Moda Tiago L, Montanari Carlos A, Andricopulo Adriano D
Laboratório de Química Medicinal e Computacional, Centro de Biotecnologia Molecular Estrutural, Instituto de Física de São Carlos, Universidade de São Paulo, 13566-970 São Carlos, SP, Brazil.
Bioorg Med Chem. 2007 Dec 15;15(24):7738-45. doi: 10.1016/j.bmc.2007.08.060. Epub 2007 Sep 1.
A drug intended for use in humans should have an ideal balance of pharmacokinetics and safety, as well as potency and selectivity. Unfavorable pharmacokinetics can negatively affect the clinical development of many otherwise promising drug candidates. A variety of in silico ADME (absorption, distribution, metabolism, and excretion) models are receiving increased attention due to a better appreciation that pharmacokinetic properties should be considered in early phases of the drug discovery process. Human oral bioavailability is an important pharmacokinetic property, which is directly related to the amount of drug available in the systemic circulation to exert pharmacological and therapeutic effects. In the present work, hologram quantitative structure-activity relationships (HQSAR) were performed on a training set of 250 structurally diverse molecules with known human oral bioavailability. The most significant HQSAR model (q(2)=0.70, r(2)=0.93) was obtained using atoms, bond, connection, and chirality as fragment distinction. The predictive ability of the model was evaluated by an external test set containing 52 molecules not included in the training set, and the predicted values were in good agreement with the experimental values. The HQSAR model should be useful for the design of new drug candidates having increased bioavailability as well as in the process of chemical library design, virtual screening, and high-throughput screening.
用于人类的药物应在药代动力学与安全性以及效力和选择性之间达到理想的平衡。不良的药代动力学可能会对许多原本很有前景的候选药物的临床开发产生负面影响。由于人们越来越认识到在药物发现过程的早期阶段就应考虑药代动力学特性，各种计算机辅助的ADME（吸收、分布、代谢和排泄）模型正受到越来越多的关注。人体口服生物利用度是一项重要的药代动力学特性，它与全身循环中可发挥药理和治疗作用的药物量直接相关。在本研究中，对一组包含250个具有已知人体口服生物利用度的结构多样分子的训练集进行了全息定量构效关系（HQSAR）分析。使用原子、键、连接和手性作为片段区分获得了最显著的HQSAR模型（q(2)=0.70，r(2)=0.93）。通过一个包含52个未包含在训练集中的分子的外部测试集评估了该模型的预测能力，预测值与实验值吻合良好。该HQSAR模型应有助于设计具有更高生物利用度的新药候选物，以及在化学文库设计、虚拟筛选和高通量筛选过程中发挥作用。
