Ghose Arup K, Herbertz Torsten, Hudkins Robert L, Dorsey Bruce D, Mallamo John P
Department of Chemistry, Discovery Research, Cephalon, Inc. , 145 Brandywine Parkway, West Chester, Pennsylvania 19380, United States.
ACS Chem Neurosci. 2012 Jan 18;3(1):50-68. doi: 10.1021/cn200100h. Epub 2011 Nov 2.
The central nervous system (CNS) is the major area that is affected by aging. Alzheimer's disease (AD), Parkinson's disease (PD), brain cancer, and stroke are the CNS diseases that will cost trillions of dollars for their treatment. Achievement of appropriate blood-brain barrier (BBB) penetration is often considered a significant hurdle in the CNS drug discovery process. On the other hand, BBB penetration may be a liability for many of the non-CNS drug targets, and a clear understanding of the physicochemical and structural differences between CNS and non-CNS drugs may assist both research areas. Because of the numerous and challenging issues in CNS drug discovery and the low success rates, pharmaceutical companies are beginning to deprioritize their drug discovery efforts in the CNS arena. Prompted by these challenges and to aid in the design of high-quality, efficacious CNS compounds, we analyzed the physicochemical property and the chemical structural profiles of 317 CNS and 626 non-CNS oral drugs. The conclusions derived provide an ideal property profile for lead selection and the property modification strategy during the lead optimization process. A list of substructural units that may be useful for CNS drug design was also provided here. A classification tree was also developed to differentiate between CNS drugs and non-CNS oral drugs. The combined analysis provided the following guidelines for designing high-quality CNS drugs: (i) topological molecular polar surface area of <76 Å(2) (25-60 Å(2)), (ii) at least one (one or two, including one aliphatic amine) nitrogen, (iii) fewer than seven (two to four) linear chains outside of rings, (iv) fewer than three (zero or one) polar hydrogen atoms, (v) volume of 740-970 Å(3), (vi) solvent accessible surface area of 460-580 Å(2), and (vii) positive QikProp parameter CNS. The ranges within parentheses may be used during lead optimization. One violation to this proposed profile may be acceptable. The chemoinformatics approaches for graphically analyzing multiple properties efficiently are presented.
中枢神经系统（CNS）是受衰老影响的主要区域。阿尔茨海默病（AD）、帕金森病（PD）、脑癌和中风是中枢神经系统疾病，其治疗将花费数万亿美元。实现适当的血脑屏障（BBB）穿透通常被认为是中枢神经系统药物发现过程中的一个重大障碍。另一方面，血脑屏障穿透对于许多非中枢神经系统药物靶点可能是一个不利因素，清楚了解中枢神经系统药物和非中枢神经系统药物之间的物理化学和结构差异可能对这两个研究领域都有帮助。由于中枢神经系统药物发现中存在众多具有挑战性的问题且成功率较低，制药公司开始降低其在中枢神经系统领域的药物发现工作优先级。受这些挑战的推动，并为了帮助设计高质量、有效的中枢神经系统化合物，我们分析了317种中枢神经系统口服药物和626种非中枢神经系统口服药物的物理化学性质和化学结构特征。所得出的结论为先导化合物选择提供了理想的性质概况以及先导优化过程中的性质修饰策略。这里还提供了一份可能对中枢神经系统药物设计有用的亚结构单元列表。还开发了一个分类树来区分中枢神经系统药物和非中枢神经系统口服药物。综合分析为设计高质量中枢神经系统药物提供了以下指导原则：（i）拓扑分子极性表面积<76 Ų（25 - 60 Ų），（ii）至少一个（一个或两个，包括一个脂肪族胺）氮原子，（iii）环外少于七条（两条至四条）线性链，（iv）少于三个（零个或一个）极性氢原子，（v）体积为740 - 970 ų，（vi）溶剂可及表面积为460 - 580 Ų，以及（vii）正的QikProp参数CNS。括号内的值可在先导优化过程中使用。对该提议概况的一次违反可能是可以接受的。本文介绍了用于高效图形化分析多种性质的化学信息学方法。
