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In vitro trans-monolayer permeability calculations: often forgotten assumptions
Kuresh A Youdim1, Alex Avdeef, N Joan Abbott
1Antioxidant Research Group Wolfson Centre for Age-Related Diseases Guy's, King's and St Thomas' School of Biomedical Sciences King's College, London, SE1 1UL, UK.
Drug Discovery Today
|December 4, 2003
Summary
Understanding drug physicochemical properties is key for effective drug design. This study examines in vitro cellular permeability assays, suggesting improvements for better drug permeation prediction.
Area of Science:
- Pharmacology
- Drug Discovery
- Biophysics
Background:
- Effective therapeutic strategies rely on drugs with favorable pharmacokinetic properties.
- Physicochemical characteristics (e.g., pKa, molecular weight, solubility, lipophilicity) dictate drug partitioning into membranes and cellular barrier crossing.
- Drug permeability influences absorption via gastrointestinal tract and blood-brain barrier penetration.
Purpose of the Study:
- To examine current in vitro cellular permeability assay methodologies.
- To identify implicit assumptions in these assays.
- To propose improvements for methodological techniques and mathematical equations in drug permeability determination.
Main Methods:
- Review of existing in vitro cellular permeability assay protocols.
- Analysis of the assumptions underlying these assays.
- Development of suggestions for enhancing assay techniques and mathematical models.
Main Results:
- Current in vitro assays present a trade-off between high throughput/low predictability and low throughput/high predictability.
- Implicit assumptions in assay performance can be overlooked, impacting results.
- Methodological and mathematical improvements can enhance the predictive potential of in vitro permeability assays.
Conclusions:
- Optimizing in vitro cellular permeability assays is crucial for accurate drug permeation prediction.
- Refined methodologies and mathematical models can improve the design of effective therapeutic strategies.
- Addressing assay assumptions leads to more reliable drug development screening.