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Industry Insight

Planning for 2021: Three Lessons the Clinical Development Sector Can Learn From 2020

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Industry Insight

Planning for 2021: Three Lessons the Clinical Development Sector Can Learn From 2020

The clinical trials industry, like many other global sectors, has been hit hard by the pandemic. One outcome has been the necessary diversion of resources to the development of COVID-19 vaccines and treatments alongside the care of critically ill patients. Other areas of vital research areas have consequently lacked resources, including oncology and dementia. Impending economic contraction around the world means trials are also taking place against a backdrop of tighter budgets, leaving clinical development on a cliff edge as 2021 approaches.

At the same time, the pandemic has exposed many underlying issues in clinical trials that have existed for much longer than COVID-19. These can be categorized into three broad categories: trial planning, design and patient enrollment; resource waste; and a duplication of efforts. The reality is that even without COVID-19, a fifth of trials were already failing to meet their objectives. So, as pharmaceutical companies look ahead to an uncertain 2021, these problems must be addressed head on through the use of predictive analytics, scenario modeling and synthetic patient data if companies are to survive and continue to deliver new therapies to patients. This will only be viable if biopharmaceutical companies take a data-driven approach to clinical development, and ensure everything they do is underpinned by the use of data in an integrated and holistic way.

1. Planning, design, and enrollment of trials


Poor study design, planning and the concomitant inability to recruit enough patients are the biggest causes of trial failure. This year, COVID-19 has intensified matters, preventing trial recruitment and causing an excess of trial suspensions – Phesi analysis in May 2020 found that of 300,000 global clinical trial sites, there had been a 38 percent increase in suspensions from the beginning of the year. It then indicated suspensions peaked in early June, and after an initial drop, rose again – with over 28,000 sites suspended in September 2020.

While many trials did begin recruiting again in June, it appears they did so without systematic root cause analysis on how well the trials were performing pre-COVID-19. Many simply added additional trial sites to overcome the problem, often on the recommendation of partner clinical research organizations (CROs) who lack the advanced predictive analytical capabilities to understand the problems and recommend the best course of action. However, even in “normal” times, adding new sites to an ongoing recruiting trial is a very risky practice without analyzing the true impact of such a move using data. This must change urgently; sponsors need to become data-led in decision making in order to recruit and conduct trials as efficiently and successfully as possible.

2. Resource waste


Currently, poorly designed and executed trials are wasting millions of dollars; only 50 percent of trials registered are ever published in full and it’s thought in total, 85 percent of medical research funding is wasted. Often, decisions made during a trial are due to established beliefs (or simply, “gut feel”), with many unnecessary contingencies added for fear of not recruiting enough patients. Yet, this approach fails to account for the many variables associated with a trial. Indeed, recent data show 37 percent of sites fail to meet their enrollment targets, and a protocol is amended on average three times per trial.

Phesi analysis shows that most of these amendments are avoidable and the number of non-active, non-enrolling centers in any given study can be reduced by up to 50 percent when using predictive analytics and design tooling. Patient populations fluctuate, regulations within a country may change, macro-economic factors may be affecting healthcare across a whole region, and a newly introduced therapy can completely overturn the dynamic in a particular disease condition. Likewise, extra contingencies and protocol amendments deployed during a trial can in fact exacerbate existing problems when decisions are not informed by data, rather than solving them. Clinical development companies must have predictive scenario modeling capabilities to accurately manage new change orders from CROs and associated costs.

3. Duplication of efforts


In clinical development, efforts are often duplicated due to a lack of data sharing and the use of control arms in most trials. However, this duplication can be prevented through the use of synthetic patient data. Synthetic patient data can be employed to define the boundaries of a trial, to model and predict what types of patients should be included and excluded and to minimize or even eradicate the need for placebo-based patient enrolment. The creation of a synthetic control arm in research areas where control group performance has been historically well characterized, and where results have been consistent from trial to trial, opens a significant ethical and financial opportunity to drug developers and regulatory agencies.

By reducing or eliminating the need to enroll control arm participants, a synthetic arm can dramatically increase efficiency, reduce delays and lower the costs of a trial to the sponsor, while also addressing ethical issues associated with placebo dosing for patients. This approach is feasible and should be utilized, as we now have previous aggregate industry clinical trials data alongside the analytics capability and potential for cross-comparison with healthcare records. Companies must be open to trying something new and should consider working with other parties – who could be seen as competitors – by sharing and pooling data.

Becoming data-driven in 2021


In reflecting on the above list, the key takeaway is that in 2021, biopharmaceutical companies need to take a data-driven approach to prevent delays to trials and manage spiraling costs. Points one and two are achievable quickly and should be “non-negotiable”. The third point, synthetic data, will take longer to achieve because it is a more complex (yet feasible) undertaking, but it must be the direction of travel for biopharmaceutical companies and trial sponsors. Improvements to the planning, design and enrollment of trials and eliminating resource waste are the minimum needed for the industry to progress. However, to succeed long term organizations must start to plan and adopt the use of synthetic data as soon as possible, which will not only help to reduce costs but provide significant benefits to patients. For this to happen regulators must also collaborate closely with the industry to help drive the changes needed.

While the COVID-19 pandemic has been enormously challenging, it could also be considered as a turning point for clinical development – an opportunity to rethink the model – having exposed long-standing issues and caused companies to reassess their operations and research practices. To make changes, the burden does not just fall on pharmaceutical organizations. Money and support is needed from governments and regulatory agencies around the world to help accelerate innovation and bring down the costs of clinical trials and drug development.

 

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