The General Solubility Equation (GSE) is a QSPR model based on the melting point and log P of a chemical substance. It is used to predict the aqueous solubility of nonionizable chemical compounds. However, its reliance on experimentally derived descriptors, particularly melting point, limits its applicability to virtual compounds. The studies presented show that the GSE is able to predict, to within 1 log unit, the experimental aqueous solubility (log S) for 81% of the compounds in a data set of 1265 diverse chemical structures (-8.48 < log S < 1.58). However, the predictive ability of the GSE is reduced to 75% when applied to a subset of the data (1160 compounds -6.00 < log S < 0.00), which discounts those compounds occupying the sparsely populated regions of data space. This highlights how sparsely populated extremities of data sets can significantly skew results for linear regression-based models. Replacing the melting point descriptor of the GSE with a descriptor which accounts for topographical polar surface area (TPSA) produces a model of comparable quality to the GSE (the solubility of 81% of compounds in the full data set predicted accurately). As such, we propose an alternative simple model for predicting aqueous solubility which replaces the melting point descriptor of the GSE with TPSA and hence can be applied to virtual compounds. In addition, incorporating TPSA into the GSE in addition to log P and melting point gives a three descriptor model that improves accurate prediction of aqueous solubility over the GSE by 5.1% for the full and 6.6% for the reduced data set, respectively.

The cytochromes P450 (P450s) are a family of heme-containing monooxygenase enzymes involved in a variety of functions, including the metabolism of endogenous and exogenous substances in the human body. During lead optimization, and in drug development, many potential drug candidates are rejected because of the affinity they display for drug-metabolising P450s. Recently, crystal structures of human enzymes involved in drug metabolism have been determined, significantly augmenting the prospect of using structure-based design to modulate the binding and metabolizing properties of compounds against P450 proteins. An important step in the application of structure-based metabolic optimization is the accurate prediction of docking modes in heme binding proteins. In this paper we assess the performance of the docking program GOLD at predicting the binding mode of 45 heme-containing complexes. We achieved success rates of 64% and 57% for Chemscore and Goldscore respectively; these success rates are significantly lower than the value of 79% observed with both scoring functions for the full GOLD validation set. Re-parameterization of metal-acceptor interactions and lipophilicity of planar nitrogen atoms in the scoring functions resulted in a significant increase in the percentage of successful dockings against the heme binding proteins (Chemscore 73%, Goldscore 65%). The modified scoring functions will be useful in docking applications on P450 enzymes and other heme binding proteins.

The ability to trigger changes to material properties with external stimuli, so‐called “smart” behavior, has enabled novel technologies for a wide range of healthcare applications. Response to small changes in temperature is particularly attractive, where material transformations may be triggered by contact with the human body. Thermoreversible gelators are materials where warming triggers reversible phase change from low viscosity polymer solution to a gel state. These systems can be generated by the exploitation of macromolecules with lower critical solution temperatures included in their architectures. The resultant materials are attractive for topical and mucosal drug delivery, as well as for injectables. In addition, the materials are attractive for tissue engineering and 3D printing. The fundamental science underpinning these systems is described, along with progress in each class of material and their applications. Significant opportunities exist in the fundamental understanding of how polymer chemistry and nanoscience describe the performance of these systems and guide the rational design of novel systems. Furthermore, barriers to translating technologies must be addressed, for example, rigorous toxicological evaluation is rarely conducted. As such, applications remain tied to narrow fields, and advancements will be made where the existing knowledge in these areas may be applied to novel problems of science.

Cytochrome P450 2D6 (CYP2D6) metabolizes approximately one third of the drugs in current clinical use. To gain insight into its structure and function, we have produced four different sets of comparative models of 2D6: one based on the structures of P450s from four different microorganisms (P450 terp, P450 eryF, P450 cam, and P450 BM3), another on the only mammalian P450 (2C5) structure available, and the other two based on alternative amino acid sequence alignments of 2D6 with all five of these structures. Principal component analysis suggests that inclusion of the 2C5 crystal structure has a profound effect on the modeling process, altering the general topology of the active site, and that the models produced differ significantly from all of the templates. The four models of 2D6 were also used in conjunction with molecular docking to produce complexes with the substrates codeine and 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP); this identified Glu 216 [in the F-helix; substrate recognition site (SRS) 2] as a key determinant in the binding of the basic moiety of the substrate. Our studies suggest that both Asp 301 and Glu 216 are required for metabolism of basic substrates. Furthermore, they suggest that Asp 301 (I-helix, SRS-4), a residue thought from mutagenesis studies to bind directly to the basic moiety of substrates, may play a key role in positioning the B'-C loop (SRS-1) and that the loss of activity on mutating Asp 301 may therefore be the result of an indirect effect (movement of the B'-C loop) on replacing this residue.