Track 38: Big Data and Machine Learning in Drug Development

Big Data and Machine Learning in Drug Development

Big Data and Machine Learning (ML) are revolutionizing the way drugs are discovered, developed, and brought to market. These technologies allow for faster, more efficient, and more personalized approaches to drug development, as well as improved predictions of drug efficacy and safety. By analyzing vast amounts of data from various sources—ranging from clinical trials, genomics, and electronic health records to real-time patient monitoring and preclinical research—Big Data and ML are transforming the pharmaceutical landscape.

Here’s an overview of how Big Data and Machine Learning are being used in drug development:

Big Data in Drug Development

Big Data refers to large, complex datasets that are too vast and diverse to be handled by traditional data-processing methods. In the context of drug development, Big Data can include a range of information such as:

• Genomic Data: Information from sequencing technologies, like whole genome sequencing and RNA sequencing, which provide insights into genetic mutations, gene expression, and molecular pathways involved in diseases.
• Clinical Trial Data: Data from both past and ongoing clinical trials, including patient outcomes, biomarkers, adverse events, and treatment responses.
Electronic Health Records (EHR): Patient data collected from hospitals, doctors' offices, and pharmacies, which can include health history, diagnosis, prescriptions, lab results, and demographic details.