The VIC Fellows Program provides an opportunity for individuals with relevant expertise and interest to learn how to identify and evaluate promising innovations from global sources. We are pleased to highlight the members of the 2023-2024 class of Fellows in our ongoing series of interviews, such as this recent discussion with Nicole Shirkey-Son, PhD
Can you share a bit about your journey into biotech and your background?
From when I was a small child to today, my journey has been consistently driven by an insatiable curiosity and fascination with the human body, a compulsion toward innovation and out-of-the-box problem solving, and a love of art and creative expression. Because I was a child of a parent with an incurable degenerative chronic disease, I became keenly aware of the potential of science and medicine to help relieve some of the devastation and sorrow of disease, providing us with more time and memories with those we love. These interests and experiences shaped all of my coursework, from high school all the way to graduate school, where I eventually earned a PhD in Neuroscience. My graduate and post-doctoral research focused on understanding fundamental principles of how cells of the nervous system develop and organize themselves into intricate and complex networks, often referred to as ‘basic science’. After gaining my foundation in basic science, I next sought to understand how scientific principles and practice could be practically applied to improve human health, which led me to various industry positions at both large companies and small start-ups.
The ability to experience and learn about life science entrepreneurship, while contributing my unique skill set, drew me to apply to the VIC fellowship. In particular, I wanted to see and experience directly how innovations and intellectual property foundations can be translated into companies that provide real benefit to patients. This space, especially being in the life sciences, brings together many things that I am passionate about - high impact work that seeks to positively impact peoples lives, challenge, strategy, team work, innovation, resource optimization, and the ability to create.
You’ve worked at both small startups and large Fortune 500 companies. What are some of the biggest differences you've experienced in those environments, and how has that shaped your approach to innovation?
I think the biggest differences I’ve experienced in small start ups and large companies all relate back to fundamental differences in the quantity and type of available resources, and how those resources are utilized. These resources and how they’re used often inform the level of risk tolerance, agility, and core goals and identity of the company. This contrast has shaped my approach to innovation in a few key ways:
- Balance Agility with Structure: I’ve learned to value the startup mindset of moving fast and testing assumptions early, while also appreciating the discipline and scalability that come from robust systems. When I innovate, I focus on building scalable solutions that can start lean but grow within more regulated environments.
- Customer-Centered Design: Startups are deeply focused on solving real pain points because they can’t afford to build something that doesn’t work. That user-centered approach is something I carry forward, even in larger organizations, where it can be easier to pursue incremental development and lose touch with end users.
- Risk Management: In a startup, you're often betting the company on a single innovation. In a Fortune 500 setting, you’re balancing risk across a portfolio. I’ve learned to scope projects in ways that manage risk strategically and judiciously- through, for example, pilot programs, staged investments, or partnerships.
Ultimately, I see the most powerful innovation happening when you can bring startup speed and creativity into the ecosystem of a large company, with its resources and global reach.
You have extensive experience in preclinical development for both biologics and medical devices. What are some of the key challenges in translating preclinical research into clinical applications?
Translating a product from preclinical development to regulatory approval and clinical use is often a long and complex process, due to a number of factors. Understanding and anticipating these challenges is key to reducing failure rates and accelerating the path to market.
Predictive Value of Preclinical Models: In vitro and/or animal models often don’t fully recapitulate human biology, especially in areas like neurology, immunology, and oncology. A therapy may show promise in preclinical models but fail to demonstrate efficacy or safety in humans.
Safety and Toxicity Profiles: Unanticipated toxicities can emerge in human trials that were not detected during preclinical testing. Dosing regimens that work in animal models might not scale safely or effectively for human subjects.
Regulatory Hurdles: Navigating FDA or global regulatory pathways (IND filings, GMP manufacturing, etc.) can be complex and resource-intensive. Ensuring the robustness of preclinical data to meet regulatory standards can be a major bottleneck.
Manufacturability and Scalability: A product that works in the lab may not be feasible to produce at scale or under quality-controlled conditions. Translational research must consider how to scale production while maintaining consistency and compliance.
Funding and Resource Constraints: Bridging the "valley of death" between preclinical success and clinical trials often requires significant capital with no guaranteed return. Many promising innovations stall here due to lack of funding or investor risk aversion.
Clinical Trial Design Challenges: Identifying appropriate patient populations, biomarkers, or endpoints can be difficult, especially for novel therapies. Ethical and logistical constraints in human trials add complexity.
Cross-Disciplinary Collaboration: Successful translation requires tight integration between scientists, clinicians, engineers, regulatory experts, and business teams. Misalignment between these stakeholders can derail progress.
As a certified GLP expert, you've served as Principal Investigator or Study Director for hundreds of preclinical studies. What are some of the most important considerations for designing and executing a successful preclinical study?
Preclinical studies are a cornerstone of successful product development. Well-designed preclinical studies don't just check a box for regulatory approval- they build confidence, guide smart decision-making, and lay the groundwork for clinical translation. Without the appropriate design, planning, conduct, and documentation, the time and cost of preclinical evaluation can quickly exceed timelines and budgets, with potentially devastating consequences for the product.
Overall, two take home suggestions: 1) don’t go it alone, or in a silo! Ensure that the study has been designed and planned by a well-rounded group of experts, as applicable to the product type and stage of development, and 2) it’s never too early to talk to regulatory and preclinical experts (such as Study Directors) about your product! Consider feedback from the following: a) experts on the product, such as lead Scientists/Engineers, that understand: how it’s made, prepared, delivered, functions, and its history, current state, and future plan for product development and testing, b) test facility staff such as: Study Director, Surgeons/Interventionalists, Veterinarian, Quality Assurance experts, animal care leads, lead technicians, c) subject matter experts such as Clinicians, Biocompatibility experts, Toxicologists, Biostatisticians, and most definitely, Regulatory experts, d) key stakeholders on all sides, such as product company leadership, and test facility management. Beyond this, ensure consideration of the following:
- Clear rationale and objectives: Why is the study being conducted, and what are the objectives? What are you trying to evaluate- early feasibility, a new prototype/formulation, usability, efficacy, safety, etc? How will this information be used, and by whom?
- Measurements: Define the endpoints, or data to be collected to assess the outcome of the study. What measurements will be taken (and why), how often, and by whom? How will the data be analyzed and interpreted? Define criteria for success, as applicable. Discuss expectations with test facility staff and other cross-functional experts to ensure validity and feasibility.
- Model system: Select relevant and predictive models that best mimic the human condition you’re targeting. Consider disease relevance, genetic background and immunological compatibility as applicable, and physiological similarities/differences to humans. Use multiple models when appropriate to strengthen translatability.
- Study integrity and quality: Use methods to ensure bias control, such as randomization and blinding, when feasible and appropriate. Include applicable control groups, including positive, negative, and sham controls. Carefully consider the sample size, and ensure statistical power as appropriate for the study. Utilize standard operating procedures/standardized protocols. Maintain strict data integrity and control. Ensure the appropriate level of compliance (GLP, non-GLP, non-GLP with Quality review). Upon completion, summarize the study and findings in a study report.
- Ensure Regulatory alignment: Design studies in consultation with regulatory experts and applicable regulatory guidelines. Think ahead to needs for subsequent stages of development. Avoid redoing studies due to incomplete design.
- Translational relevance: Consider biomarkers, imaging, or other assays/metrics that can bridge preclinical and clinical stages. Ensure preclinical readouts have clinical relevance.
- Cost and timeline management: Preclinical studies can be costly and time-consuming- build in buffers and risk mitigation strategies. Prioritize studies that will de-risk the development path the most.
- Ethical responsibility: Use the 3Rs (Replace, Reduce, Refine) for animal work. Ensure ethical and judicious use and treatment of animals, with proper sourcing, welfare, and IACUC oversight.
You’ve served as Principal Investigator on over $2 million in non-dilutive funding for startups. What advice would you give to founders or scientists seeking grant funding?
Securing grant funding is both an art and a science. Here are some suggestions I’ve found helpful:
- Start with a Clear, Compelling Value Proposition
Funders need to understand why your work matters—what unmet need you're addressing, and how your solution is innovative or differentiated. Focus on impact: clinical significance, market potential, and how your work advances the field or solves a real-world problem.
- Align with the Grant’s Mission and Priorities
Tailor your application to the specific goals of the funding agency or program (e.g., NIH, NSF, DARPA, SBIR/STTR). Study previously funded projects and the agency’s strategic roadmap. Mirror their language and objectives when applicable.
- Tell a Scientific Story with a Strategic Arc
Even technical reviewers are influenced by a well-told story. Build a narrative around your project that ties together the hypothesis, methods, and long-term vision. Anticipate concerns and address them proactively in your application.
- Have Strong Preliminary Data (Even If It’s Small)
Data builds credibility. Even early results, pilot studies, or proof-of-concept demos help reduce perceived risk. If you’re pre-data, use strong rationale, literature support, or partnerships with research institutions.
- Build a Credible, Experienced Team
Reviewers look at both the idea and the people behind it. Highlight your team's technical expertise, execution history, and advisory board if you have one. If you’re missing expertise in a key area (e.g., regulatory, clinical), show you know it and plan to bring it in, evidenced by letters of support.
- Be Realistic About Scope, Budget, and Timeline
Make sure your aims are achievable within the funding period and budget. Overpromising can be a red flag. Provide detailed, defensible budgets. Funders want to see you’re using resources wisely.
- Polish the Writing—Clarity Is Everything
Don’t assume the reviewer is an expert in your exact niche. Explain technical terms, use figures, and make it easy to follow. Get feedback from colleagues, mentors, or consultants who've successfully secured funding.
- Plan for Commercialization and Impact
For small business and translational grants (like SBIR/STTR), outline a go-to-market strategy, regulatory plan, IP status, and business model. Show you’re thinking beyond the bench and into the clinic or market.
- Use Rejections as a Learning Tool
Most grants get rejected the first time. Read reviewer comments carefully, revise, and resubmit. Persistence pays off—many successful startups secured funding after 1–2 rounds of iteration.
- Network with Program Officers
Don’t underestimate the value of reaching out to grant program officers. They can offer guidance on fit, priorities, and even help shape your application before you submit.
At VIC Tech, you're helping evaluate new investment opportunities. What do you look for in a promising biotech startup?
I look for candidates that have strong, innovative science backed by peer-reviewed research, and solid intellectual property. Targeting a clear unmet medical need with significant market potential is essential. The team should be well-rounded and experienced, with credible advisors and investors. The pipeline should have clear development milestones, and a solid understanding of regulatory and reimbursement pathways. Strong financial backing, a realistic runway, and a clear differentiation from competitors are key, along with a viable exit strategy such as acquisition or IPO potential.
What life-science trends are you most excited about?
I’m most excited about the rise of AI, but not for the reasons one might think. AI is already acting like a mirror for humanity, reflecting aspects of ourselves that we often overlook: how we think, how we create, how we communicate, and even how we make decisions. By building systems that mimic human cognition, emotion, and behavior, we’re forced to grapple with the essence of those traits in ourselves. AI challenges us to define what can’t (yet) or perhaps shouldn’t be replicated- consciousness, empathy, morality, creativity, wholeness, connection, intuition. In doing so, we start to better understand what makes us uniquely human, and who we want to be. I believe a deeper understanding of what it means to be human will profoundly shape the future of biotechnology and healthcare, making them more personalized, ethical, and effective. In short, by better understanding humanity, we won’t just make better tools- we’ll provide better care. That’s something I think we can all get excited about.