Speaking at a WIRED event Tuesday, Moderna CEO Stéphane Bancel said he was "encouraged" by the company's dialogue with the FDA--but acknowledged recent setbacks. Moderna CEO Stéphane Bancel prepares to testify before the Senate on March 22, 2023 in Washington, DC. At the WIRED Health summit on Tuesday, Moderna CEO Stéphane Bancel said the recent changes to Covid-19 vaccine policy made by Health and Human Services secretary Robert F. Kennedy, Jr. are a "step backward." Moderna is one of the manufacturers of mRNA-based Covid-19 vaccines, and last month the company received approval from the Food and Drug Administration for an updated version of the shot . But as part of that approval, the FDA imposed new restrictions on who can receive the vaccine.

However, the technology seems to be lagging when it comes to other areas, including translation into drug discovery. Despite a huge amount of media attention for its potential to accelerate this field, AI is yet to be proven as an effective solution. What needs to change for AI to advance drug discovery? AI could make the strongest impact on drug discovery by reducing the number of drugs failing in clinical trials. Currently AI is largely focused on method development using preclinical data – data from research that takes place before human clinical trials – rather than focusing on applying and generating the clinical data we need to make a real impact on drug discovery.

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.

Machine learning is starting to show its potential in multiple fields. According to Gareth Baxendale, head of technology for the NIHR Clinical Research Network, clinical trials are no exception. Could machine learning be used to enhance or improve the success of a clinical trial? There is much excitement around machine learning and the opportunities it can provide, and it's already beginning to permeate every area of our digital life. In fact, you're likely to make use of machine learning almost every day without even knowing it.