The computational prediction of pharmaceutical crystal structures and polymorphism

Area of Science:

• Solid-state chemistry
• Materials science
• Computational chemistry

Background:

• Polymorphism, the ability of a solid material to exist in multiple crystal forms, is crucial in pharmaceutical development.
• Experimental polymorph screening is resource-intensive and may not identify all possible crystal structures.
• Predictive computational methods offer a potential complement to experimental screening.

Purpose of the Study:

• To critically review current computational methodologies for predicting organic molecule polymorphs.
• To identify limitations and areas for improvement in existing predictive models.
• To assess the current utility and future potential of computational approaches in solid-state science.

Main Methods:

• Review of existing computational methods for crystal structure prediction.
• Analysis of thermodynamic approaches based on lattice energy minimization.
• Discussion of the need to incorporate kinetic effects and refine thermodynamic models.

Main Results:

• Current computational methods, primarily based on thermodynamic stability, often overestimate the propensity for polymorphism.
• Refinement of thermodynamic models and inclusion of kinetic factors are necessary for improved accuracy.
• Computational predictions have demonstrated utility in aiding the characterization of polymorphs using powder X-ray diffraction data.

Conclusions:

• Computational prediction of organic molecule polymorphs is an emerging field with significant potential.
• Further development is required to overcome limitations in current thermodynamic and kinetic modeling.
• These computational tools can provide valuable insights into molecular packing and aid in polymorph characterization, supporting pharmaceutical solid-state science.