Dissertation
Thermodynamic prediction and analysis of organic crystals in silico for the formulation of pharmaceuticals
University of Iowa
Doctor of Philosophy (PhD), University of Iowa
Summer 2024
DOI: 10.25820/etd.007703
Abstract
The formulation of active pharmaceutical ingredients involves discovering stable crystal packing arrangements or polymorphs, each of which has distinct pharmaceutically relevant properties. Traditional experimental screening techniques utilizing various conditions are commonly supplemented with in silico crystal structure prediction (CSP) to inform the crystallization process and mitigate risk. Predictions are often based on advanced classical force fields or quantum mechanical calculations that model the crystal potential energy landscape but do not fully incorporate temperature, pressure, or solution conditions during the search procedure. Chapter II of this manuscript documents an innovative alchemical path that utilizes an advanced polarizable atomic multipole force field to predict crystal structures based on direct sampling of the isobaric-isothermal (NPT) ensemble. The use of alchemical (i.e., nonphysical) intermediates, a novel Monte Carlo barostat, and an orthogonal space tempering bias combine to enhance the sampling efficiency of the deposition/sublimation phase transition. This algorithm demonstrates a step forward in the rigorous use of the NPT ensemble during the CSP search process and opens the door to future algorithms that incorporate solution conditions using continuum solvation methods.
During in silico crystal structure prediction of organic molecules, millions of candidate structures are often generated. These candidates must be compared to remove duplicates prior to further analysis (e.g., optimization with electronic structure methods) and ultimately compared with structures determined experimentally. The agreement of predicted and experimental structures forms the basis of evaluating the results from the Cambridge Crystallographic Data Centre (CCDC) blind assessment of crystal structure prediction, which further motivates the pursuit of rigorous alignments. Evaluating crystal structure packings using coordinate root-mean-square deviation (RMSD) for N molecules (or N asymmetric units) in a reproducible manner requires metrics to describe the shape of the compared molecular clusters to account for alternative approaches used to prioritize selection of molecules. Described in Chapter III is a flexible algorithm called Progressive Alignment of Crystals (PAC) to evaluate crystal packing similarity using coordinate RMSD and the use of radius of gyration (Rg) as a metric to quantify the shape of the superimposed clusters. It is shown that the absence of metrics to describe cluster shape adds ambiguity to the results of the CCDC blind assessments because it is not possible to determine whether the superposition algorithm has prioritized tightly packed molecular clusters (i.e. to minimize Rg) or prioritized reduced RMSD (i.e. via possibly elongated clusters with relatively larger Rg). For example, it is shown that when the PAC algorithm described here uses single linkage to prioritize molecules for inclusion in the superimposed clusters, the results are nearly identical to those calculated by the widely used program COMPACK. However, the lower Rg values obtained using average linkage are favored for molecule prioritization because the resulting RMSDs more equally reflect the importance of packing along each dimension. It is shown that the PAC algorithm is faster than COMPACK when using a single process and its utility for biomolecular crystals is demonstrated.
The final step of in silico crystal structure prediction typically involves the reranking of predicted polymorphs with a less efficient, but higher accuracy method (e.g., quantum mechanics, free energy differences, etc.). Free energy methods exist to determine the free energy difference between predicted polymorphs; however, these are usually intractable to perform on large datasets or requires the use of less accurate/more efficient models (e.g., fixed charge classical models) to scale up to the desired number of systems and/or system size. Described within Chapter IV is a novel dual topology sampling pathway that connects two polymorph end states via custom symmetry operator(s) to facilitate rapid sampling between polymorphs to produce a free energy difference.
Details
- Title: Subtitle
- Thermodynamic prediction and analysis of organic crystals in silico for the formulation of pharmaceuticals
- Creators
- Aaron Joseph Nessler
- Contributors
- Michael J. Schnieders (Advisor)Lewis L. Stevens (Committee Member)Dale E. Wurster (Committee Member)Terry A. Braun (Committee Member)Edward A. Sander (Committee Member)
- Resource Type
- Dissertation
- Degree Awarded
- Doctor of Philosophy (PhD), University of Iowa
- Degree in
- Biomedical Engineering
- Date degree season
- Summer 2024
- DOI
- 10.25820/etd.007703
- Publisher
- University of Iowa
- Number of pages
- xiv, 107 pages
- Copyright
- Copyright 2024 Aaron Joseph Nessler
- Grant note
- The following funding is acknowledged: National Science Foundation, Directorate for Mathematical and Physical Sciences CHE-1751688 and NIH NIDCD R01DC012049 awarded to Professor Schnieders. Mitsubishi Tanabe Pharma Corp. provided partial support for Aaron Nessler during the preparation of this work. Aaron Nessler was partially supported by a Ballard and Seashore Dissertation Fellowship from the University of Iowa. (iii)
- Language
- English
- Date submitted
- 07/19/2024
- Description illustrations
- Illustrations, tables, graphs, charts
- Description bibliographic
- Includes bibliographical references (pages 95-107).
- Public Abstract (ETD)
My research focuses on the analysis of organic molecules to predict if/how they will orient themselves to form one or more solid crystals. This is particularly useful to guide experimental efforts by drawing attention to favorable crystal packings or polymorphs that have not been discovered experimentally. To this end, I have developed a crystal structure prediction algorithm to predict crystal packings, which begins from a single molecule or protein of interest and incorporates temperature and pressure conditions. Furthermore, I have introduced a new method to quantify the difference between two crystal packing arrangements that is both faster and more interpretable compared to existing methods. Finally, we have developed an efficient method to quantify the relative thermodynamic stability differences between different packings, which opens the door to efficient ranking of putative crystal packings.
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering
- Record Identifier
- 9984698053502771
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