Can Lean Startup Principles Be Applied to Life Sciences Companies?
Lean Startup methods are often touted as the right path for every business, but are they really useful for life-science startups?
According to Mark Annett, who spoke at the Launch NJ: Life Sciences Hub meetup on Jan. 5, the answer is “No.”
In the life science industry, startups can’t legally go to market with a minimum viable product because their products must be approved by the Food and Drug Administration, and they must be built under design controls before they can be marketed, he explained.
However, Annett added that some Lean Startup principles could be extremely valuable for life-science startups, and his presentation focused on how Lean principles could be used when applying for a grant from the federal government’s Small Business Innovation Research program.
Annett acknowledged upfront that he was only giving his own opinion, but he added that his credentials supported his ability to make this judgment.
Aside from being a Lean Startup competition winner, featured speaker at numerous Lean events and mentoring 25-plus companies, Annett is a biomedical engineer who has designed spinal implements and worked on neurological stimulators and heart pumps. He is currently president of Annett Enterprises, an intellectual-property and product-development think tank and acquisition company.
Annett started off his presentation by explaining that, “in a nut shell,” Lean Startup is all about the MVP, but the MVP doesn’t fit into the life-science model.
At this point, an audience member asked, “Isn’t a pharma company that is developing and testing various assays in the lab developing an MVP?” Annett replied, “The MVP model would have you injecting those same assays into people, and then improving them based on customer feedback, which we can’t legally do!”
Annett then went on to demonstrate a standard Lean Startup business approach in order to highlight the principles that are particularly useful in the SBIR grant-application process.
According to Annett, the most important principles are: pivoting (changing your product or market based on customer feedback), determining your riskiest assumption (the one whose falsification would negate your business model), and setting your validation criteria (measures for testing your riskiest assumption).
Annett went on to show how he had used these principles in a recent SBIR grant application, and how they can help avoid the most common causes of low SBIR scores, among them: the absence of clear objectives, endpoints and criteria for success; an unfocused or poorly written research plan; and a lack of innovation.
Attendee Judith Sheft, of New Jersey Institute of Technology, told the group about some programs geared to helping life-science startups adopt aspects of the Lean methodology. For example, in June the National Institutes of Health and the National Science Foundation agreed to collaborate on a program to train researchers to evaluate their scientific discoveries for commercial potential, with the aim of expediting the entry of biomedical innovations into the marketplace.
According to an NIH release, the curriculum of the NIH Innovation Corps (“I-Corps”) Team Training Pilot Program, offered to SBIR award winners, comprises a nine-week boot camp in which experienced business-savvy instructors work closely with teams of researchers to help them explore potential markets for their federally funded innovations.
Sheft also pointed out that Lean Startup expert Steve Blank wrote a blog post about the I-Corps program. In the post, Blank noted that, while not all life-science-startup activities benefit from the Lean Startup methodology, there are many that do.
Blank wrote that, by using Lean principles, healthcare and life-science entrepreneurs could quickly define clinical utility before spending millions of dollars, understand who their core and tertiary customers are, figure out the sales and marketing process required for initial clinical sales and downstream commercialization, assess intellectual property and regulatory risk before they design and build, and know what data will be required by future partnerships or collaborations before doing the science.
