Real-World Solutions to your Drug Development Problems
The amount of data captured by pharma companies today is fast outpacing best use for it. The ever-evolving scope of the field also means that many senior-level professionals do not fully understand the importance of getting data right in their business, or missing a potential opportunity that their rivals seize.
Stemming from Front Line Genomics’ successful Data-Driven Drug Development event in Boston last March, our new report on data in pharma “Data Solutions for Drug Development: Case Studies from the Front Line” is designed to provide real, applicable case studies and knowledge to the companies and individuals best placed to use them effectively. With the report set to release later this week, we thought it was a great time to showcase how useful this information will be.
Using both in-depth contributor analysis and high-level case studies from businesses leading in this field, the report’s intent is to demonstrate the importance of data today and provide solutions to problems many pharma companies, large and small, are facing. Some of the main areas discussed include:
• Making the business case, managing next steps and improving implementation, with contributions from Peter Henstock, Senior Data Scientist, Pfizer
• How to generate better ROI with AI/ML, with contributions from Bino John, Associate Director, AstraZeneca, Pankaj Agarwal, Senior Fellow, Computational Biology, Functional Genomics, GSK, and Kristin Haraldsdottir, Business Development Director, Lantern Pharma
• Where do we begin integrating multi-omic data, with contributions from Michelle Penny, Director, Computational Biology and Genomics, Biogen
• Using data to reduce costs and increase success rates in clinical trials, with contributions from Charles Paulding, Director, Predictive Medicine Genetics & Translational Medicine, Regeneron
From this report, readers will receive:
• A better understanding of some of the major roadblocks to better data usage, and subsequently a greater idea of where to focus improvement effort
• Knowledge of how to build a business case for making improvements, and how to relay that case
• Vital ideas for solving some of the leading data problems inherent in drug discovery and development today, including around AI, multi-omics and recruitment
• A greater understanding of how other pharma companies are handling these data issues
• Notes from leading experts on solutions within the data field, and real-world examples of where improvements have been made
From making the business case for a better data lifecycle to ensuring FAIRness of data to generating better ROI with AI and machine learning, our report proves highly useful in a real, applicable sense, with real insights to which readers have had no previous access. Out now, our report is something no data experts in pharma should miss.