Objective
This study aims to calculate the global warming potential, in carbon dioxide (CO2) equivalent emissions, from all in-scope activities involved in phase 1, 2, 3 and 4 clinical trials spanning multiple disease areas.

Design
The study design involved a retrospective analysis of completed clinical trials.

Setting
Select set of seven clinical trials conducted between 2018 and 2023 and sponsored by Johnson & Johnson Innovative Medicine: TMC114FD1HTX1002, 77242113PSO2001, 42756493BLC2002, 54767414MMY3012, VAC18193RSV3006, R092670PSY3016 and 28431754DIA4032

Participants
While participants and the public were involved in all seven trials, the life cycle assessments (LCAs) were performed as an independent retrospective analysis after the clinical trials were completed. As a retrospective analysis, we leveraged clinical trial documentation and interviews with the sponsor trial staff and trial site staff. None of the participating trial subjects were involved specifically in the LCA analysis, nor was any personal identifying information from the trial subjects collected or shared.

The underlying clinical trials were performed in accordance with the Declaration of Helsinki and Guidelines for Good Pharmacoepidemiology Practice. All participating investigators were required to obtain full governing board approval for conducting research involving humans. Sponsor approval and continuing review were obtained through the appropriate Institutional Review Board/Ethics Committee (IRB) and Health Authority channels. For academic investigative sites that did not receive authorisation to use the central IRB, full board approval was obtained from their respective governing IRBs, and documentation of approval was submitted to Johnson & Johnson Innovative Medicine, LLC, before the site’s participation and initiation of any trial procedures. All registry participants provided written informed consent and authorisation before participating.

Primary outcome measure
Primary outcome measure CO2 equivalents (CO2e) for in-scope clinical trial activities calculated according to Intergovernmental Panel on Climate Change 2021 impact assessment methodology.

Results
The TMC114FD1HTX1002 phase 1 trial was the smallest trial both in terms of number of patients (39) and sites (1) and had the smallest emissions at 17 648 kgCO2e. The 54767414MMY3012 phase 3 trial was not the largest trial in terms of number of participating patients (517) but had the largest number of participating sites (129) and had the largest emissions at 3 107 436 kg CO2e. Across all seven trials analysed, the mean emissions per patient were 3260 kg CO2e. When the overall trial footprints are broken down by phase, the phase 2 mean per patient was 5722 kg CO2e and the phase 3 mean per patient emissions were 2499 kg CO2e. The five largest contributors of greenhouse gas (GHG) emissions were drug product (50% mean), patient travel (10% mean), travel for on-site monitoring visits (10% mean), collection and processing of laboratory samples (9% mean) and sponsor staff commuting (6% mean). Patient travel was the only consistent GHG hotspot across all seven trials, as other hotspots appeared intermittently in some trials but not others based on variations in trial design. Across the multisite phase 2, 3 and 4 trials we analysed, a combination of the observed five largest contributors to GHG emissions were responsible for no less than 79% of GHG emissions for any one trial.

Conclusions
Based on our LCAs of seven clinical trials spanning all four phases of development and multiple disease areas, there are five activities that drive no less than 79% of the average clinical trial’s GHG footprint. These are drug product manufacture, packaging, and distribution; patient travel; on-site monitoring visit travel; the collection, transport and processing of laboratory samples; and sponsor staff commuting between their homes and the office. Understanding the activities that drive GHG emissions in clinical trials can both guide trial designers in avoiding or minimising reliance on these activities when designing new trials and guide trial sponsors in taking targeted actions to reduce GHG emissions from these activities where their use cannot be avoided.

OBJECTIVE: This study aims to calculate the global warming potential, in carbon dioxide (CO2) equivalent emissions, from all in-scope activities involved in a phase-1 clinical study. DESIGN: Retrospective analysis. DATA SOURCE: Internal data held by Janssen Pharmaceuticals. STUDIES INCLUDED: Janssen-sponsored TMC114FD1HTX1002 study conducted between 2019 and 2021. MAIN OUTCOME: Measure CO2 equivalents (CO2e) for in-scope clinical trial activities calculated according to intergovernmental panel on climate change 2021 impact assessment methodology. RESULTS: The CO2e emissions generated by the trial were 17.65 tonnes. This is equivalent to the emissions generated by driving an average petrol-fueled family car 71 004 km or roughly 1.8 times around the circumference of the Earth. Commuting to the clinical site by the study participants generated the most emissions (5419 kg, 31% of overall emissions), followed by trial site utilities (2725 kg, 16% of overall emissions) and site staff travel (2560 kg, 15% of overall emissions). In total, the movement of people (participant travel, site staff travel and trial site staff travel) accounted for 8914 kg or 51% of overall trial emissions. CONCLUSIONS: Decentralised trial models which seek to bring clinical trial operations closer to the participant offer opportunities to reduce participant travel. The electrification of sponsor vehicle fleets and society's transition towards electric vehicles may result in further reductions. TRIAL REGISTRATION NUMBER: NCT04208061.

Background: The circular economy reshapes the linear “take, make, and dispose” approach and evolves around minimizing waste and recapturing resources in a closed-loop system. The health sector accounts for 4.6% of global greenhouse gas emissions and has, over the decades, been built to rely on single-use devices and deal with high volumes of medical waste. With the increase in the adoption of digital health solutions in the health care industry, leading the industry into a new paradigm of how we provide health care, a focus must be put on the amount of waste that will follow. Digital health solutions will shape health care through the use of technology and lead to improved patient care, but they will also make medical waste more complex to deal with due to the e-waste component. Therefore, a transformation of the health care industry to a circular economy is a crucial cornerstone in decreasing the impact on the environment. Objective: This study aims to address the lack of direction in the current literature on circular business models. It will consider micro, meso, and macro factors that would impact the operational validity of circular models using the digital health solutions ePaper label (medical packaging), smart wearable sensor (health monitoring devices), smart pill box (medication management), and endo-cutter (surgical equipment) as examples. Methods: The study will systematically perform a scoping review through a database and snowball search. We will analyze and classify the studies from a predetermined set of categories and then summarize them into an evidence map. Based on the review, the study will develop a 2D framework for businesses to follow or for future research to take a standpoint from. Results: Preliminarily, the review has analyzed 26 studies in total. The results are close to equally distributed among the micro (8/26, 31%), meso (10/26, 38%), and macro (8/26, 31%) levels. Circular economy studies emphasize several circular practices such as recycling (17/26, 65%), reusing (18/26, 69%), reducing (15/26, 58%), and remanufacturing (8/26, 31%). The value proposition in the examined business model is mostly dominated by stand-alone products (18/26, 69%) compared to product as a service (7/26, 27%), involving stakeholders such as health care professionals or hospitals (20/26, 77%), manufacturers (11/26, 42%), and consumers (9/26, 35%). All studies encompass societal (12/26, 46%), economic (23/26, 88%), and environmental (24/26, 92%) viewpoints. Conclusions: The study argues that each digital health solution would have to be accessed individually to find the optimal business model to follow. This is due to their differing life cycles and complexity. The manufacturer will need a layered value proposition, implementing several business models dependent on their respective product portfolios. The need to incorporate several business models implies an ecosystem perspective that is relevant to consider.

Background Digital health technologies (DHTs) can facilitate the execution of de-centralized trials that can offer opportunities to reduce the burden on participants, collect outcome data in a real-world setting, and potentially make trial populations more diverse and inclusive. However, DHTs can also be a significant source of electronic waste (e-waste). In recognition of the potential health and environmental impact from DHT use in trials, private and public institutions have recently launched initiatives to help measure and manage this e-waste. But in order to develop sound e-waste management policies, it will be necessary to first estimate the current volume of e-waste that results from the use of DHTs in trials. Materials and Methods A Web Ontology Language (OWL)-compliant ontology of DHTs was created using a list of 500 DHT device names derived from a mixture of public and private sources. The U.S. clinical trials registry, ClinicalTrials.gov, was then queried to identify and classify trials using any of the devices in the ontology. The ClinicalTrials.gov records from this search were then analyzed to characterize the volume and properties of trials using DHTs, as well as estimating the total volume of individual DHT units that have been provisioned (or are planned to be provisioned) for clinical research. Results Our ontology-driven search identified 2326 unique clinical trials with a reported “actual” enrollment of 200,947 participants and a “planned” enrollment of an additional 4,094,748 participants. The most-used class of DHTs in our ontology was “wearables,” (1852 trials), largely driven by the use of smart watches and other wrist-worn sensors (estimated to involve 149,391 provisioned devices). The most-used subtype of DHTs in trials was “subcutaneous” devices (367 trials), driven by the prevalent use and testing of glucose monitors (estimated to involve 17,666 provisioned devices). Conclusion Thousands of trials, involving hundreds of thousands of devices, have already been completed, and many more trials (potentially involving millions more devices) are planned. Despite the great opportunities that are afforded by DHTs to the clinical trial enterprise, if the industry lacks the ability to track DHT use with sufficient resolution, the result is likely to be a great deal of e-waste. A new ontology of DHTs, combined with rigorous data science methods like those described in this paper, can be used to provide better information across the industry, and in turn, help create a more sustainable and equitable clinical trials enterprise.