Optimization and comparison of statistical tools for the prediction of multicomponent forms of a molecule: the antiretroviral nevirapine as a case study

Abstract

In the pharmaceutical area, to obtain structures with desired properties, one can design and perform a screening of multicomponent forms of a drug. However, there is an infinite number of molecules that can be used as co-formers. Aiming to avoid spending time and money in failed experiments, scientists are always trying to optimize the selection of co-formers with high probability to co-crystallize with the drug. Here, the authors propose the use of statistical tools from the Cambridge Crystallographic Data Centre (CCDC) to select the co-formers to be used in a pharmaceutical screening of new crystal forms of the antiretroviral drug nevirapine (NVP). The H-bond propensity (HBP), coordination values (CV), and molecular complementarity (MC) tools were optimized for multicomponent analysis and a dataset of 450 molecules was ranked by a consensus ranking. The results were compared with CosmoQuick co-crystal prediction results and they were also compared to experimental data to validate the methodology. As a result of the experimental screening, three new co-crystals – NVP–benzoic acid, NVP–3-hydroxybenzoic acid, and NVP– gentisic acid – were achieved and the structures are reported. Since each tool assesses a different aspect of supramolecular chemistry, a consensus ranking can be considered a helpful strategy for selecting coformers. At the same time, this type of work proves to be useful for understanding the target molecule and analyzing which tool may exhibit more significance in co-former selection.