Future of clinical development is on the verge of a major transformation due to convergence of large new digital data sources, computing power to identify clinically meaningful patterns in the data using efficient artificial intelligence and machine-learning algorithms, and regulators embracing this change through new collaborations. This perspective summarizes insights, recent developments, and recommendations for infusing actionable computational evidence into clinical development and health care from academy, biotechnology industry, nonprofit foundations, regulators, and technology corporations. Analysis and learning from publically available biomedical and clinical trial data sets, real-world evidence from sensors, and health records by machine-learning architectures are discussed. Strategies for modernizing the clinical development process by integration of AI- and ML-based digital methods and secure computing technologies through recently announced regulatory pathways at the United States Food and Drug Administration are outlined. We conclude by discussing applications and impact of digital algorithmic evidence to improve medical care for patients.

Biomarkers are physiologic, pathologic, or anatomic characteristics that are objectively measured and evaluated as an indicator of normal biologic processes, pathologic processes, or biological responses to therapeutic interventions. Recent advances in the development of mobile digitally connected technologies have led to the emergence of a new class of biomarkers measured across multiple layers of hardware and software. Quantified in ones and zeros, these “digital” biomarkers can support continuous measurements outside the physical confines of the clinical environment. The modular software–hardware combination of these products has created new opportunities for patient care and biomedical research, enabling remote monitoring and decentralized clinical trial designs. However, a systematic approach to assessing the quality and utility of digital biomarkers to ensure an appropriate balance between their safety and effectiveness is needed. This paper outlines key considerations for the development and evaluation of digital biomarkers, examining their role in clinical research and routine patient care.

BackgroundDespite the rapid adoption of immunotherapies in advanced non–small cell lung cancer (advNSCLC), knowledge gaps remain about their real‐world (rw) performance.MethodsThis retrospective, observational, multicenter analysis used the Flatiron Health deidentified electronic health record‐derived database of rw patients with advNSCLC who received treatment with PD‐1 and/or PD‐L1 (PD‐[L]1) inhibitors before July 1, 2017 (N = 5257) and had ≥6 months of follow‐up. The authors investigated PD‐(L)1 line of treatment and PD‐L1 testing rates and the relationship between overall survival (OS) and rw intermediate endpoints: progression‐free survival (rwPFS), rw time to progression (rwTTP), rw time to next treatment (rwTTNT), and rw time to discontinuation (rwTTD).ResultsFirst‐line PD‐(L)1 inhibitor use increased from 0% (in the third quarter of 2014 [Q3 2014]) to 42% (Q2 2017) over the study period. PD‐L1 testing also increased (from 3% in Q3 2015 to 70% in Q2 2017). The estimated median OS was 9.3 months (95% CI, 8.9‐9.8 months), and the estimated rwPFS was 3.2 months (95% CI, 3.1‐3.3 months). Longer OS and rwPFS were associated with ≥50% PD‐L1 percentage staining results. Correlations (⍴) between OS and intermediate endpoints were ⍴ = 0.75 (95% CI, 0.73‐0.76) for rwPFS and ⍴ = 0.60 (95% CI, 0.57‐0.63) for rwTTP, and, for treatment‐based intermediate endpoints, correlations were ⍴ = 0.60 (95% CI, 0.56‐0.64) for rwTTNT (N = 856) and ⍴ = 0.81 (95% CI, 0.80‐0.82) for rwTTD.ConclusionsThe use of first‐line PD‐(L)1 inhibitors and PD‐L1 testing has substantially increased, with better outcomes for patients who have ≥50% PD‐L1 percentage staining. Intermediate rw tumor‐dynamics estimates were moderately correlated with OS in patients with advNSCLC who received immunotherapy, highlighting the need for optimizing and standardizing rw endpoints to enhance the understanding of patient outcomes outside clinical trials.

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