As we’re saying goodbye to the last of 2021, we wrap up the year and start reading prognoses for the new one. Naturally, we want to be prepared for what’s to come and this desire for foresight is strengthened by the unpredictability of the last 21 months. What else can catch us by surprise? Whatever it is (and I for one hope the answer is ‘nothing’ as I don’t like surprises), we can prepare by sifting out the movements from the trends.
This is why today’s article will focus on what is likely here to stay through tribulations, rather than what seems to be trendy in the clinical research space. Without further ado, what does 2022 have in store for clinical trials?
Drug development speed-up
Experts in the industry anticipate different developments as well as increased focus on what’s already been gaining traction. But all of that ultimately translates into speeding up of the drug development process.
Bhawna Lawera from Health 2.0 Conference anticipates that clinical trials will be accelerated due to the removal of bureaucratic obstacles. Another factor she points to is the implementation of AI-enabled tools for the screening of potential study participants.
Rose Higgins, CEO of HealthMyne, is hopeful about the increased use of radiomics – advanced imaging analytics – which can support clinical trials in various ways. For example, radiomics can help in the development of more precise inclusion/exclusion criteria as well as predicting patient outcomes.
“By enabling researchers to obtain real-world data that objectively quantifies the characteristics of cancer patients’ tumors, radiomics allows clinicians to make personalized, data-driven predictions about how a patient’s disease is likely to progress.”
Higgins
Of course, the drug development process doesn’t start with clinical trials. Many things precede the testing of a new therapy in the real world – for example, clinical trial feasibility. The process of planning a new study itself can be optimized with the use of technology but what is more important – better planning, in turn, can speed up drug development. How? Preventing delays, recruiting the right patients, in the right context, within the set time limit.
One key way trial feasibility can be optimized is by making the most of all available data, not just the data well structured. Here’s where Natural Language Processing (NLP) will support the industry in 2022.
NLP tools analyze and streamline unstructured data from sources like electronic health records, regulatory agencies, etc. to provide data that is otherwise hard to obtain and use. For example, TrialHub, our platform for clinical trial planning, uses NLP to extract specific information about decentralized trial components from the registries and structure it in a user-friendly way.
NLP has the potential to provide specialized data that feasibility experts can use to create more precise clinical trial strategies.
Dynamic trial design
One thing that did not begin with Covid but was certainly accelerated by it was the movement toward decentralization of clinical trials. Lisa Henderson, Editor-in-Chief of Applied Clinical Trials, predicts that in 2022 we will start seeing case studies that break down the DCT model to assess what works vs what doesn’t. Such case studies can serve as proof of applicability when it comes to the regulatory approval of therapies tested via this model.
Hand in hand with this process another one will occur – the boom of decentralized trial solutions. As the industry includes more and more DCT modalities into study designs, the need for solutions and providers that can support the DCT model will increase. Digital patient recruitment providers, solutions for remote patient monitoring, eConsent and ePRO providers will have their moment in 2022 as they become increasingly important to the industry.
Tom Lemberg, founder and CEO of Curebase, believes this development will ultimately make clinical trials much less burdensome for patients. If clinical trial design is adapted to fit different patients’ lifestyles, it can go a long way toward improving patient recruitment and minimizing delays-related losses for the industry.
Lemberg explains that certain patients will prefer to participate in trials from the comfort of their home, while others have a dynamic life of traveling that is incompatible with regular site visits. Accommodating different patient needs is what the future of clinical research holds.
On that note, clinical research will keep its focus on patient diversity in clinical trials as well. Lisa Henderson explains that this is not diversity for the sake of diversity but rather a way to better evaluate the efficacy of new therapies in the target patient population.
“Across the board, it would be a better practice if the population studied was representative of the population intended to receive the potentially approved medicine.”
Henderson
Beyond-the-pill therapeutics
The Covid healthcare crisis highlighted the need for treatments approaches that go beyond the traditional pill. Bhawna Lawera points to a recent “spike of interest” in less orthodox healthcare practices like gamification for disease prevention and treatment, the use of wearables, VR technology, and more.
Digital therapeutics is where a lot of experts put their hope for the treatment of neurodegenerative conditions like Alzheimer’s. Aniket Singh Rajput, CEO and founder of Neuroglee Therapeutics, points that traditional drug-based approaches often have only a modest effect on patients’ symptoms.
Rajput noted that “there’s an urgent need to slow dementia-related cognitive decline via digital therapies, either adjunctive therapy or monotherapy.” He imagines Alzheimer’s patients performing customized tasks on a tablet that allows healthcare providers to monitor their cognitive function virtually and intervene when needed.
This is one, digitally-driven, side of the coin. The other is personalization in medicines. Personalization can take many forms, however, one of the most prominent tendencies is pharmacogenomics. Jim Robbins, senior vice president of life sciences at Arcadia, predicts that genomic-phenomic data will accelerate pharmacogenomic discovery.
According to Robbins, pharmacogenetics will “reshape the current paradigm of disease-specific drug approvals into mutation-specific indication and usage approvals”.
Based on your observations in the clinical research space, what are your predictions for 2022? Alternatively, what are your hopes? Let us know.