Translational Roadmap for the Organs-on-a-Chip Industry toward Broad Adoption

Abstract

Organs-on-a-Chip (OOAC) is a disruptive technology with widely recognized potential to change the efficiency, effectiveness, and costs of the drug discovery process; to advance insights into human biology; to enable clinical research where human trials are not feasible. However, further development is needed for the successful adoption and acceptance of this technology. Areas for improvement include technological maturity, more robust validation of translational and predictive in vivo-like biology, and requirements of tighter quality standards for commercial viability. In this review, we reported on the consensus around existing challenges and necessary performance benchmarks that are required toward the broader adoption of OOACs in the next five years, and we defined a potential roadmap for future translational development of OOAC technology. We provided a clear snapshot of the current developmental stage of OOAC commercialization, including existing platforms, ancillary technologies, and tools required for the use of OOAC devices, and analyze their technology readiness levels. Using data gathered from OOAC developers and end-users, we identified prevalent challenges faced by the community, strategic trends and requirements driving OOAC technology development, and existing technological bottlenecks that could be outsourced or leveraged by active collaborations with academia.

Keywords: body-on-a-chip; gap analysis; microphysiological systems; organ-on-a-chip; organotypic culture models; quality management; technology-led strategy; translation; validation.

Figure 1

Examples of organs-on-a-chip models, recreating (a) the blood-brain barrier and (b) the perivascular environment in the human endometrium (Adapted and Reprinted from [1], with the permission of AIP Publishing and from [2], both licensed under a Creative Commons Attribution—CC BY license).

Figure 2

Overview of organ-on-a-chip (OOAC) developer and end-user survey respondents: (A,B) areas of business (single selection), (C) role(s) within their organization (multiple selections possible), (D) OOAC developer’s company or research group size (single selection), (E) respondent’s number of years in the OOAC business (single selection), and (F) an overview of the products and services offered by responding OOAC companies.

Figure 3

Overview of available and desired OOAC models indicated by survey respondents. The responses given by OOAC developers and end-users were statistically different (t-test, p < 0.001 comparing developers vs. current and past users, p < 0.005 comparing developers with future interests), while the end-users’ past, current, and future interests were not significantly different.

Figure 4

High-level roadmap of OOAC challenges and requirements, which begins with scaling considerations and progresses through development phases, encompassing device development, cellularization, perfusion and automation, and validation requirements before reaching a translational stage that enables specific regulations, standardization, and provides a level of ease-of-see that can lead to greater OOAC adoption.

Figure 5

A radar map, showing a consensus among survey responses regarding criteria important for OOAC robustness. Examples given to respondents were Luer-Lok for fluidic connections, media changes, pH testing, the addition of drug/toxin for automated operation/manipulation, and automated pH adjustments based on sensor readouts for closed-loop control. Responses were collected on a consistent Likert rating scale from “1—not important at all”, “2—somewhat important”, “3—neutral”, “4—somewhat important”, to “5—very important”. OOAC developers and end-users ranked the criteria for robustness (p > 0.05) of the new models in equivalent order (p = 1, Kruskal–Wallis).

Figure 6

Overview of critical platform criteria toward the broad adoption of OOAC technology. Abbreviated in this figure as Device “3Rs” and Model 3Rs” are robustness, reproducibility, and reliability of each. Examples for automated perfusion and automated sampling/injection included pumps, valves, and robotic pipettes, while examples of closed-loop control included automated pH and oxygen measurements and subsequent adjustments. OOAC developers and end-users ranked the criteria for the broad adoption of the new models in equivalent order (Kruskal–Wallis, p = 1).

Figure 7

(A) A radar map, showing survey responses comparing biological functions available for developers vs. biological functions desired by end-users. Respondents were able to select multiple-selection answers. OOAC developers and end-users ranked similarly to the essential biological functions to be modeled in an OOAC (Kruskal–Wallis, p > 0.1). (B) A radar map, showing a consensus among OOAC developers and end-users regarding the importance of certain modeling criteria toward the broad adoption of OOAC technology. Responses for this question were collected on a consistent Likert rating scale from “1—not important at all” to “5—very important”, as shown. Cardiac cell contractions were given as an example for recapitulation of basic organ functions, while the capacity for long-term culture could benefit the evaluation of chronic exposures and tolerance build-up.

Figure 8

(A) A radar map, comparing survey responses by OOAC developers and end-users and their estimation of technology readiness levels (TRL) of the OOAC field. (B) A radar map, showing overlap among users’ and developers’ responses regarding compliance with existing quality and regulatory standards. Multiple selections were possible. Answers from the two groups of respondents were not significantly different (p > 0.05).

Figure 9

A radar map, showing a consensus among survey responses regarding the importance of end-user support categories toward broad OOAC device adoption. Responses were collected on a consistent Likert rating scale from “1—not important at all”, “2—somewhat important”, “3—neutral”, “4—somewhat important”, to “5—very important”. OOAC developers and end-users ranked the criteria for support services or trainings (p > 0.05) of the new models in equivalent order (p = 1, Kruskal–Wallis).