Clinical Trials (CTs) remain the foundation of safe and effective drug development. Given the evolving data-driven and personalized medicine approach in healthcare, it is imperative for companies and regulators to utilize tailored Artificial Intelligence (AI) solutions that enable expeditious and streamlined clinical research. 

Evidence generated by CTs is widely accepted and likely to remain the gold standard for development of safe and effective drugs, despite the long-standing acknowledgment of the great investment and high risks involved for pharmaceutical companies. 

With AI being recognized as a pathway towards sustainable and optimized drug development, multiple applications in CTs are being discussed and begin to be explored in practice. This is boosted by the growth and expansion of randomized trials providing medical research with large and complex volumes of categorized and uncategorized clinical, molecular and imaging data. While data availability is critical for data-driven and personalized medicine trends, generating actionable insights from the available information requires use of comprehensive AI models, developed and trained with appropriate datasets, to effectively expedite and streamline the various activities within drug research.

AI and clinical trials

AI can contribute to address unmet medical needs by enhancing and accelerating identification of new molecular targets (genes or proteins). Access to large pharmacokinetics (PK) and pharmacodynamics (PD) datasets, from previous preclinical and clinical research (including from failed trials), is needed to develop and train effective and reliable algorithms that generate new stable molecules with real treatment potential. Lack of published PK/PD data, for competitive or proprietary reasons, is a significant hurdle to achieve full potential of AI in new drug discovery.

Prediction of clinical outcomes is essential to the advent of precision medicine and to inform trial design by eliminating the statistical variability of general populations. In fact, AI can be used to simulate data to detect more efficient statistical outcome measures. One report suggests that using an AI algorithm to predict participant outcomes and to identify those most likely to progress fast and reach endpoints sooner, could lead to shorter duration trials.

There is evidence of ML algorithms supporting early detection and prognosis of disease, thus improving overall CT success. Beyond clinical expectations, AI can be wielded in the early phases of clinical research to predict molecular features, target sensitivity, bioavailability and toxicity, as well as to reduce later stage trial failure, and thus help design PhII/PhIII trials that are more likely to transition to regulatory approval. The impact on human and financial resources, as well as protection of participant’s safety and public perception of CTs, is irrefutable.

With the inclusion of automated data collection tools and by developing novel digital biomarkers that rely on AI algorithms to interpret data and transform it into usable insights, near real-time access using wearable devices and sensors can be provided to investigational sites, to obtain visualizations of a participant’s condition. Improving the safety oversight of trial participants, especially those with life-threatening or debilitating conditions, is a clear advantage facilitated by faster access to actionable insights.

Regulatory documents

In the EU, initiatives concerning AI are taking place at different levels. EC is developing a general framework and governance model for AI, founded in excellence and trust. The proposal for EU Regulation, known as Artificial Intelligence Act, establishes conformity assessment for high-risk AI (including SaMD) covering risk management, data governance, automatic record-keeping, human interface, and cybersecurity requirements Implementation of this regulation requires the creation of an EU-shared database and full access to data sets in pre- and post-marketing phases, both particularly challenging.

Conclusion

Integration of AI in CTs is a promising and expanding field, and many articles suggest AI may be the key to overcoming the current status quo in drug development and pave the way for a new paradigm of sustainable medical research. Efforts devoted to assessing the prospective use of AI are evidence that the Industry is looking forward to unlocking AI’s full potential in conducting more successful and cost-effective trials. 

The approach to integration of AI in drug development and approval is broad and covers all phases of a drug’s lifecycle. However, despite the existing publication of AI-related reflection papers and strategic action plans by Regulators, there is still a lack of specific and detailed regulatory guidance focusing on the use of AI within CTs.