From Structure to Dose: An Silico Pipeline for Predicting Human Pharmacokinetics

GSK Data Science Symposium 2018 | Upper Providence, PA

Date: October 3, 2018

Presenter: Neha Murad, PhD

Title: From Structure to Dose: An Silico Pipeline for Predicting Human Pharmacokinetics

Abstract: Accelerating Therapeutics for Opportunities in Medicine (ATOM) is a public private consortium with GSK as one of its founding members. ATOM aims to accelerate drug discovery by applying a multidisciplinary approach that integrates high performance computing, diverse biological data, and artificial intelligence. As part of ATOM’s goal to go from structure to optimal dose with minimal in vivo or in vitro testing, we present a Physiologically Based Pharmacokinetic (PBPK) pipeline to support this objective. 

PBPK modeling investigates the ADME (Absorption, Distribution, Metabolism, and Excretion) properties of a drug over a course of time and provides drug concentration time profiles that provide information about the therapeutic window and helps ascertain the optimal dose. Besides information on physiology, other crucial input parameters into a PBPK model are tissue-to-plasma partition coefficients (Kp), hepatic clearance and renal clearance. These input parameters can be calculated using mechanistic models, which requires drug-specific information such as logP, pKa, fu, p, blood: plasma ratio and intrinsic clearance. We aim to evaluate and validate a series of machine learning and mechanistic models for both sets of input parameters and to deploy the best combination of models into the pipeline.  As a first step, the poster presents preliminary analysis and comparison of current mechanistic Kp prediction models in context of the PBPK pipeline (pipelined in Python) using 1263 compounds from the Obach Lombardo data set.  This work will eventually enable us to predict human PK in silico (e.g., volume of distribution, clearance) and will be part of the overall ATOM effort to optimizing efficacy, safety and PK parameters for de novo compounds in drug discovery.