Is Data Science the Treatment for Inefficiencies in Clinical Trial Operations?

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Written By managementconsulting

Half of the total cost of bringing a drug to market is lost to lengthy drug development timelines, and inefficiencies associated with clinical trial design and implementation make up a significant portion of those costs. One key factor that leads to delays in clinical programs is patient recruitment and enrollment. Today, nearly 80% of clinical trials fail to meet enrollment timelines, and approximately one-third (30%) of phase III study terminations are due to enrollment difficulties. Moreover, longer recruitment periods mean longer trials and increased cost. Clinical trials typically last 42% longer than expected in phase I, 31% longer in phase II, and 30% beyond planned timelines in phase III, mostly because of recruitment delays. These costly delays are getting worse. One study showed that between 2008 and 2011, the cost for a patient in a phase I trial increased 33%, phase II costs rose 75% and phase III costs rose 88%. The smallest increase occurred in phase IV, which still saw costs increase 30%.

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How can pharmaceutical companies address these seemingly insurmountable clinical trial inefficiencies? A holistic, data-driven approach to clinical trial decision-making can help companies enhance the feasibility process, design trials that allow for more efficient patient enrollment, and run those trials at the right sites that treat patients who are best suited for their trials. While we’ve found that most major pharmaceutical companies are making efforts to align with this type of approach—using real-world and publicly available data to build more sophisticated analytics capabilities—these efforts are often fragmented, implemented inconsistently and difficult to scale. Companies often budget in anticipation of rescuing struggling clinical trials; however, if they applied some of that budget to the design phase, they could save considerable time and resources in preventing such failures. Here’s how a data-driven approach can help improve clinical trial design in six key areas:

Competitive intelligence: A thorough understanding of your patient population, and the trials that will compete with yours, is essential during the planning phases. An investigator’s current trial burden can be a strong indicator of how they will perform on your trial. Selecting investigators who are participating in complementary trials (versus competing trials) will ensure that you’re able to maximize your investment in a site. For example, an investigator who has a robust trial portfolio may have resourcing issues with your trial, but if all of the investigator’s trials are in the same disease area, there may be an opportunity to capture a complementary patient population.

Another key factor to consider is the number of trials that an investigator is participating in compared with what epidemiology data tells us about the number of patients available in that region. Using anonymized patient longitudinal data, you can learn how many patients with a specific condition reside in a geography. If the incidence of a disease is low, and there are other trials in that geography, it’s important to take a closer look at how those competing trials will affect enrollment and consider alternate regions or adjustments in timelines.

Country selection: When determining global allocation for a clinical trial, pharma companies need to consider several factors:

The population: Estimates suggest that more than 35% of sites will fail to enroll the number of subjects they indicated during the site qualification process, according to the Tufts Center for the Study of Drug Development. Using electronic health records, claims data and other epidemiology data, you can determine which countries have the highest concentrations of patients eligible for your clinical trial consulting, allowing for appropriate allocation of patients in different countries and sites.

Local clinical trial history: Looking at average site initiation timelines and enrollment timelines for similar trials within your preferred countries will give you an idea of how many patients you can expect the region to deliver in the amount of time you have to enroll patients in the study. It’s also essential to consider the cost. Using internal and industry benchmark data on historical clinical spending, you can model the cost of running a specific trial in a country or region. Lastly, the availability of high-performing sites and investigators is key to country selection. Building a detailed picture of the site and investigator landscape can help inform a robust country selection strategy. Using publicly available data, combined with your own internal data on site performance and industry benchmarks, you can identify the highest performing centers across therapeutic areas. This will ensure that you’re selecting the right sites in the right countries for the right trials.

The commercial and regulatory environment: Data on the utilization of currently approved products, treatment pathways and paradigms, and reimbursement should be considered when determining country selection. Understanding the potential for commercialization and any concerns that should be addressed during product development can eliminate the need for additional and costly country-specific studies. For example, if you were developing a new therapy for arthritis that you expected would become the third product in your class to market, would you invest in clinical trials in a region where the local health authority had not agreed to reimburse for the second product, favoring the first? Regulatory factors should be considered in determining country selection, prioritization and planning. For example, study timelines can be significantly impacted by how long health authorities take to review and approve a protocol, as well as the requirements and time for institutional review boards or research ethics boards to complete protocol reviews. It’s also critical to develop a thorough understanding of local regulations. For example, some countries will require that a sponsor purchase any co-therapies involved in a trial design for patients, while others may allow patients to receive the products commercially.

KOL influence mapping: Understanding key opinion leaders in a disease state is critical to the success of a clinical development program. Beyond standard KOL mapping techniques, advanced technology can be used to identify current and future networks of influencers. Through machine learning and AI, you can create a comprehensive influence map of a disease area. You also can create an in-depth picture of current and up-and-coming influencers in your therapeutic area by analyzing their papers and practice guidelines, investigator participation in clinical trials, speaking engagements at conferences, leadership positions within associations and editorial positions in key journals.

For example, clinical trials in certain disease states are particularly challenging because a variety of specialists may treat a specific condition. Finding the key influencers requires a strategy that goes beyond traditional KOL mapping to create a comprehensive network and pathways for patients to find clinical trial sites through referrals. When you’ve identified key opinion leaders in a specific geography, you can plan a referral strategy that maximizes patient enrollment. If you create a site map that identifies where the influencers are and then open sites near clusters of KOLs, you can use these influencers to work with the community to refer patients. You can also identify referral sites based on established relationships across trial sites and non-trial sites, and adjust for established competitive relationships.

Enrollment forecasting:
Patient enrollment has been one of the most difficult and challenging steps in clinical trials for several years. There are a host of operational factors that negatively impact enrollment. However, the most significant problem is forecasting that differs considerably from reality. Today, almost 80% of trials fail to meet enrollment timelines. The issue is not that a trial might take 30 months to enroll the required number of patients, but that it was wrongly forecasted to take only 20 months. Having realistic expectations for how quickly a trial will complete enrollment has many downstream benefits.



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