Molecular-scale modelling for predicting surface energies on a face-specific and whole particle basis is applied to all the crystallographically-independent surfaces of L-glutamic acid forms. The predicted data is found to be in good general agreement with measured surface energies using inverse gas chromatography and Washburn capillary rise techniques with the former revealing higher values compared to the prediction, perhaps consistent with the polar (zwitterionic) nature of this material. This fusion of experimental and computational data provides a high-fidelity definition of the face-by-face breakdown of the energetic anisotropy of the crystals. There is increasing industrial interest in defining the potential impact of whole particle properties on the performance of formulated drug product and their manufacturability especially as the community accelerates the molecule to medicine journey. The overall molecular modelling approach highlights its application in designing ingredients for optimising face-specific particle surface energies for product formulatability particularly in early phase process development.

The solvent-mediated crystal morphologies of the α and β 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.

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.

We introduce a combination of existing and novel approaches to the assessment and prediction of particle properties intrinsic to the formulation and manufacture of pharmaceuticals. Naturally following on from established solid form informatics methods, we return to the drug lamotrigine, re-evaluating its context in the Cambridge Structural Database (CSD). We then apply predictive digital design tools built around the CSD-System suite of software, including Synthonic Engineering methods that focus on intermolecular interaction energies, to analyze and understand important particle properties and their effects on several key stages of pharmaceutical manufacturing. We present a new, robust workflow that brings these approaches together to build on the knowledge gained from each step and explain how this knowledge can be combined to provide resolutions at decision points encountered during formulation design and manufacturing processes.

In this contribution, the hydration kinetics of three anhydrous polymorphs (AH-A, AH-B, and AH-C) of fluconazole [2-(2,4-difluorophenyl)-1,3-bis (1H-1,2,4-triazol-1-yl)-propan-2-ol] were studied. T...

This chapter describes general features associated with the packing of molecular materials based on geometric considerations. The link between these packing motifs and intermolecular interactions will be established through lattice energy calculations. Any approach to use theoretical methods to help determine or indeed predict crystal structures must take into account the balance between intra- and intermolecular interaction energies present in crystal lattice. Theoretical studies of molecular and calculated solid state structures or experimental structures determined by X-ray or neutron diffraction can generate a vast amount of data which has to be visualized and analyzed often in three dimensions. The use of a combination of high-resolution X-ray diffraction and theoretical methods can provide structural information on molecular materials comparable with traditional single-crystal methods. Molecular mechanics provides a route to accurate and reliable molecular structure information on larger systems. Molecular orbital methods can be employed across a wider range of molecular classes with a greater confidence than is possible by molecular mechanics.

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.

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.

The selection of the solid form for development is a key milestone in the conversion of a new chemical entity into a drug product. An understanding of the materials science of a new active pharmaceutical is crucial at the interface of medicinal chemistry and pharmaceutical development. The physical and chemical properties of a new chemical entity that impact product performance and product robustness are strongly influenced by the solid state structure of the active pharmaceutical ingredient. Product performance can only be assured when the new chemical entity is delivered to the patient in a chemically and physically stable solid form. In this chapter we will attempt to integrate progress with cutting edge computational tools in academia to the best current industrial practices so that the medicinal chemist and pharmaceutical scientist can transform the journey from molecule to medicine.

This book highlights the current state-of-the-art regarding the application of applied crystallographic methodologies for understanding, predicting and controlling the transformation from the molecula

An understanding of the materials science of a new active pharmaceutical ingredient (API) is crucial at the interface of the chemical synthesis and drug product development. The selection of the crystallisation process and particle attributes during development is a key milestone in the conversion of a new API into a drug product. The physical and chemical properties of an API can impact product performance and process robustness and are strongly influenced by the solid state structure of the API. Product performance can only be assured when the API is delivered to the patient in a chemically and physically stable solid form. In this chapter we will attempt to integrate progress with cutting edge computational tools in academia to the best current industrial practices.

The Needle-like crystal morphologies can cause problems during downstream unit processes assoicated with pharmaceutical and fine chemical product manufacture, due to their fragility under compaction, difficulties in filtering and tendency to block pipes. Tailoring a crystal morphology from solution requires an understanding of the molecular scale surface chemistry and the crystal growth kinetic mechanisms, forming the basis of the design of a solution environment. α-para amino benzoic acid (pABA) crystallises as a needle-like morphology from most organic solvents, such as ethanol, water and acetonitrile1. Here the bulk solid-state intermolecular interactions (intrinsic synthons) are discussed in terms of the α-pABA stability, and the surface terminated intermolecular interactions of the major (101), (10-1) and (01-1) surfaces2, in terms of their interaction with the surrounding solution and crystal morphology. The experimentally elucidated crystal growth kinetic mechanisms of the side (10-1) and capping (01-1) surfaces, in ethanol, are linked to the extrinsic synthons and crystal morphology observed3. The surface entropic α-factors are calculated to estimate the interfacial roughening of the individual faces in ethanol, and how this can affect the crystal growth kinetics. A model for the temporal evolution of a equilibrium crystal morphology of α-pABA at different supersaturation is presented, in terms of guiding the residence time for industrial batch crystallisation. Finally, the effect of addition of nitromethane to the ethanol solutions on the crystal morphology of α-pABA is presented. This demonstrates that through a careful choice of solvent, based on a molecular and kinetic understanding of the crystal surface can successfully modify a crystal morphology from solution.

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 selection of the solid form for development is a milestone in the conversion of a new chemical entity into a drug product. An understanding of the materials science of a new active pharmaceutical is crucial at the interface of medicinal chemistry and pharmaceutical development. The physical and chemical properties of a new chemical entity that impact product performance and product robustness are strongly influenced by the solid state structure of the active pharmaceutical ingredient. The formation of different solid state structures (salt, co-crystal and polymorph) provides the medicinal chemist and pharmaceutical scientist an opportunity to eliminate undesirable properties thus enabling a rapid and successful development program. Product performance can only be assured when the new chemical entity is delivered to the patient in a chemically and physically stable solid form. In this chapter we will attempt to link new cutting edge academic progress to the best current industrial practices that medicinal chemists and pharmaceutical scientists can apply in selecting the optimal solid form, along with the related pharmaceutical properties that enable the rapid advancement of a new molecules to medicines.

ObjectivesTo 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.MethodsThe 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 findingsThe 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.ConclusionsThe 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.

Solid form selection is one of the key milestones in the development of any new small molecule drug candidate. It is critical not only from a regulatory perspective but also from a drug substance manufacturing and drug product-processing viewpoint. High throughput systems provide a significant increase in the number and diversity of experiments performed on a given compound, and are becoming a central feature of drug development, especially in areas concerned with solid form selection. This paper reports our automation of the polymorph screening procedure in combination with automated isolation of samples. Particular focus is placed on the analysis by high throughput PXRD with associated strategies for analysis, clustering, pattern classification, and visualization