Investigating the effects of the core nitrogen atom configuration on the thermodynamic solubility of 6,5-bicyclic heterocycles

Physicochemical properties, such as solubility, are important when prioritising compounds for progression on a drug discovery project. There is limited literature around the systematic effects of core changes on thermodynamic solubility. This work details the synthesis of nitrogen containing 6,5-bicyclic heterocyclic cores which are common scaffolds in medicinal chemistry and the analysis of their physicochemical properties, particularly, thermodynamic solubility. Crystalline solids were obtained where possible to enable a robust comparison of the thermodynamic solubility. Other parameters such as pK_a, melting point and lipophilicity were also measured to determine the key factors affecting the observed solubility.

Prediction of aqueous intrinsic solubility of druglike molecules using Random Forest regression trained with Wiki-pS0 database

The accurate prediction of solubility of drugs is still problematic. It was thought for a long time that shortfalls had been due the lack of high-quality solubility data from the chemical space of drugs. This study considers the quality of solubility data, particularly of ionizable drugs. A database is described, comprising 6355 entries of intrinsic solubility for 3014 different molecules, drawing on 1325 citations. In an earlier publication, many factors affecting the quality of the measurement had been discussed, and suggestions were offered to improve ways of extracting more reliable information from legacy data. Many of the suggestions have been implemented in this study. By correcting solubility for ionization (i.e., deriving intrinsic solubility, S0) and by normalizing temperature (by transforming measurements performed in the range 10-50 °C to 25 °C), it can now be estimated that the average interlaboratory reproducibility is 0.17 log unit. Empirical methods to predict solubility at best have hovered around the root mean square error (RMSE) of 0.6 log unit. Three prediction methods are compared here: (a) Yalkowsky's general solubility equation (GSE), (b) Abraham solvation equation (ABSOLV), and (c) Random Forest regression (RFR) statistical machine learning. The latter two methods were trained using the new database. The RFR method outperforms the other two models, as anticipated. However, the ability to predict the solubility of drugs to the level of the quality of data is still out of reach. The data quality is not the limiting factor in prediction. The statistical machine learning methodologies are probably up to the task. Possibly what's missing are solubility data from a few sparsely-covered chemical space of drugs (particularly of research compounds). Also, new descriptors which can better differentiate the factors affecting solubility between molecules could be critical for narrowing the gap between the accuracy of the prediction models and that of the experimental data.

Improvement in aqueous solubility achieved via small molecular changes

Overcoming poor solubility is a significant issue in drug discovery. The most common solution is to replace carbon atoms with polar heteroatoms such as N and O or by attaching a solubilizing appendage. This approach can lead to other issues such as poor activity and PK or the increased risk for toxicity. However, there are more subtle structural changes which can be employed that lead to an increase in solubility. These include, excising hydrophobic groups which do not efficiently contribute to binding, modifying stereo- and regiochemistry, increasing or decreasing the degree of unsaturation or adding small hydrophobic groups such as fluorine or methyl.