An in vitro toolbox to accelerate anti-malarial drug discovery and development

Authors: Charman SA, Andreu A, Barker H, Blundell S, Campbell A, Campbell M, Chen G, Chiu FCK, Crighton E, Katneni K, Morizzi J, Patil R, Pham T, Ryan E, Saunders J, Shackleford DM, White KL, Almond L, Dickins M, Smith DA, Burrows JN, Abla N
Publication: Malaria Journal
Keywords: admet predictor, drug discovery, gastroplus, machine learning, pbbm, PBPK modeling
Software: ADMET Predictor®, GastroPlus®

Abstract

Background

Modelling and simulation are being increasingly utilized to support the discovery and development of new anti-malarial drugs. These approaches require reliable in vitro data for physicochemical properties, permeability, binding, intrinsic clearance and cytochrome P450 inhibition. This work was conducted to generate an in vitro data toolbox using standardized methods for a set of 45 anti-malarial drugs and to assess changes in physicochemical properties in relation to changing target product and candidate profiles.