Predicting pharmacodynamic effects through early drug discovery with artificial intelligence-physiologically based pharmacokinetic (AI-PBPK) modelling

• DOI: 10.3389/fphar.2024.1330855
• Publication Date: 2024-02-16T18:13:46Z
• Authors: Keheng Wu; Xue Li; Zhou Zhou; Youni Zhao; Mei Su; Zhuo Cheng; Xinyi Wu; Zhijun Huang; Xiong Jin; Jingxi Li; Mengjun Zhang; Jack Liu; Bo Liu
• Keywords: Pharmacology; 0301 basic medicine; P-CABs; 0303 health sciences; PBPK modeling; machine learning (ML); PD modeling; Therapeutics. Pharmacology; RM1-950; artificial intelligence (AI); early drug discovery

Graphical AbstractMain steps used to predict PK and PD outcomes of the compounds. (Step 1) Use different AI related simulations to predict the compound’s ADME and physiochemical properties. (Step 2) Predict PK outcomes using the PBPK model. (Step 3) PD models are used to predict how changes in drug concentrations affect gastric acid secretion and gastric pH. E/E0 is the relative activity of H+/K+ ATPase by drug; ksec is the secretion rate constants for intra-gastric H+ concentration; kout is the elimination rate constant for intra-gastric H+ concentration; Hobs is the observed concentration of H+; I (Inhibition) is the current antisecretory effect (or current pH level) of the drug; Imax is the maximum possible effect (or maximum pH level) of the drug can achieve; The term (Imax -I) represents how far the current effect is from its maximum potential.