Toxicology & DMPK Research Department, Teijin Institute for Bio-medical Research, Teijin Pharma Limited, 4-3-2 Asahigaoka, Hino-shi, Tokyo 191-8512, Japan.
Mol Pharm. 2024 Oct 7;21(10):5182-5191. doi: 10.1021/acs.molpharmaceut.4c00584. Epub 2024 Sep 26.
Given the aging populations in advanced countries globally, many pharmaceutical companies have focused on developing central nervous system (CNS) drugs. However, due to the blood-brain barrier, drugs do not easily reach the target area in the brain. Although conventional screening methods for drug discovery involve the measurement of (unbound fraction of drug) brain-to-plasma partition coefficients, it is difficult to consider nonequilibrium between plasma and brain compound concentration-time profiles. To truly understand the pharmacokinetics/pharmacodynamics of CNS drugs, compound concentration-time profiles in the brain are necessary; however, such analyses are costly and time-consuming and require a significant number of animals. Therefore, in this study, we attempted to develop an prediction method that does not require a large amount of experimental data by combining modeling and simulation (M&S) with machine learning (ML). First, we constructed a hybrid model linking plasma concentration-time profile to the brain compartment that takes into account the transit time and brain distribution of each compound. Using mouse plasma and brain time experimental values for 103 compounds, we determined the brain kinetic parameters of the hybrid model for each compound; this case was defined as scenario I (a positive control experiment) and included the full brain concentration-time profile data. Next, we built an ML model using chemical structure descriptors as explanatory variables and rate parameters as the target variable, and we then input the predicted values from 5-fold cross-validation (CV) into the hybrid model; this case was defined as scenario II, in which no brain compound concentration-time profile data exist. Finally, for scenario III, assuming that the brain concentration is obtained at only one time point, we used the brain kinetic parameters from the result of the 5-fold CV in scenario II as the initial values for the hybrid model and performed parameter refitting against the observed brain concentration at that time point. As a result, the RMSE/R2-values of the brain compound concentration-time profiles over time were 0.445/0.517 in scenario II and 0.246/0.805 in scenario III, indicating the method provides high accuracy and suggesting that it is a practical method for predicting brain compound concentration-time profiles.
鉴于全球先进国家人口老龄化，许多制药公司专注于开发中枢神经系统 (CNS) 药物。然而，由于血脑屏障的存在，药物不易到达大脑的靶部位。虽然传统的药物发现筛选方法涉及（药物未结合分数）脑-血浆分配系数的测量，但很难考虑血浆和脑化合物浓度-时间曲线下面积之间的非平衡。为了真正了解 CNS 药物的药代动力学/药效学，需要对脑内化合物浓度-时间曲线下面积进行分析；然而，这些分析既昂贵又耗时，需要大量的动物。因此，在这项研究中，我们试图通过将建模与模拟（M&S）与机器学习（ML）相结合，开发一种不需要大量实验数据的预测方法。首先，我们构建了一个将血浆浓度-时间曲线与考虑到每个化合物的转运时间和脑分布的脑室联系起来的混合模型。使用 103 种化合物的小鼠血浆和脑时间实验值，我们确定了每个化合物的混合模型的脑动力学参数；这种情况被定义为情景 I（阳性对照实验），并包含完整的脑浓度-时间曲线下面积数据。接下来，我们使用化学结构描述符作为解释变量，速率参数作为目标变量，构建了一个 ML 模型，然后将 5 折交叉验证（CV）的预测值输入到混合模型中；这种情况被定义为情景 II，其中不存在脑化合物浓度-时间曲线下面积数据。最后，对于情景 III，假设仅在一个时间点获得脑浓度，我们使用情景 II 中 5 折 CV 的结果中的脑动力学参数作为混合模型的初始值，并针对该时间点的观察到的脑浓度进行参数重拟合。结果，情景 II 和情景 III 中脑化合物浓度-时间曲线下面积随时间的 RMSE/R2 值分别为 0.445/0.517 和 0.246/0.805，表明该方法具有较高的准确性，表明它是一种预测脑化合物浓度-时间曲线下面积的实用方法。
