How did the pandemic impact oncology trials? What kept them moving during lockdowns?

Oncology care was uniquely, negatively impacted by COVID-19. In the early days of the pandemic, oncology trials that were open either stopped enrolling patients or were substantially slowed, and new study starts were delayed. There were also delayed diagnoses of new patients due to the pandemic. Obviously, fewer patients being diagnosed means fewer prospective patients who may consider clinical trials. It also means that patients’ cancers will progress and that they’ll start treatment at more advanced stages.

That situation persisted from late February 2020 through the middle of the summer of 2020, when precautions had been fully integrated into practice. The standard-of-care was modified to be protective of patients and staff. In certain cancers, surgical interventions were limited in favour of less invasive initial treatment approaches or medical-only treatment. Levels of more invasive diagnostic activities, such as biopsies, were also sometimes reduced versus pre-COVID standard-of-care. This meant that expected outcomes might differ from historical outcomes or that clinical trials based on historical patterns of treatments might find fewer patients available meeting those patterns.

Once protective measures were in place, there was a new focus on restarting stalled studies and initiating new ones.

This restarting and re-initiation involved a reconsideration of the operating models for executing studies and a redesigning of studies for less burden to sites and patients. ‘Reconsideration’ has involved using remote access tools for uploading documents and study artifacts that otherwise might have required direct, on-site interactions; as well as AI-driven solutions to identify patients for study eligibility more tightly aligned to the provider workflows. There has also been encouragement to broaden eligibility criteria to allow more patients to receive consideration, in addition to leveraging AI tools to simultaneously assure meeting the study intent and reaching the targeted number of participants in as short a timeframe as possible. Many of these new planning solutions were evidence-based as opposed to past-RCT centric.

This is a subtle but critical change: standard-of-care data, combined with advanced AI, can be used to optimise trials for this broader inclusion and provide higher assurance that the study can demonstrate outcome benefits relative to the current standard-of-care for specific subpopulations of patients. It also assures that the interests of providers, patients, and sponsors can all be met, and the challenges of COVID-19 accommodated. It has been a heroic effort of many – really remarkable what we have seen in terms of new models of collaboration, cooperation, technologies, and innovative approaches.

What role will AI and data-driven processes implemented during the pandemic play once it’s over?

AI-driven approaches to trial design, site optimisation, patient identification and eScreening – even the processing of data for digital trials – are changes that will remain. These shifts made during the pandemic away from the traditional clinical trial system had been predicted for years. But it took a shock of unforeseen proportions to accelerate solutions that had been slowly maturing on the edge of the industry for years. What is especially salient is that the newly integrated approaches had their validation through smaller-scale use over the last half-decade or so. Patient identification has been accelerated by early work at the Dana Farber and elsewhere. Principles of real-world data study design emerged from work at Friends of Cancer Research, Stanford, and other oncology research centers. It’s now going into practice as the basis of all new clinical trials in a number of biopharma companies. There’s no going back, no desire to return to legacy models – rather, the focus is on how to scale the emerging approaches and AI solutions introduced over the course of the last 18 months or so.

What does a shift to alternative trial models, like those enabled by technology, mean for oncology treatment and patient outcomes?

One of the largest implications and benefits from these alternative trial models is that clinical trials will become more accessible to and lower the burden on patients. They’re more accessible in that robust clinical trial technologies that previously were only found in academic settings are now moving into the more advanced community oncology and regional health system settings – where 80% of patients in the US receive their care. Having access to standard-of-care therapeutics and clinical trials means a broader set of treatment options for the most devastating cancers. It also means that patients of different racial and economic groups now have trials more accessible to them. This also means a broader range of clinical sites can now be attractive to and viable for biopharma to activate and align to for an ongoing portfolio of studies. Finally, designing trials with evidence, conducting trials closer to standard-of-care clinical workflows, holds the potential that we can advance outcomes and benefits more rapidly than in years past with stronger form evidence.

Do you see these kinds of solutions having broader impacts? If so, how?

Oncology is approximately 40% of all clinical studies, the area of the greatest number of new therapeutic entity approvals, and the disease area of greatest real-world data and evidence depth. As such, many of the innovations being introduced and scaled in oncology will move into other therapeutic areas, especially rare diseases, acute chronic diseases, and any others where principles of precision medicine are meaningful.

On the areas that is not being discussed is that these real-world data savvy and AI-driven solutions hold the promise for substantial time and cost improvements – greater than 25% for both. The implication of this is a greater capacity for research across the industry – the ability to fund more trials on the same R&D funding base, advance more needed medicines more quickly, etc. Everyone is a beneficiary of this – innovators, providers, and most importantly, patients.

Anything else you’d like to add?

Optimistically, we are starting to exit one of the most challenging periods of medical care and biomedical innovation of our generation. Healthcare providers and biomedical innovators all moved beyond their traditional ways of working and defined new approaches that embraced real-world evidence, AI and other technologies, and deeply patient-centric approaches. It’s truly remarkable. We should use this moment to provide the strongest encouragement to make these novel and emerging clinical research approaches the ‘new way’.

Jeff Elton is chief executive officer of ConcertAI