Can the Formation of Pharmaceutical Cocrystals Be Computationally Predicted? 2. Crystal Structure Prediction

Abstract

We report a multistage lattice energy minimization methodology for generating stable packing arrangements of cocrystals containing flexible molecules. In the first approximation, the intermolecular electrostatic interactions are modeled with atomic charges and the molecular deformation energy is interpolated over a set of precomputed quantum mechanical values. At subsequent stages, the accuracy is improved by first using analytically rotated and then conformation-dependent multipole moments, computed from the isolated-molecule charge density, and "on-the-fly" quantum mechanical calculations to compute the intramolecular deformation energy. This multistage approach increases the efficiency of the search and establishes the molecule-dependent error due to the atomic charge representation of the charge density and the neglect of the conformational dependence of atomic multipole moments. The methodology is used to study the lattice energy landscapes of the cocrystals of 4-aminobenzoic acid with 2,2'-bipyridine and 4-nitrophenylacetic acid, as well as the single-component crystals. All single-component, experimentally determined crystal structures within the scope of the search were found at, or very close to, the global minimum. The experimental cocrystal with 2,2'-bipyridine is also predicted to be among the most stable packing arrangements. On the contrary, the lattice energy landscape of the cocrystal with 4-nitrophenylacetic acid contains several low energy structures that are more stable than the experimentally observed form and have different hydrogen bonding motifs. Overall, the methodology can provide worthwhile crystal energy landscapes for multicomponent organic solids and thereby contribute to understanding cocrystal formation.

Stability Order of Caffeine Co-crystals Determined by Co-crystal Former Exchange Reaction and Its Application for the Validation of in Silico Models

“…[17][18][19][20] For example, the difference in hydrogen bond energy between a co-crystal and each of the sole components (ΔE) was proposed by Musumeci et al, based on the hypothesis that the formation of a co-crystal becomes more probable as ΔE becomes larger.…”

“…16) Similarly, some in silico physicochemical parameters for CCF selection were proposed by several research groups. [17][18][19][20] For example, the difference in hydrogen bond energy between a co-crystal and each of the sole components (ΔE) was proposed by Musumeci et al, based on the hypothesis that the formation of a co-crystal becomes more probable as ΔE becomes larger. [21][22][23] However, ΔE has not been rigorously validated due to the lack of information about the stability order of co-crystals.…”

General Computational Algorithms for Ab Initio Crystal Structure Prediction for Organic Molecules

“…Over the last few years, these algorithms have been applied both by us and more extensively by others to a relatively wide variety of systems including single compound crystals [15,[29][30][31][32][33][34][35][36], co-crystals [14,[37][38][39], including chiral co-crystals [40,41], hydrates and solvates [42,43]. The codes have also been used separately, e.g.…”

“…The multistage approach to CSP has been widely adopted [18,25,38,68] and has been successfully used in the blind tests of crystal structure prediction [3][4][5][6][7]9]. Its success hinges on the hypothesis that relatively simple models of the energy surface can provide energy minima whose geometry is in reasonably good agreement with that of energy minima on a more accurate surface -even if the actual energy values differ significantly, in both absolute and relative terms, between the simpler and the more rigorous models.…”

Prediction of the crystal structures of axitinib, a polymorphic pharmaceutical molecule

“…Thus, it can be used (i) to provide reassurance that all likely polymorphs have been identified; (ii) to guide the search for further polymorphs by suggesting the specific crystal structures that might be observed, thereby helping to identify appropriate crystallization conditions ; (iii) to support crystal engineering by providing an understanding of the link between the motifs observed and molecular structure (Uzoh et al, 2012) or crystal composition (Habgood, 2013;Karamertzanis et al, 2009); (iv) to help crystallographers interpret data gathered on specific compounds (Baias et al, 2013;Friščić et al, 2010;Wu et al, 2013). This broad array of uses provides impetus for methodological improvements aimed at increasing the accuracy of the predictions and at broadening the range of molecules, co-crystals, salts and solvates that can be tackled in terms of size and complexity.…”