Abstract
The Tissue Engineering and Regenerative Medicine International Society-North America (TERMIS-NA) Industry Committee was formed in February 2009 to address the common roadblocks (i.e., hurdles) in the commercialization of tissue engineering/regenerative medicine products for its members. A semiquantitative online opinion survey instrument that delineated potentially sensitive hurdles to commercialization in each of the TERMIS constituency groups that generally participate in the stream of technology commercialization (academia, startup companies, development-stage companies, and established companies) was developed. The survey was opened to each of the 863 members of TERMIS-NA for a period of 5 weeks from October to November 2009. By its conclusion, 215 members (25%) had responded. Their proportionate numbers were closely representative of TERMIS-NA constituencies. The resulting data delineate what each group considers to be its most difficult and also its easiest hurdles in taking a technology to full product development. In addition, each group ranked its perception of the difficult and easy hurdles for all other groups, enabling an assessment of the degree of understanding between groups. The data depict not only critical hurdles in the path to commercialization at each stage in product development but also a variable understanding of perceptions of hurdles between groups. This assessment has provided the Industry Committee with activity foci needed to assist individual groups in the technology-commercialization stream. Moreover, the analysis suggests that enhanced communication between groups engaged in commercialization will be critical to the successful development of products in the tissue engineering/regenerative medicine sector.
Introduction
The committee chose to use a semiquantitative online opinion survey mechanism to gain access to the voice of its customers, that is, the members of TERMIS-NA. The survey was designed to assess the hurdles faced within four major participant groups in the commercialization stream. These groups included academia, startup companies, development-stage companies, and established companies. The committee wished to determine what each group considered to be its own challenges as well as their perceptions of the challenges of other groups. The committee also wished to determine the cross-group experience profile of the TERMIS-NA membership as a backdrop to this assessment.
Methodology
Study population
The entire membership (863 members, including 268 student members) of TERMIS-NA constituted the study population. A customized online semiquantitative opinion survey * was created.
Survey design
The survey was designed to provide data to assess three core questions:
Experience profile: What is the proportion of members having experience in single versus multiple groups?
Primary perception: For members in academia, startup companies, development-stage companies, and established companies, what do each perceive as the current hurdles ranked as most difficult and as the easiest in the commercialization process in their respective groups?
Cross perception: What do members of each group perceive to be current hurdles ranked as most difficult and as the easiest in the commercialization process as experienced by other groups?
Assignment of technology development groups
Members were asked to identify their primary activity group among the following choices (as defined below for the purposes of the survey):
Academia
Startup company (i.e., having products in early development)
Development-stage company (i.e., having products in late development or early sales)
Established company (i.e., ongoing, predictable product sales and growth)
Government
Nonprofit organization
Other (consultants, etc.)
Members assigned themselves to these groups as primary (defined as working in this group currently), secondary (defined as having had at least 3 years of experience in this group but not presently working in it), or not applicable.
Measurement of hurdle difficulty and group-specific hurdle choices
Table 1 depicts the surveyed hurdles for the academic, startup, development-stage, and established companies, respectively. Choices within each group were designed to reflect important aspects of the commercialization process that are unique to that group. For each hurdle, a respondent could vote for one of the following to describe the degree of difficulty associated with that hurdle:
Easy Some difficulty Quite difficult Extremely difficult Impossible Not applicable
Note that although some overlap exists among the hurdles, the survey choices were designed to also recognize the unique challenges faced by each group.
FDA, U.S. Food and Drug Administration; NIH, National Institutes of Health; VC, venture capital.
Members of all primary groups were allowed to vote both within their group and across all other groups to enable assessment of direct and perceived experiences of most difficult and easiest hurdles. Members of TERMIS-NA were reminded of the survey via e-mail weekly for a period of 5 weeks. Only one completed survey from any computer's Internet protocol address was accepted.
Survey validity
Although primarily intended to provide opinion trends to guide the Industry Committee in its educational activities, the survey response rate provided a 5.8% error at the 95% confidence level and a 4.9% error at the 90% confidence level, as determined using a CustomInsight.com survey algorithm.† The committee deemed this degree of confidence to be sufficient to support the following analysis.
Analysis of data
Data were analyzed to quantify the responses within groups (i.e., academics responding to the academic hurdles) and responses between groups (e.g., academics responding to established company hurdles).
General perceptions of difficulty
The committee considered four endpoints to be of interest:
The degree to which each group considered its own hurdles to be difficult or easy.
The degree to which each group saw one another's hurdles to be difficult or easy.
The actual hurdles identified as most difficult or easiest within each group.
The actual hurdles identified as most difficult or easiest for each group as estimated by other groups.
Other points that were considered by the committee
Because the vast majority of responses for each group were typically in the easy and somewhat difficult categories, the results of responses in the quite difficult and extremely difficult categories for each hurdle were pooled to complete the assessment of hurdle difficulty for each group.
The impossible category was almost never chosen and was therefore omitted from the analysis.
The data analysis was further focused on those hurdles having the top three percentage response rates (in many cases, there were ties in the first, second, and third place categories; see Appendices A and B).
The mean of the frequencies assigned to the top three hurdles for each group was designated the difficulty factor.
In the presence of percentage ties assigned to the hurdles, the first, second, and third assigned percentages were used only once in the calculation of the difficulty factor.
Specific perceptions of difficulty
Although the general perception of difficulty within and between groups was assessed using the difficulty factor, a method was needed to determine how accurately each group could identify the specific hurdles deemed most difficult by the group to which they most clearly belonged. To do this, the number of times that another group accurately chose any of the top three hurdles (in any order) that were identified by any group ranking its own experience was counted. The consonance factor could have a maximum value of 3.
The raw data underpinning this analysis is presented in Appendices A and B. Note that Appendix A contains data from group self-evaluation and Appendix B contains data from group interevaluation.
Results
Respondent profile
A total of 215 TERMIS-NA members (25%) responded. Table 2 depicts the numbers of responders who assigned themselves to groups in a primary status and Table 3 depicts the number and proportions of responders who assigned themselves to groups in a secondary status.
Percentages shown are as a function of total respondents (215). The chart should be read by identifying a primary group in column 1 and then reading to the right to see how often assignment to secondary groups occurred.
Thirty-four members assigned themselves to more than one primary designation.
Table 3 depicts the extent to which secondary group assignment occurred as a function of primary group assignment. These data provide one picture of the collective cross-experience among groups.
Findings include:
Members assigned themselves to secondary domains 68% of the time.
The most likely secondary experience when the primary domain was academia was a startup company (11.2%), whereas for a startup company it was academia (2.3%).
For development-stage companies it was a startup company (5.1%) and for an established company it was a development-stage company (5.1%).
In Table 4, overall secondary assignments are pooled to provide a measure of cross-group experience among primary group members.
Percentages are based on the number of respondents in each domain.
Findings include:
Government respondents exhibited the highest domain crossover experience, followed by academia. All industry groups and nonprofits formed a middle tier, with Others having significantly lower crossover experience.
Table 5 depicts how closely the survey groups compared with their proportionate representation in the TERMIS-NA membership. Because the latter is only classified by Industry, not by the categories of startup, development-stage, or established company, survey responder numbers in these groups were pooled for comparison.
TERMIS-NA, Tissue Engineering and Regenerative Medicine International Society-North American.
Findings include:
The proportion of responders in academia was slightly less than the proportion of academics in TERMIS-NA. The proportion of pooled Industry domains (startup company, development-stage company, established company) was slightly higher. The proportion of pooled government/nonprofit and other domains was about equal to their TERMIS-NA membership proportion.
Perceptions of hurdle difficulty ‡
Group perceptions of their own and others' hurdle difficulties were assessed by the following two methods:
General perception of hurdle difficulty: The intensity (as defined by the percentage of responses) of perceived hurdle difficulty is determined by calculating the mean of the top three hurdle rankings within and between groups. This represents the mean percentage of respondents who felt that the top three ranked hurdles were quite difficult or extremely difficult.
Specific perception of hurdle difficulty: The frequency (as defined by the rate of coincident assignment of hurdles) with which another group's top three ranked hurdles coincided with a target group's ranking of its own top three hurdles. The coincidence could occur at any position within the top three hurdles in either group's ranking. A maximum value of 3 is allowed (i.e., up to three coincident identifications of another group's top three hurdles in any order). During inspection of Appendices A and B, multiple ties are seen to occur within a group's percentile ranking of the first, second, and third hurdles in many cases. All such ties were considered as candidates for matches in the top three positions. This analysis assesses the accuracy with which any group can identify the most difficult hurdles of any other group.
General perception of hurdle difficulty
Table 6 depicts the sums of the percentage ranking of the top three hurdles within and between groups.
The groups in the first vertical column rank their own and other groups' perceived level of difficulty. The cells in gray represent self-ranking.
Findings include:
In all cases, groups perceive the difficulties of their own hurdles to be greater than other groups perceive them to be. Of all groups, startup companies considered their hurdle difficulties to be the greatest and established companies considered theirs to be the least difficult.
In Table 7, the rankings shown in Table 6 are normalized in each column to the self-ranking (gray) cells to determine the degree to which other groups agree with one's own perception of hurdle difficulty. This allows two criteria to be developed: mean inward consonance (the agreement of other groups with one's own group ranking) and mean outward consonance (a group's agreement with the rankings of other groups).
The cells in gray represent self-ranking.
Findings include:
Other groups were the most likely to understand the hurdle difficulty level of academics and least likely to understand the hurdle difficulty of development-stage companies (mean inward consonance of 0.8 and 0.4, respectively). In contrast, development-stage companies were best able to appreciate the hurdle difficulty of other groups, whereas academics and established companies appreciated these hurdle difficulties the least (mean outward consonance of 0.8, 0.5, and 0.4, respectively). Established companies were especially weak in their perception of the hurdle difficulties of startup companies (0.3) and development-stage companies (0.2).
Specific perception of hurdle difficulty
Table 8 depicts the frequency (to a maximum value of 3) with which any one group predicts another group's top three most difficult hurdles (in any order).
The cells in gray represent self-ranking.
Findings include:
The nonacademic groups were able to predict specific academic difficulty hurdles with the greatest accuracy and specific established company difficulty hurdles with the least accuracy (mean inward consonance of 3.0 and 1.3, respectively). Development-stage and established companies were able to specifically predict the difficult hurdles of other groups with the greatest accuracy and academics were the least accurate in this prediction (mean outward consonance of 2.7, 2.7, and 1.7, respectively).
For ease of interpretation, Table 9 shows a distillate of the most difficult hurdles faced by the four groups as self-ranked (as more fully outlined in Appendix A).
Perceptions of hurdle ease §
Group perceptions of their own and others' easiest hurdles are assessed in two ways:
General perception of hurdle ease: The intensity of perceived hurdle ease is determined by calculating the mean of the top three hurdle rankings within and between groups. This represents the mean percentage of respondents who felt that the top three ranked hurdles were easy.
Specific perception of hurdle ease: This is measured as the frequency with which another group's top three ranked hurdles coincided with a target group's ranking of its own top three easy hurdles. The coincidence could occur at any position within the top three hurdles in either group's ranking. A maximum value of 3 is allowed. Note in inspection of Appendices A and B that, in many cases, multiple ties occurred within a group's percentile ranking of its first, second, and third easiest hurdles. All such ties were considered as candidates for matches in the top three positions. This analysis illustrates the relative accuracy with which any group can identify the easiest hurdles of any other group.
General perception of hurdle ease
Table 10 depicts the sums of the percentage ranking of the top three hurdles identified as easy within and between groups. The groups in the first vertical column rank their own and other groups' perceived level of ease. The cells in gray represent self-ranking.
The cells in gray represent self-ranking.
Findings include:
While self-rankings tended to be similar, of all groups developing stage companies had the lowest perception of easy hurdles while academic groups gave themselves the highest perception of easy hurdles.
In Table 11, the rankings shown in Table 10 are normalized in each column to the self-ranking (gray) cells to determine the degree to which other groups agree with one's own perception of hurdle ease. This allows two parameters to be developed: mean inward consonance (the agreement of other groups with one's own group ranking) and mean outward consonance (a group's agreement with the rankings of other groups).
The cells in gray represent self-ranking.
Findings include:
Other groups were the most likely to understand the hurdle ease of academics and least likely to understand the hurdle ease of development-stage companies (mean inward consonance of 0.9 and 0.5, respectively). In contrast, development-stage companies were best able to appreciate the hurdle ease of other groups while this was least appreciated by academics (mean outward consonance of 1.0 and 0.4, respectively). Academics were especially weak in their perception of the hurdle ease of development-stage (0.3) and established companies (0.3).
Specific perception of hurdle ease
Table 12 depicts the frequency (to a maximum value of three) with which any one group predicts another group's top three easy hurdles.
The cells in gray represent self-ranking.
Findings include:
Established company easy hurdles were predicted by all other groups with the greatest accuracy and development-stage company easy hurdles with the least accuracy (mean inward consonance of 3.0 and 1.7, respectively). Startup companies were able to predict the easy hurdles of other groups with the greatest accuracy and academics were the least accurate in this prediction (mean outward consonance of 3.0 and 1.7, respectively).
For ease of interpretation, Table 13 shows a distillate of the easiest hurdles faced by the four groups, as self-ranked (as more fully outlined in Appendix A).
The special perceptions of government/nonprofit employees and others (consultants)
Because consultants and government/nonprofit employees are likely to work with all of the groups being considered in this analysis, their impressions could be particularly interesting. However, ‡ their numbers when assigned as primary members of these groups were insufficient to warrant inclusion of their limited responses in this analysis. A more focused analysis of these groups will be needed in the future.
Conclusions
The Industry Committee of TERMIS-NA performed an online survey of the full membership of TERMIS-NA to better understand the needs of its membership with respect to technology commercialization. The survey was segmented to highlight the commercialization hurdles faced by the academic, startup, development–stage, and established company subsets of the technology development continuum. Although originally designed to be an opinion-only survey, the overall response rate of 25% provided a reasonable representation of the overall TERMIS-NA membership distribution for a semiquantitative survey of this nature.
The survey was designed to identify what each group felt were its specific commercialization hurdles and the hurdles they found to be the easiest and also to identify what each group perceived to be one another's' hurdles. By this method, the Industry Committee could not only focus its activities on the most important hurdles within each group but also determine if intergroup understanding was also a barrier to commercialization. The results fulfilled both aims.
Hurdles to commercialization were evaluated both generally, that is, by intensity of difficulty and specifically, that is, by identification of specific hurdles that were the most difficult and the easiest. The results indicate that groups perceive the difficulties of their own hurdles to be greater than other groups perceive them to be. Of all groups, startup companies considered their hurdle difficulties to be the greatest and established companies considered theirs to be the least difficult. Other groups were the most likely to understand the hurdle difficulty level of academics and least likely to understand the hurdle difficulty of development-stage companies. In contrast, development-stage companies were best able to appreciate the hurdle difficulties of other groups, whereas academics and established companies appreciated these the least. Established companies were especially weak in their perception of the hurdle difficulties of startup companies and development-stage companies.
The intensity of ease in the top three hurdles ranked as Easy was quite similar between all groups. However, other groups were most likely to understand the hurdle ease of academics and least likely to understand the hurdle ease of development-stage companies. In contrast, development-stage companies were best able to appreciate the hurdle ease of other groups, whereas they were least appreciated by academics. Academics were especially weak in their perception of the hurdle ease of development-stage and established companies.
The following applied to the ability of any group to identify the specific hurdles deemed most difficult by another group ranking itself. The nonacademic groups were able to predict specific academic difficulty hurdles with the greatest accuracy and established company difficulty hurdles with the least accuracy. The development-stage and established companies were able to predict the specific difficulty hurdles of other groups with the greatest accuracy. Academics were the least accurate in this prediction.
The following applied to the ability of any group to identify the specific hurdles deemed easiest for another group ranking itself. Academic and startup company groups were able to predict established company easy hurdles with the greatest accuracy and development-stage company easy hurdles with the least accuracy. In addition, startup companies were able to identify the easy hurdles of other groups with the greatest accuracy and that academics were the least accurate in this identification.
The opinions of government, nonprofit, and other (primarily consultant) members of TERMIS-NA, although most likely to be valuable because of their unique positions with respect to all groups, could not be considered in this analysis because of low response numbers.
An analysis of the data derived from this survey will help to guide the Industry Committee in its educational activities. It is evident that specific attention must be paid to the needs of each group as an entity but that substantial cross-education is also necessary. Specific needs apply to each group:
For academics, support of funding programs and market awareness will be important, although there is little role for the committee in programs designed to assist in recruitment or research management.
For startup companies, support of management recruitment practices and technology transfer office policies and procedures will be important, although there is no need for support of the search for operating space or early product prototyping.
For development-stage companies, programs designed to support networking with financiers and to enhance awareness of market dynamics will be critical, whereas little attention needs to be paid to human resource, intellectual property (IP) or operational space issues.
For established companies, it will be important to provide access to pools of new IP and to support programs that enhance the U.S. Food and Drug Administration's ability to understand tissue engineering/regenerative medicine technology. Conversely, there will be no need to support established company activities in the areas of real estate, sales/vendor management, or competitive intelligence.
Finally, the survey results indicate that the Industry Committee would do well to craft programs that draw together and enhance the mutual understanding of the difficulties faced by each group in the continuum of technology development. It is not known how significant the misread by one group of another's difficulties affects the flow of technology, but it cannot be assumed to be a trivial issue. The data suggest specific possibilities as well. For example, under the current financial environment many startup and development stage companies are looking to finance operations through codevelopment or even mergers and acquisitions. Given that startup and development-stage companies consider managing IP to be easy, whereas established companies are interested in expanding their IP base, potential partnerships may benefit by focusing on this aspect early in any transaction discussion.
The Industry Committee of TERMIS-NA will utilize the survey data as a guide to best assist the TERMIS-NA membership to overcome both group-specific hurdles and also the perhaps more subtle intergroup communication hurdles that this survey has uncovered.
Footnotes
Acknowledgments
The authors thank Sarah E. Wilburn, TERMIS Administrator, for her support in the administration of the survey. The authors are also grateful to all members of TERMIS-NA who participated in the survey.
Disclosure Statement
No competing financial interests exist.
Appendices
Perception of Difficulty and Ease Between Groups
| Academic – Startup | |
| Difficult (Mean 33.3) | |
| Obtaining Substantial Funding (VC, Other) | 43.1% |
| Recruiting Experienced Management | 29.3% |
| Obtaining Seed Funding | 27.6% |
| Working with Technology Transfer Offices | 27.6% |
| Working with the FDA | 27.6% |
| Easy (Mean 21.7) | |
| Concept Development | 28.8% |
| Obtaining Space for Operations | 22.4% |
| Recruiting Nonexperienced Management Personnel | 13.8% |
| Academic – Developing | |
| Difficult (Mean 26.2) | |
| Generating Initial Revenues | 27.3% |
| Working with the FDA | 26.2% |
| Scaling Up Manufacture | 25% |
| Easy (Mean 9.5) | |
| Developing Follow-On Product Prototypes | 10% |
| Working with Technology Transfer Offices | 9.3% |
| Product Development (Physical Form) | 9.1% |
| Academic – Established | |
| Difficult (Mean 15) | |
| Managing Development Focus | 17.5% |
| Working with the FDA | 17.5% |
| Optimizing Strategy | 15% |
| Understanding the Evolving Market | 15% |
| Working with Technology Transfer Offices | 15% |
| Generating Sufficient Revenues | 15% |
| Securing New Intellectual Property | 12.5% |
| Maintaining Intellectual Property | 12.5% |
| Recruiting and Retaining Experienced Nonmanagement Personnel | 12.5% |
| Easy (Mean 10) | |
| Obtaining Enlarged Space for Operations | 12.5% |
| Maintaining Intellectual Property | 10% |
| Working with Vendors | 10% |
| Working with Salespersons/Distributors | 10% |
| Scaling Up Manufacture | 7.5% |
| Generating Sufficient Revenues | 7.5% |
| Securing New Intellectual Property | 7.5% |
| Retaining Experienced Management | 7.5% |
| Developing Follow-On Product Prototypes | 7.5% |
| Startup – Academic | |
| Difficult (Mean 42.2) | |
| Obtaining Substantial Research Funds (NIH/Other) | 53.3% |
| Obtaining Seed Research Funds | 46.7% |
| Orienting Research to Market Need | 26.7% |
| Easy (Mean 28.1) | |
| Concept Development | 37.5% |
| Managing Research Focus | 26.7% |
| Recruiting Research Personnel | 20% |
| Orienting Research to Market Need | 20% |
| Managing Conflict of Interest | 20% |
| Startup – Developing | |
| Difficult (Mean 33) | |
| Maintaining Sufficient Funding | 40% |
| Generating Initial Revenues | 37.5% |
| Recruiting and Retaining Experienced Nonmanagement Personnel | 21.4% |
| Managing Development Focus | 21.4% |
| Understanding the Evolving Market | 21.4% |
| Working with Technology Transfer Offices | 21.4% |
| Easy (Mean 24.4) | |
| Retaining Experienced Management | 28.6% |
| Working with Salespersons/Distributors | 23.1% |
| Product Development (Physical Form) | 21.4% |
| Recruiting and Retaining Experienced Nonmanagement Personnel | 21.4% |
| Obtaining Enlarged Space for Operations | 21.4% |
| Understanding the Evolving Market | 21.4% |
| Understanding the Competition | 21.4% |
| Working with Technology Transfer Offices | 21.4% |
| Working with Vendors | 21.4% |
| Startup – Established | |
| Difficult (Mean 24.2) | |
| Growing to Meet Market Expectation | 30.8% |
| Understanding the Evolving Market | 25% |
| Working with Technology Transfer Offices | 25% |
| Managing Development Focus | 16.7% |
| Understanding the Competition | 16.7% |
| Easy (Mean 25) | |
| Obtaining Enlarged Space for Operations | 33.3% |
| Working with Vendors | 33.3% |
| Working with Salespersons/Distributors | 33.3% |
| Generating Sufficient Revenues | 25% |
| Maintaining Intellectual Property | 25% |
| Managing Development Focus | 25% |
| Understanding the Competition | 25% |
| Managing Intercorporate Interactions | 25% |
| Developing Follow-On Product Prototypes | 25% |
| Maintaining Sufficient Funding | 16.7% |
| Securing New Intellectual Property | 16.7% |
| Recruiting and Retaining Experienced Nonmanagement Personnel | 16.7% |
| Development – Academic | |
| Difficult (Mean 34.6) | |
| Orienting Research to Market Need | 45.5% |
| Obtaining Substantial Research Funds (NIH/Other) | 33.3% |
| Obtaining Seed Research Funds | 25% |
| Easy (Mean 33.9) | |
| Concept Development | 54.5% |
| Recruiting Research Personnel | 27.3% |
| Managing Conflict of Interest | 20% |
| Development – Startup | |
| Difficult (Mean 53.5) | |
| Obtaining Substantial Funding (VC, Other) | 71.4% |
| Working with the FDA | 46.2% |
| Obtaining Seed Funding | 42.9% |
| Managing Development Focus | 42.9% |
| Easy (Mean 39.3) | |
| Obtaining Space for Operations | 46.2% |
| Concept Development | 38.5% |
| Disclosure Process Management | 33.3% |
| Development – Established | |
| Difficult (Mean 40) | |
| Understanding the Evolving Market | 50% |
| Growing to Meet Market Expectations | 50% |
| Generating Sufficient Revenues | 40% |
| Understanding the Competition | 40% |
| Working with Technology Transfer Offices | 40% |
| Working with the FDA | 40% |
| Securing New Intellectual Property | 30% |
| Managing Development Focus | 30% |
| Managing as a Public Company (If Applicable) | 30% |
| Managing Intercorporate Interactions | 30% |
| Easy (Mean 33.3) | |
| Obtaining Enlarged Space for Operations | 50% |
| Working with Vendors | 30% |
| Working with Salespersons/Distributors | 30% |
| Generating Sufficient Revenues | 20% |
| Securing New Intellectual Property | 20% |
| Maintaining Intellectual Property | 20% |
| Established – Academic | |
| Difficult (Mean 42.3) | |
| Obtaining Substantial Research Funds (NIH/Other) | 60% |
| Obtaining Seed Research Funds | 41% |
| Orienting Research to Market Need | 26% |
| Easy (Mean 35.6) | |
| Concept Development | 53.6% |
| Recruiting Research Personnel | 27.1% |
| Managing Research Focus | 26.2% |
| Established – Startup | |
| Difficult (Mean 14.7) | |
| Obtaining Substantial Funding (VC, Other) | 17% |
| Obtaining Seed Funding | 15% |
| Understanding the Market | 12% |
| Securing Credit | 12% |
| Easy (Mean 26.7) | |
| Concept Development | 32.1% |
| Obtaining Space for Operations | 29.6% |
| Developing Product Prototypes | 18.5% |
| Established – Development | |
| Difficult (Mean 10) | |
| Generating Initial Revenues | 11% |
| Scaling Up Manufacture | 10% |
| Maintaining Sufficient Funding | 10% |
| Understanding the Evolving Market | 10% |
| Working with the FDA | 10% |
| Managing Shareholder Expectations | 9% |
| Easy (Mean 17.1) | |
| Product Development (Physical Form) | 20.8% |
| Recruiting and Retaining Experienced Nonmanagement Personnel | 17.4% |
| Retaining Experienced Management | 13% |
| Obtaining Enlarged Space for Operations | 13% |
| Understanding the Evolving Market | 13% |
| Working with Salespersons/Distributors | 13% |
| Working with Vendors | 13% |
The authors are the members of the Tissue Engineering and Regenerative Medicine International Society-North America Industry Committee.
‡
The number of respondents in the government/nonprofit and other (consultants, etc.) categories who assigned themselves primary status were 2, 5, and 4, respectively. As such, their numbers were too low to include meaningfully in the following analysis.
§
An easy hurdle is any hurdle that is not perceived to be a meaningful obstruction to the commercialization of a product.