Pharmaceutical Digital Design: From Chemical Structure through Crystal Polymorph to Conceptual Crystallization Process
A workflow for the digital design of crystallization processes starting from the chemical structure of the active pharmaceutical ingredient (API) is a multi-step, multi-disciplinary process. A simple version would be to first predict the API crystal structure and from it the corresponding properties of solubility, morphology, and growth rates, assume that the nucleation would be controlled by seeding, and then use these parameters to design the crystallization process. This is usually an over-simplification as most APIs are polymorphic, and the most stable crystal of the API alone may not have the required properties for development into a drug product. This perspective, from the experience of a Lilly Digital Design project, considers the fundamental theoretical basis of crystal structure prediction (CSP), free energy, solubility, morphology and growth rate prediction, and the current state of nucleation simulation. This is illustrated by applying the modeling techniques to real examples, olanzapine and succinic acid. We demonstrate the promise of using ab initio computer modeling for solid form selection and process design in pharmaceutical development. We also identify open problems in the application of current computational modeling and achieving the accuracy required for immediate implementation that are currently limiting the applicability of the approach..
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Preprint| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
chemRxiv.org - (2024) vom: 23. Jan. Zur Gesamtaufnahme - year:2024 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Burcham, Christopher L. [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| doi: |
10.26434/chemrxiv-2024-lctpz |
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| 520 | |a A workflow for the digital design of crystallization processes starting from the chemical structure of the active pharmaceutical ingredient (API) is a multi-step, multi-disciplinary process. A simple version would be to first predict the API crystal structure and from it the corresponding properties of solubility, morphology, and growth rates, assume that the nucleation would be controlled by seeding, and then use these parameters to design the crystallization process. This is usually an over-simplification as most APIs are polymorphic, and the most stable crystal of the API alone may not have the required properties for development into a drug product. This perspective, from the experience of a Lilly Digital Design project, considers the fundamental theoretical basis of crystal structure prediction (CSP), free energy, solubility, morphology and growth rate prediction, and the current state of nucleation simulation. This is illustrated by applying the modeling techniques to real examples, olanzapine and succinic acid. We demonstrate the promise of using ab initio computer modeling for solid form selection and process design in pharmaceutical development. We also identify open problems in the application of current computational modeling and achieving the accuracy required for immediate implementation that are currently limiting the applicability of the approach. | ||
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