Michelle Lynn Hall, PhD, Associate Vice President, Genetic Medicine, Eli Lilly
Michelle Lynn Hall, PhD, Associate Vice President, Genetic Medicine, Eli Lilly
Biography
Michelle Lynn Hall, PhD, is a biotech executive and scientific leader with over 15 years of experience in small molecules, RNA therapeutics, and delivery technologies. She currently serves as Associate Vice President of Genetic Medicine at Eli Lilly, where she built and scaled the Boston-based Genetic Medicine group into a 140-person innovation hub responsible for over 30% of Lilly’s R&D pipeline.Her career includes foundational roles at Schrödinger and Moderna, where she helped shape the computational and translational basis of emerging modalities. She leads Lilly’s efforts at the intersection of nanomedicine, AI, and genetic medicines, focusing on venture creation, exploratory R&D, and delivery platform innovation. She integrates emerging delivery technologies, secure AI infrastructure, and external partnerships to shape our nanomedicine strategy and pipeline opportunities. She also advises on scientific and technical aspects of business development, represents Lilly at global innovation forums, and drives initiatives such as establishing clinical trial infrastructure for advanced genetic therapies in new geographies. Recognized as a pioneer in platform development and cross-functional team building, Michelle has led efforts in RNA delivery, AI/ML-driven design, and mRNA molecular biology. Michelle is passionate about mentoring scientific talent, supporting women in STEM, and enabling translational innovation at the intersection of computation and biology.
Interview
NanoSphere: Tell us a bit about yourself—your background, journey, and what led you to where you are today.
Michelle: I began my career immersed in theoretical chemistry pursuing a PhD at Columbia and developing quantum-level simulations and predictive models. While at Schrödinger, I explored machine learning for gene expression analysis, moving beyond traditional structure-based drug design. But what truly motivated me was my growing fascination with the larger, higher-dimensional challenges in computational biology, where systems are less defined, and the data more complex.
That curiosity led me to Moderna. I began with almost no knowledge of genetic medicine, so much so that I worried that they might eventually fire me for being completely clueless. But I was excited by the idea that someone would pay me to learn exactly that, so I was willing to take the risk. And I’m glad I did. Not only did I develop expertise in genetic medicine, but that journey led to my current role at Eli Lilly, where I founded and expanded the Genetic Medicine group in Boston. We have developed cross-functional teams and platforms that now support a significant portion of Lilly’s early pipeline across various modalities.
NanoSphere: You’ve established and led the scale-up of Lilly Boston’s genetic medicine program into 30% of the pipeline. What made that success possible? What early data signals do you look for to justify scaling a program? What should biotech startups focus on to make themselves attractive partners to big pharma?
Michelle: What made our success possible wasn’t just scientific rigor; it was mindset. We assembled the team like a venture capital origination team. We generated ideas broadly, pressure-tested them through critical brainstorming, and then carried out “killer experiments” to see which ones deserved further investment. Equally important, we focused on should rather than could. We didn’t chase technical success alone but asked whether a given idea aligned with unmet needs and therapeutic strategies.
Once we embraced an idea, we asked another key question: Are we the best suited to pursue this, or is someone else in a better position? If another group or company could do it better, we chose to partner rather than compete. That kind of egolessness was a fundamental principle. I believe many in big pharma fall into the “not invented here” trap, but coming from tech and early-stage biotech, I fundamentally reject that mindset. I’ve always believed that our job is to push science forward, whether through internal development, external collaboration, or strategic investment, and who gets credit is vastly less important than getting a therapy to the patient faster.
The team was just as vital. We enforced a strict “no brilliant jerks” policy and worked hard to recruit individuals who were not only highly skilled technically but also adaptable, cooperative, and resilient. I strongly believe in getting the right people on the bus and then collaboratively deciding where to go. My role as a leader is not to be the smartest person in the room; it’s to create an environment where talented, mission-driven people can thrive and determine the direction together. When it comes to scaling a program, I look for three main signals:
- Translatability: Does the biology replicate in human-relevant systems?
- Differentiation: Is the science clearly better than current options?
- Scalability: Can the entire approach mature scientifically, operationally, and clinically?
- The team: Do they seem like people I’d want beside me in a crisis? Are they open, transparent, and grounded?
- The thesis: Is it credible, feasible, and supported by strong early data?
- The edge: Is the platform or program meaningfully differentiated from everything else out there?
NanoSphere: With the rise of AI and high-throughput automation, what tasks do you believe should never be outsourced to machines in the drug discovery pipeline—and why? If you could design your ideal AI assistant tomorrow, what specific problems in genetic medicine would it solve first?
Michelle: Machines can speed up hypothesis creation, data analysis, and experimental execution, but they should not replace human judgment when it comes to context and nuance. Tasks like target prioritization, clinical application, and ethical decisions rely on a thorough understanding of biology, patient needs, and societal effects. If I could design my ideal AI assistant, it would first address data harmonization and metadata gaps. Much of drug discovery is hindered by low-quality or fragmented datasets. My assistant would:
Michelle: Machines can speed up hypothesis creation, data analysis, and experimental execution, but they should not replace human judgment when it comes to context and nuance. Tasks like target prioritization, clinical application, and ethical decisions rely on a thorough understanding of biology, patient needs, and societal effects. If I could design my ideal AI assistant, it would first address data harmonization and metadata gaps. Much of drug discovery is hindered by low-quality or fragmented datasets. My assistant would:
- Curate multi-modal data from literature, experiments, and real-world evidence
- Annotate it with biologically meaningful features
- Suggest orthogonal assays to resolve ambiguity
- Generate ranked therapeutic hypotheses with mechanistic rationales
NanoSphere: If there’s one key message or insight you’d like to share with readers about the future of nanomedicine, what would it be?
Michelle: The promise of nanomedicine has never been clearer, or more urgent. In one of the most powerful demonstrations to date, Baby KJ, born with a life-threatening urea cycle disorder, became the first patient to receive a personalized CRISPR base-editing therapy delivered systemically via mRNA-loaded lipid nanoparticles (Nature, 2025). The treatment corrected the mutation in his liver cells, allowing him to leave the hospital after nearly a year. It was a real-world reminder that nanomedicine can make profoundly positive impacts on patients’ lives.
Michelle: The promise of nanomedicine has never been clearer, or more urgent. In one of the most powerful demonstrations to date, Baby KJ, born with a life-threatening urea cycle disorder, became the first patient to receive a personalized CRISPR base-editing therapy delivered systemically via mRNA-loaded lipid nanoparticles (Nature, 2025). The treatment corrected the mutation in his liver cells, allowing him to leave the hospital after nearly a year. It was a real-world reminder that nanomedicine can make profoundly positive impacts on patients’ lives.
The promise of nanomedicine is advancing globally. Across the Asia-Pacific (APAC) region, governments are stepping up with focused, long-term strategies to advance cell and gene therapies (CGTs). Countries like Japan, South Korea, and Taiwan have implemented specialized regulatory frameworks that allow for earlier approvals and market access, often launching global CGT products before they reach the U.S. or Europe (Cytotherapy, 2025; Premier Research, 2020; National Academies Press, 2014). In Australia, the Clinical Trial Notification (CTN) scheme allows sponsors to initiate trials within weeks (Linical, 2025). Region-wide collaborations like the Access Consortium are harmonizing regulatory pathways across Australia, Canada, Singapore, Switzerland, and the UK to reduce friction and enable cross-border CGT access. Unfortunately, this rapidly maturing science is encountering serious political headwinds in the United States, where the Secretary of Health and Human Services has taken deliberate steps to dismantle evidence-based infrastructure. All 17 members of the CDC’s Advisory Committee on Immunization Practices (ACIP) have been replaced with vaccine-skeptical advisors (Stateline, 2025). Nearly $500 million in federal mRNA-related vaccine research funding has been cancelled or redirected, undermining not only pandemic preparedness but the very platform technologies that underpin emerging curative therapies like Baby KJ’s (The Guardian, 2025). While the U.S. grapples with the consequences of anti-science policymaking, the global engine of biomedical innovation keeps turning. Nanomedicine isn’t just about vaccines. It’s about restoring function, rewriting mutations, and redefining what’s treatable. Paired with evidence-based policy, scientific rigor, and global collaboration, it doesn’t just offer hope. It reclaims futures.