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"Leveraging Machine Learning to Accelerate Drug Development"

Bringing a drug to market is a long, arduous undertaking. When CDMOs leverage machine learning to improve both process and analytical drug substance development, pharmaceutical drug sponsors can reduce overall time to market, decrease costs, improve quality and diminish risk.

Bringing a new drug to market takes, on average, 10 to 15 years, according to the Pharmaceutical Research and Manufacturers Association, which significantly delays getting treatments to patients. This lengthy process fuels considerable frustration.

Contract Development and Manufacturing Organizations (CDMOs), however, can apply machine learning (ML) to the drug substance development process, including:

Process development, which includes optimizing the drug substance production processes to ensure consistency, scalability, and cost-effectiveness. ML algorithms are employed to assess complex datasets and analyze trends.

Analytical development, which focuses on developing and validating methods that can accurately and reliably measure the properties of the pharmaceutical compound or product. ML can be leveraged to refine techniques such as gas chromatography.

Through these innovations, machine learning can contribute to improving the accuracy, speed, and reproducibility of pharmaceutical analyses, ultimately supporting better decision-making in drug substance development and quality assurance processes. Machine learning tools can help address core challenges in drug substance development, leading to:

• Reduced costs
• Faster time to market
• Improved product quality
• Less risk of delays in quality control

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