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A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Pharmacology".

Deadline for manuscript submissions: 20 September 2023 | Viewed by 6195

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Special Issue Editors

Dr. Zhiyao He

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Guest Editor

Department of Pharmacy, West China Hospital, Sichuan University, Chengdu, China
Interests: drug delivery; gene therapy; pharmacotherapy; biotherapy; siRNA

Dr. Zhijie Xu

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Guest Editor

Department of Pathology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha 410008, China
Interests: anti-tumor pharmacology; programmed cell death; non-coding RNA; native compounds.

Dr. Chenyu Sun

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Guest Editor

AMITA Health Saint Joseph Hospital Chicago, Chicago, IL, USA
Interests: evidence based medicine; translational medical research; clinical studies of novel medication
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Special Issue Information

Dear Colleagues,

This special issue focuses on current research trends in clinical, translational and experimental pharmacotherapeutics. It serves as an international forum for sharing and exchange of research outcomes among health practitioners, scientists and researchers associated with experimental, translational and clinical pharmacotherapeutics.

Clinical pharmacotherapeutics refers to the rational use of drugs to diagnose, prevent and treat diseases in clinical practice. Clinical pharmacotherapeutics includes but not limited to clinical pharmacokinetics, clinical pharmacodynamics, clinical medication therapy management, pharmacogenomics, rational drug use, adverse drug reaction/event, etc.

Translational pharmacotherapeutics is a novel concept, which devotes to translating novel theranostics from bench to bedside. Translational pharmacotherapeutics includes but not limited to new theories, new therapies, and new technologies for potential clinical application.

Experimental pharmacotherapeutics refers to the experimental & basic research for purpose of pharmacotherapeutics. Experimental pharmacotherapeutics includes but not limited to target identification & validation, compound designing and screening, activity evaluation, mechanism of action, drug delivery, pharmacokinetics, pharmacodynamics, chronotoxicology, preclinical safety & toxicity, etc.

Dr. Zhiyao He
Dr. Zhijie Xu
Dr. Chenyu Sun
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Clinical Medicine is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

pharmacotherapeutics
pharmacokinetics
pharmacodynamics
pharmacogenomics
chronotoxicology
drug delivery
drug discovery

Published Papers (7 papers)

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Open AccessEditorial

Clinical, Translational and Experimental Pharmacotherapeutics Advances

and

J. Clin. Med. 2023, 12(3), 1136; https://doi.org/10.3390/jcm12031136 - 01 Feb 2023

Viewed by 595

Abstract

Pharmacotherapeutics is the prevention and treatment of ailments, whether disorders or diseases, with medication [...] Full article

(This article belongs to the Special Issue Clinical, Translational and Experimental Pharmacotherapeutics Advances)

Research

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Open AccessArticle

Reporting of Drug-Induced Myopathies Associated with the Combination of Statins and Daptomycin: A Disproportionality Analysis Using the US Food and Drug Administration Adverse Event Reporting System

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J. Clin. Med. 2023, 12(10), 3548; https://doi.org/10.3390/jcm12103548 - 18 May 2023

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Abstract

Background: Myopathy is one of the most common adverse reactions of daptomycin and statins. We aimed to evaluate the muscular toxicity of the combination therapy of daptomycin and statins in a large pharmacovigilance database. Methods: This was a retrospective disproportionality analysis based on real-world data. All cases reported between the first quarter of 2004 and the fourth quarter of 2022 where daptomycin and statins were reported were gathered from the US Food and Drug Administration Adverse Event Reporting System (FAERS) database. Disproportionality analyses were conducted by estimating the proportional reporting ratios (PRRs), reporting odds ratio (ROR), and information component (IC). Results: A total of 971,861 eligible cases were collected from the FAERS database. Data analysis showed that rosuvastatin (ROR: 124.39, 95% CI: 87.35–178.47), atorvastatin (ROR: 68.53, 95% CI: 51.93–90.43), and simvastatin (ROR: 94.83, 95% CI: 71.12–126.46) combined with daptomycin increased the reporting frequency of myopathy. Moreover, myopathy was reported more frequently with the 3-drug combination (ROR: 598.01, 95% CI: 231.81–1542.71). For rhabdomyolysis, the frequency of reports also increased when daptomycin was combined with rosuvastatin (ROR: 156.34, 95% CI: 96.21–254.05), simvastatin (ROR: 72.65, 95% CI: 47.36–111.44), and atorvastatin (ROR: 66.31, 95% CI: 44.06–99.81). Conclusions: The combination of daptomycin and statins increased the association of myopathy and rhabdomyolysis, especially with rosuvastatin, simvastatin, and atorvastatin. Full article

(This article belongs to the Special Issue Clinical, Translational and Experimental Pharmacotherapeutics Advances)

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Open AccessArticle

Developing a Warning Model of Potentially Inappropriate Medications in Older Chinese Outpatients in Tertiary Hospitals: A Machine-Learning Study

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J. Clin. Med. 2023, 12(7), 2619; https://doi.org/10.3390/jcm12072619 - 30 Mar 2023

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Abstract

Due to multiple comorbid illnesses, polypharmacy, and age-related changes in pharmacokinetics and pharmacodynamics in older adults, the prevalence of potentially inappropriate medications (PIMs) is high, which affects the quality of life of older adults. Building an effective warning model is necessary for the early identification of PIMs to prevent harm caused by medication in geriatric patients. The purpose of this study was to develop a machine learning-based model for the warning of PIMs in older Chinese outpatients. This retrospective study was conducted among geriatric outpatients in nine tertiary hospitals in Chengdu from January 2018 to December 2018. The Beers criteria 2019 were used to assess PIMs in geriatric outpatients. Three problem transformation methods were used to tackle the multilabel classification problem in prescriptions. After the division of patient prescriptions into the training and test sets (8:2), we adopted six widely used classification algorithms to conduct the classification task and assessed the discriminative performance by the accuracy, precision, recall, F1 scores, subset accuracy (ss Acc), and Hamming loss (hm) of each model. The results showed that among 11,741 older patient prescriptions, 5816 PIMs were identified in 4038 (34.39%) patient prescriptions. A total of 41 types of PIMs were identified in these prescriptions. The three-problem transformation methods included label power set (LP), classifier chains (CC), and binary relevance (BR). Six classification algorithms were used to establish the warning models, including Random Forest (RF), Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost), CatBoost, Deep Forest (DF), and TabNet. The CC + CatBoost model had the highest accuracy value (97.83%), recall value (89.34%), F1 value (90.69%), and ss Acc value (97.79%) with a good precision value (92.18%) and the lowest hm value (0.0006). Therefore, the CC + CatBoost model was selected to predict the occurrence of PIM in geriatric Chinese patients. This study’s novelty establishes a warning model for PIMs in geriatric patients by using machine learning. With the popularity of electronic patient record systems, sophisticated computer algorithms can be implemented at the bedside to improve medication use safety in geriatric patients in the future. Full article

(This article belongs to the Special Issue Clinical, Translational and Experimental Pharmacotherapeutics Advances)

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Open AccessArticle

Mitochondria-Associated Endoplasmic Reticulum Membrane (MAM) Is a Promising Signature to Predict Prognosis and Therapies for Hepatocellular Carcinoma (HCC)

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J. Clin. Med. 2023, 12(5), 1830; https://doi.org/10.3390/jcm12051830 - 24 Feb 2023

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Abstract

Background: The roles of mitochondria and the endoplasmic reticulum (ER) in the progression of hepatocellular carcinoma (HCC) are well established. However, a special domain that regulates the close contact between the ER and mitochondria, known as the mitochondria-associated endoplasmic reticulum membrane (MAM), has not yet been investigated in detail in HCC. Methods: The TCGA-LIHC dataset was only used as a training set. In addition, the ICGC and several GEO datasets were used for validation. Consensus clustering was applied to test the prognostic value of the MAM-associated genes. Then, the MAM score was constructed using the lasso algorithm. In addition, uncertainty of clustering in single-cell RNA-seq data using a gene co-expression network (AUCell) was used for the detection of the MAM scores in various cell types. Then, CellChat analysis was applied for comparing the interaction strength between the different MAM score groups. Further, the tumor microenvironment score (TME score) was calculated to compare the prognostic values, the correlation with the other HCC subtypes, tumor immune infiltration landscape, genomic mutations, and copy number variations (CNV) of different subgroups. Finally, the response to immune therapy and sensitivity to chemotherapy were also determined. Results: First, it was observed that the MAM-associated genes could differentiate the survival rates of HCC. Then, the MAM score was constructed and validated using the TCGA and ICGC datasets, respectively. The AUCell analysis indicated that the MAM score was higher in the malignant cells. In addition, enrichment analysis demonstrated that malignant cells with a high MAM score were positively correlated with energy metabolism pathways. Furthermore, the CellChat analysis indicated that the interaction strength was reinforced between the high-MAM-score malignant cells and T cells. Finally, the TME score was constructed, which demonstrated that the HCC patients with high MAM scores/low TME scores tend to have a worse prognosis and high frequency of genomic mutations, while those with low MAM scores/high TME scores were more likely to have a better response to immune therapy. Conclusions: MAM score is a promising index for determining the need for chemotherapy, which reflects the energy metabolic pathways. A combination of the MAM score and TME score could be a better indicator to predict prognosis and response to immune therapy. Full article

(This article belongs to the Special Issue Clinical, Translational and Experimental Pharmacotherapeutics Advances)

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Open AccessArticle

Predicting Hepatotoxicity Associated with Low-Dose Methotrexate Using Machine Learning

Qiaozhi Hu

Hualing Wang

and

Ting Xu

J. Clin. Med. 2023, 12(4), 1599; https://doi.org/10.3390/jcm12041599 - 17 Feb 2023

Viewed by 818

Abstract

An accurate prediction of the hepatotoxicity associated with low-dose methotrexate can provide evidence for a reasonable treatment choice. This study aimed to develop a machine learning-based prediction model to predict hepatotoxicity associated with low-dose methotrexate and explore the associated risk factors. Eligible patients with immune system disorders, who received low-dose methotrexate at West China Hospital between 1 January 2018, and 31 December 2019, were enrolled. A retrospective review of the included patients was conducted. Risk factors were selected from multiple patient characteristics, including demographics, admissions, and treatments. Eight algorithms, including eXtreme Gradient Boosting (XGBoost), AdaBoost, CatBoost, Gradient Boosting Decision Tree (GBDT), Light Gradient Boosting Machine (LightGBM), Tree-based Pipeline Optimization Tool (TPOT), Random Forest (RF), and Artificial Neural Network (ANN), were used to establish the prediction model. A total of 782 patients were included, and hepatotoxicity was detected in 35.68% (279/782) of the patients. The Random Forest model with the best predictive capacity was chosen to establish the prediction model (receiver operating characteristic curve 0.97, accuracy 64.33%, precision 50.00%, recall 32.14%, and F1 39.13%). Among the 15 risk factors, the highest score was a body mass index of 0.237, followed by age (0.198), the number of drugs (0.151), and the number of comorbidities (0.144). These factors demonstrated their importance in predicting hepatotoxicity associated with low-dose methotrexate. Using machine learning, this novel study established a predictive model for low-dose methotrexate-related hepatotoxicity. The model can improve medication safety in patients taking methotrexate in clinical practice. Full article

(This article belongs to the Special Issue Clinical, Translational and Experimental Pharmacotherapeutics Advances)

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Open AccessArticle

Association between Serum Uric Acid and Liver Enzymes in Adults Aged 20 Years and Older in the United States: NHANES 2005–2012

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J. Clin. Med. 2023, 12(2), 648; https://doi.org/10.3390/jcm12020648 - 13 Jan 2023

Viewed by 922

Abstract

Although the relationship between serum uric acid (SUA) and nonalcoholic fatty liver disease has been widely reported, the relationship between SUA and liver enzymes has rarely been reported. The purpose of this study was to evaluate the association of SUA levels with alanine aminotransferase (ALT) and aspartate aminotransferase (AST) in populations aged 20 years and older in the United States. We analyzed 7165 individuals aged 20 years and older from the National Health and Nutrition Examination Survey (NHANES) in the United States. Weighted multiple linear regression models were used to analyze the relationship between SUA and ALT and AST. A generalized additive model and a smooth curve fitting were used to observe the linear relationship. SUA was positively correlated with ALT and AST. In addition, the overall increasing trend of ALT and SUA was observed across the SUA quartile groups. In the stratified analysis by sex and race, the SUA levels in male, female, Mexican American, and Non-Hispanic White individuals, and those of another race, were positively correlated with ALT and AST. However, the SUA levels in Non-Hispanic Black individuals had a nonlinear relationship with ALT and AST. In individuals aged 20 years and older in the United States (excluding Non-Hispanic Black individuals), SUA levels were positively associated with ALT and AST. Therefore, with a rise in SUA levels, liver function should be monitored or intervened with in people aged 20 years and older in the United States. Full article

(This article belongs to the Special Issue Clinical, Translational and Experimental Pharmacotherapeutics Advances)

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Open AccessArticle

Anlotinib Suppressed Ovarian Cancer Progression via Inducing G2/M Phase Arrest and Apoptosis

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J. Clin. Med. 2023, 12(1), 162; https://doi.org/10.3390/jcm12010162 - 25 Dec 2022

Viewed by 757

Abstract

Ovarian cancer remains the most common gynecologic malignancy, because of its chemotherapy resistance and relapse. Anlotinib, a new oral multi-targeted tyrosine kinase inhibitor, has shown encouraging antitumor activity in several preclinical and clinical trials, while its effect on ovarian cancer has not been reported. In this study, we investigated the antitumor activity and underlying mechanism of anlotinib in ovarian cancer. Cell viability was analyzed by Cell Counting Kit-8 assay. Migration was measured by wound-healing assay. The cell cycle distribution and cell apoptosis rate were detected by flow cytometry. In vivo antitumor effect was analyzed in mouse ovarian carcinoma peritoneal metastasis model. We found that anlotinib inhibited the proliferation of ovarian cancer cells in a dose- and time- dependent manner by inducing G2/M phase arrest and apoptosis. Moreover, anlotinib upregulated the the phosphorylation of Histone H3, and expression of p21 protein in vitro. In addition, anlotinib inhibited the migration of ovarian cancer cells in vitro. Furthermore, anlotinib inhibited tumor growth by inhibiting cell proliferation and suppressing ovarian cancer angiogenesis in vivo. This study demonstrated the extraordinary anti-ovarian cancer effect of anlotinib, which may provide a promising therapeutic strategy for ovarian cancer. Full article

(This article belongs to the Special Issue Clinical, Translational and Experimental Pharmacotherapeutics Advances)

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