Comprehensive Network Map of ADME-Tox Databases

In silico design of diacylglycerol acyltransferase-1 (DGAT1) inhibitors based on SMILES descriptors using Monte-Carlo method

Diabetes, obesity and other diseases related to metabolism are worldwide health problems. These syndromes can be well treated when a particular enzyme-based therapy is developed. Diacylglycerol acyltransferase (DGAT; EC 2.3.1.20) is a microsomal enzyme which is responsible for the synthesis of triglycerides from 1,2-diacylglycerol by catalyzing the acyl-CoA-dependent acylation. The obesity and type-II diabetes can be checked by the inhibition of DGAT1 enzyme. Quantitative structure-activity relationship (QSAR) modelling is an essential technique in drug design and development. To study the aspect of DGAT1 inhibitors, Monte-Carlo method-based QSAR was developed for 197 DGAT1 inhibitors. QSAR models were derived by using the optimal descriptor based on SMILES notation. Different statistical parameters including the novel index of ideality of correlation were applied to validate the generated QSAR models. Four random splits were prepared from the data set. The statistical criteria r² = 0.8129, CCC = 0.8979 and Q² = 0.7962 of the validation set of split 1 were the best; therefore, the developed QSAR model of split 1 was decided to be the leading model. The molecular fragments, which were promoter of endpoint increase or decrease were also determined. Thirteen new DGAT1 inhibitors were designed from the lead compound DGAT011.

Identification of a key candidate gene‑phenotype network mediated by glycyrrhizic acid using pharmacogenomic analysis

Glycyrrhizic acid (GA) is primarily used as an anti-inflammatory agent in cases of chronic hepatitis. However, its underlying mechanisms in diverse biological processes and its reported benefits are yet to be fully elucidated. In the current study, an analytical method based on pharmacogenomics was established to mine disease-modulatory activities mediated by GA. Five primary protein targets and 138 functional partners were identified for GA by querying open-source databases, including Drugbank and STRING. Subsequently, GA-associated primary and secondary protein targets were integrated into Cytoscape to construct a protein-protein interaction network to establish connectivity. GA-associated target genes were then clustered based on Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis. The tumor necrosis factor axis was revealed to be a primary module regulated by GA-associated targets. Furthermore, 12 hub genes were queried to assess their potential anti-cancer effects using cBioPortal. The results indicated that pharmacogenomics-based analysis improved understanding of the underlying drug-target events of GA and provided predictive and definitive leads for future studies.

Predicting and Understanding the Human Microbiome's Impact on Pharmacology

Our bodies each possess a unique and dynamic collection of microbes and viruses, collectively the 'microbiome', with distinct metabolic capacities from our human cells. Unforeseen modification of drugs by the microbiome can drastically alter their clinical effectiveness, with the most dramatic cases leading to fatal drug interactions. Pharmaceuticals can be activated, deactivated, toxified, or release metabolites that alter the 'canonical' pharmacokinetics of the drug. Thus, predicting and characterizing microbe-drug interactions is necessary to develop and implement personalized drug administration protocols and, more broadly, to improve drug safety and efficacy. In this review, we focus on microbiome-driven alterations to drug pharmacokinetics and provide a research framework for pharmacologists interested in characterizing microbiome interactions with any drug of interest.

Computational modeling of human oral bioavailability: what will be next?

INTRODUCTION

The oral route is the most convenient way of administrating drugs. Therefore, accurate determination of oral bioavailability is paramount during drug discovery and development. Quantitative structure-property relationship (QSPR), rule-of-thumb (RoT) and physiologically based-pharmacokinetic (PBPK) approaches are promising alternatives to the early oral bioavailability prediction. Areas covered: The authors give insight into the factors affecting bioavailability, the fundamental theoretical framework and the practical aspects of computational methods for predicting this property. They also give their perspectives on future computational models for estimating oral bioavailability. Expert opinion: Oral bioavailability is a multi-factorial pharmacokinetic property with its accurate prediction challenging. For RoT and QSPR modeling, the reliability of datasets, the significance of molecular descriptor families and the diversity of chemometric tools used are important factors that define model predictability and interpretability. Likewise, for PBPK modeling the integrity of the pharmacokinetic data, the number of input parameters, the complexity of statistical analysis and the software packages used are relevant factors in bioavailability prediction. Although these approaches have been utilized independently, the tendency to use hybrid QSPR-PBPK approaches together with the exploration of ensemble and deep-learning systems for QSPR modeling of oral bioavailability has opened new avenues for development promising tools for oral bioavailability prediction.