Trends in Phenotypic Drug Discovery – A Discussion with Brent Cezairliyan

Brent Cezairliyan is the Vice President of Biology at Octagon Therapeutics, a company focusing on novel drug discovery for autoimmune disease, using unique insights from cell metabolism and phenotypic methods to discover innovative product candidates. Phenotypic drug discovery remains a potent method in understanding the effects of pharmaceuticals on entire cells, and offers potential for opening up previously undruggable targets.

Thank you for joining us today, Dr. Brent Cezairliyan. It’s such a pleasure to talk to you – you have a long history in the life sciences, particularly in academia. What would you say motivated your career? From your PhD studying protein homeostasis, your postdoctoral work on bacterial growth and pathogenesis, to moving on to industry with Octagon Therapeutics, mostly working on autoimmune disorders, your interests are varied. Do they have a common denominator?

Almost every project I have worked on has focused on a form of metabolic regulation. In graduate school I studied the regulation of the cellular unfolded protein response. My curiosity about how metabolism influences the interactions of cells with one another led me to study how bacteria respond to their environment under different conditions, in particular what they produce and secrete– things like hydrolytic enzymes and toxins– and how that affects survival and reproduction. Now, at Octagon, we are looking at how the metabolic state of immune cells affects their interaction with their environment and vice versa. Until recently, most of the emphasis in the field has been characterization based on immunophenotyping – essentially protein expression. Octagon is moving beyond that to what we think is the logical next step: catalytic activity.

Octagon concentrates on studying the environment of cells and their responses to it through cell-screening methods, rather than utilizing cell-screening in optimal growth conditions as is usual. Why is this important, and what advantages can it offer in drug discovery compared to traditional methods?

Most cell culture media have been formulated to support survival and robust growth. These media have been extremely valuable in helping us to learn a lot of the fundamental biology of different cell types. However, in autoimmune and other diseases, the cellular environment isn’t static. Changes during disease exacerbation and remission can result in huge shifts in cellular metabolism. This idea isn’t new—it is already appreciated to a great degree, for example, with cytokines. And yet we do not have a good understanding of a lot of the metabolic effects of the environment on cells during disease pathogenesis. We have found that accounting for the disease-associated cellular environment during screening allows us to uncover previously unappreciated changes in metabolism that can be leveraged in drug discovery.

Whole cell-based drug discovery is one of the methods for phenotypic drug discovery – the other often being animal model-based. Both of these approaches face common challenges – such as hit validation and target deconvolution. Their chief advantage is their great ability to offer novel, first-in-class drugs – whereas target-based discovery is often better at optimizing known mechanisms of action. How do you think the industry should aim to overcome the challenges presented by phenotypic drug discovery?

It is a hard problem to state a general case solution. We have an expanding set of tools to tackle target deconvolution. Activity-based probes, ever-improving mass spectrometric techniques, and growing libraries of chemical modulators of known function have helped us a lot. Future improvements in cost, time, and automation of these methods will undoubtedly help. As with all things, we appreciate the shortcomings of each method – but we believe all tools have their place in an investigative arsenal, and approaches that complement each other can overcome such traditional deficiencies. 

Do you think novel technologies, such as AI’s machine and deep learning models, could offer the solution for these challenges? Researchers have already been putting deep learning models to use in target identification – but the adoption of AI in pharma has somewhat lagged behind other fields. This may perhaps be due to the industry’s unique combination of complex problems, regulatory hurdles and broad scope.

After seeing what AI has become capable of in the past ten years, I think it is only a question of when. There is a way to go before we can rely on it for direct target identification from experimental data, but until then we can leverage AI in other ways. One of the most powerful uses of AI in drug discovery right now is for the optimization of experimental design. Take the example of screening a compound library with a set of different cell genotypes under different growth conditions. The search space is potentially large in each of its dimensions. AI can be used to choose the number and identity of the elements in each dimension in order to maximize the likelihood of success for a given set of resource constraints.

You will be joining us at Proventa International’s Drug Discovery Biology Strategy Meeting in Boston this May. Are there any insights you would like to share with us prior to the event – or any developments in drug discovery that you are excited to hear about?

There is a lot to look forward to. I am curious to hear about some of the recent advances in drug/target interaction measurement in live cells and whole organisms – and how whole-cell methods can be used to propel drug discovery processes forward. 

Nick Zoukas, Former Editor, PharmaFEATURES

Caitlene Joy Limon, Producer, Proventa International

Join Proventa International’s Drug Discovery Biology Strategy Meeting in Boston to hear more on the strengths of phenotypic drug discovery and its unique applications throughout pharma, as well as how the industry is overcoming the traditional challenges of the method.