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

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.