Clinical trials are an essential step in the development of new drugs and therapeutics. However, they can be extremely expensive and take years to complete before a drug can be authorized for sale and use, and there is no guarantee of success.
In recent years, several innovative technologies have emerged which have disrupted the wider pharmaceutical and medical industries, offering significant benefits for the design and operation of clinical trials.
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Foundations of Automation in Clinical Trials
Clinical trials are extremely complex, requiring rational design and management to succeed. Despite the best efforts of experts working in these trials, many can fail due to several dynamic and interconnected factors.
Clinical trials cost a lot of money, for instance. Complex regulations can cause additional burdens on trial design and management. Insufficient management of patient cohorts can cause negative impacts such as skewed patient data, which can affect the efficacy of trialled drugs.1
Automation is especially useful in key areas such as patient management, data collection, and trial management. Clinical metadata management is made easier due to centralized systems. Automation enables better and more efficient case report form development and dataset conversion processes.1
Many technologies come under the umbrella of automation. AI, robotics, machine learning, big data, and advanced analytics and algorithms are some of the main examples of innovative technologies streamlining and improving clinical trial success rates.
Automation benefits the clinical trial process, from initial study design to submission and approval.1
Benefits of Automation in Clinical Trials
Automation can overcome many of the key issues with clinical trials. It can streamline processes, accelerate time-to-market, optimize resources, and enhance data accuracy, thereby reducing costs and improving effectiveness and efficiency.
Automations ability to expedite the development of therapies and treatments can be hugely beneficial for patients.
Automation speeds up the time needed to develop a drug or therapy, from the initial study design to regulatory approval and mass market release. It saves time, resources, and costs.
Additionally, more accurate data collection and quality improve the safety of trials and end products. Furthermore, automation improves productivity over the complete trial life cycle and ensures faster results when compiling and analyzing.1
Patient cohorts are not monolithic: to succeed, a clinical trial must consider several differences between participants.
AI can pinpoint particular group participants and groups and differences in lifestyles and genetic profiles, meaning that it can play a pivotal role in trial management and personalized medicine.3
What is a Clinical Trial?
Applications of Automation
Machine learning (ML) and AI are two of the main automation technologies currently employed in clinical trials. Chatbots are an interesting use of these technologies. They can be utilised for different users, such as patients and researchers, reducing the burden on support staff.2
Machine learning can also be used in patient enrolment by providing predictive analytics. Variables such as randomization, adverse events, study duration, and therapeutic area can be selected for relevance, and finalized models can be used for future clinical trials. ML can also be used for risk-based monitoring.2
Technologies such as AI and ML can also be used to template and automate documentation, helping to speed up and comply with regulatory submissions.
Machine learning can also be employed in data management, offering benefits such as “smart querying” by reading trial data and identifying potential queries that can be used for various purposes within a clinical trial.
Remote patient monitoring can be enhanced using technologies such as chatbots, and electronic informed consent can be automated, removing burdens from clinical staff.
Several papers have been published in recent decades, highlighting the successful use of automation in clinical trials. Insilico Medicine has employed AI to design INS018_055, which has reached Phase 2 clinical trials.3
This is just one example, with many papers reporting research into using emerging technologies to improve clinical trials.
Challenges and Limitations
Despite the numerous benefits of automation, there are still some key challenges and limitations to its use in clinical trials.
Firstly, data quality, which AI relies on to make decisions, can be inconsistent, biased, or incomplete. This can lead to inaccurate data processing and potential risks to patients.
Secondly, there are data security and privacy concerns surrounding the use of emerging technologies in clinical trials. Additionally, there are ethical and regulatory considerations surrounding these technologies.3
Initial start-up costs can present a roadblock to adopting emerging technologies as organizations running trials may lack the necessary funds and prefer to rely on cheaper but less efficient processes.
Additionally, integration with existing infrastructure can be technically challenging, and adequate staff training in these emerging technologies is lacking.
Case Studies and Success Stories
As mentioned above, Insilico’s AI-designed drug is a notable example of the success of automation in improving clinical trials. AI was used to identify both a target and drug design, which would be the most successful. The company also has two partially AI-generated drugs in the development pipeline.3
Another success story is Tempus. The company uses a library of molecular and clinical data to provide insights for the application of AI in clinical trials.
This approach streamlines clinical trial management and cohort recruitment, improving efficiency and reducing costs.3 Recursion Pharmaceuticals successfully employs computer vision and other technologies in clinical trials.
Future Directions and Conclusions
Automation in clinical trial management has a promising future. AI is increasingly central in this area, accelerating drug development and overcoming several challenges and barriers.
Predictive analytics and better decision-making will help improve trial management. Less human error will improve patient outcomes, and automation could profoundly impact future trials.
Automation provides huge benefits in terms of cost, efficiency, and drug safety, and the above case studies (albeit a brief exploration of this area) highlight the growing trend of using automation in clinical trials. Indeed, AI and automation have had a huge impact already.
Continued investment in innovative technologies will be needed to realize their full potential.
References
- Formedix (2020) Automating clinical trials: Why’s it’s essential for success [online] Pharma Phorum. Available at: https://pharmaphorum.com/partner-content/automating-clinical-trials-why-its-essential-for-success (Accessed on 09 July 2024)
- Vangipurapu, M (2022) AI and Automation in Clinical Trials [online] Clinion. Available at: https://www.clinion.com/insight/ai-and-automation-in-clinical-trials/ (Accessed on 09 July 2024)
- Srivastava, S (2023) The Future of Clinical Trials – Unlocking AI’s Potential to Revolutionize Healthcare Research [online] Appinventiv. Available at: https://appinventiv.com/blog/artificial-intelligence-in-clinical-trials (Accessed on 09 July 2024)
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