Is there a difference between leads and drugs? A historical perspective
Is there a difference between leads and drugs? A historical perspective
复制标题
DOI：
10.1021/ci010366a
复制
发表时间：
2001-09-01
期刊：
影响因子：
--
通讯作者：
Leeson, PD
中科院分区：
文献类型：
--
作者：
Oprea, TI;Davis, AM;Leeson, PD
为了考虑进一步开发，先导化合物结构应显示以下特性：（1）简单的化学特征，适合化学优化;（2）属于已建立的SAR系列;（3）有利的专利情况;和（4）良好的吸收、分布、代谢和排泄（ADME）特性。存在两种不同类别的导联：缺乏任何治疗用途的导联（即，“纯”先导化合物），以及那些本身是市售药物但已被改变以产生新药的先导化合物。我们先前已经分析了从18个先导和药物结构对开始的类先导组合文库的设计（S. J.蒂格等人Angew. Chcm.，国际教育英语1999，38，3743-3748）。在这里，我们报告了基于96个铅-药物对的扩展数据集的结果，其中62个是未作为药物销售的铅结构，75个是可能未用作铅的药物。我们检查了以下属性：MW（分子量），CMR（计算的分子生物活性），RNG（环数），RTB（可旋转键的数目）、氢键供体（HDO）和受体（HAC）的数目、正辛醇/水分配的计算对数（CLogP）、在pH 7.4下分配系数的计算对数（LogD（74））、日光指纹药物样评分（DFPS）和性质和药效团特征评分（PPFS）。在药物和电极导线的中位数之间观察到以下差异：Delta MW = 69; Delta CMR = 1.8; Delta RNG = Delta HAC =1; Delta RTB = 2; Delta CLogP = 0.43; Delta LogD（74）= 0.97，Delta HDO = 0; Delta DFPS = 0.15; Delta PPFS = 0.12。平均而言，先导化合物结构表现出较低的分子复杂性（较低的分子量，较少的环和可旋转键），较低的疏水性（较低的CLogP和LogD（74））和较低的药物样（较低的药物样评分）。这些发现表明，将铅优化为药物的过程会导致更复杂的结构。这些信息应用于设计新的组合库，旨在铅发现。
To be considered for further development, lead structures should display the following properties: ( 1) simple chemical features, amenable for chemistry optimization; (2) membership to an established SAR series; (3) favorable patent situation; and (4) good absorption, distribution, metabolism, and excretion (ADME) properties. There are two distinct categories of leads: those that lack any therapeutic use (i.e., "pure" leads), and those that are marketed drugs themselves but have been altered to yield novel drugs. We have previously analyzed the design of leadlike combinatorial libraries starting from 18 lead and drug pairs of structures (S. J. Teague et al. Angew. Chcm., Int. Ed. Engl. 1999, 38, 3743-3748). Here, we report results based on an extended dataset of 96 lead-drug pairs, of which 62 are lead structures that are not marketed as drugs, and 75 are drugs that are not presumably used as leads. We examined the following properties: MW (molecular weight), CMR (the calculated molecular refractivity), RNG (the number of rings), RTB (the number of rotatable bonds), the number of hydrogen bond donors (HDO) and acceptors (HAC), the calculated logarithm of the n-octanol/water partition (CLogP), the calculated logarithm of the distribution coefficient at pH 7.4 (LogD(74)), the Daylight-fingerprint druglike score (DFPS), and the property and pharmacophore features score (PPFS). The following differences were observed between the medians of drugs and leads: Delta MW = 69; Delta CMR = 1.8; Delta RNG = Delta HAC =1; Delta RTB = 2; Delta CLogP = 0.43; Delta LogD(74) = 0.97, Delta HDO = 0; Delta DFPS = 0.15; Delta PPFS = 0.12. Lead structures exhibit, on the average, less molecular complexity (less MW, less number of rings and rotatable bonds), are less hydrophobic (lower CLogP and LogD(74)), and less druglike (lower druglike scores). These findings indicate that the process of optimizing a lead into a drug results in more complex structures. This information should be used in the design of novel combinatorial libraries that are aimed at lead discovery.