Caron Giulia, Ermondi Giuseppe
University of Torino, Molecular Biotechnology and Health Sciences Department, Via Quarello, 15, 10135 Torino, Italy.
University of Torino, Molecular Biotechnology and Health Sciences Department, Via Quarello, 15, 10135 Torino, Italy.
Drug Discov Today. 2017 Jun;22(6):835-840. doi: 10.1016/j.drudis.2016.11.017. Epub 2016 Nov 23.
Current multiparameter optimization (MPO) strategies make use of few experimental physicochemical descriptors (i.e., solubility at physiological pH and lipophilicity in the octanol/water system). Here, we show how new trends in drug discovery (i.e., large and flexible molecules for 'difficult' targets) call for the integration of ad hoc descriptors in MPO approaches. In particular, to rank, select, and optimize drug candidates, it could be relevant to have experimental data relating to the acid-base properties and the folding of the molecule to mask polar groups (so-called 'chameleonic' properties). We propose two strategies to quantify ionization and chameleonic properties and discuss their practical integration in property criteria profiles.
当前的多参数优化(MPO)策略利用了很少的实验物理化学描述符(即生理pH下的溶解度和辛醇/水系统中的亲脂性)。在此,我们展示了药物发现的新趋势(即针对“难成药”靶点的大且灵活的分子)如何要求在MPO方法中整合特殊描述符。特别是,为了对候选药物进行排名、选择和优化,获取与分子的酸碱性质和掩盖极性基团的折叠相关的实验数据(所谓的“变色龙”性质)可能是相关的。我们提出了两种量化电离和变色龙性质的策略,并讨论了它们在性质标准概况中的实际整合。