Publications by authors named "Sugden I"

Crystal structure determination is a crucial aspect of almost every branch of the chemical sciences, bringing us closer to understanding crystallization, polymorphism, phase transitions, and the relationship between a structure and its physicochemical and functional properties. Unfortunately, many molecules notoriously crystallize as microcrystalline powders, providing a significant challenge in establishing their structures. In this work, we describe the crystal structure determination of three elusive polymorphs of the anti-inflammatory drug meloxicam (MLX) using three approaches, of which only one was successful for each crystal phase.

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Crystal structure prediction (CSP) seeks to identify all thermodynamically accessible solid forms of a given compound and, crucially, to establish the relative thermodynamic stability between different polymorphs. The conventional hierarchical CSP workflow suggests that no single energy model can fulfill the needs of all stages in the workflow, and energy models across a spectrum of fidelities and computational costs are required. Hybrid /empirical force-field (HAIEFF) models have demonstrated a good balance of these two factors, but the force-field component presents a major bottleneck for model accuracy.

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Article Synopsis
  • Participants from 22 research groups utilized various methods, including periodic DFT-D methods, machine learning models, and empirical force fields to assess crystal structures generated from standardized sets.
  • The findings indicate that DFT-D methods generally aligned well with experimental results, while one machine learning approach showed significant promise; however, the need for more efficient research methods was emphasized due to resource consumption.
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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.

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Article Synopsis
  • Cocrystallization helps control the physical properties of active pharmaceutical ingredients (APIs) during drug development, but identifying suitable coformers is challenging and resource-intensive.
  • A new high-throughput computational approach is proposed to quickly identify which API/coformer pairs won't likely form cocrystals, reducing unnecessary experimental work.
  • Testing the approach on 30 API/coformer combinations led to the discovery of five new cocrystals and demonstrated potential for significant efficiency gains in the early stages of pharmaceutical research.
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Article Synopsis
  • * Current methodologies for crystal structure prediction (CSP) are becoming viable for practical applications, and this article reviews their development and categorizes them based on similarities and differences.
  • * The article also highlights ongoing research areas aimed at enhancing the accuracy and broadened use of CSP techniques, while offering insights into future advancements in the field over the next ten years.
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The discovery of molecular ionic cocrystals (ICCs) of active pharmaceutical ingredients (APIs) widens the opportunities for optimizing the physicochemical properties of APIs whilst facilitating the delivery of multiple therapeutic agents. However, ICCs are often observed serendipitously in crystallization screens and the factors dictating their crystallization are poorly understood. We demonstrate here that mechanochemical ball milling is a versatile technique for the reproducible synthesis of ternary molecular ICCs in less than 30 min of grinding with or without solvent.

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Article Synopsis
  • The study introduces a new model for crystal structure prediction (CSP) that addresses issues with molecular flexibility in larger compounds, improving upon previous models by eliminating discontinuities.
  • The revised model, implemented in the CrystalPredictor code, demonstrates significant reductions in computational effort (up to 65%) and enhances reliability when analyzing various compounds.
  • Additionally, the approach successfully identifies all three known polymorphs of flufenamic acid for the first time, indicating its effectiveness in computational studies.
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The global search stage of crystal structure prediction (CSP) methods requires a fine balance between accuracy and computational cost, particularly for the study of large flexible molecules. A major improvement in the accuracy and cost of the intramolecular energy function used in the CrystalPredictor II [Habgood et al. (2015).

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The sixth blind test of organic crystal structure prediction (CSP) methods has been held, with five target systems: a small nearly rigid molecule, a polymorphic former drug candidate, a chloride salt hydrate, a co-crystal and a bulky flexible molecule. This blind test has seen substantial growth in the number of participants, with the broad range of prediction methods giving a unique insight into the state of the art in the field. Significant progress has been seen in treating flexible molecules, usage of hierarchical approaches to ranking structures, the application of density-functional approximations, and the establishment of new workflows and `best practices' for performing CSP calculations.

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Article Synopsis
  • In crystal structure prediction (CSP), generating a large array of potential crystal structures is essential, and this is achieved by exploring the lattice energy surface using a basic energy approximation.
  • The paper introduces a new algorithm that accounts for molecular flexibility by utilizing local approximate models (LAMs) created from quantum mechanical calculations, effectively simulating energy and geometry variations.
  • The method's performance is showcased through its application to three flexible molecules, including the challenging 5-methyl-2-[(2-nitrophenyl)amino]-3-thiophenecarbonitrile (ROY) and two other complex compounds, demonstrating its efficiency in this context.
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Ab initio MD and potential energy surface sampling has been used to study the rearrangement processes in carboranes and their derivatives. A new mechanism is found, in addition to those previously proposed. The fact that theoretical activation energies are lower than those observed experimentally, and the differing activity of technetium and rhenium complexes, are rationalised by orbital symmetry constraints.

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