Using Big and Small Data to Bridge the Gap in Pharmaceutical Design and Clinical Translation

In modern drug discovery and pharmaceutical development there is a growing emphasis on the use of complex data to drive innovation in pharmaceutical design, delivery, and translation for variable patient populations. With new modalities emerging each year, and a focus on rapid and efficient translation from bench-scale to bedside, we look to the use of both Big Data and Small Data to drive both our mechanistic understanding and decision making. Big Data is defined by large data sets and includes tools such as A/I and Machine Learning; Big Data encompasses large and complex data sets such as population pharmacokinetics and the results of in silico simulations that can be leveraged to guide us in the design and development of pharmaceutical products to address unmet need globally. The rise of Big Data in pharmaceutical development does not, however, overshadow the need to continue to leverage Small Data – especially in the era of personalized and precision medicine. Topics for this conference sit at the intersection of in silico computations, in vitro experimentation, and in vivo performance driving discussion around leveraging Big and Small Data together to advance new ideas in form and formulation for drug discovery.