Streamlining the Complex Respiratory Clinical Trial Infrastructure


The lung is a complex organ, and the clinical trials processes that support new therapies for lung and respiratory disease are equally complex. Streamlining these processes with new approaches, including artificial intelligence (AI), could significantly improve the speed and quality of respiratory clinical trials while lowering costs. This article will discuss steps investigators and sponsors can take now to streamline trials and accelerate the time to market for lifesaving and life-changing therapies.

According to Eastern Research Group, it can take more than a decade to get a new drug to market, and respiratory trials are among the most expensive. And, according to the Biotechnology Innovation Organization (BIO), 58% of respiratory drugs fail in the last phase of the clinical trial process.1

A first reason cited for failure is that pulmonary trials require a high number of subjects to achieve strong statistical significance. The conventional measures used to assess trial participants, such as the six-minute walk test, can introduce variability that is not directly related to lung health, and this affects the data. A more precise quantitative method for assessing lung structure and function in clinical trials would complement existing measures and potentially require fewer subjects.2

Finding and recruiting appropriate subjects that meet inclusion criteria is a challenge for nearly all trials. Unfortunately, patient data is not readily accessible to investigators, and physicians have a difficult time staying informed about current trial opportunities for their patients.

Lastly, Roots Analysis data show that recruitment and retention issues make it difficult to find viable clinical trial sites. In fact, 37% of research sites do not meet subject accrual goals and more than 10% fail to enroll a single patient.3 Respiratory trials that involve imaging are especially challenging for sites given the complexities of subject breathing training, acquisition protocols, data security and more. These challenges create costly delays for new therapies and stop some promising trials altogether. These delays come at a time of continual increases in incidence, prevalence and mortality from lung disease and a need for technological and therapeutic advances that can be addressed with better approaches to imaging, recruiting and clinical site preparedness.

Quantitative imaging data is key

Over the past decade, better hardware and software have enabled trial sponsors to rely on more precise quantitative imaging-based data for respiratory clinical trials, providing much more detailed information about the condition of the lung.

Quantitative imaging has several advantages over conventional measures. First, imaging biomarkers offer reliable, objective measures of structural lung changes, such as the ability to measure airway wall thickness with millimeter precision using quantitative CT (QCT). A second advantage is repeatability. While a subject might have some variability in a spirometry effort, imaging data 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.4

If highly precise imaging measures can be used 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.5 Reducing the number of subjects can speed trials and lower costs.

Focus on the right clinical trial subjects

Reducing the number of required trial subjects is a great start, but it can still be challenging to find the right patients for a respiratory trial; research firm Roots estimates $3.2 billion is spent on patient recruitment alone.6 Rich insights contained within imaging data can help address this challenge, however. Thanks to AI, sponsors can use imaging-based biomarkers to objectively and quantitatively determine which patients are ideal candidates for a clinical study.

Markers of emphysema, COPD, interstitial lung disease (ILD), asthma, and lung cancer are now used to identify viable trial participants. If, for example, a new emphysema drug trial is targeting individuals with upper-lobe predominant emphysema with a disease burden of 50% or more, an automated AI-powered analysis of multiple chest CT scans can surface candidates who match the specific criteria. The entire sourcing process is automated, which is an obvious benefit for trial sponsors as well as treating physicians whose patients may be included in promising trials based purely on their existing scan data.

Clinical trial site training and best practices

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.


Technology, including AI, that supports earlier and more accurate diagnosis and management of lung and respiratory disease, is now more accepted across healthcare and biopharma. For clinical trials specifically, bringing quantification and objectivity to patient selection and trial management will improve the economics and patient impact of drug discovery and commercialization. It’s not hard to see how greater lung imaging precision, providing AI-enabled data on exact location and nature of damage, is invaluable in drug development, especially when this same technology is also becoming the standard of care for patient diagnosis and therapeutic guidance around the world.

Susan Wood, PhD, is the President & CEO of VIDA Diagnostics


  1. Thomas DW, Burns J, Audette J, et al. Clinical development success rates 2006-2015. Biotechnology Innovation Organization, Washington DC. June 2016.
  2. Dirksen and colleagues found that “using quantitative CT (QCT) measures, power analysis showed that the effect would be significant in a similar trial with 130 patients” vs. 550 subjects with conventional measures. Source: 1 - Dirksen et al, A Randomized Clinical Trial of a1-Antitrypsin Augmentation Therapy, Am J Respiratory Critical Care, Vol 160, 1468-1472, 1999
  3. Source: Roots Analysis
  4. Biotechnology Innovation Organization (BIO), Pharma Intelligence, Quantitative Life Sciences; “Clinical Development Success Rates, 2011-2020,” February 2021, Page 18
  5. Dirksen et al, A Randomized Clinical Trial of a1-Antitrypsin Augmentation Therapy, Am J Respiratory Critical Care, Vol 160, 1468-1472, 1999
  6. 5 Ways to Lower Clinical Trial Patient Recruitment Costs, Antidote, 2021
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