Molecular, Solid-State and Surface Structures of the Conformational Polymorphic Forms of Ritonavir in Relation to their Physicochemical Properties.

PURPOSE: Application of multi-scale modelling workflows to characterise polymorphism in ritonavir with regard to its stability, bioavailability and processing. METHODS: Molecular conformation, polarizability and stability are examined using quantum mechanics (QM). Intermolecular synthons, hydrogen bonding, crystal morphology and surface chemistry are modelled using empirical force fields. RESULTS: The form I conformation is more stable and polarized with more efficient intermolecular packing, lower void space and higher density, however its shielded hydroxyl is only a hydrogen bond donor. In contrast, the hydroxyl in the more open but less stable and polarized form II conformation is both a donor and acceptor resulting in stronger hydrogen bonding and a more stable crystal structure but one that is less dense. Both forms have strong 1D networks of hydrogen bonds and the differences in packing energies are partially offset in form II by its conformational deformation energy difference with respect to form I. The lattice energies converge at shorter distances for form I, consistent with its preferential crystallization at high supersaturation. Both forms exhibit a needle/lath-like crystal habit with slower growing hydrophobic side and faster growing hydrophilic capping habit faces with aspect ratios increasing from polar-protic, polar-aprotic and non-polar solvents, respectively. Surface energies are higher for form II than form I and increase with solvent polarity. The higher deformation, lattice and surface energies of form II are consistent with its lower solubility and hence bioavailability. CONCLUSION: Inter-relationship between molecular, solid-state and surface structures of the polymorphic forms of ritonavir are quantified in relation to their physical-chemical properties.

A Digital Mechanistic Workflow for Predicting Solvent-Mediated Crystal Morphology: The α and ß Forms of l-Glutamic Acid.

The solvent-mediated crystal morphologies of the α and ß polymorphic forms of l-glutamic acid are presented. This work applies a digital mechanistically based workflow that encompasses calculation of the crystal lattice energy and its constituent intermolecular synthons, their interaction energies, and their key role in understanding and predicting crystal morphology as well as assessing the surface chemistry, topology, and solvent binding on crystal habit growth surfaces. Through a comparison between the contrasting morphologies of the conformational polymorphs of l-glutamic acid, this approach highlights how the interfacial chemistry of organic crystalline materials and their inherent anisotropic interactions with their solvation environments direct their crystal habit with potential impact on their further downstream processing behavior.

De-Risking Early-Stage Drug Development With a Bespoke Lattice Energy Predictive Model: A Materials Science Informatics Approach to Address Challenges Associated With a Diverse Chemical Space.

The solid-state properties of new chemical entities are critical to the stability and bioavailability of pharmaceutical drug products. The stability of the solid-state packing is described by the packing energy and an accurate prediction of this property for drug molecules would therefore be desirable. However, this has been difficult to achieve because of the lack of fundamental thermodynamic data on drug molecules. A potential solution would be to use calculated lattice energies to build a model and design molecules with desired physicochemical properties from an early stage, aligning with a "design by first intent" strategy for physicochemical properties. We first demonstrate the high correlation and interchangeability between QSPR models built using calculated lattice energies and experimental sublimation enthalpies for small organic molecules. We then present a QSPR model trained on in-house molecules using lattice energies calculated from crystal structures. The result is a model that enables fast prediction of the lattice energies of in-house molecules from 2-D molecular structure with reasonable accuracy (R2 = 0.92, root mean square error = 3.58 kcal/mol). We explore the model elements to improve our understanding of the molecular properties that contribute to lattice energy and then suggest potential cross-industry aspects that may enhance the application of the concept.

The CSD Drug Subset: The Changing Chemistry and Crystallography of Small Molecule Pharmaceuticals.

We report the generation and statistical analysis of the CSD drug subset: a subset of the Cambridge Structural Database (CSD) consisting of every published small-molecule crystal structure containing an approved drug molecule. By making use of InChI matching, a CSD Python API workflow to link CSD entries to the online database Drugbank.ca has been produced. This has resulted in a subset of 8632 crystal structures, representing all published solid forms of 785 unique drug molecules. We hope that this new resource will lead to improvements in targeted cheminformatics and statistical model building in a pharmaceutical setting. In addition to this, as part of the Advanced Digital Design of Pharmaceutical Therapeutics collaboration between academia and industry, we have been given the unique opportunity to run comparative analysis on the internal crystal structure databases of AstraZeneca and Pfizer, alongside comparison to the CSD as a whole.

Low solubility in drug development: de-convoluting the relative importance of solvation and crystal packing.

OBJECTIVES: An increasing trend towards low solubility is a major issue for drug development as formulation of low solubility compounds can be problematic. This paper presents a model which de-convolutes the solubility of pharmaceutical compounds into solvation and packing properties with the intention to understand the solubility limiting features. METHODS: The Cambridge Crystallographic Database was the source of structural information. Lattice energies were calculated via force-field based approaches using Materials Studio. The solvation energies were calculated applying quantum chemistry models using Cosmotherm software. KEY FINDINGS: The solubilities of 54 drug-like compounds were mapped onto a solvation energy/crystal packing grid. Four quadrants were identified were different balances of solvation and packing were defining the solubility. A version of the model was developed which allows for the calculation of the two features even in absence of crystal structure. CONCLUSION: Although there are significant number of in-silico models, it has been proven very difficult to predict aqueous solubility accurately. Therefore, we have taken a different approach where the solubility is not predicted directly but is de-convoluted into two constituent features.

The integration of solid-form informatics into solid-form selection.

OBJECTIVES: To demonstrate how the use of structural informatics during drug development assists with the assessment of the risk of polymorphism and the selection of a commercial solid form. METHODS: The application of structural chemistry knowledge derived from the hundreds of thousands of crystal structures contained in the Cambridge Structural Database to drug candidates is described. Examples given show the comparison of intermolecular geometries to database-derived statistics, the use of Full Interaction Maps to assess polymorph stability and the calculation of hydrogen bond propensities to provide assurance of a stable solid form. The software tools used are included in the Cambridge Structural Database System and the Solid Form Module of Mercury. KEY FINDINGS: The early identification of an unusual supramolecular motif in the development phase of maraviroc led to further experimental work to find the most stable polymorph. Analyses of two polymorphs of a pain candidate drug demonstrated how consideration of molecular conformation and intermolecular interactions were used for the assessment of relative stability. Informatics analysis confirmed that the solid form of crizotinib, a monomorphic system, had a low risk of polymorphism. CONCLUSIONS: The application of informatics-based assessment of new chemical entities complements experimental studies and provides a deeper understanding of the qualities of the structure. The information provided by structural analyses is incorporated into the assessment of risk. Informatics techniques are quick to apply and are straightforward to use, allowing an assessment of progressing drug candidates.