Wang Junmei, Hou Tingjun
Department of Pharmacology, University of Texas Southwestern Medical Center at Dallas, 75390-9050, USA.
Comb Chem High Throughput Screen. 2011 Jun 1;14(5):328-38. doi: 10.2174/138620711795508331.
Aqueous solubility is one of the major physiochemical properties to be optimized in drug discovery. It is related to absorption and distribution in the ADME-Tox (Absorption, Distribution, Metabolism, Excretion, and Toxicity). Aqueous solubility and membrane permeability are the two key factors that affect a drug's oral bioavailability. Because of the importance of aqueous solubility, a lot of efforts have been spent on developing reliable models to predict this physiochemical property. Although some progress has been made and a lot of models have been constructed, it is concluded that accurate and reliable aqueous models targeted to predict solubility of drug-like molecules, have not emerged based on the outcome of an aqueous solubility prediction campaign sponsored by Goodman et al. In this review paper, we provide a snapshot of the latest development in the field. The challenges of developing high quality aqueous solubility models as well as the strategies of surmounting those challenges have been discussed. We conclude that the biggest challenge of modeling aqueous solubility is to collect more high quality, unskewed and drug-relevant solubility data which are sufficient diverse to cover most the chemical space of drugs. The second challenge is to develop good descriptors to account for the lattice energy of solvation. In order to develop accurate and predictable in silico solubility models, the key is to collect a sufficient number of high quality experimental data and the suspicious data must be verified. In addition, the molecular descriptors must be relevant to the energies in the solvation process (the lattice energy for crystal packing, the energy of forming cavity in solvent, and the solvation energy), and the models must be carefully cross-validated and evaluated using the external data sets.
水溶性是药物研发中需要优化的主要物理化学性质之一。它与药物代谢动力学（吸收、分布、代谢、排泄和毒性）中的吸收和分布相关。水溶性和膜通透性是影响药物口服生物利用度的两个关键因素。由于水溶性的重要性，人们已花费大量精力来开发可靠的模型以预测这一物理化学性质。尽管已取得一些进展并构建了许多模型，但根据古德曼等人发起的一项水溶性预测活动的结果得出结论，针对预测类药物分子溶解度的准确可靠的水溶性模型尚未出现。在这篇综述论文中，我们概述了该领域的最新进展。讨论了开发高质量水溶性模型所面临的挑战以及克服这些挑战的策略。我们得出结论，构建水溶性模型面临的最大挑战是收集更多高质量、无偏差且与药物相关的溶解度数据，这些数据要足够多样化以覆盖大多数药物化学空间。第二个挑战是开发能解释溶剂化晶格能的良好描述符。为了开发准确且可预测的计算机模拟溶解度模型，关键是收集足够数量的高质量实验数据，并且必须对可疑数据进行验证。此外，分子描述符必须与溶剂化过程中的能量（晶体堆积的晶格能、在溶剂中形成空穴的能量以及溶剂化能）相关，并且必须使用外部数据集对模型进行仔细的交叉验证和评估。
