Welcome to the Woman of the Week podcast, a weekly discussion that illuminates the unique stories of women leaders who are catalyzing change throughout the life sciences industry. You can check out all our podcast episodes here.
Editor’s note: Earlier this week, Najat Khan spoke at the HLTH conference in Las Vegas about how data insights will fuel the next health revolution. In this interview with PharmaVoice Editor-in-chief Taren Grom, first published in June, Khan provides more insights into how Johnson & Johnson is using tech and data to redefine drug development.
While volunteering as a translator for a prenatal and postnatal services company in the slums of Bangladesh, 15-year-old Najat Khan, Ph.D. got a wakeup call that permanently changed the course of her life. In her travels, she witnessed first-hand the terrible health inequities among those less fortunate. She was particularly struck by an encounter with a girl her own age who was dying of leukemia, and for whom there was no hope.
“As a 15-year-old, there are all these things that go through your head: this party, curfews and whatnot,” Khan says. “I thought, ‘Look at the life that she will not have.’ I felt so incredibly helpless standing there. And that was the moment I said to myself, ‘Come hell or high water I’m going to devote my life to helping patients like her. Because if I have the opportunity to live another month or another year, I better make every single day worth it.’”
Today, the 38-year-old trailblazer is not only making every single day worth it, she is driving transformation within Janssen R&D as chief data science officer and global head of strategy and operations. In her role, Khan is looking across a broad range of technologies, such as AI, machine learning, real-world evidence and digital health, to “reimagine” how Janssen discovers and develops medicines for its patients.
“My role as the global head of R&D strategy and operations focuses on our overall end-to-end R&D strategy, where we are going to go in the next two, three, five, 10 years,” she says. “And then ensuring that the right pipeline and portfolio decisions are actually being made. The reason this is important is that the R&D engine is core to the medicines of the future. Data science is critical in terms of how we are reimagining the discovery and development of medicines. So, the duality of the role means that I’m using data science in a very targeted way to look at the end-to-end pipeline and ask questions around how we improve the medicines we make for the patients that we serve.”
This unique role within life sciences is the culmination of a career journey — computer science, science and medicine, and business and leadership — that has not followed the “well-trodden path.”
“I think you have one life, and at the end of the day, my core mission and purpose personally has always been about transforming the lives of patients in their darkest hour,” she says. “If we can leverage different disciplines to create transformation, that’s the highest calling for me.”
Khan, who started as a scientist, quickly recognized that to have the impact she was seeking, she needed to accelerate how drugs got developed.
“I realized that it would take me months to make a molecule and often, it wouldn’t work,” she admits.
As an undergrad, she learned computer science, and through the process of coding and working with algorithms that got smarter as more data was entered, a “light bulb” went on: She fully understood how the combination of the two disciplines could make a difference.
Two years ago, when she got her start as chief data science officer, Janssen R&D had about five projects ongoing.
“When I looked at the portfolios, I said, we are not asking the right questions,” Khan remarks. “These programs are not going to make a meaningful difference. We have to be really clear on what we focus on. Today, we have 120-plus data science projects ongoing, covering the entirety of our clinical development programs, including oncology, immunology, neuroscience, cardiovascular, rare diseases such as pulmonary hypertension, infectious disease vaccines, discovery and development.”
Through all of her successes, Khan has never forgotten her roots, and she remains committed to the ideal of ensuring equity for patients and her teams.
“Equity is about ensuring everyone has the best opportunity and the best foundation,” she says. “In life, if you want to make a difference, you start by making a change within yourself. You have to be fearless — bold and disciplined at the same time.”
In this WoW podcast, Khan details how Janssen R&D is approaching the process of finding targeted therapies by understanding the drivers of disease, her personal commitment to health equity and how she learned to break the boxes of convention to bring a differentiated voice to the table.
Welcome to WoW, the Woman of the Week podcast by PharmaVoice, powered by Industry Dive. Please join us June 9 for our Second Annual Wow virtual program. Go to PharmaVoice.com/events to register for a complimentary pass.
In this episode, Taren Grom, editor-in-chief emeritus at PharmaVoice visits with Najat Khan, Ph.D., chief data science officer and global head of R&D strategy and operations, Janssen R&D.
Taren: Welcome to the Wow podcast program.
Najat: Thank you, Taren. Thank you so much for having me today.
Taren: It’s our pleasure. Dr. Khan, can you please briefly share what your role as chief data science officer and global head of strategy and operations at Janssen R&D entails. That’s a big title.
Najat: It is a big title, but maybe, let me explain it a little bit. It is a dual role, which is very, very unique in the life science and I would even say, the healthcare industry.
My role as the chief data science officer really entails looking across AI, machine learning, real-world evidence, and digital health. And leveraging all of these new techniques to reimagine how we discover and develop medicines for our patients.
My role as the global head of R&D strategy and operations really focuses on our overall end to end R&D strategy, where are we going to go in the next two, three, five, 10 years. And then ensuring that the right pipeline and portfolio decisions are actually being made. And the reason why that’s important is, for any pharmaceutical company, the R&D engine is core to the medicines of what we’re going to make in the future. And data science is critical in terms of how we are reimagining the discovery and development of medicines. So the duality of the role means that you’re using data science in a very targeted way looking at the end-to-end pipeline and say, how do we improve the medicines we make for the patients that we serve.
Taren: That’s fascinating. How do you go about making some of those strategic decisions?
Najat: In terms of the R&D strategy and operations role, the specific decisions are really focused on what’s the unmet need? How well do we understand the disease, the actionability of what we can do with our knowledge of the disease? And then how do we ensure that we have the right access for our patients?
On the data science side, it’s really focused on two dimensions. One – what’s the impact you can have on our pipeline and portfolio using data science? And the second, is doing something I call data science diligence, the feasibility. Is the data ready? Are the algorithms appropriate? Are the regulatory conditions appropriate for us to actually have an impact using data science? So we take a very thoughtful pragmatic approach in terms of truly what’s going to make a difference then the curve, as I like to say, for the trajectory of human health, and that’s what we do. That is our mission. That’s our credo at J&J. And both roles, the mission and the vision is one and the same.
Taren: I love that. When you’re creating this strategy, I can’t imagine you do this alone. Who are your main collaborators when you have to put all the puzzle pieces together?
Najat: That’s a great question. None of this and nothing in, I would say, what we do is done on our own. It truly is a team effort. So in R&D, the collaborators are the various therapeutic and functional heads focusing in oncology, immunology, cardiovascular, as six therapeutic areas all the way to vaccines. Our head of clinical operations and trials, our head of discovery. And then it’s not just R&D, right. We have our collaborators in commercial. We have our collaborators in supply chain. We have our collaborators across IT. So it really spans end-to-end to be able to look and say…and hold hands together and say, what are these areas that we’re going to invest in strategically that’s going to make a difference for patients and is something that’s feasible? And it’s not a one-time decision, Taren. We are constant. This is what I love about the role as heading up strategy for Janssen R&D, we have such a robust pipeline and robust, deep diverse pipeline and it’s not a one-time decision. You’re looking at the portfolio regularly as new data comes and new evidence comes in. So that you’ll be dispassionate and objective in terms of where you focus your investment that is really truly going to make a difference for patients. That’s something that we do collectively across the organization.
Taren: That’s wonderful. I know that you all have a robust pipeline and there are so many unmet needs. Is there a particular area that you’re looking at, let’s say in the next two to three years and let’s go out, 10 years. Anything in particular is striking hot right now?
Najat: I can share a few thoughts. First, we have a really great pipeline in the oncology space with the recent launch of our first CAR-T therapy called Carvykti, for relapsed/refractory multiple myeloma. And multiple myeloma, we have a history of different regimens that are really directing us towards how can we actually cure the disease. And cell therapy, I remember when I was at UPenn doing my PhD it was an idea about a decade ago and now, it’s become a reality and a reality that actually has really high effectiveness, efficacy rates in patients. So it’s great to see and again, for those listening, cell therapies when you’re basically taking a person’s immune system re-engineering it, so that they can actually fight a specific cancer.
So to me, it’s seeing that arc going from an idea to an action to becoming a real therapy that works and makes a difference for patients. I think you’re going to start to see more of that to some of these emerging modalities. Cell therapy is one example, but also in gene therapy, in sRNA, mRNA, and so forth. So this whole area of cell therapy and genetic medicines, I think is going to be a real breakthrough in terms of being able to treat certain diseases that we couldn’t treat before.
The other thing I would also say, we are looking in different diseases such as in the immunology space – IBD, in the neuroscience space and mental health, Taren, following COVID, we have been in the space for a long time, whether it’s depression, neurodegeneration, neuropsychiatry. But all in all, while all of these areas are very excited about, data science and digital health is central to our strategy. It’s central to our differentiation as to how we treat patients, define disease, find the right molecules is going to be a good therapy, and then actually run the right trials more effectively and efficiently with more diverse population. That’s completely, I would say, I get excited end-to-end regardless of which disease area we’re referring to is going to transform and it is transforming how we do things. I’m happy to share a couple of maybe examples as to how we’re doing that, but that’s an area of huge focus and differentiation for us.
Taren: I would love to get into that. Let’s talk about the data science and digital health area, please.
Najat: So, maybe taking a step back before getting into examples. One thing I’d like to share is, when you look at how we discover and develop medicines today, data science is an opportunity and we are already doing this end-to-end changing how we discover and develop our medicines. So, starting from the very beginning, let’s talk about when we start looking at a certain disease. What’s the driver of the disease? Is it just one disease or is it multiple subdiseases? So redefining diseases, as we know it based on the driver of the disease. So it’s not to say, you have prostate cancer or colon cancer, because the cancer is appearing in the prostate or colon, but what is actually driving it, so a certain mutation. Are there other predictive factors, mechanistic factors? The disease pathophysiology. That’s something now with more and more longitudinal human data everything from all-mixed single seat, to real-world data and even data from sensors and wearables were able to decipher and understand what’s driving the disease. That Taren, is where everything starts.
If you understand the root causes something well, then the next step is, how do we develop the right molecule that has really the chance of success of being effective and safe when you actually start human trials. So optimizing the design of these molecules whether it’s biologics or small molecules is something where we’re applying a lot data science in AI, machine learning. I’ll share some examples.
And then once you go into clinical trials, we call it clinical development, there’s so much happening there. Using and understanding the natural history of how patients are in the real world to say, how do you target the right patients, precision medicine? Who’s going to respond better to certain therapy versus not and why? Being able to use, commonly used data points, like ECT, echo, histopathology images to be able to diagnose patients earlier. And then, running our trials in a completely different way. Go to where the patients are. Not the sites and clinical trial sites that we know where we can actually now tell in an anonymous fashion eligible patients for trial where they are. And also, over in depths are more diverse population. So that the trials that we’re doing represent and reflect those that we’ll treat in the real world. And there’s many elements of decentralization real-world data, I’ll get to that more. But I’m just trying to sort of give a flavor of how every single step of what we do, end-to-end is being redefined and transformed by using different elements of AI, machine learning, real-world evidence, and digital health.
Taren: It’s fascinating and because some of these new technologies are relatively new. And I use that term with the bunny close, relatively new. How do you go about embedding these new ways of thinking within an organization as matrix as the one you’re leading? Because there’s going to be some resistance, I would think, from some of the old ways, but you are about the future. So how do you manage those two worlds?
Najat: It’s a great question. I think, I’ll say, three or four ways. And we have been on the journey for the past couple of years and I think we’ve make some endless progress. It starts off with the super laser focus on solving for opportunities and challenges, Taren is going to make a difference from patients and the therapies we’re developing. Everybody can stand behind that.
Najat: So what I mean by that is, this is where my dual role where understanding the pipeline at an intimate level and understanding the programs, what are the challenges in each program and then see where it can be applied data science. So being very fit for purpose. Focusing on the question, so you’re not just answering something or doing something with data science because it’s a cool technology. It cannot be technology first. It has to be patient and question-led. So I think that’s one big area, because then you get the organization behind you to say – we are doing things that are good way today. How can we be great? How can we do better, right? Every company has to evolve. So that’s one.
The second thing I’ll say, is if you pick the right questions and you have a good portfolio questions that you’re solving with data science, some near-term, some longer-term. What we focused on is having concrete impact. When people start to see that you actually have examples where you’re changing things, so it’s not just an idea, but you take it from an abstract idea to actionable to impact. That’s where you see a huge cultural change curve that happens. And we are a very scientific data-driven organization. So, we have those examples and I can share a few. Everybody likes to be on a winning team, Taren. Everybody wants to be on a winning team. But you have to go through that arc to how you can have success and that can take 10 years. So one of our strategy was we started in the clinical development space. Because we said, okay, there’s some questions here and we can actually have an impact in the next year, year and a half. Whereas, some of the areas in discovery will take longer. You can’t do one or the other, you’ve got to do both, but you have to start somewhere so you can build that momentum in the organization of making it real.
And then the third thing I’d also say, is it’s about the talent you hire. We have hired, we have a team of 100 plus data scientists that are bilingual. They’re proficient in both data science of course, that’s first, whether it’s AI, machine learning, real-world evidence, etc. But they’re also extremely knowledgeable in clinical development and discovery and just the healthcare space. Why does that matter? That means, if you don’t end up having a siloed organization working on a zone, but they speak the same language which creates a bridge with the great and outstanding clinicians we have maybe have done things differently, or operations heads or commercial or different parts of the organization. But being able to speak that same language is extremely important because the domain expertise in the life science and healthcare space is non-trivial. And having that same mission to help patients, not just software or algorithms for the sake of it, but for the benefit of a patient. That’s how you hire your team, is also extremely important.
And then the last thing I’ll say, is how you set up that team for success. For us, we treat data scientists and scientists as close citizens, and we’ve created a structural ways of working where they have an equal share of voice. This Taren, was personally very important for me, because you can have the best idea in the world, but if you don’t have a seat and a voice around that table, it will never actually make it to true impact. And then all of this investment and hard work has gone to nothing. So this is where our senior leadership whether it’s Joaquin Duato, Mathai Mammen, our CEO and our head of R&D for Janssen, they are true believers, true inspirational leaders. They have actually put in the effort to say, how do we infuse this end-to-end across the organization? That’s the support from the top and engagement from the rest of the organization, to have that equal share of voice has made it, I think is one of our secret sauce, in terms of how we have integrated and therefore, have had collective impact with data science.
Taren: That’s fascinating. And I loved that it’s end-to-end. And I loved that you call your data scientist bilingual and speaking two different, it’s not two different languages, but it is almost two different languages. And then that support from the top, which is absolutely critical and to the culture of the organization to move it forward. Thank you so much for that. That was fascinating. Through all this transformation, because change management is hard. People don’t like to change. How are you creating that team dynamic? And I know you talked about some of those early wins. But what are some of the specific things that you are doing to encourage others to embrace a new way of thinking and a new way of looking at asking questions and solving problems?
Najat: It’s a great question. And one thing I didn’t mention before, I also co-chair the J&J data science council. So that’s bringing all of the data science leaders across J&J. So I lead Janssen Pharma R&D and one of the efforts there is, how do we create that adoption of data science and impact across all of J&J. So I can maybe share a couple of thoughts there, both of the enterprise level and the R&D level.
Let’s start with some of the examples because that is what’s creating that change and you know, Taren two years ago, when I started the role as the chief data science officer, we had about four or five projects ongoing and about less than 10 data scientists. When I looked at the portfolios, I said, these are not the right questions. These are not going to make a meaningful difference. We have to really be clear on what we focus on. Two years now, and we have about 120 plus data science projects, covering the entirety of our clinical development programs.
Najat: And that’s diverse oncology to immunology, so it’s oncology, immunology, and neuroscience, cardiovascular, rare disease like pulmonary hypertension, infectious disease vaccines, discovery and development – it’s huge. And the way it happened, so let’s get to a few examples. Number one, pulmonary hypertension, it’s a rare disease. Janssen has medicines in the market, in market that can help patients. However, patients get misdiagnosed on average by about four years. So think about it from time where you have the disease it takes four years to get diagnosed. Because a lot of the symptoms early on are very similar to other diseases. And one of the questions be decided early on is we have to close that diagnosis gap. There’s no point having really good medicines if patients aren’t getting it on time. It’s a travesty that snap. So what we said is, okay, from a data science perspective, data science Taren, and really, AI is really good in the computer vision space. So this is like looking at images, structured data, ECGs, echos, MRI images, histopathology slides. And we said, hey, let’s look at the entire journey of the patient that has PH. What is that first procedure that they get – usually common ECGs. So we said, can you look at the ECGs very, very early on. Six months within the first onset of symptoms and be able to say that this person may have flag, that they may have PH. Because what happens is this is a rare disease Taren, so physicians, that doesn’t come to mind. And if that happens then they can go for a confirmation test, which is more invasive.
So we did just that. We created an algorithm. So this is an idea and we work with a startup company, Mayo, other health systems. We created the algorithm and we validated across different systems. We are able to pick up pulmonary hypertension with really good accuracy about more than two almost three years in advance. And based on the work we’ve done, we we’re just granted FDAs breakthrough device designation about…it actually went public yesterday. That’s a huge deal to say, and the work is not done, because we have more to do. But to say, how do you actually completely change, how patients for this devastating disease are diagnosed early? And AI can pick up features that will flag that somebody has this disease many years in advance that we can see ourselves. And the plan for us is to continue the work, hopefully get approval and then actually deploy this across many hospital systems and we’re working with different partners. So that we can really start picking up the patients and flagging those patients earlier on. Think about that impact that it has on the patient and their medicines that they can have across the industry, not just ours, that can actually help them get better.
So that’s an example that from start to finish is using these new approaches, doing them in a very rigorous, thoughtful way. But the benefit and the ultimate outcome is always for patients. And we have scientists in the pulmonary hypertension therapeutic area that we partner very closely with great external collaborators and also other internal collaborators. So everybody can get behind that. Yes, there’s a lot of concerns in the beginning. Is this going to work or not? But hey, this is part of innovation. You have to be resilient. You have to do the right thing. And you have to work with the right people and be agnostic to where good ideas can come from. So we never do this on our own. We do it as a team.
Taren: It’s fascinating. And I have to ask, you talked about looking at that disease in particular as a rare disease and that seems like it’s a shift. Now, we’re looking at bigger diseases that were may be managed by large blockbuster drugs, but now it seems like there’s a focus on these niche areas and we’re looking at truly personalized medicine from a rare disease lens. Is that a fair assessment?
Najat: I think if you want to treat the right patient at the right time with the right therapy, you need to understand what’s the driver of disease and then it becomes more personalized. And it’s happening across the board. I mean, for instance, let’s say, bladder cancer. A subset of patients with bladder cancer with FGFR mutations, that’s the driver mutation. So talk about truly personalized medicines and we have therapies for treating that disease. But here’s the conundrum, Taren, while it’s great to know exactly the biomarker, the mutation that’s driving the disease and you create more personalized medicines. I think it’s about a 10 or 12 percent of people in the real world are getting sequence for that mutation. So, how do you bridge that gap between more personalized medicine and then ensuring that patients are actually being sequenced or being flagged, so that they can actually get that right medicine at the right time for the right patient.
And so, what we have done and this is just another example is, we said, I wonder if you can close that gap by looking at biopsy data. So when you do a biopsy, everybody, if your physician is suspicious if you have a tumor or you do, you get a biopsy. So you have these histopathology images. So we said, instead of adding new data and burden to our health system that’s already overburden, can you look at that data? Use AI, machine learning to predict the mutation of that patient. Can you predict that somebody has an FGFR mutation just from digital histopath image? And we use internal data, external data, again partnering and we have built an algorithm that can actually predict that somebody has FGFR mutation just from the image, histopathology image. That’s a game changer, because what that does to you is you can actually then flag that this patient might have FGFR and then the person can get sequenced. And you can replace FGFR with different mutations. I’m just giving you the specific example. And why is that important? Again, you can have a patient that can be diagnosed or misdiagnosed that now can be diagnosed appropriately and get the right treatment – the Holy Grail of precision medicine.
The other thing is also, when we run clinical trials it’s really hard to find the right patient because there are still going more niche like you were saying. It’s almost effectively becoming like a rare disease. And how do you get that right patient? This will enable us and we’re actually deploying this. We’re the first in industry now to be doing this in our clinical trials, to be able to screen and say, can we actually find the right patients. So that you can do it faster, but then also do it more effectively. We don’t miss patients will have FGFR and we can actually ensure that they’re being enrolled in clinical trials.
And the last thing I’ll say is, a lot of what we talk about here that’s important to us at Janssen and we focus a lot on is diversity in our trials. And when we talk about whether it’s getting sequence or whether it’s running trials, the burden is even higher for those from different backgrounds, different economic status. So if you use data to kind of level the playing field, I call it, AI for good, you can actually ensure that your trials are more representative of those that you’re going to treat. We did that with one of our vaccines program, where we actually develop machine learning algorithms to be able to predict where the hotspot are going to be, this was for COVID-19. That was important because we wanted to go to the right places and ensure you get rich datasets to understand the right evidence generation of how the vaccine is holding up. But in that model, we partnered with MIT, we actually, over index in areas that had more diversity African-American, Latinx, actually, also those that are more elderly. So around nursing homes actually, because we knew those were the high-risk patient population. Guess what? – Once we did the trial, our phase 3 trial was extremely diverse, higher census numbers of African-American and Latinx.
This doesn’t happen by accident. So my point of all of these examples, is whether its precision medicine, whether it’s having the right diversity and trials, you can actually use data that exists today, design your trial. So, you’re over indexing and going to areas of solve those problems that are still challenges for us today.
Taren: That is fascinating. And so, I have to ask, when you’re looking at this future state and we’re talking about AI and machine learning right now, what’s next? What’s that next thing that you’re going to be looking at in terms of a technology or has it yet to be defined or identified even?
Najat: I would say a lot of what I think about what’s next is less around the technology and what’s going to be defined is how we harness that technology in a systematic way and scale end-to-end way from an idea to impact.
Taren: Fair enough.
Najat: I think that’s where we are. Let me maybe share a bit more detail on that. When I think about clinical development, designing our trials for the right patient in mind, stratifying who’s going to respond versus not. Like I was saying, doing the trials with the right spots where the patients are and not where the sites that we’ve worked with before, that’s kind of upending how we do things. But we need to do that at scale and was trying to do that, because then you’re going to see a huge amount of impact that you can have. I would say on the discovery front, I think it’s really exciting time with all of the rise of omics data, single-seat data were at a cellular level you’re trying to understand what’s happening to someone’s immune system. Like understanding, when you go from a disease to a non-disease state how sort of someone’s immune system is changing. And therefore, being able to predict who can respond versus not – super, super interesting. And that technology, being able to understand what’s the driver of this, that’s one of the discovery front from being able to design antibodies using AI, machine learning versus an empirical way of doing that. Same thing with small molecules being able to predict earlier on. Are they going to be off target effects? To me, that’s the other huge area that’s coming close to primetime, it’s not fully there yet, but it’s coming close to primetime and why this is so important?
One of the biggest challenges as an industry we have today, is the attrition of molecules from preclinical to clinical. You do proof of concept studies and things don’t work. If we can get better earlier on, we can change that. We can narrow that from the right compounds, the right disease areas, the right targets and have a better probability of success as something becomes a therapy eventually. That’s what I’m most excited about. Yes, the time, efficiency, cost, that’s good. But I’m more interested in that value and insight early on that improves your probability of success. That all of this work and time is going to lead to the right medicine for the right patient, and we can provide access in the right way. I think that’s going to change and the things that are enabling that change is the fact that not only is there more data. Because people talk with this more data. More data is not the answer. It’s about higher quality data with the right standards and just being linked. So you have your electronic health record, that’s anonymous link to your claims data, link to your -omics data, and link to your lab data. So you can create that longitudinal map of the patient’s journey.
The only way you’re going to solve for diseases is the more we know and can learn about what really is happening to people to begin. And for that, you need this longitudinal patient journey, that’s getting better. Our AI methods and approaches are getting better. Regulators are trying to provide more guidance – just fantastic. Compute power is going up. All that is great. But to me, it’s more around, how are we harnessing all of this to answer the right questions. Again, clinical to get so many examples of impact, we just got to scale that. But in discovery, the time is close to coming. And I think that’s going to have a huge impact for our overall probability of success of these medicines that we’re making.
Taren: I’m excited about the future and I am so excited to hear how you are transforming from discovery as well as in the clinical trial space. Because it was certainly in need of, I don’t want to say, a complete overhaul because that’s unfair. But some major tweaks to get to the right medicine to get the right patient. So congratulations to you and your team for really undertaking such a huge amount of work and applying such a discipline to it as well. If we could switch a little bit, because I’d love to hear about your personal journey as well and how you got involved in all of this and being a trailblazer in disruption and drug discovery and in data science. As a 38-year old woman of color, originally from Bangladesh, you’re not typical of a chief data officer. So how does it feel to be a trailblazer and do you have a sense of responsibility as a role model?
Najat: In terms of my own journey, I would say, Taren, I’ve always been comfortable with not following the well-trodden path. Because I think you have one life at the end of the day and my core mission and purpose, personally has always been transforming the lives of people, of patients in the darkest hour. As I grew up, my parents are both physicians. I did a lot of nonprofit work in Bangladesh as a kid. Starting from when I was actually nine, and I saw the suffering that happens to people, patients, and families and communities when a health scare or even worse, when a health incident actually happens. And that is something if you can transform that, if we can leverage different disciplines to do that, that’s the highest calling personally for me.
The other thing that’s important is about equity. Equity is about ensuring that whether it’s my team. Whether its patients, people around the world that you are actually giving everyone the best opportunity, the best foundation by making differential investment to be successful. I think we have an opportunity, I think and this is what gets me excited about being in J&J, about the role to do both. I started as a scientist. So I did my PhD at UPenn in chemistry developing molecules targeted for certain cancer lines. But pretty quickly in that journey, I realized that it would take me months to make a molecule and sometimes it wouldn’t work or often, it wouldn’t work. So, in my undergrad, I also did a lot in computer science. So I started coding and be in silico and predicting which molecules would work versus not. And I remember, things started working. The algorithms get smarter, the more you put in beta learning, et cetera. And that was the light bulb for me that, the combination of the two disciplines can truly make a difference.
But I remember many people in my lab that said, why are you spending all this time in the computer lab? Why not just be in the lab-lab, the wet lab? But to me, there’s a value of doing both targeted to the right question. But then after that, I was in academic and I felt like if you want to go back to that purpose statement, which is how do you transform lives of patients. You definitely, it’s important to know science and computer science, but it’s also important to know having business acumen, how do you drive decision making for the right mission and purpose. And that’s when I ended up going to the Boston Consulting Group. And I did a lot of work in both R&D and commercial in pharma companies for some of the top 10 pharma companies, payer, and provider. I worked in the US. I worked in Europe. I worked in Asia, and it’s a global mindset coupled with the end-to-end healthcare such as even beyond life science. That was an incredible learning experience for me. I understand how decisions get made, what’s the current state of affairs, what can we do differently? And it’s the confluence of those three computer science, science and medicine, and the business acumen strategy leadership. Those are the three things that have come to play in the role that I did today.
And I’ll tell you looking back, it’s so easy to connect the dots. If I told you that story like how I lived it prospectively, there were so many questions like, why do you have such nonlinear path and why not just do things a certain way, this is too risky? But in life, if you want to make a difference, if you want to make a change – you start to change within yourself? You have to be able to be fearless in some ways to be bold and to be disciplined at the same time. And the other is quite frankly, you have to work with good people. I have been fortunate to have really good mentors that have given me advice at my lowest moments. Often, I would feel like an impostor syndrome. When I was at BCG I remember my first six months, everyone had an MBA. I talk to myself, “oh my gosh, I can’t speak the language. I’m in academic. What am I doing here?” And I had this amazing partner who sat me down and said, “listen Najat, don’t think about yourself as what you don’t have. Think about what makes you differentiated.” And I look at him thinking, nothing. He said, scientists are great at going deep. If you have the right business acumen, you’re going to be able to go broad and think about the big picture too. He said, it’s much harder to learn science and medicine and be able to go deep and be that analytical than it is to understand the broader scope. You can do both. Lead with descent, which is you can go deep and learn and observe how to go broad and have that business acumen. If you can do both, you’re going to be differentiated and you’re going to make that impact that you really wanted.
That made a huge difference for me, because I noticed, I was putting myself in a box, and that’s happened my entire career. People put you in a box if you don’t look, speak, and think a certain way. And my entire career has been not to let people put me in a box and I feel like when you have the right mentors, they reminds you that don’t let people put you in a box. And that is something that’s really important for me to pay it forward and mentor these 25, 30 different people. And my advice is always, nonlinear careers because if you really want to change the future and don’t ever let anyone put you in a box. Because you know yourself better than anyone else, don’t you. And if you’re trying to create a new path, it’s natural for people to think that that’s not possible. But once you figure it out and you have to be resilient and you have to wait a problem solving and look at the details and really work hard. But when you do that, the reward is extremely, extremely, extremely worth it, because it fulfills your purpose.
Taren: That’s fantastic. And talk about such pearls of wisdom. Don’t adhere to a nonlinear career or don’t take a nonlinear career. Don’t let anyone put you in a box and most importantly, lead with your strength to distinguish yourself, no matter where you are. No matter what role you’re taking on. And that really takes a lot of soul searching and looking in the mirror and saying – how can I do this? So what tremendous advice and I’m so glad that you were the recipient of that advice because look at where you are today. That’s quite something. And I love the fact that you’re paying it forward and you’re mentoring all those other individuals. What’s the best part of that mentoring relationship for you?
Najat: It’s funny, I had two folks that I saw yesterday that I mentor, people always think when you mentor someone you’re helping and to me, I’m constantly learning. I’m learning from them.
As an example, one of the girls that I mentor, she’s doing data science. She’s doing a degree in data science at UPenn. She’s an extremely bright African-American scholar, I would say, and I remember her interest and her passion is diversification in clinical science. And she said to me, “I really want to do this.” And we do a lot of work in this space, Taren, so I’m going to introduce her to a couple of folks in my team. And I said to her, I said, “listen, a lot of people are going to say that they’ve done this and this doesn’t work, that doesn’t work. Just ignore that or people are going to say, I’ve done it before. Just ignore that. Just stay your course.” She said to me, “well yes.” She said, “they could have done it before, but I’m going to do it with more substance and it’s going to be better.” And I love that and I said, “oh my gosh, if I have mine said 10 years ago,” but I thought that was fantastic. That is the right attitude to have. So I am constantly learning. So it’s never just a one-way. I really treasure those conversations.
The other thing, I’ll also say, even on my own team, Taren about half our team is women for data science. In an industry, which is I would say 15 percent max gender diversity and racial diversity is in the low digits and ours is much much higher, but again, more to do. But that doesn’t happen by accident
Najat: It happens because people want to follow purpose. People want to also work in a company that invests in them as an individual. Not for that job. Not for that exact role, but for them to be a leader. That means understanding what their aspiration is. Understanding the inequity that exists and let’s be fair, it does and then actually, doing something about it, creating the right opportunity to overcome that. I spent a lot of time in that space because I’ve always seen better answers and solutions for this data science-science come if you have the right diverse team. And it’s an inclusive culture where they are encouraged to speak up. I haven’t always had that in my career, and I’ve had some great mentors that have helped me throughout. But that is a huge focus for me, for the team.
And the last thing I’ll say, is you ask the question around, it takes a lot of courage. It takes a lot of and I always tell people that when times get really tough, you have to do a lot of like you said, soul-searching. But remember the reason why you’re here. Because as you get more for senior in roles, just things get complicated and if you’re trying to do changes it just complicate it. And for me, it goes back to when I was 15 when I was volunteering in Bangladesh and I was looking at pre and postnatal care and just the story. Because pre and postnatal care in Bangladesh the mortality rates for women and children it’s so high, and my role there was just to be a translator. I was working with Oxfam on that. Anyways, as I was going through the, we were in a slum and we were doing these interviews and a father, clearly the thought came up, and he was so so upset. And he had these piles of receipts and medical reports and said, “please, help my daughter. “And he thought I was a physician, which of course, I was not. And I looked through it and she had leukemia. I went to see her, it wasn’t even a house. It was a little shed. There’s nothing — they sold everything to feed her. And I knew she only had a few months to live and there’s nothing I could do. We were the same age and I looked at her and I thought to myself, as a 15-year-old, there’s all these things going through your head, this party, that day, curfews, and whatnot. And for that moment I said, look at the life that she will not have and I felt so incredibly helpless standing there. And that was the moment, I said to myself, no matter come hell or high water, I’m going to devote my life to helping patients like her. Because I have the opportunity to live, maybe another month, another year and I better make every single day worth it.
And I share that story because when things get really hard and when you’re in your lowest point, I remind myself that and I tell everybody that I mentor, it has to be a story that speaks to your purpose and you have to be unmoved from that. And if you do that and you do things in the right way, with the right people, you will be successful. And if you failed you’ll at least learn. We will always learn. And so that’s…my mom now also had severe Parkinson’s and every day, I don’t see her in Bangladesh. I think I’m losing that special time with her. I’d better make it worth it by doing something that’s going to transform the lives of patients.
So that’s the advice I give to folks like there’s going to be so many examples of life being tough, but really go back to that core moments that makes you do what you do and stick to what grounds you and hold onto that and don’t let other dethrone you from it.
Taren: Wow. I have nothing else to say but wow. Thank you so much for sharing that very deeply personal story and how that has transformed your life and given you a life of purpose. I can’t imagine what it be like standing there and looking at this 15-year-old child and thinking, this is it for her and I have my future ahead of me and to be so mature in that moment and say, this is going to change my life. That’s quite a wow moment.
Najat: It is. And there’s a lot of guilt that comes from it. Because I was like, why, she deserves so much more. I still think about it, like, every week and I measure myself with am I doing enough? As, you talked about, Taren like data science, like that one patient, who can be diagnosed three years in advance, if that happens again, we’re pushing really hard, think about the impact it has for their family. Think about that women in there, usually it affects women in their 40s and 50s and their children and their families, mother like, it makes a difference. So, to me it’s about we have to change. We have to evolve. And the reason is not because our peers are doing it or who’s doing it – It’s because that’s the mission that we have.
To me, that’s what gets me excited in terms of, everything won’t work out, but we will learn from every experience. And quite frankly, a lot of things are working out. Like for instance, we just launched our first fully remote decentralized trial in the immuno-derm space. Like, COVID had its challenges, but we should learn from it and ensure the learning stick because those of color, it’s really difficult to take the whole day off and be in a trial. And actually, do all the different procedures. So, how can we level that playing field for everybody? And things aren’t perfect. Data isn’t perfect. We spend so much time, Taren on like privacy considerations, AI and ethics, rigorous methodology like, my background as a scientist, it has to be rigorous. And we do all of that, but there are solutions that are coming through. We are having these examples of impact. And there is data available today where we’ve been able to show that for certain disease, infectious disease that leads to success, what are the risk factors? How do you target those patients? How do you made sure that they get those therapies first?
This is possible. It’s happening. It’s happening all around us. It just requires for us to look deep inside, not get threatened. But really focus on change is good and why are we doing it? – We’re doing it for patients. They win. If they win, we all win.
Taren: And I have nothing more. That is the perfect ending to our time together. I could talk with you for another hour. This has been a fascinating conversation. Thank you so much for sharing all that you’re doing at Janssen and J&J to create transformation on behalf of patients. And thank you so much for sharing so much of your personal story with our listeners. It’s been truly a pleasure, Najat. Thank you so much.
Najat: Thank you, Taren. It was wonderful to speak with you. And thank you for having me.
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