Predictive models of aqueous solubility of organic compounds built on A large dataset of high integrity.

Aqueous solubility is one of the most important properties in drug discovery, as it has profound impact on various drug properties, including biological activity, pharmacokinetics (PK), toxicity, and in vivo efficacy. Both kinetic and thermodynamic solubilities are determined during different stages of drug discovery and development. Since kinetic solubility is more relevant in preclinical drug discovery research, especially during the structure optimization process, we have developed predictive models for kinetic solubility with in-house data generated from 11,780 compounds collected from over 200 NCATS intramural research projects. This represents one of the largest kinetic solubility datasets of high quality and integrity. Based on the customized atom type descriptors, the support vector classification (SVC) models were trained on 80% of the whole dataset, and exhibited high predictive performance for estimating the solubility of the remaining 20% compounds within the test set. The values of the area under the receiver operating characteristic curve (AUC-ROC) for the compounds in the test sets reached 0.93 and 0.91, when the threshold for insoluble compounds was set to 10 and 50 µg/mL respectively. The predictive models of aqueous solubility can be used to identify insoluble compounds in drug discovery pipeline, provide design ideas for improving solubility by analyzing the atom types associated with poor solubility and prioritize compound libraries to be purchased or synthesized.

Application of in vitro Drug Metabolism Studies in Chemical Structure Optimization for the Treatment of Fibrodysplasia Ossificans Progressiva (FOP).

Currently no approved treatment exists for fibrodysplasia ossificans progressiva (FOP) patients, and disease progression results in severe restriction of joint function and premature mortality. LDN-193189 has been demonstrated to be efficacious in a mouse FOP disease model after oral administration. To support species selection for drug safety evaluation and to guide structure optimization for back-up compounds, in vitro metabolism of LDN-193189 was investigated in liver microsome and cytosol fractions of mouse, rat, dog, rabbit, monkey and human. Metabolism studies included analysis of reactive intermediate formation using glutathione and potassium cyanide (KCN) and analysis of non-P450 mediated metabolites in cytosol fractions of various species. Metabolite profiles and metabolic soft spots of LDN-193189 were elucidated using LC/UV and mass spectral techniques. The in vitro metabolism of LDN-193189 was significantly dependent on aldehyde oxidase, with formation of the major NIH-Q55 metabolite. The piperazinyl moiety of LDN-193189 was liable to NADPH-dependent metabolism which generated reactive iminium intermediates, as confirmed through KCN trapping experiments, and aniline metabolites (M337 and M380), which brought up potential drug safety concerns. Subsequently, strategies were employed to avoid metabolic liabilities leading to the synthesis of Compounds 1, 2, and 3. This study demonstrated the importance of metabolite identification for the discovery of novel and safe drug candidates for the treatment of FOP and helped medicinal chemists steer away from potential metabolic liabilities.