Department of Ophthalmology and Visual Sciences, McGill University, Montreal, QC H4H 3S5, Canada.
Drug Discovery Core, Research Institute, McGill University Health Centre, Montreal, QC H4A 3J1, Canada.
Molecules. 2021 Apr 25;26(9):2505. doi: 10.3390/molecules26092505.
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.
膦硼烷复合物是具有神经和眼科疾病模型临床前疗效的新型化学实体。体外和体内研究表明，这些化合物的代谢物能够切割轴突损伤下游效应涉及的二硫键。使用标准的计算方法研究这些药物存在一个困难，即大多数计算工具不是为含硼化合物设计的。本研究采用计算和机器学习方法评估了这些独特化合物的吸收分布特性。通过计算方法检查的特征包括细胞通透性、辛醇-水分配系数、血脑屏障通透性、口服吸收和血清蛋白结合。所得神经网络显示出适当的准确性水平，与现有的计算方法相当。具体来说，它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物具有口服活性候选药物所需的必要药代动力学特征。这项研究表明，将标准的计算预测和机器学习模型与神经网络相结合，可有效地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。
