Shots:
- Clinical trial sponsors are rapidly moving toward objective, continuous respiratory monitoring, adopting technologies like the RESP Biosensor to improve endpoint sensitivity, safety assessments, and real-world patient insight.
- Beyond cough frequency, emerging acoustic biomarkers, such as wheezes, crackles, cough intensity, and cough bout patterns, are redefining how respiratory function and therapeutic impact are measured in modern trials.
- PharmaShots welcomes Nick Delmonico, Founder & CEO of Strados Labs, as he shares how RESP is shaping the future of digital respiratory endpoints and paving the way for AI-driven prediction of respiratory decline.
Saurabh: How are clinical trial sponsors increasingly adopting objective respiratory monitoring solutions such as the RESP Biosensor to enhance endpoint precision and patient safety assessments?
Nick: Over the past several years, we’ve seen a significant shift across clinical research toward the greater use of wearables and digital health technologies to capture objective, continuous data directly from patients. Sponsors are increasingly recognizing the limitations of traditional, episodic, and subjective measurements, tools like clinic-based assessments or patient-reported outcomes, which can introduce recall bias or fail to reflect real-world patient experience.
This trend is especially visible in neurological research, such as Parkinson’s disease trials, where digital technologies have become essential for measuring motor symptoms unobtrusively and continuously. A similar evolution is now occurring in respiratory medicine.
In respiratory trials, cough has become an especially important target for objective measurement because it strongly correlates with patient quality of life yet has historically been captured using PROs that can miss day–to–day variability and nighttime symptoms. As a result, sponsors are adopting high-fidelity tools like the RESP Biosensor to quantify cough with much greater precision. This shift is enabling more sensitive endpoints, more confident safety assessments, and a more complete picture of how therapies truly impact patients outside the clinic.
Saurabh: Beyond traditional cough frequency measurement, which emerging respiratory biomarkers or acoustic parameters are proving most valuable for objective, data-driven monitoring in clinical research?
Nick: While cough frequency remains a leading metric for evaluating respiratory symptom burden, sponsors are increasingly looking for richer, multidimensional respiratory biomarkers that offer deeper clinical insight.
Acoustic lung sounds, particularly wheezes and crackles, are among the most promising. These signals traditionally require clinician auscultation, which is inherently intermittent and subjective. By capturing them continuously and objectively, researchers gain a window into airway obstruction dynamics and even early signs of pulmonary edema. This opens new possibilities in diseases such as asthma, COPD, cystic fibrosis, and congestive heart failure.
At the same time, cough itself is being re-examined through a more nuanced lens. Beyond frequency, measurements such as cough bouts (clusters of coughs) and cough sound intensity (a measure for the forcefulness of the cough) are proving valuable. These parameters provide a more complete understanding of cough burden, helping sponsors distinguish meaningful changes that frequency alone may overlook.
Saurabh: What are the most significant hurdles sponsors encounter when incorporating wearable respiratory sensors into study protocols, particularly concerning data reliability, patient adherence, and evolving regulatory expectations?
Nick: Deploying any wearable in a clinical trial introduces layers of operational and regulatory complexity, and respiratory technologies are no exception. The first major hurdle is ensuring that the device is validated, reliable, and capable of producing high-quality data under real-world conditions. Sponsors must look for technologies with strong compliance track records and validation, including FDA clearances where applicable.
Another critical consideration is patient adherence. Even the most advanced device cannot generate value if patients struggle to use it correctly, wear it consistently, or integrate it into daily routines. Manufacturers must demonstrate thoughtful design, frictionless user experience, and proven multi-day wear performance.
Finally, regulatory expectations are evolving quickly. For example, using cough frequency as a primary endpoint requires strict procedures such as human review and annotation of cough events to ensure accuracy and auditability. Sponsors need clarity on whether a vendor has established processes aligned with FDA requirements. In short, success requires not only an accurate device, but an end-to-end ecosystem that meets regulatory, operational, shipping/logistical and data integrity expectations.
Saurabh: In an era of decentralized and adaptive trials, how critical is real-world respiratory data access for sponsors and investigators, and in what ways does RESP translate this data into meaningful, actionable insights?
Nick: Real-world data has become increasingly important in clinical development because it can better reflect how patients feel, function, and respond to therapy outside structured clinic visits. The FDA continues to reinforce the role of digital health technologies in capturing these meaningful, patient-centered outcomes, especially in indications where symptom burden fluctuates throughout the day or night.
While many indications do not yet require using digital health technologies, these data can substantially strengthen a drug’s clinical narrative. Demonstrating improvements in day-to-day function, sleep quality, or overall symptom burden can meaningfully differentiate a therapy in a competitive market.
The RESP platform is designed to translate respiratory acoustics into clinically interpretable insights — trends in cough burden, nocturnal symptom patterns, fluctuations in wheeze or crackle prevalence, and more. These outputs help sponsors understand not just whether a drug works, but how it impacts quality of life in ways traditional endpoints may not fully capture.
Saurabh: How can continuous monitoring of lung sounds—including wheezes, crackles, and cough patterns—help detect early indicators of therapeutic efficacy or potential respiratory safety concerns?
Nick: Continuous monitoring of lung sounds can bring value to clinical trials in a few key ways. First, it provides a more sensitive and objective readout of how therapies impact real-world symptom burden, particularly at night when patients may be unaware of or unable to report symptoms like coughing or wheezing. These insights can complement or enhance questionnaires by offering evidence of changes that patients may not consciously perceive.
Second, acoustic monitoring can help confirm whether a therapy is contributing to adverse events. Cough, for example, is a known side effect of many orally inhaled drugs and certain cardiovascular agents. Objective monitoring can help sponsors confirm onset, severity, and timing with precision.
Third, there is growing interest in using cough and lung sounds as early indicators of exacerbations. Exacerbations are clinically meaningful events—they increase healthcare utilization, predict long-term decline, and influence regulatory and payer decisions. Early detection through rising cough frequency or adventitious lung sounds could allow investigators to intervene sooner, reduce exacerbation severity, and generate important insights into disease trajectories and treatment response.
Saurabh: Looking ahead, how is Strados Labs shaping the next generation of digital respiratory endpoints, and are there plans to leverage predictive analytics or AI-driven algorithms to anticipate respiratory decline before clinical symptoms emerge?
Nick: Looking ahead, Strados Labs is deeply focused on expanding the role of lung acoustics as a next-generation class of digital respiratory endpoints. We’re actively engaged in several academic and healthcare collaborations aimed at understanding how patterns in cough, wheeze, crackles, and nighttime symptoms can inform disease activity, treatment response, and patient well-being across a broad range of respiratory conditions.
We’re also exploring new form factors, including watch-based sensors, to bring these capabilities into more patient-friendly, widely deployable devices.
As for AI-driven predictive analytics, that is absolutely part of our long-term roadmap. As we continue to collect a growing body of high-quality respiratory acoustic data across diverse populations and indications, we are building the foundation needed to train models that may one day help predict respiratory decline, identify exacerbations earlier, or guide therapy optimization. We see this as a natural evolution of our platform and a key opportunity to transform how respiratory diseases are monitored and managed in clinical research.
About the Author:
Nick Delmonico
Founder and CEO, Strados Labs
Nick Delmonico is the co-founder and CEO of Strados Labs, a medical technology startup focused on improving respiratory disease surveillance and management. He founded the company based on his personal experience as an asthma patient and a desire to improve how respiratory symptoms are monitored and understood. Prior to Strados Labs, Nick worked in financial consulting at a Fortune 100 financial institution and in public accounting at a Big Four firm, where he supported multinational pharmaceutical companies, biotechnology firms, and major hospitals and health systems in the Philadelphia region. He holds a master’s degree in Healthcare Management from Temple University’s Fox School of Business.
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