Fast-Tracking Cancer Drug Development Using Data Science | Client Case Study
The stakes are high in oncology drug development: The process is costly, the competition is fierce, and the mission — saving live is critical. And the traditional method was worthless and took an extra effort and time for cancer drug development. Now, the time has changed and evolved with high-end technologies to streamline healthcare effortlessly and effectively. Here’ is one of the example of successful project which saw a ray of hope in darkness.
The Challenge
A major pharmaceutical 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 increasing the effectiveness and safety of the drug development process.
The organization chose us- The Expert Community as a partner for this ongoing initiative because of our proven experience in data science and artificial intelligence, as well as our deep expertise in life science and pharmaceutical industry.
The Solution
Our overall goal was to use AI to enhance decision-making in the clinical trials phases of cancer 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 worked closely with the company’s pharmaceutical development & commercialization organization to build an automated process for data analysis in preclinical trials. The power of AI helped us predict adversely drug reactions, which resulted not only in a safer and faster process but also a more streamlined regulatory approval process.
The Result
The data science and AI-powered approach to cancer drug development yielded significant improvements for our pharmaceutical client:
Reduced Preclinical Trial Time: The automated data analysis process, powered by AI, led to a 3-4 year reduction in preclinical trial times. This translates to faster development of potentially life-saving cancer drugs.
Enhanced Safety and Efficiency: AI’s ability to predict adverse drug reactions streamlined the process, leading to safer drug development and a more efficient regulatory approval process.
Cost Reduction: By shortening trial times and optimizing processes, the client achieved significant cost savings per patient.
Reusable Assets and Knowledge Base: Each project phase generated reusable assets like case studies and technical knowledge. This valuable information can be applied to future research and development initiatives, further accelerating drug development and improving success rates.
By partnering with an experienced data science team of The Expert Community, our client’s pharmaceutical company accelerated the development of life-saving cancer treatments, improving safety, and optimizing costs.
Therefore, are you facing challenges in your own cancer drug development pipeline? We can help! Contact us today to discuss how our expertise in data science and AI can simplify your clinical trials, reduce costs, and ultimately, bring life-saving treatments to patients faster.