7 major trends shaping clinical trials: Implications for Life Sciences companies

The going is certainly getting tougher for life sciences companies. The industry today faces rising pressure from consumers and policy makers to lower drug prices, reduce time-to-market for new products, and comply with increasingly onerous regulations. Consequently, the clinical research ecosystem is seeking to improve trial outcomes and patient experience, slash trial costs, better manage underlying risks, and shorten product development lifecycles.

The good news is that trial sponsors and contract research organizations (CROs), like yours, can now leverage commercially viable digital technologies and design concepts to fundamentally reimagine existing operating models for enhanced trial efficacy. In that context, it is useful to factor in some of the following major trends impacting clinical trials:

Design thinking: Companies across industries are redesigning their entire value proposition around the theme of customer experience, striving relentlessly to deliver superior, distinct and enjoyable interactions across various touch points. Accordingly, some of the leading pharmaceutical organizations have started using design thinking to improve patient experience during trials. The core idea here is to ensure trials are centered around the patient, rather than around the drug being tested. That means constantly interacting with subjects, and spending more time at sites to identify existing gaps and iteratively build solutions.

For example, Novartis has adopted the Human-Centered Design Toolkit from IDEO to optimize various phases of the trial cycle, including participant recruitment, protocol feasibility, informed consent and patient reported outcomes.

Eli Lilly has launched a collaborative program called CoLAB to simulate trial protocols. Under this exercise, the pharma major's clinical research teams collect inputs from patients, study coordinators, physicians and other external partners regarding trial elements such as study visits, samples and medication dosing. Accordingly, Lilly simulates both the site and patient experience, covering trial pain points like screening and baseline visits, and dose modifications. This design thinking-based approach, the company hopes, will help expedite trials, boost patient satisfaction, avert protocol amendments, and meet or exceed enrollment timelines.

Artificial intelligence (AI): So-called 'chatbots,' virtual assistants and other AI-driven, self-learning machines are springing up all over the place, from ecommerce and internet banking sites to various consumer mobile apps. These machines have starting making their presence felt in clinical trials too. Companies can now leverage AI to map and study molecular structures for formulating drug therapies faster, thereby accelerating the initial discovery and testing process in trials.

Stanford University has created a bioinformatic map of common diseases, using computers to access massive volumes of raw data from various sources, and analyze the same to flag recurring phenotypes and other patterns. Start-ups like AiCure are developing medication adherence tools that harness AI facial recognition to confirm whether patients have taken the correct medication. Such tools are expected to foster better compliance with trial settings, hopefully resulting in improved results and effective remote monitoring support.

Robotics: Completing trials faster, and at lesser costs, is clearly one of the industry's biggest goals going forward. And help is at hand from robots! MIT has built a robot to gauge the effectiveness of drugs intended to treat acute stroke or aid in recovery from cardiac arrests. Such machines could help sponsors take early decisions on whether to persist with a specific drug or pursue alternative therapeutic avenues. This, in turn, MIT estimates, could pave the way for a 70% reduction in the number of patients required to be tested, thereby reducing the trial completion time and associated costs.

Meanwhile, Boston Children's Hospital has introduced a robotic teddy bear, named Huggable, to engage patients during clinical visits, and alleviate their anxiety and inconvenience for a more pleasant trial experience. As part of a randomized pediatric trial, the hospital introduced this therapeutic tool to track physiological changes and patient reactions, and assess the therapeutic value of social robotics in such settings.

Wearables: Pharma and biotech companies have also begun embracing wearables to collect personalized, high-quality patient data on the go. Sanofi carried out a fully remote clinical trial in Europe last year for diabetic patients to test a 3G-enabled wireless blood glucose monitor.

Israeli drug manufacturer Teva, in partnership with Intel, is building wrist watches and other wearables that sense and track daily movements of patients suffering from Huntington's disease, Parkinson's and epilepsy. The company intends to mine these rich data sets with advanced algorithms and data analytics technologies to identify disease patterns that could prove valuable during future trials.

Mobility: As consumers increasingly rely on their mobile devices for a whole range of activities, they are becoming comfortable with the idea of sharing personal health information with trusted service providers. Recognizing this significant trend, drug makers are aggressively using mobile messaging and social media for patient recruitment and investigator interactions to boost pre-trial enrolment, and minimize subject dropouts during trials. Simultaneously, pharma and biotech companies are using mobile apps to remind patients about appointments and medication schedules, and deliver instructions.

Cloud computing: Stakeholders across the clinical trial value chain are using the cloud for hosting different applications and infrastructure, as well as for promoting smooth integration and coordination with external partners. The research and development (R&D) function, in particular, is attracting a lot of attention in this regard, with pre-competitive collaboration consortia like TransCelerate creating cloud-based, common databases for hypotheses development.

Some pharma organizations are looking at developing a common 'research cloud' to harmonize transactional processes, outsource the same if required, and focus on trial execution to solve core biomedical questions. These experiments, if successful, could ensure consistent workflows, data federation and standardization, as well as business process streamlining. These would eventually result in frugal and faster product innovation.

Big Data / Analytics: Companies across the life sciences industry today need to effectively, and swiftly, collect and mine the huge volumes of structured clinical and unstructured real-world data being collected from various sources. Toward this end, organizations are adopting Big Data tools to organize terabytes of unstructured data from myriad systems such as EMRs and mHealth devices into comparable formats, analyzing the same, and then visualizing the findings.

Through this approach, drug makers and CROs hope to improve their understanding of known relational trial factors, and discover unforeseen patterns that could result in the formulation of new hypotheses. Further, analytics-driven insights could help pharma and biotech firms identify and choose prospective trial sites and investigators per therapeutic area, indication and geography based on historical performance.

Conclusion

Shortening trial timelines, while simultaneously enhancing outcomes, improving patient experience and rationalizing underlying costs, is not an impossible task. Life sciences companies can realize each of these business goals over the medium and long term if they embrace a more experimental, iterative way of configuring and executing trials. By harnessing innovative technologies and design frameworks, they will be able to empathize with evolving patient needs, and accordingly, ideate and develop relevant, compelling solutions.