Abstract
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 drugs 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.
Keywords: Solubility, solubility challenge, in silico modeling, drug design, Prediction, physiochemical properties, ADME-Tox, bioavailability, combinatorial chemistry, high-throughput screening
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Current Computer-Aided Drug Design
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