In this research, we examined how public regulatory agencies respond to rapidly emerging technologies. Our study examined the role of the Food and Drug Administration as it manages first-in-human clinical trials using CRISPR/Cas-9 gene editing technologies and we examined both state and federal regulators who are managing the introduction of connected and autonomous vehicle (CAV) technologies. The research builds on a concept of "absorptive capacity" that examines the types of investments firms make in fields of rapidly emerging technologies that allow them to remain competitive. Prior to our study, this concept had not been applied in the public sector. We found that public sector agencies, when they have the budget to do so, make similar kinds of investments to ensure that they can perform their legislatively mandated roles.  Because public sector agencies are sometimes constrained in their ability to make such investments (e.g., lack of budget; barriers to recruitment), many agencies find creative ways to bolster their own technical capacity. In fact, this can be a preferred strategy when building up internal capacity might take too much time. Our research indicates that the public sector agencies asked to determine, as a matter of public policy, the likely risks and benefits of CRISPR/Cas-9 and CAV emerging technologies bring significant expertise to the table and, in many cases, do not lag their private sector counterparts in their technical understanding of the technologies they are asked to review. An important feature of our research is that we studied agencies that, because they provide access to markets for new technologies, often have the support of regulated actors who enjoy the benefits of having received a stamp of safety or effectiveness.  We believe that this support from the regulated sector may be important in providing agencies with the resources, including political legitimacy, they need to be able to maintain expertise. The research findings are based on interviews with private sector actors in the process of developing CRISPR/Cas-9 medical treatments or CAV technologies and with agency personnel at the state and federal level. 

This data stems from 33 in-depth interviews with regulatory experts and innovators leading CAV (14) and CRISPR-Cas9 (19) technological development. This includes interviews with regulatory reviewers and those who have experience working with government agencies such as the FDA, Department of Transportation, and Department of Motor Vehicles, as well as individuals working in technology innovation including leaders of private biotechnology or automobile development companies, consulting agencies, academic and industry scientific researchers, and legal experts. Interviews were semi-structured and typically lasted an hour, with a few closer to two hours. These interviews took place via various mediums – in-person, over the phone, and video conferencing – in order to efficiently accommodate interviewees across the United States. During interviews, individuals were asked about their perspectives regarding the technology, CRISPR-Cas9 or CAVs, specifically pertaining to future development, priorities and concerns for regulation, and potential risks. Each interviewee who consented to audio recording was provided with the opportunity to review their de-identified transcript before it became a part of this public repository of collected data.

The sample of interviewees for this study consist of a combination of purposive samples, from targeted search strategies, and snowball samples resulting from references from interviewees. This approach to sampling allowed us to reach experts who are otherwise challenging to connect with and enlist for academic data collection, especially given the high-level, often private and proprietary nature of their work. In order to record perspectives from the gamut of stakeholders, we purposefully searched for interviewees who had relevant technological or regulatory experience and were affiliated with related organizations. Further, our team’s ability to network with relevant experts was bolstered by our location near to a substantial number of laboratories, research hospitals, technology and regulatory consultants, and private companies innovating genomic and automated vehicle technologies. The target population of interviewees thus consisted of employees or ex-employees working at government agencies and private companies that inform the development and regulation of CAVs and CRISPR-Cas9 for biomedical purposes. The sampling frame consisted of public directories and meeting rosters of those sitting on regulatory advisory committees (i.e. the FDA’s Cellular, Tissue, and Gene Therapies Advisory Committee), professional societies (i.e. American Society of Gene & Cell Therapy), and private stakeholders with expertise navigating the regulatory landscape for genome editing and automated vehicles. We also utilized LinkedIn search functionalities, which allowed us to identify and reach out to individuals with relevant expertise, employer histories, and professional trajectories. Because a significant focus of this study was concerned with government regulatory agencies, target individuals’ contact information was often publicly listed. Our team contacted all relevant individuals. We interviewed all those who agreed to be a respondent. Most interviewees also provided a list of references for further interviews, generating a snowball sample in addition to the purposive sample.

Almost all interviewees provided consent to be audio recorded. Using this audio recording, accurate transcripts were generated by a professional human transcription service and then de-identified by the research team to the standard described below.

Per our human subjects approval letter, which subjects were able to review prior to consenting to an interview and inclusion in the repository, transcripts were de-identified to the following standard: "the research team will review transcripts to ensure that they contain no information that would allow a reasonably knowledgeable and determined person to identify the individual who gave the interview." Redactions made to the interview transcripts are marked in the text with [redacted] where applicable.

As described in the methods section, this dataset is comprised of de-identified interview transcripts from 33 interviews. All subjects were given the option to consent to inclusion or opt for exclusion of their redacted transcript in the repository at the time of the interview and again upon review of the redacted transcripts.

Based on this review process, 14 of the 33 interviews are included in this repository (9 CRISPR and 5 CAV). All interviews conducted (including those which are NOT in the repository) are indexed in the overview file for citation and reference purposes.

Files:

1. README.xlsx
   1. Description: Master overview file of the entire interview dataset, including meta-data for interviews which are not included in the repository)
   2. Columns: 
      1. Interview Number (serves as GUID)
      2. Month
      3. Year
      4. Case (CAV or Gene Therapy (GT)
      5. In Repository? (Included or Excluded)
      6. Filename
2. De-Identified Interview Transcripts
   1. One .docx and one PDF per interview (PDF for stable page numbering, docx for easy restructuring)
   2. Naming convention: Capacity Challenge_Interview [Interview Number]_[Case Abbreviation CAV or GT]_[Month][Year]
      1. i.e. Capacity Challenge_Interview 1_CAV_07-2018.docx AND Capacity Challenge_Interview 1_CAV_July2018.pdf
   3. Total number of interview transcripts: 14

Recommend In-Text Citation style for interviews:

(Posch et al. 2021, Interview #, page [# from PDF])

i.e. (Posch et al. 2021, Interview 1, page 4)