Integrated in Silico−in Vitro Strategy for Addressing Cytochrome P450 3A4 Time-Dependent Inhibition

Abstract

Throughout the past decade, the expectations from the regulatory agencies for safety, drug-drug interactions (DDIs), pharmacokinetic, and disposition characterization of new chemical entities (NCEs) by pharmaceutical companies seeking registration have increased. DDIs are frequently assessed using in silico, in vitro, and in vivo methodologies. However, a key gap in this screening paradigm is a full structural understanding of time-dependent inhibition (TDI) on the cytochrome P450 systems, particularly P450 3A4. To address this, a number of high-throughput in vitro assays have been developed. This work describes an automated assay for TDI using two concentrations at two time points (2 + 2 assay). Data generated with this assay for over 2000 compounds from multiple therapeutic programs were used to generate in silico Bayesian classification models of P450 3A4-mediated TDI. These in silico models were validated using several external test sets and multiple random group testing (receiver operator curve value >0.847). We identified a number of substructures that were likely to elicit TDI, the majority containing indazole rings. These in vitro and in silico approaches have been implemented as a part of the Pfizer screening paradigm. The Bayesian models are available on the intranet to guide synthetic strategy, predict whether a NCE is likely to cause a TDI via P450 3A4, filter for in vitro testing, and identify substructures important for TDI as well as those that do not cause TDI. This represents an integrated in silico-in vitro strategy for addressing P450 3A4 TDI and improving the efficiency of screening.

Novel Bayesian classification models for predicting compounds blocking hERG potassium channels

“…Where A is the classification of model, B is the observed value, P(A) is the prior probability or marginal probability of classification A without considering any B factors, P(B) is the prior or marginal probability of the value, P(B|A) is the posterior probability (the probability of value B if classification A is true), and P(A|B) is the probability that classification A is true given the observed data B (also called the posterior probability) [22] .…”

Using Open Source Computational Tools for Predicting Human Metabolic Stability and Additional Absorption, Distribution, Metabolism, Excretion, and Toxicity Properties

“…A visualization of the chemical space covered by a test and training set can be created using principal component analysis (PCA) (Zientek et al, 2010). Such a visual chemical space map was generated for the HLM dataset by converting the CDK and SMARTS descriptors (total ϳ579 descriptors) into principal components (PCs).…”

“…This process would allow significant cost savings by not screening each compound. A recent study by us suggested an approximately 30% saving in in vitro testing by implementing computational models (Zientek et al, 2010). Clearly, we should also be cognizant of the chemical space coverage of the model.…”

Quantifying and Predicting the Promiscuity and Isoform Specificity of Small-Molecule Cytochrome P450 Inhibitors

“…We have previously found the related technique principal components analysis useful in understanding how individual probe substrates report on different regions of the functional space of single P450 isoforms (Nath and Atkins, 2008b) and also in the prediction of a compound's propensity to cause time-dependent inhibition of CYP3A4 (Zientek et al, 2010). In this study, we extend this approach from considering multiple probe substrates of a single enzyme to a panel of different enzymes, with the goal of understanding how P450s are distributed in functional space with respect to their susceptibility toward inhibition.…”