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 co-formers. 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.