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: Pharmacodynamics; Drug Development

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