目标:目标是评估,对于亲脂性药物,logP对人体稳态分布体积(VDss)预测的影响,包括中间fut和Kp值,从六种方法:Oie-Tozer,罗杰斯-罗兰(组织特异性Kp和仅肌肉Kp),GastroPlus,Korzekwa-Nagar,和TCM-新。
方法:通过保持四种药物的pKa和fup恒定,进行了以logP为重点的敏感性分析。而改变logP。还计算了特定文献logP值的VDss。误差预测分析是通过分析LogP值来源的预测误差进行的,药物,和整体价值。
结果:罗杰斯-罗兰方法对logP值高度敏感,其次是GastroPlus和Korzekwa-Nagar。Oie-Tozer和TCM-New方法对logP仅适度敏感。因此,这些方法的相对性能取决于logP值的来源。随着logP值的增加,TCM-New和Oie-Tozer是最准确的方法。无论logP值来源如何,TCM-New是唯一准确的方法。Oie-Tozer为灰黄霉素提供了准确的预测,泊沙康唑,和异氟康唑;GastroPlus用于伊曲康唑和异氟康唑;Korzekwa-Nagar用于泊沙康唑;和中药-灰黄霉素的新产品,泊沙康唑,和伊沙武康唑.由于VDss的过度预测,Rodgers-Rowland方法都提供了不准确的预测。
结论:TCM-New是四种药物和三种logP来源对人类VDss的最准确预测,其次是Oie-Tozer.TCM-New被证明是高亲脂性药物VDss预测的最佳方法,表明BPR是组织中药物分配的有利替代品,避免使用fup。
OBJECTIVE: The goal was to assess, for lipophilic drugs, the impact of logP on human volume of distribution at steady-state (VDss) predictions, including intermediate fut and Kp values, from six methods: Oie-Tozer, Rodgers-Rowland (tissue-specific Kp and only muscle Kp), GastroPlus, Korzekwa-Nagar, and TCM-New.
METHODS: A sensitivity analysis with focus on logP was conducted by keeping pKa and fup constant for each of four drugs, while varying logP. VDss was also calculated for the specific literature logP values. Error prediction analysis was conducted by analyzing prediction errors by source of logP values, drug, and overall values.
RESULTS: The 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 VDss.
CONCLUSIONS: TCM-New was the most accurate prediction of human VDss across four drugs and three logP sources, followed by Oie-Tozer. TCM-New showed to be the best method for VDss prediction of highly lipophilic drugs, suggesting BPR as a favorable surrogate for drug partitioning in the tissues, and which avoids the use of fup.