Computational and Experimental Screening Approaches to Aripiprazole Salt Crystallization.

Area of Science:

• Pharmaceutical Science
• Materials Science
• Computational Chemistry

Background:

• Multicomponent crystal system (MCC) screening is vital for enhancing active pharmaceutical ingredient (API) properties.
• Experimental salt screening faces challenges like numerous counterions, solvents, and disproportionation risks.
• Combining experimental and computational methods can overcome these screening challenges.

Purpose of the Study:

• To evaluate existing counterion screening methods.
• To propose and validate new virtual solvent screening approaches for MCC crystallization.
• To improve the efficiency and success rate of API salt selection and crystallization.

Main Methods:

• Validated the ΔpK_a > 3 rule for counterion selection using screening reports.
• Developed novel computational models for virtual solvent screening to prevent incongruent MCC crystallization.
• Conducted experimental aripiprazole (APZ) salt screening with 10 counterions and 10 solvents to validate models.

Main Results:

• Identified eight MCCs for aripiprazole (APZ), including glucuronate, mesylate, oxalate, tartrate, salicylate, and mandelate.
• The novel virtual solvent screening model showed good agreement with experimental findings for optimal solvent selection.
• Successfully validated computational models for predicting successful MCC crystallization.

Conclusions:

• A rational strategy for MCC crystallization using combined computational and experimental methods was demonstrated.
• The validated virtual solvent screening model is applicable to new active pharmaceutical ingredients (APIs).
• This approach enhances the selection of counterions and solvents for improved API physicochemical properties.