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Optimizing DMPK Properties: Experiences from a Big Pharma DMPK Department.
Anna-Karin Sohlenius-Sternbeck1, Juliette Janson, Johan Bylund
1DMPK & Bioanalysis, Medivir AB, SE-141 44 Huddinge, Sweden. annakarin.sternbeck@gmail.com.
Optimizing drug development involves understanding drug metabolism and pharmacokinetics (DMPK) and physicochemical properties. Integrating these insights accelerates compound optimization and informed decision-making in drug discovery.
Area of Science:
- Pharmacology and Drug Discovery
- Medicinal Chemistry
- Biotechnology
Background:
- Drug disposition relies on drug properties, body interactions, and biological barriers.
- Achieving therapeutic effects requires drugs to reach target sites at adequate concentrations.
- This review focuses on the role of drug metabolism and pharmacokinetics (DMPK) and physicochemical properties in compound optimization at AstraZeneca.
Purpose of the Study:
- To present key assays for evaluating DMPK properties of new chemical entities.
- To guide the interpretation of assay outcomes for effective compound optimization.
- To describe an integrated approach for predicting drug fate in humans early in discovery.
Main Methods:
- Utilized assays for solubility, LogD, permeability, and metabolic stability in early drug discovery.
- Implemented high-throughput methods for efficient bioanalysis and sample handling.
- Integrated in vitro data for early "dose to man" predictions, refined with in vivo pharmacokinetic (PK) and pharmacodynamic (PD) data.
Main Results:
- Developed a streamlined workflow for DMPK activities from lead identification to candidate selection.
- Achieved cost-effective and efficient optimization of chemical series.
- Facilitated informed decision-making throughout the drug discovery project lifecycle.
Conclusions:
- A robust DMPK workflow enhances efficiency and cost-effectiveness in drug development.
- Early and integrated assessment of DMPK properties is crucial for successful compound optimization.
- Informed decision-making, supported by comprehensive data, drives successful drug candidate selection.
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