ArticlePharmaceutical research2024
Relative Performance of Volume of Distribution Prediction Methods for Lipophilic Drugs with Uncertainty in LogP Value.
Article in Pharmaceutical research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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Who cites it
4 citing papers in PubMed.
- Integrating Static and Dynamic Models to Predict Cannabidiol-Mediated Metabolic Drug-Drug Interactions.Clinical pharmacology and therapeutics · 2026Article
- PBK modelling of phthalates and their metabolites and the application in next generation risk assessment.Archives of toxicology · 2026Article
- Accelerated discovery of new organic photovoltaic dyes for OPVs using light absorbance as the primary screening criterionRSC advances · 2025Article
- Exploring the degree of long-range order/disorder in indaceno-based photovoltaic small molecules using data-driven machine learning analysis.RSC advances · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
purposeThe goal was to assess, for lipophilic drugs, the impact of logP on human volume of distribution at steady-state (VD
methodA sensitivity analysis with focus on logP was conducted by keeping pKa and fup constant for each of four drugs, while varying logP. VD
resultsThe Rodgers-Rowland methods were highly sensitive to logP values, followed by GastroPlus and Korzekwa-Nagar. The Oie-Tozer and TCM-New methods were only modestly sensitive to logP. Hence, the relative performance of these methods depended upon the source of logP value. As logP values increased, TCM-New and Oie-Tozer were the most accurate methods. TCM-New was the only method that was accurate regardless of logP value source. Oie-Tozer provided accurate predictions for griseofulvin, posaconazole, and isavuconazole; GastroPlus for itraconazole and isavuconazole; Korzekwa-Nagar for posaconazole; and TCM-New for griseofulvin, posaconazole, and isavuconazole. Both Rodgers-Rowland methods provided inaccurate predictions due to the overprediction of VD
conclusionsTCM-New was the most accurate prediction of human VD
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