EXPERIMENTAL LIPOPHILICITY PROFILES OF DRUG-LIKE MOLECULES: COMPARATIVE ANALYSIS OF PH-DEPENDENT PARTITION AND DISTRIBUTION COEFFICIENTS
DOI:
https://doi.org/10.65327/bp.v12i3.2544Keywords:
Lipophilicity, logP, logD, pH-dependent distribution, Drug-like moleculesAbstract
Lipophilicity is one of the main physicochemical properties that determine the absorption, distribution, permeability and pharmaceutical behaviour of drug-like molecules. In the present study the experimental lipophilicity profiles were assessed by comparing the partition and distribution coefficients at various pHs with a focus on the molecular ionization behaviour. LogPN, LogPI, LogD, pKa, pH, acid–base classification, measurement method information was also used in a secondary QSAR. A total of 297 experimental observations for 114 different compounds were analysed. Descriptive statistics, correlation analysis, Mann–Whitney U testing and graphical pH-dependent profiling were used. The mean logD was 0.55 ± 1.93, while experimental pH ranged from 0.50 to 13.40. The overall results indicated a weak to moderate negative correlation between the pH and the logD (ρ = −0.286, p < 0.001). However, on stratified analysis, there were opposite associations for acidic compounds (moderate negative association, ρ = −0.586, p < 0.001) and basic compounds (positive association, ρ = 0.282, p = 0.001). A good correlation was found between LogD and logPN or logPI, which indicated that there were close relationships between the experimental lipophilicity measures. The pKa and experimental pH of acidic and basic compounds were very different, but the logD distributions were similar. The results indicate that the pH-dependent lipophilicity profiling is more informative than a single partition coefficient, especially for the characterization of ionizable drug-like molecules. It is recommended that standardized measurements be made over wider pH ranges.
Downloads
References
Akumu, E. O. (2026). Balancing Favourable Drug-Likeness Against Respiratory and Renal Liabilities: An Integrated In Silico ADME–Toxicity Profile of a Novel 3-{[2-(Formimidoylamino) ethyl] sulfanyl}-6-(1-methoxyethyl)-4-methyl-7-oxo-1-azabicyclo [3.2. 0] hept-2-ene-2-carbonyl fluoride. Editon Consortium Journal of Research in Medical and Health Sciences, 6(1), 28-40.
Aliagas, I., Gobbi, A., Lee, M. L., & Sellers, B. D. (2022). Comparison of logP and logD correction models trained with public and proprietary data sets. Journal of Computer-Aided Molecular Design, 36(3), 253-262.
Bertsch Aguilar, E., Suñer Sánchez, S., Pinheiro, S., & Zamora Ramírez, W. J. (2023). Critical Assessment of pH-Dependent Lipophilic Profiles of Small Molecules for Drug Design: Which One Should We Use and In Which Cases?.
Bertsch-Aguilar, E., Quirós, N., Schosinsky, F., Pinheiro, S., Medina-Franco, J. L., & Zamora, W. J. (2026). Data-driven critical evaluation of the General Solubility Equation: When is it a valid solubility predictor for Drug-like Molecules?.
Bodiwala, K. B., Dhameliya, T. M., Sureja, D. K., & Patel, M. P. (2024). Determination of pH-Solubility Profile and Development of Prediction Models for pH-Dependent Solubility of Atorvastatin Calcium. Current Pharmaceutical Analysis, 20(8), 808-824.
Castagnini, F. (2026). CHROMATOGRAPHIC METHODS FOR PHYSICO-CHEMICAL EVALUATION AND PERMEABILITY PREDICTION IN EARLY DRUG DISCOVERY.
da Silva, C. G., Niero, C. V., Duque, M. D., & Nordi, C. S. F. (2025). Comparative analysis of the applicability of prediction software to microcystin toxicity evaluation. Toxicon, 108620.
Duan, Y. J., Fu, L., Zhang, X. C., Long, T. Z., He, Y. H., Liu, Z. Q., ... & Cao, D. S. (2023). Improved GNNs for log D 7.4 prediction by transferring knowledge from low-fidelity data. Journal of Chemical Information and Modeling, 63(8), 2345-2359.
Esteban Bertsch, Sebastian Suñer, Silvana Pinheiro, & William J. Zamora. (2024). Experimental n-octanol/water Partition/Distribution Coefficients Database for Small Molecules [Data set]. Zenodo. https://doi.org/10.5281/zenodo.13208946.
Formanek, A., Vincze, A., Bicsak, R., Balogh, G. T., Moreau, Y., & Arany, A. (2026). A Comparative Study of QSPR Methods on a Unique Multitask PAMPA Data Set. Journal of Chemical Information and Modeling, 66(9), 5508-5518.
Garcia Jimenez, D., Ermondi, G., Jandova, Z., Vallaro, M., Caron, G., & Arnhof, H. (2025). Linker methylation as a strategy to enhance PROTAC oral bioavailability: insights from molecular properties and conformational analysis. Journal of Medicinal Chemistry, 68(15), 16666-16677.
Goyzueta-Mamani, L. D., Barazorda-Ccahuana, H. L., Candia-Puma, M. A., Galdino, A. S., Machado-de-Avila, R. A., Giunchetti, R. C., ... & Chávez-Fumagalli, M. A. (2024). Targeting Leishmania infantum Mannosyl-oligosaccharide glucosidase with natural products: potential pH-dependent inhibition explored through computer-aided drug design. Frontiers in Pharmacology, 15, 1403203.
Holm, R. (2023). Physicochemical Properties of Drugs for Use in the Pharmaceutical Industry. In Drug Discovery and Evaluation: Safety and Pharmacokinetic Assays (pp. 1-43). Cham: Springer International Publishing.
Jurowski, K., & Krośniak, A. (2025). ADME profile of AP-238-opioid designer drug (CAS: 140924-11-4): first application of multi-in silico approach methodology for comprehensive prediction of ADME profile (absorption, distribution, metabolism and excretion) important for clinical toxicology and forensic purposes. Chemico-Biological Interactions, 415, 111493.
Kabdraisova, A. Z., Umbetova, A. K., Kairalapova, G. Z., Litvinenko, Y. A., Sassykova, L. R., Yelibayeva, N. S., ... & Sadvakas, A. M. (2026). Machine-Learning-Driven Molecular Design and Structure–Property–Performance Relationships in Pharmaceutical Chemistry. Molecules, 31(12), 2162.
Karami, T. K., Hailu, S., Feng, S., Graham, R., & Gukasyan, H. J. (2022). Eyes on Lipinski's rule of five: A New “rule of thumb” for physicochemical design space of ophthalmic drugs. Journal of ocular pharmacology and therapeutics, 38(1), 43-55.
Kusaczuk, M., Tovar-Ambel, E., Martín-Cabrera, P., Lorente, M., Salvador-Tormo, N., Mikłosz, A., ... & Naumowicz, M. (2024). Cytotoxicity, proapoptotic activity and drug-like potential of quercetin and kaempferol in glioblastoma cells: preclinical insights. International Journal of Molecular Sciences, 25(19), 10740.
Llompart, P., Minoletti, C., Marcou, G., & Varnek, A. (2025). What Works, What Doesn’t, and Why? An Industrial Perspective on Absorption Modeling. Journal of Medicinal Chemistry, 68(14), 14609-14627.
Nunna, N., Ghoshal, T. S., Yadav, I., Arun, T., & Salgotra, V. (2026). Comparative In Vitro ADME Assessment and Physiologically Based Pharmacokinetic (PBPK) Modeling of Andrographolide and Its Tert-Butyldiphenylsilyl (TBDPS)-Protected Analogue.
Stegemann, S., Moreton, C., Svanbäck, S., Box, K., Motte, G., & Paudel, A. (2023). Trends in oral small-molecule drug discovery and product development based on product launches before and after the Rule of Five. Drug Discovery Today, 28(2), 103344.
Tapia, L., Pérez, Y., Carreira-Barral, I., Bujons, J., Bolte, M., Bedia, C., ... & Alfonso, I. (2024). Tuning pH-dependent cytotoxicity in cancer cells by peripheral fluorine substitution on pseudopeptidic cages. Cell Reports Physical Science, 5(9).
Venturi, A., Di Bona, S., Desantis, J., Eleuteri, M., Bartalucci, M., Baroni, M., ... & Cruciani, G. (2024). Between theory and practice: computational/experimental integrated approaches to understand the solubility and lipophilicity of PROTACs. Journal of Medicinal Chemistry, 67(18), 16355-16380.
Vidović, D., Milošević, N., Pavlović, N., Todorović, N., Panić, J. Č., Ćurčić, J., ... & Lalić‐Popović, M. (2022). In silico–in vitro estimation of lipophilicity and permeability association for succinimide derivatives using chromatographic anisotropic systems and parallel artificial membrane permeability assay. Biomedical Chromatography, 36(9), e5413.
Volkova, T. V., Simonova, O. R., & Perlovich, G. L. (2022). Physicochemical profile of antiandrogen drug bicalutamide: Solubility, distribution, permeability. Pharmaceutics, 14(3), 674.
Wagen, C. (2025). Physics-Informed Machine Learning Enables Rapid Macroscopic pKa Prediction.
Wang, Y., Xiong, J., Xiao, F., Zhang, W., Cheng, K., Rao, J., ... & Zheng, M. (2023). LogD7. 4 prediction enhanced by transferring knowledge from chromatographic retention time, microscopic pKa and logP. Journal of cheminformatics, 15(1), 76.
Wu, J., Wang, J., Wu, Z., Zhang, S., Deng, Y., Kang, Y., ... & Hou, T. (2022). ALipSol: an attention-driven mixture-of-experts model for lipophilicity and solubility prediction. Journal of Chemical Information and Modeling, 62(23), 5975-5987.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Zaharaddeen Muhammad Adam
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
