Office of Clinical Pharmacology, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, Maryland 20993, United States.
Department of Pharmaceutical Sciences, Binghamton University-SUNY, 96 Corliss Ave, Johnson City, New York 13790, United States.
Mol Pharm. 2022 Jul 4;19(7):2506-2517. doi: 10.1021/acs.molpharmaceut.2c00193. Epub 2022 Jun 8.
Determining the amount of drug transferred into human milk is critical for benefit-risk analysis of taking medication while breastfeeding. In this study, we developed an in vitro and in vivo extrapolation (IVIVE) model to predict human milk/plasma (/) drug concentration ratios. Drug unionized fractions at pH 7.0 () and 7.4 (), drug fractions unbound in human plasma () and milk (), and in vitro cell permeability in both directions (efflux ratio, ER) were incorporated into the IVIVE model. A multiple regression model was chosen to predict from and polar surface area (PSA). A total of 97 drugs with experimental ER from Caco-2 cells were used to test the IVIVE model. The / ratios predicted by the IVIVE model had a 1.93-fold geometric mean fold error (GMFE) and 72% of predictions were within two-fold error (Pw2FE), which were superior to the performance of previously reported five models. The IVIVE model showed a reasonable prediction accuracy for passive diffusion drugs (GMFE = 1.71-fold, Pw2FE = 82%, = 50), BCRP substrates (BCRP: GMFE = 1.91-fold, Pw2FE = 60%, = 5), and substrates of P-gp and BCRP (GMFE = 1.74-fold, Pw2FE = 75%, = 8) and a lower prediction performance for P-gp substrates (GMFE = 2.51-fold, Pw2FE = 55%, = 22). By fitting the observed / ratios of 39 P-gp substrates, an optimized ER (1.61) was generated to predict the / ratio of P-gp substrates using the developed IVIVE model. Compared with currently available in vitro models, the developed IVIVE model provides a more accurate prediction of the drug / ratio, especially for passive diffusion drugs. The model performance is expected to be further improved when more experimental and ER data are available.
测定药物向人乳中转移的量对于分析哺乳期妇女用药的获益-风险至关重要。在这项研究中，我们开发了一种体外-体内推断（IVIVE）模型，以预测人乳/血浆（/）药物浓度比。模型中纳入了药物在 pH7.0（）和 7.4（）时的非离子化分数（）、人血浆（）和乳（）中未结合的药物分数、双向的体外细胞通透性（外排比，ER）。选择多元回归模型来预测从和比表面积（PSA）。使用来自 Caco-2 细胞的 97 种具有实验 ER 的药物来测试 IVIVE 模型。IVIVE 模型预测的/比值的几何均数倍误差（GMFE）为 1.93 倍，72%的预测值在两倍误差内（Pw2FE），优于之前报道的 5 种模型的性能。IVIVE 模型对被动扩散药物（GMFE = 1.71 倍，Pw2FE = 82%， = 50）、BCRP 底物（BCRP：GMFE = 1.91 倍，Pw2FE = 60%， = 5）和 P-gp 和 BCRP 的底物（GMFE = 1.74 倍，Pw2FE = 75%， = 8）具有合理的预测准确性，而对 P-gp 底物的预测性能较低（GMFE = 2.51 倍，Pw2FE = 55%， = 22）。通过拟合 39 种 P-gp 底物的观察到的/比值，生成了优化的 ER（1.61），用于使用开发的 IVIVE 模型预测 P-gp 底物的/比值。与目前可用的体外模型相比，开发的 IVIVE 模型对药物/比值的预测更准确，特别是对被动扩散药物。当获得更多的实验和 ER 数据时，预计模型性能将进一步提高。
