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Artificial intelligence can reduce clinical trial cycle times while improving the costs of productivity and outcomes of clinical development. This report is the third in our series on the impact of AI on the biopharma value chain.
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
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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. Unlocking RWD using predictive AI models and analytics tools can accelerate the understanding of diseases, identify suitable patients and key investigators to inform site selection, and support novel clinical study designs.
AI algorithms, combined with an effective digital infrastructure, could enable the continuous stream of clinical trial data to be cleaned, aggregated, coded, stored and managed.3 In addition, improved electronic data capture (EDC) should can also reduce the impact of human error in data collection and facilitate seamless integration with other databases (figure 2).
The adoption of AI technologies is therefore becoming a critical business imperative; specifically in the following six areas.
1. Clinical trial design: Biopharma companies are adopting a range of strategies to innovate trial design. Increasing amounts of scientific and research data, such as current and past clinical trials, patient support programmes and post-market surveillance, have energised trial design. AI-enabled technologies, having unparalleled potential to collect, organise and analyse the increasing body of data generated by clinical trials, including failed ones, can extract meaningful patterns of information to help with design.
2. Patient enrichment, recruitment and enrolment: AI-enabled digital transformation can improve patient selection and increase clinical trial effectiveness, through mining, analysis and interpretation of multiple data sources, including electronic health records (EHRs), medical imaging and ‘omics’ data. The FDA has published guidance that identifies three strategies to assist the biopharma industry to improve patient selection and optimise a drug’s effectiveness, all of which could benefit from AI technologies (figure 3).4
3. Investigator and site selection: One of the most important aspects of a trial is selecting high-functioning investigator sites. Site qualities such as administrative procedures, resource availability, clinicians with in-depth experience and understanding of the disease, can influence both study timelines and data quality and integrity.5 AI technologies can help biopharma companies identify target locations, qualified investigators, and priority candidates, as well as collect and collate evidence to satisfy regulators that the trial process complies with Good Clinical Practice requirements.
4. Patient monitoring, medication adherence and retention: AI algorithms can help monitor and manage patients by automating data capture, digitalising standard clinical assessments and sharing data across systems. AI algorithms, in combination with wearable technology, can enable continuous patient monitoring and real-time insights into the safety and effectiveness of treatment while predicting the risk of dropouts, thereby enhancing engagement and retention.6
5. Using operational data to drive AI-enabled clinical trial analytics: Trials generate immense operational data, but functional data silos and disparate systems can hinder companies from having a comprehensive view of their clinical trials portfolio over multiple global sites. Consolidating all data – whatever the source – on a shared analytics platform, supported by open data standards, can foster collaboration and integration and provide insights across vital metrics. Incorporating a self-learning system, designed to improve predictions and prescriptions over time, together with data visualisation tools can proactively deliver reliable analytics insights to users.7
6. Outsourcing and strategic relationships to obtain necessary AI skills and talent: Biopharma companies are looking to strategic and operational relationships based on outsourcing and partnership models. A number of companies increasingly see Contract Research Organisations (CROs) that have invested in data science skills as strategic partners, providing access not only to specialised expertise, but also to a wide range of potential trial participants.8 Biopharma companies have attracted the attention of the tech giants. For biopharma, tech giants can be either potential partners or competitors; and present both an opportunity and a threat as they disrupt specific areas of the industry.9 At the same time, an increasing number of digital technology startups are now working in the clinical trials space, including partnering or contracting with biopharma. These partnerships combine tech giants and startups core expertise in digital science with biopharma’s knowledge and skills in medical science.10
For the next few years, RCTs are likely to remain the gold standard for validating the efficacy and safety of new compounds in large populations. However, the life sciences and health care industries are on the brink of large-scale disruption driven by interoperable data, open and secure platforms, consumer-driven care and a fundamental shift from health care to health.
Biopharma companies are set to develop tailored therapies that cure diseases rather than treat symptoms. Clinical trials will need to accommodate the increased number of more targeted approaches required. Regulators around the globe have released guidance to encourage biopharma companies to use RWD strategies.11 Innovative trials using RWD are likely to play an increasing role in the regulatory process by defining new, patient-centred endpoints.
In the future, all stakeholders involved in the clinical trial process will align their decisions with the patient’s needs. Sponsors will channel information about the trial, the process and the people involved through the patient. The use of AI-enabled digital health technologies and patient support platforms can revolutionise clinical trials with improved success in attracting, engaging and retaining committed patients throughout study duration and after study termination (figure 4).
In the future, AI, together with enhanced computer simulations and advances in personalised medicine, will lead to in silico trials, which use advanced computer modelling and simulations in the development or regulatory evaluation of a drug.12 The next decade will also see an increase in the implementation of virtual trials that leverage the capabilities of innovative digital technologies to lessen the financial and time burdens that patients incur. Virtual trials enable faster enrolment of more representative groups in real-time and in their normal environment and monitoring of these patients remotely. As many as half of all trials could be done virtually, with convenience improving patient retention and accelerating clinical development timelines.13
While AI is yet to be widely adopted and applied to clinical trials, it has the potential to transform clinical development. The applications of AI could lead to faster, safer and significantly less expensive clinical trials. The potential of AI to improve the patient experience will also help deliver the ambition of biopharma to embed patient-centricity more fully across the whole R&D process.
Ultimately, transforming clinical trials will require companies to work entirely differently, drawing on change management skills, as well as partnerships and collaborations. If biopharma succeeds in capitalising on AI’s potential, the productivity challenges driving the decline in
Read the full report, Intelligent clinical trials: Transforming through AI-enabled engagement, for more insights.