Improving the Accuracy of Protein-Ligand Binding Mode Prediction Using a Molecular Dynamics-Based Pocket Generation Approach.

Protein-drug binding mode prediction from the apo-protein structure is challenging because drug binding often induces significant protein conformational changes. Here, the authors report a computational workflow that incorporates a novel pocket generation method. First, the closed protein pocket is expanded by repeatedly filling virtual atoms during molecular dynamics (MD) simulations. Second, after ligand docking toward the prepared pocket structures, binding mode candidates are ranked by MD/Molecular Mechanics Poisson-Boltzmann Surface Area. The authors validated our workflow using CDK2 kinase, which has an especially-closed ATP-binding pocket in the apo-form, and several inhibitors. The crystallographic pose coincided with the top-ranked docking pose for 59% (34/58) of the compounds and was within the top five-ranked ones for 88% (51/58), while those estimated by a conventional prediction protocol were 9% (5/58) and 50% (29/58), respectively. Our study demonstrates that the prediction accuracy is significantly improved by preceding pocket expansion, leading to generation of conformationally-diverse binding mode candidates. © 2018 Wiley Periodicals, Inc.

An Evolutionary Search Algorithm for Covariate Models in Population Pharmacokinetic Analysis.

Building a covariate model is a crucial task in population pharmacokinetics. This study develops a novel method for automated covariate modeling based on gene expression programming (GEP), which not only enables covariate selection, but also the construction of nonpolynomial relationships between pharmacokinetic parameters and covariates. To apply GEP to the extended nonlinear least squares analysis, the parameter consolidation and initial parameter value estimation algorithms were further developed and implemented. The entire program was coded in Java. The performance of the developed covariate model was evaluated for the population pharmacokinetic data of tobramycin. In comparison with the established covariate model, goodness-of-fit of the measured data was greatly improved by using only 2 additional adjustable parameters. Ten test runs yielded the same solution. In conclusion, the systematic exploration method is a potentially powerful tool for prescreening covariate models in population pharmacokinetic analysis.

Computer-based evolutionary search for a nonlinear conversion function for establishing in vitro-in vivo correlation (IVIVC) of oral drug formulations.

Establishment of in vitro-in vivo correlation (IVIVC) accelerates optimization of desirable drug formulations and/or modification of the manufacturing processes in the scale-up and post-approval periods. This article presents a method of finding the optimal conversion function for establishing Level A point-to-point IVIVC, based on a computer-based evolutionary search technique. Gene expression programming (GEP) is a technique for optimizing a mathematical expression tree with the help of a genetic algorithm. A parameter optimization routine, which minimizes the number of parameters in the mathematical expression trees and estimates the best-fit parameter values, was implemented in the GEP algorithm. Feasibility of the computer program was investigated using the in vitro and in vivo data for sustained release diltiazem formulations. It provided a mathematical equation that, from their in vitro dissolution profiles, successfully predicts the plasma concentration profiles of three different formulations of diltiazem following oral administration. Because the present approach does not use intravenous injection data like conventional IVIVC analyses, it is widely applicable to the evaluation of various oral formulations.