Welcome to Partnology’s Biotech Leader Spotlight Series, where we highlight the remarkable accomplishments and visionary leadership of biotech industry pioneers. This series is about showcasing the groundbreaking strides made by exceptional leaders who have transformed scientific possibilities into tangible realities. Through insightful interviews, we invite you to join us in following the inspiring journeys of these executives who continue to shape the landscape of the biotech industry. This week we are recognizing:
Venkataraman Sriram is an accomplished researcher with more than 15 years of biopharmaceutical research experience developing biological and small molecule therapeutics against tumor immunotherapy targets. Prior to joining Max and Michel in starting Foundery Biosciences, Sriram was an early employee at Pionyr Immunotherapeutics. At Pionyr, Sriram played a key role in building the research team. He also led the operational move from the incubator space to an independent facility and served as the nonclinical lead for the PY314 (anti-TREM2) program. Further, Sriram worked with a cross-functional team in filing Pionyr’s Pre-IND and IND regulatory documents. Before his time at Pionyr, Sriram worked at Gilead Sciences. Sriram established and successively led several immuno-oncology programs at Gilead Sciences and Merck. At Merck Research Laboratories (formerly the Schering-Plough Biopharma prior to its acquisition by Merck), Sriram also served as the cross-site pharmacology lead for all the immuno-oncology programs, including the anti-PD-1 antibody, Keytruda. Sriram received his Ph.D. in Biochemistry and Immunology from the University of Madras, India, and completed his post-doctoral training in immunology at the Indiana University School of Medicine.
Walk me through your career, highlighting key moments or decisions that shaped your path toward becoming a biotech CSO:
In a recent New Yorker article, a writer mentions that drug development is like foraging in an unfamiliar forest. I am absolutely thrilled to be foraging and I love the forest that I am foraging in. I never dreamt of being a biotech CSO — that wasn’t the goal I started out with. I come from India, from a farming family. I’m one of the first in my family to attend college — a first-generation graduate. My career was really shaped by two key moments. The first was during my time in India. After completing my undergraduate and master’s degrees I had planned to pursue a PhD in plant tissue culture. It felt adjacent to my roots in agriculture and was something I was already doing in a respected lab.
But then a close family member was diagnosed with cancer. That moment changed everything for me. If that hadn’t happened when it did, I might have stayed in plant biology. I might even be teaching high school biology in some remote part of India. That experience shifted my focus from plant biology to cancer research. I was drawn to immunology, and I believed that through immunology I could help tackle cancer. After completing my PhD in India, I still felt underprepared for the kind of research I wanted to do. So, I applied for a postdoc in the U.S.
I ended up at Indiana University in the lab of Dr. Randy Brutkiewicz. That was the second key turning point in my life. He was a true, hands-on immunologist — a “card-carrying” one, as we say. He had just started his lab and personally taught me everything I know about immunology. That foundational experience fundamentally reshaped my career.
After my postdoc, I entered industry. I joined DNAX Research Institute, which later became Schering-Plough Biopharma. At DNAX, I really honed my skills in immunology. It was an exciting time — a lot of work on cytokines, chemokines, and how to modulate cancer and autoimmunity using those tools. My role was as a pharmacologist, studying rodent pharmacology for the molecules we were developing.
I was also fortunate to be part of the Keytruda team at Merck, which has since become a multi-billion-dollar drug. That experience also contributed significantly to where I am today. After Merck, I joined Gilead and spent about two and a half years helping build their immunology portfolio. That’s where I met Michel Streuli, who left Gilead to join a biotech founded by Max Krummel called Pionyr Immunotherapeutics. I followed shortly after.
At Pionyr, we developed “myeloid tuning” as a therapeutic strategy. Eventually, we sold part of the company back to Gilead. That success made us reflect: maybe the venture model needed tweaking. And rather than wait for someone else to do it, we decided to take it on ourselves.
We knew that academic innovation is often where great science starts — Max is embedded at UCSF, and we had ties with institutions like the University of Arizona. So, we thought, why not bridge the translational “valley of death” ourselves by applying rigorous industry vetting to academic discoveries?
That’s how Foundery Biosciences was born. Foundery was launched as a venture studio model by myself, Max Krummel, and Mitch Streuli. Rather than spinning out companies the traditional way, we decided to incubate a portfolio of early ideas in-house — say 20 — and advance the few that showed the most promise. The goal is to identify the gems in the rough and turn them into fully independent drug discovery companies that can achieve true value inflection points and real impact for patients.
At Foundery, I wear two hats. I’m the CSO of the management company, where I oversee scientific strategy across our programs. I’m also a co-managing partner in the Foundery Fund, where we invest in and guide these translational efforts.
You’ve led R&D efforts at both large biopharma and nimble startups. What have been the biggest lessons learned from operating across such different environments?
A lot of people in this field have the opportunity to work in both big biopharma and startups — and each has its own pros and cons. For my career, having experience in both has been incredibly helpful. The key difference between the two isn’t the science. At both large companies and startups, the science is rigorous. That old notion that industry science isn’t published or is kept secret just isn’t true anymore. Today, there’s great science being done and published across the board.
The real differences lie in strategy and the type of resources available. In big pharma — at companies like Merck or Gilead — you have to be thorough because you’re working within a matrixed organization, collaborating across different functional teams. You tend to get specialized, focused in one area. But that also means you learn how to interact across functions, and you have access to deep expertise across the company — in regulatory, clinical, toxicology, and more. And of course, they have the financial resources to invest heavily. Big biopharma also thinks in terms of long-term goals and timelines, which is a luxury relative to startups.
In contrast, at a startup, you have to operate like you own the company — no matter your title. Whether you’re a CSO, an associate, or a scientist, you’re expected to wear multiple hats. And that’s not just a figure of speech — it’s absolutely true. You need to show ownership, make fast decisions, and be resourceful.
When I was at Pionyr Immunotherapeutics, a VC-backed biotech, we had to make tough calls every day — deciding which data mattered most, how to design experiments that could quickly get us to a decision point. Every quarter, we had a board meeting, and we had to show tangible progress. The pressure in big pharma is different. You’re accountable for your project, but you’re not involved in every single decision across the company the way you are at a startup.
I learned so much in that startup environment that I simply couldn’t have learned in big pharma. The breadth of knowledge you gain is enormous. You may not go as deep in one specific area, but if you’ve already built that depth in big pharma, a startup complements it perfectly. That’s my trajectory: I started in big pharma, where I developed deep pharmacology and preclinical drug development expertise. Then I moved into startups, where I expanded my breadth — I can now talk to you about CMC, regulatory strategies, investor relations — all of it. That came from my time in VC-backed biotech.
The most important thing I’ve learned is that success in either environment requires a mix of scientific credibility, resourcefulness, and communication. In big biopharma, it’s about dealing with complexity and working together across different teams. In startups, it’s about having a clear vision, acting quickly, and making the most of every dollar and every experiment.
Now, I have a mix of mindsets. I’m comfortable building rigorous, scalable science with limited resources, but I always keep an eye on how each decision will help create long-term value and make a real impact on patients.
With your deep roots in immunology, how do you think the field is evolving, especially in autoimmune and inflammatory diseases?
To me, immunology is more than just a therapeutic area—it’s a systems-level way of thinking about disease. I’ve loved the subject from very early in my career. The immune system is incredibly interconnected—it’s nodal and central to almost everything in the body. It touches nearly every organ system, whether we’re tackling cancer, fibrosis, or autoimmune disease.
In all of these diseases, our goal is the same: to identify the key receptor, pathway, or subpopulation of immune cells that is driving the pathology. Physiologically, the immune system is performing essential functions. But in disease, something has gone awry. Our job is to find that pathological node and modulate it—to restore balance, or ideally, achieve a cure. So, for me, whether or not we label immunology as a “therapeutic area” is secondary. The system itself—and how we study, understand, and leverage it—is what matters most.
Early in my career, I focused on rodent immunology and preclinical models, because that’s what was available to us. But we quickly learned that murine systems don’t always translate well to humans. That led us to pivot toward building human-relevant immunoassays and models—surrogate systems that could better reflect what’s happening in patients.
While our tools and methods have evolved, our core belief in the power of the immune system has remained constant. Initially, we had broad immunomodulators like anti-TNF therapies, which have generated billions in revenue. Drugs like PegIntron for melanoma were also more general in their mechanism. But the arrival of Keytruda marked a turning point—demonstrating that with a single drug, targeting a single receptor, you could manipulate the immune system in a precise way and even achieve cures.
I was fortunate to be part of the early development and acceleration of the Keytruda clinical program. Today, though, the field has shifted. The focus—and the capital—has moved away from immuno-oncology toward autoimmune and inflammatory diseases. This is largely because, despite many attempts, nothing has matched the success of PD-1 inhibitors. VCs have grown more cautious about I-O.
Now, the opportunity lies in applying the same methodologies and systems-level thinking to autoimmunity—where there remains significant unmet medical need. That’s where the money is flowing, and I believe that’s where we’ll see the next big wave of breakthroughs.
Whether it’s through RNA therapeutics, targeted protein degradation, cell therapies, or systemic immune modulators, there’s still so much work to be done. And while the specific indications may shift depending on the economics, the immune system remains at the center of it all. What’s required now is what’s always been required: resilience, focus, and disciplined execution. We have to learn from clinical data—even setbacks—and loop those insights back into our preclinical hypotheses. That mindset has driven recent success stories, including China-centered development of PD-1 x VEGF bispecifics, where speed, scientific clarity, and tight integration between research and clinical execution made a measurable impact.
Can you share a time when early translational insights meaningfully shifted the direction of a program — for better or worse?
In my view, a translational insight is knowledge or understanding—typically from preclinical research—that bridges the gap (or “valley of death”) between discovery and therapeutic benefit. It’s about taking an idea or a concept and identifying how to bring it into the clinic in a way that meaningfully impacts patients. That missing link—that actionable knowledge—is what I refer to as a translational insight. It’s essentially what we mean by “bench to bedside.”
One example where early translational insights meaningfully influenced the direction of a program was from the early days of the Keytruda program. I was just starting out in my career, and I was fortunate to be in the right place at the right time. I was a preclinical pharmacologist on the program, mentored by two amazing individuals—Joe Phillips and Michele Squarci.
At the time, we knew that BMS was at least a year ahead with their clinical program, and we at Merck were trying to catch up. Our team was tasked with answering two critical questions. First: could we identify the optimal dose using preclinical models, since we didn’t have the luxury of time for extensive dose exploration in the clinic? Second: as the program gained momentum, which combination therapies should we prioritize first, given the many opportunities?
We focused on generating high-quality preclinical data, particularly around PK/PD relationships, receptor occupancy, and dose-response. Merck was a massive organization, so we had access to deep expertise across many functions. Our job was to narrow it down—not provide a range of doses, but pinpoint the best one. Was it 2 mg/kg, 3 mg/kg, or 10 mg/kg? We used robust preclinical modeling to guide that decision, and that insight likely played a small but important role in helping our program accelerate past the competition.
Similarly, on the combination side, the field was still heavily reliant on xenograft models, and few people understood the utility—or limitations—of syngeneic models. I was learning how to use each model appropriately and understand which combinations made the most sense biologically and translationally. It felt like we were building the plane as we were flying it—but in the end, that work made a real impact on clinical execution, and I’m proud of that. The sheer volume and depth of this preclinical data provided the clinical team with a substantial head start. In retrospect, this work was crucial for several reasons: it directly informed dose optimization, enabled faster clinical execution by narrowing down the most promising combination partners, and helped us strategically target indications based on evolving tumor microenvironment (TME) data. This early, data-driven approach allowed us to be much more efficient and effective in our clinical strategy.
What emerging technologies or therapeutic approaches do you believe will redefine immunology drug development over the next 5–10 years?
Because it’s July 2025, a lot of people will likely answer this question by saying AI and machine learning—but I’m going to say no, at least for now. What excites me personally isn’t exactly “emerging” anymore, because it’s already proven valuable in certain areas: spatial transcriptomics and spatial proteomics. These technologies allow us to see where RNA is expressed or differentially expressed, or where a specific protein is located within tissue.
In the past, we’d take a tissue sample, homogenize it, and then analyze the overall transcriptomic or proteomic profile. Insights were inferred from that bulk data—such as comparing responders vs. non-responders. But now, especially in immunology, context—both spatial and temporal—matters immensely. These new technologies allow us to map cell-to-cell interactions within the native tissue environment. We can now pinpoint when and where specific genes and proteins are active, within particular cells and microenvironments. That level of detail is transformative.
This spatial context is especially crucial in immunology. It reveals how different immune and non-immune cells interact within diseased or inflamed tissues—insights that can significantly accelerate therapeutic development. For example, fibroblasts were historically overlooked, but are now even considered immune cells by some immunologists, thanks to this kind of spatial analysis. We now understand how fibroblasts interact with macrophages, monocytes, or T cells in disease contexts.
As for emerging therapeutic approaches, I do believe AI and ML are overhyped—but they are also becoming indispensable for analyzing complex biological data. Tools like AlphaFold, for example, have revolutionized protein structure prediction.
However, it’s equally important to approach the claims around AI/ML with a healthy degree of caution. There’s a palpable “hype cycle,” as Derek Lowe, the prolific science blogger behind ‘In the Pipeline,’ often points out. He frequently reminds us that while AI holds immense long-term promise, the immediate challenges in areas like small molecule discovery and clinical translation are significant, largely due to the sheer complexity of human biology and the need for consistently high-quality, large datasets.
For instance, claims about ‘AI-designed molecules’ being in clinical trials need careful qualification. While AI can certainly accelerate various stages – from target identification and lead optimization to predicting properties – the reality is that these molecules still undergo rigorous experimental validation and refinement by human scientists every step of the way. It’s more accurate to say they are ‘AI-assisted’ or ‘AI-accelerated’ rather than entirely ‘AI-designed.’ We need to be precise with our language, especially given the heightened scrutiny on technological advancements in the biopharma space.
Therefore, for claims like de novo antibody generation or novel target identification driven purely by AI, I believe a higher burden of proof is necessary. We must be transparent about what these technologies can actually achieve in practice today, demonstrating tangible, reproducible results, rather than over-promising on future capabilities. The goal should be to leverage AI to enhance our scientific endeavors and accelerate discovery, not to create unrealistic expectations that could ultimately undermine trust in these powerful tools.
On the therapeutic front, I’m excited by multi-modality therapeutics, like code-integrated RNA therapies, cell therapies, and gated cell approaches. These innovations are opening up entirely new treatment paradigms. So, overall, there are many exciting technological advancements on the horizon—and I’m thrilled to continue contributing to this evolving landscape.
You’ve successfully secured partnerships and engaged investors across multiple ventures. What do scientific leaders need to understand about ‘speaking the language’ of business and capital?
I didn’t speak the language of business or capital until about five years ago. I didn’t know it—it’s an entirely different language. For most scientists, it’s helpful to be multilingual in that sense, because it is important.
Most of what we do in science is good science—but if we can’t communicate, it gets lost. What’s the value of our research if we can’t explain it clearly? Communication becomes especially critical when you’re speaking to people who manage capital. It’s not just about saying, “I cured this cancer in mice.” The real question is: What’s the value of that? How does it translate? How important is it in the broader context?
No matter the setting—a boardroom, SAB, team meeting, classroom, or even at home—if we can’t communicate effectively, our message won’t land. And beyond just delivering a message, it’s also about who you are when you’re delivering it. In my experience, whether you’re dealing with businesspeople, investors, or fellow scientists, if you don’t build trust, the message will fall flat. You could have the most polished, data-driven pitch in the world, but if it’s not grounded in authenticity, it’s not going to win.
As scientific leaders, we often focus on getting the science right—of course. That’s our job. But if we want to build companies, raise capital, or develop the next transformative therapy, we have to go beyond the science. We need to understand what matters to each stakeholder—whether it’s a VC, a board member, or a collaborator. What are they solving for? What’s their risk tolerance? How do we earn their trust? It’s not just about being technically sound—it’s about aligning strategically and ethically, and speaking their language.
When we work with investors, academic partners (like UCSF or University of Arizona), or even internally with our team, we try to build trust and clarity. That only comes from being transparent and consistent. We shouldn’t try to oversell. Pitch the science in terms of its real-world impact. Outline clear milestones—what we are committing to, what success looks like, and what the consequences are if we fall short. At the end of the day, capital follows conviction, and conviction is built on trust. You earn that by being authentic, by listening well, and by showing up with an ability to receive feedback.
We need to make honest assessments of both risk and value. That’s what gets people to lean in. It’s not hype—like I said with AI and ML. It’s about building credibility.
