Ph.D. Grad Uses Computational Chemistry for Drug Discovery
Biochemistry alumna moves into pharma with a goal to impact people’s lives.
As a computational chemist, Anjela Manandhar (Ph.D. ’19, Biochemistry) uses data to create models to better understand biochemical processes.
The Nepal native has long aspired to make a difference through science, and this month she’s getting her chance, as she begins a new job with Takeda Pharmaceutical Company in Cambridge, Massachusetts.
“I wanted to apply my research in something translational, somewhere it was impacting people's lives directly,” she said, but it took time for her to develop the skills she needed to do this.
The new position follows a postdoctoral fellowship at Temple University, where she studied chemical compounds with the potential to inhibit SARS-CoV-2, the virus that causes COVID-19. The postdoc, she says, gave her the experience that prepared her for the new role.
Manandhar spoke with the Graduate Center last month about using computational chemistry for the discovery of new drugs.
The Graduate Center: Tell us about your research to develop treatments for COVID-19.
Manandhar: When COVID happened, everyone was trying to do something to help understand the system, if there were compounds that would inhibit COVID. We did two different kinds of work. The first was the repurposing of drugs, where we looked at already approved FDA drugs.
We looked at the interaction of those drugs with one of the proteins of COVID-19. The protein I'm working with is called Mpro. It has a long name, SARS-CoV-2 main cysteine protease. Basically, it’s a very important protein that is required for the replication and the maturation of the COVID-19 virus.
The goal was to find compounds that might inhibit that protein to limit the life cycle of COVID-19. I looked at different small compounds that would interact with the Mpro and, based on those interactions, predicted different kinds of optimization that might create more potent small compounds.
GC: What is computational chemistry?
Manandhar: It's basically using different kinds of models to understand different experimental phenomena. So, it’s using computational tools and models of the system that you want to study to get the answers.
Take my current project, for example. I look at different compounds and their interaction with biological targets, and the proteins that I'm interested in. Based on those interactions, I have some idea of what compounds might be better to proceed with.
Let's say I start with 100 compounds and I see five compounds have good interactions with the target I'm interested in. Then I talk to the experimental collaborators to say, ‘Hey, these five compounds look good, can you test them?’ So, besides the interactions, they’re looking at things like physiological properties and toxicity.
Basically, it’s the initial screening or prediction that will help with the research.
GC: How does computational chemistry support scientific research?
Manandhar: What computational chemistry is good at is saving time and money for the experimental work. Here we’re using computational resources and we don't need to buy chemicals and wait for them to do different sets of assays. Here we’re just testing, computationally, so one can get a rough idea of how to proceed experimentally.
Let's say, experimentally, some kind of phenomena was observed. That can be explained by computational work because we can see at the molecular level where, at times, experiments cannot.
GC: How has being a Nepali woman shaped your career?
Manandhar: I come from a developing country. I might be one of the very few women Ph.D. holders in Nepal. I'm in a small minority and I’m also in a technology field. So, I feel a responsibility. I know I’ve made my family proud. But it also comes with the knowledge that it should not just be me. I should be helping other people if they want to consider Ph.D. research, especially women.
So, with that, if somebody contacts me asking for help with Ph.D. research in the U.S., I’ve always been open and I try as much as I can to help them.
I might be one of the very few women Ph.D. holders in Nepal … I know I’ve made my family proud. But it also comes with the knowledge that it should not just be me.
GC: What advice do you have for current Ph.D. students?
Manandhar: From my experience, you need to have a comfortable lab environment and very good communication with your adviser. I really enjoyed my work in my lab, and I felt my adviser and postdocs were there to help and support me whenever I needed it. I was in an environment that was very motivating and very encouraging.
When I came to CUNY, my adviser Sharon Loverde (GC/College of Staten Island, Biochemistry, Chemistry) emailed me asking me if I would like to rotate. I said ‘yes,’ just to experience what it would be like. So, I did a rotation in her lab. I liked her.
More than the computational work, I really felt comfortable working with her for my Ph.D. because I knew it was five years, minimum, that I would be working there.
When I joined the lab, it was more than a project. I basically signed up for the environment that I liked. It can be different for different people, because some students have a very specific research interest and so they end up choosing a specific research lab. I was more open-minded, I chose the lab where I felt I could grow and learn.
GC: Is there any other advice you’d like to share?
Manandhar: After my Ph.D., I wanted to go into pharma directly. That was my goal. But then, I figured out that my skill set didn't match up very well. My postdoc became the detour to gain those skills.
Once you finish your Ph.D., you might not land the dream job you want right away. Sometimes it's not straightforward. I joined a postdoc lab, and there I learned those missing skills, which helped me to land another job.
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