Susan Wood, PhD - President & CEO, VIDA Diagnostics, Inc.
We are amid a worldwide lung crisis. In the 40 years prior to COVID, lung disease mortality grew by 40%, while the mortality rate for other major diseases all decreased.1 Now, with 39% of COVID survivors experiencing long-term lung dysfunction,2 an additional 200 million patients3 have joined a crowded population of 500 million people with respiratory conditions.4
Common respiratory conditions are increasingly prevalent worldwide,1 and lung health is among the most pressing priorities for health systems, policy makers and, of course, those who suffer. Despite this increasing focus, progress on therapeutic development and delivery has been frustratingly slow, in part because respiratory diseases are difficult to diagnose, categorize, monitor, and manage. Tools commonly used to assess lung health, disease progression and therapy response produce data that are subjective and imprecise.
Inadequate assessment tools affect the quality of clinical trial data, leading to costly inefficiencies, delays, and failures. Clinical trials for respiratory therapies are notoriously expensive — costing 47% more than oncology trials and 80% more than cardiovascular trials4 — with more than half of those trials failing at or after Phase 3 studies.5 Many pipelines have stalled and investment in new lung and respiratory therapies has lagged.
Why are respiratory clinical trials especially susceptible to failure? How can study sponsors better approach site training, subject recruitment, and endpoint metrics to deliver much-needed therapeutic options to patients? To overcome these challenges, a more pragmatic, technology-driven approach to study design is needed, and below are three best practices for success.
1. Improve Endpoint Precision by Using Objective Metrics That Directly Measure Lung Changes
Respiratory studies have historically been plagued by weak data quality. Metrics commonly used to assess a patient’s disease status, progression, or treatment response are insensitive, subjective, or easily skewed by comorbidities and environmental factors unrelated to a patient’s actual lung health. The Six-Minute Walk Test (6MWT) and spirometry exam are good examples.
The 6MWT, measuring how far a person can walk in six minutes, is commonly cited as a metric of aerobic endurance in patients with Chronic Obstructive Pulmonary Disease (COPD), pulmonary hypertension, Interstitial Lung Disease (ILD), and other respiratory conditions. While this test is routinely used in clinical studies to establish an outcome measure of treatment effectiveness, its lack of sensitivity requires a large subject population to show signal strength.
Another common endpoint measure in respiratory trials, spirometry, is also prone to imprecision. Spirometry measures the ability of a subject to exhale air (volume and force). It is effort dependent and lacks the ability to pinpoint why there are airflow deficiencies. Most importantly, spirometry often lacks the sensitivity to detect early or subtle changes in lung anatomy.
Reliance on subjective and imprecise endpoints has been more a function of what is available, not what is best. Increasingly, AI-driven, image-based biomarkers are used to provide objective data on structural lung characteristics with pinpoint precision. These data reduce variability and are highly reproducible. Clinical trial sponsors can now use more precise, quantitative imaging-based data for endpoint analysis. This precision approach can dramatically reduce prolonged study timelines and unnecessary costs, encouraging investment in promising new therapies.
2. Improve Trial Site Onboarding and Success By Empowering High-Quality Standards And Driving Consistency
Sponsors must ensure that trial sites are adequately prepared and staff properly trained to execute protocols smoothly and retain participants. A single trial site costs $25,000 to onboard,4 so sites that fail to complete onboarding or who drop-out add significant cost and delays to a trial. Unfortunately, more than 10% fail to enroll even one patient.6 For trials that include imaging endpoints, complexity and the risk of site drop-out increases. High quality data requirements include proper subject breathing training, adherence to CT protocols, and regular scanner calibration. When multiple sites are involved, the complexity and variability increase further.
To reduce study location variability, sponsors must invest heavily in training and operations across clinical trial sites, ensuring protocols are executed consistently, equipment is regularly calibrated, and that rigorous standards are met for documentation and compliance. Site management is not easy as trial sites are in constant flux during studies, with employee departures, new hire onboarding, and regular equipment upgrades. Without structure and assiduous management, retraining, instrument revalidation and other critical steps can unnecessarily extend trial time and increase costs.
Fortunately, with intelligent platforms and systems, clinical trial imaging operations can be easier and unintimidating. AI-powered data quality controls play a key role in ensuring consistent, high-quality data. Automated algorithms can screen incoming data for common compliance errors, flagging problem datasets early, and maintaining the integrity of the data pool. Technology can also be utilized to proactively monitor global trial site performance using data quality measures, training certification, and more. AI is easing imaging operations for trial sites, resulting in more successful sites operating with efficiency and producing higher quality trial data.
3. Overcome Subject Recruiting Challenges by Using Objective Data and Inclusion Criteria to Recruit Smaller, More Representative Study Populations
Identifying qualified study candidates is a common trial failure point, especially in respiratory trials; it is fraught with risk and accounts for a significant portion of trial cost. Recruitment for respiratory clinical trials can cost up to $3.2 billion, with 37% of studies failing to reach target study sizes.7,8 Investigators recruiting for respiratory trials face unique challenges, including the imprecision and subjectivity of data that is generated by available assessment tools.
Patients with identical respiratory deficiency can report different triggers, present with a range of symptoms, and respond differently to treatment mechanisms. Population heterogeneity and symptom variability make it challenging to define and implement objective inclusion and exclusion criteria.
Objective measures to tighten recruitment criteria is an exceedingly high value in clinical trials, and increasingly sponsors are turning to data and artificial intelligence. If, for example, a trial is targeting individuals with upper-lobe predominant moderate emphysema, an AI-powered analysis of multiple chest Computerized Tomography (CT) scans can objectively identify candidates who meet these criteria, providing a more efficient, reliable, and unbiased method for recruiting ideal study populations.
With a statistically more representative population, researchers can reduce study size while improving quality and outcomes. Smaller patient numbers speed trial recruiting and decrease costs, and research supports this claim: According to a 2021 study, the probability of success for a drug to move from Phase I to approval doubles when preselection biomarkers are used.7 A second study further validates this claim, finding while 550 subjects would be required to confirm therapeutic improvement using conventional measures, only 130 patients would be required if imaging data were used.8
Conclusion
The costs of lung disease are crippling health systems, and the pandemic has further stretched limited resources. Addressing the subjectivity and imprecision of clinical trials significantly advances trial quality for the entire biopharma industry and benefits the patients who will receive new therapies. These advances are especially true with lung and respiratory related trials.
With AI-powered lung intelligence delivering proven efficiencies at trial sites and emboldening sponsors to confidently invest in R&D, we can blunt the looming crisis of the lung within this decade and bring much-needed therapies to patients faster and for far less cost.
References
- GBD Chronic Respiratory Disease Collaborators. Prevalence and attributable health burden of chronic respiratory diseases, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet Respir Med. 2020;8(6):585-596. Available at: Prevalence and attributable health burden of chronic respiratory diseases, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017 Accessed April 15, 2022
- Sanchez-Ramirez DC, Normand K, Zhaoyun Y, Torres-Castro R. Long-Term Impact of COVID-19: A Systematic Review of the Literature and Meta-Analysis. Biomedicines. 2021;9(8):900. Published 2021 Jul 27. doi:10.3390/biomedicines9080900. Available at: Long-Term Impact of COVID-19: A Systematic Review of the Literature and Meta-Analysis. Accessed May 4, 2022
- Our World Data, Johns Hopkins University, CSSE COVID-19 Data, Cumulative Confirmed COVID-19 Cases. Available at: https://ourworldindata.org/explorers/coronavirus-data-explorer. Accessed May 4, 2022.
- Sertkaya A, Birkenbach A, Berlind A, et al. Examination of Clinical Trial Costs and Barriers for Drug Development. Available at: https://aspe.hhs.gov/sites/default/files/private/ pdf/77166/rpt_erg.pdf. Accessed April 15, 2022.
- Biotechnology Innovation Organization (BIO), Pharma Intelligence, Quantitative Life Sciences. Clinical Development Success Rates, 2011-2020. Available at: https://pharmaintelligence. informa.com/~/media/informa-shop-window/pharma/2021/files/reports/2021-clinical-development-success-rates-2011-2020-v17.pdf. Accessed April 15, 2022.
- Dirksen A, Dijkman JH, Madsen F, et al. A randomized clinical trial of alpha(1)-antitrypsin augmentation therapy. Am J Respir Crit Care Med. 1999;160(5 Pt 1):1468-1472. Available at: A randomized clinical trial of alpha(1)-antitrypsin augmentation therapy Accessed May 4, 2022
- Antidote. 5 Ways to Lower Clinical Trial Patient Recruitment Costs. Available at: https:// www.antidote.me/blog/5-ways-to-lower-clinical-trial-patient-recruitment-costs. Accessed April 15, 2022.
- Roots Analysis. Patient Recruitment and Retention Services Market (2nd Edition) by Therapeutic Areas (Cardiovascular Diseases, Oncological Disorders, Infectious Diseases, CNS Disorders, Respiratory Disorders, Hematological Disorders and Others), Patient Recruitment Steps (Pre-screening and Screening), Trial Phases (Phase I, Phase II, Phase III and Phase IV), and Key Geographies (North America, Europe, Asia-Pacific, Latin America, MENA, and RoW): Industry Trends and Global Forecasts, 2021-2030. Available at: https:// www.rootsanalysis.com/reports/view_document/patient-recruitment-and-retention-services-market/245.html. Accessed April 15, 2022.
Dr. Susan Wood has 25+ years of experience championing innovative clinical solutions into routine clinical use. Dr. Wood received her Ph.D. from the Johns Hopkins Medical Institutions, School of Hygiene and Public Health. Her Ph.D. work combined quantifying three-dimensional lung structure with changes in lung function using high-resolution CT imaging. She also holds a Master of Science degree in Biomedical Engineering from Duke University, and a Bachelor of Science in Engineering from the University of Maryland, College Park.
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