AI-driven clinical trials: The full potential of AI in drug development

Artificial intelligence (AI) is rapidly advancing drug discovery by expediting the identification of promising drugs. However, the focus is shifting towards the next frontier: AI-driven clinical trials.

Why does this matter?

Drug development is lengthy and resource-intensive. From discovery to approval, it can take up to 14 years and cost more than $1 billion. The initial stages involve extensive research into identifying drug targets. Once a potential drug has been tested in a laboratory setting, it must then undergo multiple phases of clinical trials. However, trials notoriously face high costs, lengthy timelines, and challenges regarding patient enrollment and retention.

The clinical research industry continues to be hindered by a widespread reliance on outdated methods and siloed systems which fragment the research process. A lack of unified, end-to-end solutions forces clinical researchers to pay for and coordinate between multiple systems. These factors lead to inadequate participant recruitment, extended trial timelines, escalating costs, and a trial failure rate of 86.2%.

AI has the ability to automate processes across the clinical trial life cycle, accelerating timelines, increasing cost efficiency, and bringing medicines to market sooner. That’s why Research Grid (R.grid) has built the only AI-driven, end-to-end platform that automates the entire back office of clinical trials. Transforming trial management in this way has a wide range of effects.

Our work

R.grid’s mission is to enable faster, more successful clinical trials by engineering smart software that safely automates back-office admin across the full trial life cycle. This innovative technology directly addresses the administrative and recruitment challenges which unnecessarily stifle medical progress.

Research Grid has built comprehensive solutions for patient and community engagement (Inclusive) and clinical trial management (TrialEngine). Automating operations at every trial stage with these tools reduces tasks that would have otherwise taken weeks or months down to seconds.

What does AI mean for our lives?

Faster drug development

At present, getting a drug to market can take over a decade, with 85% of clinical trials experiencing delays. These extended timelines not only impede the availability of potentially life-saving therapies but also increase the financial burden on medical research institutions.

Utilizing applied AI can fix bottlenecks and streamline clinical admin to compress drug development timelines. R.grid does this by using specialized AI algorithms to automate back-office tasks, increasing efficiency by an average of 98%. This can cut months off timelines at every stage of clinical research, the combined effect of which will bring novel medications to patients years sooner.

No more wasted expertise

60% of clinical researchers spend more than 2 hours per patient per day on manual data re-entry. This significantly detracts from the time they can dedicate to high-impact activities and complex problem-solving. In turn, this hampers innovation and wastes human and financial resources. AI process automation offers a solution to this structural inefficiency.

R.grid’s AI-driven tools support research professionals by automating tedious tasks such as protocol and document creation, recruitment management, patient anonymization, and data entry, completing them in seconds. This frees thousands of hours of researchers’ time across trials, allowing them to redirect their resources to advancing medicine.

Increased throughput

Clinical researchers are often too overwhelmed with time-consuming, repetitive tasks to deal with challenges such as patient recruitment effectively. Insufficient patient recruitment is a significant issue which often stems from time and resource constraints. As a result, recruitment difficulties cause delays in 80% of clinical trials and play a role in 55% of all trial cancellations.

R.grid removes the burden of manual processes by completing them automatically with AI tools, allowing researchers to focus more fully on the most pressing research issues. This increased capacity combined with R.grid’s extensive patient network results in a 145% increase in patient engagement across all age groups, driving recruitment, retention, and trial success.

Reduced costs

Delays are an enormous source of excess costs in clinical research, with each day of delay costing between $600,000 and $8 million. However, AI can expedite clinical trials and avoid the expenses associated with delays, reducing costs at this pivotal stage of research.

R.grid uses AI to increase cost efficiency on multiple levels. First, it reduces time and resources spent on administration and management. Second, it helps mitigate one of the main causes of delays, participant enrollment, though increasing engagement. As a result, using R.grid can cut costs by 45%.

An exciting future for clinical trials

Artificial intelligence is increasingly gaining traction in the clinical research industry. Approximately 60% of pharmaceutical and biotechnology professionals plan to increase their use of artificial intelligence within 2 years. It’s clear that the clinical research industry is ready to step into the AI generation, and its application in areas such as admin is opening up unprecedented possibilities. From accelerating drug discovery to automating trial processes, the breadth of AI applications in clinical research is extremely promising. It’s already been shown that using specialized AI algorithms to optimize processes delivers significant efficiency gains at every stage of research, both in the short and long term. R.grid’s AI solutions are just one way that researchers can incorporate AI into their workflow to make their trials faster and more successful.