Translating the results of preclinical studies in animal models for the human studies is tricky in the best of times, but the challenges are particularly stark when investigating diseases of the central nervous system. The complexity of the human brain and its differences from the mouse brain can often lead to side effects in human trials. To help overcome these longstanding translational challenges, Cerevance has developed its proprietary Nuclear Enriched Transcript Sort sequencing (NETSseq) platform to analyze postmortem human brain tissues to produce rich cell type–specific data sets. Cerevance’s Chief Executive Officer, Brad Margus, met with Pharma’s Almanac Editor in Chief David Alvaro, Ph.D., to discuss how the platform facilitates more informed drug development strategies for a variety of neurodegenerative disorders.
David Alvaro (DA): Brad, can you tell us a little bit about where you identified shortcomings in traditional CNS drug development using animal models and where you see opportunities to change this paradigm?
Brad Margus (BM): Many companies, including my last company, have long relied on animal models to study human diseases, especially late-onset neurodegenerative diseases or psychiatric diseases. However, what we have increasingly learned is that animals just aren’t that sufficient. To really understand these diseases and to develop treatments, we truly needed a way to study the human brain. Even then, some of the solutions that have been possible, like looking at stem cells or even neural progenitor cells derived from patient blood and skin samples, still present an immature and artificial model of the brain. So, to enable research on actual, natural human brain cells, we sought to access mature human brain tissue from donors.
DA: Can you explain the fundamental concept and the technology underlying your NETSseq platform?
BM: Essentially, we apply the very old school approach called fluorescence-activated cell sorting (FACS) but implemented several innovative tweaks. It has never been easy to sort neurons, because they have a huge range of structures and get damaged very easily. Our approach is unique in that we pull the nuclei out of the cells, after which we can very effectively sort them. If you have the right marker or probe for a specific cell type, you can pull out a lot of cells or nuclei from one cell type, even very rare cell types, so that you can study them. And, because we can get a lot of nuclei for cell type, we can extract much more RNA and measure the expression of many more genes than other approaches like single cell or single nucleus analysis.
DA: What new drug development strategies can the NETSseq platform enable?
BM: Every program begins with something that you want to change when your drug is used. Long before you worry about whether a drug is specific or selective or potent, you need to define the target. Typically, this is a critical protein that you believe, if you can change its activity — either up or down — it will prove to be therapeutic for people who have the disease. What Cerevance is all about is finding novel targets that no one has worked on before: targets that may have been changed with disease, targets that are selectively produced only in vulnerable cell types, cell types that cause neuroinflammation, or cell types that lie in circuits disrupted by disease. Cerevance’s mission is to help researchers find new targets to enable therapeutics that can help treat these neurological diseases.
DA: How has Cerevance used the NETSseq platform to enable drug discovery efforts and build a pipeline?
BM: For common diseases that affect large patient populations, like Alzheimer’s and dementia, maybe 10% or less of cases are attributed to a known genetic mutation — the cause for most cases is a mystery. Currently, most of the industry focuses on a few well-known targets, such as those involved in the formation of plaques or tangles in the brain, or a protein called tau. These are all important, but we think there are other critical approaches that are mostly overlooked.
At Cerevance, we’ve worked with 23 brain banks around the world and have so far collected over 11,000 brain tissue samples. We’ve looked at people who died from causes other than brain diseases — from age eight to 104 — to see how aging affects different cell types in the brain. We’ve also applied this same methodology to diseases like Alzheimer’s, Parkinson’s and Amyotrophic Lateral Sclerosis (ALS) to find targets for which we believe, based on the known biology, changing their activity would be therapeutic. We have now taken these novel targets into drug discovery and started developing compounds against them and are advancing those closer to the clinic.
DA: Can you highlight any of those individual candidates in your pipeline or tell us a little bit about where they are in their clinical journey?
BM: Usually, investors and media are only interested in your lead program, and so I’d be thrilled to tell you about another program we’re working on that is in earlier stages that is really exciting to us.
A really popular area right now in neuroscience is neuroinflammation. The central idea is that in a condition like Alzheimer’s disease, there may be a range of different initial causes for why the disease develops, but the brain’s immune system overreacts to that pathology, which then causes the disease to get much worse. So, if you could somehow dampen the inflammatory response in the brain, it could be highly therapeutic — if not by affecting the source of the disease, then at least by decreasing its impact. There are a number of companies that have been founded on this idea. What we did at Cerevance, using our unique approach, was look at the microglia — the immune cells in the brain — to find genes and proteins that are expressed only in that one cell type.
The idea was that if we could find a target expressed selectively in microglia that we could affect, then maybe we could come up with an anti-inflammatory approach for the brain that would be really precise. It’s not an option to fully shut down the immune system or the inflammatory response, especially in the elderly patients who typically experience diseases like Alzheimer’s and Parkinson’s, because it could cause a wide range of problems. We wanted was a precise way to reduce the inflammatory response specifically in the brain. Using our platform, we found a unique protein that’s expressed only in microglia in the brain. When we drugged that protein — at least so far in cells and in animals — with a compound we’ve developed, we found that it dampened the inflammatory response with real precision. This is really exciting, because while the role for inflammation is pretty widely accepted, largely on the basis of genetic evidence, our technology was able to reveal a unique, novel target that may not have been uncovered using other approaches.
DA: That brings up a point I’m curious about. Lately there has been more discussion of the amyloid hypothesis for Alzheimer’s disease and whether Alzheimer’s drug development may have been too focused on amyloid to the exclusion of other models and targets. How important do you see reopening the search for targets for Alzheimer’s and potentially correcting that movement?
BM: The amyloid hypothesis — the plaques, all of that — absolutely plays a role in Alzheimer’s. There’s no doubt about that, but it’s not the whole story. There are people with plaques who don’t get Alzheimer’s disease. And there are people with Alzheimer’s who don’t have plaques. So, it’s clearly not the whole story.
At Cerevance, we believe that finding novel targets is really important for the whole field; however, it is risky. There are understandable reasons why pharmaceutical company executives don’t want to stake their careers pursuing something that has a higher risk of failure, and investors don’t necessarily want to invest in a program that may not get evidence of efficacy for many years and millions of dollars down the road, when you finally get to a Phase II clinical trial. We understand the risks, but it’s what we’re passionate about and committed to doing. Progress is also being made with biomarkers and with other kinds of data sets. We’re overlaying all of these approaches to try to get to the best hypothesis we can and ones that we can test quickly, and we think the world needs it.