ArticlePharmaceutical research2025
Predicting Distribution Coefficients (LogD) of Cyclic Peptides Using Molecular Dynamics Simulations.
Article in Pharmaceutical research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- Considerations for the dynamic role of bile acids during oral drug discovery and development.npj drug discovery · 2026Review
- Systematic Search for Blood-Brain Barrier Modulating Peptides Based on Exhaustive E-Cadherin Domain-Domain Docking.Journal of chemical information and modeling · 2025Article
- Efficacy of sulfonamides targeting malic enzyme in an animal model of Chagas disease.Frontiers in pharmacology · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
purposeThe distribution coefficient (LogD) is a critical property for oral peptide drug design. In this study, we focused on cyclic peptides (octreotide and its analogs) and aimed to determine their LogD values at four pHs using both the simulation and experimental approaches.
methodsFor the experimental approach, the shake-flask method with LCMS quantification was employed to determine LogD values. For the simulation approach, the partition coefficient (LogP) was obtained from the solvation free energy calculations using molecular dynamics (MD) simulation. The LogD values were then calculated from the obtained LogP values considering the predicted pKa and ionization states of each peptide residue. More peptide properties such as polar surface area (PSA), number of intramolecular hydrogen bonds, solvent accessible surface area (SASA), and radius of gyration (R
resultsFor a total of 28 LogD values across four pHs, the predicted values from the simulation under the OPLS-AA forcefield agreed with the experimental values, with an average deviation of 1.39 ± 0.86 log units, displaying better predictions compared to the data generated under the CHARMM forcefield or using commercial software. In addition, the analysis of PSA, SASA, and R
conclusionsThe method developed in this study can predict the LogD values at a wide pH range covering multiple formulation/physiological conditions and therefore can provide insights into designing oral peptide drugs, especially for early-stage projects.
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Registered trials
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