We will start off with a problem statement describing how incomplete characterization of the polymorph landscape and polymorph-dependent properties makes efficient drug product formulation and manufacturing process development to produce a quality, stable product a challenge.
Then a brief description of the types of computation, particularly, crystal structure and polymorph prediction augmented by polymorph-dependent property prediction often in combination with and complementary to typical collections of experimental data can enable and de-risk drug product and manufacturing process development.
Distinct case studies, illustrating how drug products would be influenced when: * The structure of a relevant polymorph is unknown, * A new lower energy stable polymorph structure has likely been identified, * The most stable polymorph structure is confirmed and the implications from the relative stabilities of the other polymorphs influence development strategy. Additionally, results across a large number of crystal structure and polymorph predictions in comparison with experimental results will be provided to give a perspective on how reliably the most important polymorphs can be predicted.
We will then describe how physical property prediction, based upon the selected polymorph, typically the most stable one, can aid in manufacturing, including: * Powder X-ray diffraction patterns for comparison with experimental patterns to confirm/refute predicted structures * Young’s modulus and the shear modulus can be used to influence tableting strategies * Hygroscopicity prediction in selected cases * Crystal morphology prediction to influence solvent and excipient selection for optimal powder flowability
The methodology and limitations of the current method will also be described with examples: * Applicability (complexity) and methodology limitations * Accuracy and reliability * Current limitations: - Salts - Cocrystals - More than one drug molecule in the unit cell
Learning Objectives:
Upon completion, participant will be able to understand the limitation of experimental data encountered in drug product and manufacturing process development with respect to API polymorphs and the resulting risks.
Upon completion, participant will be able to identify projects for which computation tools can contribute to de-risking during final API form selection
Upon completion, participant will be able to combine the results from experiments and computations to gain the most insight into manufacturing issues related to a crystal polymorph