Minimizing Polymorphic Risk through Cooperative Computational and Experimental Exploration.

Area of Science:

• Pharmaceutical Science
• Materials Science
• Computational Chemistry

Background:

• Polymorphism in pharmaceuticals presents challenges in drug development and manufacturing.
• Unanticipated polymorphs can emerge late in development, increasing risks.
• Computational Crystal Structure Prediction (CSP) offers a potential solution for exploring solid form landscapes.

Purpose of the Study:

• To integrate state-of-the-art CSP with experimental methods to explore pharmaceutical crystal structures.
• To rationalize the difficulty in obtaining specific polymorphs and minimize risks of late-appearing forms.
• To investigate the solid form landscape of isoniazid and iproniazid.

Main Methods:

• Utilized advanced computational crystal structure prediction (CSP) techniques.
• Employed a wide range of experimental crystallization methods, including high-pressure experiments.
• Applied free energy calculations to rationalize experimental observations.

Main Results:

• CSP successfully predicted the structure of isoniazid Form III, previously unsolved for decades.
• CSP accurately predicted the risk of polymorphism for iproniazid.
• Experimentally obtained and characterized the first three known nonsolvated crystal forms of iproniazid, all predicted by CSP.

Conclusions:

• The synergistic computational-experimental approach effectively de-risks pharmaceutical solid form landscapes.
• CSP is a powerful tool for predicting and understanding polymorphs, aiding drug development.
• High-pressure experiments were successful in obtaining elusive crystal forms, guided by CSP predictions.