scholarly journals Interpreting computed crystal energy landscapes for pharmaceutical molecules

2014 ◽  
Vol 70 (a1) ◽  
pp. C1615-C1615
Author(s):  
Sarah Price
Keyword(s):  
Crystal Structure ◽  
Crystal Structures ◽  
Structure Prediction ◽  
Energy Landscape ◽  
Crystal Structure Prediction ◽  
Energy Landscapes ◽  
Pyridinecarboxylic Acid ◽  
Solid Form ◽  
Pharmaceutical Molecules ◽  
Polymorph Screening

Crystal Structure Prediction (CSP) algorithms aim to generate the thermodynamically feasible crystal structures of a molecule from the chemical diagram, ranking their relative stability by a necessarily approximate estimate of the crystal energy. Such calculations are becoming feasible for molecules of a size and flexibility of small molecule pharmaceuticals. Contrasting the crystal energy landscape, the computer generated structures that are thermodynamically plausible as polymorphs, with the results of experimental polymorph screening, shows that CSP studies are not limited to being a search for the most thermodynamically stable crystal structure but can play a valuable role in understanding polymorphism and the potential complexity of crystallisation behaviour.[1] This presentation will illustrate the use of CSP as a complement to industrial-type solid form screening activities. Examples will include olanzapine, [2] tazofelone, two closely related 5-HT2a agonists and 6-[(5-chloro-2-([(4-chloro-2-fluorophenyl)methyl]oxy)phenyl)methyl]-2-pyridinecarboxylic acid (GSK269984B).[3] This illustrates the use of the crystal energy landscape to understand disorder, help structurally characterise metastable polymorphs and suggest whether there are additional polymorphs to be targeted. Since crystal energy landscapes usually include a wider range of crystal structures than known polymorphs, it raises the scientific question as to what determines which structures can be observed as metastable polymorphs. Thus both scientific as well as technological challenges need to be overcome before we can predict polymorphs.

scholarly journals Can computed crystal energy landscapes help understand pharmaceutical solids?

2016 ◽  
Vol 52 (44) ◽  
pp. 7065-7077 ◽  
Author(s):  
Sarah L. Price ◽  
Doris E. Braun ◽  
Susan M. Reutzel-Edens
Keyword(s):  
Crystal Structure ◽  
Case Studies ◽  
Structure Prediction ◽  
Crystal Structure Prediction ◽  
Energy Landscapes ◽  
Pharmaceutical Solids ◽  
Solid Form

Case studies illustrate how crystal structure prediction calculations can complement industrial solid form screening.

scholarly journals Crystal structure prediction of flexible pharmaceutical-like molecules: density functional tight-binding as an intermediate optimisation method and for free energy estimation

2018 ◽  
Vol 211 ◽  
pp. 275-296 ◽  
Author(s):  
Luca Iuzzolino ◽  
Patrick McCabe ◽  
Sarah L. Price ◽  
Jan Gerit Brandenburg
Keyword(s):  
Crystal Structure ◽  
Free Energy ◽  
Crystal Structures ◽  
Structure Prediction ◽  
Density Functional ◽  
Tight Binding ◽  
Crystal Structure Prediction ◽  
Energy Estimation ◽  
Molecular Conformations ◽  
Pharmaceutical Molecules

Periodic DFTB3-D3 calculations allow the refinement of molecular conformations within crystal structures and estimates of phonons for flexible pharmaceutical molecules.

scholarly journals Data-driven learning and prediction of inorganic crystal structures

2018 ◽  
Vol 211 ◽  
pp. 45-59 ◽  
Author(s):  
Volker L. Deringer ◽  
Davide M. Proserpio ◽  
Gábor Csányi ◽  
Chris J. Pickard
Keyword(s):  
Crystal Structure ◽  
Machine Learning ◽  
Crystal Structures ◽  
Structure Prediction ◽  
Data Driven ◽  
Interatomic Potentials ◽  
Crystal Structure Prediction ◽  
Energy Landscapes ◽  
Inorganic Crystal

Machine learning-based interatomic potentials, fitting energy landscapes “on the fly”, are emerging and promising tools for crystal structure prediction.

scholarly journals Minimizing Polymorphic Risk Through Cooperative Computational and Experimental Exploration

2020 ◽  
Author(s):  
Christopher R. Taylor ◽  
Matthew T. Mulvee ◽  
Domonkos S. Perenyi ◽  
Michael R. Probert ◽  
Graeme Day ◽  
...  
Keyword(s):  
Crystal Structure ◽  
High Pressure ◽  
Structure Prediction ◽  
Experimental Approach ◽  
Significant Risk ◽  
Free Energy Calculations ◽  
Crystal Structure Prediction ◽  
Crystal Forms ◽  
Wide Range ◽  
Solid Form

<div> <p>We combine state-of-the-art computational crystal structure prediction (CSP) techniques with a wide range of experimental crystallization methods to understand and explore crystal structure in pharmaceuticals and minimize the risk of unanticipated late-appearing polymorphs. Initially, we demonstrate the power of CSP to rationalize the difficulty in obtaining polymorphs of the well-known pharmaceutical isoniazid and show that CSP provides the structure of the recently discovered, but unsolved, Form III of this drug despite there being only a single known form for almost 70 years. More dramatically, our blind CSP study predicts a significant risk of polymorphism for the related iproniazid. Employing a wide variety of experimental techniques, including high-pressure experiments, we experimentally obtained the first three known non-solvated crystal forms of iproniazid, all of which were successfully predicted in the CSP procedure. We demonstrate the power of CSP methods and free energy calculations to rationalize the observed elusiveness of the third form of iproniazid, the success of high-pressure experiments in obtaining it, and the ability of our synergistic computational-experimental approach to “de-risk” solid form landscapes.</p> </div>

scholarly journals Crystal structures of two furazidin polymorphs revealed by a joint effort of crystal structure prediction and NMR crystallography

2020 ◽  
Vol 76 (3) ◽  
pp. 322-335 ◽  
Author(s):  
Marta K. Dudek ◽  
Piotr Paluch ◽  
Edyta Pindelska
Keyword(s):  
Crystal Structure ◽  
Crystal Structures ◽  
Structure Determination ◽  
Structure Prediction ◽  
Structural Features ◽  
Crystal Structure Determination ◽  
Crystal Structure Prediction ◽  
Crystal Forms ◽  
Space Groups ◽  
Nmr Crystallography

This work presents the crystal structure determination of two elusive polymorphs of furazidin, an antibacterial agent, employing a combination of crystal structure prediction (CSP) calculations and an NMR crystallography approach. Two previously uncharacterized neat crystal forms, one of which has two symmetry-independent molecules (form I), whereas the other one is a Z′ = 1 polymorph (form II), crystallize in P21/c and P 1 space groups, respectively, and both are built by different conformers, displaying different intermolecular interactions. It is demonstrated that the usage of either CSP or NMR crystallography alone is insufficient to successfully elucidate the above-mentioned crystal structures, especially in the case of the Z′ = 2 polymorph. In addition, cases of serendipitous agreement in terms of 1H or 13C NMR data obtained for the CSP-generated crystal structures different from the ones observed in the laboratory (false-positive matches) are analyzed and described. While for the majority of analyzed crystal structures the obtained agreement with the NMR experiment is indicative of some structural features in common with the experimental structure, the mentioned serendipity observed in exceptional cases points to the necessity of caution when using an NMR crystallography approach in crystal structure determination.

scholarly journals Insight from energy surfaces: structure prediction by lattice energy exploration

2014 ◽  
Vol 70 (a1) ◽  
pp. C28-C28
Author(s):  
Graeme Day
Keyword(s):  
Crystal Structure ◽  
Structure Prediction ◽  
Crystal Packing ◽  
Lattice Energy ◽  
Organic Crystals ◽  
Crystal Structure Prediction ◽  
Stability Order ◽  
The Stability ◽  
Solid Form ◽  
Energy Surfaces

A long-standing challenge for the application of computational chemistry in the field of crystallography is the prediction of crystal packing, given no more than the chemical bonding of the molecules being crystallised. Recent years have seen significant progress towards reliable crystal structure prediction methods, even for traditionally challenging systems involving flexible molecules and multi-component solids [1]. These methods are based on global searches of the lattice energy surface: a search is performed to locate all possible packing arrangements, and these structures are ranked by their calculated energy [2]. One aim of this lecture is to provide an overview of advances in methods for crystal structure prediction, focussing on molecular organic crystals, and highlighting strategies that are being explored to extend the reach of these methods to more complex systems. A second aim is to discuss the range applications of crystal structure prediction calculations, which have traditionally included solid form screening, particularly of pharmaceutically active molecules, and structure determination. As energy models become more reliable at correctly ranking the stability order of putative structures, and the timescale required for structure searching decreases, crystal structure prediction has the potential for the discovery of novel molecular materials with targeted properties. Prospects for computer-guided discovery of materials will be discussed.

scholarly journals First-year experience with fully automated crystal structure prediction

2014 ◽  
Vol 70 (a1) ◽  
pp. C1618-C1618
Author(s):  
Marcus Neumann ◽  
Bernd Doser
Keyword(s):  
Crystal Structure ◽  
Structure Prediction ◽  
Density Functional ◽  
Method Development ◽  
Force Fields ◽  
Crystal Structure Prediction ◽  
Blind Test ◽  
Structure Generation ◽  
Statistical Correlations ◽  
Pharmaceutical Molecules

With improving hardware and software performance, usability has become one of the main obstacles to a more widespread use of Crystal Structure Prediction (CSP) with the GRACE program. In terms of method development, important milestones had already been passed by the time of the 5th blind test [1] in 2010, including the parameterization of dispersion-corrected Density Functional Theory (DFT-D) [2], the generation of tailor-made force fields from ab-initio reference data [3], a Monte-Carlo parallel tempering crystal structure generation engine and a DFT-d reranking procedure exploiting statistical correlations. These components have now been incorporated in automated data flow processes that remove the burden of scores of expert decisions from the user. Summarizing the results of CSP studies performed with the new Force Field Factory and CSP Factory modules throughout a year, the current performance of CSP is critically assessed and further method development needs are pinpointed. Studied compounds include 20 small molecules with competing hydrogen bonds motifs, 4 mono-hydrates of non-ionic molecules and the hydrates and chloride salts of several amino acids. The ability to handle flexible pharmaceutical molecules is demonstrated by a validation study on aripiprazole with one and two molecules per asymmetric unit. Salient features of the energy landscapes of other pharmaceutical molecules are discussed. Statistics are presented for the accuracy of tailor-made force fields, and the energy ranking performance of several DFT-d flavors is compared.

scholarly journals Powder diffraction and crystal structure prediction identify four new coumarin polymorphs

2017 ◽  
Vol 8 (7) ◽  
pp. 4926-4940 ◽  
Author(s):  
Alexander G. Shtukenberg ◽  
Qiang Zhu ◽  
Damien J. Carter ◽  
Leslie Vogt ◽  
Johannes Hoja ◽  
...  
Keyword(s):  
Crystal Structure ◽  
Crystal Structures ◽  
Powder Diffraction ◽  
Structure Prediction ◽  
Prediction Method ◽  
Crystal Structure Prediction ◽  
Free Energies ◽  
Structure Prediction Method

Crystal structures of four new coumarin polymorphs were solved by crystal structure prediction method and their lattice and free energies were calculated by advanced techniques.

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