Predicting Absorption-Distribution Properties of Neuroprotective Phosphine-Borane Compounds Using In Silico Modeling and Machine Learning.
Predicting Absorption-Distribution Properties of Neuroprotective Phosphine-Borane Compounds Using In Silico Modeling and Machine Learning.
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DOI：
10.3390/molecules26092505
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发表时间：
2021-04-25
期刊：
影响因子：
--
通讯作者：
Levin LA
中科院分区：
文献类型：
--
作者：
Remtulla R;Das SK;Levin LA
膦-硼烷复合物是在神经元和眼科疾病模型中具有临床前功效的新型化学实体。体外和体内研究表明，这些化合物的代谢产物能够裂解轴突损伤下游效应中涉及的二硫键。使用标准的计算机模拟方法研究这些药物的一个困难是，大多数计算工具不是为含硼烷的化合物设计的。使用计算机模拟和机器学习方法，评估了这些独特化合物的吸收分布特性。用计算机模拟方法检查的特征包括细胞渗透性、辛醇-水分配系数、血脑屏障渗透性、口服吸收和血清蛋白结合。由此产生的神经网络表现出适当的准确性水平，并与现有的计算机方法相媲美。具体来说，他们能够可靠地预测已知含硼化合物的药代动力学特征。这些方法预测，膦硼烷化合物及其代谢产物满足口服活性药物候选物的必要药代动力学特征。这项研究表明，标准的计算机预测和机器学习模型与神经网络的结合，可有效预测新型含硼化合物作为神经保护药物的药代动力学特征。
Phosphine-borane complexes are novel chemical entities with preclinical efficacy in neuronal and ophthalmic disease models. In vitro and in vivo studies showed that the metabolites of these compounds are capable of cleaving disulfide bonds implicated in the downstream effects of axonal injury. A difficulty in using standard in silico methods for studying these drugs is that most computational tools are not designed for borane-containing compounds. Using in silico and machine learning methodologies, the absorption-distribution properties of these unique compounds were assessed. Features examined with in silico methods included cellular permeability, octanol-water partition coefficient, blood-brain barrier permeability, oral absorption and serum protein binding. The resultant neural networks demonstrated an appropriate level of accuracy and were comparable to existing in silico methodologies. Specifically, they were able to reliably predict pharmacokinetic features of known boron-containing compounds. These methods predicted that phosphine-borane compounds and their metabolites meet the necessary pharmacokinetic features for orally active drug candidates. This study showed that the combination of standard in silico predictive and machine learning models with neural networks is effective in predicting pharmacokinetic features of novel boron-containing compounds as neuroprotective drugs.
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