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LIFE SCIENCES

Data Science Fast-Tracks Development Time for Potentially Life-Saving Cancer Treatments


The Challenge

The stakes are high in oncology drug development: The process is costly; the competition is fierce; and the mission—saving lives—is critical. A major pharmaceuticals company wanted to improve its highly manual process for conducting clinical trials for its cancer drugs. The company wanted to reduce the time it takes to conduct clinical trials for cancer drugs while making the drug-development process more effective and safer for patients. They partnered with Cognizant to carry out this ongoing initiative because of our skills in data science and artificial intelligence (AI) as well as our deep experience in life sciences and the pharmaceutical industry.


Our Approach

Our overall goal was to use AI to enhance decision-making in the clinical trial phases of oncology drug development. AI improves the process of selecting candidates for specific drugs by collecting evidence of drug effectiveness based on chemical structure and how the targeted body tissue responds.

We are working closely with the company’s Pharmaceutical Development & Commercialization organization to build an automated process for data analysis in clinical trials. The power of AI helps us predict adverse drug reactions, not only making the process safer and faster but also helping to streamline the regulatory approval process.

 

AI and Data Science Improve Clinical Trial Processes

The project is part of an ongoing research and development initiative, with each phase producing assets that can be reused as case studies for future research problems. This knowledge provides recommendations for improving the process of capturing data in other trials.

Using AI and data science helps shorten clinical trial times by three to four years and cut per-patient costs while improving safety and producing reusable assets and technical knowledge that can be utilized in future initiatives.

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3–4 years

reduction in clinical oncology trials

8% to 10%

cost savings per patient

AI-enabled

deployment of next generation candidate drug evaluation methods


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