Wanat Karolina, Michalak Klaudia, Brzezińska Elżbieta
Department of Analytical Chemistry, Faculty of Pharmacy, Medical University of Lodz, 90-419 Lodz, Poland.
Pharmaceutics. 2024 Nov 30;16(12):1534. doi: 10.3390/pharmaceutics16121534.
The penetration of drugs through the blood-brain barrier is one of the key pharmacokinetic aspects of centrally acting active substances and other drugs in terms of the occurrence of side effects on the central nervous system. In our research, several regression models were constructed in order to observe the connections between the active pharmaceutical ingredients' properties and their bioavailability in the CNS, presented in the form of the log BB parameter, which refers to the drug concentration on both sides of the blood-brain barrier.
Predictive models were created using the physicochemical properties of drugs, and multiple linear regression and a data mining method, i.e., MARSplines, were used to build them. Retention values from protein-affinity chromatography (TLC and HPLC) were introduced into the analyses. In both experiments, the stationary phases were modified with serum albumin, which enriched the obtained chromatographic data, and were then introduced into the models with good results.
The conducted analyses confirm that the variables that influence the log BB include high degree of lipophilicity, ionisation capacity and low capability of forming hydrogen bonds. However, the addition of chromatographic data improved the obtained regression results and increased the robustness of the models against an increased number of cases. The linear regression model with chromatographic parameters explains 85% of the log bb variability, whereas the MARSplines model explains 91%. Based on the obtained results, it can be concluded that the use of chromatographic data can increase the robustness of predictive regression models related to penetration through biological barriers.
就中枢神经系统副作用的发生而言，药物透过血脑屏障是中枢作用活性物质及其他药物关键的药代动力学方面之一。在我们的研究中，构建了几个回归模型，以观察活性药物成分的性质与其在中枢神经系统中的生物利用度之间的联系，生物利用度以log BB参数的形式呈现，该参数指血脑屏障两侧的药物浓度。
利用药物的物理化学性质创建预测模型，并使用多元线性回归和一种数据挖掘方法即MARSplines来构建模型。将蛋白质亲和色谱（薄层色谱和高效液相色谱）的保留值引入分析中。在两个实验中，固定相用血清白蛋白进行修饰，这丰富了获得的色谱数据，然后将其引入模型，结果良好。
进行的分析证实，影响log BB的变量包括高度的亲脂性、电离能力和形成氢键的能力较低。然而，添加色谱数据改善了获得的回归结果，并提高了模型针对更多案例数量的稳健性。具有色谱参数的线性回归模型解释了log bb变异性的85%，而MARSplines模型解释了91%。基于获得的结果，可以得出结论，使用色谱数据可以提高与透过生物屏障相关的预测回归模型的稳健性。
