Clinical, Translational and Experimental Pharmacotherapeutics Advances

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

Deadline for manuscript submissions: closed (20 September 2023) | Viewed by 13740

Special Issue Editors


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

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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

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Keywords

  • pharmacotherapeutics
  • pharmacokinetics
  • pharmacodynamics
  • pharmacogenomics
  • chronotoxicology
  • drug delivery
  • drug discovery

Published Papers (8 papers)

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Editorial

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2 pages, 151 KiB  
Editorial
Clinical, Translational and Experimental Pharmacotherapeutics Advances
by Zhiyao He, Lei Yu, Zhijie Xu and Chenyu Sun
J. Clin. Med. 2023, 12(3), 1136; https://doi.org/10.3390/jcm12031136 - 01 Feb 2023
Viewed by 1055
Abstract
Pharmacotherapeutics is the prevention and treatment of ailments, whether disorders or diseases, with medication [...] Full article

Research

Jump to: Editorial

13 pages, 1226 KiB  
Article
Cardiovascular Safety Evaluation of Febuxostat and Allopurinol: Findings from the FDA Adverse Event Reporting System
by Yang Bai, Bin Wu, Liangwen Gou, Zhenwei Fang, Ting Xu, Tiejun Zhang and Yuwen Li
J. Clin. Med. 2023, 12(18), 6089; https://doi.org/10.3390/jcm12186089 - 20 Sep 2023
Cited by 1 | Viewed by 2102
Abstract
Background: Febuxostat and allopurinol are the most commonly used uric acid-lowering medications, and their safety is of great concern, especially the cardiovascular adverse reactions associated with febuxostat. We propose to study the cardiovascular toxicity of febuxostat and allopurinol using the FDA Adverse Event [...] Read more.
Background: Febuxostat and allopurinol are the most commonly used uric acid-lowering medications, and their safety is of great concern, especially the cardiovascular adverse reactions associated with febuxostat. We propose to study the cardiovascular toxicity of febuxostat and allopurinol using the FDA Adverse Event Reporting System (FAERS) database. Methods: A total of 64 quarters of FAERS data were downloaded from 2004 to 2019. Febuxostat- and allopurinol-related cardiovascular adverse events were extracted after data cleaning. Signal detection was conducted by reporting odds ratio (ROR) and proportional reporting ratio (PRR). Results: There were 2939 and 25,219 reports of febuxostat- and allopurinol-related cardiovascular adverse events (CVAEs), respectively. The most frequent CVAEs with febuxostat and allopurinol were edema peripheral (14.38%) and peripheral swelling (8.76%), respectively. In elderly gout patients, febuxostat is associated with an increased risk of heart failure, ischemic heart disease, hypertension, and cardiomyopathy. Febuxostat in combination with acetic acid derivatives nonsteroidal anti-inflammatory drug (NSAIDS) also increases the risk of cardiovascular adverse events. Conclusions: Compared with allopurinol, febuxostat may increase cardiovascular toxicity in patients with gout. Full article
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23 pages, 7637 KiB  
Article
Novel Molecular Subtyping Scheme Based on In Silico Analysis of Cuproptosis Regulator Gene Patterns Optimizes Survival Prediction and Treatment of Hepatocellular Carcinoma
by Heng Jiang, Hao Chen, Yao Wang and Yeben Qian
J. Clin. Med. 2023, 12(18), 5767; https://doi.org/10.3390/jcm12185767 - 05 Sep 2023
Cited by 1 | Viewed by 1343
Abstract
Background: The liver plays an important role in maintaining copper homeostasis. Copper ion accumulation was elevated in HCC tissue samples. Copper homeostasis is implicated in cancer cell proliferation and angiogenesis. The potential of copper homeostasis as a new theranostic biomarker for molecular imaging [...] Read more.
Background: The liver plays an important role in maintaining copper homeostasis. Copper ion accumulation was elevated in HCC tissue samples. Copper homeostasis is implicated in cancer cell proliferation and angiogenesis. The potential of copper homeostasis as a new theranostic biomarker for molecular imaging and the targeted therapy of HCC has been demonstrated. Recent studies have reported a novel copper-dependent nonapoptotic form of cell death called cuproptosis, strikingly different from other known forms of cell death. The correlation between cuproptosis and hepatocellular carcinoma (HCC) is not fully understood. Materials and Methods: The transcriptomic data of patients with HCC were retrieved from the Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) and were used as a discovery cohort to construct the prognosis model. The gene expression data of patients with HCC retrieved from the International Cancer Genome Consortium (ICGC) and Gene Expression Omnibus (GEO) databases were used as the validation cohort. The Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis was used to construct the prognosis model. A principal component analysis (PCA) was used to evaluate the overall characteristics of cuproptosis regulator genes and obtain the PC1 and PC2 scores. Unsupervised clustering was performed using the ConsensusClusterPlus R package to identify the molecular subtypes of HCC. Cox regression analysis was performed to identify cuproptosis regulator genes that could predict the prognosis of patients with HCC. The receiver operating characteristics curve and Kaplan–Meier survival analysis were used to understand the role of hub genes in predicting the diagnosis and prognosis of patients, as well as the prognosis risk model. A weighted gene co-expression network analysis (WGCNA) was used for screening the cuproptosis subtype-related hub genes. The functional enrichment analysis was performed using Metascape. The ‘glmnet’ R package was used to perform the LASSO regression analysis, and the randomForest algorithm was performed using the ‘randomForest’ R package. The ‘pRRophetic’ R package was used to estimate the anticancer drug sensitivity based on the data retrieved from the Genomics of Drug Sensitivity in Cancer database. The nomogram was constructed using the ‘rms’ R package. Pearson’s correlation analysis was used to analyze the correlations. Results: We constructed a six-gene signature prognosis model and a nomogram to predict the prognosis of patients with HCC. The Kaplan–Meier survival analysis revealed that patients with a high-risk score, which was predicted by the six-gene signature model, had poor prognoses (log-rank test p < 0.001; HR = 1.83). The patients with HCC were grouped into three distinct cuproptosis subtypes (Cu-clusters A, B, and C) based on the expression pattern of cuproptosis regulator genes. The patients in Cu-cluster B had poor prognosis (log-rank test p < 0.001), high genomic instability, and were not sensitive to conventional chemotherapeutic treatment compared to the patients in the other subtypes. Cancer cells in Cu-cluster B exhibited a higher degree of the senescence-associated secretory phenotype (SASP), a marker of cellular senescence. Three representative genes, CDCA8, MCM6, and NCAPG2, were identified in patients in Cu-cluster B using WGCNA and the “randomForest” algorithm. A nomogram was constructed to screen patients in the Cu-cluster B subtype based on three genes: CDCA8, MCM6, and NCAPG2. Conclusion: Publicly available databases and various bioinformatics tools were used to study the heterogeneity of cuproptosis in patients with HCC. Three HCC subtypes were identified, with differences in the survival outcomes, genomic instability, senescence environment, and response to anticancer drugs. Further, three cuproptosis-related genes were identified, which could be used to design personalized therapeutic strategies for HCC. Full article
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14 pages, 1806 KiB  
Article
Vincristine-Induced Neuropathy in Patients Diagnosed with Solid and Hematological Malignancies: The Role of Dose Rounding
by Abdulrahman M. Alwhaibi, Ali A. Alshamrani, Miteb A. Alenazi, Shroog F. Altwalah, Nouf N. Alameel, Noura N. Aljabali, Sara B. Alghamdi, Abdulwahab I. Bineid, Monira Alwhaibi and Mohamed N. Al Arifi
J. Clin. Med. 2023, 12(17), 5662; https://doi.org/10.3390/jcm12175662 - 31 Aug 2023
Cited by 1 | Viewed by 1104
Abstract
Background: Vincristine is a vital constituent of chemotherapeutic regimens. Vincristine-induced neuropathy is a challenging adverse effect that impacts quality of life and treatment course. The dose rounding of chemotherapies is a strategy that is commonly used in clinical practice. Nevertheless, the frequency of [...] Read more.
Background: Vincristine is a vital constituent of chemotherapeutic regimens. Vincristine-induced neuropathy is a challenging adverse effect that impacts quality of life and treatment course. The dose rounding of chemotherapies is a strategy that is commonly used in clinical practice. Nevertheless, the frequency of developed neuropathy in vincristine first-time users and the potential association with dose rounding remains elusive. Methods: A retrospective analysis was conducted on patients administered vincristine for the first time between 2016 and 2022 using the King Saud University Medical City (KSUMC) database. Patients were stratified into pediatric and adult groups. Neuropathy frequency, its association with demographic and clinical parameters, and the Impact of dose rounding were assessed using SPSS software version 28. Results: Approximately 34.6% of patients were diagnosed with neuropathy after vincristine administration. Autonomic neuropathy was common among affected adults and pediatric patients (55.1% and 56.1%, respectively), while cranial neuropathy was more frequent in pediatric patients. Higher BSA (p = 0.038) and Scr (p = 0.044) in the pediatric group, the presence of respiratory comorbidities (p = 0.044), and the use of azole antifungals (p < 0.001) in the adult group were significantly associated with neuropathy episodes. The rounding-up of vincristine doses was significantly associated with increased neuropathy occurrence (p < 0.001), while dose rounding-down was significantly associated with a decrease in neuropathy in both groups of patients (p < 0.001). Conclusions: Our findings demonstrate that autonomic neuropathy is the most common vincristine-related neuropathy, regardless of the patient’s age. Dose rounding is a significant determinant of vincristine-induced neuropathy in both groups. Further studies are needed to evaluate the variables that exacerbate or prevent neuropathy associated with the first-time use of vincristine. Full article
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16 pages, 2970 KiB  
Article
The Development and Validation of a Predictive Model for Voriconazole-Related Liver Injury in Hospitalized Patients in China
by Guirong Xiao, Yiyao Liu, Yanhua Chen, Zhiyao He, Yan Wen and Ming Hu
J. Clin. Med. 2023, 12(13), 4254; https://doi.org/10.3390/jcm12134254 - 25 Jun 2023
Viewed by 902
Abstract
Voriconazole is widely used in the treatment and prevention of invasive fungal diseases. Common drug-induced liver injuries increase the economic burdens and the risks of premature drug withdrawal and disease recurrence. This study estimated the disposal cost of voriconazole-related liver injury, explored the [...] Read more.
Voriconazole is widely used in the treatment and prevention of invasive fungal diseases. Common drug-induced liver injuries increase the economic burdens and the risks of premature drug withdrawal and disease recurrence. This study estimated the disposal cost of voriconazole-related liver injury, explored the risk factors of voriconazole-related liver injury in hospitalized patients, and established a predictive model of liver injury to assist clinicians and pharmacists in estimating the probability or risk of liver injury after voriconazole administration to allow for early identification and intervention in patients at high risk of liver injury. A retrospective study was conducted on the selected inpatients whose blood concentration of voriconazole was measured in the West China Hospital of Sichuan University from September 2016 to June 2020. The incidence and disposal cost of voriconazole-related liver injuries were calculated. The incidence of voriconazole-related liver injury was 15.82% (217/1372). The disposal cost has been converted to 2023 at a discount rate of 5%. The median (P25, P75) disposal cost of severe liver injury (n = 42), general liver injury (n = 175), and non-liver injury (n = 1155) was 993.59 (361.70, 1451.76) Chinese yuan, 0.00 (0.00, 410.48) yuan, and 0.00 (0.00, 0.00) yuan, respectively, with a statistically significant difference (p < 0.001). Single factor analysis and multiple factor logistic regression were used to analyze the risk factors of voriconazole-related liver injury. The voriconazole-related liver injury was related to the trough concentration (Cmin, OR 1.099, 95% CI 1.058–1.140), hypoproteinemia (OR 1.723, 95% CI 1.126–2.636), and transplantation status (OR 0.555, 95% CI 0.325–0.948). The prediction model of liver injury was Logit (P)= −2.219 + 0.094 × Cmin + 0.544 × Hydroproteinemia − 0.589 × Transplantation, and the prediction model nomogram was established. The model validation results showed that the C-index of the derivation set and validation set was 0.706 and 0.733, respectively. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was 0.705 and 0.733, respectively, indicating that the model had good prediction ability. The prediction model will be helpful to develop clinical individualized medication of voriconazole and to identify and intervene in the cases of patients at high risk of voriconazole-related liver injury early on, in order to reduce the incidence of voriconazole-related liver injuries and the cost of treatment. Full article
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13 pages, 1308 KiB  
Article
Developing a Warning Model of Potentially Inappropriate Medications in Older Chinese Outpatients in Tertiary Hospitals: A Machine-Learning Study
by Qiaozhi Hu, Fangyuan Tian, Zhaohui Jin, Gongchao Lin, Fei Teng and Ting Xu
J. Clin. Med. 2023, 12(7), 2619; https://doi.org/10.3390/jcm12072619 - 30 Mar 2023
Cited by 2 | Viewed by 1245
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 [...] Read more.
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
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20 pages, 5927 KiB  
Article
Mitochondria-Associated Endoplasmic Reticulum Membrane (MAM) Is a Promising Signature to Predict Prognosis and Therapies for Hepatocellular Carcinoma (HCC)
by Yuyan Chen, Senzhe Xia, Lu Zhang, Xueqian Qin, Zhengyi Zhu, Tao Ma, Shushu Lu, Jing Chen, Xiaolei Shi and Haozhen Ren
J. Clin. Med. 2023, 12(5), 1830; https://doi.org/10.3390/jcm12051830 - 24 Feb 2023
Viewed by 2133
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 [...] Read more.
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
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13 pages, 1629 KiB  
Article
Predicting Hepatotoxicity Associated with Low-Dose Methotrexate Using Machine Learning
by Qiaozhi Hu, Hualing Wang and Ting Xu
J. Clin. Med. 2023, 12(4), 1599; https://doi.org/10.3390/jcm12041599 - 17 Feb 2023
Cited by 1 | Viewed by 1838
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 [...] Read more.
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
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