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Review

Current Trends in Toxicity Assessment of Herbal Medicines: A Narrative Review

by
Alexandra Jităreanu
1,
Adriana Trifan
2,*,
Mădălina Vieriu
3,*,
Ioana-Cezara Caba
1,
Ioana Mârțu
4 and
Luminița Agoroaei
1
1
Department of Toxicology, Faculty of Pharmacy, “Grigore T. Popa” University of Medicine and Pharmacy Iași, 700115 Iasi, Romania
2
Department of Pharmacognosy and Phytotherapy, Faculty of Pharmacy, “Grigore T. Popa” University of Medicine and Pharmacy Iași, 700115 Iasi, Romania
3
Department of Analytical Chemistry, Faculty of Pharmacy, “Grigore T. Popa” University of Medicine and Pharmacy Iași, 700115 Iasi, Romania
4
Department of Dental Technology, Faculty of Dental Medicine, “Grigore T. Popa” University of Medicine and Pharmacy Iași, 700115 Iasi, Romania
*
Authors to whom correspondence should be addressed.
Processes 2023, 11(1), 83; https://doi.org/10.3390/pr11010083
Submission received: 28 November 2022 / Revised: 11 December 2022 / Accepted: 24 December 2022 / Published: 28 December 2022

Abstract

:
Even in modern times, the popularity level of medicinal plants and herbal medicines in therapy is still high. The World Health Organization estimates that 80% of the population in developing countries uses these types of remedies. Even though herbal medicine products are usually perceived as low risk, their potential health risks should be carefully assessed. Several factors can cause the toxicity of herbal medicine products: plant components or metabolites with a toxic potential, adulteration, environmental pollutants (heavy metals, pesticides), or contamination of microorganisms (toxigenic fungi). Their correct evaluation is essential for the patient’s safety. The toxicity assessment of herbal medicine combines in vitro and in vivo methods, but in the past decades, several new techniques emerged besides conventional methods. The use of omics has become a valuable research tool for prediction and toxicity evaluation, while DNA sequencing can be used successfully to detect contaminants and adulteration. The use of invertebrate models (Danio renio or Galleria mellonella) became popular due to the ethical issues associated with vertebrate models. The aim of the present article is to provide an overview of the current trends and methods used to investigate the toxic potential of herbal medicinal products and the challenges in this research field.

1. Introduction

Herbal medicine has always represented an important component of primary health care. It is estimated that approximately 80% of the world’s population uses herbal medicinal products for their therapeutic virtues [1,2,3]. The industry of phyto-preparations experienced a high growth rate during the last decades, leading to a wide variety of products circulating in the market, used in both alternative and complementary medicine. In the context of a high demand from consumers, there is an increased pressure to assess the efficacy of the products also to ensure their safety. The burgeoning demand was also accompanied by the escalation of the rate of fraudulent practices (e.g., the substitution of herbal material and the addition of synthetic compounds) [1,4], so standardization and quality control processes are of most importance. However, in some cases, the possible negative effects are not only attributed to adulteration, contamination, or misidentification of plant species but are related to the inherent toxicity of the plants. To address this aspect, an attentive toxicological assessment is required to eliminate potential safety concerns. Last, but not least, the adverse effects can be caused by foreign contaminants—chemicals (e.g., pesticide residues, heavy metals) or microbiological [5]. This review focuses on presenting the current strategies and techniques used and recommended by regulatory authorities to investigate the authenticity and toxicity of medicinal herbal products. The data collection was performed using worldwide databases, including Scopus, PubMed, Web of Science, Google Scholar, and Science Direct. The analyzed literature sources included original articles, review articles, and books.

2. The Importance of Standardization for Safety and Toxicity Profiling of Herbal Medicine—Assuring the Herbs’ Authenticity

Research performed during the last decades has pointed out the problems associated with the poor quality of the herbal medicinal products available on the market. Some of these issues are caused by species substitution and adulteration. Newmaster et al. tested several herbal products marketed in North America and found a considerable rate of product substitution, contamination, and use of fillers not listed on the label [6].
In some cases, contaminations and substitutions can cause important adverse reactions for consumers. For example, Senna alexandrina was detected in some Hypericum perforatum products, and the sennosides could determine a laxative effect, with diarrhea and abdominal pain [6]. Juglans nigra was also found in Ginkgo and Echinacea products. Juglans nigra contains a toxic compound (juglone), and furthermore, the contamination can be very dangerous for persons with nut allergies [6,7]. The substitution of Stephania tetrandra (“hang fang ji”) with Aristolochia fangchi (“guang fang ji”) in traditional Chinese medicine products may lead to renal toxicity and cancer, due to the presence of aristolochic acid, a carcinogenic and nephrotoxic agent [8,9]. Datura stramonium is a plant species frequently used in Ayurvedic medicine. In case of adulteration with Brugmansia arborea, an anticholinergic toxidrome can appear, with potentially serious outcomes for the patients [10,11].
In light of the above, the accurate identification of medicinal plant materials is essential for their safety profile. For consumer protection, it is imperative to authenticate the raw herbal material and also the final marketed products. Morphological, microscopic, and chemical identification are the traditional methods used for authentication, but other new and innovative techniques have emerged and gained popularity in the last decade [12].

2.1. Morphological Identification

For accurate morphological identification, the expertise of professional taxonomists is of utmost importance, but there are some significant limitations associated with morphological diagnosis. For example, morphological keys are often valid for plants in the flower or fruit stage. Furthermore, phenotypic and genetic variations can affect the morphology of the plants and cause misidentification [13].

2.2. The Metabolomics Approach in Herbal Medicinal Products Identification and Standardization-Chemical Fingerprinting

Metabolomics represents an omics-based approach for both qualitative and quantitative assessment of metabolites in a living system. The metabolome comprises only organic compounds with low molecular masses (called metabolites) and no polymerized structures (macromolecules) [14]. Plants produce a wide variety of phytometabolites (Figure 1). The secondary metabolites play different roles in the physiology of the plants (self-defense, environmental adaptation), but they also exhibit several pharmacological actions and are active compounds used in the treatment of human diseases. Secondary metabolites can be species-specific, and their investigation can be used in the identification and quality control of herbal medicinal products [14,15,16].
Metabolomics has become an important tool for assessing the quality and safety of herbal medicinal products, and it addresses the need for reliable methods that evaluate herbal products’ authenticity and help discriminate between samples [17,18].
There are two approaches in metabolomics—targeted (only a specific group of known metabolites are investigated) and global (non-targeted—all metabolites are analyzed). Analytical chemistry and biostatistics play essential roles in modern metabolomics. The progress made in the field of analytical techniques had a remarkable impact on the profiling of secondary plant metabolites [14,15,16]. Several studies used metabolomics (alone or jointly with other methods) to investigate the identity and authenticity of plant species in different herbal products [19,20,21,22]. For example, Fukuda et al. recommend licochalcone A as a candidate biomarker to distinguish between Glycyrrhiza species [22].
Analytical techniques like high-performance thin layer chromatography (HPTLC), high-performance liquid chromatography (HPLC) with a diode-array detector (DAD), high performance liquid chromatography (HPLC)—high-resolution mass spectrometry (HRMS), capillary electrophoresis (CE), gas chromatography (GC), gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), ultra-high-performance liquid chromatography (UHPLC) connected with OrbiTrap mass spectrometry (MS), rapid resolution liquid chromatography (RRLC)—quadrupole time-of-flight mass spectrometry (QTOF-MS), electrospray ionization quadrupole time-of-flight mass spectrometry (ESI-Q-TOF-MS), matrix assisted laser desorption/ionization mass spectrometry (MALDI-MS), supercritical fluid chromatography (SFC), liquid chromatography-nuclear magnetic resonance (LC-NMR), Fourier-transform infrared spectroscopy (FTIR), UV-Visible spectroscopy, NMR spectroscopy, Laser-induced breakdown spectroscopy (LIBS), and Raman spectroscopy have been successfully used for phytometabolites assessment (Table 1).

2.3. DNA-Based Techniques for Herbal Products Authentication

Traditional methods used for the authentication of medicinal plants have limitations. In the case of plant species that are morphologically and phytochemically indistinguishable, DNA fingerprinting has become an indispensable and trustworthy tool for the authentication of herbal drugs for the reason that the genomic composition of each plant species is unique and specific. The DNA molecule is stable, and it is not affected by external factors (physiological and storage conditions). DNA sequencing methods allow the identification of the precise order of nucleotides in a DNA sample. These methods have good repeatability and high universality and are rapidly gaining popularity. The British, the United States, Japanese, and Chinese Pharmacopoeias have introduced guidelines for DNA barcoding and approved annotated DNA barcodes for the individual identification of some plant species [4,61,62]. In the USA, in 2016, the New York State Office of the Attorney General came to an agreement with herbal supplement manufacturers regarding the use of DNA barcoding during quality control stages [63].
DNA-based identification techniques can be applied for herbal material, but also for final herbal medicinal products [64]. However, the greater levels of gene tree paraphyly in plants make the task of distinguishing between species more challenging in comparison to animals [65].
DNA barcoding is a micro-genomic identification technique used for different biological samples. It analyzes the variability of a standard DNA region. After the amplification of a relatively short (<1000 bp) DNA sequence (from nuclear or organelle genomes), bar-code-like patterns are generated. The DNA sequence fragment is considered a molecular marker for species identification [16,66]. The standardization of the method can be achieved by creating unified databases and identification platforms [13].
Several barcode libraries are currently available: The barcode of life data system (BOLD), Consortium for the barcode of life (CBOL), Medicinal Materials DNA Barcode Database (MMDBD), International Barcode of Life project (iBOL), GenBank (NCBI), Traditional Chinese Medicine Database [66].
While in the animal kingdom, the mitochondrial genome (mitochondrial cytochrome c oxidase unit I—COI) proved to be a fine option for the generation of DNA barcodes [67], for plant barcoding, the chloroplast and nuclear genomes were found to be suitable for identification, due to higher nucleotide substitution rates. Hypothetically, matK (maturase K gene) is the closest plant analogue of COI due to its high rate of evolution, but despite some successful experiments [68], no single locus can be used as a universal plant barcode. The choice of barcodes represents a challenge in the authentication process, and the evaluation of the efficiency of different candidate barcodes led to the conclusion that a combination of two or more loci is preferred to single-locus for plant barcoding. However, since the three (or more)-loci approach did not show significant improvement in the species discrimination process, to avoid additional expenses, the standard barcode for land plants is the two-loci barcode approach [65].
The ideal barcode should have a rapid enough evolution and sufficient nucleotide sequence variation to distinguish unambiguously between species but also limited intraspecific variation. The conserved regions will function as primers for PCR [4].
Several multi-loci combinations from chloroplast and nuclear genomes were assessed to find the best combination for plant barcoding [66]. The Consortium for the Barcode of Life adopted the combination of chloroplast regions rbcL (ribulose-1,5-bisphosphate carboxylase gene) and matK (maturase K gene) as DNA barcodes for plants, but several studies have also investigated over DNA barcodes. Tnah et al. used a two-tiered approach (rbcL and trnH-psbA—chloroplast intergenic spacer) for the authentication of common herbal plants in the tropics, with good species resolution [4].
Chen et al. [69] investigated trnH-psbA, matK, rbcL, rpoC1, ycf5, ITS2, and ITS as the best candidate DNA barcodes suitable for medicinal plant identification. They established that the rate of successful identification at the species level (92.7%) using ITS2 was superior to that of rbcL and matK, therefore validating ITS2 as a standard DNA barcode for plant identification [69]. Their findings were also supported by Miao et al., who identified ITS2 as a universal barcode that can be used for the identification of poisonous medicinal plants found in the Chinese Pharmacopeia, with a similar rate of correct identification (92.59%) [13]. The findings of other studies also support the use of ITS2 as a barcode for the identification of medicinal plant species and confirm its effectiveness. ITS2 is a short sequence (200–230 bp on average) with rapid concerted evolution [70,71,72,73]. However, ITS2 cannot be used for the global identification of plants due to potential intragenomic variations of ITS copies within individuals, which can cause incorrect identification of species [70,74].
At present, the best option for unequivocal plant species identification consists in an approach that combines noncoding intergenic spacers (e.g., ITS, trnH-psbA) and plastidial coding sequences (e.g., matK, rbcL) as barcodes [75].
Traditional DNA barcoding can be difficult to use in the case of highly processed medicinal plant products or on poorly preserved samples since the degradation of the DNA can affect the polymerase chain reaction [75,76].
Pawar et al. used the nuclear ITS2 gene and the chloroplast gene trnH-psbA to investigate the authenticity of some herbal dietary supplements, and they pointed out the problems of using traditional DNA barcoding with complex finished herbal products [77].
DNA mini-barcoding can represent an alternative technique in such cases. DNA mini-barcoding uses shorter (≤200 bp) DNA fragments from standardized matK and rbcL barcode regions [70]. The mini-barcodes have the advantage of being amplified more rapidly. Furthermore, they are more diversified and allow a more accurate identification. However, the method has some major limitations (e.g., it cannot be applied for the identification of unknown adulterants/ contaminants, and the reduced length of DNA fragments can cause the loss of relevant information) [76]. Several studies successfully used DNA mini-barcode for the identification of plant species in health products [75,78,79,80,81]. However, in the case of authentication of highly processed herbal products, the use of analytical techniques to identify key active compounds should come to complete the data collected from DNA barcoding [82].
There are different DNA sequencing methods—the early Sanger method (the chain termination method, developed in the 1970s) and the next-generation high-throughput methods. The applicability of the traditional Sanger sequencing method for the authentication of complex mixtures containing several plant species proved to be limited. Ivanova et al. pointed out that Sanger sequencing can lead to biased and misleading results during the analysis of samples containing multiple species, being unable to resolve the issue of mixed signals. The various stages covered during the production of standardized herbal products (e.g., extractions, fermentations with microorganisms) can determine the degradation of plant DNA or the contamination from foreign DNA sources. Other sources of foreign DNA can be represented by weed species or pollen from neighboring plants that can contaminate the herbal material during harvesting [8,83]. Furthermore, symbiotic, parasitic, and pathogenic plant-fungal interactions can also affect the DNA-based identification process. When the DNA template coming from the contaminant is preferentially amplified, false conclusions can be drawn because only the contaminant DNA is detected. These shortcomings were resolved by the development of next-generation sequencing (NGS) methods. Ivanova et al. elaborated an NGS workflow that enabled the simultaneous detection of plant and fungal DNA and can be used for quality and fungal contamination assessment [83].
The PCR-dependent DNA metabarcoding and PCR-free genome skimming/shotgun metagenomics are two important approaches used for herbal product identification [62].
Conventional DNA barcoding can be used exclusively for the authentication of herbal products containing a single herbal species. DNA metabarcoding (a combination of high-throughput sequencing and DNA barcoding) is applicable for the simultaneous identification of multiple taxa in a sample, suitable for quality control and contaminant species identification in various herbal products [82,84,85,86,87]. However, the discriminatory power and the success of the identification with DNA metabarcoding is still a challenge for highly processed herbal products because of the low quality of DNA samples. Metabarcoding of multiple mini-barcode loci can represent an option in such cases [62,88].
NGS can perform simultaneous sequencing of thousands of molecules. This method is appropriate for samples derived from multiple plant species, with varying levels of DNA degradation, even when fillers or contaminants are present, because, with NGS, numerous DNA sequences are determined at the same time, while the traditional Sanger sequencing identification requires only homogeneous DNA sequences. NGS allows the determination of genomic sequences and species identification even at concentrations of 1% in the complex mixture. For a successful analysis, comprehensive and well-developed sequence libraries are essential. On the other hand, the high sensitivity of the technique also requires extra precautions to ensure sterile work conditions and avoid potential contamination of the samples during analysis [8,16,83,89]. NGS was successfully used by Zhang et al. to unambiguously differentiate between Echinacea species [90].
To improve the performance of the method, DNA barcoding was integrated with HRM (high-resolution melting) analysis (Bar-HRM). Bar-HRM is a post-PCR detection method of sequence variation without the need for sequencing that uses a DNA-binding fluorescent dye. The dye specifically binds to double-stranded DNA (the previously amplified PCR products), and as the DNA fragments dissociate during the denaturation process with increasing temperatures, the dye is released, and the fluorescent signal diminishes. The DNA “melting” kinetics is monitored, and a melting curve is obtained. The discrimination between species can be performed based on the characteristics (shape and peak) of the distinct melting curves that are generated. In this way, specific species or adulterants that are present in herbal medicinal products can be identified [91,92,93].
There are several studies that successfully used Bar-HRM for medicinal plant authentication. Costa et al. investigated ITS1 and matK as mini-barcode candidates for the distinction of Hypericum species using species-specific PCR and real-time PCR coupled with HRM analysis. Only matK was suitable for identification using the PCR-HRM assay due to some degree of intra-species variability displayed by the ITS1 region [75]. Ounjai et al. used the hybrid Bar-HRM method to distinguish between species in the Zingiberaceae family [93]. Zhao et al. successfully identified Ardisia gigantifolia and its adulterants (Rhododendron molle and Clerodendrum cyrtophyllum) using the Bar-HRM and a two-loci approach (ITS2 and trnH-psbA) [94].
Other medicinal plant species for whose identification and quality control the Bar-HRM method was applied included Phyllanthus spp. [95], Sideritis spp. [96], Paris polyphylla [97], Gentiana rhodantha [98], Hyoscyamus niger [99], Artemisia spp. [100], Acanthaceae spp. [101], Hippophae spp. [102], Panax spp. [103], and Eurycoma longifolia [104].
Due to its effectiveness and reliability in authenticating medicinal herbal material, DNA barcoding represents an area of interest for many research teams. A selection of studies on this topic and their conclusions are summarized in Table 2.
Sometimes, the toxicity of herbal products can be caused by the replacement of the traditionally prescribed plant part with another part of the same plant species. In these cases, DNA-based techniques are not enough to assess the safety of the products, and other complementary analyses must be used (omics analyses) [65].
Sometimes, herbal dietary supplements have a complex composition, and in these cases, it is best to use an integrative approach for quality control. Thongkhao et al. used NGS, microscopic characterization, and HPTLC techniques to reveal the presence in a commercial product of an adulterant specie unlisted on the label [136].
To sum up, although DNA barcoding and chemical fingerprinting using complex analytical techniques exhibit some challenges and critical aspects, their implementation in the process of quality and safety assessment in the herbal supplements industry is essential for the discovery of fraud and adulteration. Ensuring the authenticity and correct identification of botanical species is a fundamental request for the security of the products.

3. Intrinsic Toxicity Evaluation

The toxicity of medicinal plants and herbal medicinal products is closely related to the presence of bioactive compounds in the plant material and their toxic potential [1]. The issue is even more complicated when it comes to heterogenous, complex mixtures of herbs which can cause unpredictable effects [137].
There are many examples of toxic endogenous compounds in the plant kingdom such as pyrrolizidine alkaloids (hepatotoxic, genotoxic, cytotoxic, phototoxic), furan derivatives (hepatotoxic, possible carcinogenic), epoxy-diterpenoids (hepatotoxic), anthraquinones (hepatotoxic), bis-benzylisoquinoline alkaloids (pulmonary toxicity), alkenylbenzenes (genotoxic, carcinogenic), ginkgolic acids (embryotoxic, cytotoxic, neurotoxic) [138]. In the case of phytometabolites with proven toxic potential (as the ones mentioned above), the regulatory authorities responsible for the quality and safety of herbal medicines have imposed concentration limits. In this context, efforts were made in the field of developing technologies to remove these compounds and efficient methods to detect and assess their concentration (for example, chromatographic methods and immunoassays using monoclonal antibodies) [139,140,141].

3.1. Acute/Sub-Acute/Chronic Toxicity Evaluation

Toxicological preclinical evaluation of herbal medicines uses both in vitro and in vivo models.
To evaluate acute and chronic toxicity (Figure 2), animal models are usually used. The Organization for Economic Cooperation and Development (OECD) elaborated guidelines for testing the toxicity of chemical compounds.
Acute toxicity assessment implies the administration of a single dose from the tested product to each test animal. The signs of toxicity and the mortality of the animals are recorded for a period of 14 days in order to determine parameters like the maximally tolerated dose (MTD) and the half-lethal dose (LD50). For the sub-acute and chronic toxicity evaluation, the animal is exposed repeatedly to the tested products daily over a variable period (28 days—OECD 407 [142], 90 days—OECD 408 [143], 12 months—OECD 452 [144]). Toxicity parameters like NOAEL (no observed adverse effect level), NOEL (no observed effect level), LOAEL (lowest observed adverse effect level), and LOEL (lowest observed effect level) can be determined. In the case of medicinal plants and herbal medicine, the oral route of administration is used most frequently, but dermal, intraperitoneal, and inhalation are also available. Exposure via inhalation is usually applied to essential oils, while dermal toxicity is evaluated for herbal medicinal products used in the treatment of dermatological disorders [145,146,147]. The preferred animal species are rodents (mice or rats), but for dermal exposure, rabbits can also be used [148,149,150,151,152,153,154].
In vivo studies are the subject of many ethical issues, and the general tendency is to reduce to a minimum the number of experimental animals. In this context, preclinical toxicology was oriented toward other approaches.
Technological progress has led to the development of in vitro models that are superior in mimicking in vivo conditions than the original ones. These new models have evolved exponentially (Figure 3), from 2D cell cultures and 3D cell cultures to microfluidic devices and miniature artificial organs capable of imitating key elements of human physiology (organ-on-chip systems). By integrating and connecting more organ-on-chip devices, complex systems result in body-on-chip systems that can reproduce complex interactions that take place between different organs inside the organism [155,156].
In 3D-cell culturing systems, some of the disadvantages encountered in 2D cell cultures (e.g., altered cell morphology, modified division process, the lack of interactions between cells and the extracellular environment, homogenous distribution of resources, and unlimited access) have been overcome. Therefore, 3D cell culture models mimic better the in vivo conditions of tissues and offer improved prediction of bioactivity [157].
3D-tissue models have been used to study the activity of medicinal plants. The EpiAirway™ in vitro human airway model is a potential alternative for in vivo testing to assess inhalation toxicity [158,159]. This 3-D tissue model was previously used in medicinal plant studies and can represent a reliable method to evaluate the effect of herbal products on the integrity of the tissues, cell viability, and preservation of cilia [160].
Another in vitro tissue model is Episkin™ (reconstituted human epidermis), and it can be used as an alternative in dermal toxicity studies [161]. Ng’uni et al. used this method to evaluate the irritation potential of Galenia africana [151].
There are not many examples in the literature of studies on medicinal plants (or herbal medicinal products) that use organ-on-chip technology. Chang et al. linked two organ-on-chip systems (a kidney-on-a-chip and a liver-on-a-chip) and investigated the nephrotoxic and carcinogenic potential of aristolochic acid. The mechanisms of the hepatic biotransformation and transport into the kidney tissue were elucidated [162].
However, although these new technologies offer many advantages, and have the potential to revolutionize the field of medicinal products safety evaluation [163], the switch from conventional toxicity assessment methods is currently in a transition phase, with some obstacles to overcome [157].
Respecting the desiderate to restrict the use of higher animals in toxicity studies, alternative invertebrate animal models were explored. Zebrafish (Danio renio) assay is a highly used and appreciated toxicity screening tool, being simple and inexpensive. Adult zebrafish can be used, but also embryos, this animal model being suitable for various toxicity tests, including embryotoxicity (Figure 4). One advantage is the transparency of the embryos, which facilitates toxicity examinations during different developmental stages (the visualization of organogenesis is possible using fluorescence and transgenic strains) [164].
The zebrafish model was used for the assessment of different medicinal plants, for example, Hypericum lanceolatum [165], Enydra fluctuans [166], Piper sarmentosum [167], Graptophyllum pictum [168], Murraya koenigii [169], Urena lobata [170] and Endopleura uchi [171]. In their review, Modarresi Chahardehi et al. make an up-to-date presentation of studies that used zebrafish for toxicity screening of medicinal plants [164]. Falcão et al. also reviewed this method as an alternative to the embryotoxicity assessment of plant products [172].
Another eco-friendly approach uses an insect model—Galleria mellonella (greater wax moth) as a test organism. Mbarga et al. evaluated the acute toxicity of some traditional medicinal plants from Cameroon (Cymbopogon citratus, Moringa oleifera, Vernonia amygdalina, Cinchona officinalis, Enantia chlorantha, Garcinia lucida, and Azadirachta indica). Their results pointed out that Galleria mellonella can be a potential invertebrate model for toxicity assessment, but further studies are needed [173].
Although much progress has been made in the field of toxicity evaluation, for a reliable prediction of potential risks for humans, in vivo vertebrate toxicity studies remain the gold standard.

3.2. Genotoxicity and Carcinogenicity Evaluation

Genotoxicity is defined as “the property of a compound to induce genetic damage by various mechanisms” [174], and genotoxic agents can be carcinogens and/or mutagens for humans. Therefore, genotoxicity testing is very important for safety assessment. The International Conference on Harmonisation (Guidance on Genotoxicity Testing and Data Interpretation for Pharmaceuticals Intended for Human Use) recommends a battery of tests for genotoxicity testing (Figure 5), emphasizing that a singular test is not conclusive. There are several indicators of genotoxicity that can be analyzed: mutagenic potential (the ability to induce gene mutations), primary DNA damage, and chromosomal damage (structural and numerical aberrations) [175].
Genotoxicity assessment is important for the safety evaluation of herbal medicinal products because there are several examples of plants with phytometabolites that present genotoxic/carcinogenic properties. Da Silva Dantas et al. screened the electronic databases for articles published between 1975 and 2020 that investigated the genotoxic/mutagenic effects of medicinal plants. A significant percentage of medicinal plants presented mutagenic properties, emphasizing the importance of accurate testing for natural products [176]. Plants belonging to the genus of Symphytum, Senecio, Crotalaria, and Heliotropium biosynthesize pyrrolizidine alkaloids. The compounds that contain a double bond in the molecule (e.g., retronecine, heliotridine, otonecine) can form 6,7-dihydro-7-hydroxy-1-hydroxymethyl-5H-pyrrolizine-derived DNA adducts, responsible for genotoxicity, explaining tumor induction. Other categories of genotoxic phytometabolites are represented by the alkenyl-benzenes from essential oils (e.g., safrole, myristicin, estragole, asarones, and eugenol), anthraquinones (from Rubia tinctorum and Morinda officinalis), and aristolochic acids [177]. Plant-derived alkylating agents are considered pro-carcinogenic substances; they interact with DNA bases, and if the affected cells do not undergo apoptosis and continue to proliferate, carcinogenesis is initiated [178].
There are different assays (in vitro or in vivo) available to assess genotoxicity, and they cover the different mechanisms of genotoxicity mentioned above (gene mutations, primary DNA damage, and numerical and structural chromosomal damage).
To get a more reliable evaluation, more than one assay is required. The typical assessment is performed using a battery of tests that includes a gene mutation test (on bacteria), another in vitro assay, and an in vivo assay (on mammals). EMEA recommends in its Guideline on the assessment of genotoxicity of herbal substances/preparations the following combination of tests: the Ames test, mouse lymphoma assay (or other mammalian cell assays), and rodent micronucleus test (or other in vivo genotoxicity tests) [176,179]. The EFSA Scientific Committee recommends for the initial screening step a combination of two in vitro tests: the bacterial reverse mutation assay, which covers gene mutations, and the in vitro micronucleus test, which covers chromosome aberrations. If in vitro testing identifies positive genotoxic endpoints, then further in vivo testing is required. The recommended in vivo tests by EFSA are the mammalian erythrocyte micronucleus test, transgenic rodent somatic and germ cell gene mutation assays, and an in vivo Comet assay [180].
Table 3 presents a selection of genotoxicity assessment studies performed on herbal medicinal products.
Genotoxicity evaluation using in vitro and in vivo test systems can sometimes generate false positive or false negative results and is not in accordance with the carcinogenic potential identified using in vivo rodent carcinogenicity studies. These differences can be linked to the limited metabolization capability of in vitro systems (in the case of xenobiotics with genotoxic metabolites) but also to the fact that genotoxicity assessment cannot reveal the nongenotoxic carcinogens. The S9 fraction of liver homogenate can be used to simulate mammalian metabolism [207]. On the other hand, a significant percentage of non-carcinogenic compounds gave false positive results in in vitro chromosomal damage assays due to the low specificity of the methods [208,209]. Based on these considerations, Kirkland et al. divided the chemicals into three groups—the first group (“true positives”) includes the known mutagenic carcinogens that should give positive results in in vitro genotoxicity tests; the second group (“true negatives”) contains non-DNA reactive compounds and should give negative results, while the substances in the third group (“false positive”) give positive results in in vitro testing, but the in vivo genotoxicity results are negative. However, some of the compounds in groups two and three can pose risks for humans, being non-genotoxic carcinogens [210].
The refinement of in vitro genotoxicity methods is necessary to avoid excessive in vivo testing and to improve predictive capacity. New evaluation tools were developed (e.g., ToxTracker®, Vitotox®, the TGx-DDI (DDI = DNA damage-inducing) transcriptomic biomarker assay, in vitro MicroFlow® micronucleus assay, the in vitro CometChip® assay), and a combination of these tools has proven efficient in differentiating between relevant and irrelevant positive results in in vitro genotoxicity assays [208,209,211].
Some of these new genotoxicity tools were also used in the study of medicinal plants. Vitotox®, a test that evaluates the bacterial SOS-response triggered by exposure to genotoxic agents [212], was used to investigate the mutagenic potential of extracts from different medicinal plants [213,214,215]. The combination of CometChip® and the TGx-DDI biomarker assay was used to evaluate eugenol’s ability to induce DNA damage and classified the compound as a DNA damage-inducing (DDI) agent at high concentrations [216].
When it comes to genotoxicity testing for herbal products, there are some specific challenges due to the complexity of the mixtures, usually containing a large number of constituents [177]. Furthermore, the characteristics of plants can also have an important influence on the results. For example, the Bacterial Reverse Mutation Test (Ames assay) is one of the most popular genotoxicity studies used in herbal product research. The test uses histidine-deficient bacteria (Salmonella) strains which can be converted into histidine-independent strains by mutagen compounds. But sometimes, the accuracy of these tests is questioned. Verschaeve pointed out that the high histidine content of some plants can influence the outcome of the test, generating false-positive results (the growth of the bacterial colonies can be sustained by the higher concentration of histidine available in the medium and not by histidine reverse mutation). Furthermore, some plants have antibacterial properties, which can affect bacterial growth and influence the results of the Ames test [217].
At present, there are no specific guidelines for the carcinogenicity assessment of herbal medicinal products. The existing OECD 451—Carcinogenicity studies and OECD 453—Combined chronic toxicity/carcinogenicity studies designed for pharmaceutical compounds could be extrapolated to herbal products as well when this is required [177]. There are some examples of older studies that used animal models to investigate the carcinogenicity potential of herbal medicinal products [218,219], but currently, long-term rodent carcinogenicity testing is not frequently used for herbal products [220]. A more recent study is the 2-year bioassay performed by Hoenerhoff et al., where they evaluated the hepatocellular carcinogenicity risk in mice associated with long-term consumption of Ginkgo biloba leaf extract, and found considerable evidence connecting ginkgo extracts to oncogenic events [221]. Their findings were further supported by other studies [222,223]. The National Toxicology Program within the US Department of Health and Human Services also conducted several in vivo carcinogenicity studies on animal models (rodents) for herbal medicines (Aloe vera, Panax ginseng, Hydrastis canadensis, Piper methysticum, Silybum marianum), and for components isolated from different essential oils (e.g., pulegone, αβ-thujone) [224,225,226]. The issue of herbal medicine with genotoxic and carcinogenic potential is also emphasized by International Agency for Research on Cancer (IARC). Aloe vera, Hydrastis canadensis, Ginkgo biloba, Piper methysticum, and pulegone have monographs in Volume 108 of IARC Monographs on the Evaluation of Carcinogenic Risks to Humans (that focuses on the carcinogenicity of herbal products). They have all been classified as possibly carcinogenic to humans (Group 2B) [227].
In summary, as there is clear evidence of genotoxicity and carcinogenicity associated with herbal medicine, this aspect needs to be carefully investigated to ensure the safety of the consumers.

3.3. Omics-Based Toxicology

The increasing demand for the toxicological evaluation of herbal medicinal products has led to the development of various methods for toxicity testing [1]. The “Omics” approach (from the Latin “ome”—many, a totality) relies on comprehensive, integrated evaluations [228].
Omics-based toxicology is based on the reflection at the cellular level of the interaction between toxic xenobiotics and biological systems. Toxico-transcriptomics, toxico-proteomics, and toxico-metabonomics are powerful tools for the toxicity assessment of herbal products (Figure 6). These technologies evaluate the outcome of the interactions between xenobiotics and living cells at different levels. The final goal is to discover the mechanisms behind the effects of a compound [137].
In some cases, the toxic effects of medicinal plants can be closely related to the metabolic activation inside the exposed organism of the phytometabolites. In these situations, omics technologies can be successfully used to evaluate the toxicity of natural products, identify reactive metabolites of natural molecules, and identify the targeted peptides modified by interaction with these metabolites [138].

3.3.1. Toxico-Transcriptomics

Toxico-transcriptomics investigates the effects of exposure to xenobiotics on genes transcription, toxico-proteomics identifies protein alterations, while toxic-metabonomics analyzes the physiological changes (perturbation of metabolic profile) that appear as a response to the chemical stressors [1].
Transcriptomics studies the RNA transcripts produced by cells under specific circumstances. The transcriptome is very dynamic and sensitive to xenobiotic agents, and the investigation of the genes with significantly affected expression levels after exposure can give insights into the toxicity of the tested agent. Therefore, transcriptome analysis can be considered an early indicator of toxicity [229]. There are different transcriptome technologies: serial analysis of gene expression (SAGE), microarray technologies, and sequencing technologies [230]. One of the most popular techniques used is DNA microarray because it allows the simultaneous monitoring of the expression of thousands of genes, and it is accurate, specific, sensitive, and reproducible. Transcriptomics (toxico-transcriptomics) has been successfully used to elucidate the toxicological effects and mechanisms of natural compounds [231]. For example, the aristolochic acid from the Aristolochia species inhibits the nuclear factor-kappa B (NF-κB) signaling in kidney cells, explaining the nephrotoxicity [232]. Emodin, a bioactive compound from Reum palmatum, alters the expression of genes involved in the apoptosis of cancer cells, exhibiting anti-cancer properties [233,234]. But, at the same time, with the help of transcriptomics, the testicular toxicity of the compound was explained via the IGF-1 (insulin-like growth factor-1) receptor signaling pathway [235]. Another category of toxic phytometabolites is pyrrolizidine alkaloids, found in species belonging to Boraginaceae, Asteraceae, and Fabaceae families. They exhibit cytotoxic, genotoxic, and carcinogenic potential. Several research teams investigated the liver and lung toxicity of these alkaloids in rats, and transcriptomics analysis revealed altered expression patterns for genes involved in DNA damage response and cell-cycle regulation [236,237,238,239].

3.3.2. Toxico-Proteomics

The purpose of this approach is to assess protein alterations as a consequence of exposure to xenobiotics. The changes can consist of modified protein levels, increased/decreased activation of key proteins (for example, apoptosis-related proteins), structural modifications, and post-translational protein modifications (e.g., phosphorylation, glycosylation, acetylation, proteolysis). The proteomics research has different areas of interest: protein profiling proteomics (quantitative evaluation), structural proteomics, functional proteomics, and protein-protein interactions. The identification of biomarkers that are closely connected to the toxicity signature of xenobiotic compounds represents an important issue for the use of proteomics in toxicology [1,240,241,242].
Several techniques can be used for proteomics studies. The traditional approach is two-dimensional polyacrylamide gel electrophoresis coupled with mass spectrometry (2D-PAGE-MS) protocol, but, at present, there are also available improved methods, like Matrix-assisted laser desorption/ionization coupled to time-of-flight mass spectrometry (MALDI-TOF-MS) or Surface-enhanced laser desorption/ionization coupled to time-of-flight mass spectrometry (SELDI-TOF-MS) [1,240].
The proteome analysis was used to investigate both the therapeutic and toxicity mechanisms of herbal medicinal products [242,243,244,245].
As we previously mentioned, pyrrolizidine alkaloids are a group of highly hepatotoxic phytometabolites. The proteomic approach was used in several studies to investigate the molecular mechanism of hepatotoxicity for these compounds. Among the affected structures are proteins with importance for cellular energy metabolism and whose activity was also related to the oxidative stress process. Carbamoyl-phosphate synthase (CPS1), ATP synthase subunit beta (ATP5B), and thrombospondin 1 (TSP1) were among the affected protein structures [246,247,248].
The toxicity of aristolochic acids is a subject of interest, and Liu et al. analyzed the changes in proteome profiles in liver and kidney tissues from rats exposed to aristolochic acid. The most downregulated proteins were SEC14-like protein 2 and synaptic vesicle membrane protein VAT-1 homolog [249].
Tripterygium wilfordii is frequently used in Chinese traditional medicine for its immunosuppressant properties, but it is associated with male reproductive toxicity. Dai et al. performed a proteomics analysis of testis tissue samples collected from rats exposed to Tripterygium glycosides, and their results confirmed the alteration of proteins involved in sperm production and differentiation. The glycosides exhibited an inhibitory effect on the expression of proteins related to the PI3K-Akt signaling pathway, which can explain reproductive dysfunction [250].
Phosphorylation is one of the most important post-translational modifications suffered by proteins, and it influences many biological processes. Phosphoproteomics (the study of protein phosphorylation) has become a popular tool to evaluate phosphorylation-mediated signaling [251], also used in the field of medicinal plants [252]. Gelsenicine is a neurotoxic alkaloid from Gelsemium elegans (Gelsemiaceae). Huang et al. used phosphoproteomics to assess the changes in brain protein phosphorylation. Different brain regions were investigated, but the protein phosphorylation was mainly affected in the hippocampus area [253].

3.3.3. Toxico-Metabonomics

Both metabolomics and metabonomics are used in the scientific community. However, there is still some disagreement regarding the exact differences between the two terms. Metabolomics was defined as a “comprehensive and quantitative analysis of all metabolites”. Metabonomics is considered a subset of metabolomics and represents “the quantitative measurement of the time-related multi-parametric metabolic response of living systems to pathophysiological stimuli or genetic modification” [254,255,256]. Toxico-metabonomics allows the detection of alterations in the homeostasis and metabolic control of the organism secondary to exposure to a toxic compound. The modifications in the pattern of endogenous metabolites can provide important insight into the targettissues and the mechanism of the toxic action [255]. To sum up, toxico-metabonomics has several applications: to evaluate the levels of endogenous biochemicals potentially affected by the interaction with the toxic agent, to identify the metabolites of the toxic in order to reveal the molecular mechanisms of action and to identify novel biomarkers of toxicity. NMR and MS-coupled with chromatographic techniques are used for metabolic profiling [1,137,256].
Toxico-metabonomics can also be used in the field of herbal medicinal products to evaluate herb/host interactions. Most of the studies use experimental animal models to evaluate the metabolic response of the interaction (Table 4).
The evidence presented in the scientific literature supports the fact that omics-based approaches have changed the field of herbal medicine research and are essential tools in toxicity assessment. They allow the identification of toxicity biomarkers and target tissues and reveal relevant details regarding the mechanism of toxicity.

4. Evaluation of Toxicity Arising from Extrinsic Sources

Several external factors can represent a source of toxicity enhancement for herbal medicine (microbial and mycotoxins contamination, pesticides, and fumigation agent residues, radioactive contamination, residual solvents, or toxic heavy metal content—Figure 7).
Mycotoxins, pesticides, and heavy metals are the most common aspects of concern when it comes to the toxicity of herbal medicine arising from extrinsic sources.

4.1. Mycotoxins Control

Mycotoxins (aflatoxins, ochratoxin A, fumonisins, zearalenone, deoxynivalenol) are secondary metabolites produced by fungal species (e.g., Penicillium, Aspergillus, Fusarium, Alternaria, Claviceps). Their occurrence in food and medicinal products represents a research subject of interest, due to their highly toxic potential (carcinogenic, mutagenic, neurotoxic, hepatotoxic, nephrotoxic, teratogenic, and immunotoxic). Aflatoxin B1 was included in the Group-1 carcinogen by the IARC and is considered the most toxic mycotoxin [275]. To ensure the safety of consumers, regulations for mycotoxins in herbal medicinal products were established by international organizations and agencies (World Health Organization, European Food Standards Agency, European Medicine Agency, United States Food and Drug Administration). Many research groups investigated the contamination of medicinal herbal products with mycotoxins and revealed that this subject could represent a potentially serious issue of concern because several studies found significant levels of mycotoxins above the maximum admitted limits [276,277,278].
The detection and quantification of mycotoxins are essential for assuring the safety of herbal medicinal products. Most methods used for mycotoxins assessment rely on chromatographic techniques. In the 1960s, after the discovery of aflatoxins, the traditional method of analysis was TLC, but at present, its usage is limited. However, this technique is still recommended as a screening tool when a large number of samples are investigated, being simple, inexpensive, and rapid [279,280,281,282].
The need for the separation of various mycotoxins and accurate qualitative and quantitative determination has shifted the interest towards more performant methods. There are several chromatographic techniques available (HPLC, GC/MS, LC/MS, LC-MS/MS, LC-FLD) that can perform both quantitative and qualitative analyses with high sensitivity, recommended in international pharmacopeias [280,283,284,285,286,287,288]. Liquid-chromatography techniques are preferred to gas-chromatography because no complex derivatization procedure is required during the analysis, as most mycotoxins are nonvolatile. Chromatographic methods have high specificity and sensitivity and can be used for the simultaneous determination of multiple mycotoxins [280,284,285,289]. However, these methods are more laborious and require several sample preparation steps (extraction and clean-up), and rapid detection technologies were developed for simpler and more convenient monitoring of the mycotoxins’ contamination [290].
Immunological techniques developed on the specific affinity of an antibody for an antigen are very popular for rapid mycotoxins screening. Mycotoxins detection using enzyme-linked immunosorbent assay (ELISA) is based on the interaction between the antigen (mycotoxin) and an antibody, and various test kits for the determination of mycotoxins are currently available on the market [280,289]. Shim et al. used ELISA and investigated the aflatoxins levels in 70 types of herbal medicine distributed in South Korea and confirmed the potential risk to consumers [291].
Another alternative is represented by immunochromatographic assays (ICA). Lateral Flow Immunoassay (LFIA) has been successfully used for the detection of a single mycotoxin but also for the simultaneous determination of multiple mycotoxins (e.g., multi-component assay for aflatoxin B1, ochratoxin A, and zearalenone) [292].
Colloidal gold-based immunochromatographic assay (GICA) uses colored colloidal gold-antibody conjugates, and it is currently a popular method used for mycotoxins detection [280,293,294,295].
The aptamer-based lateral flow assay is an alternative that presents the advantage of using nucleic acid-based aptamers instead of antibodies. The aptamers showed better resistance to aggressive medium conditions (pH, solvents) and had improved sensitivity and specificity [280,288].
Cytometric bead array is a new flow cytometry technique based on the quantification of the signal of fluorescence-encoded microspheres for the assessment of the desired analytes. It allows a rapid determination of multiple analytes in reduced sample volumes. It has many applications in various biological and environmental samples [296], and it was also applied to the investigation of mycotoxins. Chang-Bin et al. were the first to use this technique for rapid mycotoxins analysis in the field of medicinal herbal products, investigating the Chinese herb ‘Mai Ya’ (barley sprout, Malt) [280,297].
The development of simple, rapid, and effective methods to determine and quantify mycotoxins in herbal products is needed to lower the risks to human health.

4.2. Pesticides and Fumigation Agent Residues Control

The presence of pesticide residues in medicinal herbs is another issue of concern. There are different classes of pesticides: organochlorine, organophosphorus, nitrogen-containing pesticides, and pesticides of plant origin [298]. Several studies revealed the presence of these contaminants in herbal products. The most studied types of products are Asian herbal medicines. To ensure the safety of consumers, Maximum Residual Levels (MRLs) were established for pesticides [299]. Wang et al. analyzed 1017 samples of 10 Chinese herbal medicines and determined 168 pesticides. Of the analyzed samples, 76.0% contained multiple residues, and only 10.8% were residue-free. However, they did not classify the potential health risk associated with the consumption of these products as high [300].
Chromatographic methods (GC and HPLC coupled with different detectors) are usually used to carry out the identification and quantification of pesticides from herbal medicinal products. One of the most challenging aspects of the analysis is the preparation of the samples (using extraction and clean-up procedures) because of the complex nature of the herbal matrix, which contains pigments (carotenoids, chlorophyll), phenolic compounds, and essential oils—Table 5 [301,302,303].
The chromatographic methods are a sensitive, reliable tool for pesticide analysis, but the development of rapid detection techniques is needed for the efficient screening of herbal medicinal products. Immunological assays were also developed for pesticide residues (for example, IC-ELISA (indirect competitive enzyme-linked immunosorbent assay), and colloidal gold ICA for the detection of organophosphorus pesticides [319,320], ELISA for the monitoring of pyrethroids [321,322], immunosensors and immuno-loop-mediated isothermal amplification assays (iLAMP) for the determination of organophosphates) [323,324]. To expand the number of compounds analyzed simultaneously in a single step, pesticide biochips were also designed [325]. These technologies are widely used in environmental analyses, but they can be successfully applied to medicinal plants or other herbal medicines.

4.3. Heavy Metals Control

Heavy metal contamination can be another safety issue for some herbal medicines, especially Asian traditional medicines. Sometimes, metals are voluntarily incorporated in the formulas for their therapeutic virtues, but in other cases, the contamination occurs via the plants that accumulated heavy metals from the soil, or during the manufacturing process [326].
The presence of heavy metals like Pb, Cd, As, and Hg in herbal medicine in quantities exceeding the maximum permissible limits can cause negative health effects (abdominal discomfort, liver damage, damage to the nervous, renal, and respiratory systems, and skin conditions) [327,328,329].
The quantification of heavy metal levels in herbal medicine is a subject of interest, approached by many research groups (Table 6). Inductively Coupled Plasma Mass Spectrometry (ICP-MS), Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES), and atomic absorption spectroscopy (AAS) are used for the quantification of heavy metal contamination [330,331,332,333,334,335]. Luo et al. used ICP-MS and determined cadmium, lead, arsenic, mercury, and copper in 1773 extract samples from 86 different kinds of herbal medicines. Most products were within acceptable risks, but there were samples where at least one heavy metal was detected to be over the maximum acceptable limit. The authors also pointed out the higher risks associated with As contamination and recommended improved monitoring activity [328].
In the context of environmental pollution, the contamination of herbal medicinal products with different agents can represent a potential health safety concern, and the development of sensitive, simple methods to identify and quantify contaminants is needed to avoid negative consequences for consumers.

5. Conclusions

In the context of the growing popularity of herbal medicinal products, ensuring the safety of consumers must become a priority for producers and regulatory authorities. The assessment of the toxicologic potential is a complex process, as many factors (both plants -related and environmental) can contribute to the toxicity of medicinal herbal products. The scientific community is making a continuous, joined effort to improve the techniques used for the authentication of herbal species, the detection of harmful phytochemicals, the elucidation of toxicity mechanisms, and the quantification of environmental pollutants in herbal materials.

Author Contributions

Conceptualization, A.J. and A.T.; methodology, L.A.; investigation, I.-C.C., I.M. and A.J.; resources, I.M.; data curation, M.V.; writing—original draft preparation, A.J. and M.V.; writing—review and editing, A.T.; visualization, I.-C.C.; supervision, A.T.; project administration, A.J. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Woo, C.S.J.; Lau, J.S.H.; El-Nezami, H. Herbal medicine: Toxicity and recent trends in assessing their potential toxic effects. In Advances in Botanical Research; Shyur, L.-F., Lau, A.S.Y., Eds.; Elsevier Academic Press: Cambridge, MA, USA, 2012; Volume 62, pp. 365–384. [Google Scholar]
  2. Islam, S.U.; Dar, T.U.; Khuroo, A.A.; Bhat, B.A.; Mangral, Z.A.; Tariq, L.; Tantray, W.W.; Malik, A.H. DNA barcoding aids in identification of adulterants of Trillium govanianum Wall. ex D. Don. J. Appl. Res. Med. Aromat. Plants 2021, 23, 100305. [Google Scholar] [CrossRef]
  3. Aware, C.B.; Patil, D.N.; Suryawanshi, S.S.; Mali, P.R.; Rane, M.R.; Gurav, R.G.; Jadhav, J.P. Natural bioactive products as promising therapeutics: A review of natural product-based drug development. S. Afr. J. Bot. 2022, 151, 512–528. [Google Scholar] [CrossRef]
  4. Tnah, L.; Lee, S.; Tan, A.; Lee, C.; Ng, K.; Ng, C.; Farhanah, Z.N. DNA barcode database of common herbal plants in the tropics: A resource for herbal product authentication. Food Control 2019, 95, 318–326. [Google Scholar] [CrossRef]
  5. Jordan, S.A.; Cunningham, D.G.; Marles, R.J. Assessment of herbal medicinal products: Challenges, and opportunities to increase the knowledge base for safety assessment. Toxicol. Appl. Pharmacol. 2010, 243, 198–216. [Google Scholar] [CrossRef]
  6. Newmaster, S.G.; Grguric, M.; Shanmughanandhan, D.; Ramalingam, S.; Ragupathy, S. DNA barcoding detects contamination and substitution in North American herbal products. BMC Med. 2013, 11, 222. [Google Scholar] [CrossRef] [Green Version]
  7. Wallace, L.J.; Boilard, S.M.; Eagle, S.H.; Spall, J.L.; Shokralla, S.; Hajibabaei, M. DNA barcodes for everyday life: Routine authentication of Natural Health Products. Food Res. Int. 2012, 49, 446–452. [Google Scholar] [CrossRef]
  8. Lo, Y.T.; Shaw, P.C. Application of next-generation sequencing for the identification of herbal products. Biotechnol. Adv. 2019, 37, 107450. [Google Scholar] [CrossRef]
  9. Tankeu, S.; Vermaak, I.; Chen, W.; Sandasi, M.; Viljoen, A. Differentiation between two “fang ji” herbal medicines, Stephania tetrandra and the nephrotoxic Aristolochia fangchi, using hyperspectral imaging. Phytochemistry 2016, 122, 213–222. [Google Scholar] [CrossRef]
  10. Kerchner, A.; Farkas, Á. Worldwide poisoning potential of Brugmansia and Datura. Forensic Toxicol. 2020, 38, 30–41. [Google Scholar] [CrossRef] [Green Version]
  11. Han, J.; Li, M.; Luo, K.; Liu, M.; Chen, X.; Chen, S. Identification of Daturae flos and its adulterants based on DNA barcoding technique. Yao Xue Xue Bao 2011, 46, 1408–1412. [Google Scholar]
  12. Pandey, R.; Tiwari, R.K.; Shukla, S.S. Omics: A newer technique in herbal drug standardization & quantification. J. Young Pharm. 2016, 8, 76. [Google Scholar]
  13. Liu, M.; Li, X.-W.; Liao, B.-S.; Luo, L.; Ren, Y.-Y. Species identification of poisonous medicinal plant using DNA barcoding. Chin. J. Nat. Med. 2019, 17, 585–590. [Google Scholar] [CrossRef]
  14. Gowthaman, N.; Lim, H.; Gopi, S.; Amalraj, A. Identification of toxicology biomarker and evaluation of toxicity of natural products by metabolomic applications. In Inflammation and Natural Products, 1st ed.; Gopi, S., Amalraj, A., Kunnumakkara, A., Thomas, S., Eds.; Elsevier Academic Press: Cambridge, MA, USA, 2021; pp. 407–436. [Google Scholar]
  15. Gajula, S.N.R.; Nanjappan, S. Metabolomics: A recent advanced omics technology in herbal medicine research. In Medicinal and Aromatic Plants; Aftab, T., Hakeem, K.R., Eds.; Elsevier Academic Press: Cambridge, MA, USA, 2021; pp. 97–117. [Google Scholar]
  16. Mukne, A.; Momin, M.; Betkar, P.; Joshi, V. Standardization of herbal biomolecules. In Herbal Biomolecules in Healthcare Applications, 1st ed.; Mandal, S.C., Nayak, A.K., Dhara, A.K., Eds.; Elsevier Academic Press: Cambridge, MA, USA, 2022; pp. 643–667. [Google Scholar]
  17. Putri, S.P.; Nakayama, Y.; Matsuda, F.; Uchikata, T.; Kobayashi, S.; Matsubara, A.; Fukusaki, E. Current metabolomics: Practical applications. J. Biosci. Bioeng. 2013, 115, 579–589. [Google Scholar] [CrossRef] [PubMed]
  18. Braconi, D.; Millucci, L.; Parisi, M.L.; Spiga, O.; Santucci, A. Omics-based technologies for food authentication and traceability. In Food Authentication and Traceability; Galanakis, C.M., Ed.; Elsevier Academic Press: Cambridge, MA, USA, 2021; pp. 215–245. [Google Scholar]
  19. Sánchez, M.; González-Burgos, E.; Divakar, P.K.; Gómez-Serranillos, M.P. DNA-based authentication and metabolomics analysis of medicinal plants samples by DNA barcoding and ultra-high-performance liquid chromatography/triple quadrupole mass spectrometry (UHPLC-MS). Plants 2020, 9, 1601. [Google Scholar] [CrossRef] [PubMed]
  20. Xiao, Q.; Mu, X.; Liu, J.; Li, B.; Liu, H.; Zhang, B.; Xiao, P. Plant metabolomics: A new strategy and tool for quality evaluation of Chinese medicinal materials. Chin. Med. 2022, 17, 45. [Google Scholar] [CrossRef]
  21. Pan, H.; Yao, C.; Yao, S.; Yang, W.; Wu, W.; Guo, D. A metabolomics strategy for authentication of plant medicines with multiple botanical origins, a case study of Uncariae Rammulus Cum Uncis. J. Sep. Sci. 2020, 43, 1043–1050. [Google Scholar] [CrossRef]
  22. Fukuda, E.; Baba, M.; Iwasaki, N.; Uesawa, Y.; Arifuku, K.; Kamo, O.; Tsubono, K.; Okada, Y. Identification of Glycyrrhiza species by direct analysis in real time mass spectrometry. Nat. Prod. Commun. 2010, 5, 1755–1758. [Google Scholar] [CrossRef] [Green Version]
  23. Bārzdiņa, A.; Paulausks, A.; Bandere, D.; Brangule, A. The Potential Use of Herbal Fingerprints by Means of HPLC and TLC for Characterization and Identification of Herbal Extracts and the Distinction of Latvian Native Medicinal Plants. Molecules 2022, 27, 2555. [Google Scholar] [CrossRef]
  24. Syed, M.; Khan, M.N.; Khadim, A.; Shadab, H.; Perveen, A.; El-Seedi, H.R.; Musharraf, S.G. Chemical fingerprinting of three Anemone species and an adulteration study to detect cross mixing of medicinal plants by HPLC-HR-ESI-MS/MS method. J. King Saud Univ. Sci. 2021, 33, 101461. [Google Scholar] [CrossRef]
  25. Soininen, T.H.; Jukarainen, N.; Auriola, S.O.; Julkunen-Tiitto, R.; Karjalainen, R.; Vepsäläinen, J.J. Quantitative metabolite profiling of edible onion species by NMR and HPLC–MS. Food Chem. 2014, 165, 499–505. [Google Scholar] [CrossRef]
  26. Zhang, J.; Wang, C.; Wu, W.; Jin, Q.; Wu, J.; Yang, L.; An, Y.; Yao, C.; Wei, W.; Song, J.; et al. Authentication of herbal medicines from multiple botanical origins with cross-validation mebabolomics, absolute quantification and support vector machine model, a case study of Rhizoma Alismatis. Arab. J. Chem. 2022, 15, 104118. [Google Scholar] [CrossRef]
  27. Rakhesh, K.V.; Ashalatha, S.N.; Mahima, K. In vitro regeneration and chromatographic fingerprint analysis of Aphanamixis polystachya (Wall.) Parker by HPTLC technique. S. Afr. J. Bot. 2022, 151, 259–265. [Google Scholar] [CrossRef]
  28. Jiménez-Sánchez, C.; Lozano-Sánchez, J.; Rodríguez-Pérez, C.; Segura-Carretero, A.; Fernández-Gutiérrez, A. Comprehensive, untargeted, and qualitative RP-HPLC-ESI-QTOF/MS2 metabolite profiling of green asparagus (Asparagus officinalis). J. Food Compos. Anal. 2016, 46, 78–87. [Google Scholar] [CrossRef]
  29. Dawane, V.; Pathak, B. Assessment of secondary metabolite profile and quantification method development for Lupeol and Caffeic acid by HPTLC in Avicennia marina pneumatophore roots. Biocatal. Agric. Biotechnol. 2020, 26, 101573. [Google Scholar] [CrossRef]
  30. Shah, A.P.; Travadi, T.; Sharma, S.; Pandit, R.; Joshi, C.; Joshi, M. Comprehensive analysis using DNA metabarcoding, PCR, and HPLC unveils the adulteration in Brahmi herbal products. bioRxiv 2022. [Google Scholar] [CrossRef]
  31. Ullah, I.; Subhan, F.; Rudd, J.A.; Rauf, K.; Alam, J.; Shahid, M.; Sewell, R.D. Attenuation of cisplatin-induced emetogenesis by standardized Bacopa monnieri extracts in the pigeon: Behavioral and neurochemical correlations. Planta Med. 2014, 80, 1569–1579. [Google Scholar]
  32. Ullah, I.; Subhan, F.; Lu, Z.; Chan, S.W.; Rudd, J.A. Action of Bacopa monnieri to antagonize cisplatin-induced emesis in Suncus murinus (house musk shrew). J. Pharmacol. Sci. 2017, 133, 232–239. [Google Scholar] [CrossRef]
  33. Wang, Y.; Harrington, P.d.B.; Chen, P. Metabolomic profiling and comparison of major cinnamon species using UHPLC–HRMS. Anal. Bioanal. Chem. 2020, 412, 7669–7681. [Google Scholar] [CrossRef]
  34. Petrakis, E.A.; Cagliani, L.R.; Polissiou, M.G.; Consonni, R. Evaluation of saffron (Crocus sativus L.) adulteration with plant adulterants by 1H NMR metabolite fingerprinting. Food Chem. 2015, 173, 890–896. [Google Scholar] [CrossRef]
  35. Windarsih, A.; Rohman, A.; Swasono, R.T. Application of H-NMR metabolite fingerprinting and chemometrics for the authentication of Curcuma longa adulterated with Curcuma manga. J. Appl. Pharm. Sci. 2018, 8, 075–081. [Google Scholar]
  36. Rohman, A.; Wijayanti, T.; Windarsih, A.; Riyanto, S. The authentication of Java turmeric (Curcuma xanthorrhiza) using thin layer chromatography and 1H-NMR based-metabolite fingerprinting coupled with multivariate analysis. Molecules 2020, 25, 3928. [Google Scholar] [CrossRef] [PubMed]
  37. Windarsih, A.; Rohman, A.; Swasono, R.T. Application of 1H-NMR based metabolite fingerprinting and chemometrics for authentication of Curcuma longa adulterated with C. heyneana. J. Appl. Res. Med. Aromat. Plants 2019, 13, 100203. [Google Scholar] [CrossRef]
  38. Gu, X.; Zhu, S.; Du, H.; Bai, C.; Duan, X.; Li, Y.; Hu, K. Comprehensive multi-component analysis for authentication and differentiation of 6 Dendrobium species by 2D NMR-based metabolomic profiling. Microchem. J. 2022, 176, 107225. [Google Scholar] [CrossRef]
  39. Ullrich, S.F.; Averesch, N.J.; Castellanos, L.; Choi, Y.H.; Rothauer, A.; Kayser, O. Discrimination of wild types and hybrids of Duboisia myoporoides and Duboisia leichhardtii at different growth stages using 1H NMR-based metabolite profiling and tropane alkaloids-targeted HPLC-MS analysis. Phytochemistry 2016, 131, 44–56. [Google Scholar] [CrossRef]
  40. Ibragic, S.; Barbini, S.; Oberlerchner, J.T.; Potthast, A.; Rosenau, T.; Böhmdorfer, S. Antioxidant properties and qualitative analysis of phenolic constituents in Ephedra spp. by HPTLC together with injection port derivatization GC–MS. J. Chromatogr. B 2021, 1180, 122877. [Google Scholar] [CrossRef]
  41. Kim, H.K.; Choi, Y.H.; Erkelens, C.; Lefeber, A.W.; Verpoorte, R. Metabolic fingerprinting of Ephedra species using 1H-NMR spectroscopy and principal component analysis. Chem. Pharm. Bull. 2005, 53, 105–109. [Google Scholar] [CrossRef] [Green Version]
  42. Singh, P.; Ranjan, R.; Tripathi, R.; Dixit, J.; Sinha, N.; Singh, A.K.; Tiwari, K.N. GC-MS and NMR spectroscopy based metabolite profiling of Panchvalkal kwath (polyherbal formulation). Nat. Prod. Res. 2021, 1–6. [Google Scholar] [CrossRef]
  43. Wang, Z.; Xie, H.; Ren, J.; Chen, Y.; Li, X.; Chen, X.; Chan, T.-W.D. Metabolomic approach for rapid differentiation of Fritillaria bulbs by matrix-assisted laser desorption/ionization mass spectrometry and multivariate statistical analysis. J. Pharm. Biomed. Anal. 2020, 185, 113177. [Google Scholar] [CrossRef]
  44. Xiao, J.-J.; Huang, H.; Lei, Y.-C.; Lin, T.-W.; Ma, Y.; Zhang, J.; Zhang, X.-G.; Zhang, D.-Q.; Lv, G.-H. Development of Tianma HPLC fingerprint and discriminant analysis. Zhongguo Zhong Yao Za Zhi 2017, 42, 2524–2531. [Google Scholar]
  45. Guo, J.; Wu, Y.; Jiang, M.; Wu, C.; Wang, G. An LC–MS-based metabolomic approach provides insights into the metabolite profiles of Ginkgo biloba L. at different developmental stages and in various organs. Food Res. Int. 2022, 159, 111644. [Google Scholar] [CrossRef]
  46. Farag, M.A.; Afifi, S.M.; Rasheed, D.M.; Khattab, A.R. Revealing compositional attributes of Glossostemon bruguieri Desf. root geographic origin and roasting impact via chemometric modeling of SPME-GC-MS and NMR metabolite profiles. J. Food Compos. Anal. 2021, 102, 104073. [Google Scholar] [CrossRef]
  47. Zhao, J.; Wang, M.; Adams, S.J.; Lee, J.; Chittiboyina, A.G.; Avula, B.; Ali, Z.; Raman, V.; Li, J.; Wu, C.; et al. Metabolite variation and discrimination of five licorice (Glycyrrhiza) species: HPTLC and NMR explorations. J. Pharm. Biomed. Anal. 2022, 220, 115012. [Google Scholar] [CrossRef] [PubMed]
  48. Farag, M.A.; Porzel, A.; Wessjohann, L.A. Comparative metabolite profiling and fingerprinting of medicinal licorice roots using a multiplex approach of GC–MS, LC–MS and 1D NMR techniques. Phytochemistry 2012, 76, 60–72. [Google Scholar] [CrossRef] [PubMed]
  49. Rizzato, G.; Scalabrin, E.; Radaelli, M.; Capodaglio, G.; Piccolo, O. A new exploration of licorice metabolome. Food Chem. 2017, 221, 959–968. [Google Scholar] [CrossRef] [PubMed]
  50. Frommenwiler, D.A.; Maire-Widmer, V.; Upton, R.; Nichols, J.; Heubl, G.; Reich, E. Qualitative and quantitative characterization of two licorice root species (Glycyrrhiza glabra L. and Glycyrrhiza uralensis Fisch.) by HPTLC, validated by HPLC and DNA sequencing. JPC-J. Planar Chromatogr.-Mod. TLC 2017, 30, 467–473. [Google Scholar] [CrossRef]
  51. Song, W.; Qiao, X.; Chen, K.; Wang, Y.; Ji, S.; Feng, J.; Li, K.; Lin, Y.; Ye, M. Biosynthesis-based quantitative analysis of 151 secondary metabolites of licorice to differentiate medicinal Glycyrrhiza species and their hybrids. Anal. Chem. 2017, 89, 3146–3153. [Google Scholar] [CrossRef]
  52. Shawky, E.; El Sohafy, S.M. Untargeted and targeted chemical profiling for efficacy-directed discrimination of Hedera helix L. subspecies using HPTLC-image analysis and HPTLC/MS. Ind. Crops Prod. 2020, 145, 111980. [Google Scholar] [CrossRef]
  53. Trute, A.; Nahrstedt, A. Identification and quantitative analysis of phenolic compounds from the dry extract of Hedera helix. Planta Med. 1997, 63, 177–179. [Google Scholar] [CrossRef]
  54. Xie, G.-Y.; Zhu, Y.; Shu, P.; Qin, X.-Y.; Wu, G.; Wang, Q.; Qin, M.-J. Phenolic metabolite profiles and antioxidants assay of three Iridaceae medicinal plants for traditional Chinese medicine “She-gan” by on-line HPLC–DAD coupled with chemiluminescence (CL) and ESI-Q-TOF-MS/MS. J. Pharm. Biomed. Anal. 2014, 98, 40–51. [Google Scholar] [CrossRef]
  55. Gao, W.; Yang, H.; Qi, L.-W.; Liu, E.-H.; Ren, M.-T.; Yan, Y.-T.; Chen, J.; Li, P. Unbiased metabolite profiling by liquid chromatography–quadrupole time-of-flight mass spectrometry and multivariate data analysis for herbal authentication: Classification of seven Lonicera species flower buds. J. Chromatogr. A 2012, 1245, 109–116. [Google Scholar] [CrossRef]
  56. Brahmi, F.; Nguyen, A.T.; Nacoulma, A.P.; Sheridan, H.; Wang, J.; Guendouze, N.; Madani, K.; Duez, P. Discrimination of Mentha species grown in different geographical areas of Algeria using 1H-NMR-based metabolomics. J. Pharm. Biomed. Anal. 2020, 189, 113430. [Google Scholar] [CrossRef] [PubMed]
  57. Kustiati, U.; Wihadmadyatami, H.; Kusindarta, D.L. Dataset of Phytochemical and secondary metabolite profiling of holy basil leaf (Ocimum sanctum Linn) ethanolic extract using spectrophotometry, thin layer chromatography, Fourier transform infrared spectroscopy, and nuclear magnetic resonance. Data Brief 2022, 40, 107774. [Google Scholar] [CrossRef] [PubMed]
  58. Nguyen, H.T.; Lee, D.-K.; Choi, Y.-G.; Min, J.-E.; Yoon, S.J.; Yu, Y.-H.; Lim, J.; Lee, J.; Kwon, S.W.; Park, J.H. A 1H NMR-based metabolomics approach to evaluate the geographical authenticity of herbal medicine and its application in building a model effectively assessing the mixing proportion of intentional admixtures: A case study of Panax ginseng: Metabolomics for the authenticity of herbal medicine. J. Pharm. Biomed. Anal. 2016, 124, 120–128. [Google Scholar] [PubMed]
  59. Santos, M.S.; Pereira-Filho, E.R.; Ferreira, A.G.; Boffo, E.F.; Figueira, G.M. Authenticity study of Phyllanthus species by NMR and FT-IR Techniques coupled with chemometric methods. Quím Nova 2012, 35, 2210–2217. [Google Scholar] [CrossRef] [Green Version]
  60. Ibrahim, R.S.; Fathy, H. Targeted and untargeted-metabolite profiling to track the compositional integrity of ginger during processing using digitally-enhanced HPTLC pattern recognition analysis. J. Chromatogr. B 2018, 1080, 59–63. [Google Scholar] [CrossRef]
  61. British Pharmacopoeia Commission. Deoxyribonucleic acid (DNA) based identification techniques for herbal drugs. In British Pharmacopoeia Appendix XI; TSO: London, UK, 2017; pp. 354–357. [Google Scholar]
  62. Wu, H.-Y.; Shaw, P.-C. Strategies for molecular authentication of herbal products: From experimental design to data analysis. Chin. Med. 2022, 17, 38. [Google Scholar] [CrossRef]
  63. Wong, T.-H.; But, G.W.-C.; Wu, H.-Y.; Tsang, S.S.-K.; Lau, D.T.-W.; Shaw, P.-C. Medicinal materials DNA barcode database (MMDBD) version 1.5—One-stop solution for storage, BLAST, alignment and primer design. Database 2018, 2018, bay112. [Google Scholar] [CrossRef]
  64. Kazi, T.; Hussain, N.; Bremner, P.; Slater, A.; Howard, C. The application of a DNA-based identification technique to over-the-counter herbal medicines. Fitoterapia 2013, 87, 27–30. [Google Scholar] [CrossRef]
  65. Mishra, P.; Kumar, A.; Nagireddy, A.; Mani, D.N.; Shukla, A.K.; Tiwari, R.; Sundaresan, V. DNA barcoding: An efficient tool to overcome authentication challenges in the herbal market. Plant Biotechnol. J. 2016, 14, 8–21. [Google Scholar] [CrossRef]
  66. Techen, N.; Parveen, I.; Pan, Z.; Khan, I.A. DNA barcoding of medicinal plant material for identification. Curr. Opin. Biotechnol. 2014, 25, 103–110. [Google Scholar] [CrossRef]
  67. Hebert, P.D.; Cywinska, A.; Ball, S.L.; DeWaard, J.R. Biological identifications through DNA barcodes. Proc. R. Soc. Lond. B Biol. Sci. 2003, 270, 313–321. [Google Scholar] [CrossRef] [Green Version]
  68. Lahaye, R.; Van der Bank, M.; Bogarin, D.; Warner, J.; Pupulin, F.; Gigot, G.; Maurin, O.; Duthoit, S.; Barraclough, T.G.; Savolainen, V. DNA barcoding the floras of biodiversity hotspots. Proc. Natl. Acad. Sci. USA 2008, 105, 2923–2928. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  69. Chen, S.; Yao, H.; Han, J.; Liu, C.; Song, J.; Shi, L.; Zhu, Y.; Ma, X.; Gao, T.; Pang, X.; et al. Validation of the ITS2 region as a novel DNA barcode for identifying medicinal plant species. PLoS ONE 2010, 5, e8613. [Google Scholar] [CrossRef] [PubMed]
  70. Parveen, I.; Gafner, S.; Techen, N.; Murch, S.J.; Khan, I.A. DNA barcoding for the identification of botanicals in herbal medicine and dietary supplements: Strengths and limitations. Planta Med. 2016, 82, 1225–1235. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  71. Gao, T.; Yao, H.; Song, J.; Liu, C.; Zhu, Y.; Ma, X.; Pang, X.; Xu, H.; Chen, S. Identification of medicinal plants in the family Fabaceae using a potential DNA barcode ITS2. J. Ethnopharmacol. 2010, 130, 116–121. [Google Scholar] [CrossRef]
  72. Sun, Z.; Chen, S. Identification of cortex herbs using the DNA barcode nrITS2. J. Nat. Med. 2013, 67, 296–302. [Google Scholar] [CrossRef]
  73. Pang, X.; Shi, L.; Song, J.; Chen, X.; Chen, S. Use of the potential DNA barcode ITS2 to identify herbal materials. J. Nat. Med. 2013, 67, 571–575. [Google Scholar] [CrossRef]
  74. Xu, B.; Zeng, X.-M.; Gao, X.-F.; Jin, D.-P.; Zhang, L.-B. ITS non-concerted evolution and rampant hybridization in the legume genus Lespedeza (Fabaceae). Sci. Rep. 2017, 7, 40057. [Google Scholar] [CrossRef] [Green Version]
  75. Costa, J.; Campos, B.; Amaral, J.S.; Nunes, M.E.; Oliveira, M.B.P.; Mafra, I. HRM analysis targeting ITS1 and matK loci as potential DNA mini-barcodes for the authentication of Hypericum perforatum and Hypericum androsaemum in herbal infusions. Food Control 2016, 61, 105–114. [Google Scholar] [CrossRef]
  76. Gao, Z.; Liu, Y.; Wang, X.; Wei, X.; Han, J. DNA mini-barcoding: A derived barcoding method for herbal molecular identification. Front. Plant Sci. 2019, 10, 987. [Google Scholar] [CrossRef]
  77. Pawar, R.S.; Handy, S.M.; Cheng, R.; Shyong, N.; Grundel, E. Assessment of the authenticity of herbal dietary supplements: Comparison of chemical and DNA barcoding methods. Planta Med. 2017, 83, 921–936. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  78. Ming, S.; Gang-Qiang, D.; Zhang, Y.-Q.; Xia, L.; Wei, S. Identification of processed Chinese medicinal materials using DNA mini-barcoding. Chin. J. Nat. Med. 2017, 15, 481–486. [Google Scholar]
  79. Little, D.P. Authentication of Ginkgo biloba herbal dietary supplements using DNA barcoding. Genome 2014, 57, 513–516. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  80. Frigerio, J.; Gorini, T.; Galimberti, A.; Bruni, I.; Tommasi, N.; Mezzasalma, V.; Labra, M. DNA barcoding to trace Medicinal and Aromatic Plants from the field to the food supplement. J. Appl. Bot. Food Qual. 2019, 92, 33–38. [Google Scholar]
  81. Zhang, W.; Yang, S.; Zhao, H.; Huang, L. Using the ITS2 sequence-structure as a DNA mini-barcode: A case study in authenticating the traditional medicine “Fang Feng”. Biochem. Syst. Ecol. 2016, 69, 188–194. [Google Scholar] [CrossRef]
  82. Raclariu, A.C.; Mocan, A.; Popa, M.O.; Vlase, L.; Ichim, M.C.; Crisan, G.; Brysting, A.K.; de Boer, H. Veronica officinalis product authentication using DNA metabarcoding and HPLC-MS reveals widespread adulteration with Veronica chamaedrys. Front. Pharmacol. 2017, 8, 378. [Google Scholar] [CrossRef] [Green Version]
  83. Ivanova, N.V.; Kuzmina, M.L.; Braukmann, T.W.; Borisenko, A.V.; Zakharov, E.V. Authentication of herbal supplements using next-generation sequencing. PLoS ONE 2016, 11, e0156426. [Google Scholar] [CrossRef]
  84. Raclariu, A.C.; Heinrich, M.; Ichim, M.C.; de Boer, H. Benefits and limitations of DNA barcoding and metabarcoding in herbal product authentication. Phytochem. Anal. 2018, 29, 123–128. [Google Scholar] [CrossRef]
  85. Raclariu, A.C.; Ţebrencu, C.E.; Ichim, M.C.; Ciupercǎ, O.T.; Brysting, A.K.; de Boer, H. What’s in the box? Authentication of Echinacea herbal products using DNA metabarcoding and HPTLC. Phytomedicine 2018, 44, 32–38. [Google Scholar] [CrossRef]
  86. Raclariu, A.C.; Paltinean, R.; Vlase, L.; Labarre, A.; Manzanilla, V.; Ichim, M.C.; Crisan, G.; Brysting, A.K.; de Boer, H. Comparative authentication of Hypericum perforatum herbal products using DNA metabarcoding, TLC and HPLC-MS. Sci. Rep. 2017, 7, 1291. [Google Scholar] [CrossRef] [Green Version]
  87. Coghlan, M.L.; Haile, J.; Houston, J.; Murray, D.C.; White, N.E.; Moolhuijzen, P.; Bellgard, M.I.; Bunce, M. Deep sequencing of plant and animal DNA contained within traditional Chinese medicines reveals legality issues and health safety concerns. PLoS Genet. 2012, 8, e1002657. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  88. Arulandhu, A.J.; Staats, M.; Hagelaar, R.; Peelen, T.; Kok, E.J. The application of multi-locus DNA metabarcoding in traditional medicines. J. Food Compos. Anal. 2019, 79, 87–94. [Google Scholar] [CrossRef]
  89. Mahgoub, Y.A.; Shawky, E.; Eldakak, M.; Bahey-El-Din, M.; Darwish, F.A.; El Sebakhy, N.A.; El-Hawiet, A. Plant DNA barcoding and metabolomics for comprehensive discrimination of German Chamomile from its poisonous adulterants for food safety. Food Control 2022, 136, 108840. [Google Scholar] [CrossRef]
  90. Zhang, N.; Erickson, D.L.; Ramachandran, P.; Ottesen, A.R.; Timme, R.E.; Funk, V.A.; Luo, Y.; Handy, S.M. An analysis of Echinacea chloroplast genomes: Implications for future botanical identification. Sci. Rep. 2017, 7, 216. [Google Scholar] [CrossRef] [PubMed]
  91. Osathanunkul, M.; Suwannapoom, C.; Ounjai, S.; Rora, J.A.; Madesis, P.; De Boer, H. Refining DNA barcoding coupled high resolution melting for discrimination of 12 closely related croton species. PLoS ONE 2015, 10, e0138888. [Google Scholar] [CrossRef]
  92. Thongkhao, K.; Tungphatthong, C.; Phadungcharoen, T.; Sukrong, S. The use of plant DNA barcoding coupled with HRM analysis to differentiate edible vegetables from poisonous plants for food safety. Food Control 2020, 109, 106896. [Google Scholar] [CrossRef]
  93. Ounjai, S.; Osathanunkul, R.; Madesis, P.; Osathanunkul, M. Multi chloroplast genes for species identification in bar-HRM analysis of taxonomical complex medicinal plants group (Zingiberaceae). Chiang Mai J. Sci. 2017, 44, 1311–1321. [Google Scholar]
  94. Zhao, B.; Xiong, C.; Wu, L.; Xiang, L.; Shi, Y.; Sun, W.; Chen, S. DNA barcoding coupled with high resolution melting for rapid identification of Ardisia gigantifolia and its toxic adulterants. Biotechnol. Biotechnol. Equip. 2021, 35, 641–649. [Google Scholar] [CrossRef]
  95. Buddhachat, K.; Osathanunkul, M.; Madesis, P.; Chomdej, S.; Ongchai, S. Authenticity analyses of Phyllanthus amarus using barcoding coupled with HRM analysis to control its quality for medicinal plant product. Gene 2015, 573, 84–90. [Google Scholar] [CrossRef]
  96. Kalivas, A.; Ganopoulos, I.; Xanthopoulou, A.; Chatzopoulou, P.; Tsaftaris, A.; Madesis, P. DNA barcode ITS2 coupled with high resolution melting (HRM) analysis for taxonomic identification of Sideritis species growing in Greece. Mol. Biol. Rep. 2014, 41, 5147–5155. [Google Scholar] [CrossRef]
  97. Duan, B.-Z.; Wang, Y.-P.; Fang, H.-L.; Xiong, C.; Li, X.-W.; Wang, P.; Chen, S.-L. Authenticity analyses of Rhizoma Paridis using barcoding coupled with high resolution melting (Bar-HRM) analysis to control its quality for medicinal plant product. Chin. Med. 2018, 13, 8. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  98. Liu, Y.-Y. Identification of Gentiana rhodantha and its adulterants by DNA barcoding coupled with high resolution melting analysis. Chin. Pharm. J. 2019, 24, 687–692. [Google Scholar]
  99. Xiong, C.; Zhi-Gang, H.; Yuan, T.; He-Gang, L.; Ping, W.; Ming-Ming, Z.; SHIi, Y.-H.; Lan, W.; Wei, S.; Shi-Lin, C. ITS2 barcoding DNA region combined with high resolution melting (HRM) analysis of Hyoscyami Semen, the mature seed of Hyoscyamus niger. Chin. J. Nat. Med. 2016, 14, 898–903. [Google Scholar] [CrossRef] [PubMed]
  100. Song, M.; Li, J.; Xiong, C.; Liu, H.; Liang, J. Applying high-resolution melting (HRM) technology to identify five commonly used Artemisia species. Sci. Rep. 2016, 6, 34133. [Google Scholar] [CrossRef] [Green Version]
  101. Osathanunkul, M.; Madesis, P.; De Boer, H. Bar-HRM for authentication of plant-based medicines: Evaluation of three medicinal products derived from Acanthaceae species. PLoS ONE 2015, 10, e0128476. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  102. Liu, Y.; Xiang, L.; Zhang, Y.; Lai, X.; Xiong, C.; Li, J.; Su, Y.; Sun, W.; Chen, S. DNA barcoding based identification of Hippophae species and authentication of commercial products by high resolution melting analysis. Food Chem. 2018, 242, 62–67. [Google Scholar] [CrossRef]
  103. Grazina, L.; Amaral, J.S.; Costa, J.; Mafra, I. Towards authentication of Korean ginseng-containing foods: Differentiation of five Panax species by a novel diagnostic tool. LWT 2021, 151, 112211. [Google Scholar] [CrossRef]
  104. Fadzil, N.F.; Wagiran, A.; Mohd Salleh, F.; Abdullah, S.; Mohd Izham, N.H. Authenticity testing and detection of Eurycoma longifolia in commercial herbal products using bar-high resolution melting analysis. Genes 2018, 9, 408. [Google Scholar] [CrossRef] [Green Version]
  105. He, Y.; Hou, P.; Fan, G.; Song, Z.; Arain, S.; Shu, H.; Tang, C.; Yue, Q.; Zhang, Y. Authentication of Angelica anomala Avé-Lall cultivars through DNA barcodes. Mitochondrial DNA 2012, 23, 100–105. [Google Scholar] [CrossRef]
  106. Liu, J.; Shi, L.; Han, J.; Li, G.; Lu, H.; Hou, J.; Zhou, X.; Meng, F.; Downie, S.R. Identification of species in the angiosperm family Apiaceae using DNA barcodes. Mol. Ecol. Resour. 2014, 14, 1231–1238. [Google Scholar] [CrossRef]
  107. Lee, S.Y.; Ng, W.L.; Mahat, M.N.; Nazre, M.; Mohamed, R. DNA barcoding of the endangered Aquilaria (Thymelaeaceae) and its application in species authentication of agarwood products traded in the market. PLoS ONE 2016, 11, e0154631. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  108. Xiao, W.-L.; Motley, T.J.; Unachukwu, U.J.; Lau, C.B.S.; Jiang, B.; Hong, F.; Leung, P.-C.; Wang, Q.-F.; Livingston, P.O.; Cassileth, B.R. Chemical and genetic assessment of variability in commercial Radix Astragali (Astragalus spp.) by ion trap LC− MS and nuclear ribosomal DNA barcoding sequence analyses. J. Agric. Food Chem. 2011, 59, 1548–1556. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  109. Selvaraj, D.; Shanmughanandhan, D.; Sarma, R.K.; Joseph, J.C.; Srinivasan, R.V.; Ramalingam, S. DNA barcode ITS effectively distinguishes the medicinal plant Boerhavia diffusa from its adulterants. Genom. Proteom. Bioinform. 2012, 10, 364–367. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  110. Saarela, J.M.; Sokoloff, P.C.; Gillespie, L.J.; Consaul, L.L.; Bull, R.D. DNA barcoding the Canadian Arctic flora: Core plastid barcodes (rbcL+ matK) for 490 vascular plant species. PLoS ONE 2013, 8, e77982. [Google Scholar] [CrossRef] [Green Version]
  111. Jamdade, R.; Mosa, K.A.; El-Keblawy, A.; Al Shaer, K.; Al Harthi, E.; Al Sallani, M.; Al Jasmi, M.; Gairola, S.; Shabana, H.; Mahmoud, T. DNA barcodes for accurate identification of selected medicinal plants (Caryophyllales): Toward barcoding flowering plants of the United Arab Emirates. Diversity 2022, 14, 262. [Google Scholar] [CrossRef]
  112. Nio, S.A.; Kolondam, B.J.; Tallei, T.E. Evaluation of matK and rbcL genes as markers in DNA barcoding of Codiaeum variegatum (L.) Blume. Biosci. Res. 2018, 15, 192–198. [Google Scholar]
  113. Gismondi, A.; Fanali, F.; Labarga, J.M.M.; Caiola, M.G.; Canini, A. Crocus sativus L. genomics and different DNA barcode applications. Plant Syst. Evol. 2013, 299, 1859–1863. [Google Scholar] [CrossRef]
  114. Parvathy, V.; Swetha, V.; Sheeja, T.; Sasikumar, B. Detection of plant-based adulterants in turmeric powder using DNA barcoding. Pharm. Biol. 2015, 53, 1774–1779. [Google Scholar] [CrossRef]
  115. Li, Q.; Wu, J.; Wang, Y.; Lian, X.; Wu, F.; Zhou, L.; Huang, Z.; Zhu, S. The phylogenetic analysis of Dalbergia (Fabaceae: Papilionaceae) based on different DNA barcodes. Holzforschung 2017, 71, 939–949. [Google Scholar] [CrossRef]
  116. Yu, M.; Liu, K.; Zhou, L.; Zhao, L.; Liu, S. Testing three proposed DNA barcodes for the wood identification of Dalbergia odorifera T. Chen and Dalbergia tonkinensis Prain. Holzforschung 2016, 70, 127–136. [Google Scholar] [CrossRef]
  117. Wong, K.-L.; But, P.-H.; Shaw, P.-C. Evaluation of seven DNA barcodes for differentiating closely related medicinal Gentiana species and their adulterants. Chin. Med. 2013, 8, 16. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  118. Zhu, X.; Zhang, Y.; Liu, X.; Hou, D.; Gao, T. Authentication of commercial processed Glehniae Radix (Beishashen) by DNA barcodes. Chin. Med. 2015, 10, 35. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  119. Poovitha, S.; Stalin, N.; Balaji, R.; Parani, M. Multi-locus DNA barcoding identifies matK as a suitable marker for species identification in Hibiscus L. Genome 2016, 59, 1150–1156. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  120. Sun, Z.; Gao, T.; Yao, H.; Shi, L.; Zhu, Y.; Chen, S. Identification of Lonicera japonica and its related species using the DNA barcoding method. Planta Med. 2011, 77, 301–306. [Google Scholar] [CrossRef] [Green Version]
  121. Kumar, J.S.; Ramakrishan, M.; Seethapathy, G.; Krishna, V.; Shaanker, R.U.; Ravikanth, G. DNA barcoding of Momordica species and assessment of adulteration in Momordica herbal products, an anti-diabetic drug. Plant Gene 2020, 22, 100227. [Google Scholar] [CrossRef]
  122. Gogoi, B.; Bhau, B.S. DNA barcoding of the genus Nepenthes (Pitcher plant): A preliminary assessment towards its identification. BMC Plant Biol. 2018, 18, 153. [Google Scholar] [CrossRef]
  123. Chen, X.; Liao, B.; Song, J.; Pang, X.; Han, J.; Chen, S. A fast SNP identification and analysis of intraspecific variation in the medicinal Panax species based on DNA barcoding. Gene 2013, 530, 39–43. [Google Scholar] [CrossRef]
  124. Zuo, Y.; Chen, Z.; Kondo, K.; Funamoto, T.; Wen, J.; Zhou, S. DNA barcoding of Panax species. Planta Med. 2011, 77, 182–187. [Google Scholar] [CrossRef]
  125. Srirama, R.; Senthilkumar, U.; Sreejayan, N.; Ravikanth, G.; Gurumurthy, B.; Shivanna, M.; Sanjappa, M.; Ganeshaiah, K.; Shaanker, R.U. Assessing species admixtures in raw drug trade of Phyllanthus, a hepato-protective plant using molecular tools. J. Ethnopharmacol. 2010, 130, 208–215. [Google Scholar] [CrossRef]
  126. Sousa, A.I.; Ferreira, I.M.; Faria, M.A. Sensitive detection of Piper nigrum L. adulterants by a novel screening approach based on qPCR. Food Chem. 2019, 283, 596–603. [Google Scholar] [CrossRef]
  127. Parvathy, V.; Swetha, V.; Sheeja, T.; Leela, N.; Chempakam, B.; Sasikumar, B. DNA barcoding to detect chilli adulteration in traded black pepper powder. Food Biotechnol. 2014, 28, 25–40. [Google Scholar] [CrossRef]
  128. Zhang, J.-Q.; Meng, S.-Y.; Wen, J.; Rao, G.-Y. DNA barcoding of Rhodiola (Crassulaceae): A case study on a group of recently diversified medicinal plants from the Qinghai-Tibetan Plateau. PLoS ONE 2015, 10, e0119921. [Google Scholar] [CrossRef]
  129. Liu, Y.; Zhang, L.; Liu, Z.; Luo, K.; Chen, S.; Chen, K. Species identification of Rhododendron (Ericaceae) using the chloroplast deoxyribonucleic acid psbA-trnH genetic marker. Pharmacogn. Mag. 2012, 8, 29. [Google Scholar] [PubMed] [Green Version]
  130. Al-Qurainy, F.; Khan, S.; Tarroum, M.; Al-Hemaid, F.; Ali, M. Molecular authentication of the medicinal herb Ruta graveolens (Rutaceae) and an adulterant using nuclear and chloroplast DNA markers. Genet. Mol. Res. 2011, 10, 2806–2816. [Google Scholar] [CrossRef] [PubMed]
  131. Zhang, J.; Chen, M.; Dong, X.; Lin, R.; Fan, J.; Chen, Z. Evaluation of four commonly used DNA barcoding loci for Chinese medicinal plants of the family Schisandraceae. PLoS ONE 2015, 10, e0125574. [Google Scholar] [CrossRef] [PubMed]
  132. Pansa, M.; Runglawan, S.; Tawatchai, T.; Kowit, N.; Nat, B.; Arunrat, C. Species diversity, usages, molecular markers and barcode of medicinal Senna species (Fabaceae, Caesalpinioideae) in Thailand. J. Med. Plant Res. 2011, 5, 6173–6181. [Google Scholar]
  133. Vassou, S.L.; Kusuma, G.; Parani, M. DNA barcoding for species identification from dried and powdered plant parts: A case study with authentication of the raw drug market samples of Sida cordifolia. Gene 2015, 559, 86–93. [Google Scholar] [CrossRef]
  134. Wang, X.; Xue, J.; Zhang, Y.; Xie, H.; Wang, Y.; Weng, W.; Kang, Y.; Huang, J. DNA barcodes for the identification of Stephania (Menispermaceae) species. Mol. Biol. Rep. 2020, 47, 2197–2203. [Google Scholar] [CrossRef]
  135. Chen, Q.; Liu, Y.; Liu, Z.; Chen, S.; Chen, K. DNA bacording of Verbenaceae medicinal plant by using ITS2 and psbA-trnH region. Zhongguo Zhong Yao Za Zhi 2012, 37, 1107–1113. [Google Scholar]
  136. Thongkhao, K.; Prombutara, P.; Phadungcharoen, T.; Wiwatcharakornkul, W.; Tungphatthong, C.; Sukrong, M.; Sukrong, S. Integrative approaches for unmasking hidden species in herbal dietary supplement products: What is in the capsule? J. Food Compos. Anal. 2020, 93, 103616. [Google Scholar] [CrossRef]
  137. Efferth, T.; Greten, H.J. Potential of ‘Omics’ technologies for implementation in research on phytotherapeutical toxicology. In Advances in Botanical Research; Shyur, L.-F., Lau, A.S.Y., Eds.; Elsevier Academic Press: Cambridge, MA, USA, 2012; Volume 62, pp. 343–363. [Google Scholar]
  138. Wang, Y.-K.; Li, W.Q.; Xia, S.; Guo, L.; Miao, Y.; Zhang, B.-K. Metabolic activation of the toxic natural products from herbal and dietary supplements leading to toxicities. Front. Pharmacol. 2021, 12, 758468. [Google Scholar] [CrossRef] [PubMed]
  139. Boateng, I.D. A critical review of Ginkgolic acid in Ginkgo biloba leaves extract (EGb). Toxicity, technologies to remove the ginkgolic acids and its promising bioactivities. Food Funct. 2022, 13, 9226–9242. [Google Scholar] [CrossRef]
  140. Shang, M.-Y.; Tian, M.; Tanaka, H.; Li, X.-W.; Cai, S.-Q.; Shoyama, Y. Quality control of traditional chinese medicine by monoclonal antibody method. Curr. Drug Discov. Technol. 2011, 8, 60–65. [Google Scholar] [CrossRef]
  141. Loungratana, P.; Tanaka, H.; Shoyama, Y. Production of monoclonal antibody against ginkgolic acids in Ginkgo biloba Linn. Am. J. Chin. Med. 2004, 32, 33–48. [Google Scholar] [CrossRef] [PubMed]
  142. OECD. Test No. 407: Repeated Dose 28-day Oral Toxicity Study in Rodents. In OECD Guidelines for the Testing of Chemicals, Section 4; OECD Publishing: Paris, France, 2008. [Google Scholar] [CrossRef]
  143. OECD. Test No. 408: Repeated Dose 90-Day Oral Toxicity Study in Rodents. In OECD Guidelines for the Testing of Chemicals, Section 4; OECD Publishing: Paris, France, 2018. [Google Scholar] [CrossRef]
  144. OECD. Test No. 452: Chronic Toxicity Studies. In OECD Guidelines for the Testing of Chemicals, Section 4; OECD Publishing: Paris, France, 2018. [Google Scholar] [CrossRef]
  145. Toukourou, H.; Uwambayinema, F.; Yakoub, Y.; Mertens, B.; Laleye, A.; Lison, D.; Quetin-Leclercq, J.; Gbaguidi, F. In vitro and in vivo toxicity studies on Cymbopogon giganteus Chiov. leaves essential oil from Benin. J. Toxicol. 2020, 2020, 8261058. [Google Scholar] [CrossRef] [Green Version]
  146. Mishra, A.K.; Mishra, A.; Chattopadhyay, P. Screening of acute and sub-chronic dermal toxicity of Calendula officinalis L essential oil. Regul. Toxicol. Pharmacol. 2018, 98, 184–189. [Google Scholar] [CrossRef] [PubMed]
  147. Mekonnen, A.; Tesfaye, S.; Christos, S.G.; Dires, K.; Zenebe, T.; Zegeye, N.; Shiferaw, Y.; Lulekal, E. Evaluation of skin irritation and acute and subacute oral toxicity of Lavandula angustifolia essential oils in rabbit and mice. J. Toxicol. 2019, 2019, 5979546. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  148. Teke, G.N.; Kuete, V. Acute and subacute toxicities of African medicinal plants. In Toxicological Survey of African Medicinal Plants; Kuete, V., Ed.; Elsevier Academic Press: Cambridge, MA, USA, 2014; pp. 63–98. [Google Scholar]
  149. Ahmed, M. Acute toxicity (lethal dose 50 calculation) of herbal drug somina in rats and mice. Pharm. Pharmacol. 2015, 6, 185. [Google Scholar] [CrossRef] [Green Version]
  150. Zhao, Y.-L.; Su, M.; Shang, J.-H.; Wang, X.; Njateng, G.S.S.; Bao, G.-L.; Ma, J.; Sun, Q.-D.; Yuan, F.; Wang, J.-K.; et al. Acute and chronic toxicity of indole alkaloids from leaves of Alstonia scholaris (L.) R. Br. In mice and rats. Nat. Prod. Bioprospect. 2020, 10, 77–88. [Google Scholar] [CrossRef]
  151. Ng’uni, T.; Klaasen, J.A.; Fielding, B.C. Acute toxicity studies of the South African medicinal plant Galenia africana. Toxicol. Rep. 2018, 5, 813–818. [Google Scholar] [CrossRef]
  152. Liyanagamage, D.S.N.K.; Jayasinghe, S.; Attanayake, A.P.; Karunaratne, V. Acute and subchronic toxicity profile of a polyherbal drug used in Sri Lankan traditional medicine. Evid.-Based Complement. Altern. Med. 2020, 2020, 2189189. [Google Scholar] [CrossRef] [PubMed]
  153. Alelign, T.; Chalchisa, D.; Fekadu, N.; Solomon, D.; Sisay, T.; Debella, A.; Petros, B. Evaluation of acute and sub-acute toxicity of selected traditional antiurolithiatic medicinal plant extracts in Wistar albino rats. Toxicol. Rep. 2020, 7, 1356–1365. [Google Scholar] [CrossRef] [PubMed]
  154. Maimaiti, A.; Jing-Jing, L.; Shi, L. Investigating the acute and sub-acute toxicity of medicinal Cuscuta chinensis Lam plant. J. Ethnopharmacol. 2021, 273, 114005. [Google Scholar] [CrossRef] [PubMed]
  155. Sung, J.H.; Wang, Y.I.; Narasimhan Sriram, N.; Jackson, M.; Long, C.; Hickman, J.J.; Shuler, M.L. Recent advances in body-on-a-chip systems. Anal. Chem. 2018, 91, 330–351. [Google Scholar] [CrossRef] [PubMed]
  156. Wu, Q.; Liu, J.; Wang, X.; Feng, L.; Wu, J.; Zhu, X.; Wen, W.; Gong, X. Organ-on-a-chip: Recent breakthroughs and future prospects. Biomed. Eng. Online 2020, 19, 9. [Google Scholar] [CrossRef] [Green Version]
  157. Badr-Eldin, S.M.; Aldawsari, H.M.; Kotta, S.; Deb, P.K.; Venugopala, K.N. Three-Dimensional In Vitro Cell Culture Models for Efficient Drug Discovery: Progress So Far and Future Prospects. Pharmaceuticals 2022, 15, 926. [Google Scholar] [CrossRef]
  158. Jackson Jr, G.R.; Maione, A.G.; Klausner, M.; Hayden, P.J. Prevalidation of an acute inhalation toxicity test using the EpiAirway in vitro human airway model. Appl. In Vitro Toxicol. 2018, 4, 149–158. [Google Scholar] [CrossRef] [Green Version]
  159. Jang, S.Y.; Park, M.K.; Im, J.M.; Park, H.S.; Seo, H.S.; Park, H.J.; Nah, S.S. In vitro acute inhalation toxicity for TiO2 (GST) using 3D human tissue model (EpiAirway™). Environ. Anal. Health Toxicol. 2021, 36, e2021015. [Google Scholar]
  160. Sharma, M.; Schoop, R.; Hudson, J. The efficacy of Echinacea in a 3-D tissue model of human airway epithelium. Phytother. Res. 2010, 24, 900–904. [Google Scholar] [CrossRef]
  161. Słoczyńska, K.; Popiół, J.; Gunia-Krzyżak, A.; Koczurkiewicz-Adamczyk, P.; Żmudzki, P.; Pękala, E. Evaluation of Two Novel Hydantoin Derivatives Using Reconstructed Human Skin Model EpiskinTM: Perspectives for Application as Potential Sunscreen Agents. Molecules 2022, 27, 1850. [Google Scholar] [CrossRef]
  162. Chang, S.-Y.; Weber, E.J.; Sidorenko, V.S.; Chapron, A.; Yeung, C.K.; Gao, C.; Mao, Q.; Shen, D.; Wang, J.; Rosenquist, T.A.; et al. Human liver-kidney model elucidates the mechanisms of aristolochic acid nephrotoxicity. JCI Insight 2017, 2, e95978. [Google Scholar]
  163. Oleaga, C.; Bernabini, C.; Smith, A.S.; Srinivasan, B.; Jackson, M.; McLamb, W.; Platt, V.; Bridges, R.; Cai, Y.; Santhanam, N.; et al. Multi-Organ toxicity demonstration in a functional human in vitro system composed of four organs. Sci. Rep. 2016, 6, 20030. [Google Scholar] [CrossRef] [PubMed]
  164. Modarresi Chahardehi, A.; Arsad, H.; Lim, V. Zebrafish as a successful animal model for screening toxicity of medicinal plants. Plants 2020, 9, 1345. [Google Scholar] [CrossRef] [PubMed]
  165. Gence, L.; Fernezelian, D.; Bringart, M.; Veeren, B.; Christophe, A.; Brion, F.; Meilhac, O.; Bascands, J.-L.; Diotel, N. Hypericum lanceolatum Lam. Medicinal Plant: Potential Toxicity and Therapeutic Effects Based on a Zebrafish Model. Front. Pharmacol. 2022, 13, 832928. [Google Scholar] [PubMed]
  166. Xavier, J.; Kripasana, K. Acute toxicity of Leaf extracts of Enydra fluctuans Lour in Zebrafish (Danio rerio Hamilton). Scientifica 2020, 2020, 3965376. [Google Scholar] [CrossRef] [Green Version]
  167. Zainol Abidin, I.Z.; Fazry, S.; Jamar, N.H.; Ediwar Dyari, H.R.; Zainal Ariffin, Z.; Johari, A.N.; Ashaari, N.S.; Johari, N.A.; Megat Abdul Wahab, R.; Zainal Ariffin, S.H. The effects of Piper sarmentosum aqueous extracts on zebrafish (Danio rerio) embryos and caudal fin tissue regeneration. Sci. Rep. 2020, 10, 14165. [Google Scholar] [CrossRef]
  168. Abidin, A.; Visepomaran, S.; Balan, S.; Bahari, H. Evaluation of Effect of Ethanol Extraction of Graptophyllum pictum on Zebrafish (Danio rerio) Embryo Model through Toxicity Assay Assessment. J. Toxicol. Risk Assess. 2021, 7, 040. [Google Scholar]
  169. Dinesh, D.; Nanjappa, D.P.; Babu, N.; Kalladka, K.; Chakraborty, G.; Chakraborty, A. Evaluation of Toxicity and Antiangiogenic Activity of Murraya koenigii Leaf Extracts in Zebrafish. J. Health Allied Sci. NU 2020, 10, 79–85. [Google Scholar]
  170. Purnomo, Y.; Aini, N.; Noerhayati, E. Acute Toxicity Level of Pulutan (Urena lobata) Leaf Extract on Zebrafish (Danio rerio) and its Analysis by In Silico Study. Res. J. Pharm. Technol. 2022, 15, 2477–2482. [Google Scholar]
  171. de Sá Hyacienth, B.M.; Sánchez-Ortiz, B.L.; Picanço, K.R.T.; Pereira, A.C.M.; de Sá Hyacienth, D.C.; de Souza, G.C.; Sarquis, R.d.S.F.R.; Aduanga, G.M.G.; Navarrete, A.; Carvalho, J.C.T. Endopleura uchi (Huber) Cuatrec.: A medicinal plant for gynecological treatments–A reproductive toxicity assessment in zebrafish (Danio rerio). J. Ethnopharmacol. 2020, 250, 112457. [Google Scholar] [CrossRef]
  172. Falcão, M.A.P.; de Souza, L.S.; Dolabella, S.S.; Guimarães, A.G.; Walker, C.I.B. Zebrafish as an alternative method for determining the embryo toxicity of plant products: A systematic review. Environ. Sci. Pollut. Res. 2018, 25, 35015–35026. [Google Scholar] [CrossRef]
  173. Mbarga, M.J.A.; Podoprigora, I.V.; Anyutoulou, K.L.D. Galleria mellonella (greater wax moth) as an eco-friendly in vivo approach for the assessment of the acute toxicity of medicinal plants: Application to some plants from Cameroon. Open Vet. J. 2021, 11, 651–661. [Google Scholar]
  174. ICH. Guidance on Genotoxicity Testing and Data Interpretation for Pharmaceuticals Intended for Human Use S2 (R1). 2011. Available online: https://database.ich.org/sites/default/files/S2_R1_Guideline.pdf (accessed on 10 November 2022).
  175. Baldrick, P. Genotoxicity test battery–An assessment of its utility in early drug development. Mutat. Res. Genet. Toxicol. Environ. Mutagen. 2021, 868, 503388. [Google Scholar] [CrossRef] [PubMed]
  176. da Silva Dantas, F.G.; de Castilho, P.F.; de Almeida-Apolonio, A.A.; de Araújo, R.P.; de Oliveira, K.M.P. Mutagenic potential of medicinal plants evaluated by the Ames Salmonella/microsome assay: A systematic review. Mutat. Res. Rev. Mutat. Res. 2020, 786, 108338. [Google Scholar] [CrossRef] [PubMed]
  177. Poivre, M.; Nachtergael, A.; Bunel, V.; Philippe, O.N.; Duez, P. Genotoxicity and carcinogenicity of herbal products. In Toxicology of Herbal Products; Pelkonen, O., Duez, P., Vuorela, P.M., Vuorela, H., Eds.; Springer: Cham, Switzerland, 2017; pp. 179–215. [Google Scholar]
  178. Kristanc, L.; Kreft, S. European medicinal and edible plants associated with subacute and chronic toxicity part I: Plants with carcinogenic, teratogenic and endocrine-disrupting effects. Food Chem. Toxicol. 2016, 92, 150–164. [Google Scholar] [CrossRef]
  179. European Medicines Agency. Guideline on the Assessment of Genotoxicity of Herbal Substances/Preparations. 2008. Available online: https://www.ema.europa.eu/en/documents/scientific-guideline/guideline-assessment-genotoxicity-herbal-substances/preparations_en.pdf (accessed on 10 November 2022).
  180. European Food Safety Authority. Scientific Opinion on genotoxicity testing strategies applicable to food and feed safety assessment. EFSA J. 2011, 9, 2379. [Google Scholar]
  181. Tsuboy, M.S.; Marcarini, J.C.; Ferreira, D.T.; Ferraz, E.R.A.; Chequer, F.M.D.; Oliveira, D.P.d.; Ribeiro, L.R.; Mantovani, M.S. Evaluation of extracts from Coccoloba mollis using the Salmonella/microsome system and in vivo tests. Genet. Mol. Biol. 2010, 33, 542–548. [Google Scholar] [CrossRef]
  182. Eren, Y.; Özata, A. Determination of mutagenic and cytotoxic effects of Limonium globuliferum aqueous extracts by Allium, Ames, and MTT tests. Rev. Bras. Farmacogn. 2014, 24, 51–59. [Google Scholar] [CrossRef] [Green Version]
  183. Florinsiah, L.; Farida Zuriana, M.; Nurshahirah, N.; Norfazlina, N.; Suziaina Zaila, C. Mutagenicity effect of Centella asiatica in aqueous extract by using Ames test. Open Conf. Proc. J. 2014, 4, 83–87. [Google Scholar] [CrossRef] [Green Version]
  184. Lee, M.-Y.; Seo, C.-S.; Ha, H.; Park, E.; Kim, J.-Y.; Shin, H.-K. The genotoxicity of an aqueous extract of Gyejibokryeong-hwan. BMC Complement. Altern. Med. 2018, 18, 21. [Google Scholar] [CrossRef] [Green Version]
  185. Saravanan, V.; Murugan, S.; Kumaravel, T. Genotoxicity studies with an ethanolic extract of Kalanchoe pinnata leaves. Mutat. Res. Genet. Toxicol. Environ. Mutagen. 2020, 856, 503229. [Google Scholar] [CrossRef] [PubMed]
  186. Schultz, F.; Osuji, O.F.; Nguyen, A.; Anywar, G.; Scheel, J.R.; Caljon, G.; Pieters, L.; Garbe, L.-A. Pharmacological assessment of the antiprotozoal activity, cytotoxicity and genotoxicity of medicinal plants used in the treatment of Malaria in the greater Mpigi Region in Uganda. Front. Pharmacol. 2021, 12, 678535. [Google Scholar]
  187. Santiago Quiles, M.R.; Oquendo-Jiménez, I.; Herreño-Saénz, D.; Antoun, M.D. Genotoxicity of alkaloid-rich extract from Lupinus termis seeds. Pharm. Crops 2010, 1, 18–23. [Google Scholar] [CrossRef] [Green Version]
  188. Njoya, E.M.; Moundipa, P.F.; Stopper, H. In vitro genotoxic and mutagenic evaluation of the aqueous extract of Codiaeum variegatum and its amoebicidal sub-fraction. J. Ethnopharmacol. 2014, 155, 823–829. [Google Scholar] [CrossRef] [PubMed]
  189. Liu, S.X.; Cao, J.; An, H. Molecular analysis of Tripterygium hypoglaucum (level) Hutch-induced mutations at the HPRT locus in human promyelocytic leukemia cells by multiplex polymerase chain reaction. Mutagenesis 2003, 18, 77–80. [Google Scholar] [CrossRef] [Green Version]
  190. Arora, S.; Brits, E.; Kaur, S.; Kaur, K.; Sohi, R.S.; Kumar, S.; Verschaeve, L. Evaluation of genotoxicity of medicinal plant extracts by the comet and VITOTOX® tests. J. Environ. Pathol. Toxicol. Oncol. 2005, 24, 193–200. [Google Scholar]
  191. Grujičić, D.; Marković, A.; Vukajlović, J.T.; Stanković, M.; Jakovljević, M.R.; Ćirić, A.; Djordjević, K.; Planojević, N.; Milutinović, M.; Milošević-Djordjević, O. Genotoxic and cytotoxic properties of two medical plants (Teucrium arduini L. and Teucrium flavum L.) in relation to their polyphenolic contents. Mutat. Res. Genet. Toxicol. Environ. Mutagen. 2020, 852, 503168. [Google Scholar] [CrossRef]
  192. de Quadros, A.P.O.; Mazzeo, D.E.C.; Marin-Morales, M.A.; Perazzo, F.F.; Rosa, P.C.P.; Maistro, E.L. Fruit extract of the medicinal plant Crataegus oxyacantha exerts genotoxic and mutagenic effects in cultured cells. J. Toxicol. Environ. Health A 2017, 80, 161–170. [Google Scholar] [CrossRef]
  193. Kahaliw, W.; Hellman, B.; Engidawork, E. Genotoxicity study of Ethiopian medicinal plant extracts on HepG2 cells. BMC Complement. Altern. Med. 2018, 18, 45. [Google Scholar] [CrossRef] [Green Version]
  194. Demma, J.; Hallberg, K.; Hellman, B. Genotoxicity of plumbagin and its effects on catechol and NQNO-induced DNA damage in mouse lymphoma cells. Toxicol. In Vitro 2009, 23, 266–271. [Google Scholar] [CrossRef]
  195. Ahmadi, A.; Gandomi, H.; Derakhshandeh, A.; Misaghi, A.; Noori, N. Phytochemical composition and in vitro safety evaluation of Ziziphora clinopodioides Lam. ethanolic extract: Cytotoxicity, genotoxicity and mutagenicity assessment. J. Ethnopharmacol. 2021, 266, 113428. [Google Scholar] [CrossRef] [PubMed]
  196. de Moura, D.F.; Rocha, T.A.; de Melo Barros, D.; da Silva, M.M.; de Lira, M.A.d.C.; dos Santos Souza, T.G.; da Silva, C.J.A.; de Aguiar Júnior, F.C.A.; Chagas, C.A.; da Silva Santos, N.P.; et al. Evaluation of the cytotoxicity, oral toxicity, genotoxicity, and mutagenicity of the latex extracted from Himatanthus drasticus (Mart.) Plumel (Apocynaceae). J. Ethnopharmacol. 2020, 253, 112567. [Google Scholar] [CrossRef]
  197. Mohamed, H.M.; Aly, M.S. Evaluation of genotoxicity of Euphorbia triaculeata Forssk. extract on mice bone marrow cells in vivo. Toxicol. Rep. 2018, 5, 625–631. [Google Scholar] [CrossRef] [PubMed]
  198. Hwang, Y.-H.; Kim, T.; Cho, W.-K.; Yang, H.J.; Kwak, D.H.; Ha, H.; Song, K.H.; Ma, J.Y. In vitro and in vivo genotoxicity assessment of Aristolochia manshuriensis Kom. Evid.-Based Complement. Altern. Med. 2012, 2012, 412736. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  199. Soumaya, K.-J.; Dhekra, M.; Fadwa, C.; Zied, G.; Ilef, L.; Kamel, G.; Leila, C.-G. Pharmacological, antioxidant, genotoxic studies and modulation of rat splenocyte functions by Cyperus rotundus extracts. BMC Complement. Altern. Med. 2013, 13, 28. [Google Scholar] [CrossRef] [Green Version]
  200. Kulkarni, P.; Paul, R.; Ganesh, N. In vitro evaluation of genotoxicity of avocado (Persea americana) fruit and leaf extracts in human peripheral lymphocytes. J. Environ. Sci. Health C Environ. Carcinog. Ecotoxicol. Rev. 2010, 28, 172–187. [Google Scholar] [CrossRef]
  201. Harutyunyan, K.; Balayan, K.; Tadevosyan, G.; Hayrapetyan, M.; Musayelyan, R.; Grigoryan, R.; Khondkaryan, L.; Sarkisyan, N.; Babayan, N. Genotoxic potential of selected medicinal plant extracts in human whole blood cultures. J. Herbmed Pharmacol. 2019, 8, 160–162. [Google Scholar] [CrossRef]
  202. Asare, G.; Bugyei, K.; Sittie, A.; Yahaya, E.; Gyan, B.; Adjei, S.; Addo, P.; Wiredu, E.; Nyarko, A. Genotoxicity, cytotoxicity and toxicological evaluation of whole plant extracts of the medicinal plant Phyllanthus niruri (Phyllanthaceae). Genet. Mol. Res. 2012, 11, 100–111. [Google Scholar] [CrossRef]
  203. Asita, A.O.; Maga-ma, S.; Moahloli, T.M.; Baholo, S. Evaluation of extracts of wild Cannabis sativa L. for genotoxicity and phytochemical composition. Caryologia 2021, 74, 135–150. [Google Scholar] [CrossRef]
  204. Bruck de Souza, L.; Leitão Gindri, A.; de Andrade Fortes, T.; Felli Kubiça, T.; Enderle, J.; Roehrs, R.; Moura e Silva, S.; Manfredini, V.; Gasparotto Denardin, E.L. Phytochemical analysis, antioxidant activity, antimicrobial activity, and cytotoxicity of Chaptalia nutans leaves. Adv. Pharmacol. Sci. 2020, 2020, 3260745. [Google Scholar] [CrossRef]
  205. Paw, M.; Gogoi, R.; Sarma, N.; Pandey, S.K.; Borah, A.; Begum, T.; Lal, M. Study of anti-oxidant, anti-inflammatory, genotoxicity, and antimicrobial activities and analysis of different constituents found in rhizome essential oil of Curcuma caesia Roxb., collected from north east India. Curr. Pharm. Biotechnol. 2020, 21, 403–413. [Google Scholar] [CrossRef] [PubMed]
  206. Madić, V.; Stojanović-Radić, Z.; Jušković, M.; Jugović, D.; Popović, A.Ž.; Vasiljević, P. Genotoxic and antigenotoxic potential of herbal mixture and five medicinal plants used in ethnopharmacology. S. Afr. J. Bot. 2019, 125, 290–297. [Google Scholar] [CrossRef]
  207. Abdelmigid, H.M. New trends in genotoxicity testing of herbal medicinal plants. In New Insights into Toxicity and Drug Testing; InTech: Rijeka, Croatia, 2013; pp. 89–120. [Google Scholar]
  208. Li, H.-H.; Yauk, C.L.; Chen, R.; Hyduke, D.R.; Williams, A.; Frötschl, R.; Ellinger-Ziegelbauer, H.; Pettit, S.; Aubrecht, J.; Fornace Jr, A.J. TGx-DDI, a transcriptomic biomarker for genotoxicity hazard assessment of pharmaceuticals and environmental chemicals. Front. Big Data 2019, 2, 36. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  209. Allemang, A.; De Abrew, K.N.; Shan, Y.K.; Krailler, J.M.; Pfuhler, S. A comparison of classical and 21st century genotoxicity tools: A proof of concept study of 18 chemicals comparing in vitro micronucleus, ToxTracker and genomics-based methods (TGx-DDI, whole genome clustering and connectivity mapping). Environ. Mol. Mutagen. 2021, 62, 92–107. [Google Scholar] [CrossRef]
  210. Kirkland, D.; Kasper, P.; Martus, H.-J.; Müller, L.; van Benthem, J.; Madia, F.; Corvi, R. Updated recommended lists of genotoxic and non-genotoxic chemicals for assessment of the performance of new or improved genotoxicity tests. Mutat. Res. Genet. Toxicol. Environ. Mutagen. 2016, 795, 7–30. [Google Scholar]
  211. Verschaeve, L. Investigations of plant-derived products with the in vitro comet assay. In Proceedings of the ICAW 2015—11th International Comet Assay Workshop, Antwerpen, Belgium, 1–4 September 2015. [Google Scholar]
  212. Ates, G.; Favyts, D.; Hendriks, G.; Derr, R.; Mertens, B.; Verschaeve, L.; Rogiers, V.; Doktorova, T.Y. The Vitotox and ToxTracker assays: A two-test combination for quick and reliable assessment of genotoxic hazards. Mutat. Res. Genet. Toxicol. Environ. Mutagen. 2016, 810, 13–21. [Google Scholar] [CrossRef]
  213. Edziri, H.; Mastouri, M.; Mahjoub, A.; Anthonissen, R.; Mertens, B.; Cammaerts, S.; Gevaert, L.; Verschaeve, L. Toxic and mutagenic properties of extracts from Tunisian traditional medicinal plants investigated by the neutral red uptake, VITOTOX and alkaline comet assays. S. Afr. J. Bot. 2011, 77, 703–710. [Google Scholar] [CrossRef] [Green Version]
  214. Chichioco-Hernandez, C.; Wudarski, J.; Gevaert, L.; Verschaeve, L. Evaluation of cytotoxicity and genotoxicity of some Philippine medicinal plants. Pharmacogn. Mag. 2011, 7, 171. [Google Scholar] [CrossRef] [Green Version]
  215. Elgorashi, E.E.; Taylor, J.L.; Maes, A.; van Staden, J.; De Kimpe, N.; Verschaeve, L. Screening of medicinal plants used in South African traditional medicine for genotoxic effects. Toxicol. Lett. 2003, 143, 195–207. [Google Scholar] [CrossRef]
  216. Buick, J.K.; Williams, A.; Meier, M.J.; Swartz, C.D.; Recio, L.; Gagné, R.; Ferguson, S.S.; Engelward, B.P.; Yauk, C.L. A modern genotoxicity testing paradigm: Integration of the high-throughput CometChip® and the TGx-DDI transcriptomic biomarker in human HepaRG™ cell cultures. Front. Public Health 2021, 9, 694834. [Google Scholar] [CrossRef]
  217. Verschaeve, L. Genotoxicity and antigenotoxicity studies of traditional medicinal plants: How informative and accurate are the results? Nat. Prod. Commun. 2015, 10, 1489–1493. [Google Scholar] [CrossRef] [PubMed]
  218. Kapadia, G.J.; Chung, E.; Ghosh, B.; Shukla, Y.; Basak, S.; Morton, J.; Pradhan, S. Carcinogenicity of some folk medicinal herbs in rats. J. Natl. Cancer Inst. 1978, 60, 683–686. [Google Scholar] [CrossRef] [PubMed]
  219. Kapadia, G.J.; Paul, B.; Chung, E.; Ghosh, B.; Pradhan, S. Carcinogenicity of Camellia sinensis (tea) and some tannin-containing folk medicinal herbs administered subcutaneously in rats. J. Natl. Cancer Inst. 1976, 57, 207–209. [Google Scholar] [CrossRef] [PubMed]
  220. Moreira, D.d.L.; Teixeira, S.S.; Monteiro, M.H.D.; De-Oliveira, A.C.A.; Paumgartten, F.J. Traditional use and safety of herbal medicines. Rev. Bras. Farmacogn. 2014, 24, 248–257. [Google Scholar] [CrossRef]
  221. Hoenerhoff, M.J.; Pandiri, A.R.; Snyder, S.A.; Hong, H.-H.L.; Ton, T.-V.; Peddada, S.; Shockley, K.; Witt, K.; Chan, P.; Rider, C.; et al. Hepatocellular carcinomas in B6C3F1 mice treated with Ginkgo biloba extract for two years differ from spontaneous liver tumors in cancer gene mutations and genomic pathways. Toxicol. Pathol. 2013, 41, 826–841. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  222. Wang, H.; Wu, X.; Lezmi, S.; Li, Q.; Helferich, W.G.; Xu, Y.; Chen, H. Extract of Ginkgo biloba exacerbates liver metastasis in a mouse colon cancer Xenograft model. BMC Complement. Altern. Med. 2017, 17, 516. [Google Scholar] [CrossRef] [Green Version]
  223. Zhang, Z.; Chen, S.; Mei, H.; Xuan, J.; Guo, X.; Couch, L.; Dobrovolsky, V.N.; Guo, L.; Mei, N. Ginkgo biloba leaf extract induces DNA damage by inhibiting topoisomerase II activity in human hepatic cells. Sci. Rep. 2015, 5, 14633. [Google Scholar] [CrossRef] [Green Version]
  224. Program, N.T. Toxicology and carcinogenesis studies of pulegone (CAS No. 89-82-7) in F344/N rats and B6C3F1 mice (gavage studies). Natl. Toxicol. Program Tech. Rep. Ser. 2011, 563, 1–201. [Google Scholar]
  225. NTP. Toxicology and carcinogenesis studies of alpha, beta-thujone (CAS No. 76231-76-0) in F344/N rats and B6C3F1 mice (gavage studies). National Toxicology Program. Natl. Toxicol. Program Tech. Rep. Ser. 2011, 570, 1–260. [Google Scholar]
  226. Dunnick, J.K.; Nyska, A. The toxicity and pathology of selected dietary herbal medicines. Toxicol. Pathol. 2013, 41, 374–386. [Google Scholar] [CrossRef] [Green Version]
  227. WHO International Agency for Research on Cancer (IARC). IARC Monographs on the Evaluation of Carcinogenic Risks to Humans. Available online: https://monographs.iarc.who.int/ (accessed on 12 November 2022).
  228. Ouedraogo, M.; Baudoux, T.; Stévigny, C.; Nortier, J.; Colet, J.-M.; Efferth, T.; Qu, F.; Zhou, J.; Chan, K.; Shaw, D. Review of current and “omics” methods for assessing the toxicity (genotoxicity, teratogenicity and nephrotoxicity) of herbal medicines and mushrooms. J. Ethnopharmacol. 2012, 140, 492–512. [Google Scholar] [CrossRef] [PubMed]
  229. Joseph, P. Transcriptomics in toxicology. Food Chem. Toxicol. 2017, 109, 650–662. [Google Scholar] [CrossRef]
  230. Gupta, A.P.; Bozdech, Z.; Preiser, P.R. Transcriptomics and proteomics. In Advances in Malaria Research; Gaur, D., Chitnis, C.E., Chauhan, V.S., Eds.; John Wiley & Sons, Inc.: Hoboken, NJ, USA, 2016; Volume 197. [Google Scholar]
  231. Lo, H.-Y.; Li, C.-C.; Huang, H.-C.; Lin, L.-J.; Hsiang, C.-Y.; Ho, T.-Y. Application of transcriptomics in Chinese herbal medicine studies. J. Tradit. Complement. Med. 2012, 2, 105–114. [Google Scholar] [CrossRef] [Green Version]
  232. Chen, Y.-Y.; Chiang, S.-Y.; Wu, H.-C.; Kao, S.-T.; Hsiang, C.-Y.; Ho, T.-Y.; Lin, J.-G. Microarray analysis reveals the inhibition of nuclear factor-kappa B signaling by aristolochic acid in normal human kidney (HK-2) cells. Acta Pharmacol. Sin. 2010, 31, 227–236. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  233. Fu, Z.-y.; Han, J.-x.; Huang, H.-y. Effects of emodin on gene expression profile in small cell lung cancer NCI-H446 cells. Chin. Med. J. 2007, 120, 1710–1715. [Google Scholar] [CrossRef] [PubMed]
  234. Saunders, I.T.; Mir, H.; Kapur, N.; Singh, S. Emodin inhibits colon cancer by altering BCL-2 family proteins and cell survival pathways. Cancer Cell Int. 2019, 19, 98. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  235. Oshida, K.; Hirakata, M.; Maeda, A.; Miyoshi, T.; Miyamoto, Y. Toxicological effect of emodin in mouse testicular gene expression profile. J. Appl. Toxicol. 2011, 31, 790–800. [Google Scholar] [CrossRef]
  236. Mei, N.; Guo, L.; Liu, R.; Fuscoe, J.C.; Chen, T. Gene expression changes induced by the tumorigenic pyrrolizidine alkaloid riddelliine in liver of Big Blue rats. BMC Bioinform. 2007, 8 (Suppl. 7), S4. [Google Scholar] [CrossRef] [Green Version]
  237. Ebmeyer, J.; Rasinger, J.D.; Hengstler, J.G.; Schaudien, D.; Creutzenberg, O.; Lampen, A.; Braeuning, A.; Hessel-Pras, S. Hepatotoxic pyrrolizidine alkaloids induce DNA damage response in rat liver in a 28-day feeding study. Arch. Toxicol. 2020, 94, 1739–1751. [Google Scholar] [CrossRef]
  238. Buchmueller, J.; Sprenger, H.; Ebmeyer, J.; Rasinger, J.D.; Creutzenberg, O.; Schaudien, D.; Hengstler, J.G.; Guenther, G.; Braeuning, A.; Hessel-Pras, S. Pyrrolizidine alkaloid-induced transcriptomic changes in rat lungs in a 28-day subacute feeding study. Arch. Toxicol. 2021, 95, 2785–2796. [Google Scholar] [CrossRef]
  239. Abdelfatah, S.; Naß, J.; Knorz, C.; Klauck, S.M.; Küpper, J.-H.; Efferth, T. Pyrrolizidine alkaloids cause cell cycle and DNA damage repair defects as analyzed by transcriptomics in cytochrome P450 3A4-overexpressing HepG2 clone 9 cells. Cell Biol. Toxicol. 2022, 38, 325–345. [Google Scholar] [CrossRef] [PubMed]
  240. Wetmore, B.A.; Merrick, B.A. Invited review: Toxicoproteomics: Proteomics applied to toxicology and pathology. Toxicol. Pathol. 2004, 32, 619–642. [Google Scholar] [CrossRef] [PubMed]
  241. Merrick, B.A.; Witzmann, F.A. The role of toxicoproteomics in assessing organ specific toxicity. EXS 2009, 99, 367–400. [Google Scholar] [PubMed] [Green Version]
  242. Lao, Y.; Wang, X.; Xu, N.; Zhang, H.; Xu, H. Application of proteomics to determine the mechanism of action of traditional Chinese medicine remedies. J. Ethnopharmacol. 2014, 155, 1–8. [Google Scholar] [CrossRef] [Green Version]
  243. Yang, Y.-Y.; Yang, F.-Q.; Gao, J.-L. Differential proteomics for studying action mechanisms of traditional Chinese medicines. Chin. Med. 2019, 14, 1. [Google Scholar] [CrossRef] [Green Version]
  244. Huang, Q.; Zhao, H.; Zhang, C.; Hao, Y.; Ma, Q.; Dong, S.; Liu, Z.; Wang, S.; Shi, Y. Quantitative Proteomics Reveal the Protective Effects of the Combined Chinese Herbal Extracts Against Alzheimer’s Disease via Modulating the Expression of Synapse-associated Proteins in Mouse Model. 2021. Available online: https://www.researchsquare.com/article/rs-956329/v1 (accessed on 5 November 2022).
  245. Hung, Y.-C.; Wang, P.-W.; Pan, T.-L. Functional proteomics reveal the effect of Salvia miltiorrhiza aqueous extract against vascular atherosclerotic lesions. Biochim. Biophys. Acta Proteins Proteom. BBA-Proteins Proteom. 2010, 1804, 1310–1321. [Google Scholar] [CrossRef]
  246. Li, Y.-H.; Tai, W.C.-S.; Khan, I.; Lu, C.; Lu, Y.; Wong, W.-Y.; Chan, W.-Y.; Wendy Hsiao, W.-L.; Lin, G. Toxicoproteomic assessment of liver responses to acute pyrrolizidine alkaloid intoxication in rats. J. Environ. Sci. Health C 2018, 36, 65–83. [Google Scholar] [CrossRef]
  247. Li, Y.-H.; Tai, W.C.-S.; Xue, J.-Y.; Wong, W.-Y.; Lu, C.; Ruan, J.-Q.; Li, N.; Wan, T.-F.; Chan, W.-Y.; Hsiao, W.-L.W.; et al. Proteomic study of pyrrolizidine alkaloid-induced hepatic sinusoidal obstruction syndrome in rats. Chem. Res. Toxicol. 2015, 28, 1715–1727. [Google Scholar] [CrossRef]
  248. Wang, W.; Chen, Y.; Yin, Y.; Wang, X.; Ye, X.; Jiang, K.; Zhang, Y.; Zhang, J.; Zhang, W.; Zhuge, Y.; et al. A TMT-based shotgun proteomics uncovers overexpression of thrombospondin 1 as a contributor in pyrrolizidine alkaloid-induced hepatic sinusoidal obstruction syndrome. Arch. Toxicol. 2022, 96, 2003–2019. [Google Scholar] [CrossRef]
  249. Liu, J.; Dong, W.; Wong, T.Y.; Qiu, C.; Wu, J.; Zhao, J.; Xia, J.; Xie, S.; Song, X. Proteome-wide analysis of protein alterations in response to aristolochic acids in rat kidney and liver tissues. Mol. Omics 2021, 17, 405–412. [Google Scholar] [CrossRef]
  250. Dai, Y.; Sun, L.; Han, S.; Xu, S.; Wang, L.; Ding, Y. Proteomic Study on the Reproductive Toxicity of Tripterygium Glycosides in Rats. Front. Pharmacol. 2022, 13, 888968. [Google Scholar] [CrossRef]
  251. Gerritsen, J.S.; White, F.M. Phosphoproteomics: A valuable tool for uncovering molecular signaling in cancer cells. Expert Rev. Proteom. 2021, 18, 661–674. [Google Scholar] [CrossRef] [PubMed]
  252. Li, J.; Silva-Sanchez, C.; Zhang, T.; Chen, S.; Li, H. Phosphoproteomics technologies and applications in plant biology research. Front. Plant Sci. 2015, 6, 430. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  253. Huang, S.-J.; Zuo, M.-T.; Qi, X.-J.; Huang, C.-Y.; Liu, Z.-Y. Phosphoproteomics reveals NMDA receptor-mediated excitotoxicity as a key signaling pathway in the toxicity of gelsenicine. Food Chem. Toxicol. 2021, 156, 112507. [Google Scholar] [CrossRef] [PubMed]
  254. Ramsden, J.J. Metabolomics and metabonomics. In Bioinformatics, 2nd ed.; Ramsden, J.J., Ed.; Springer: London, UK, 2009; pp. 1–6. [Google Scholar]
  255. Nicholson, J. Metabonomics-A Generic Platform for the Investigation of Drug Toxicity, Altered Gene Function and Disease Processes. Drug Discov. World 2004, 5, 23–29. [Google Scholar]
  256. Robertson, D.G. Metabonomics in toxicology: A review. Toxicol. Sci. 2005, 85, 809–822. [Google Scholar] [CrossRef]
  257. Sun, B.; Wu, S.; Li, L.; Li, H.; Zhang, Q.; Chen, H.; Li, F.; Dong, F.; Yan, X. A metabolomic analysis of the toxicity of Aconitum sp. alkaloids in rats using gas chromatography/mass spectrometry. Rapid Commun. Mass Spectrom. 2009, 23, 1221–1228. [Google Scholar] [CrossRef]
  258. Tan, Y.; Ko, J.; Liu, X.; Lu, C.; Li, J.; Xiao, C.; Li, L.; Niu, X.; Jiang, M.; He, X.; et al. Serum metabolomics reveals betaine and phosphatidylcholine as potential biomarkers for the toxic responses of processed Aconitum carmichaelii Debx. Mol. BioSyst. 2014, 10, 2305–2316. [Google Scholar] [CrossRef] [Green Version]
  259. Xu, L.; Wang, Y.; Ma, Z.; Tang, X.; Gao, Y. Urine metabolomics study on potential hepatoxic biomarkers identification in rats induced by aurantio-obtusin. Front. Pharmacol. 2020, 11, 1237. [Google Scholar] [CrossRef]
  260. Zhou, Y.; Liao, Q.; Lin, M.; Deng, X.; Zhang, P.; Yao, M.; Zhang, L.; Xie, Z. Combination of 1H NMR-and GC-MS-based metabonomics to study on the toxicity of Coptidis Rhizome in rats. PLoS ONE 2014, 9, e88281. [Google Scholar] [CrossRef] [Green Version]
  261. Zhao, D.-S.; Wu, Z.-T.; Li, Z.-Q.; Wang, L.-L.; Jiang, L.-L.; Shi, W.; Li, P.; Li, H.-J. Liver-specific metabolomics characterizes the hepatotoxicity of Dioscorea bulbifera rhizome in rats by integration of GC-MS and 1H-NMR. J. Ethnopharmacol. 2018, 226, 111–119. [Google Scholar] [CrossRef] [PubMed]
  262. Zhao, D.-S.; Jiang, L.-L.; Fan, Y.-X.; Wang, L.-L.; Li, Z.-Q.; Shi, W.; Li, P.; Li, H.-J. Investigation of Dioscorea bulbifera rhizome-induced hepatotoxicity in rats by a multisample integrated metabolomics approach. Chem. Res. Toxicol. 2017, 30, 1865–1873. [Google Scholar] [CrossRef] [PubMed]
  263. Liu, Y.; Huang, R.; Liu, L.; Peng, J.; Xiao, B.; Yang, J.; Miao, Z.; Huang, H. Metabonomics study of urine from Sprague–Dawley rats exposed to Huang-yao-zi using 1H NMR spectroscopy. J. Pharm. Biomed. Anal. 2010, 52, 136–141. [Google Scholar] [CrossRef] [PubMed]
  264. Chen, Q.; Zhang, K.; Jiao, M.; Jiao, J.; Chen, D.; Yin, Y.; Zhang, J.; Li, F. Study on the Mechanism of Mesaconitine-Induced Hepatotoxicity in Rats Based on Metabonomics and Toxicology Network. Toxins 2022, 14, 486. [Google Scholar] [CrossRef] [PubMed]
  265. Ma, C.; Bi, K.; Zhang, M.; Su, D.; Fan, X.; Ji, W.; Wang, C.; Chen, X. Toxicology effects of morning glory seed in rat: A metabonomic method for profiling of urine metabolic changes. J. Ethnopharmacol. 2010, 130, 134–142. [Google Scholar] [CrossRef] [PubMed]
  266. Zhang, Z.-H.; Zhao, Y.-Y.; Cheng, X.-l.; Dai, Z.; Zhou, C.; Bai, X.; Lin, R.-C. General toxicity of Pinellia ternata (Thunb.) Berit. in rat: A metabonomic method for profiling of serum metabolic changes. J. Ethnopharmacol. 2013, 149, 303–310. [Google Scholar] [CrossRef]
  267. Li, C.-Y.; Tu, C.; Gao, D.; Wang, R.-L.; Zhang, H.-Z.; Niu, M.; Li, R.-Y.; Zhang, C.-E.; Li, R.-S.; Xiao, X.-H.; et al. Metabolomic study on idiosyncratic liver injury induced by different extracts of Polygonum multiflorum in rats integrated with pattern recognition and enriched pathways analysis. Front. Pharmacol. 2016, 7, 483. [Google Scholar] [CrossRef] [Green Version]
  268. Zhang, C.-e.; Niu, M.; Li, Q.; Zhao, Y.-l.; Ma, Z.-j.; Xiong, Y.; Dong, X.-p.; Li, R.-y.; Feng, W.-w.; Dong, Q.; et al. Urine metabolomics study on the liver injury in rats induced by raw and processed Polygonum multiflorum integrated with pattern recognition and pathways analysis. J. Ethnopharmacol. 2016, 194, 299–306. [Google Scholar] [CrossRef]
  269. Li, Y.-X.; Gong, X.-H.; Liu, M.-C.; Peng, C.; Li, P.; Wang, Y.-T. Investigation of liver injury of Polygonum multiflorum Thunb. in rats by metabolomics and traditional approaches. Front. Pharmacol. 2017, 8, 791. [Google Scholar] [CrossRef] [Green Version]
  270. Huo, T.; Fang, Y.; Zhao, L.; Xiong, Z.; Zhang, Y.; Wang, Y.; Feng, C.; Yuan, M.; Wang, S.; Chen, M.; et al. 1HNMR-based metabonomic study of sub-chronic hepatotoxicity induced by realgar. J. Ethnopharmacol. 2016, 192, 1–9. [Google Scholar] [CrossRef]
  271. Wei, L.; Liao, P.; Wu, H.; Li, X.; Pei, F.; Li, W.; Wu, Y. Metabolic profiling studies on the toxicological effects of realgar in rats by 1H NMR spectroscopy. Toxicol. Appl. Pharmacol. 2009, 234, 314–325. [Google Scholar] [CrossRef] [PubMed]
  272. Chen, J.; Zhang, C.; Wu, X.; Ji, H.; Ma, W.; Wei, S.; Zhang, L.; Chen, J. 1 H NMR-based nontargeted metabonomics study of plasma and urinary biochemical changes in Kudouzi treated rats. Rev. Bras. Farmacogn. 2018, 28, 474–480. [Google Scholar] [CrossRef]
  273. Zeng, M.; Cai, J.; Su, K.; Chen, B.; Dong, Y.; Wen, C.; Wang, X.; Zheng, H. Urine metabolic changes in rats after Tripterygium wilfordii poisoning. Int. J. Clin. Exp. Med. 2016, 9, 18074–18079. [Google Scholar]
  274. Lu, F.; Cao, M.; Wu, B.; Li, X.-z.; Liu, H.-y.; Chen, D.-z.; Liu, S.-m. Urinary metabonomics study on toxicity biomarker discovery in rats treated with Xanthii Fructus. J. Ethnopharmacol. 2013, 149, 311–320. [Google Scholar] [CrossRef] [PubMed]
  275. Lyon, F. IARC monographs on the evaluation of carcinogenic risks to humans. Some Ind. Chem. 1994, 60, 389–433. [Google Scholar]
  276. Ałtyn, I.; Twarużek, M. Mycotoxin contamination concerns of herbs and medicinal plants. Toxins 2020, 12, 182. [Google Scholar] [CrossRef] [Green Version]
  277. Ashiq, S.; Hussain, M.; Ahmad, B. Natural occurrence of mycotoxins in medicinal plants: A review. Fungal Genet. Biol. 2014, 66, 1–10. [Google Scholar] [CrossRef] [PubMed]
  278. Santos, L.; Marín, S.; Sanchis, V.; Ramos, A.J. Screening of mycotoxin multicontamination in medicinal and aromatic herbs sampled in Spain. J. Sci. Food Agric. 2009, 89, 1802–1807. [Google Scholar] [CrossRef]
  279. Rizzo, I.; Vedoya, G.; Maurutto, S.; Haidukowski, M.; Varsavsky, E. Assessment of toxigenic fungi on Argentinean medicinal herbs. Microbiol. Res. 2004, 159, 113–120. [Google Scholar] [CrossRef]
  280. Zhang, L.; Dou, X.-W.; Zhang, C.; Logrieco, A.F.; Yang, M.-H. A review of current methods for analysis of mycotoxins in herbal medicines. Toxins 2018, 10, 65. [Google Scholar] [CrossRef] [Green Version]
  281. Ezekwesili-Ofili, J.; Onyemelukwe, N.; Agwaga, P.; Orji, I. The bioload and aflatoxin content of herbal medicines from selected states in Nigeria. Afr. J. Tradit. Complement. Altern. Med. 2014, 11, 143–147. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  282. Aiko, V.; Mehta, A. Prevalence of toxigenic fungi in common medicinal herbs and spices in India. 3 Biotech 2016, 6, 159. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  283. Weaver, C.M.; Trucksess, M.W. Determination of aflatoxins in botanical roots by a modification of AOAC official method SM 991.31: Single-laboratory validation. J. AOAC Int. 2010, 93, 184–189. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  284. Sarkar, R.; Shinde, R.; Dhanshetty, M.; Banerjee, K. Multi-mycotoxin analysis method using liquid chromatography with tandem mass spectrometry and fluorescence detection in Indian medicinal herbs: Development and validation. J. Chromatogr. A 2022, 1677, 463310. [Google Scholar] [CrossRef]
  285. Saha, A.; Gajbhiye, N.A.; Basak, B.B.; Manivel, P. High-performance liquid chromatography tandem mass spectrometry for simultaneous detection of aflatoxins B1, B2, G1 and G2 in Indian medicinal herbs using QuEChERS-based extraction procedure. Int. J. Environ. Anal. Chem. 2018, 98, 622–643. [Google Scholar] [CrossRef]
  286. Arroyo-Manzanares, N.; García-Campaña, A.M.; Gámiz-Gracia, L. Multiclass mycotoxin analysis in Silybum marianum by ultra high performance liquid chromatography–tandem mass spectrometry using a procedure based on QuEChERS and dispersive liquid–liquid microextraction. J. Chromatogr. A 2013, 1282, 11–19. [Google Scholar] [CrossRef]
  287. Yue, Y.-T.; Zhang, X.-F.; Ou-Yang, Z.; Gao, W.-W.; Wu, J.; Yang, M.-H. Determination of T-2 toxin in traditional Chinese herbal medicines by GC-ECD. Chromatographia 2009, 70, 1495–1499. [Google Scholar] [CrossRef]
  288. Victor Jeyaraj, S.V.; Loy, M.J.; Goh, K.W.; Lean, Y.L.; Chan, S.Y.; Ming, L. Aflatoxin tests in herbal products and its quantification: Latest updates. Front. Nutr. 2022, 9, 956077. [Google Scholar] [CrossRef]
  289. Abdolmaleki, K.; Khedri, S.; Alizadeh, L.; Javanmardi, F.; Oliveira, C.A.; Khaneghah, A.M. The mycotoxins in edible oils: An overview of prevalence, concentration, toxicity, detection and decontamination techniques. Trends Food Sci. Technol. 2021, 115, 500–511. [Google Scholar] [CrossRef]
  290. Wang, L.; Kong, W.; Yang, M.; Han, J.; Chen, S. Safety issues and new rapid detection methods in traditional Chinese medicinal materials. Acta Pharm. Sin. B 2015, 5, 38–46. [Google Scholar] [CrossRef] [Green Version]
  291. Shim, W.-B.; Kim, K.; Ofori, J.A.; Chung, Y.-C.; Chung, D.-H. Occurrence of aflatoxins in herbal medicine distributed in South Korea. J. Food Prot. 2012, 75, 1991–1999. [Google Scholar] [CrossRef]
  292. Li, X.; Li, P.; Zhang, Q.; Li, R.; Zhang, W.; Zhang, Z.; Ding, X.; Tang, X. Multi-component immunochromatographic assay for simultaneous detection of aflatoxin B1, ochratoxin A and zearalenone in agro-food. Biosens. Bioelectron. 2013, 49, 426–432. [Google Scholar] [CrossRef] [PubMed]
  293. Fadlalla, M.H.; Ling, S.; Wang, R.; Li, X.; Yuan, J.; Xiao, S.; Wang, K.; Tang, S.; Elsir, H.; Wang, S. Development of ELISA and lateral flow immunoassays for ochratoxins (OTA and OTB) detection based on monoclonal antibody. Front. Cell Infect. Microbiol. 2020, 10, 80. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  294. Xiulan, S.; Xiaolian, Z.; Jian, T.; Zhou, J.; Chu, F. Preparation of gold-labeled antibody probe and its use in immunochromatography assay for detection of aflatoxin B1. Int. J. Food Microbiol. 2005, 99, 185–194. [Google Scholar] [CrossRef] [PubMed]
  295. You, K.; Luo, X.; Hu, W.; Xu, Y.; Guo, J.; He, Q. Environmental-friendly gold nanoparticle immunochromatographic assay for ochratoxin A based on biosynthetic mimetic mycotoxin-conjugates. World Mycotoxin J. 2020, 13, 267–276. [Google Scholar] [CrossRef]
  296. Morgan, E.; Varro, R.; Sepulveda, H.; Ember, J.A.; Apgar, J.; Wilson, J.; Lowe, L.; Chen, R.; Shivraj, L.; Agadir, A.; et al. Cytometric bead array: A multiplexed assay platform with applications in various areas of biology. Clin. Immunol. 2004, 110, 252–266. [Google Scholar] [CrossRef]
  297. Chang-Bin, X.; Qiu-Tao, L.; Xiao-Wen, D.; Mei-Hua, Y.; Wei-Jun, K.; Li, W. Rapid detection of ochratoxin A in malt by cytometric bead array based on indirect competition principle. Chin. J. Anal. Chem. 2016, 44, 625–632. [Google Scholar]
  298. Shaban, N.S.; Abdou, K.A.; Hassan, N.E.-H.Y. Impact of toxic heavy metals and pesticide residues in herbal products. Beni-Suef Univ. J. Basic Appl. Sci. 2016, 5, 102–106. [Google Scholar] [CrossRef] [Green Version]
  299. Tripathy, V.; Basak, B.; Varghese, T.S.; Saha, A. Residues and contaminants in medicinal herbs—A review. Phytochem. Lett. 2015, 14, 67–78. [Google Scholar] [CrossRef]
  300. Wang, Y.; Gou, Y.; Zhang, L.; Li, C.; Wang, Z.; Liu, Y.; Geng, Z.; Shen, M.; Sun, L.; Wei, F.; et al. Levels and Health Risk of Pesticide Residues in Chinese Herbal Medicines. Front. Pharmacol. 2021, 12, 818268. [Google Scholar] [CrossRef]
  301. Chen, L.; Song, F.; Liu, Z.; Zheng, Z.; Xing, J.; Liu, S. Multi-residue method for fast determination of pesticide residues in plants used in traditional Chinese medicine by ultra-high-performance liquid chromatography coupled to tandem mass spectrometry. J. Chromatogr. A 2012, 1225, 132–140. [Google Scholar] [CrossRef] [PubMed]
  302. Tong, H.; Tong, Y.; Xue, J.; Liu, D.; Wu, X. Multi-residual pesticide monitoring in commercial Chinese herbal medicines by gas chromatography–triple quadrupole tandem mass spectrometry. Food Anal. Method 2014, 7, 135–145. [Google Scholar] [CrossRef]
  303. Łozowicka, B.; Jankowska, M.; Rutkowska, E.; Hrynko, I.; Kaczyński, P.; Miciński, J. The evaluation of a fast and simple pesticide multiresidue method in various herbs by gas chromatography. J. Nat. Med. 2014, 68, 95–111. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  304. Zuin, V.G.; Yariwake, J.H.; Bicchi, C. Fast supercritical fluid extraction and high-resolution gas chromatography with electron-capture and flame photometric detection for multiresidue screening of organochlorine and organophosphorus pesticides in Brazil’s medicinal plants. J. Chromatogr. A 2003, 985, 159–166. [Google Scholar] [CrossRef]
  305. Tripathy, V.; Saha, A.; Patel, D.J.; Basak, B.; Shah, P.G.; Kumar, J. Validation of a QuEChERS-based gas chromatographic method for analysis of pesticide residues in Cassia angustifolia (senna). J. Environ. Sci. Health B 2016, 51, 508–518. [Google Scholar] [CrossRef]
  306. Zhang, R.; Tan, Z.-C.; Huang, K.-C.; Wen, Y.; Li, X.-Y.; Zhao, J.-L.; Liu, C.-L. A vortex-assisted dispersive liquid-liquid microextraction followed by UPLC-MS/MS for simultaneous determination of pesticides and aflatoxins in herbal tea. Molecules 2019, 24, 1029. [Google Scholar] [CrossRef] [Green Version]
  307. Tripathy, V.; Saha, A.; Kumar, J. Detection of pesticides in popular medicinal herbs: A modified QuEChERS and gas chromatography–mass spectrometry based approach. J. Food Sci. Technol. 2017, 54, 458–468. [Google Scholar] [CrossRef] [Green Version]
  308. Wang, Y.; Han, J.; Zhang, J.; Li, X.; Bai, R.; Hu, F. A monitoring survey and health risk assessment for pesticide residues on Codonopsis Radix in China. Sci. Rep. 2022, 12, 8133. [Google Scholar] [CrossRef]
  309. Wu, T.; Qi, P.; Wang, J.; Wang, Z.; Di, S.; Xu, H.; Zhao, H.; Zhao, C.; Wang, X. Simultaneous determination of 114 pesticides in complex Chinese herbal medicine Fritillaria using ordered mesoporous carbon CMK-3 as a reversed-dispersive solid phase extraction sorbent. RSC Adv. 2021, 11, 4129–4137. [Google Scholar] [CrossRef]
  310. Guo, Q.; Deng, M.; Yu, B.; Tan, L. Analysis of the Residues of 20 Organochlorine Pesticides in Herba epimedii, a Chinese Herbal Medicine, by Solid-Phase Extraction with Gas Chromatography/Negative Chemical Ionization–Mass Spectrometry. J. AOAC Int. 2010, 93, 295–305. [Google Scholar] [CrossRef]
  311. Jeong, H.I.; Han, J.-E.; Shin, B.-C.; Jang, S.B.; Won, J.-H.; Kim, K.H.; Sung, S.-H. Monitoring of Heavy Metals and Pesticide Residues of Herbal Decoctions in Traditional Korean Medicine Clinics in Korea. Int. J. Environ. Res. Public Health 2022, 19, 8523. [Google Scholar] [CrossRef]
  312. Słowik-Borowiec, M.; Szpyrka, E.; Walorczyk, S. Analysis of pesticide residues in fresh peppermint, Mentha piperita L., using the quick easy cheap effective rugged and safe method (QuEChERS) followed by gas chromatography with electron capture and nitrogen phosphorus detection. Bull. Environ. Contam. Toxicol. 2012, 89, 633–637. [Google Scholar] [CrossRef] [PubMed]
  313. Kowalska, G. Pesticide residues in some Polish herbs. Agriculture 2020, 10, 154. [Google Scholar] [CrossRef]
  314. Kowalska, G.; Pankiewicz, U.; Kowalski, R. Estimation of pesticide residues in selected products of plant origin from Poland with the use of the HPLC-MS/MS technique. Agriculture 2020, 10, 192. [Google Scholar] [CrossRef]
  315. Du, G.; Xiao, Y.; Yang, H.R.; Wang, L.; Song, Y.l.; Wang, Y.T. Rapid determination of pesticide residues in herbs using selective pressurized liquid extraction and fast gas chromatography coupled with mass spectrometry. J. Sep. Sci. 2012, 35, 1922–1932. [Google Scholar] [CrossRef]
  316. Du, G.; Song, Y.; Wang, Y. Rapid simultaneous determination of multiple pesticide residues in traditional Chinese medicines using programmed temperature vaporizer injection–fast gas chromatography coupled with mass spectrometry. J. Sep. Sci. 2011, 34, 3372–3382. [Google Scholar] [CrossRef]
  317. Miao, S.; Lu, J.; Jia, Z.; Mao, X.; Li, W.; Wang, K.; Ji, S. Simultaneous determination of 53 pesticide residues in traditional Chinese herbal medicines by GC/MS. Chin. Pharm. J. 2010, 45, 1263–1270. [Google Scholar]
  318. Wan-E, Z.; Zhen-Bin, G. Gel permeation chromatography purification and gas chromatography-mass spectrometry detection of multi-pesticide residues in traditional Chinese medicine. Am. J. Anal. Chem. 2012, 3, 16811. [Google Scholar]
  319. Tang, J.; Zhang, M.; Cheng, G.; Li, A.; Lu, Y. Development of IC-ELISA for detection of organophosphorus pesticides in water. J. Environ. Sci. Health B 2008, 43, 707–712. [Google Scholar] [CrossRef]
  320. Hua, X.; Yang, J.; Wang, L.; Fang, Q.; Zhang, G.; Liu, F. Development of an enzyme linked immunosorbent assay and an immunochromatographic assay for detection of organophosphorus pesticides in different agricultural products. PLoS ONE 2012, 7, e53099. [Google Scholar] [CrossRef] [Green Version]
  321. Chen, X.; Xu, L.; Ma, W.; Liu, L.; Kuang, H.; Wang, L.; Xu, C. General immunoassay for pyrethroids based on a monoclonal antibody. Food Agric. Immunol. 2014, 25, 341–349. [Google Scholar] [CrossRef]
  322. Hao, X.; Kuang, H.; Li, Y.; Yuan, Y.; Peng, C.; Chen, W.; Wang, L.; Xu, C. Development of an enzyme-linked immunosorbent assay for the α-cyano pyrethroids multiresidue in Tai lake water. J. Agric. Food Chem. 2009, 57, 3033–3039. [Google Scholar] [CrossRef]
  323. Reynoso, E.C.; Torres, E.; Bettazzi, F.; Palchetti, I. Trends and perspectives in immunosensors for determination of currently-used pesticides: The case of glyphosate, organophosphates, and neonicotinoids. Biosensors 2019, 9, 20. [Google Scholar] [CrossRef] [Green Version]
  324. Hua, X.; Yin, W.; Shi, H.; Li, M.; Wang, Y.; Wang, H.; Ye, Y.; Kim, H.J.; Gee, S.J.; Wang, M.; et al. Development of phage immuno-loop-mediated isothermal amplification assays for organophosphorus pesticides in agro-products. Anal. Chem. 2014, 86, 8441–8447. [Google Scholar] [CrossRef] [Green Version]
  325. Aamand, J.; Bruun, L.; Christensen, B.V. Immunological analysis of pesticides: A new tool in groundwater testing. GEUS Bull. 2004, 4, 29–32. [Google Scholar] [CrossRef] [Green Version]
  326. Elvin-Lewis, M. Safety issues associated with herbal ingredients. Adv. Food Nutr. Res. 2005, 50, 219–313. [Google Scholar]
  327. Rao, M.M.; KumarMeena, A. Detection of toxic heavy metals and pesticide residue in herbal plants which are commonly used in the herbal formulations. Environ. Monit. Assess. 2011, 181, 267–271. [Google Scholar] [CrossRef]
  328. Luo, L.; Wang, B.; Jiang, J.; Fitzgerald, M.; Huang, Q.; Yu, Z.; Li, H.; Zhang, J.; Wei, J.; Yang, C.; et al. Heavy metal contaminations in herbal medicines: Determination, comprehensive risk assessments, and solutions. Front. Pharmacol. 2021, 11, 595335. [Google Scholar] [CrossRef]
  329. Sarma, H.; Deka, S.; Deka, H.; Saikia, R.R. Accumulation of heavy metals in selected medicinal plants. In Reviews of Environmental Contamination and Toxicology; Springer: New York, NY, USA, 2012; pp. 63–86. [Google Scholar]
  330. Street, R. Heavy metals in medicinal plant products—An African perspective. S. Afr. J. Bot. 2012, 82, 67–74. [Google Scholar] [CrossRef] [Green Version]
  331. Nkansah, M.A.; Hayford, S.T.; Borquaye, L.S.; Ephraim, J.H. Heavy metal contents of some medicinal herbs from Kumasi, Ghana. Cogent. Environ. Sci. 2016, 2, 1234660. [Google Scholar] [CrossRef]
  332. Dghaim, R.; Al Khatib, S.; Rasool, H.; Ali Khan, M. Determination of heavy metals concentration in traditional herbs commonly consumed in the United Arab Emirates. J. Environ. Public Health 2015, 2015, 973878. [Google Scholar] [CrossRef] [Green Version]
  333. Behera, B.; Bhattacharya, S. The importance of assessing heavy metals in medicinal herbs: A quantitative study. CELLMED 2016, 6, 3.1–3.4. [Google Scholar] [CrossRef] [Green Version]
  334. Abdulla, N.M.; Adam, B.; Blair, I.; Oulhaj, A. Heavy metal content of herbal health supplement products in Dubai–UAE: A cross-sectional study. BMC Complement. Altern. Med. 2019, 19, 276. [Google Scholar] [CrossRef] [Green Version]
  335. Annan, K.; Dickson, R.A.; Amponsah, I.K.; Nooni, I.K. The heavy metal contents of some selected medicinal plants sampled from different geographical locations. Pharmacogn. Res. 2013, 5, 103. [Google Scholar] [CrossRef] [Green Version]
  336. Adepoju-Bello, A.; Issa, O.; Oguntibeju, O.O.; Ayoola, G.; Adejumo, O. Analysis of some selected toxic metals in registered herbal products manufactured in Nigeria. Afr. J. Biotechnol. 2012, 11, 6918–6922. [Google Scholar] [CrossRef]
Figure 1. Classification and characteristics of metabolites in a living organism.
Figure 1. Classification and characteristics of metabolites in a living organism.
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Figure 2. The Organization for Economic Cooperation and Development guidelines for toxicology studies.
Figure 2. The Organization for Economic Cooperation and Development guidelines for toxicology studies.
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Figure 3. Evolution of in vitro models for toxicology assessment.
Figure 3. Evolution of in vitro models for toxicology assessment.
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Figure 4. Zebrafish model—applicability for toxicity screening of xenobiotic agents.
Figure 4. Zebrafish model—applicability for toxicity screening of xenobiotic agents.
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Figure 5. Genotoxicity assessment methods—examples and mechanisms.
Figure 5. Genotoxicity assessment methods—examples and mechanisms.
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Figure 6. Omics-based approaches in toxicology—evaluation of the interactions between xenobiotics and living cells at different levels.
Figure 6. Omics-based approaches in toxicology—evaluation of the interactions between xenobiotics and living cells at different levels.
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Figure 7. Evaluation of toxicity arising from extrinsic sources—potential contaminants and methods of quantification. Abbreviations: GC, Gas-Chromatography; LC, Liquid-Chromatography; TLC, Thin Layer Chromatography.
Figure 7. Evaluation of toxicity arising from extrinsic sources—potential contaminants and methods of quantification. Abbreviations: GC, Gas-Chromatography; LC, Liquid-Chromatography; TLC, Thin Layer Chromatography.
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Table 1. Examples of metabolomic approaches for plant species authentication and differentiation.
Table 1. Examples of metabolomic approaches for plant species authentication and differentiation.
Analyzed MaterialAnalytical Technique UsedReference
Achillea millefoliumTLC
HPLC-UV
[23]
Anemone spp.HPLC-HR-ESI-MS/MS[24]
Allium spp.NMR
HPLC–MS
[25]
Alisma plantago-aquatica and A. orientaleUHPLC/Orbitrap-MS[26]
Aphanamixis polystachyaHPTLC[27]
Asparagus officinalisRP-HPLC-ESI-QTOF/MS/MS[28]
Avicennia marinaHPTLC[29]
Bacopa monnieriHPLC-PAD
HPLC-UV
[30]
[31]
[32]
Calendula officinalisTLC
HPLC-UV
[23]
Centella asiaticaHPLC-PAD[30]
Cinnamomum spp.UHPLC-HRMS[33]
Crocus sativus1H-NMR[34]
Curcuma spp.TLC
1H-NMR
[35,36,37]
Dendrobium spp.2D NMR[38]
Duboisia spp.1H-NMR
HPLC-MS
[39]
Ephedra spp.HPTLC
GC–MS
[40]
1H-NMR[41]
Ficus spp.FTIR, GC-MS
NMR
[42]
Fritillaria spp.MALDI-MS[43]
Gastrodia eletaHPLC-DAD-MS[44]
Ginkgo bilobaLC–MS[45]
Glossostemon bruguierGC-MS
NMR
[46]
Glycyrrhiza spp.HPTLC
NMR
[47]
GC-MS
LC-MS
1H NMR
[48]
HPLC-HRMS [49]
HPTLC
HPLC
[50]
LC-UV
LC-MS-MS
[51]
Hedera helix subspp.HPTLC- image analysis
HPTLC-MS
[52]
HPLC
CE
[53]
Hibiscus sabdariffaTLC
HPLC-UV
[23]
Iris domestica, I. tectorum and I. dichotomaHPLC–DAD-CL
ESI-QTOF-MS/MS
[54]
Lonicera spp.RRLC-QTOF-MS[55]
Matricaria recutitaTLC
HPLC-UV
[23]
Mentha spp.1H-NMR[56]
Ocimum sanctumTLC
FTIR
1H-NMR
[57]
Panax ginseng1H NMR[58]
Phyllanthus spp.FTIR
NMR
[59]
Uncaria spp.UHPLC/QTOF-MS[21]
Zingiber officinaleHPTLC (digitally-enhanced method)[60]
Abbreviations: 1H-NMR, Proton nuclear magnetic resonance spectroscopy; 2D NMR, Two-dimensional nuclear magnetic resonance spectroscopy; CE, Capillary electrophoresis; ESI-QTOF-MS/MS, Electrospray ionization-Quadrupole time-of-flight-Mass spectrometry; FTIR, Fourier-transform infrared spectroscopy; GC-MS, Gas chromatography–mass spectrometry; HPLC-DAD-CL, High-performance liquid chromatography with diode array detection coupled with chemiluminescent detection; HPLC-DAD-MS, High-performance liquid chromatography coupled with diode array detector and tandem mass spectrometry; HPLC-HR-ESI-MS/MS, High-performance liquid chromatography-High resolution electrospray ionization-Mass spectrometry; HPLC-HRMS, High-performance liquid chromatography-High resolution mass spectrometry; HPLC-MS, High-performance liquid chromatography-Mass spectrometry; HPLC-PAD, High-performance liquid chromatography with Photodiode Array Detector; HPLC-UV, High-performance liquid chromatography with Ultraviolet detection; HPTLC, High-performance thin-layer chromatography; LC-MS, Liquid chromatography–mass spectrometry; LC-UV, Liquid chromatography with Ultraviolet detection; MALDI-MS, Matrix-assisted laser desorption/ionization-Mass spectrometry; NMR, Nuclear magnetic resonance spectroscopy; RP-HPLC-ESI-QTOF/MS/MS, Reverse phase High-performance liquid chromatography-Electrospray ionization- Quadrupole time-of-flight-Mass spectrometry; RRLC-QTOF-MS, Rapid resolution liquid chromatography-Quadrupole time-of-flight-Mass spectrometry; TLC, Thin layer chromatography; UHPLC/Orbitrap-MS, Ultra-high-performance liquid chromatography-Orbitrap-Mass spectrometry; UHPLC/QTOF-MS, Ultra-high-performance liquid chromatography-Quadrupole time-of-flight-Mass spectrometry; UHPLC-HRMS, Ultra-high-performance liquid chromatography-High resolution mass spectrometry.
Table 2. A selection of studies using DNA barcoding for herbal material authentication.
Table 2. A selection of studies using DNA barcoding for herbal material authentication.
Analyzed MaterialAnalyzed Genomic RegionConclusion of the StudyReference
Angelica spp.matK, ITS, ITS2, rbcL, psbA-trnH
  • ITS—DNA barcoding to identify A. anomala and A. dahurica
[105]
Apiaceae familyrbcL, matK, ITS, ITS2, psbA-trnH
  • ITS and ITS2—superior results in intra- and interspecific divergence assessments
  • ITS—the highest identification efficiency (73.3%)
  • ITS + psbA-trnH combination—82.2% identification efficiency
  • ITS/ITS2 + psbA-trnH—potential standard DNA barcode for Apiaceae identification
[106]
Aquilaria spp.matK, rbcL, rpoB, rpoC1, psbA-trnH, trnL-trnF, ITS, ITS2
  • trnL-trnF + ITS / trnL-trnF + ITS2—the greatest species resolution using the least number of loci combination
  • trnL-trnF + ITS2—the best candidate barcode for Aquilaria spp. (ITS2 has a shorter sequence length compared to ITS → easier PCR amplification of degraded DNA samples)
[107]
Astragalus spp.ITS
  • ITS—potential barcode marker for quality control of Radix Astragali
[108]
Boerhavia diffusaITS, ITS1, ITS2, psbA-trnH
  • ITS and ITS1—potential candidate regions for identification and authentication of B. diffusa products
[109]
Canadian Arctic Flora
(490 vascular plant species—half of the Canadian Arctic flora and 93% of the flora of the Canadian Arctic Archipelago)
rbcL, matK
  • Higher sequence recovery for rbcL than matK (93% and 81%)
  • rbcL—easier to recover than matK from herbarium specimens (92% and 77%).
[110]
CaryophyllalesITS2, rbcL, matK
  • ITS2—the most successful in distinguishing between examined species to detect the contamination and adulteration
[111]
Codiaeum variegatumrbcL, matK
  • rbcL—more reliable for identification of C. variegatum than matK
[112]
Crocus spp.rbcL, matK, trnH-psbA, ITS
  • ITS—the most variable and informative region in C. sativus species
[113]
Curcuma spp.ITS, rbcL, matK
  • rbcL and ITS—100% PCR and sequencing success rate
  • matK—no amplification
  • ITS—ideal locus in discriminating the Curcuma species (showed greater variability than rbcL).
[114]
Dalbergia spp. ITS, matK, rbcL
  • ITS + matK + rbcL—a suitable barcode combination for identifying Dalbergia spp.
[115]
Dalbergia odorifera and D. tonkinensistrnH-psbA
  • trnH-psbA—proposed as DNA barcode for differentiation between D. odorifera and D. tonkinensis
[116]
Fabaceae familyITS2
  • ITS2—efficient and powerful marker and potential barcode to distinguish various species in Fabaceae family
[71]
Gentiana spp.rbcL, matK, ITS, 5S rRNA, trnH-psbA, trnL-F, rpl36-rps8
  • All seven tested loci—able to differentiate medicinal Gentiana species from adulterants.
  • Only 5S rRNA and trnL-F—able to discriminate the closely related species G. triflora, G. scabra and G. manshurica
[117]
Glehnia littoralisITS2
  • ITS2 distinguished commercial processed Glehniae Radix from common herbal adulterants
[118]
Hibiscus spp.matK, rbcL, trnH-psbA, ITS2
  • matK—differentiation of all the species of Hibiscus
  • Only the two-marker combinations with matK differentiated all the species
  • Better species resolution when the matK was in the three-marker combination
  • matK—more suitable than rbcL, trnH-psbA, ITS2 for Hibiscus spp. identification
[119]
Hippophae spp.ITS2
  • Bar-HRM targeting ITS2 assay—suitable to identify Hippophae species and authenticate commercial sea buckthorn products
[102]
Hypericum spp. (H. perforatum and H. androsaemum)ITS1, matK
  • Both regions (ITS1, matK)—successful in the species-specific PCR identification
  • Only matK—adequate for real-time PCR—HRM analysis differentiation
[75]
Lonicera spp.rbcl, matK, psbA-trnH, ITS2, ITS, trnL, trnL-F
  • psbA-trnH—the highest interspecific divergence intergenic spacer
[120]
Matricaria recutitamatK, rbcL, psbA-trnH, ITS, ITS2
  • ITS2—the maximum genetic diversity
  • ITS2—adequate polymorphic sites to detect interspecific variation with high amplification and sequencing success.
[89]
Momordica spp.ITS, matK
  • Both regions (matK, ITS)—good PCR amplification and sequencing results
[121]
Nepenthes spp.ITS, rbcl, matK
  • The combination of ITS + matK—barcode for Nepenthes genus identification
[122]
Panax spp.ITS2, matK,
psbA-trnH
  • ITS2 potential specific marker for the identification Panax species and their adulterants
[123]
atpF-atpH, rbcL, rpoB, and rpoC1, matK, psbK-I, psbM-trnD, rps16 and nad1, psbA-trnH, ITS
  • psbA-trnH and ITS—the most variable loci
  • The combination of psbA-trnH and ITS—sufficient for identifying all the species and clusters in the genus
[124]
Paris polyphyllaITS2
  • ITS2—successful for P. polyphylla authentication and quality control
[97]
Phyllanthus spp.rbcL, trnL
  • trnL derived primer pair (PhylltrnL)—higher specificity and power of discrimination for Phyllanthus species than rbcL derived primer pair (PhyllrbcL)
[95]
psbA-trnH
  • psbA-trnH can effectively discriminate Phyllanthus species
[125]
Piper nigrumtrnL, psbA-trnH
  • trnL, psbA-trnH—suitable for specific detection of contaminants (Carica papaya, Zea mays and Capsicum annuum)
[126]
psbA-trnH, rbcL, rpoC1
  • psbA-trnH—ideal for detection of chilli adulteration in black pepper
[127]
Rhodiola spp.rbcL, matK, trnH-psbA, trnL-F, ITS
  • ITS—the best single-locus barcode (resolved 66% of the Rhodiola species)
  • rbcL, matK, trnH-psbA, trnL-F, ITS combination—the highest discrimination power, (resolved 80.9% of the species)
[128]
Rhododendron spp.rbcL, matK, psbA-trnH, ITS2
  • psbA-trnH—DNA marker for identifying the Rhododendron species
[129]
Ruta graveolensrpoB, rpoC1, ITS
  • ITS, rpoB, rpoC1—the best markers to differentiate Ruta graveolens and Euphorbia dracunculoides (adulterant)
[130]
Schisandraceae familyITS, trnH-psbA, matK, rbcL
  • ITS and trnH-psbA—higher species-resolving power than matK and rbcL
  • ITS + trnH-psbA + matK + rbcL—the most ideal DNA barcode for discriminating the medicinal plants of Schisandra and Kadsura
  • ITS + trnH-psbA—the most suitable barcode for Illicium species
[131]
Senna spp.trnH-psbA
  • trnH-psbA—effective at distinguishing Senna species
[132]
Sida cordifoliarbcL, matK, psbA-trnH, ITS2
  • psbA-trnH and ITS2—the best two-marker combination for S. cordifolia identification
[133]
Stephania spp.ITS, ITS2, psbA-trnH, matK, rbcL, trnL-F
  • ITS—the highest number of variable and informative sites, followed by psbA-trnH
  • ITS + psbA-trnH—the highest discrimination power
[134]
Trillium govanianumITS, matK, trnH-psbA, rbcL
  • ITS, matK and trnH-psbA—ideal reference barcodes for T. govanianum, ITS and trnH-psbA—suitable for Paris. polyphylla (adulterant)
[2]
Verbenaceae familypsbA-trnH, rbcL, matK, ITS2, ITS
  • ITS2 and psbA-trnH—promising sequence for the identification of the species in Verbenaceae
[135]
Abbreviations: Bar-HRM, Barcode DNA-High Resolution Melting; PCR, Polymerase Chain Reaction; PCR—HRM, Polymerase Chain Reaction-High Resolution Melting.
Table 3. Genotoxicity assessment studies for herbal medicinal products—a selection.
Table 3. Genotoxicity assessment studies for herbal medicinal products—a selection.
Genotoxic EndpointsTestReference
Gene mutationsAmes (Bacterial Reverse Mutation Test)[176,181,182,183,184,185,186,187]
Mouse lymphoma assay[185,187,188]
In Vitro Mammalian Cell Gene Mutation Tests[189]
DNA damageComet assay[181,188,190,191,192,193,194,195,196]
Chromosomal damage (structural and numerical aberrations)Chromosome aberration test[184,197,198,199,200]
Micronucleus assay[181,185,187,188,192,197,198,201,202]
Allium cepa test[182,203,204,205,206]
Table 4. A selection of toxico-metabonomics studies applied for the potential toxic assessment of herbal medicinal products and bioactive phytometabolites.
Table 4. A selection of toxico-metabonomics studies applied for the potential toxic assessment of herbal medicinal products and bioactive phytometabolites.
Analyzed SampleAnalytical MethodsAim of the StudyReference
Aconitum spp. alkaloidsGC-TOF/MSIdentification of altered metabolites associated with induced metabolic disorders[257]
Aconitum carmichaeliiLC-QTOF-MSIdentification of serum biomarkers of toxicity[258]
Aurantio-obtusin
(anthraquinone from Cassia seed)
UPLC-QTOF-MSIdentification of urinary biomarkers associated with hepatotoxicity[259]
Coptidis rhizoma1H NMR
GC-MS
Investigation of gastro-intestinal toxicity via the alteration of gut microbiota[260]
Dioscorea bulbifera rhizome1H NMR
GC-MS
Assessment of hepatotoxicity and identification of altered metabolites in blood and tissue samples[261]
GC-MSIdentification of altered metabolites from rat plasma, urine, and feces associated with hepatotoxicity[262]
Dioscorea bulifera root 1H NMRPrediction of hepatotoxicity based on urinary metabolic perturbations[263]
Mesaconitine (diterpenoid alkaloid from Aconitum spp.)UPLC-Q-Exactive Orbitrap-MSInvestigation of the hepatotoxicity mechanism[264]
Pharbitis nil seedsUPLC-MSInvestigation of changes of urine metabolite biomarkers associated with nephrotoxicity[265]
Pinellia ternataUPLC Q-TOF-MSInvestigation of general toxicity (liver, kidney, heart) via profiling serum metabolic alterations[266]
Polygonum multiflorumUHPLC-MSIdentification of hepatotoxicity biomarkers and pathways[267]
HPLC-MS[268]
UPLC-QTOF-MS[269]
Realgar (Traditional Chinese medicine)1H NMRAssessment of hepatotoxicity using plasma and urine samples metabolic profiling[270]
1H NMRAssessment of hepato- and nephrotoxicity via metabonomic analysis of urine, serum, and liver tissue[271]
Sophora alopecuroides1H NMREvaluation of metabonomic profiling alterations associated with hepatic and renal toxicity[272]
Tripterygium wilfordiiGC-MSIdentification of urine biomarkers to elucidate Tripterygium wilfordii poisoning[273]
Xanthii fructusUPLC-QTOF-MSIdentification of urinary toxicity biomarkers associated with lipid metabolism alteration [274]
Abbreviations: 1H-NMR, Proton nuclear magnetic resonance spectroscopy; GC-MS, Gas chromatography–Mass spectrometry; GC-QTOF-MS, Gas chromatography- Time-of-flight-Mass spectrometry; HPLC-MS, High-performance liquid chromatography-Mass spectrometry; LC-QTOF-MS, Liquid chromatography–Time-of-flight-mass spectrometry; UHPLC-MS, Ultra-high-performance liquid chromatography-Mass spectrometry; UPLC-Q-Exactive Orbitrap MS, Ultra-performance liquid chromatography-Quadrupole-Electrostatic field Orbitrap-Mass spectrometry; UPLC-QTOF-MS, Ultra- performance liquid chromatography-Quadrupole time-of-flight-Mass spectrometry.
Table 5. A selection of techniques used for the identification and quantification of pesticides in herbal medicinal products.
Table 5. A selection of techniques used for the identification and quantification of pesticides in herbal medicinal products.
Analyzed SamplePurification and Analysis MethodsReference
Brazilian medicinal plantsSFE
HRGC–ECD/FPD
[304]
Cassia angustifoliaQuEChERS
GC–MS
[305]
China herbal teaUPLC-MS/MS coupled with vortex-assisted DLLME[306]
Commercial market medicinal plant samples in IndiaQuEChERS
GC–MS
[307]
Codonopsis Radix (in China)QuEChERS-GC–MS/MS
QuEChERS-LC–MS/MS
[308]
Fritillaria spp.QuEChERS
LC-MS/MS
[309]
Herba epimediiSPE
GC-MS
[310]
Herbal Decoctions in Traditional Korean MedicineGC/ECD
GC/MSD
[311]
Mentha piperitaQuEChERS
GC/ECD/NPD
[312]
Polish herbal raw materialsQuEChERS
HPLC–MS/MS
[313]
Polish products of plant originQuEChERS
HPLC–MS/MS
[314]
A selection of medicine and food herbsSPLE
GC-MS/MS
[315]
A selection of medicinal plants—Matricaria chamomilla, Tilia spp., Pulmonaria spp., Melissa spp., Mentha piperita, Thymus vulgaris.MSPD
LSE
GC
[303]
Traditional Chinese herbal medicinesGC/MS/MS[302]
QuEChERS
GC-MS with PTV-LVI-SV
[316]
HPLC-MS/MS
GC/MS/MS
[300]
QuEChERS
UHPLC–MS/MS
[301]
SPE
GC-MS
[317]
GPC
GC-MS
[318]
Abbreviations: DLLME, Dispersive liquid-liquid microextraction; GC-ECD-NPD, Gas chromatography coupled to electron capture and nitrogen phosphorus detectors; GC-ECD, Gas chromatography with electron capture detection; GC-MSD, Gas chromatography with mass selective detection; GC–MS, Gas chromatography–Mass spectrometry, GPC, Gel Permeation Chromatography; HPLC–MS-MS, High-performance-liquid chromatography–Mass spectrometry; HRGC–ECD-FPD, High-resolution gas chromatography with electron-capture and flame photometric detection; LC-MS, Liquid chromatography–Mass spectrometry; LSE, Liquid–solid extraction; MSPD, Matrix solid phase dispersion; PTV-LVI-SV, Programmed temperature vaporizer in solvent vent mode; QuEChERS, Quick, Easy, Cheap, Effective, Rugged, and Safe extraction method; SPLE, Selective pressurized liquid extraction; SPE, Solid-phase extraction; SFE, Supercritical fluid extraction; UHPLC–MS-MS, Ultra-high-performance-liquid chromatography–Mass spectrometry; UPLC-MS/MS, Ultra-performance-liquid chromatography–Mass spectrometry.
Table 6. Heavy metals quantification in herbal medicinal products—a selection of studies.
Table 6. Heavy metals quantification in herbal medicinal products—a selection of studies.
Analyzed MaterialAnalysis MethodConclusion of the StudyReference
Emblica officinalis
Terminalia chebula
Terminalia belerica
Withania somnifera
AAS
  • Pb, Cd, Hg, and As were within permissible limits
[327]
A selection of herbal medicinesICP-MS
  • 70.93% of the herbal plants were within the acceptable risks
  • As posed the highest risk
  • Tetradium ruticarpum, Plantago asiatica, and Desmodium styracifolium—the highest risk
[328]
A selection of herbal products in NigeriaAAS
  • As and Hg were present in all the samples at concentrations below the USP limits
  • Cd—above the USP limits in 55% of the samples
[336]
A selection of medicinal herbs from GhanaAAS
  • Fe, Zn, Pb levels were within the WHO maximum permissible limits
  • For some samples, Cd levels were above the WHO limits
[331]
Petroselinum crispum
Ocimum basilicum
Salvia officinalis
Origanum vulgare
Mentha spicata
Thymus vulgaris
Matricaria chamomilla
AAS
  • Most herbs contained levels of heavy metals (Cd, Pb, Cu, Fe, Zn) that exceeded the WHO permissible limits
[332]
A selection of medicinal herbs from IndiaAAS
  • Except for the Cr content in three plants, the levels of As, Pb, Cd, Hg, Ni were below the permissible limits
[333]
A selection of health supplement productsICP-OES
  • The majority of products—low levels of heavy metals
  • Exception—higher Cd levels for 3 products
[334]
A selection of medicinal plantsAAS
  • Pb was present in all plant species, with one exception
  • 40% of the plant species exceeded the limit for Cd
[335]
Abbreviations: AAS, Atomic Absorption Spectroscopy; ICP-MS, Inductively Coupled Plasma Mass Spectrometry, ICP-OES, Inductively Coupled Plasma Optical Emission Spectrometry, USP, United States Pharmacopeia; WHO, World Health Organization.
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Jităreanu, A.; Trifan, A.; Vieriu, M.; Caba, I.-C.; Mârțu, I.; Agoroaei, L. Current Trends in Toxicity Assessment of Herbal Medicines: A Narrative Review. Processes 2023, 11, 83. https://doi.org/10.3390/pr11010083

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Jităreanu A, Trifan A, Vieriu M, Caba I-C, Mârțu I, Agoroaei L. Current Trends in Toxicity Assessment of Herbal Medicines: A Narrative Review. Processes. 2023; 11(1):83. https://doi.org/10.3390/pr11010083

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Jităreanu, Alexandra, Adriana Trifan, Mădălina Vieriu, Ioana-Cezara Caba, Ioana Mârțu, and Luminița Agoroaei. 2023. "Current Trends in Toxicity Assessment of Herbal Medicines: A Narrative Review" Processes 11, no. 1: 83. https://doi.org/10.3390/pr11010083

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