【作者】 陈丽娟；

【导师】 陈新；

【作者基本信息】 浙江大学 ， 植物学， 2011， 硕士

【摘要】 小分子的代谢动力学特征(ADME)决定着化合物在生物体内能否真正达到生物学作用,已经成为是药物临床前研究的重要考量因素。目前导致药物不能成功上市的原因中,40%是由于ADME性质不佳所造成的。为了有效降低药物的淘汰率,降低药物开发成本,高通量的测定候选药物前体的性质,计算机ADME建模成为了值得研究的重要途径。目前的ADME建模方法主要是利用定量结构活性关系模型,所用的描述符基本上都基于小分子结构。以前研究证明,分子对接可以分析小分子与蛋白之间的相互作用,可以用药物活性的分类预测。然而,但很少有研究用于药物动力学的定量活性预测。因此,本研究主要以人类血浆白蛋白结合率研究为例,探索对接分数能否作为描述符用于定量建模预测。第一部分利用传统的小分子结构描述符来进行人类血浆白蛋白结合率(log K’HSA)的预测。所得模型结果为Q2=0.84,MSE=0.06；r2=0.83,mse=0.06；R2=0.87,mse=0.05.与前人所报导的模型相比,这些预测能力要稍好一些,证明我们可以重复前人的工作并达到相似或者更好的结果。第二部分使用对接软件利用小分子结构和蛋白质结构计算对接分数。然后将这些对接分数进行整合,基于计算所得对接分数描述符来建立模型预测log K’HSA,最终所得的模型结果为r2=0.78,mse=0.09；Q2=0.75,MSE=0.09；R2=0.79,mse=0.07.该结果可以与前人报导的模型达到相似的结果,很好地证明了对接分数可以作为描述符来定量预测结合率。进一步,本论文研究了蛋白质柔性和小分子柔性对预测模型结果的影响。结果发现,相比较小分子柔性的影响,蛋白质柔性对预测模型结果的影响会更大一些。这一结果与现今对接软件所存在的问题是一致的。更多还原

【Abstract】 Pharmacokinetic properties of a compound are the determinant factors for achieving its biological role in organisms. Inappropriate ADME properties were the most significant cause of drug withdrawn from development and account for 40% of all the reasons. To reduce drug attrition effectively, cut down the cost of drug discovery and development, and measure properties of drug lead in high throughput ways, in silico ADME modeling is a good choice.For the present, ADME properties are most often estimated from the structural properties of a compound by the quantitative structural activity relationship (QSAR) approach. Previous studies had shown that molecular docking, capable of analyzing compound-protein interaction, could be used to make categorical estimation of a pharmacokinetic property. However, there were few researches to make numeric estimation. Therefore, this research takes human serum albumin (HSA) binding affinity as an example to show that docking descriptors might also be useful to estimate the exact value of a pharmacokinetic property.First, we used traditional compound structure descriptors to predict Human Serum Binding Affinity (log K’HSA). The model showed a result of Q2= 0.84, MSE =0.06; r2= 0.83, mse= 0.06 and R2= 0.87, mse= 0.05. This result was a little better than previously reported model and showed our method was able to reproduce the accuracy reported before.Second, we used docking scores as descriptors to predict log K’HSA. The final result were r2= 0.78, mse= 0.09; Q2= 0.75, MSE= 0.09; R2= 0.79, mse= 0.07. This accuracy was comparable to the known QSAR model based on compound descriptors and proved that docking scores can be used as descriptors in numeric prediction. Further, our research showed that a descent account of protein flexibility was essential to get better prediction model.更多还原