Efficient Screening of Coformers for Active Pharmaceutical Ingredient Cocrystallization

Area of Science:

• Solid-state chemistry
• Pharmaceutical development
• Computational materials science

Background:

• Controlling active pharmaceutical ingredient (API) solid-state properties via cocrystallization is crucial for drug product development.
• Discovering suitable coformers for APIs is challenging, often requiring extensive, material-limited experimental screening.
• Current cocrystal discovery methods are time-consuming and costly, especially in early-stage pharmaceutical research.

Purpose of the Study:

• To develop a systematic, high-throughput computational approach for predicting API/coformer cocrystal formation.
• To identify API/coformer pairs unlikely to form cocrystals, enabling their early elimination from experimental investigation.
• To reduce the experimental effort and cost associated with cocrystal screening.

Main Methods:

• A crystal structure prediction (CSP) methodology was adapted for high-throughput screening of API/coformer pairs.
• The approach leverages existing CSP calculations for neat APIs, avoiding additional quantum mechanical computations.
• Computational screening was performed on 30 potential 1:1 multicomponent systems involving three APIs and nine coformers/solvents.

Main Results:

• A computational investigation was complemented by experimental validation of all 30 systems.
• Five new cocrystals were discovered, including novel API-coformer combinations, a polymorphic cocrystal, and a system with different stoichiometry.
• A new polymorph of cis-aconitic acid was also identified experimentally.

Conclusions:

• The proposed computational approach effectively predicts API/coformer pairs that are unlikely to form cocrystals.
• This method can significantly reduce experimental screening efforts by prioritizing promising candidates.
• The strategy offers a more efficient pathway for cocrystal discovery in pharmaceutical development.