Publications by authors named "I J Sugden"

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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