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14 pages, 4932 KiB

Open AccessArticle

Development of Analytical Quality by Design Compliant Chaotropic Chromatography Method for Ziprasidone and Its Five Impurities Determination

by Milena Rmandić, Đorđe Vasilić, Marija Rašević, Mira Zečević, Biljana Otašević, Ana Protić and Anđelija Malenović

Pharmaceuticals 2023, 16(9), 1296; https://doi.org/10.3390/ph16091296 - 14 Sep 2023

Viewed by 813

Abstract

In this study, an AQbD-compliant chaotropic chromatography method for ziprasidone and the determination of its five impurities was developed. The influence of critical method parameters (initial and final methanol fraction in the mobile phase, gradient duration) on the set of selected critical method [...] Read more.

In this study, an AQbD-compliant chaotropic chromatography method for ziprasidone and the determination of its five impurities was developed. The influence of critical method parameters (initial and final methanol fraction in the mobile phase, gradient duration) on the set of selected critical method attributes (t_{_imp. V}, t_{_imp. V} − t_{_imp. I}, S and <W_USP>) was studied by Box–Behnken design. The errors resulting from the calculation of the model coefficients were propagated to the selected responses by Monte Carlo simulations, and their predictive distribution was obtained. The design space was computed (π ≥ 80%), and a working point was selected: initial methanol fraction 38.5%, final methanol fraction 77.5%, and gradient duration 16.25 min. Furthermore, the quantitative robustness of the developed method was tested using the Plackett–Burman design. P_{_imp II} and P_{_imp V} were found to be significantly affected, the first by mobile phase flow rate and the second by gradient duration. Finally, the method was validated, and its reliability for routine quality control in capsules was confirmed. Full article

(This article belongs to the Special Issue The Age of In-Silico Methods in Drug Discovery, Development, Manufacture and Quality Control)

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16 pages, 1461 KiB

Open AccessArticle

Dried Blood Spots in Neonatal Studies: A Computational Analysis for the Role of the Hematocrit Effect

by Chrysa Daousani, Vangelis Karalis, Yannis L. Loukas, Kleopatra H. Schulpis, Konstantinos Alexiou and Yannis Dotsikas

Pharmaceuticals 2023, 16(8), 1126; https://doi.org/10.3390/ph16081126 - 10 Aug 2023

Viewed by 838

Abstract

Dried blood spot (DBS) microsampling is extensively employed in newborn screening (NBS) and neonatal studies. However, the impact of variable neonatal hematocrit (Ht) values on the results can be a source of analytical error, and the use of fixed Ht for calibration (Ht [...] Read more.

Dried blood spot (DBS) microsampling is extensively employed in newborn screening (NBS) and neonatal studies. However, the impact of variable neonatal hematocrit (Ht) values on the results can be a source of analytical error, and the use of fixed Ht for calibration (Ht_cal) is not representative of all neonatal subpopulations. A computational approach based on neonatal demographics was developed and implemented in R^® language to propose a strategy using correction factors to address the Ht effect in neonatal DBS partial-spot assays. A rational “tolerance level” was proposed for the Ht effect contribution to the total analytical error and a safe Ht range for neonatal samples, where the correction of concentrations can be omitted. Furthermore, an “alert zone” for a false positive or negative result in NBS was proposed, where the Ht effect has to be considered. Results point toward the use of Ht_cal values closely representative of populations under analysis and an acceptable level of percentage relative error can be attributed to the Ht effect, diminishing the probability of correction. Overall, the impact of the Ht effect on neonatal studies is important and future work may further investigate this parameter, correlated to other clinical variables potentially affecting results. Full article

(This article belongs to the Special Issue The Age of In-Silico Methods in Drug Discovery, Development, Manufacture and Quality Control)

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23 pages, 4502 KiB

Open AccessArticle

An In Silico Study Investigating Camptothecin-Analog Interaction with Human Protein Tyrosine Phosphatase, SHP2 (PTPN11)

by Donald Bajia and Katarzyna Derwich

Pharmaceuticals 2023, 16(7), 926; https://doi.org/10.3390/ph16070926 - 26 Jun 2023

Cited by 1 | Viewed by 1141

Abstract

The human PTPN11 gene encodes for the src tyrosine phosphatase protein (SHP2) is now gaining much attention in many disorders, particularly its oncogenic involvement in many types of cancer. Efforts in developing molecules targeting SHP2 with high efficacy are the future of drug [...] Read more.

The human PTPN11 gene encodes for the src tyrosine phosphatase protein (SHP2) is now gaining much attention in many disorders, particularly its oncogenic involvement in many types of cancer. Efforts in developing molecules targeting SHP2 with high efficacy are the future of drug discovery and chemotherapy. However, the interaction of a new camptothecin analog with the catalytic domain of SHP2 protein remains unknown. Therefore, this study aims to provide in silico rationale for the recognition and binding of FL118 and irinotecan with the catalytic domain of human protein tyrosine phosphatase-SHP2 (PTPc-SH2-SHP2, chain A). The docking interaction of the human SHP2 protein’s catalytic domain as well as Y279C and R465G mutants with FL118 and irinotecan ligands were calculated and analyzed using the Autodock 4.2 programme, setting the docking grid to target the protein’s active site. The camptothecin analog FL118 had the best lowest negative affinity energies with PTPc-SHP2 wildtype and SHP2-Y279C mutant model (−7.54 Kcal/mol and −6.94 Kcal/mol, respectively). Moreover, the protein-ligand complexes revealed several hydrogen bond interactions reflecting the degree of stability that each structure possesses, with the FL118-SHP2-wildtype forming the most stable complex among the structures. In addition, the FL118-SHP2 wildtype complex was validated for RMSD, RMSF, hydrogen bonds, and salt bridges. This revealed that the complex generated became stable over time. This in silico rationale identifies the novel FL118 camptothecin analog as a potent selective inhibitor of PTPc-SH2 domain of SHP2 protein, paving way for further in vitro investigations into the interactions and binding activity of analogs with SHP2 for potential therapeutic applications in PTPN11-associated disorders. Full article

(This article belongs to the Special Issue The Age of In-Silico Methods in Drug Discovery, Development, Manufacture and Quality Control)

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24 pages, 6601 KiB

Open AccessArticle

An In Silico Approach toward the Appropriate Absorption Rate Metric in Bioequivalence

by Vangelis D. Karalis

Pharmaceuticals 2023, 16(5), 725; https://doi.org/10.3390/ph16050725 - 10 May 2023

Cited by 2 | Viewed by 1396

Abstract

In bioequivalence, the maximum plasma concentration (Cmax) is traditionally used as a metric for the absorption rate, despite the fact that there are several concerns. The idea of “average slope” (AS) was recently introduced as an alternative metric to reflect absorption rate. This [...] Read more.

In bioequivalence, the maximum plasma concentration (Cmax) is traditionally used as a metric for the absorption rate, despite the fact that there are several concerns. The idea of “average slope” (AS) was recently introduced as an alternative metric to reflect absorption rate. This study aims to further extend the previous findings and apply an in silico approach to investigate the kinetic sensitivity of AS and Cmax. This computational analysis was applied to the C-t data of hydrochlorothiazide, donepezil, and amlodipine, which exhibit different absorption kinetics. Principal component analysis (PCA) was applied to uncover the relationships between all bioequivalence metrics. Monte Carlo simulations of bioequivalence trials were performed to investigate sensitivity. The appropriate programming codes were written in Python for the PCA and in MATLAB^® for the simulations. The PCA verified the desired properties of AS and the unsuitability of Cmax to reflect absorption rate. The Monte Carlo simulations showed that AS is quite sensitive to detecting differences in absorption rate, while Cmax has almost negligible sensitivity. Cmax fails to reflect absorption rate, and its use in bioequivalence gives only a false impression. AS has the appropriate units, is easily calculated, exhibits high sensitivity, and has the desired properties of absorption rate. Full article

(This article belongs to the Special Issue The Age of In-Silico Methods in Drug Discovery, Development, Manufacture and Quality Control)

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17 pages, 1812 KiB

Open AccessArticle

Analytical Quality by Design: Achieving Robustness of an LC-CAD Method for the Analysis of Non-Volatile Fatty Acids

by Rasmus Walther, Jovana Krmar, Adrian Leistner, Bojana Svrkota, Biljana Otašević, Andjelija Malenović, Ulrike Holzgrabe and Ana Protić

Pharmaceuticals 2023, 16(4), 478; https://doi.org/10.3390/ph16040478 - 23 Mar 2023

Viewed by 1570

Abstract

An alternative to the time-consuming and error-prone pharmacopoeial gas chromatography method for the analysis of fatty acids (FAs) is urgently needed. The objective was therefore to propose a robust liquid chromatography method with charged aerosol detection for the analysis of polysorbate 80 (PS80) [...] Read more.

An alternative to the time-consuming and error-prone pharmacopoeial gas chromatography method for the analysis of fatty acids (FAs) is urgently needed. The objective was therefore to propose a robust liquid chromatography method with charged aerosol detection for the analysis of polysorbate 80 (PS80) and magnesium stearate. FAs with different numbers of carbon atoms in the chain necessitated the use of a gradient method with a Hypersil Gold C₁₈ column and acetonitrile as organic modifier. The risk-based Analytical Quality by Design approach was applied to define the Method Operable Design Region (MODR). Formic acid concentration, initial and final percentages of acetonitrile, gradient elution time, column temperature, and mobile phase flow rate were identified as critical method parameters (CMPs). The initial and final percentages of acetonitrile were fixed while the remaining CMPs were fine-tuned using response surface methodology. Critical method attributes included the baseline separation of adjacent peaks (α-linolenic and myristic acid, and oleic and petroselinic acid) and the retention factor of the last compound eluted, stearic acid. The MODR was calculated by Monte Carlo simulations with a probability equal or greater than 90%. Finally, the column temperature was set at 33 °C, the flow rate was 0.575 mL/min, and acetonitrile linearly increased from 70 to 80% (v/v) within 14.2 min. Full article

(This article belongs to the Special Issue The Age of In-Silico Methods in Drug Discovery, Development, Manufacture and Quality Control)

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30 pages, 11091 KiB

Open AccessArticle

Identification of a Family of Glycoside Derivatives Biologically Active against Acinetobacter baumannii and Other MDR Bacteria Using a QSPR Model

by Francisco José Palacios-Can, Jesús Silva-Sánchez, Ismael León-Rivera, Hugo Tlahuext, Nina Pastor and Rodrigo Said Razo-Hernández

Pharmaceuticals 2023, 16(2), 250; https://doi.org/10.3390/ph16020250 - 07 Feb 2023

Cited by 3 | Viewed by 2024

Abstract

As the rate of discovery of new antibacterial compounds for multidrug-resistant bacteria is declining, there is an urge for the search for molecules that could revert this tendency. Acinetobacter baumannii has emerged as a highly virulent Gram-negative bacterium that has acquired multiple resistance mechanisms [...] Read more.

As the rate of discovery of new antibacterial compounds for multidrug-resistant bacteria is declining, there is an urge for the search for molecules that could revert this tendency. Acinetobacter baumannii has emerged as a highly virulent Gram-negative bacterium that has acquired multiple resistance mechanisms against antibiotics and is considered of critical priority. In this work, we developed a quantitative structure-property relationship (QSPR) model with 592 compounds for the identification of structural parameters related to their property as antibacterial agents against A. baumannii. QSPR mathematical validation (

R^{2}

= 70.27,

R^{N}

= −0.008,

a (R^{2})

= 0.014, and

δ K

= 0.021) and its prediction ability (

Q^{2}

_LMO= 67.89,

Q^{2}

_EXT = 67.75,

a (Q^{2})

= −0.068,

δ Q

= 0.0,

\bar{r_{m}^{2}}

= 0.229, and

Δ r_{m}^{2}

= 0.522) were obtained with different statistical parameters; additional validation was done using three sets of external molecules (

R^{2}

= 72.89, 71.64 and 71.56). We used the QSPR model to perform a virtual screening on the BIOFACQUIM natural product database. From this screening, our model showed that molecules 32 to 35 and 54 to 68, isolated from different extracts of plants of the Ipomoea sp., are potential antibacterials against A. baumannii. Furthermore, biological assays showed that molecules 56 and 60 to 64 have a wide antibacterial activity against clinically isolated strains of A. baumannii, as well as other multidrug-resistant bacteria, including Staphylococcus aureus, Escherichia coli, Klebsiella pneumonia, and Pseudomonas aeruginosa. Finally, we propose 60 as a potential lead compound due to its broad-spectrum activity and its structural simplicity. Therefore, our QSPR model can be used as a tool for the investigation and search for new antibacterial compounds against A. baumannii. Full article

(This article belongs to the Special Issue The Age of In-Silico Methods in Drug Discovery, Development, Manufacture and Quality Control)

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24 pages, 5492 KiB

Open AccessArticle

SIRT2i_Predictor: A Machine Learning-Based Tool to Facilitate the Discovery of Novel SIRT2 Inhibitors

by Nemanja Djokovic, Minna Rahnasto-Rilla, Nikolaos Lougiakis, Maija Lahtela-Kakkonen and Katarina Nikolic

Pharmaceuticals 2023, 16(1), 127; https://doi.org/10.3390/ph16010127 - 14 Jan 2023

Cited by 1 | Viewed by 1945

Abstract

A growing body of preclinical evidence recognized selective sirtuin 2 (SIRT2) inhibitors as novel therapeutics for treatment of age-related diseases. However, none of the SIRT2 inhibitors have reached clinical trials yet. Transformative potential of machine learning (ML) in early stages of drug discovery [...] Read more.

A growing body of preclinical evidence recognized selective sirtuin 2 (SIRT2) inhibitors as novel therapeutics for treatment of age-related diseases. However, none of the SIRT2 inhibitors have reached clinical trials yet. Transformative potential of machine learning (ML) in early stages of drug discovery has been witnessed by widespread adoption of these techniques in recent years. Despite great potential, there is a lack of robust and large-scale ML models for discovery of novel SIRT2 inhibitors. In order to support virtual screening (VS), lead optimization, or facilitate the selection of SIRT2 inhibitors for experimental evaluation, a machine-learning-based tool titled SIRT2i_Predictor was developed. The tool was built on a panel of high-quality ML regression and classification-based models for prediction of inhibitor potency and SIRT1-3 isoform selectivity. State-of-the-art ML algorithms were used to train the models on a large and diverse dataset containing 1797 compounds. Benchmarking against structure-based VS protocol indicated comparable coverage of chemical space with great gain in speed. The tool was applied to screen the in-house database of compounds, corroborating the utility in the prioritization of compounds for costly in vitro screening campaigns. The easy-to-use web-based interface makes SIRT2i_Predictor a convenient tool for the wider community. The SIRT2i_Predictor’s source code is made available online. Full article

(This article belongs to the Special Issue The Age of In-Silico Methods in Drug Discovery, Development, Manufacture and Quality Control)

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Review

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39 pages, 1752 KiB

Open AccessReview

Model-Informed Drug Development: In Silico Assessment of Drug Bioperformance following Oral and Percutaneous Administration

by Jelena Djuris, Sandra Cvijic and Ljiljana Djekic

Pharmaceuticals 2024, 17(2), 177; https://doi.org/10.3390/ph17020177 - 30 Jan 2024

Viewed by 1233

Abstract

The pharmaceutical industry has faced significant changes in recent years, primarily influenced by regulatory standards, market competition, and the need to accelerate drug development. Model-informed drug development (MIDD) leverages quantitative computational models to facilitate decision-making processes. This approach sheds light on the complex [...] Read more.

The pharmaceutical industry has faced significant changes in recent years, primarily influenced by regulatory standards, market competition, and the need to accelerate drug development. Model-informed drug development (MIDD) leverages quantitative computational models to facilitate decision-making processes. This approach sheds light on the complex interplay between the influence of a drug’s performance and the resulting clinical outcomes. This comprehensive review aims to explain the mechanisms that control the dissolution and/or release of drugs and their subsequent permeation through biological membranes. Furthermore, the importance of simulating these processes through a variety of in silico models is emphasized. Advanced compartmental absorption models provide an analytical framework to understand the kinetics of transit, dissolution, and absorption associated with orally administered drugs. In contrast, for topical and transdermal drug delivery systems, the prediction of drug permeation is predominantly based on quantitative structure–permeation relationships and molecular dynamics simulations. This review describes a variety of modeling strategies, ranging from mechanistic to empirical equations, and highlights the growing importance of state-of-the-art tools such as artificial intelligence, as well as advanced imaging and spectroscopic techniques. Full article

(This article belongs to the Special Issue The Age of In-Silico Methods in Drug Discovery, Development, Manufacture and Quality Control)

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