Vyant Bio is an innovative biotech company focused on neurology and oncology, with a mission to discover novel therapeutics that can speed up the drug discovery process, from very early biologic target identification to novel molecular indication. In this interview, President and CEO Jay Roberts discusses advancements in modern drug development and their potential impact on the future of healthcare.
Please describe the story of Vyant Bio: What sparked the idea, and how has it evolved since?
Historically, we were in cancer diagnostics, supporting patients in pharma clinical trials. We acquired a preclinical business in 2017, and we spun off a couple of our business divisions to raise cash and to change the profile of our business focus.
At the beginning of 2020, we got highly focused on drug discovery, bringing robust molecules and biologics into the discovery process. It is a very iterative process that leads to the identification of novel drugs that we can bring into clinical trials. That’s the point where we hand off the therapeutic asset to our pharma or biotech partners.
Pharmaceutical companies are very good at doing large clinical trials, but when it comes to drug discovery, in many ways, the industry has failed. The cost and time it takes to get drugs to patients are unacceptable. We’ve set out to try to make a difference with that in mind.
We use genomic, and in some cases, proteomic data to bring clinical patient data back into our drug discovery engine. With our digital platform, we can use those large data sets to iterate and design or discover drugs based on a whole host of data that is relevant for us.
We set out in early 2020 to participate with a small cohort of companies that were redefining drug discovery. We were working to strategically align with key collaborators which in the near term will maximize the value of Vyant Bio to our shareholders.
It was the beginning of the pandemic and everybody in the pharmaceutical industry, governments, and society at large were asking why it took so long to get vaccines approved and administered to patients.
We started to take a careful look at the drug discovery sector, and we realized that the breakdown and the real failure wasn’t in clinical trials but rather much earlier while still in the discovery phase.
Regulators are very good at making sure that when drugs are approved for patient clinical trials, they do not kill or endanger patients, but they have less focus on the efficacy of the drug. We believe our platforms can meaningfully assess the effective impact that potential drugs have on human cells.
We saw that there were only a handful of companies making good progress using data technologies and sophisticated data modeling together with biology. We are making meaningful investments in a digital platform that allows us to leverage the use of data science and unique computer-generated modeling.
We believe the use of human-derived cells to create disease models in our labs allows us to incorporate innovative technologies onto our platform. We are applying induced pluripotent stem cells (“iPSC”), a technology that uses fibroblasts (adult human cells) that first get regressed to a stem cell state, and then progress to grow and form 2D spheroids and 3D organoids. We have perfected a biological process to grow and standardize these cells at scale, which gives us high throughput screening capacity and very consistent output data.
Finally, we own in-vivo testing facilities in a few locations around the world, so we can test for toxicity, safety, and effectiveness in our animal testing labs, which is currently a final step in drug discovery prior to gaining approval to bring new drugs into clinical trials. So, our platform combines in silico computer models that simulate and then incorporate in vitro and in vivo human cells to give insights that allow us to efficiently bring drugs into the clinic.
What kind of diseases do you simulate with these micro organs?
We create disease models on two parallel paths. In neurology, we’re currently simulating neurological degenerative diseases like CDKL5, Parkinson’s, Alzheimer’s, and neurodevelopmental diseases that affect children, like Rett Syndrome.
One of the fascinating things about human cells is that they tend to naturally organize themselves and as they do, we can induce certain kinds of reactions or functions in the brain. Our microbrains are continuing to become more complex, as we further develop our capabilities.
Similarly, on a parallel track in oncology, we’re bringing human primary cells from cancer patients into the lab. We grow tumor cell lines to evaluate how novel compounds that are screened across these tumor cells can suppress or inhibit tumor cell growth or metastasis.
If we think about precision medicine, this is a place where we can reproduce a person’s cells and screen them to indicate what drugs are going to be effective for that particular patient. Of course, there is still a lot of work to be done in the future to make this part of a practical standard of care, but it shows very good early promise.
We bring a specific patient’s cells into our lab, grow them, and run genomic and proteomic patient data through our AI engine to design proteins and digitally print them to see how the binding effect of certain proteins and receptors can restrict and suppress tumor growth. That gives us an indication of how to design proteins as a therapeutic, iterate through our discovery engine in what we define as an “avatar clinical trial”, and focus on how to get our novel therapeutics into the regulatory track efficiently, to be approved for patient clinical trials.
All of this work can be done on a condensed lifecycle. What used to take five or seven years to discover novel candidates in the past can now be done in two to three years.
We are collecting a significant amount of data during our R&D efforts. In addition to processing data from large libraries of chemical compounds, and using genomic and proteomic data to be informative about promising biomarkers, we also analyze this data along with imaging data that we collect when screening compounds in our high throughput systems to evaluate significant biological impacts on our spheroid and organoid platforms. This analysis is done using our proprietary machine learning system, AnalytiX.
What other trends or technologies do you find exciting these days?
We’re employing very smart and experienced data scientists who know how to manage very large amounts of data to design drugs. We built it on a common digital platform that allows us to interrogate biology and chemistry using large datasets. The ecosystem is going to allow us to gain better insights over time because we’ll have access to better data. We get to learn from all of those data sources to be able to design therapeutics more effectively.
Regulators across the globe are eager to understand how they can better use other forms of data. At Vyant Bio, we spend a subset of our time working with regulatory agencies around the world to learn from them, but we also want to educate them so we can reach common acceptance of in vitro biological systems and in silico computer models as a credible and validated part of the criteria for approving drugs. We hope we can get that kind of momentum going.
In today’s environment, animal testing is still an important component of the regulatory track. Regulators do not want to test unsafe drugs on humans. We can bring the human back into the discovery cycle, even before animal testing, to see the impact in advance and predict the safety of those drugs.
With certain diseases, computer models and human-derived cell models are more indicative of what’s going to happen in a patient clinical trial than animal testing. For example, it would be hard to test an antidepressant drug on animals and ask them how they feel. In humans, you can see different kinds of reactions in the brain and get real-world data and evidence that is far more predictive and helps to classify the drug’s effect.
We are excited to be in an ecosystem of constituents with a real interest in drug discovery. It’s a rapidly changing and evolving field, and we’re proud to be amongst some of the leaders that are creating innovation in this field.
How do you envision the future of biotechnology?
As the rapid evolution of this sector continues, many biopharma companies are now embracing the use of technology and innovative science like micro-organs in their discovery research efforts.
The evolution and the rapid change will become more and more evident. I think we can almost compare that to the diagnostic industry 15 years ago when I first came into this field. We were just starting to think about biomarkers as a diagnostic tool. Soon enough, the use of biomarkers and the development of instruments that can identify unique genes became a common practice. This was very much driven by the sequencing of the DNA to not only diagnose diseases but also treat them.
I think the same events are taking place now in drug discovery, where the use of genomic, genetic, and proteomic data is giving us the necessary information to be able to design therapeutics that will be more effective.
