In a recent perspective published in Nature Medicine, the researcher shared his vision and future perspectives for the domains of clinical research and evidence-based therapeutics.
Study: The next generation of evidence-based medicine. Image Credit: Kitreel/Shutterstock
Background
In recent times, advancements in technological wearables, machine learning, and data mining have initiated the transformation of evidence-based medical practices to revolutionize next-generation trials and treatments. Despite scientific advances in technology, the clinical translation of medicinal achievements is lacking. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has exposed existing clinical trial limitations and improved trial design by increasing focus on the prime subject of research, i.e., the patient.
About the perspective
In the present perspective, the researcher envisioned the future of clinical research, emphasizing key areas of improvement in clinical trial design, conduct, and the generation of evidence.
Drawbacks in the current research scenario
Recent times have witnessed unparalleled advances in clinical research; however, their translation into clinics has lagged in most research domains and remains outpaced. Drug development is expensive, with high rates of failure and enormous financial and societal burdens. Most data on chronic diseases reside in data silos, and multidisciplinary collaborative efforts are required to increase clinical trial efficiency and generate the best quality of evidence.
Randomized controlled trials (RCTs) are not always feasible due to challenges in generating evidence in a timely and cost-effective manner. Moreover, the narrow sample population sizes limit generalizability, and many clinical questions remain unanswered. Ethical consent ethical barriers increase RCT durations. Prognostic or predictive biomarkers (pathological, physiological, or clinical) need to be defined, and endpoints must be defined clearly to accelerate drug development. Further, trials are challenged by staffing limitations.
Future of clinical trials
Innovative research strategies are required to increase patient engagement and produce the highest-quality evidence for the swift translation of diagnostics and therapeutics from research settings to clinical settings for improved population health. The coronavirus disease 2019 (COVID-19) has accelerated the development of next-generation clinical trials by forcing the trials to be more patient-centric.
Future trials would be based on artificial intelligence (AI), machine learning, and deep neural networks to improve drug discovery, interpretation of images, streamlining electronic medical record data, and improving trial workflow, and adapted to recent advances in immunology, precision medicine, and immunology.
Master protocols, comprising sub-studies such as umbrella studies, basket studies, platform studies, and master observational trials, are used to enhance clinical trial design progress. Future trials would be more decentralized, virtualized, and comprise digitalized endpoints for more realistic, globally harmonized, standardized real-world tracking of patient experiences and enable remote monitoring. Trials with collaborative efforts by academic institutions, patients, sponsors (pharmaceutical, government, cooperative groups), regulatory organizations, and CROs (contract research organizations) would improve the research landscape in times of time.
Navigation services and site-agnostic matching would improve trial accessibility and patient participation in clinical trials. Priority reviews, orphan designations, fast-track designations, and breakthrough designations would reduce the premarketing process duration and increase the speed of drug development. Transportation analyses may increase the external validity of trial findings.
N-of-1 individualized genomic trials may improve rare disease assessments, and the trials may potentially be enhanced by the ‘digital twins’ concept of patient-specific medical analogs for AI-based combinations of polynomial mechanistic information and medical history data. Moreover, external or synthetic control arms may be incorporated in trial design comparator arms that can mirror the comparator arms of RCTs. More pediatric RCTs may be conducted to improve understanding of rare diseases, based on AI methods, used as an adjunct for, and not as a substitute to, human intelligence.
Conclusions
To conclude, based on the findings, the success of clinical trials requires transformations in trial design, conduct, assessments, and documentation, to optimize the use of digital, AI-based data and technological advancements, and bridge the gap between the trial world and the real world.
The main challenges in clinical research development in the future would be tapping the potential of multidimensional and real-world evidence generation by retrieving, assimilating, and analyzing large datasets and the information obtained by advanced scientific techniques.
Global funding partnerships need to be increased, and social media and online platforms could be used to increase awareness and community engagement. Breaking silos, digitalization, decentralization, and personalized, pragmatic, preventive, and precision medicine are required to improve public health and address diversity, equity, and worldwide accessibility to therapeutics.
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