Neuroscience Medicinal Chemistry, Pfizer PharmaTherapeutics Research and Development, 558 Eastern Point Road, Groton, Connecticut, USA.
ACS Chem Neurosci. 2010 Jun 16;1(6):435-49. doi: 10.1021/cn100008c. Epub 2010 Mar 25.
The interplay among commonly used physicochemical properties in drug design was examined and utilized to create a prospective design tool focused on the alignment of key druglike attributes. Using a set of six physicochemical parameters ((a) lipophilicity, calculated partition coefficient (ClogP); (b) calculated distribution coefficient at pH = 7.4 (ClogD); (c) molecular weight (MW); (d) topological polar surface area (TPSA); (e) number of hydrogen bond donors (HBD); (f) most basic center (pK(a))), a druglikeness central nervous system multiparameter optimization (CNS MPO) algorithm was built and applied to a set of marketed CNS drugs (N = 119) and Pfizer CNS candidates (N = 108), as well as to a large diversity set of Pfizer proprietary compounds (N = 11 303). The novel CNS MPO algorithm showed that 74% of marketed CNS drugs displayed a high CNS MPO score (MPO desirability score ≥ 4, using a scale of 0-6), in comparison to 60% of the Pfizer CNS candidates. This analysis suggests that this algorithm could potentially be used to identify compounds with a higher probability of successfully testing hypotheses in the clinic. In addition, a relationship between an increasing CNS MPO score and alignment of key in vitro attributes of drug discovery (favorable permeability, P-glycoprotein (P-gp) efflux, metabolic stability, and safety) was seen in the marketed CNS drug set, the Pfizer candidate set, and the Pfizer proprietary diversity set. The CNS MPO scoring function offers advantages over hard cutoffs or utilization of single parameters to optimize structure-activity relationships (SAR) by expanding medicinal chemistry design space through a holistic assessment approach. Based on six physicochemical properties commonly used by medicinal chemists, the CNS MPO function may be used prospectively at the design stage to accelerate the identification of compounds with increased probability of success.
考察了药物设计中常用的理化性质之间的相互作用,并利用这些性质创建了一个有前景的设计工具,该工具侧重于关键类药性属性的调整。使用一组六个理化参数((a)脂溶性,计算分配系数(ClogP);(b)在 pH = 7.4 时的计算分配系数(ClogD);(c)分子量(MW);(d)拓扑极性表面积(TPSA);(e)氢键供体数(HBD);(f)最碱性中心(pK(a))),构建了一个药物类药性中枢神经系统多参数优化(CNS MPO)算法,并将其应用于一组上市的中枢神经系统药物(N = 119)和辉瑞中枢神经系统候选药物(N = 108),以及辉瑞专有的化合物多样性集(N = 11303)。新型 CNS MPO 算法显示,与 60%的辉瑞中枢神经系统候选药物相比,74%的上市中枢神经系统药物具有较高的 CNS MPO 评分(MPO 适宜性评分≥4,采用 0-6 的评分范围)。该分析表明,该算法可能可用于识别具有更高成功通过临床试验假说的化合物的可能性。此外,在上市中枢神经系统药物组、辉瑞候选药物组和辉瑞专有的化合物多样性组中,都观察到 CNS MPO 评分的增加与关键药物发现体外属性(有利的渗透性、P-糖蛋白(P-gp)外排、代谢稳定性和安全性)的一致性之间存在关系。CNS MPO 评分函数通过整体评估方法扩展药物化学设计空间,相对于硬性截止值或使用单个参数来优化结构活性关系(SAR)具有优势。该函数基于药物化学家常用的六个理化性质,可以在设计阶段前瞻性地使用,以加速识别具有更高成功可能性的化合物。