Precision patient finding has the potential to radically reduce the cost of trials and create a more centralized approach which serves to benefit a multitude of stakeholders, from biotechs to HCPs and patients.
One approach that has high cost-saving potential is comprehensive genome profiling (CGP). In the majority of genetics-based clinical trials, potential participants are genetically sequenced to verify the presence of a single specific variant and the results are then retained by the sponsor or relevant participating institution. Often, the reason single gene tests are employed is due to regulatory requirements.
Using a genetic screening test that costs $500 per person to screen for a variant present in 1 out of 100 people, a clinical trial requiring 20 eligible participants would need a sequencing budget of $1 million. If 10 trials each require 20 participants that carry a genetic variant present in 1% of the population, and each trial uses an individual, biomarker specific test, the total screening costs for those studies will be $10 million (the cost of 20,000 tests).
However, if all 10 biomarkers could be covered in a single comprehensive genome profiling test, this means that 10x fewer tests (only 2,000) would be required for all of the trials to identify 20 eligible patients. Therefore, if a CPG test costs $2,500 per person, the total screening costs for those 10 studies would be $5 million – half that of a traditional single variant approach.
In trials where speed is the priority rather than cost saving, the same $10 million screening budget could be used to sequence 4,000 patients, enabling identification of eligible patients twice as fast and halving the time required to complete trial enrollment.
In a 2023 analysis of 2,542 randomized clinical trials registered on ClinicalTrials.gov, researchers found that approximately 1 in 5 studies were completed within the planned time frame and that the median trial delay was 12.2 months. With the average daily cost of running a clinical trial estimated to be approximately $40,000, that works out as the average clinical trial losing more than $17 million to delays alone.
However, it’s important to note that this figure varies significantly depending on the disease area of focus and the stage of the clinical trial. For instance, the average daily trial costs by phase are as follows:
Precision patient finding methods such as CGP therefore not only help reduce the cost of screening and make more efficient use of resources and operational budgets, but also have a wider impact on trial costs. Such methods also contribute to a reduction in delays through rapid identification of highly relevant participants, enhanced enrollment efficiency and reduced numbers of screening failures. When tools such as genomic profiling and AI data analysis are brought together, this helps generate a highly relevant pool of patients from the outset and reduces the number of individuals who screen fail at cost to the sponsor.
These technologies can also help reduce delays associated with underperforming trial sites by identifying locations that have higher concentrations of eligible patients, enabling sponsors to focus trial recruitment efforts more effectively.
Losing patients to trial drop out is a costly process. When recruiting a single patient can cost more than $6,500, replacing them can then cost even more. Reducing patient dropout is a key area in which biotechs can create cost and time savings. The use of analytics and patient profiling is fundamental to helping reduce dropout, enabling the identification and exclusion of patients who, for example, live too far from trial sites or whose stage of disease means participation is impractical.
Precision patient finding approaches can also minimize dropout by identifying highly relevant patients from the outset, in turn resulting in a better quality patient experience and a more positive view of trial participation.
Reducing the risk of trial failure is a fundamental objective of precision patient finding programs in a highly competitive sector where the overall success rate of clinical trials is only 7.9%.
The approaches discussed above play a key role in helping de-risk trials and enabling the dynamic adaptation of trial design as the study progresses. When these methods are used to identify highly relevant patients from the outset, insufficient enrollment, high screen fail rates, and patient drop-out are significantly reduced, leading to an overall reduced risk of trial failure. Additionally, enhanced accuracy in patient selection leads to overall better quality of trial data, resulting in enhanced patient outcomes and a greater chance of therapeutic success within a reduced timeframe.
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