Given that you have led big pharma (J&J) and smaller firms (Theravance and now FogPharma), what are the career prospects and trajectory for mid-career / newly graduating students wanting to enter this vertical? What would be your advice for prospective professionals seeking a career in big pharma versus smaller firms, including startups?
Mathai: There is no better time to join the biopharmaceutical industry than now, whether the company is small or large, intending to make medicines or in healthcare services or diagnostic companies. A vibrant ecosystem of varied companies aims to make medicines to transform a person’s life: extend life, reduce pain and suffering, and improve overall quality of life. Two parallel revolutions in biology and data science vastly expand what is possible in our field.
Although the revolution is well underway in biology, we are only maybe one-third of the way up the inflection curve; there is much more to understand. However, the mechanism by which diseases occur, their pathophysiology, and the mechanisms by which health is maintained are increasingly clearer. Biological processes are also more trackable with tools and data to understand how the disease may work.
The second major revolution is occurring in data science. Most broadly defined, data science means all things data, whether speaking about the sensors that collect key data, the measurements being employed, the manipulation of large data sets, or the algorithms that help with the analytics applied to all that data. Collectively, those analytics can be either sophisticated advanced computational methods or AI. I define data science in the broadest possible way to mean all the above, and they are all undergoing massive changes right now.
These two revolutions are not independent of one another; they intersect. In my work at J&J and at FogPharma, we draw on these arenas to inform others and drive our R&D towards medicines that would otherwise not have been possible and with speed as patients wait.
As for prospective professionals weighing big pharma versus smaller startup environments or anything in between, they have distinctive features that may appeal to different people. You should begin with self-reflection on who you are, what things you most derive joy from, and what you find most upsetting or annoying.
For instance, I previously worked at Merck and J&J, and I think entering as a junior person in a large organization is an amazing experience because you benefit from decades of understanding and knowledge in that environment. A terrific degree of refinement is brought to your attention as part of your onboarding and training in any of those large company environments. If you are attentive and have a learning personality, you absorb information through direct education and indirect diffusion, and you can gain a lot from that environment. One negative aspect of that environment might be that it can be slow, so agility and navigation skills are sometimes required if you have an innovative idea. It may not be implementable that day. Walking the square and gaining alignment on a directional change may take some time. But sometimes, slowness is good as ideas mature, so depending on what kind of person you are, you can have a terrific long-term career there. There are lots of resources to bring to bear, and a large organization like J&J or Merck can have an amazing impact on healthcare, which I have been fortunate to experience. And there is a joy in making such an impact using such a vast platform.
I started my first company, Theravance, after an MD-PhD program at Harvard, based on my PhD thesis with George Whitesides. We started with a handful of people and grew from there. In smaller companies, when you build and grow something, joy comes from it that is hard to describe. While using that platform in large companies to create medicines, you are not building a company per se. And so that feels different. I can tell you from personal experience that there is a lot of joy in building a small organization. You can make decisions very quickly to implement your idea when you drive in the morning, even as a junior member. In the first year of employment, you will wear many hats beyond the discipline you were hired for. So many people around you represent other disciplines that you are learning at a rate that can be remarkably high.
That said, you do not have the depth of company around you, so one of the things that I have done, and that I recommend to anyone going to any small company, is proactively seeking out advisors. Since you do not have in your 10-person or 50-person company experts in every single aspect of the industry, you must make a network for yourself who will be your mentors and educators. You will be a mentor and educator, too, in the future. They are there for you as part of your community and at the other end of the phone when you need advice or guidance. I found that it is a joyful thing to build such a network and have a community that goes on to have different experiences at different companies.
So, just in summary, there is no better time to join the medicine-making enterprise than now, and you can build an incredibly meaningful career. And I have found pleasure and joy of different sorts in all the environments I have been in.
Many people are now considering the following question: How will the advent of Gen AI impact the industry positively and negatively?
Mathai: This is a big question. I have devoted a lot of time to it in the last 10 years, as it was the core central tenet at J&J during the evolution of the J&J R&D organization. And it is already leading us as an industry to make medicines differently.
I think of generative AI as a component of the data science revolution alongside analytical AI, which has existed for a long time. In analytical AI, you use machine learning methods to categorize A versus B, for instance, a responder versus a non-responder to your medicine. Generative AI, on the other hand, might suggest what set of proteins or molecules to make, how to construct a promoter region on a gene therapy, how to construct a capsid or how to make a cell therapy, etc. It can be used to develop medicine for the right patients, select clinical trials on-site, or manufacture engineering and science. Other forms of advanced computation are less about categorization and generation. Physics-based approaches such as molecular dynamics for the design of drug candidates are not AI, for example, but they are part of the revolution in new computational advances that help one discover and develop medicines. The AI and all these new advanced computational approaches that leverage growing high-quality datasets, faster and faster computing and extensive storage are all part of the data science revolution.
So generative AI, I would say, in general, for the alumni and students at MIT Sloan, is just part of a core education of the future. What should an educated person learn? It does not necessarily mean you need to code in Python, but it helps to understand how that would be done. Try to understand how large language models are combined, how they might work, what a llama is, and what ChatGPT is. How do those operate? How does Alpha Fold work?
One of the things that I would have the strongest guidance on, for anyone wanting to be a general manager in the future, is picking up some of the basics of AI/machine learning methods and gaining an understanding well beyond the superficial of why certain tools are being used to tackle certain problems and the ins and outs of the specific approaches. It is less useful for a general manager to learn the ins and outs of biology, the ins and outs of clinical medicine, or the ins and outs of chemistry. I do not think that is as necessary as getting into the weeds of data science.
Given that Gen AI is taking over, what are the implications for a career in life sciences?
Mathai: You must be proficient and bilingual. Data science becomes integral if you have expertise in biological science or any other domain.
It is also key to learning the fundamentals. Learn the why, what, and how in data science. What are the problems? Why are those problems solvable today, and do you understand why certain tools are being applied to certain problems? So then, as the tools change and evolve, it becomes easier to understand why. For example, convolutional neural networks are the tool of choice for image vision. Transformer approaches are currently the tool for languages like ChatGPT, etc. But that may change. Depending on what happens, it may invert, or a third kind of tool will come, so you cannot leave it to know what tools are used for what problems. Today, you must understand what it is about that tool that makes it most appropriate so that when the field changes, you can keep up. Otherwise, it will seem like brand new knowledge every two years, which is no good.
Further Reading/Listening:
- Founder Stories: Mathai Mammen, CEO of FogPharma (www.youtube.com/watch?v=SD7tDzj-Uq0)
- The BioCenturyShow: Mathai Mammen in conversation with Editor-in-Chief Simone Fishburn. (www.youtube.com/watch?v=M25xiFblCbE)
- JPM 2024: FogPharma’s new CEO, Mathai Mammen, unveils ‘contrarian’ plan in oncology (https://www.statnews.com/2024/01/09/jpm-2024-fogpharmas-new-ceo-mathai-mamman-unveils-contrarian-plan-in-oncology/)
- Mathai Mammen on being back to company building at Fog Pharma (https://www.biotechtv.com/post/fog-pharma-february-28-2024)
- Innovation, leadership, and purpose in the biotech industry: A conversation with Mathai Mammen, CEO of FogPharma (www.heidrick.com/en/insights/podcasts/e125_a-conversation-withmathai-mammen-ceo-of-fogpharma)
Mathai Mammen is a world-renowned innovator in drug discovery, development, and team- and company-building responsible for creating thirteen transformational medicines from discovery through commercial launch and more than twenty-five other potential medicines that have achieved clinical proof-of-concept, many of these practice-changing. Mathai is the Chief Executive Officer of Cambridge-based FogPharma, a clinical-stage biopharmaceutical company advancing a new category of peptide therapeutics for people affected by cancer and other severe diseases. Previously, Mathai was Executive Vice President of Pharmaceuticals and R&D at Johnson & Johnson, one of the largest R&D organizations in the world. Before that, Mathai was the leader of much of the research at Merck, including Oncology, Immunology, and cardiovascular disease. He began his career as head of R&D at Theravance, Inc., a company he co-founded from graduate school based on his work at Harvard University with Dr. George Whitesides, which split to form Theravance Biopharma and Innoviva.
Partha Anbil is a Contributing Writer for the MIT Sloan Career Development Office and an alum of MIT Sloan. Besides being VP of Programs of the MIT Club of Delaware Valley, Partha is a long-time life sciences consulting industry veteran, currently with an NYSE-listed WNS, a digital-led business transformation company, as Senior Vice President and Practice Leader for their Life Sciences practice.
Michael Wong is a Contributing Writer for the MIT Sloan Career Development Office, an emeritus co-president, and a board member of the Harvard Business School Healthcare Alumni Association. He is also a Part-time Lecturer for the Wharton Communication Program at the University of Pennsylvania, and his ideas have been shared in the MIT Sloan Management Review and Harvard Business Review.
