Respiratory Clinical Trials Across Key Indications

Respiratory diseases remain a substantial global health burden, affecting hundreds of millions of people worldwide and contributing to morbidity, mortality, and healthcare utilisation.[1,2]

The respiratory therapeutic landscape includes a wide range of conditions such as asthma, chronic obstructive pulmonary disease (COPD), interstitial lung disease (ILD), idiopathic pulmonary fibrosis (IPF), pulmonary hypertension, respiratory infections, chronic cough, pulmonary nodules, long COVID, and other airway and parenchymal lung disorders.[1,2] Although these conditions affect the same organ system, they differ considerably in disease biology, progression, symptom burden, treatment objectives, and evidence requirements.

As a result, respiratory clinical trials require careful, indication-specific design. Symptoms may fluctuate over time, treatment effects can be challenging to measure, and endpoints often combine different measures depending on the disease and treatment objective.[3]

In Brief

• Respiratory clinical trials evaluate interventions for diseases affecting the lungs and airways, including medicines, devices, diagnostics, monitoring, and rehabilitation.

• Trial design needs to reflect the indication, because respiratory diseases differ in biology, progression, symptoms, and treatment goals.
• Operational planning often centres on recruitment feasibility, eligibility criteria, repeated assessments, endpoint derivation, and missing data risk.
• A common risk is treating respiratory endpoints as interchangeable when lung function, imaging, PROs, biomarkers, and safety measures answer different questions.
• The main takeaway is that respiratory clinical trials need aligned populations, endpoints, follow-up, and data-quality processes from the start.
  
  

What are respiratory clinical trials?

Respiratory clinical trials evaluate interventions for diseases affecting the lungs and airways. These studies may assess medicines, biologics, medical devices, diagnostics, prevention strategies, rehabilitation programmes, disease-monitoring tools, or quality-of-life interventions.[3]

Depending on the indication and intervention, respiratory clinical trials may aim to improve lung function, reduce symptoms, prevent exacerbations, slow disease progression, improve exercise capacity and daily functioning, support earlier diagnosis, evaluate long-term safety, or improve health-related quality of life.

Respiratory studies range from early-phase trials assessing pharmacodynamic effects on airway inflammation or lung function to large confirmatory studies evaluating exacerbation reduction, disease progression, hospitalisation, mortality, or long-term safety.

Why respiratory clinical trials need indication-specific design

Respiratory diseases differ significantly in their underlying biology, progression, clinical presentation, and treatment goals. Trial design should therefore be tailored to the disease under investigation, the intervention’s mechanism of action, the target population, and the outcomes most relevant to clinical and regulatory decision-making.[3,5,12]

Asthma

Asthma trials often require careful patient characterisation because the disease involves multiple phenotypes, inflammatory patterns, and levels of severity. Studies may focus on symptom control, exacerbation reduction, lung function improvement, corticosteroid reduction, or biomarker-defined treatment response.[5,11]

Chronic obstructive pulmonary disease

COPD studies frequently assess exacerbation rates, symptom burden, lung function decline, exercise capacity, and health-related quality of life. Smoking history, prior exacerbations, background therapy, and comorbidities can all contribute to variability in study populations.[6,12]

Idiopathic pulmonary fibrosis and interstitial lung disease

IPF and other forms of ILD often require endpoints that reflect disease progression rather than short-term symptom improvement. Forced vital capacity (FVC), imaging findings, exercise capacity, and longer-term assessment are commonly important.[7]

Pulmonary hypertension

Pulmonary hypertension trials often evaluate exercise capacity, functional class, haemodynamic measures, clinical worsening, and disease progression. These studies require careful diagnostic confirmation and longitudinal assessment.[8]

Respiratory infections

Respiratory infection studies may focus on prevention, symptom duration, recovery time, pathogen clearance, complications, hospitalisation, or mortality. Seasonal patterns, circulating pathogens, vaccination status, and epidemiological variability can influence study conduct and interpretation.[17]

Chronic cough

Chronic cough studies often rely on cough-monitoring technologies and patient-reported outcomes. Endpoint selection and symptom measurement are particularly important because cough frequency and severity may vary considerably over time.[9]

Pulmonary nodules

Studies involving pulmonary nodules are often diagnostic or risk-stratification studies rather than therapeutic efficacy trials. They may evaluate imaging strategies, molecular diagnostics, or clinical decision-support tools.[10]

Long COVID

Long COVID can involve persistent respiratory symptoms alongside fatigue, reduced exercise tolerance, cognitive symptoms, and other systemic manifestations. This heterogeneity can complicate patient selection, endpoint choice, and follow-up requirements.[4] Where Long COVID studies include respiratory, functional and systemic outcomes, multidisciplinary assessment may be relevant to endpoint selection and follow-up planning.

Respiratory clinical trial design considerations

Respiratory clinical trials must balance scientific objectives with operational feasibility. Disease variability, patient diversity, endpoint complexity, and assessment burden can all affect study performance and interpretation.[3]

Managing disease variability

Respiratory symptoms and physiological measures may fluctuate because of environmental exposures, infections, seasonal patterns, exacerbations, adherence changes, or comorbid disease.[6,9,12]

This variability can affect endpoint selection, sample size assumptions, statistical power, and interpretation. Measures such as forced expiratory volume in one second (FEV1), symptom scores, and exacerbation rates may show within-patient and between-patient variation, which should be considered during protocol development and statistical planning.[12,13]

Balancing feasibility and scientific precision

Eligibility criteria, visit schedules, diagnostic procedures, and endpoint assessments should be specific enough to support reliable interpretation without making recruitment or participation unnecessarily difficult.

This balance is particularly important when studies involve repeated spirometry, imaging, exercise testing, home monitoring, wearable devices, or long-term follow-up. Protocol complexity can increase participant burden, site workload, missing data, and the risk of operational inconsistency.[3,13]

Statistical planning

Early statistical input is important because many respiratory endpoints are repeated, variable, event-based, or affected by missing data. Planning should consider endpoint hierarchy, sample size assumptions, longitudinal models, multiplicity, and the handling of intercurrent events.

Relevant intercurrent events may include treatment discontinuation, rescue medication use, exacerbations, hospitalisation, changes in background therapy, or death. The estimand framework can help clarify the treatment effect of interest and how post-randomisation events should be reflected in the analysis.[15]

Early planning also supports consistent endpoint derivation, efficient management of repeated assessments, clear analysis specifications, and production of analysis-ready datasets.

Recruitment and eligibility in respiratory clinical trials

Successful respiratory clinical trials depend on enrolling an appropriate patient population while maintaining recruitment feasibility. Screening procedures, eligibility criteria, and diagnostic confirmation should support the study objectives without unnecessarily restricting enrolment.

Screen and diagnostic confirmation

Respiratory diseases can vary considerably in presentation and severity. Screening procedures are typically designed to confirm diagnosis and disease severity using combinations of spirometry, imaging, laboratory testing, biomarker assessment, specialist review, medical history, or confirmation of prior exacerbations.[5,7,12,13]

Diagnostic confirmation can improve the interpretability of trial results, but it may also increase screen failure rates and site workload. These practical implications should be considered during feasibility planning.

Eligibility criteria and population selection

Defining an appropriate study population is essential for generating interpretable results. Disease severity, symptom burden, smoking history, comorbidities, prior exacerbations, background treatment, and previous treatment exposure can all influence outcomes and treatment response.[5,6,11,12]

Many respiratory diseases also include distinct phenotypes or endotypes. Biomarker-driven eligibility criteria may improve population characterisation and help identify treatment effects in the intended patient population.[5,11]

Eligibility criteria should support the primary scientific objective without becoming unnecessarily restrictive. While stricter criteria may reduce variability, they can also slow recruitment and reduce generalisability.

Randomisation, comparator treatment, placebo use or assignment to an intervention should also be explained clearly during consent where relevant. This is particularly important when respiratory studies involve standard-of-care comparators, sham-like procedures, rehabilitation programmes, or home-based monitoring.

Informed consent

Respiratory studies frequently involve repeated testing, imaging, home monitoring, symptom diaries, wearable devices, or long-term follow-up. Participants should clearly understand study requirements, potential risks, assessment burden, data collection methods, and their right to withdraw at any time.[3]

Practical recruitment challenges

Practical recruitment challenges are common in respiratory research. Patients with severe respiratory disease may have mobility limitations, oxygen requirements, fatigue, or significant comorbidities. Rare respiratory diseases and biomarker-defined studies may also require extensive pre-screening.

Recruitment planning may therefore benefit from specialist centres, disease registries, referral networks, diagnostic databases, and robust site feasibility assessments.

Respiratory clinical trial endpoints and assessments

Endpoint selection is central to respiratory clinical trial design. Endpoints should be clinically meaningful, measurable with acceptable precision, and aligned with the study objective, population, and regulatory context.[3,5,12]

Long function assessments

Lung function testing remains a core component of many respiratory studies. Common measures include FEV1, FVC, and peak expiratory flow rate (PEFR).[13]

The choice of lung function endpoint depends on the indication. FEV1 is widely used in asthma and COPD studies, whereas FVC is commonly used in pulmonary fibrosis and other interstitial lung diseases.[7,12,13]

Exacerbation endpoints

Exacerbation endpoints are commonly used in asthma and COPD studies. These may include annualised exacerbation rates, time to first exacerbation, exacerbation severity, hospitalisations, or rescue medication use.[5,6,12]

Clear event definitions and consistent event capture are important for reliable interpretation.

Imaging assessments

Imaging techniques such as computed tomography (CT), high-resolution computed tomography (HRCT), and positron emission tomography (PET) can provide insight into structural lung changes, disease progression, inflammatory activity, or diagnostic risk.[7,10]

Imaging is particularly important in pulmonary fibrosis, ILD, pulmonary nodules, and post-infectious lung disease.

Exercise capacity and functional outcomes

Functional assessments help determine whether physiological changes translate into meaningful clinical benefit. Measures such as the six-minute walk test (6MWT) are commonly used in pulmonary hypertension, pulmonary fibrosis, and Long COVID studies.[4,8]

Patient-reported outcomes

Patient-reported outcomes (PROs) provide insight into symptoms, daily functioning, treatment burden, and health-related quality of life.[14]

Because improvements in physiological measures do not always correspond directly with symptom improvement, PROs can provide an important patient-centred perspective on treatment benefit.

Biomarkers

Biomarkers may be used for patient stratification, treatment selection, and response evaluation. Depending on the indication, studies may assess inflammatory markers, eosinophil counts, cytokines, gene-expression profiles, or treatment-specific biomarkers.[5,11] Any biomarker or scale used as an outcome should be validated for the intended context before it is used to support interpretation.

Safety assessments

Safety evaluation remains central throughout respiratory clinical development. Assessments may include adverse events, serious adverse events, exacerbations, hospitalisations, disease progression, respiratory failure, and mortality.[3]

Pulmonary fibrosis, long COVID and longer-term respiratory follow-up

Some respiratory diseases require follow-up beyond the initial treatment period to understand progression, sustained benefit or delayed safety outcome.[3,4,7]

Pulmonary fibrosis provides a common example. Progressive fibrotic changes may lead to declining lung function, worsening symptoms, reduced exercise capacity, and deterioration in quality of life over time. Clinical trials often require repeated longitudinal assessments to evaluate whether treatment slows disease progression.[7,16]

Experience from Long COVID has also highlighted the importance of understanding longer-term respiratory outcomes following severe respiratory infections. Some individuals experience persistent breathlessness, reduced exercise tolerance, abnormal imaging findings, or broader systemic symptoms after acute infection.[4]

Extended follow-up may increase the risk of missing data due to withdrawal, worsening disease, or loss to follow-up. These considerations should be incorporated into study design, data management plans, and statistical analyses.[3,15]

Managing data quality and standardisation in respiratory trials

High-quality data are essential for reliable trial interpretation. Many respiratory assessments depend on patient effort, operator technique, equipment performance, and standardised procedures, making them particularly susceptible to variability.[13]

Current Good Clinical Practice expectations place emphasis on proportionate, risk-based quality management and fit-for-purpose systems. In respiratory trials, this means identifying data and processes that are critical to participant safety and endpoint reliability, then focusing oversight on those areas.

Spirometry standardisation

Spirometry should be conducted according to recognised American Thoracic Society (ATS) and European Respiratory Society (ERS) standards.[13]

Standardisation may include equipment calibration, acceptability and repeatability criteria, consistent patient instructions, quality review procedures, and appropriate handling of technically inadequate tests.

Assessment consistency and training

Consistent assessment schedules, equipment, procedures, and site training help improve comparability across visits and study locations.

This applies to spirometry, imaging, exercise-capacity testing, home monitoring, biomarker sampling, wearable devices, and patient-reported outcomes. Appropriate staff and participant training can reduce avoidable measurement variability and improve data reliability.[13,14] 

Where respiratory trials use electronic diaries, connected devices, wearable devices or remote data capture, sponsors should also consider data integrity across the data lifecycle. This includes validated systems, clear data ownership, access controls, audit trails, query handling, and documented processes for reviewing incoming data.

Central review and data quality monitoring

Centralised review processes can help identify inconsistent spirometry performance, imaging variability, missing data, protocol deviations, and site-level quality issues early in the study.

For sponsors, this also supports clearer endpoint derivation, more consistent data handling, and earlier resolution of discrepancies before database lock.

Blinding and bias mitigation

Randomised, controlled, and blinded study designs remain the preferred approach for reducing bias where feasible.[3]

However, blinding can be challenging in studies involving devices, rehabilitation programmes, behavioural interventions, or procedures with obvious treatment differences. Independent endpoint assessment, objective outcome measures, adjudication processes, and central review can help strengthen study integrity when full blinding is not possible.

Conclusion

Respiratory clinical trials present specific design and delivery challenges. Differences in disease biology, patient populations, endpoint characteristics, and follow-up requirements mean that study designs should be aligned with the indication, treatment objective, and evidence needs of the programme.

Need support with respiratory clinical trials? Quanticate's statistical consultancy team helps sponsors plan, manage, analyse, and report respiratory studies across all phases, with experience spanning COPD, asthma, pulmonary fibrosis, interstitial lung disease, cystic fibrosis, pulmonary arterial hypertension, respiratory infections, and other respiratory indications. Request a consultation and a member of our team will be in touch.

References:

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[3] International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use. ICH E8(R1): General Considerations for Clinical Studies. Final version adopted 6 October 2021. Available at: https://database.ich.org/sites/default/files/E8-R1_Guideline_Step4_2021_1006.pdf. Accessed 7 July 2026.

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[15] International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use. ICH E9(R1): Addendum on Estimands and Sensitivity Analysis in Clinical Trials to the Guideline on Statistical Principles for Clinical Trials. Final version adopted 20 November 2019. Available at: https://database.ich.org/sites/default/files/E9-R1_Step4_Guideline_2019_1203.pdf. Accessed 7 July 2026.

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