Oprea T I, Davis A M, Teague S J, Leeson P D
AstraZeneca R&D Mölndal, EST Lead Informatics, S 431 83 Mölndal, Sweden.
J Chem Inf Comput Sci. 2001 Sep-Oct;41(5):1308-15. doi: 10.1021/ci010366a.
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. Chem., 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: DeltaMW = 69; DeltaCMR = 1.8; DeltaRNG = DeltaHAC =1; DeltaRTB = 2; DeltaCLogP = 0.43; DeltaLogD(74) = 0.97; DeltaHDO = 0; DeltaDFPS = 0.15; DeltaPPFS = 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.
为了被考虑进一步开发,先导结构应具备以下特性:(1)简单的化学特征,便于进行化学优化;(2)属于已确立的构效关系(SAR)系列;(3)有利的专利情况;以及(4)良好的吸收、分布、代谢和排泄(ADME)特性。先导物有两类不同的类型:一类是没有任何治疗用途的(即“纯”先导物),另一类是本身为上市药物但经过改造以产生新型药物的。我们之前从18对先导物和药物结构出发分析了类先导物组合文库的设计(S. J. 蒂格等人,《德国应用化学》,国际英文版,1999年,38卷,3743 - 3748页)。在此,我们报告基于96对先导物 - 药物对的扩展数据集的结果,其中62个是未作为药物上市的先导结构,75个是大概未用作先导物的药物。我们研究了以下特性:分子量(MW)、计算的分子折射度(CMR)、环数(RNG)、可旋转键数(RTB)、氢键供体(HDO)和受体(HAC)的数量、正辛醇/水分配系数的计算对数(CLogP)、pH 7.4时分布系数的计算对数(LogD(74))、Daylight指纹药物类分数(DFPS)以及特性和药效团特征分数(PPFS)。在药物和先导物的中位数之间观察到以下差异:ΔMW = 69;ΔCMR = 1.8;ΔRNG = ΔHAC = 1;ΔRTB = 2;ΔCLogP = 0.43;ΔLogD(74) = 0.97;ΔHDO = 0;ΔDFPS = 0.15;ΔPPFS = 0.12。先导结构平均表现出较低的分子复杂性(分子量较小、环数和可旋转键数较少)、疏水性较低(CLogP和LogD(74)较低)以及药物类性质较低(药物类分数较低)。这些发现表明将先导物优化为药物的过程会产生更复杂的结构。该信息应用于旨在发现先导物的新型组合文库的设计中。