Application of New Cellular and Microphysiological Systems to Drug Metabolism Optimization and Their Positioning Respective to In Silico Tools. (June 2019)

• Record Type: Journal Article
• Authors: Docci, Luca; Parrott, Neil; Krähenbühl, Stephan; Fowler, Stephen
• Is Part Of: SLAS discovery. Volume 24:Number 5(2019)
• Page Start: 523
• Page End: 536
• Publication Date: 2019-06
• Subjects: hepatocytes -- microphysiological systems -- drug interactions -- machine learning -- PBPK; Drugs -- Analysis -- Periodicals; Drugs -- Testing -- Periodicals; Biomolecules -- Analysis -- Periodicals; Biomolecules -- Analysis; Drugs -- Analysis; Drugs -- Testing; Drug Evaluation, Preclinical; Molecular Biology -- methods; Periodicals; Periodicals; 615.1
• DOI: 10.1177/2472555219831407
• Languages: English
• ISSNs: 2472-5552

Abstract

New cellular model systems for drug metabolism applications, such as advanced 2D liver co-cultures, spheroids, and microphysiological systems (MPSs), offer exciting opportunities to reproduce human biology more closely in vitro with the aim of improving predictions of pharmacokinetics, drug–drug interactions, and efficacy. These advanced cellular systems have quickly become established for human intrinsic clearance determination and have been validated for several other absorption, distribution, metabolism, and excretion (ADME) applications. Adoption will be driven through the demonstration of clear added value, for instance, by more accurate and precise clearance predictions and by more reliable extrapolation of drug interaction potential leading to faster progression to pivotal proof-of-concept studies. New experimental systems are attractive when they can (1) increase experimental capacity, removing optimization bottlenecks; (2) improve measurement quality of ADME properties that impact pharmacokinetics; and (3) enable measurements to be made that were not previously possible, reducing risk in ADME prediction and candidate selection. As new systems become established, they will find their place in the repository of tools used at different stages of the research and development process, depending on the balance of value, throughput, and cost. In this article, we give a perspective on the integration of these new methodologies into ADME optimization during drug discovery, and the likely applications and impacts on drug development.