Knowledge-Based, Central Nervous System (CNS) Lead Selection and Lead Optimization for CNS Drug Discovery.
Knowledge-Based, Central Nervous System (CNS) Lead Selection and Lead Optimization for CNS Drug Discovery.
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DOI：
10.1021/cn200100h
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发表时间：
2012-01-18
影响因子：
5
通讯作者：
Mallamo, John P.
中科院分区：
文献类型：
--
作者：
Ghose, Arup K.;Herbertz, Torsten;Hudkins, Robert L.;Dorsey, Bruce D.;Mallamo, John P.
关键词：
中枢神经系统（CNS）是受衰老影响的主要区域。阿尔茨海默氏病（AD）、帕金森氏病（PD）、脑癌和中风是CNS疾病，其治疗将花费数万亿美元。实现适当的血脑屏障（BBB）渗透通常被认为是CNS药物发现过程中的重大障碍。另一方面，血脑屏障渗透可能是许多非中枢神经系统药物靶点的责任，并且清楚地了解中枢神经系统和非中枢神经系统药物之间的理化和结构差异可能有助于这两个研究领域。由于中枢神经系统药物发现中的众多挑战性问题和低成功率，制药公司开始降低其在中枢神经系统竞技场中的药物发现工作的优先级。面对这些挑战，为了帮助设计高质量，有效的CNS化合物，我们分析了317种CNS和626种非CNS口服药物的理化性质和化学结构特征。所得结论为引线的选择和引线优化过程中的性能修改策略提供了理想的性能曲线。本文还提供了可能对CNS药物设计有用的亚结构单元列表。还开发了分类树，以区分CNS药物和非CNS口服药物。综合分析为设计高质量的CNS药物提供了以下指南：（i）拓扑分子极性表面积<76 μ 2（25-60 μ 2），（ii）至少一种（iii）少于七个（一个或两个，包括一个脂族胺）氮，（2至4个）环外的线性链，（iv）少于3个（零个或一个）极性氢原子，（v）740-970 μ m3的体积，（vi）460-580 μ m2的溶剂可及表面积，和（vii）正QikProp参数CNS。括号内的范围可在电极导线优化期间使用。一次违反此建议的配置文件可能是可接受的。提出了一种有效地图形化分析多个性质的化学信息学方法。
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 Å2 (25–60 Å2), (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 Å3, (vi) solvent accessible surface area of 460–580 Å2, 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.
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影响因子：
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作者：
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通讯作者：
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DOI：
10.1021/ci034205d
发表时间：
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期刊：
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影响因子：
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DOI：
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发表时间：
2009-03-27
期刊：
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