Predicting and improving the membrane permeability of peptidic small molecules
Salma B Rafi1, Brian R Hearn, Punitha Vedantham
1Department of Pharmaceutical Chemistry, University of California, San Francisco, California, USA.
Journal of Medicinal Chemistry
|March 8, 2012
Summary
Introducing hydrogen bond acceptor-donor pairs in peptidic molecules enhances membrane permeability and drug-like properties. Computational methods accurately predict these permeability trends, outperforming traditional clogP calculations.
Area of Science:
- Medicinal Chemistry
- Computational Chemistry
- Biophysics
Background:
- Peptidic small molecules are promising drug candidates.
- Membrane permeability is a critical factor for oral bioavailability.
- Understanding factors influencing membrane permeability is essential for drug design.
Purpose of the Study:
- To investigate the impact of hydrogen bond acceptor-donor pairs on peptidic small molecule membrane permeability.
- To compare experimental findings with computational predictions.
- To evaluate the utility of advanced computational methods over traditional ones.
Main Methods:
- Experimental evaluation of membrane permeability for natural and unnatural amino acid-containing peptides.
- Computational analysis using all-atom force field methods to calculate free energy of transfer from water to membrane.
- Comparison of computational predictions with experimental data and clogP values.
Main Results:
- Introduction of hydrogen bond acceptor-donor pairs significantly improved membrane permeability.
- These modifications also maintained or enhanced other favorable drug-like properties.
- All-atom force field calculations accurately predicted the rank order of experimental permeability trends.
- Computational method showed superior predictive power compared to clogP.
Conclusions:
- Strategic incorporation of hydrogen bond acceptor-donor pairs is an effective strategy to enhance the membrane permeability of peptidic molecules.
- All-atom force field simulations provide a reliable and predictive computational tool for optimizing drug-like properties of peptides.
- This approach offers a more accurate alternative to traditional methods for predicting permeability.
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