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Predicting Distribution Coefficients (LogD) of Cyclic Peptides Using Molecular Dynamics Simulations.

Hao Lou¹, Mei Feng², Zahraa Al-Tamimi³

¹Department of Pharmaceutical Chemistry, University of Kansas, Lawrence, KS, 66047, USA. lou0@ku.edu.

Pharmaceutical Research

|March 27, 2025

Summary

This study developed a simulation method to accurately predict the distribution coefficient (LogD) of cyclic peptides, aiding oral peptide drug design. The findings offer valuable insights for early-stage drug development projects.

Keywords:

hydrophobicity logD prediction molecular dynamics simulation oral peptide delivery solvation free energy

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Published on: December 16, 2013

Area of Science:

Computational Chemistry
Drug Design
Pharmacokinetics

Background:

The distribution coefficient (LogD) is crucial for designing orally administered peptide drugs.
Cyclic peptides like octreotide are important therapeutic agents.

Purpose of the Study:

To determine the LogD values of cyclic peptides (octreotide and analogs) at four pH values.
To validate a simulation approach against experimental data for LogD prediction.

Main Methods:

Experimental determination of LogD using the shake-flask method with LC-MS quantification.
Computational prediction of LogP via molecular dynamics (MD) simulations and solvation free energy calculations.
Calculation of LogD from LogP, pKa, and ionization states; additional calculation of peptide properties (PSA, hydrogen bonds, SASA, Rg) using MD.

Main Results:

Simulated LogD values using the OPLS-AA forcefield showed good agreement with experimental data (average deviation 1.39 ± 0.86 log units).
The OPLS-AA forcefield provided superior prediction accuracy compared to the CHARMM forcefield or commercial software.
Analysis of peptide properties indicated conformational flexibility in both aqueous and organic phases.

Conclusions:

The developed simulation method accurately predicts LogD values across a broad pH range, relevant for various formulation and physiological conditions.
This approach provides valuable insights for the design of oral peptide drugs, particularly in early development stages.