Internet of Things (IoT) is a concept first proposed by Ashton [1] at a presentation at Procter & Gamble (Cincinnati, Ohio) in 1999. By collecting data from individual things and using these data efficiently to facilitate the exchange of information within the network, material and product flows can be tracked to achieve more accurate and real time visibility of the supply chain, which improves communication between entities and streamlines the different steps in the supply chain.

Over the last two decades, IoT has been successfully implemented in various industries including aerospace, aviation, automotive, telecommunications, medicine and health care, independent living, pharmaceutical, retail, logistics and supply chains, manufacturing, process, environmental, transportation, agricultural and breeding, media and entertainment, recycling, and insurance [2,3]. Various benefits have been realized through the application of IoT. Constant monitoring and real-time data acquisition and analysis lead to benefits such as earlier detection of risks, faster response times, and cost savings. This has a wide range of usages from mine production safety [4] to home health care applications [5]. Usage of radio frequency identification (RFID), sensor technologies, and networking architecture, allows better perception and transmission of underground environment information, which leads to better and more timely decision making and safer mine production. Miniature wearable sensors are used to monitor older adults and patients with chronic conditions in order to allow monitoring beyond the boundaries of hospitals. This improves the standard of health care reducing labor costs by lowering the required amount of contact hours with medical staff. Interoperability from connecting information from one domain to others allows better use of resources [6]. One of the most significant applications is in smart highway systems where sensors and cameras are used to collect traffic information that then feeds into a real-time update of an indicator’s operating rules on the highway such as speed limit [7].

A survey of the trends and topics in IoT research conducted by Whitmore et al [8] in 2015 looked into the evidence base available in the IoT research space and identified gaps in literature for specific applications and governance frameworks for the regulation of IoT. The implementation of IoT is generally divided into three layers—the sensing layer, the architecture layer, and the application layer [9]. In the context of cell therapies, which are therapies where whole living cells are administered to patients for treatment of diseases, Harrison et al [10] reviewed the application of fluorescent optical sensors on cell therapy manufacturing, and connecting real-time metabolic measurements to quality attributes of products, which could allow more scalable processes and better quality products. Outside of manufacturing, sensors can also be used for in vivo monitoring postadministration [11].

Technology platforms for facilitating information flow and exchange have been made available by industry and academic partnerships. Vitruvian Networks, for instance, is a partnership set up by GE Ventures (Menlo Park, California) and Mayo Clinic (Rochester, Minnesota) to make use of biomarkers, cell therapy processes, and clinical outcome data to guide therapy development [12]. IoT allows real-time control, more integrated maintenance, better adaptability to changing demands, better collaboration across the supply chain of cell therapies, better traceability, smarter products, and new business models for continuous improvement of products [13]. Frameworks for distributed manufacturing and service-oriented IoT deployments have been proposed and discussed in previous literature [9,14-17], but for highly regulated manufacturing environments such as for cell-based therapies, the interface between IoT architecture and Good Automated Manufacturing Practice [18] has not been documented. To allow successful implementation of IoT, the challenges specific to the area have to be identified. Issues such as privacy and security encountered in other IoT applications [19] have to be identified and considered in a manner specific to cell therapy manufacturing and delivery to inform a tailored IoT approach that facilitates translation and delivery of cell-based products.

This paper aims to systematically review all technologies available in the area, and inspect the current IoT application in cell-based therapies to identify the benefits, challenges, and future directions of using IoT to allow safe and cost-effective delivery of cell-based therapies.

The aim of this review is to understand the current available technologies for IoT, the extent of implementation in cell-based therapy delivery, and the drivers and challenges for implementation of IoT. This will allow the gaps for IoT implementation to be identified and will inform future directions in cell therapy supply chain optimization.

Our research questions are as follows:

This systematic review will be conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines (Multimedia Appendix 1) [20].

In accordance with the PRISMA-P (Protocols) checklist recommendation, the inclusion criteria of this protocol are in accordance with the PICO (Participant, Intervention, Comparator, and Outcomes) standards. Details of the PICO to be included in the review are described in Textbox 1.

The fields of both IoT and cell therapy are rapidly advancing; therefore, to ensure that studies found are up to date, only publications from 2008-2018 will be considered. Studies not published in the English language are excluded due to a language barrier. Studies relevant only to the discovery, research, and development phases of cell-based therapies are not in the scope of this review.

The following databases will be searched: Medline, EMBASE (OvidSP), Web of Science, Cochrane Library & HEED, Scopus, ACM digital library, INSPEC, ScienceDirect, and the IEEE Xplore Digital Library. In addition, a search on Google to identify gray literature will be conducted. Table 1 shows the search concepts and keywords to be searched for this review. Search strings will be constructed using cell-based therapy keywords and keywords from other concepts. A sample search was conducted on the PubMed database, and the search strings used are described in Table 2.

EndNote X8 software will be used for the removal of duplicates. Textbox 1 describes the inclusion criteria of the review. Two independent reviewers will screen the titles and abstracts of papers to ensure no bias occurs. Papers that are ineligible will be eliminated, and the full text of those that appear to meet the review’s eligibility criteria will be obtained and read in full to ensure eligibility. Any contradictions or discrepancies between the reviewers that arise will be discussed until consensus is reached.

Two reviewers will independently check each article to minimize bias using the collaboration’s risk of bias tool as described in the Cochrane Handbook for Systematic Review of Interventions [21]. All selected articles will be judged for their quality based on the Critical Appraisal Skills Programme systematic review checklist [22] and data analysis.

The eligible literature will then be described for its three-layer architecture of IoT proposed in various literature [9,23]. Any observed benefits and challenges of IoT in cell therapy delivery will be extracted and mapped against its status of implementation identified in the included studies. A sample data abstraction form can be found in Textbox 2. The data extracted will then be analyzed qualitatively.