Selecting the optimal solid‑state form of an active pharmaceutical ingredient (API) is essential for ensuring bioavailability, stability, manufacturability, and long‑term IP protection.
Cocrystals can offer significant advantages—such as improved solubility and enhanced physical stability—but identifying the right coformer from thousands of possibilities remains a major challenge. Traditional screening methods are slow, resource‑heavy, and often rely on trial‑and‑error, increasing the risk of discovering critical issues late in development.
This white paper explores how AI‑driven modeling transforms cocrystal screening by predicting high‑potential candidates earlier, reducing unnecessary experiments, and guiding scientists toward the most viable solid forms. By combining computational insights with targeted laboratory evaluation, development teams can accelerate decision‑making, reduce costs, and avoid downstream surprises that impact timelines and manufacturability. These capabilities align with emerging predictive tools already validated across extensive experimental datasets within Lonza’s solid‑form services.
Whether your goal is to improve solubility, strengthen IP, or de‑risk your formulation strategy, AI‑enhanced cocrystal screening offers a clearer, smarter path to selecting the right solid form earlier in the API lifecycle.
Key Objectives:
- Explain why early solid‑form selection is critical for bioavailability, stability, manufacturability, and IP strategy—helping prevent downstream reformulation and delays.
- Highlight limitations of traditional cocrystal screening, including high experimental burden, long timelines, and the risk of missing viable coformers.
- Show how integrating AI‑based prediction with targeted experimentation improves outcomes, reduces trial‑and‑error, and supports confident, data‑driven decisions early in development.
Offered Free by: Lonza
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