(Clinical) Trial or Error: The Modernization of Respiratory Clinical Trials

Contributed Commentary by Susan Wood, Vida

November 23, 2021 | Historically, clinical trials—essential for bringing new drugs to market to improve patient outcomes—are slow and costly. On average, it takes 10-15 years to get a new drug to market, according to Eastern Research Group, Inc. Of all therapeutic areas, none is more expensive than respiratory—in fact, respiratory trials cost 47% more than oncology trials and 80% more than cardiovascular trials.

Why are respiratory trials so expensive? While the reasons are numerous, there are three major factors worth dissecting:

  1. Conventional measures: The primary measures for pulmonary trials today often require a high number of subjects to achieve a strong statistical signal. Measures like the six-minute walk test (6MWT), for example, are prone to confounding variables not directly related to lung health. A more precise, quantitative method for assessing lung structure and function in clinical trials would complement existing processes and potentially require fewer subjects.
  2. Proper subject population: Finding and recruiting appropriate subjects that meet inclusion criteria is a challenge for nearly all trials. Data is not readily accessible, and physicians have a difficult time staying informed on current trial opportunities for their patients.
  3. Site recruitment and retention: Strong respiratory trial sites can be difficult to find. Root Analysis reports that 37% of research sites do not meet subject accrual goals and more than 10% fail to enroll a single patient. Trials that involve imaging are especially challenging for sites given the complexities of subject breathing training, CT protocols, data security and more.

The results of these challenges are slow and expensive paths to market for new therapies, if they make it that far. Unfortunately, most do not. 58% of respiratory drugs fail in the last phase of the clinical trial process, according to the Biotechnology Innovation Organization (BIO).

So how can the pulmonary field that is still struggling to cope with COVID-19, massive wildfires, and the epidemic of lung injury due to vaping, meet the critical need for accelerated drug development and improved clinical care? The answer must come directly from investigators and clinicians doing the work, applying technology in new ways based on what they’ve learned.

Utilizing Precise, Quantitative Data

Respiratory trials are increasingly utilizing precise quantitative imaging-based data, either as endpoints, inclusion/exclusion criteria or both. These imaging endpoints allow sponsors to leverage tremendous advances in imaging technology, both in acquisition (scanner hardware) and data processing (software).

Quantitative imaging offers several key advantages over conventional measures. First, imaging biomarkers offer reliable, objective measures of structural lung changes. One advantage is measuring airway wall thickness with quantitative CT (QCT) with millimeter precision. Another advantage of quantitative imaging is its repeatability. While a subject might have some variability in a six-minute walk test or spirometry effort, imaging data from time point to time point, if properly acquired, is likely more reliable and repeatable, especially when sites are adequately trained to collect imaging for clinical trials.

Imaging biomarkers can also be useful for subject selection by applying inclusion and exclusion criteria. A BIO study found that the probability of success for a given drug to graduate from Phase I to approval doubles when preselection biomarkers are used.

If highly precise imaging measures can be utilized in a trial, it’s likely that fewer subjects will be necessary to reach a statistically significant result. One study on this topic showed that a trial with 550 subjects using conventional measures could have been reduced to 130 patients if QCT measures were used. Another study on the impact of QCT measure on study subjects found that “population sizes needed to detect meaningful changes ranged from a few (20-40) to a few hundred participants.

Since the number of subjects greatly impacts trial cost and speed, fewer subjects mean less expensive and faster trials.

Finding Clinical Trial Subjects

Reducing the number of required trial subjects is a great start, but it can still be a major challenge to find suitable patients for a respiratory trial. In fact, research firm Roots estimates $3.2 billion is spent on patient recruitment for clinical trials. Fortunately, imaging data contains rich, insightful information that can help to remedy this challenge. Increasingly, artificial intelligence (AI) is gaining favor in cardiovascular and pulmonary care and makes it possible to identify imaging-based biomarkers in the lungs that can indicate whether a patient is an ideal candidate for a clinical study.

Markers of emphysema, COPD, interstitial lung disease (ILD), asthma, and lung cancer can be identified and used to pinpoint viable trial participants. For example, let’s say a new emphysema drug trial is targeting individuals with upper-lobe damage with a disease burden of 50% or more. If an automated AI-powered analysis of all chest CT scans can quantify imaging features by lobe, candidates for the trial can be easily surfaced. This automation helps to reduce the cognitive load from treating physicians, who are otherwise tasked with recalling trial opportunities, their inclusion/exclusion criteria, and matching those with any particular patient on the fly. 

Clinical Trial Site Management

The challenges of managing a clinical trial site include proper training, consistency, equipment calibration, and maintaining high standards for efficiency and compliance. It’s not easy. Staff turnover is high, scanners get upgraded, and the need to retrain makes the trial process even longer and more costly. Adding imaging-based biomarkers to this process needn’t add complexity. We’ve successfully trained site personnel to ensure images are acquired using specific CT protocols and process controls that ensure scanner settings and breathing instructions are executed properly by a trained, certified technician on a calibrated CT scanner.

Conclusion

Tech companies applying data and AI with medical experience can be the vanguard for changing the practice of respiratory care. Pharma companies are clear about the challenges they face, especially at this time when the COVID-19 pandemic has sharpened the world’s focus on lung and respiratory health. Lessening the pain to find the proper clinical trial population, modernizing conventional methods for gathering data, and site recruitment and retention will help new pulmonary drugs get to market more efficiently.

 

Dr. Susan Wood, president & CEO of VIDA Diagnostics, 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. She can be reached at  Susan@vidalung.ai.

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