Beyond CL and V_SS: A comprehensive approach to human pharmacokinetic predictions

Area of Science:

• Pharmacokinetics and Drug Metabolism
• Preclinical Drug Development
• Computational Chemistry

Background:

• Accurate prediction of human pharmacokinetics (PK) is vital for efficient clinical drug candidate selection.
• Existing methods often rely on limited data or single parameters, necessitating improved predictive models.
• Boehringer Ingelheim has developed a portfolio of new chemical entities (NCEs) requiring robust PK assessment.

Purpose of the Study:

• To systematically evaluate and guide a structured process for predicting human pharmacokinetics (PK).
• To achieve high-quality predictions of full concentration-time profiles for orally administered compounds.
• To provide insights for scientists involved in drug metabolism and PK (DMPK) or PK/pharmacodynamics (PK/PD) modeling.

Main Methods:

• Integration of in vitro absorption, distribution, metabolism, and excretion (ADME) data.
• Incorporation of in vivo PK data and expert judgment.
• Application of state-of-the-art methodologies for predicting concentration-time profiles.

Main Results:

• High-quality human PK predictions were achieved for 40 NCE compounds.
• The study successfully predicted full concentration-time profiles, moving beyond single-parameter estimations.
• A structured, data-driven approach demonstrated its efficacy in enhancing predictive accuracy.

Conclusions:

• The presented methodology offers a reliable framework for predicting human PK, supporting informed drug candidate selection.
• This approach provides valuable guidance for optimizing preclinical development and reducing late-stage attrition.
• Continued advancements in DMPK and PK/PD modeling are crucial for future drug discovery efforts.