The term “biomarker” is an abbreviation of “biological marker” which has existed since 1980. This is a marker of a biological process which can be normal, pathological, a response to therapy or exposure according to the definition by the FDA-NIH working group.[1] This is a measurement done objectively by means of, for example, laboratory instrumentation, in vivo imaging techniques, or electrophysiological methods. The biomarker identity often includes the biomarker type (e.g., molecular, histologic, radiographic, digital, or physiologic). Digital biomarker refers to the method of data acquisition that can be achieved via various forms of digital health technologies (DHTs) and may include wearable sensors, smartphones, and other types of technologies.

Digital biomarkers are aiming to address the shortcomings of current clinical trial outcome assessments which often represent snapshots in time, are prone to high variability, depend on patient motivation at the exact time of assessment, and do not reflect what is happening to patients in their natural environment. Additionally, the requirements for regular in-clinic assessments represent a burden for patients and limit participation in clinical trials. Designing and developing digital biomarkers offers an opportunity to address these challenges by providing frequent or semicontinuous monitoring of patients in the work and home settings by means of DHT. These measures may constitute digital biomarkers or eCOA, can be deployed conveniently for patients, and may provide novel data sets complementary to the in-clinic assessments. The findings from DHT-enabled measures may have a significant impact on the outcome of clinical trials by reducing clinical trial sample size,[2] measure objectively appropriate signs and symptoms,[3] and understand better disease features and subtypes. Additionally, the emerging data indicate potential in improved and augmented healthcare delivery.

Digital biomarkers have multiple applications in drug development which may vary depending on the phase of drug development. In early-stage development, these tools can be useful at safety monitoring,[4] pharmacodynamic assessments,[5] (e.g., at-home blood pressure monitoring for hypertensive therapies), and early proof of concept studies aimed at establishing an efficacy signal in small clinical trials.[6] Moreover, digital biomarkers have the potential to increase the confidence in POC by measuring clinically meaningful endpoints objectively. For example, daily step count is a prognostic factor for survival in locally advanced lung cancer and a dynamic predictor of short-term hospitalizations in cancer patients undergoing chemoradiotherapy.[7] For late-stage clinical trials, the biggest potential is the ability to capture objectively efficacy data related to functional, physiological, or cognitive ability. Moreover, the remote nature of digital data collection can improve clinical trial accessibility, at the same time offering near real-time data for review by the medical staff. Furthermore, remote deployment of digital biomarkers makes it possible to collect the data in a natural environment, complementing standard physician assessments done during clinic visits, which can lead to a smaller size clinical trial of a shorter duration for registration studies.[8]

FDA Biomarker Qualification: Evidentiary Framework[9] guidance is a result of a multiyear precompetitive collaboration between the agency, public-private partnership consortium (FNIH), and the industry to create comprehensive requirements for qualifying novel biomarkers. This approach is agnostic to the measurement method and covers all biomarkers.

It is still important to note other regulatory documents that are pertinent to DHT-derived measures acquired in clinical trials, such as the latest FDA guidance on remote data acquisition[10] and the EMA’s outlook on digital technology-based methodologies that support the approval of medicinal products.[11]

In the case of more traditional biomarker methods, for example based on laboratory methods, development and validation of biomarker assays done in laboratories is an internal biopharma activity as it is leveraging laboratory expertise, core to drug discovery, and development. However, DHTs and digital biomarkers are different in this regard. Successful deployment of DHT-derived measures requires in-depth knowledge of DHTs, a platform enabling data collection and integration, data analytics, and statistical analysis tailored to the specific nature of a measure of interest. This infrastructure takes place often in conjunction with data processing algorithms and development which traditionally resided outside of biopharma R&D. Koneksa provides a full capability platform and experienced staff that can effectively partner with pharma companies to make digital biomarker data collection happen in clinical trials.

Koneksa provides a unique integrated solution that includes (a) in-depth expertise in biomarker development and validation and (b) full integration into clinical study protocols and procedures. Our device agnostic data integration platform offers complete adaptability, and our extensive experience in evaluating and developing algorithms yields optimal data capture and robust analysis backed by our proven experience with trial success.

Koneksa can improve patient generated data collection in multiple ways. Our ability to integrate different devices provides an opportunity for capturing different modalities of data including wearable sensors, patient questionnaires/ePRO, and digitally instrumented assessments. These data points are integrated in a single platform enhanced by a dashboard which allows data visualization for sponsors and sites to monitor data collection compliance and view trends over time. Our data processing algorithms provide a unique opportunity to capture data from multiple sensors and provide insights into patient physiology, disease characteristics, and function.

Our validation approach starts with the selection of appropriate sensors and is matched to applicable data processing algorithms. We have our own internal processes to evaluate externally available hardware and publicly available, device vendor or novel Koneksa data processing algorithms to assess utility, validity, and fit for purpose in the intended and context of use, according to regulations. Additionally, we follow the community best practices, for example the V3 framework[12] and regulatory guidance[13],[14], for evaluating and validating biomarkers which we publish on a regular basis.[15]


[2] Huang, C., Izmailova, E.S., and Jackson, N., et al. Remote FEV1 Monitoring in Asthma Patients: A Pilot Study. Clin Transl Sci. 2021 Mar;14(2):529-535.

[3] Lipsmeier, F., Taylor, K.I., and Postuma, R.B., et al. Reliability and validity of the Roche PD Mobile Application for remote monitoring of early Parkinson’s disease. Sci Rep. 2022 Jul 15;12(1):12081.

[4] Izmailova, E.S., McLean, I.L., and Hather, G., et al. Continuous Monitoring Using a Wearable Device Detects Activity-Induced Heart Rate Changes After Administration of Amphetamine. Clin Transl Sci. 2019 Nov;12(6):677-686.

[5] Huang, Q., Crumley, T., and Walters, C., et al. “In-House” Data on the Outside-A Mobile Health Approach. Clin Pharmacol Ther. 2020 Apr;107(4):948-956.

[6] Huang, C., Izmailova, E.S., and Jackson, N., et al. Remote FEV1 Monitoring in Asthma Patients: A Pilot Study. Clin Transl Sci. 2021 Mar;14(2):529-535.

[7] Izmailova, E., Huang, C., and Cantor, M., et al. Daily step counts to predict hospitalizations during concurrent chemoradiotherapy for solid tumors. Journal of Clinical Oncology. 2019;37(27_suppl):293-293.

[8] Mori, H., Wiklund, S.J., and Zhang, J.Y., et al. Quantifying the Benefits of Digital Biomarkers and Technology-Based Study Endpoints in Clinical Trials: Project Moneyball. Digit Biomark. 2022 Jun 29;6(2):36-46.




[12] Goldsack, J.C., Coravos, A., and Bakker, J.P., et al. Verification, analytical validation, and clinical validation (V3): the foundation of determining fit-for-purpose for Biometric Monitoring Technologies (BioMeTs). NPJ Digit Med. 2020 Apr 14;3:55.



[15] Ellis, R., Kelly, P., and Huang, C., et al. Sensor Verification and Analytical Validation of Algorithms to Measure Gait and Balance and Pronation/Supination in Healthy Volunteers. Sensors (Basel). 2022 Aug 20;22(16):6275.