Application of machine learning techniques to the analysis and prediction of drug pharmacokinetics.

In this review, we describe the current status and challenges in applying machine-learning techniques to the analysis and prediction of pharmacokinetic data. The theory of pharmacokinetics has been developed over decades on the basis of physiology and reaction kinetics. Mathematical models allow the reduction of pharmacokinetic data to parameter values, giving insight and understanding into ADME processes and predicting the outcome of different dosing scenarios.

Cytosolic Ca2+-dependent Ca2+ release activity primarily determines the ER Ca2+ level in cells expressing the CPVT-linked mutant RYR2.

Type 2 ryanodine receptor (RYR2) is a cardiac Ca2+ release channel in the ER. Mutations in RYR2 are linked to catecholaminergic polymorphic ventricular tachycardia (CPVT). CPVT is associated with enhanced spontaneous Ca2+ release, which tends to occur when [Ca2+]ER reaches a threshold.

Prediction of HIV drug resistance based on the 3D protein structure: Proposal of molecular field mapping.

A method for predicting HIV drug resistance by using genotypes would greatly assist in selecting appropriate combinations of antiviral drugs. Models reported previously have had two major problems: lack of information on the 3D protein structure and processing of incomplete sequencing data in the modeling procedure. We propose obtaining the 3D structural information of viral proteins by using homology modeling and molecular field mapping, instead of just their primary amino acid sequences.

A model-based comparative meta-analysis of the efficacy of dolutegravir-based and efavirenz-based regimens in HIV-infected patients.

Currently, combinations of typical types of antiretroviral agents have been adopted as chemotherapy for human immunodeficiency virus (HIV) infection, comprising two nucleoside analogue reverse transcriptase inhibitors plus one of a non-nucleoside reverse transcriptase inhibitor, an integrase strand-transfer inhibitor, and a protease inhibitor. Although several meta-analyses have been conducted to determine first-line combination antiretroviral therapy, this has yet to be confirmed due to the technical limitation associated. In the present study, we applied a model-based meta-analysis (MBMA) approach, because it allows integration of information from clinical trials with varying dosing, duration, and sampling time points, resulting in enlargement of available data sources.