A Practical Method for Predicting Compound Brain Concentration-Time Profiles: Combination of PK Modeling and Machine Learning.

Given the aging populations in advanced countries globally, many pharmaceutical companies have focused on developing central nervous system (CNS) drugs. However, due to the blood-brain barrier, drugs do not easily reach the target area in the brain. Although conventional screening methods for drug discovery involve the measurement of (unbound fraction of drug) brain-to-plasma partition coefficients, it is difficult to consider nonequilibrium between plasma and brain compound concentration-time profiles.

Prediction of Inhibitory Activity against the MATE1 Transporter via Combined Fingerprint- and Physics-Based Machine Learning Models.

Renal secretion plays an important role in excretion of drug from the kidney. Two major transporters known to be highly involved in renal secretion are MATE1/2 K and OCT2, the former of which is highly related to drug-drug interactions. Among published in silico models for MATE inhibitors, a previous model obtained a ROC-AUC value of 0.

Development of a Novel In Silico Classification Model to Assess Reactive Metabolite Formation in the Cysteine Trapping Assay and Investigation of Important Substructures.

Predicting whether a compound can cause drug-induced liver injury (DILI) is difficult due to the complexity of drug mechanism. The cysteine trapping assay is a method for detecting reactive metabolites that bind to microsomes covalently. However, it is cumbersome to use 35S isotope-labeled cysteine for this assay.

Development of Novel Methods for QSAR Modeling by Machine Learning Repeatedly: A Case Study on Drug Distribution to Each Tissue.

Artificial intelligence is expected to help identify excellent candidates in drug discovery. However, we face a lack of data, as it is time-consuming and expensive to acquire raw data perfectly for many compounds. Hence, we tried to develop a novel quantitative structure-activity relationship (QSAR) method to predict a parameter more precisely from an incomplete data set via optimizing data handling by making use of predicted explanatory variables.

Development of a 2D-QSAR Model for Tissue-to-Plasma Partition Coefficient Value with High Accuracy Using Machine Learning Method, Minimum Required Experimental Values, and Physicochemical Descriptors.

Background: The demand for physiologically based pharmacokinetic (PBPK) model is increasing currently. New drug application (NDA) of many compounds is submitted with PBPK models for efficient drug development. Tissue-to-plasma partition coefficient (K) is a key parameter for the PBPK model to describe differential equations.

Prediction of Compound Plasma Concentration-Time Profiles in Mice Using Random Forest.

Pharmacokinetic (PK) parameters such as clearance (CL) and volume of distribution (Vd) have been the subject of previous predictive models. However, having information of the concentration over time profile explicitly can provide additional value like time above MIC or AUC, etc., to understand both the efficacy and safety-related aspects of a compound.

Predicting Total Drug Clearance and Volumes of Distribution Using the Machine Learning-Mediated Multimodal Method through the Imputation of Various Nonclinical Data.

Pharmacokinetic research plays an important role in the development of new drugs. Accurate predictions of human pharmacokinetic parameters are essential for the success of clinical trials. Clearance (CL) and volume of distribution (Vd) are important factors for evaluating pharmacokinetic properties, and many previous studies have attempted to use computational methods to extrapolate these values from nonclinical laboratory animal models to human subjects.

Prediction of Total Drug Clearance in Humans Using Animal Data: Proposal of a Multimodal Learning Method Based on Deep Learning.

Research into pharmacokinetics plays an important role in the development process of new drugs. Accurately predicting human pharmacokinetic parameters from preclinical data can increase the success rate of clinical trials. Since clearance (CL) which indicates the capacity of the entire body to process a drug is one of the most important parameters, many methods have been developed.

Three-dimensional quantitative structure-activity relationship analysis for human pregnane X receptor for the prediction of CYP3A4 induction in human hepatocytes: structure-based comparative molecular field analysis.

The pregnane X receptor [PXR (NR1I2)] induces the expression of xenobiotic metabolic genes and transporter genes. In this study, we aimed to establish a computational method for quantifying the enzyme-inducing potencies of different compounds via their ability to activate PXR, for the application in drug discovery and development. To achieve this purpose, we developed a three-dimensional quantitative structure-activity relationship (3D-QSAR) model using comparative molecular field analysis (CoMFA) for predicting enzyme-inducing potencies, based on computer-ligand docking to multiple PXR protein structures sampled from the trajectory of a molecular dynamics simulation.

In silico study on the inhibitory interaction of drugs with wild-type CYP2D6.1 and the natural variant CYP2D6.17.

The natural variant of the cytochrome P450 enzyme CYP2D6.1, CYP2D6.17, is most common in African populations, has three amino acid substitutions (T107I, R296C, and S486T) compared to the wild-type, and is known to have a different ligand preference from CYP2D6.

Three-dimensional quantitative structure-activity relationship analysis of inhibitors of human and rat cytochrome P4503A enzymes.

Cytochrome P450 3A4 (CYP3A4) is a member of the CYP family and is an important enzyme in drug metabolism. A compound that inhibits CYP3A4 activity could also affect the pharmacokinetics of other substrates, resulting in drug-drug interactions (DDIs) that could cause side effects. Pharmacokinetic data from drug-development studies in rats often determine the dosage used in human clinical trials.

Calcium ions are involved in the delay of plant cell cycle progression by abiotic stresses.

Higher plants respond to environmental stresses by a sequence of reactions which include the reduction of growth by affecting cell division. It has been shown that calcium ions plays a role as a second messenger in mediating various defence responses under environmental stresses. In this study, the role of calcium ions on cell cycle progression under abiotic stresses has been examined in tobacco BY-2 suspension culture cells.