The solid form screening of galunisertib produced many solvates, prompting an extensive investigation into possible risks to the development of the favored monohydrate form. Inspired by crystal structure prediction, the search for neat polymorphs was expanded to an unusual range of experiments, including melt crystallization under pressure, to work around solvate formation and the thermal instability of the molecule. Ten polymorphs of galunisertib were found; however, the structure predicted to be the most stable has yet to be obtained. We present the crystal structures of all ten unsolvated polymorphs of galunisertib, showing how state-of-the-art characterization methods can be combined with emerging computational modeling techniques to produce a complete structure landscape and assess the risk of late-appearing, more stable polymorphs. The exceptional conformational polymorphism of this prolific solvate former invites further development of methods, computational and experimental, that are applicable to larger, flexible molecules with complex solid form landscapes.

A seventh blind test of crystal structure prediction was organized by the Cambridge Crystallographic Data Centre featuring seven target systems of varying complexity: a silicon and iodine-containing molecule, a copper coordination complex, a near-rigid molecule, a cocrystal, a polymorphic small agrochemical, a highly flexible polymorphic drug candidate, and a polymorphic morpholine salt. In this first of two parts focusing on structure generation methods, many crystal structure prediction (CSP) methods performed well for the small but flexible agrochemical compound, successfully reproducing the experimentally observed crystal structures, while few groups were successful for the systems of higher complexity. A powder X-ray diffraction (PXRD) assisted exercise demonstrated the use of CSP in successfully determining a crystal structure from a low-quality PXRD pattern. The use of CSP in the prediction of likely cocrystal stoichiometry was also explored, demonstrating multiple possible approaches. Crystallographic disorder emerged as an important theme throughout the test as both a challenge for analysis and a major achievement where two groups blindly predicted the existence of disorder for the first time. Additionally, large-scale comparisons of the sets of predicted crystal structures also showed that some methods yield sets that largely contain the same crystal structures.

A seventh blind test of crystal structure prediction has been organized by the Cambridge Crystallographic Data Centre. The results are presented in two parts, with this second part focusing on methods for ranking crystal structures in order of stability. The exercise involved standardized sets of structures seeded from a range of structure generation methods. Participants from 22 groups applied several periodic DFT-D methods, machine learned potentials, force fields derived from empirical data or quantum chemical calculations, and various combinations of the above. In addition, one non-energy-based scoring function was used. Results showed that periodic DFT-D methods overall agreed with experimental data within expected error margins, while one machine learned model, applying system-specific AIMnet potentials, agreed with experiment in many cases demonstrating promise as an efficient alternative to DFT-based methods. For target XXXII, a consensus was reached across periodic DFT methods, with consistently high predicted energies of experimental forms relative to the global minimum (above 4 kJ mol⁻¹ at both low and ambient temperatures) suggesting a more stable polymorph is likely not yet observed. The calculation of free energies at ambient temperatures offered improvement of predictions only in some cases (for targets XXVII and XXXI). Several avenues for future research have been suggested, highlighting the need for greater efficiency considering the vast amounts of resources utilized in many cases.

Direct-acting antiviral regimens have transformed therapeutic management of hepatitis C across all prevalent genotypes. Most of the chemical matter in these regimens comprises molecules well outside the traditional drug development chemical space and presents significant challenges. Herein, the implications of high conformational flexibility and the presence of a 15-membered macrocyclic ring in paritaprevir are studied through a combination of advanced computational and experimental methods with focus on molecular chameleonicity and crystal form complexity. The ability of the molecule to toggle between high and low 3D polar surface area (PSA) conformations is underpinned by intramolecular hydrogen bonding (IMHB) interactions and intramolecular steric effects. Computational studies consequently show a very significant difference of over 75 Ų in 3D PSA between polar and apolar environments and provide the structural basis for the perplexingly favorable passive permeability of the molecule. Crystal packing and protein binding resulting in strong intermolecular interactions disrupt these intramolecular interactions. Crystalline Form I benefits from strong intermolecular interactions, whereas the weaker intermolecular interactions in Form II are partially compensated by the energetic advantage of an IMHB. Like Form I, no IMHB is observed within the receptor-bound conformation; instead, an intermolecular H-bond contributes to the potency of the molecule. The choice of metastable Form II is derisked through strategies accounting for crystal surface and packing features to manage higher form specific solid-state chemical reactivity and specific processing requirements. Overall, the results show an unambiguous link between structural features and derived properties from crystallization to dissolution, permeation, and docking into the protein pocket.