Ulenberg Szymon, Belka Mariusz, Król Marek, Herold Franciszek, Hewelt-Belka Weronika, Kot-Wasik Agata, Bączek Tomasz
Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Medical University of Gdańsk, Gdańsk, Poland.
Department of Drug Technology and Pharmaceutical Biotechnology, Medical University of Warsaw, Warsaw, Poland.
PLoS One. 2015 Mar 31;10(3):e0122772. doi: 10.1371/journal.pone.0122772. eCollection 2015.
Other than efficacy of interaction with the molecular target, metabolic stability is the primary factor responsible for the failure or success of a compound in the drug development pipeline. The ideal drug candidate should be stable enough to reach its therapeutic site of action. Despite many recent excellent achievements in the field of computational methods supporting drug metabolism studies, a well-recognized procedure to model and predict metabolic stability quantitatively is still lacking. This study proposes a workflow for developing quantitative metabolic stability-structure relationships, taking a set of 30 arylpiperazine derivatives as an example. The metabolic stability of the compounds was assessed in in vitro incubations in the presence of human liver microsomes and NADPH and subsequently quantified by liquid chromatography-mass spectrometry (LC-MS). Density functional theory (DFT) calculations were used to obtain 30 models of the molecules, and Dragon software served as a source of structure-based molecular descriptors. For modeling structure-metabolic stability relationships, Support Vector Machines (SVM), a non-linear machine learning technique, were found to be more effective than a regression technique, based on the validation parameters obtained. Moreover, for the first time, general sites of metabolism for arylpiperazines bearing the 4-aryl-2H-pyrido[1,2-c]pyrimidine-1,3-dione system were defined by analysis of Q-TOF-MS/MS spectra. The results indicated that the application of one of the most advanced chemometric techniques combined with a simple and quick in vitro procedure and LC-MS analysis provides a novel and valuable tool for predicting metabolic half-life values. Given the reduced time and simplicity of analysis, together with the accuracy of the predictions obtained, this is a valid approach for predicting metabolic stability using structural data. The approach presented provides a novel, comprehensive and reliable tool for investigating metabolic stability, factors that affect it, and the proposed structures of metabolites at the same time. The performance of the DFT-SVM-based approach provides an opportunity to implement it in a standard drug development pipeline.
除了与分子靶点相互作用的功效外，代谢稳定性是决定化合物在药物研发流程中成败的主要因素。理想的候选药物应具有足够的稳定性，以抵达其治疗作用位点。尽管近年来在支持药物代谢研究的计算方法领域取得了许多卓越成就，但仍缺乏一种公认的定量建模和预测代谢稳定性的程序。本研究以一组30种芳基哌嗪衍生物为例，提出了一种用于建立定量代谢稳定性-结构关系的工作流程。在人肝微粒体和NADPH存在的情况下，通过体外孵育评估化合物的代谢稳定性，随后通过液相色谱-质谱联用（LC-MS）进行定量。采用密度泛函理论（DFT）计算获得分子的30种模型，Dragon软件作为基于结构的分子描述符的来源。基于获得的验证参数，发现支持向量机（SVM）这种非线性机器学习技术在构建结构-代谢稳定性关系模型方面比回归技术更有效。此外，通过对Q-TOF-MS/MS谱图的分析，首次确定了带有4-芳基-2H-吡啶并[1,2-c]嘧啶-1,3-二酮系统的芳基哌嗪的一般代谢位点。结果表明，将最先进的化学计量技术之一与简单快速的体外程序及LC-MS分析相结合，为预测代谢半衰期值提供了一种新颖且有价值的工具。鉴于分析时间的减少和简便性，以及所获得预测的准确性，这是一种利用结构数据预测代谢稳定性的有效方法。所提出的方法为同时研究代谢稳定性、影响代谢稳定性的因素以及代谢物的推测结构提供了一种新颖、全面且可靠的工具。基于DFT-SVM的方法的性能为在标准药物研发流程中实施该方法提供了机会。
