Embrace Innovation to Dodge Rising Trial Costs

The cost of conducting a clinical trial has been rising in recent years and that trend shows no signs of abating anytime soon. And that means that the clinical research industry needs to overcome its traditional resistance to change and adopt innovations that can save both money and time to mitigate the impact of those rising costs.
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But it’s not enough to merely adopt new technologies or other innovative practices and hope that costs go down. It is important to carefully select the innovations adopted based on four tests:

• Does it reduce cost and/or time?
• Does it cause creative destruction, or replacement of old processes with improved ones?
• Does it require doing business differently, in a better, more efficient way?
• Do users love it and actually use it?

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The cost of everything is rising rapidly, and the cost of developing new pharmaceuticals is no exception. Increases in areas like food, gas and airfare play a role in the increased cost of pharma R&D, but clinical trials also face unique factors that add to their expense. According to 2021 estimates by Deloitte, the average cost of development has soared from $450 million in 2013 to $2.45 billion in 2020.

In response to pressures—including those due to increasing costs—many industries will seek out innovations that improve the efficiency of their processes. The clinical research industry, however, has traditionally been resistant to innovations out of fear of disturbing the status quo.

But the rising costs facing biopharma R&D mean that continuing to do everything the same way is not going to work for long. Clinical research sites and sponsors must find ways of reducing costs wherever possible in order for pharma R&D to continue at the same brisk pace currently seen.

Innovation and cost control

Innovation in key areas will be key to keeping costs under control in the future. Industries must evolve to survive; it’s neither wise nor safe to continue doing things the same way indefinitely. And the clinical research industry is capable of pivoting quickly to innovate when needed. For instance, COVID-driven adoption of fully or partially decentralized clinical trials proves the benefits of judicious innovation. 
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In fact, the Tufts Center for the Study of Drug Development (CSDD) concluded earlier this year that decentralized clinical trials provide net financial benefits, with ROI of about $10 million for Phase 2 studies and $39 million for Phase 3 trials. CSDD analyzed real financial data from Medable-enabled clinical trials. Among the key findings were than decentralized research led to:

• Shorter development cycle times;
• Lower trial screen failure rates; and
• Fewer protocol amendments.

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Reductions in cycle times have the greatest impact on ROI, the CSDD study said. And decentralized or hybrid trials look likely to remain in place for the long-term. These factors indicate that adoption of innovations that allow decentralized trials meet the four tests of a useful innovation. 

Application of new technology is the form that innovation tends to take. For instance, use of artificial intelligence (AI) and machine learning (ML) is another area of innovation that shows promise in reducing the cost of clinical trials. An article in Pharmanews Intelligence earlier this year suggested that greater use of AI and ML can decrease some costs associated with drug research by as much as $26 billion per year.

And technology allowing for remote or partially remote trials—such as wearables that automatically track key patient metrics and use of telehealth—is featured frequently in trial decentralization efforts. But industry adoption of new technology, as well as new methods of operation, remains somewhat slow, especially compared to other industries.

But a 2018 study by Tufts Center for the Study of Drug Development (CSDD) showed that although 80% of respondents who have invested in technology report time savings for site initiation through activation, most indicate that their tools could be improved. 

Protocol deviations and the role of training

While many innovations in clinical trial operations aim to reduce costs by keeping cycle times shorter, one of the primary reasons clinical trials take longer—and cost more—than expected is simple error. Protocol deviations are common, consistently topping the list of BIMO inspection observations over the last several years, according to regular FDA reports. 

And some of the most promising innovations also seem to come with the risk of more frequent errors and the associated costs for dealing with them. Risks include protocol deviations, queries, reconciliations, SUSARs and data rejections. Remediation of protocol deviations can make up 27% of costs.

Decentralization, for instance, can lead to larger and more complex trials. In early 2022, CSDD noted that protocols have been growing increasingly complex since 2009, with no end in sight to that trend. For instance, the CSDD report said that Phase II and III protocols as of 2020 generally had about 20 endpoints, with an average of 1.6 primary endpoints, up 27% since 2009. And the mean number of distinct procedures required for a typical protocol rose 44% during the same timeframe.

DCTs, adaptive study designs and use of synthetic control arms are among some of the shifts in recent years that contribute to protocol complexity.

In addition, most clinical trials will use multiple vendors and technologies. This makes it ever-more-complex to communicate information about the study, as well as managing all the roles and rosters at both sites and vendors.

The solution to these problems could well lie with more innovative approaches to training. Traditionally, sponsors have provided protocol training via massive slide decks, often accompanied by an in-person or web-based lecture. But this is not the best approach for ensuring that clinical research staff perform protocol-mandated tasks and procedures correctly.

In fact, a 2019 Harvard study indicated that slide-based training and education is not effective in imparting knowledge in a way that alters performance. And the entire purpose of protocol training is supposed to be ensuring that staff perform optimally in carrying out tasks exactly as described in the protocol. 

Where slides and lectures fall down, however, simulation-based training can shine. Protocol simulation training provides actual practice in critical study skills, ranging from GCPs through informed consent, proper handling of the investigational product, patient screening and more. 
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For example, the inclusion requirements in a protocol for the study of a major depressive disorder (MDD) treatment might specify that patients meet five specific criteria, such as:

• The patient must have experienced a previous MDD episode.
• That episode must have lasted more than 10 weeks.
• The patient must currently be taking two antidepressant medications for a current MDD episode.
• The patient must be experiencing treatment failure with those medications, defined as reduction in symptoms of less than 40%, according to the ATRQ scale.
• The patient must have been in treatment for at least eight weeks.

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Standard lecture-and-slide training might quiz research staff on these criteria by offering a multiple-choice question asking them to choose the correct criteria from among various options, most of which included at least one incorrect item. And the researchers may very well pass this test immediately after sitting through the lecture or slide presentation.

But whether they can correctly apply the inclusion criteria in real-life situations is neither trained nor tested in this approach.

Conversely, in a simulation approach, learners might be presented with a variety of patient charts. Some of the patients will meet all the inclusion criteria, while others meet some or none. In this approach, research staff will be able to play-act the actual screening process. This trains them to correctly identify patients who meet or do not meet the criteria, as well as providing actual practice in conducting the patient screening that more closely mirrors real life.

Broad application of simulation training for healthcare providers in Ethiopia for the CDC showed significant improvement in performance after simulation training compared to more traditional training methods. After simulation training, 97% of healthcare providers successfully executed a simulated patient encounter, compared to a previous pass rate of 69%.

And the ability to track performance during simulation training can help sponsors predict site performance and intervene to address weak areas before a trial even begins. For example, sponsors can track performance of staff at different sites in accurately applying inclusion metrics, a critical part of enrollment.

In addition to reducing errors, simulation can reduce training time by 50%, further contributing to lower costs. 

In these ways, simulation-based training passes the first three tests of valuable innovation with ease. Additionally, sponsors that have applied this type of training consistently respond positively and continue to use simulation, meaning that it also passes the fourth test while emphasizing its potential for creative destruction. 

The effectiveness of simulation-based training has been borne out in other industries. Aviation crashes, for instance, are greatly reduced when pilots are trained via simulation. And in the healthcare arena, simulation training reversed an industry-threatening volume of anesthesia mishaps. 

While the clinical research industry traditionally has been slower than average to adopt innovations, that needs to change. Judicious adoption of innovations can help keep clinical trial costs under control. Of particular value is adoption of new training approaches, like simulation, that can prevent time-wasting and cost-increasing errors, as well as reducing the overall cost of training from the start of the process.
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Visit our Pro-Active Protocol webpage to learn about our approach to protocol optimization: http://proficiency1.wpenginepowered.com/pro-active-protocol/

David Hadden

Dave Hadden is an entrepreneur and technology innovator, founding Pro-ficiency and pioneering the fields of A.I.-based medical decision-support, Training Analytics, and Virtual Patient Simulation (VPS). Dave has focused his passion for technological innovation and learning systems in the field of clinical trials, helping sponsors make their studies more accurate and efficient through finding the right technology mix such as virtualization, performance management and applied behavioral sciences to produce the most effective, lasting, and engaging results for clinical trials.