Transforming clinical trials

It’s an exciting time in medicine. As our understanding of the causes and complexities of disease has grown, so too have the approaches to treating them. Thanks, in part, to medications developed in recent years, we’ve seen a dramatic decrease in overall mortality from common health problems such as heart disease and cancer.^1,2 Drug advances have even drastically changed the prognosis for people with less common conditions, such as HIV/AIDS, which once had no effective treatments.³

The future may be even brighter. There are close to 7,000 new medications in development worldwide, most of which could be first-in-class treatments.⁴ These drugs hold the potential to cure diseases, improve quality of life and lower economic costs by reducing the need for medical care and hospitalizations and improving personal productivity. Research has estimated, for example, that a new type of medication for migraines could save $396 billion over 10 years by reducing the number of migraines people experience, helping them continue to raise a family, work and remain productive without disruption.⁵

With benefits like these, it’s clear that getting new drugs to market as quickly as possible is a priority. Data and analytics can help make it happen. Recent advances in artificial intelligence enabled analytics and technologies create the foundation of breakthrough drugs.

Drug development: The trials of clinical trials

It’s often reported that it takes 10 to 15 years for a newly discovered drug to enter the marketplace — the true number may be closer to 30 years.⁶ The average cost is $2.7 billion.7 Clinical trials and the time to recruit qualified patients are behind most of that expense.

Clinical trials typically proceed in four phases:

A new medication must successfully complete each phase before advancing to the next. Unfortunately, nine of every 10 drugs in clinical trials fail.⁸

Recruitment at each phase poses a challenge

Locating and enrolling people to participate can often be challenging, especially if the study involves an uncommon medical condition. Investigators must frequently double the enrollment period to reach recruitment targets, and sometimes, they never do meet their goals. One study found that nearly one-fifth of cancer-related clinical trials failed because they didn’t recruit enough participants.⁹

Barriers to recruitment may include:

• The extensive number of clinical trials that exist
• Lack of recommendation from providers
• Potential time burdens for participants
• Lack of desire to participate if other previously approved treatments are available
• Costs associated with finding participants¹⁰

Issues may also arise after participants enroll. One study found trial outcomes were missing from almost 90% of reviewed clinical trials.¹¹ Participants who fail to adhere to study protocols or who drop out are among the biggest problems.¹² Recruiting additional study participants may be necessary in order to collect complete, accurate data.

Tackling the cost and time of conducting clinical trials

Optum has developed a process for identifying and contacting those who qualify, and alerting them to the trial’s existence. It streamlines the recruitment and enrollment process and, just as importantly, offers hope to people who previously had no treatment options — and may not have otherwise heard of the trial.

The Digital Research Network (DRN) is an example of a new way pharmaceutical companies recruit patients and conduct clinical trials. Working with preapproved site partners, the DRN captures research-ready patients by prescreening for eligibility through 91 million unique electronic health records.

People who meet a trial’s inclusion and exclusion criteria are identified using a variety of data including ICD-10 codes, pharmacy claims, lab tests and other data tools as screening filters. This means what could take years to do manually in some cases, could potentially be accomplished in weeks. Faster enrollment translates into trials being conducted faster. This can reduce development costs and/or time to market for successfully tested products by 20% to 30%.

Using data and analytics to address barriers

Time to market and recruitment come at a high cost. And while we should not cut trials where efficacy and quality are primary objectives, advancements in available data and technology offer hope to improve some challenging areas in the process.

Apellis Pharmaceuticals, a biotech company, illustrates how DRN is being used to help in the development of a first-ever treatment for geographic atrophy (GA). GA is a form of late-stage age-related macular degeneration (AMD), a progressive eye disorder that leads to vision loss. According to the BrightFocus Foundation, there are no proven ways to prevent or slow down the progression of AMD.

Apellis explored new ways to recruit for their phase III trial. On their own, it was difficult to find people with GA to take part in their phase II trial. With Optum DRN, Apellis was able to identify and locate potential trial participants. Using data and analytics, the DRN searched health care claims and electronic health record data, working with physicians. Potential participants were identified for the larger phase III trials in a fraction of the time it would have otherwise taken.

Advanced analytics and technology create additional benefits

Looking to the future

The Apellis trial is ongoing, but already the company is recognizing how data and analytics are helping transform the drug development process to one that contains costs and responsibly brings new drugs to market faster. While this is important, there are additional benefits that can be seen that also deliver high value such as having a better understanding about the disease progression and improving the design of a clinical study so that people struggling with diseases are given more hope for the future.

1. Mensah GA, Wei GS, Sorlie PD, et al. Decline in cardiovascular mortality: Possible causes and Implications. Circ Res. 2017; 120(2): 366–380. ahajournals.org/doi/full/10.1161/CIRCRESAHA.116.309115. Accessed June 10, 2019.
2. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2018. CA Cancer J Clin. 2018;68:7–30. onlinelibrary.wiley.com/doi/full/10.3322/caac.21442. Accessed June 8, 2019.
3. Diamond, F. HIV/AIDS treatment drastically reduces mortality and helps to reduce transmission. Manag Care. managedcaremag.com/archives/2018/11/hivaids-treatment-drastically-reduces-mortality-and-helps-limit-transmission. Published November 4, 2018. Accessed June 10, 2019.
4. Pharmaceutical Researchers and Manufacturers of America. In the pipeline: What’s next in drug discovery. phrma.org/science/in-the-pipeline. Accessed June 8, 2019.
5. Soltoff S, Koenig L, Halker Singh R, et al. The effects of calcitonin gene-related peptide inhibitors on migraine days, healthcare use, and workplace productivity: A Markov model approach. KNG Health Consulting LLC. knghealth.com/kngwp/wp-content/uploads/2018/05/KNG-Health-Final-CGRP-Inhibitor-r05262018.pdf. Published May 21, 2018. Accessed June 10, 2019.
6. Ledley, FD. 30 years is too long to wait for new medicines. There are ways to speed up drug development. STAT. statnews.com/2018/06/06/drug-development-speed-new-medicines/. Published June 6, 2018. Accessed June 10, 2019.
7. Herper, M. The cost of developing drugs is insane. That paper that says otherwise is insanely bad. Forbes. forbes.com/sites/matthewherper/2017/10/16/the-cost-of-developing-drugs-is-insane-a-paper-that-argued-otherwise-was-insanely-bad/#15a141392d45. Published October 16, 2017. Accessed June 10, 2019.
8. Ibid.
9. Bennette CS, Ramsey SD, McDermott CL, et al. Predicting low accrual in the National Cancer Institute’s Cooperative Clinical Trials. J Natl Cancer Inst. doi.org/10.1093/jnci/djv324. Published December 29, 2015. Accessed June 11, 2019.
10. Kaufman J. The innovative startups improving clinical trial recruitment, enrollment, retention and design. Mobi Health News. mobihealthnews.com/content/innovative-startups-improving-clinical-trial-recruitment-enrollment-retention-and-design. Published November 30, 2018. Accessed June 11, 2018.
11. Ibid.
12. Ibid.