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Models and Methods to Evaluate Transport of Drug Delivery Systems Across Cellular Barriers
Published on: October 17, 2013
Classifying Effluxable Versus Non-Effluxable Compounds Using a Permeability Threshold Based on Fundamental Energy
Soné Kotze1, Kai-Uwe Goss1,2, Andrea Ebert1
1Department of Computational Biology and Chemistry, Helmholtz Centre for Environmental Research (UFZ), Permoserstraße 15, 04318 Leipzig, Germany.
A new passive membrane permeability (Pm) threshold predicts active efflux in drug development. Compounds exceeding this energy-based limit are unlikely to be actively transported, aiding early drug discovery.
Area of Science:
- Pharmacokinetics and Drug Metabolism
- Cellular Biology
- Computational Chemistry
Background:
- Predicting active transport is vital for drug development.
- Cellular energy limitations influence compound transport.
- Existing methods often rely on empirical observations.
Purpose of the Study:
- To establish a mechanistic threshold for predicting active efflux based on cellular energy constraints.
- To identify compounds unlikely to undergo active efflux.
- To provide a practical tool for early drug discovery.
Main Methods:
- Analysis of literature-reported efflux ratios (ERs) in MDCKII cells.
- Experimental determination of passive membrane permeability (Pm) values.
- Concentration-dependent measurements for borderline compounds.
- Development of a threshold: Pm × Cext = 10⁻¹·⁷ cm/s×µM for MDCK cells.
Main Results:
- A permeability threshold was derived from cellular energy limitations.
- Over 60% of compounds without significant ER values fell above the threshold.
- Only three outliers were identified with high ER and above the threshold.
- The threshold successfully reclassified compounds based on active efflux potential.
Conclusions:
- The derived permeability threshold offers a simple criterion to identify compounds unlikely to be actively effluxed.
- This approach is grounded in fundamental cellular energy constraints.
- The threshold can serve as a practical tool for early drug discovery and optimization.
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