A Pragmatic PBPK-Driven Strategy to Guide ADME Characterization and Early Human PK Prediction for ECCS Class 1B/3B...

Area of Science:

• Pharmacokinetics and Drug Metabolism
• Computational Chemistry and Molecular Modeling
• Drug Discovery and Development

Background:

• Predicting human pharmacokinetics (PK) for ECCS class 1B/3B compounds is challenging due to resource-intensive experiments and empirical scaling, particularly when transporter-mediated processes are dominant.
• Existing methods often require extensive experimental characterization, hindering early-stage drug discovery efficiency.

Purpose of the Study:

• To propose and validate a tiered physiologically based pharmacokinetic (PBPK) framework for early drug discovery.
• To guide human PK prediction for zwitterionic and acidic compounds using minimal or targeted experimental inputs.
• To identify which ADME (Absorption, Distribution, Metabolism, and Excretion) processes require specific characterization based on chemical class.

Main Methods:

• Development of a tiered PBPK framework incorporating different levels of complexity for compound classes.
• Benchmarking the PBPK framework against well-characterized molecules including Fexofenadine, Valsartan, Rosuvastatin, and the active metabolite of Valategrast.
• Evaluation of allometric scaling of rat and nonhuman primate data for zwitterions, and assessment of tissue partition coefficient (Kp) adjustments for acids.

Main Results:

• For zwitterions, allometrically scaled rat biliary/renal or nonhuman primate plasma clearance accurately predicted human PK within a 2-fold margin.
• For acidic compounds, adjustment of the tissue partition coefficient (Kp) was necessary to successfully recapitulate observed plasma profiles.
• The structured PBPK strategy demonstrated its utility in identifying essential experiments for acidic compounds.

Conclusions:

• The proposed tiered PBPK framework enables reliable early human PK predictions for zwitterions with minimal inputs.
• The strategy provides guidance for targeted experimental measurements for acidic compounds, improving prediction accuracy.
• Implementation of this PBPK approach can lead to more informed screening cascade design and accelerate drug discovery timelines.