Jan 17, 2019 · 59m · a16z
a16z Podcast | The Science and Business of Innovative Medicines
Vas Narasimhan · 37m spoken Sonal Chokshi · 7m spoken Jorge Conde · 6m spoken Vijay Pande · 4m spoken
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gold bands on the timeline = statements, start to end. Hover to read, click to jump. CC turns on captions
In this episode of the a16z Podcast, Novartis CEO Vas Narasimhan joins Sonal Chokshi, Jorge Conde, and Vijay Pande to discuss the strategic, technological, and operational transformations shaping the future of pharmaceutical R&D. They explore advanced therapeutic modalities like cell and gene therapies, clinical trial modernization, AI integration, and evolving partnerships between big pharma and tech startups.
How this conversation actually went
Every chapter scored 0–10 on four independent dynamics. Hover any point for the reasoning behind the score. The host holds 12.7% of the talking time here. How this is scored →
The host as informed peer 5.3 Guest teaching 4.2 Guest disagreement 1.1 The host pushing back 2.1
speaking balance: gold is the host, purple is the guest (3 minute bins)
Sharpest disagreement ▶ 43:09 Rejection of sensor data replacing trials
Vas explicitly adopts a skeptical stance against host Sonal's enthusiasm for continuous sensor wearables, rejecting the premise that observational sensor data can bypass randomized blinded trials.
Hardest push from the host ▶ 11:55 Calling out generics as me-too drugsSonal directly highlights the contradiction between Vas's stated refusal to invest in me-too drugs and Novartis's massive reliance on generic manufacturing.
Biggest teaching moment ▶ 4:16 Constant 5% in-human success rateVas educates the hosts with industry data showing that despite decades of scientific expansion, clinical trial success rates in humans remain stuck between 5% and 10%.
The host holds their own ▶ 51:22 Citing Pasteur's Quadrant and Real Options AnalysisSonal demonstrates deep academic grounding by framing portfolio management around Pasteur's Quadrant and Xerox PARC's real options analysis framework.
the scores for every segment, with the reasoning behind each
| Chapter | Topic | The host as informed peer | Guest teaching | Guest disagreement | The host pushing back | Why |
|---|---|---|---|---|---|---|
| Managing Big Pharma R&D Portfolios and Strategic Exits | 3 | 4 | 1 | 1 | Sonal asks a sharp strategic question about how CEO leadership teases signal from noise when exiting subscale therapeutic areas. Vas elaborates on executing $50 billion in portfolio shifts, exiting consumer health and infectious diseases. | |
| Innovation Cycles, Clinical Trial Attrition, and Rising Costs | 4 | 5 | 1 | 2 | Vijay draws sharp analogies regarding rising costs and extracting oil from the ground. Vas educates the hosts with the striking statistic that in-human clinical trial success rates remain stuck at 5% to 10% despite massive advances in science. | |
| Redefining Standards of Care and R&D Execution Discipline | 6 | 3 | 1 | 1 | Vijay and Jorge contribute strong venture capital domain knowledge, articulating how reimbursement serves as a proxy for value proposition and why founders must work backward from payers rather than just the FDA. | |
| The Role of Generics and Manufacturing Scale | 5 | 5 | 2 | 4 | Sonal challenges Vas directly on the apparent irony of Novartis maintaining a massive generic drug business while claiming to avoid me-too drugs. Vas reframes generics around volume scale and global patient access rather than high-margin innovation. | |
| Building Infrastructure vs. M&A Strategy in Gene Therapy | 7 | 3 | 1 | 2 | Sonal cites classic corporate M&A literature including the Chesbrough acquisition studies and Not-Invented-Here syndrome. Vas explains why Novartis incubates cell and gene therapy acquisitions as independent units to protect them from core corporate bureaucracy. | |
| Next-Generation Therapeutic Modalities and Regenerative Medicine | 4 | 4 | 0 | 1 | Jorge probes whether aging itself could become an official therapeutic area. Vas explains the biological difficulty of targeting multifactorial conditions like sarcopenia compared to targeted tissue regeneration. | |
| RNA Interference, Genetic Editing, and Platform Modularization | 5 | 5 | 1 | 1 | Jorge contrasts bespoke drug development with iterative modular platforms. Vas provides a comprehensive history of pharma transitions from small molecule chemistry to biologics and now genetic delivery systems like AAV vectors. | |
| Applying Engineering, AI, and Machine Learning in Pharma Operations | 6 | 4 | 1 | 2 | Vijay references Greenspan's economic history on artisanal vs factory production to discuss cultural shifts in pharma. Vas cuts through tech hype by explaining that cleaning dirty operational data takes years before machine learning algorithms can provide value. | |
| Reimagining Clinical Trial Design and Real-World Evidence | 6 | 5 | 3 | 6 | Sonal pushes back against Vas's skepticism of real-world evidence and continuous wearable data in trial design. Vas firmly defends randomized placebo-controlled trials as the only reliable safeguard against complex biological noise. | |
| Ecosystem Partnerships, Startup Collaborations, and Global Talent Hubs | 5 | 4 | 1 | 2 | Jorge addresses the anxiety biotech founders feel when approaching big pharma, asking how small startups can avoid being crushed. Vas explains how Novartis created dedicated digital units like the Biome to operate like tech startups. | |
| Measuring R&D Success, Executive Leadership, and Biomedical Miracles | 7 | 4 | 0 | 1 | Sonal demonstrates advanced familiarity with R&D valuation models by citing Pasteur's Quadrant and Xerox PARC real options analysis. Vas offers closing reflections on executive leadership, crisis management, and the biological wonder of new molecular entities. |
Statements from this episode (27)
Chokshi: Novartis produces 70 billion doses of medicine annually
“In terms of volume, they're the largest producer of medicines, With seventy billion doses a year across a wide range of therapeutic areas from cancer to cardiovascular disease and more.”
Novartis executed $50 billion in deals in 2018
“We transacted in 2018 around fifty billion dollars of deals to really change the shape of the company.”
Rebuilding an abandoned pharmaceutical R&D division takes 10 years
“Because you can't change your mind now, you know, in three or four years and say, I wish I had it back. It'll take you another 10 years to build it back up again.”
Pharma clinical trial success rates have stayed flat at 5 percent
“And actually, when you look at attrition rates in our industry, really the chances of success that we have, they haven't moved in the last 15 years. Still, when we bring a medicine into human beings, on average, only one out of 20 works.”
Narasimhan: Novartis averages an 8% to 10% clinical trial success rate
“We've actually been fortunate at our company. We average in that same metric about eight to 10%.”
Narasimhan: Scaling technology in clinical trials could cut costs by 20%
“There are estimates now from various sources that believe you could take out 20% of clinical trials costs if you were to actually to really deploy technology at scale.”
Novartis will only advance drugs that replace the standard of care
“We internally have just set a very clear bar now for ourselves, primarily because we live in a world now where nobody wants a me too medicine or a medicine that's just incrementally better. We say to ourselves, it has to replace the standard of care.”
Narasimhan: Novartis considers drug reimbursement early in development, not at launch
“It used to be, we think about reimbursement as we got to launch. Now we're thinking about it really early in development.”
Novartis launched first FDA-approved prescription app for opioid addiction
“I mean, one example we launched in the U S a digital medicine. I mean, with paratherapeutics, this is the first digital app with an FDA label that's being used for opioid addiction and other psychiatric illnesses. And it is literally an app That has run clinic…”
Novartis spent $15 billion on gene therapy acquisitions in 2018
“We've done fifteen billion dollars of acquisitions just last year in the space, not, not including all of our internal work in each of these areas.”
Viable animal-to-human organ xenotransplantation will arrive within 10 to 20 years
“I think a couple of things will likely come. I think xenotransplantation, I mean, which has been in and out and worked on. And what's interesting is every one of these comes up and down. So, you know, gene therapies, cell therapies popped up in the nineties, k…”
Novartis CEO: Novartis previously ran an internal aging program targeting sarcopenia
“You know, we had actually an aging program, a small aging program for some time where we were trying to work on things like sarcopenia, which is muscle wasting and similar, similar kinds of conditions. It turns out to be very, very difficult to, because again,…”
Narasimhan: RNA therapies are the only way to drug LP(a)
“And so the only way to really target it turns out to be using RNA based therapies. These RNA based therapies are able to block the production of the gene translation of the gene into the protein and then actually reduce the LP little a in the blood.”
Narasimhan: CAR-T therapy development will shift to an iterative model
“I think in the specific example of car T I do think that's, what's going to happen because you have such a complex manufacturing that you're going to have the first generation, let's say of a CD-nineteen card, which is a cart that targets B cell cancers. And y…”
Narasimhan: AAV vectors enable reusable platform delivery in gene therapy
“When you think about AAV vectors, these are ways to deliver these gene therapies into the body. Once you solve it, the process, let's say for one of these vectors, you can apply it to multiple different diseases and not have to Recreate everything again. That'…”
AI in digital health currently offers very little actual impact
“Well, I have to first say, I completely agree about the hype cycle here. I mean, as we've gotten quite scaled and working on digital health and data science, we've learned that there's a lot of talk and very little in terms of actual delivery of impact.”
Novartis spent years just cleaning healthcare data before running AI algorithms
“We've had to spend most of the time just cleaning the data sets before you can even run the algorithm. That's just taken us years just to clean the data sets. And I think people underestimate how little clean data there is out there and how hard it is”
Novartis built AI command center Sense to track global clinical trials
“So we've built an operational command center. Take us, as I said, two and a half years to build it. We call it sense. And what it enables us to do a team sitting centrally in our headquarters to look at all of our clinical trials in the world. And AI is predic…”
AI outperforms internal human analysts at forecasting Novartis product sales
“The other area, interestingly, in the financial area as well, we find that AI does a great job predicting our free cashflow, predicting a lot of our sales for key products, and it does better than our internal people because it doesn't have the biases and the …”
Unstructured machine learning on clinical data lakes has failed to yield insights
“I mean, I think the holy grail of kind of having unstructured machine learning Go into big clinical data lakes and then suddenly find new insights. We've not been able to crack mostly because the data to link it up.”
Real-world evidence cannot replace randomized blinded clinical trials
“The other thing people talk about, but I mean, I'll take a skeptical voice around it, is the ability to use real world evidence to try to get at these things. But as somebody who's worked in clinical trials for most of their time in, in the industry I do belie…”
Wearable sensors have failed to meet clinical trial validation standards
“When I think about, first of all, I would say just in general in sensors is another place where there's been a lot of hype Above what, of expectations. I mean, we've been really trying to explore the use of sensors in clinical trials now for, in my own experie…”
Novartis employs 17,000 R&D staff and spends $9B+ annually
“We have 17,000 R&D people and spend nine, nine billion dollars plus a year in, in R&D.”