In recent years, access to patient medical information, coupled with rapid advancements in data analytics tools and technologies, has significantly altered many areas of healthcare, from early-stage discovery and research to patient treatment. One of the most significant applications of new technology is in efforts to streamline and advance clinical research. Advanced technologies, including artificial intelligence (AI), offer the promise of addressing many of the most challenging aspects of Drug development, with potential benefits for pharmaceutical companies, investigators, patients, regulators, and payers. While protocols and standards in clinical research have become increasingly complex, slowing progress and increasing costs, companies from startups to Big Pharma are identifying opportunities to apply AI to enhance trial efficiency, patient enrollment, and outcomes targeting. The fact that we are at the dawn of AI technology indicates that its role in clinical research could grow exponentially in the years ahead.
To assess the applications of these technologies in research now and in the future, the Life Sciences Practice team at the global consulting firm CRA recently conducted an analysis of trends in the use of AI in Drug development and identified several areas where manufacturers now consider AI to be essential, including in discovery of new drugs and drug targets, identification of eligible patients for clinical trials, screening and diagnosis of patients, and optimization of drug administration and dosing regimens. The analysis also highlighted the need for drug developers to keep pace with innovations in AI to ensure that advanced technologies are applied as quickly and cost-efficiently as possible and that specialized expertise, resources, and infrastructures are in place to support their use.
On a global scale, the number of investigational drugs in development has increased dramatically in recent years. Many represent potentially historic advances in the treatment of a range of serious diseases, including different types of cancer and infectious and autoimmune diseases. The fact that there are many drugs in clinical development, including some targeting the same or similar indications, also introduces new levels of competition both for patients to participate in clinical research and in planning for commercialization.
In many cases, these development programs follow the long-established approach of selecting and targeting cells with higher proliferation activity associated with disease. More recently, however, manufacturers have focused clinical research on targeting the root cause of disease – the underlying biological pathways that are associated with disease onset and progression – in efforts to deliver optimal and potentially curative benefit. This shift has also introduced the need for more advanced protocols in patient screening and in execution of clinical research.
Within this environment, many drug manufacturers are now aggressively exploring the use of automated algorithms and advanced predictive models to help identify potential molecular targets faster. These models also are being used to project the potential of new drugs to advance through regulatory reviews and ultimately succeed commercially. In some cases, the application of these technologies and approaches requires drug developers to collaborate with other industry stakeholders, including academic researchers and technology suppliers. Groups such as the Machine Learning Ledger Orchestration for Drug Discovery (MELLODDY) and the Machine Learning for Pharmaceutical Discovery and Synthesis (MLPDS) consortia were created from such partnerships in efforts to develop and apply more accurate predictive models and make drug discovery more efficient.
In addition to supporting drug discovery, AI tools also are being used to identify patients for clinical research, with the potential to deliver many significant advantages in drug development.
