In recent years, the increase in accessible, large-scale computing power and storage has ushered in a new dawn for artificial intelligence (AI), with the technology appearing to finally catch up with the existing algorithms to bring machine learning (ML) to realisation. In this article, we discuss some of the key applications of ML that have shown success in clinical research. Moreover, we consider how machine learning is impacting on clinical trials, utilising decades of structured clinical trial data alongside real-world data (RWD) and other valuable data sources to support clinical trial design, execution and analysis. Combining computational skills and drug development experience, data science teams can support the pharma and biotech industry to generate business value through the application of machine learning. For ML algorithms to be successful they require large, quality data sets for their application.
Traditional'linear and sequential' clinical trials remain the accepted way to ensure the efficacy and safety of new medicines. However, the lengthy tried and tested process of discrete and fixed phases of randomised controlled trials (RCTs) was designed principally for testing mass-market drugs and has changed little in recent decades (figure 1).1 RCTs lack the analytical power, flexibility and speed required to develop complex new therapies that target smaller and often heterogeneous patient populations. In addition, suboptimal patient selection, recruitment and retention, together with difficulties managing and monitoring patients effectively, are contributing to high trial failure rates and raising the costs of research and development.2 Over the past few years, biopharma companies have been able to access increasing amounts of scientific and research data from a variety of sources, known collectively as real-world data (RWD). However, they have often lacked the skills and technologies to enable them to utilise this data effectively.
Randomized control trials – RCTs – rose to prominence in the twentieth century as physicians and regulators sought to evaluate rigorously the performance of new medical therapies; by century's end, RCTs had become, as medical historian Laura Bothwell has noted, "the gold standard of medical knowledge," occupying the top position of the "methodologic heirarch[y]." The value of RCTs lies in the random, generally blinded, allocation of patients to treatment or control group, an approach that when properly executed minimizes confounders (based on the presumption that any significant confounder would be randomly allocated as well), and enables researchers to discern the efficacy of the intervention (does it work better – or worse – than controls) and begin to evaluate the safety and side-effects. The power and value of RCTs can be seen with particular clarity in the case of proposed interventions that made so much intuitive sense (at the time) that it seemed questionable, perhaps even immoral, to conduct a study. Examples include use of a particular antiarrhythmic after heart attacks (seemed sensible, but actually caused harm); and use of bone marrow transplants for metastatic breast cancer (study viewed by many as unethical yet revealed no benefit to a procedure associated with significant morbidity). In these and many other examples, a well-conducted RCT changed clinical practice by delivering a more robust assessment of an emerging technology than instinct and intuition could provide.
Digital technologies can transform how companies approach clinical development by incorporating valuable insights from multiple sources of data, radically improving the patient experience, enhancing clinical trial productivity, and increasing the amount and quality of data collected in trials. But where is the industry in adopting these transformative technologies? We interviewed 43 leaders across the clinical development ecosystem to understand the current level of adoption of digital technologies and how it can be accelerated. We found that the industry has been slow to digitize its clinical development processes, and that digital adoption varies widely. Even the most advanced organizations are simply piloting several technologies in different areas of clinical development, focusing on piecemeal solutions or new tools to support the existing process. Our research and client experience suggest that digital transformation is a complex, resource-intensive, and lengthy undertaking. But the rewards can be significant: Early adopters can benefit from better access to and engagement with patients, deeper insights, and faster cycle times for products in development. Many in our study expressed a desire to be fast followers, but given the complexity of operationalizing a digital strategy, the reality is that undue delay could put organizations at a competitive disadvantage. At the same time, our research also indicates that biopharma companies and contract research organizations (CROs) will need to overcome several challenges to realize the potential of digital in clinical development: immature data infrastructure and analytics, regulatory considerations, and internal organizational and cultural barriers. Biopharma companies should consider building updated data infrastructure and governance, engaging early with regulators to discuss new technologies, and developing a measured approach to evaluating and implementing technologies within their organizations. CROs can enable this change by advancing interoperable digital platforms and vetting promising technology applications. Cross-industry consortia could help advance the industry as a whole by offering a forum to share early successes and supporting the development of standards. The time to act is now.
Technology that can mine the clinical patient data that already exists is finally available - now is the time to speculate to accumulate. The intersection of technology and health has thrown up almost as many questions as it has solutions. "Innovation through collaboration: Bringing disruption to the patient" was the title of the recent Financial Times Digital Health Summit Europe, which took place in London in June. Speaking at the event was ICON's Chief Information Officer, Tom O'Leary. Since its inception in 1990, ICON has been offering a range of drug development services to the pharmaceutical industry, running clinical trials worldwide across all therapeutic areas, diseases, and indications, on behalf of pharmaceutical, biotechnology, and medical device companies.