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Clinial trials and next-generation digital health innovation

From a drug’s discovery until it gains FDA approval there is an average of 10 to 15 years. An average of $2.6 billion in investments is needed along a drug’s development lifecycle. Both of these components deem the biopharma R&D cycle an anomaly in the world we live in, where everything is on-demand. In order to develop a COVID-19 vaccine as quick as possible, academic institutions, pharmaceutical companies and government agencies have found themselves in a race. A race where the lengthy process involved in drug making has been felt more accutely than ever before.

The need to shift toward decentralized or virtual clinical trials and the major impact that COVID-19 has had on clinical trials has been discussed extensively. This post will deal with the involvement of digital in clinical trials, explore the opportunities in transforming drug discovery and discuss the current state.

Clinical Trials and the Evolution of Digital

There were 3 main points of focus involved in the initial efforts to integrate technology with clinical trials. Focus was on electronic data capture systems (EDC), electronic patient reported outcomes (ePRO), and electronic clinical outcome assessments (eCOA). Clinical research documents can be digitally stored with the use of EDC and ePRO and eCOA allow the digital distribution of assessments and surveys. Each of these serve as a digital tool within a traditional trial. The notion of decentralized or virtual clinical trials, meaning clinical trials without traditional study sites, surfaced in the last 10 years. Initial attemps failed which were due to poor enrollment and engagement but some successed followed. In 2017 with Science37 in partnership with AOBiome and in 2019 with Janssen’s partnership with PRA Health Sciences.

Decentralization and the immersion of digital health solutions into traditional clinical trials has lately increased rapidly and most of this innovation is reflected in startups which have seen steady capital growth in the last years. Startups, since 2016, have secured more than $300M. The third quarter of 2020, saw the digital health solutions focused on clinical trials raising $787M, a massive acceleration comparing to their last high in 2016, where they raised $403M. The large increase in numbers is due to a few large rounds, which also resulted in deal sizes much higher than average, and not an increase in the total amount of deals made. The importance of decentralized clinical trials in light of COVID-19 presumably drove many investors. Clinical trial solutions’ largest funding rounds in 2020’s third quarter have been: Verana Health ($100M), Aetion ($82M), THREAD ($50M), and ConcertAI ($150M).

Adopting digital health solutions for clinical trials has increased also due to the FDA changing its policies. Initially, the 21st Century Cures Act, passed by the FDA, supports data interoperability and real world evidence with implications for regulatory approvals. Secondly, in 2018 the former FDA Commissioner, Scott Gottlieb, expressed the need to rethink clinical trials. A digital health center of excellence was launched by the FDA recently, showcasing the agency’s broad dedication to advancing digital health technology, inclusive of technologies used in research.

The Digital Clinical Trial Landscape

The lifecycle of a clinical trial involves three stages, the study set-up, activation and evaluation, and all of these stages are now being influenced by digital health innovation. Digital health solutions can be divided into each of those stages, and although most startups deliver solutions targetting one stage, some are providing end to end solutions.

  • Study set-up: The solutions involved in the study set-up focus on the planning stage of clinical trials. From protocol development to approval to selection of sites and set-up. Protocol design is something that pharma, CROs and medtech are very familiar with, however, there is a growing need for trials to be more patient-focused, resulting in solutions to crowdsource opinion from patients, caregivers and health professionals. An example of this is Transparency Life Sciences. TriNetX is a tool which informs site selection based on localization of patients matching the protocol criteria. TrialSpark, utilizes big data analytics in order to spot locations with high potential where trial patricipants can be found. Prior to launching a site, some companies are now focusing on growing the efficiency of training for investigators and staff.
  • Activation:  These digital health products allow sponsors to recognize, screen, and enroll participants for clinical trials at a faster speed. Over two-thirds of trial sites do not manage to meet the initial recruitment targets and recruitments stand for about 40% of the total clinical trial budget of the U.S. pharma companies. Although the approaches chosen vary, a lot of solutions rely on artifical intelligence (AI), algorithms, and/or natural language processing (NLP). Deep6AI or Mendel.AI. use NLP to match patients to trials, based on their medical records. Some other companies provide real time insights, having developed the required platforms, and drive recruitment efficiencies for trial sites and sponsors, like Reify. Lately, there has been a noteable increase in efforts to improve diversity and inclusion in clinical trial recruitment, for example by collecting and de-identification of patient data, targetting minority communities from DrugViu.
  • Evaluation: Passively or actively, these solutions gather and manage patient data associated with outcomes. Collecting data actively is what the categories of digital health companies which initially emerged for clinical trials do such as electronic patient reported outcomes (ePROs), electronic data capture (EDC) and electonic clinical outcome assessments (eCOA). uMotif and Clincapture are some of the companies with EDC platforms. Lately, importance has been given to passive data collection, leveraging sensors in mobile phones and some biosensors. As in the case of AiCure, some solutions use smartphones cameras in order to capture data, such as ingesting a medication. Emerald is one of the companies that track movement in the home and others monitor phone usage in order to track conditions. For instance, nQmedical utilizes typing and touch screen kinematics to assess neurodegeneration.
  • End-to-end: Covering all of the stages, these solutions enable facilitation of decentralized or virtual trials. Some of the key players in the end-to-end solutions arena are Medable, Science 37, THREAD and Evidation. The latter, Evidation, makes use of their patient community, Achievement, and digital platform in order to carry out decentralized or virtual trials, as in the lately published study made by Omada Health’s chronic disease management program. Showcasing the large interest of enterprise healthcare organizations to shift to decentralized trials, is the fact that all of these companies have active partnerships with pharma and/or contract research organizations (CROs). For instance, THREAD and Science37 has partnered with Novarties, Science37 has also partnered with Boehringer Ingelheim and Medable has partnered with LabCorp.

The New Horizon of Digital Clinical Trials

Clinical trials being digitally transformed has already begun. Instead of doing the same tasks in a more efficient way, the companies on the front line are seeking new ways to utilize technology in order to do things differently. Some of these solutions are within reach, while others are still well beyond. Below are some of the enthralling developments in this space.

  • Enhancing a clinical trial’s success before it even starts Before a clinical trial even starts there is an opportunity for worthwhile innovation by enhancing the chances of success. Intelligencia.AI, has chosen such an aproach, by making use of artificial intelligence to predict the chances of success and adapting design features to eliminate risks from the trial. GNS Healthcare is another example, that leverages AI to create ”in silico” patients and point out which of a patient’s characteristics are the best match for a given clinical trial.
  • Replacing traditional control arms with synthetic control arms or digital twins
    Usually, in a traditional setting, a placebo is given, or the currenty standard therapy, to control groups in randomized controlled trials in order to serve as a comparison for the intervention under investigation. However, ethical questions have been raised about the use of traditional control groups, even more so for exhausting conditions such as rare diseases or cancer, or even when the standard of care has limited efectiveness. One possible substitute are synthetic control arms. In contrast to traditional control arms, synthetic arms use historic data or real-word evidence. UnlearnAI, one digital health startup, is taking the idea even further with their machine learning platform DiGenesis that creates ”digital twins”. These digital twins are longitudinal, comprehensive and computationally generated clinical records that describe the outcomes that would occur if a patient received a placebo.
  • Real-world data and real-world evidence in order to achieve regulatory approval
    Outside of the confines of randomized controlled trials, real-word data (RWD) and real-world evidence (RWE) is produced during routine clinical care. The 21st Centure Cures Act focused on using RWD and RWE in relation to regulatory decisions, paving the way for its use to suport follow-on indications for drug approvals. Flatiron Health was acquired in 2018 by Roche for $1.9B and provides a RWE platform for oncology and to continue the use of RWE in regulatory decision for oncology has partnered with NICE and FDA. Supporting later stage R&D are also ConcertAI which also focuses on oncology and Aetion which uses artificial intelligence to create RWE from medical and pharmacy claims data. Aetion has partnered with the FDA in order to re-create randomized clinical trials (RCTs) through RWE. COVID-19 is expected to further increase the use of RWE in regulatory decisions as interventions made available via emergency use authorization have been given to opportunity to collect RWD in ways that have not occured in the past.
  • Breathing new life into pipeline assets
    Approximately 50% of phase 3 clinical trials are destined to fail, despite the remarkable time and financial investment, and the percentage only accelerates when it comes to earlier phases. However, advanced analytics and algorithms are now being used to rescue clinical trials that are close to failing and even those which have already failed. One of the popular approaches, advanced by Healx, a startup focused on rare diseases based in the UK and Netramark, uses artificial intelligence to identify alternative indications or certain subpopulations for which an asset might be a good match.

Dealing With Innovation

Observing the explosive innovations in this space is exciting, but it can also be somewhat daunting. Where do you start as a biopharma or a digital health company in regards to your evidence generation plan? The first step is to recognize where you stand in incorporating digital solutions into clinical trials. Secondly, one must create a strategy to make sure that your evidence generation is a match with the first wave of digital health innovation. After that, to figure out which therapeutic areas or specific pipeline assets could benefit the most from reshaping the traditional approach and to begin the process of transformation.

It is exciting to think of the next horizon of digital health innovatons, bringing treatments to patients faster and more effectively!

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