Case Study

Predicting Excipient Properties: How Sanofi Accelerated Drug Formulation Design

Best Practices In Formulation And Lyophilization Development

CHALLENGE: STREAMLINING AUTOXIDATION RISK ASSESSMENTS IN DRUG FORMULATIONS

Developing the proper formulation of active ingredients (APIs) and excipients is almost as important in designing a new therapeutic as the APIs themselves. The wrong mix could result in the drug not being absorbed by the body, being absorbed at the wrong rate or being absorbed by the wrong tissue. Sanofi’s challenge was removing the formulations bottleneck in its pharmaceutical development workflow by improving its autoxidation risk assessments of potential formulations. Autoxidation refers to the likelihood an API will react with ambient oxygen or trace impurities in the excipients, resulting in shorter shelf life, incorrect dosage, and reduced efficacy. If a potential API is susceptible to autoxidation, it has to undergo more testing to determine if this can be mitigated. Testing for autoxidation is often a laborious, manual process, as every potential formulation must be manually tested before it can pass to the next phase of development, clinical trials, and manufacturing scale up. Beyond creating a bottleneck for the pharmaceutical development team, the extensive physical testing required drives up unnecessary spend on materials and equipment. As a result, the pharmaceutical development team at Sanofi actively sought solutions to remove this bottleneck and create a leaner, more sustainable process.

THE SOLUTION: IN SILICO API SIMULATION IN BIOVIA MATERIALS STUDIO

After exploring different materials modeling and simulation tools, Sanofi selected BIOVIA Materials Studio for its comprehensive collection of capabilities and overall user-friendliness. Through BIOVIA Pipeline Pilot, BIOVIA Materials Studio could be accessed as a web service through Sanofi’s internal science portal, with both custom and ready-made protocols available to speed up predictive analyses and identify top candidates. This removed the need for “trial-and-error” driven experiments and maximized the value of scientists’ time.

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