As Digital Transformation takes hold, new technologies fuel virtual clinical trials

The COVID-19 pandemic has affected all areas of life, requiring us to adapt and rethink traditional ways of doing business. Yet necessity breeds invention and innovation. As work places and conferences adopt a hybrid approach of in-person and virtual due to the COVID-19 pandemic, digital transformation is taking hold across the broader healthcare industry. As a result, remote and virtual elements of clinical trials are increasing in prevalence. (1)

Enter digital transformation: the adoption and integration of digital technology into all areas of an industry, changing and expanding its capacity to serve. Today, digital health solutions including virtual insilico clinical trials, telehealth, and AI (artificial intelligence programs) (2) have the potential to change how pharmaceutical companies discover new and effective drugs and treatments.

The movement towards remote clinical trials is not solely due to COVID-19. In 2011, one company began Phase 4 randomized clinical trials to study an Investigational New Drug and determine if the previous trial results could be replicated virtually. (Results were consistent with the outcome of the previous trials.) (3) FDA recommendations in 2013 suggested ‘greater use of centralized monitoring techniques’ (offsite monitoring) in trials, guidance that was further updated in 2015 and 2019. (4) However, the pandemic appears to be accelerating this shift after the initial public health emergency caused major disruptions to clinical research. The number of stopped or halted oncology trials peaked in May 2020 (most restarted within a few months). (5)

Even traditional clinical trials now include more remote components, out of necessity. A JAMA survey of 245 clinical trial investigators reported that the percentage of participant communications conducted remotely increased from 9% in January 2020 to 57% in May 2020. (6) Trial drugs or devices can be delivered remotely, and patient check-ins frequently take place over phone, video, or web-based platforms.

Some of the latest digital transformation technologies used in clinical trials include:

AI data allows researchers to design clinical trials which are more informed by patient data and may reach more patients. (7) One example of an AI program used in clinical trials today is Trial Pathfinder, which uses algorithms to expand the pool of eligible patients for clinical trials. Potential participants can be selected based on their electronic health records, and/or their predicted likelihood of enrolling in the trial. (8) Relaxing restrictive eligibility criteria can have the beneficial effect of increasing and diversifying the participant pool. Perhaps most importantly, certain patient populations who may have previously been excluded but would benefit from the drug or treatment can also be included, thus addressing a blind spot in trial recruitment. With more participants, researchers may gain a clearer picture of how diverse populations may respond to the drug or treatment.

Nature estimates that use of an AI tool can significantly increase the eligible patient pool, simply by working around trials with restrictive criteria. In one specific case study, 10 non-small cell lung cancer (NSCLC) trials were analyzed using Trial Pathfinder. By relaxing trial eligibility criteria for these studies, the total number of eligible patients doubled from 1553 to 3209. (8) Other examples of AI programs used in clinical trials are SYNERGY-AI or Ancora, both AI-based tools which facilitate clinical trial enrollment by matching cancer patients considering clinical trials with relevant trials. (9)

Clinical trial recruitment and retention posed challenges even before the pandemic; globally, more than 80% of trials fail to enroll on time resulting into an extension of study and/or addition of new study sites. Poor recruitment and retention remain the single largest cause of canceled trials. (10) Thus, a larger participant pool is not only advantageous, but necessary for clinical research to continue through times of uncertainty and disruption.

Similarly, insilico modeling involves combining previously reported data and findings with computer-based models of biological systems in order to conduct research virtually, or entirely in a computer laboratory. (11) Digital simulations and existing data stand in for groups of patients. In practice, insilico models may include:

? Data simulations of a drug or treatment’s potential results (12)

? Synthetic cohort arms and real-world evidence simulation trials (such as synthetic control arms), which can repurpose prior real-world clinical trial data to match patients more accurately. (13)

When a drug is tested by insilico modelor simulated trial before it is tested on patients, the model can conceivably predict the outcome of the trial. Through predictive analytics, insilico models can make trials more scalable and replicable. (14)

One specific case study - the first all-in silico clinical imaging trial - shows how in silico modeling may lead to less burdensome regulatory evaluation approaches, potentially expanding patients’ access to new options. In the 2019 VICTRE trial (Virtual Imaging Clinical Trial for Regulatory Evaluation), investigators used computer-simulated imaging of 2,986 in silico patients to compare digital mammography and digital breast tomosynthesis. The findings revealed that tomosynthesis provided improved lesion detection performance for all breast sizes and lesion types. The increased performance of tomosynthesis was consistent with results from a comparative trial using human patients and radiologists. (15)

Remote or wearable patient monitoring devices are another tool that can facilitate clinical trials. Wearables appear to be another area in which the pandemic is accelerating digital transformation. In April and May of 2020, as healthcare systems faced unprecedented strain, the FDA announced new policies to promote the use of digital health devices. They awarded Emergency Use Authorization (EUA) to six new noninvasive remote patient monitoring and telehealth devices that “measure or detect common physiological parameters... or wirelessly transmit patient information to their health care provider or other monitoring entity”. These devices monitor patients’ vital signs without increasing immunocompromised patients’ chances of exposure to pathogens. Devices can monitor heart rate, blood pressure, and signs of some adverse effects of treatments during a clinical trial, or ensure patients are following clinical trial protocols - without requiring additional in-person visits from patients. (15)

Since 2020, the FDA has prioritized delivery of new remote patient monitoring devices to the market during this public health emergency. In a world where even people without chronic conditions use smartphones, apps, and other devices to track their own personal health data, remote patient monitoring devices can become a viable tool for clinical trials. (16)

Digital or virtual clinical trials likely will not replace traditional trials entirely, as they do come with potential drawbacks. The ‘digital divide’ remains a factor in healthcare inequities. Lack of internet access, limited technology literacy, age, or disability, all of which perpetuate health disparities, could be barriers to participation in virtual trials as well. Patients on Medicaid are also underrepresented in clinical trials, although a new policy as of January 2022 requires Medicaid to cover clinical trial costs, which may lessen the financial burden (17). These remain human health care issues that researchers must consider. Still, the arrival of digital transformation holds promise for the future of healthcare and its potential to better serve ‘the digital patient’ as a whole.

References

1. Hillman A, K, Andrew. 2022 forecast: Decentralised trials to reach new heights with 28% jump. Clinical Trials Arena. https://www.clinicaltrialsarena.com/analysis/2022-forecast-decentralised-trials-to-reach-new-heights-with-28-jump/. Published February 4, 2022. Accessed March 21, 2022.
2. Gopal, Gayatri, Suter-Crazzolara, Clemens, Toldo, Luca and Eberhardt, Werner. "Digital transformation in healthcare –architectures of present and future information technologies" Clinical Chemistry and Laboratory Medicine (CCLM), vol. 57, no. 3, 2019, pp. 328-335. https://doi.org/10.1515/cclm-2018-0658
3. Orri M, Lipset CH, Jacobs BP, Costello AJ, Cummings SR. Web-based trial to evaluate the efficacy and safety of tolterodine ER 4 mg in participants with overactive bladder: REMOTE trial. Contemp Clin Trials. 2014 Jul; 38 (2):190-7. doi: 10.1016/j.cct.2014.04.009. Epub 2014 May 2. PMID: 24792229.
4. Fda.gov. 2013. Oversight of Clinical Investigations —A Risk-Based Approach to Monitoring. [online] Available at: <https://www.fda.gov/media/116754/download>
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6. McDermott MM, Newman AB. Remote Research and Clinical Trial Integrity During and After the Coronavirus Pandemic. JAMA. 2021; 325(19):1935–1936. doi: 10.1001/jama.2021.4609
7. Lange A. 4 ways AI can transform clinical trials. MedCity News. https://medcitynews.com/2021/12/4-ways-ai-can-transform-clinical-trials/. Published December 15, 2021. Accessed March 17, 2022.
8. Liu, R., Rizzo, S., Whipple, S.et al. Evaluating eligibility criteria of oncology trials using real-world data and AI. Nature 592,629–633 (2021). https://doi.org/10.1038/s41586-021-03430-5
9. Synergy-ai: Artificial Intelligence Based Precision Oncology Clinical Trial Matching and Registry -Full Text View. Full Text View - ClinicalTrials.gov. https://clinicaltrials.gov/ct2/show/NCT03452774. Accessed January 22, 2022.
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11. Trisilowati, D. G. Mallet, "In Silico Experimental Modeling of Cancer Treatment", International Scholarly Research Notices, vol. 2012, Article ID 828701, 8 pages, 2012. https://doi.org/10.5402/2012/828701
12. Francesco Pappalardo, Giulia Russo, Flora Musuamba Tshinanu, Marco Viceconti,In silicoclinical trials: concepts and early adoptions, Briefings in Bioinformatics, Volume 20, Issue 5, September 2019, Pages 199–1708, https://doi.org/10.1093/bib/bby043
13. Synthetic Control Arms in clinical trials and regulatory applications. Executive Education at HMS. https://executiveeducation.hms.harvard.edu/thought-leadership/webinar-series/recorded-presentations/synthetic-control-arms-clinical-trials-regulatory-applications. Published January 29, 2020. Accessed February 11, 2022.
14. Badano, A. In silico imaging clinical trials: cheaper, faster, better, safer, and more scalable. Trials 22, 64 (2021). https://doi.org/10.1186/s13063-020-05002-w
15. https://www.fda.gov/science-research/about-science-research-fda/victre-trial-silico-replica-clinical-trial-evaluating-digital-breast-tomosynthesis-replacement-full
16. Remote or wearable patient monitoring devices EUAs. U.S. Food and Drug Administration. https://www.fda.gov/medical-devices/coronavirus-disease-2019-covid-19-emergency-use-authorizations-medical-devices/remote-or-wearable-patient-monitoring-devices-euas. Accessed January 19, 2022.
17. Kadakia, K., Patel, B. & Shah, A. Advancing digital health: FDA innovation during COVID-19.npj Digit. Med.3,161 (2020). https://doi.org/10.1038/s41746-020-00371-7
18. Takvorian SU, Guerra CE, Schpero WL. A hidden opportunity —Medicaid’s role in supporting equitable access to clinical trials. New England Journal of Medicine. 2021; 384(21):1975-1978. doi:10.1056/nejmp2101627

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