No abstract

No abstract

Over the past decade, drug discovery programs have started to address the optimization of key ADME properties already at an early stage of the process. Hence, analytical chemists have been confronted with tremendously rising sample numbers and have had to develop methodologies accelerating quantitative liquid chromatography/tandem mass spectrometry (LC/MS/MS). This article focuses on the application of a generic and fully automated LC/MS/MS, named Rapid and Integrated Analysis System (RIAS), as a high-throughput platform for the rapid quantification of drug-like compounds in various in vitro ADME assays. Previous efforts were dedicated to the setup and feasibility study of a workflow-integrated platform combining a modified high-throughput liquid handling LC/MS/MS system controlled by a customized software interface and a customized data-processing and reporting tool. Herein the authors present an extension of this previously developed basic application to a broad set of ADME screening campaigns, covering CYP inhibition, Caco-2, and PAMPA assays. The platform is capable of switching automatically between various ADME assays, performs MS compound optimization if required, and provides a speed of 8 s from sample to sample, independently of the type of ADME assay. Quantification and peak review are adopted to the high-throughput environment and tested against a standard HPLC-ESI technology.

The cytochrome P450 (CYP) enzyme superfamily plays a major role in the metabolism of commercially available drugs. Inhibition of these enzymes by a drug may result in a plasma level increase of another drug, thus leading to unwanted drug-drug interactions when two or more drugs are coadministered. Therefore, fast and reliable in silico methods predicting CYP inhibition from calculated molecular properties are an important tool which can be applied to assess both already synthesized as well as virtual compounds. We have studied the performance of support vector machines (SVMs) to classify compounds according to their potency to inhibit CYP3A4. The data set for model generation consists of more than 1300 structural diverse drug-like research molecules which were divided into training and test sets. The predictive power of SVMs crucially depends on a careful selection of parameters specifying the kernel function and the penalty for misclassifications. In this study we have investigated a procedure to identify a valid set of SVM parameters which is based on a sampling of the parameter space on a regular grid. From this set of parameters, either single SVMs or SVM committees were trained to distinguish between strong and weak inhibitors or to achieve a more realistic three-class assignment, with one class representing medium inhibitors. This workflow was studied for several kernel functions and descriptor sets. All SVM models performed significantly better than PLS-DA models which were generated from the corresponding descriptor sets. As a very promising result, simple two-dimensional (2D) descriptors yield a three-class model which correctly classifies more than 70% of the test set. Our work illustrates that SVMs used in combination with simple 2D descriptors provide a very effective and reliable tool which allows a fast assessment of CYP3A4 inhibition potency in an early in silico filtering process.

No abstract