QSAR model to predict K_p,uu,brain with a small dataset, incorporating predicted values of related parameter

The unbound brain-to-plasma concentration ratio (K_p,uu,brain) is a parameter that indicates the extent of central nervous system penetration. Pharmaceutical companies build prediction models because many experiments are required to obtain K_p,uu,brain. However, the lack of data hinders the design of an accurate prediction model. To construct a quantitative structure–activity relationship (QSAR) model with a small dataset of K_p,uu,brain, we investigated whether the prediction accuracy could be improved by incorporating software-predicted brain penetration-related parameters (BPrPs) as explanatory variables for pharmacokinetic parameter prediction. We collected 88 compounds with experimental K_p,uu,brain from various official publications. Random forest was used as the machine learning model. First, we developed prediction models using only structural descriptors. Second, we verified the predictive accuracy of each model with the predicted values of BPrPs incorporated in various combinations. Third, the K_p,uu,brain of the in-house compounds was predicted and compared with the experimental values. The prediction accuracy was improved using five-fold cross-validation (RMSE = 0.455, <i>r</i>² = 0.726) by incorporating BPrPs. Additionally, this model was verified using an external in-house dataset. The result suggested that using BPrPs as explanatory variables improve the prediction accuracy of the K_p,uu,brain QSAR model when the available number of datasets is small.

游离脑-血浆浓度比（unbound brain-to-plasma concentration ratio, K_p,uu,brain）是反映中枢神经系统穿透程度的参数。制药企业常需构建相关预测模型，因获取K_p,uu,brain需开展大量实验。然而，数据匮乏制约了精准预测模型的构建。为基于少量K_p,uu,brain数据集构建定量构效关系（quantitative structure–activity relationship, QSAR）模型，本研究探讨了将软件预测的脑穿透相关参数（brain penetration-related parameters, BPrPs）作为药代动力学参数预测的解释变量，是否可提升预测精度。本研究从各类官方出版物中收集了88个带有实验测得K_p,uu,brain值的化合物，并采用随机森林（Random Forest）作为机器学习模型。首先，仅使用结构描述符构建预测模型；其次，将不同组合的BPrPs预测值纳入模型，验证各模型的预测精度；第三，对内部化合物的K_p,uu,brain值进行预测，并与实验值进行对比。通过纳入BPrPs，采用五折交叉验证得到的预测精度得以提升，均方根误差（Root Mean Square Error, RMSE）为0.455，决定系数（coefficient of determination, r²）为0.726。此外，本研究还使用外部自研数据集对该模型进行了验证。结果表明，当可用数据集规模较小时，将BPrPs作为解释变量可提升K_p,uu,brain QSAR模型的预测精度。

搜集汇总

背景与挑战

背景概述

该数据集是一个用于预测未结合脑-血浆浓度比（Kp,uu,brain）的QSAR模型，基于88个化合物的实验数据，旨在解决小数据集下预测准确性不足的问题。研究通过整合脑渗透相关参数（BPrPs）的预测值作为解释变量，使用随机森林方法，显著提高了模型性能（RMSE = 0.455，r² = 0.726），并进行了外部验证，证明了该方法在小数据集场景下的有效性。