The Transition from Academia: Overcoming the ... - ACS Publications

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The Transition from Academia: Overcoming the Barrier to a Career in the Drug Discovery industry Kira A Armacost J. Chem. Inf. Model., Just Accepted Manuscript • DOI: 10.1021/acs.jcim.8b00262 • Publication Date (Web): 04 May 2018 Downloaded from http://pubs.acs.org on May 5, 2018

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Journal of Chemical Information and Modeling

The Transition from Academia: Overcoming the Barrier to a Career in the Drug Discovery Industry Kira A. Armacost* Chemistry Capabilities and Screening, Modeling & Informatics, MRL, Merck & Co., Inc., West Point, PA 19486, USA

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ABSTRACT

This perspective describes the transition from academic training (specifically graduate school and a postdoctoral fellowship) to a career in a pharmaceutical industry as a computational chemist. My personal journey from childhood to senior scientist is described, along with suggestions and insights into a career in the pharmaceutical industry.


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TEXT Since I was young, I dreamed of the ability to cure diseases, specifically juvenile diabetes. I was diagnosed with diabetes as a 7 year old, less than a month away from my favorite holiday, Halloween. While this was a set back at the time, it encouraged me to become a researcher. I currently am a senior scientist at Merck & Co., Inc. West Point, PA, USA, working on discovery chemistry teams as the modeling and informatics representative. I have always had a passion for helping people and continue to fuel that passion by researching new treatments for diseases and representing myself and my company with pride. My journey into the medical field began at a young age. At the time I was diagnosed with diabetes, genetics was the main understanding for getting the disease. However, there was no family history of Type 1 diabetes going back multiple generations. I was an anomaly. Luckily, a study at the time had just appeared in the literature describing that chicken pox could be a cause for an increase in Type 1 diabetes in children.1 I had the chicken pox just a few months before being diagnosed. Could this really be the cause? It was at that moment that I realized my dream was to have a career in science. During high school, I decided that I wanted to go to medical school, so I attended college pursuing a degree in biochemistry with the eventual goal of becoming an endocrinologist. Through my coursework, I quickly realized that organic chemistry was my passion, and I changed my path to pursue medical research. I continued onto a graduate program in theoretical organic chemistry at Auburn University working under the guidance of Dr. Orlando Acevedo. My doctoral research included quantum mechanical/molecular mechanical (QM/MM) coupled to free energy perturbation (FEP) to understand solvent effects on organic reactions (i.e. the Claisen


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rearrangement,2 and Aldol reaction3). I was able to branch out of QM/MM to learn molecular dynamics (MD), which quickly became my favorite computational tool. I learned how to run MD simulations on alkanesulfonate monooxygenase (SsuD) in order to understand its mechanism.4 Developing a background in MD and FEP allowed me to pursue a postdoctoral research fellowship at the University of Michigan with Dr. Charles L. Brooks, III to develop and validate a free energy method called multi-site lambda dynamics to predict binding affinities of proteinligand complexes (i.e. drug-protein interactions).5 During my postdoc, I developed a strong interest in free energy methodologies and developed methods to predict binding affinities for a large number of molecules in a few simulations.6, 7 While I was able to predict these binding affinities, I did not have the opportunity to see the best predicted compounds advance to experimental study (i.e. synthesis, assay, etc.). It felt like something was missing. It was at the University of Michigan where I developed my niche: free energy calculations, but what could one do with a specialty in development of free energy methodologies? Could these methods be used in the pharmaceutical industry to design better drug molecules, faster to help cure or treat disease? After doing some research regarding careers in industry, I decided to pursue industrial options. Applying computational chemistry in the pharmaceutical industry has been impactful in the discovery of new medicines. As an example, Dr. Kate Holloway contributed to multiple drug discovery efforts that resulted in marketed drugs (Crixivan,8-10 Isentress,11 Zepatier,12 and Vanihep13-15) during her career. The HIV protease work that Holloway performed was one of the first examples of the direct impact that modeling can have on drug discovery programs and she continued to have computational impact with her work on HCV protease programs (Zepatier and Vanihep). After speaking to her and one of her colleagues at an American Chemical Society


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National Meeting, I decided to apply for industrial positoins, and happily accepted an offer in April 2016. During my interview, I learned more about the job role and career path. The major role of pharmaceutical companies is to discover and develop new treatments that can impact patients worldwide. In order to address these goals, cross-functional collaborations are formed to answer key scientific questions. There are many tools and areas of expertise required in drug discovery and the exposure to this knowledge allows you to learn and grow as a scientist. With all of these factors in mind, the pharmaceutical industry was definitely the path that I wanted to pursue. Before joining the modeling group at my company, I took various steps to prepare for a career in drug discovery. I read articles describing the modeling and informatics tools being used in drug discovery, started learning the basic principles of drug-like properties, and began examining off-target effects of drug-like molecules. These publications describe the optimization of small molecules to address pharmacokinetic (PK) and pharmacodynamics (PD) properties, solubility, transporters, brain penetration and so much more. Was I ready? Did I have the right background to join a pharmaceutical company? These were all questions that I had running through my head, and nerves started to form. In my first week on the job, I met my group and was assigned my first projects. I met the chemistry and biology leads on those programs, and began reading papers focused on the earlyspace drug discovery projects to which I was assigned. I was researching biology, chemistry, organic synthesis, how to optimize solubility and the many other aspects that go into a typical drug discovery campaign. One thing that I did not expect is that projects can be drastically different from one another, and that was the case for my projects. While the diversity was great to help increase my breadth of knowledge, it was also overwhelming at first. The same question


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kept haunting me: was I ready for this? Gradually, I felt comfortable enough to embrace the challenge and move forward. After a few months on the job, I got the assignment to join a project in the lead optimization (LO) space, an area that is more mature than the projects I initially supported. This program had been running for almost two years by the time I joined, which meant the breadth of chemical matter associated with the program was vast. Thousands of compounds had been made and assessed in biochemical, cell-based and off-target assays. Many of the compounds had also been assessed in in vivo PK studies and PD efficacy studies. Animal studies! As a graduate student or a postdoc, I would have never imagined that any of the compounds I designed would advance so far. While exciting, LO programs often have hundreds to thousands of compounds with enormous amounts of data associated with them. Imagine this: a thousand compounds each having biochemical activity, cell-based activity, off-target activities, solubility measurements, and PK measurements – in other words, tens of thousands of data points. It was at this point of my career where I once again felt overwhelmed and underprepared for a career in the pharmaceutical industry. One thing I have yet to mention is the amount of help I have had in my career at my company. Though I felt overwhelmed, I was encouraged to ask questions during meetings, reach out to teammates to learn about other aspects of drug discovery, and seek training where I needed it. I met individually with my colleagues in chemistry, biology, pharmacology, safety and pharmacokinetics, pharmacodynamics and drug metabolism (PPDM) groups to increase my expertise for the chemical matter that I was working with. Realizing that my colleagues are experts in their respective fields and willing to share this expertise was an eye-opening experience. Having all of this help at my fingertips allowed me to overcome being overwhelmed


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by embracing what I am able to learn from my colleagues. Having the willingness to ask questions and learn from others has had a large impact on my growth. I continue to ask questions and benefit from my colleagues and now I find that I am able to ask questions that help others to think about projects differently. During my academic career, I had never seen a molecule that was designed with computational tools be synthesized or assayed, but that quickly changed for me after I started my new job. Here, we work as a team to drive towards molecules that solve key program issues, and I was encouraged to see how I could contribute to the design of a molecule, and see it assayed and structurally characterized within a matter of weeks. It amazes me that this is routine. This is one of the best feelings that a computational chemist can have in the pharmaceutical industry. I want to share a conversation I had a few months after I started with a chemist I met during my interview. This chemist stopped me in the hallway and asked, “So, is this everything you expected?” I paused, stumbled and with a confused look said, “No, not at all.” We both laughed and agreed that my experience is fairly typical, so go into a career with the expectation that few things may be simple, straightforward or as expected. While this is one person’s perspective on the transition into industry, I hope it is helpful. Industrial drug discovery can be very rewarding experience but the scope and scale of the effort can also be daunting initially. I would like to offer a couple of suggestions for anyone wanting to pursue a career in the pharmaceutical industry. (1) Industry is a great place to expand your knowledge and passion for discovering new compounds that could evolve into treatments for disease, (2) A strong computational chemistry scientific background, regardless of application, provides the foundation for you to be trained to perform the role of a modeler on a program. Do not discount your scientific background if it does not match the job description directly, (3) Be open to asking


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questions and learning from your colleagues, not knowing something is not a sign of weakness and reaching out to others will support your growth and (4) Approach new problems with openness and enthusiasm – the sheer willingness to contribute can be a strong attribute to your program and company. AUTHOR INFORMATION Corresponding Author [email protected]* Author Contribution The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript. Conflict of Interest The author declares the following competing financial interest(s): Author is a current or former employee of Merck & Co., Inc., Whitehouse Station, NJ, U.S., and potentially owns stock and/or holds stock options in the Company. ACKNOWLEDGMENT I would like to thank John Sanders and M. Kate Holloway for their discussions and review of this manuscript and Melissa Ford, Zhe Wu and Juan Alvarez for their review of the manuscript. ABBREVIATIONS QM/MM, quantum mechanical/molecular mechanical; FEP, free energy perturbation; MD, molecular dynamics; SsuD, alkanesulfonate monooxygenase; PK, pharmacokinetic; PD,


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pharmacodynamics; LO, lead optimization; PPDM, pharmacokinetics, pharmacodynamics and drug metabolism; REFERENCES 1. Dokheel, T. M., An Epidemic of Childhood Diabetes in the United States? Diabetes Care 1993, 16, 1606-1611. 2. Acevedo, O.; Armacost, K., Claisen Rearrangements: Insight into Solvent Effects and "on Water" Reactivity from QM/MM Simulations. J. Am. Chem. Soc. 2010, 132, 1966-1975. 3. Armacost, K.; Acevedo, O., Exploring the Aldol Reaction using Catalytic Antibodies and "On Water" Organocatalysts from QM/MM Calculations. J. Am. Chem. Soc. 2014, 136, 147-156. 4. Armacost, K.; Musila, J.; Gathiaka, S.; Ellis, H. R.; Acevedo, O., Exploring the Catalytic Mechanism of Alkanesulfonate Monooxygenase Using Molecular Dynamics. Biochemistry 2014, 53, 3308-3317. 5. Knight, J. L.; Brooks III, C. L., Multisite λ Dynamics for Simulated Structure-Activity Relationship Studies. J. Chem. Theory Comput. 2011, 7, 2728-2739. 6. Armacost, K. A.; Goh, G. B.; Brooks III, C. L., Biasing Potential Replica Exchange Multisite λ-Dynamics for Efficient Free Energy Calculations. J. Chem. Theory Comput. 2015, 11, 1267-1277. 7. Hayes, R. L.; Armacost, K. A.; Vilseck, J. Z.; Brooks III, C. L., Adaptive Landscape Flattening Accelerates Sampling of Alchemical Space in Multisite λ Dynamics. J. Phys. Chem. B 2017, 121, 3626-3635. 8. Dorsey, B. D.; Levin, R. B.; McDaniel, S. L.; Vacca, J. P.; Guare, J. P.; Darke, P. L.; Zugay, J. A.; Emini, E. A.; Schleif, W. A.; Quintero, J. C.; Lin, J. H.; Chen, I.-W.; Holloway, M. K.; Fitzgerald, P. M. D.; Axel, M. G.; Ostovic, D.; Anderson, P. S.; Huff, J. R., L-735,524: The Design of a Potent and Orally Bioavailable HIV Protease Inhibitor. J. Med. Chem. 1994, 37, 3443-3451. 9. Holloway, M. K.; Wai, J. M.; Halgren, T. A.; Fitzgerald, P. M. D.; Vacca, J. P.; Dorsey, B. D.; Levin, R. B.; Thompson, W. J.; Chen, L. J.; deSolms, S. J.; Gaffin, N.; Ghosh, A. K.; Giuliani, E. A.; Graham, S. L.; Guare, J. P.; Hungate, R. W.; Lyle, T. A.; Sanders, W. M.; Tucker, T. J.; Wiggins, M.; Wiscount, C. M.; Woltersdorf, O. W.; Young, S. D.; Darke, P. L.; Zugay, J. A., A Priori Prediction of Activity for HIV-1 Protease Inhibitors Employing Energy Minimization in the Active Site. J. Med. Chem. 1995, 38, 305-317. 10. Vacca, J. P.; Dorsey, B. D.; Schleif, W. A.; Levin, R. B.; McDaniel, S. L.; Darke, P. L.; Zugay, J.; Quintero, J. C.; Blahy, O. M.; Roth, E.; Sardana, V. V.; Schlaback, A. J.; Graham, P. I.; Condra, J. H.; Gotlib, L.; Holloway, M. K.; Lin, J.; Chen, I.-W.; Vastag, K.; Ostovic, D.; Anderson, P. S.; Emini, E. A.; Huff, J. R., L-735,524: An Orally Bioavailable Human Immunodeficiency Virus Type 1 Protease Inhibitor. Proc. Natl. Acad. Sci. USA 1994, 91, 40964100. 11. Hazuda, D. J.; Anthony, N. J.; Gomez, R. P.; Jolly, S. M.; Wai, J. S.; Zhuang, L.; Fisher, T. E.; Embrey, M.; Guare, J. P.; Egbertson, M. S.; Vacca, J. P.; Huff, J. R.; Felock, P. J.; Witmer, M. V.; Stillmock, K. A.; Danovich, R.; Grobler, J.; Miller, M. D.; Xu, W.; Pearson, P. G.; Schleif, W. A.; Cortese, R.; Emini, E.; Summa, V.; Holloway, M. K.; Young, S. D., A Naphthyridine Carboxamide Provides Evidence for Discordant Resistance Between Mechanisticaly Identical Inhibitors of HIV-1 Integrase. PNAS 2004, 101, 11233-11238.


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12. Harper, S.; McCauley, J. A.; Rudd, M. T.; Ferrara, M.; DiFilippo, M.; Crescenzi, B.; Kock, U.; Petrocchi, A.; Holloway, M. K.; Butcher, J. W.; Romano, J. J.; Bush, K. J.; Gilbert, K. F.; McIntyre, C. J.; Nguyen, K. T.; Nizi, E.; Carroll, S. S.; Ludmerer, S. W.; Burlein, C.; DiMuzio, J. M.; Graham, D. J.; McHale, C. M.; Stahlhut, M. W.; Olsen, D. B.; Monteagudo, E.; Cianetti, S.; Giuliano, C.; Pucci, V.; Trainor, N.; Fandozzi, C. M.; Rowley, M.; Coleman, P. J.; Vacca, J. P.; Summa, V.; Liverton, N. J., Discovery of MK-5172, a Macrocyclic Hepatitive C Virus NS3/4a Protease Inhibitor. ACS Med. Chem. Lett. 2012, 3, 332-336. 13. Liverton, N. J.; Carroll, S. S.; DiMuzio, J. M.; Fandozzi, C. M.; Graham, D. J.; Hazuda, D.; Holloway, M. K.; Ludmerer, S. W.; McCauley, J. A.; McIntyre, C. J.; Olsen, D. B.; Rudd, M. T.; Stahlhut, M.; Vacca, J. P., MK-7009, a Potent and Selective Inhibitor of Hepatitis C Virus NS3/4A Protease. Antimicrob. Agents Chemother. 2010, 54, 305-311. 14. Liverton, N. J.; Holloway, M. K.; McCauley, J. A.; Rudd, M. T.; Butcher, J. W.; Carroll, S. S.; DiMuzio, J. M.; Fandozzi, C. M.; Gilbert, K. F.; Mao, S.-S.; McIntyre, C. J.; Nguyen, K. T.; Romano, J. J.; Stahlhut, M. W.; Wan, B.-L.; Olsen, D. B.; Vacca, J. P., Molecular Modeling Based Approach to Potent P2-P4 Macrocyclic Inhibitors of Hepatitis C NS3/4a Protease. J. Am. Chem. Soc. 2008, 130, 4607-4609. 15. McCauley, J. A.; McIntyre, C. J.; Rudd, M. T.; Nguyen, K. T.; Romano, J. J.; Butcher, J. W.; Gilbert, K. F.; Bush, K. J.; Holloway, M. K.; Swestock, J.; Wan, B.-L.; Carroll, S. S.; DiMuzio, J. M.; Graham, D. J.; Ludmerer, S. W.; Mao, S.-S.; Stahlhut, M. W.; Fandozzi, C. M.; Trainor, N.; Olsen, D. B.; Vacca, J. P.; Liverton, N. J., Discovery of Vaniprevir (MK-7009), a Macrocyclic Hepatitis C Virus NS3/4a Protease Inhibitor. J. Med. Chem. 2010, 53, 2443-2463.


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