EXECUTIVE INTERVIEW – GATC Health & Liquid Biosciences: Faster, Cheaper, More Effective Drug Discovery
Drug prices are often driven by the high level of risk and an uncertainty in drug development, along with the lengthy timeline – a process that may ultimately require millions of man hours and billions of dollars.
Therefore, to save costs and time, GATC Health and the company Liquid Biosciences are collaborating to leverage genomics, predictive algorithms, and biomarker discoveries. They aim to help pharma companies accelerate biomarker discovery and identify potential treatments while minimizing risks and reducing costs. Each company brings its unique offering to the partnership: GATC Health focuses on whole genome/expresome testing, drug discovery, and new molecule development using a proprietary technology platform while Liquid Biosciences is at the forefront of using predictive algorithms in biomarker discovery.
GATC Health is a pioneering technology company using Predictive Multiomics™ to advance drug discovery and improve human health. The company’s proprietary Multiomics Advanced TechnologyTM (MAT) platform analyzes billions of biological data points, including whole genome/exome data and multiple omics to make accurate predictions about disease states and individual response to diseases and treatments.
Drug Development & Delivery recently spoke with Jenkins and Jeff Moses, GATC Chief Marketing Officer, about the combined benefits of their MAT platform and Liquid Biosciences’ Emerge mathematical evolution platform. These two platforms help identify the right biology sooner and focus on a smaller set of potential compounds early in the pre-clinical development process to enable pharma companies to develop drugs with more efficiency and a higher success rate.
Q: What do you see as a pain point in pharma and how are GATC and Liquid Biosciences working to address that?
Ian: There are two big pain points in pharma right now. One is the standard model that 95% of pharma relies on, which is finding a semi-functional molecule and giving it to patients in massive quantities in hopes that it will reduce a symptom or group of symptoms. That works for about 90% of drugs and has served us well in the past, but can be time consuming and expensive. Pharma is becoming frustrated using this model because it’s expensive and not particularly effective. Especially with more complex disease processes, like chronic disease states, this model has become less and less effective. The second big problem in pharma is the difficulty of minimizing side effects. Recently, a major pharma company had a drug that they spent 8 years and $3 billion developing and it had a negative side effect when they took it to clinical trials. They wound up shutting the entire drug operation down. So, you’re looking at huge risks, large quantities of money, and a flawed, antiquated system. This is causing all the pain: time consuming, expensive, and unintended side effects. The idea behind what we are doing is to eliminate those “pain points” that inhibit the speed to market and the efficacy of drugs.
Q: What is meant by Predictive Multiomics and how do you apply that to the human body?
Ian: This is essentially a digital imprint of a human. GATC utilizes Predictive Multiomics, which means we are looking at many different biomes, or biological components, of the whole human body to create a virtual human. From a physiological standpoint, we take a more holistic view of a human to predictively model what may happen with a new drug in the human model before it’s given to a human. To break it down more simply, by imprinting the disease state over the top of our human model, we can have a large interactive predictive model that determines what the biotarget and the biotarget pathway looks like, what’s around it, and how it interacts in the body. Secondarily, our technology can see whether or not the drug will be binding to, or affecting, unintended locations or sites. We can also look at this quantitatively to determine how much drug is effective and as important, how much is needed.
Q: How does digital human testing compare to traditional lab testing?
Ian: GATC’s predictive model saves a lot of time because the work goes much faster when you are running a digital model. For example, a typical drug model may take 7 million man-hours. We are taking about 1.5 million of those hours and condensing them down to three weeks. So, our unique technology shortens the time frame and reduces costs for speed to market. We can get from raw biodata (sampling), or biopsy, of the diseased tissue to potential drugs in about three weeks.
Some pharma companies would take thousands of molecules and manually test them at the target in the lab. That is a very time-consuming process. However, in our platforms, we are looking at the function of the biotarget. The GATC platform can view hundreds of thousands of targets and focus on only the ones most likely to interact and make interaction predictions using neural networks and decoding models to have real-time interaction modeling. It’s a significant difference in time. We’re talking about potentially taking years and years down to a few days as the platform matures.
Q: Can you explain how GATC, Liquid Biosciences, and a pharma partner work together? Once a molecule is discovered, what happens next?
Ian: It all starts with a sample or group of samples taken from disease patients. This is typically initiated by the pharma partner. This sample is processed into raw data. Liquid Biosciences runs a program that is akin to an “evolutionary environment.” Basically, there are competing species that are algorithmic so that the best algorithm surfaces. That algorithm defines the vector analysis of a biotarget. So, raw data comes in and the target is defined at Liquid Biosciences. Once the target is discovered, all of the data is sent to the GATC’s Multiomics Advanced Technology platform where it is analyzed in the context of the whole human. We take the target and the raw data and create potential drug targets and a model for interaction. That may look like a small molecule or a larger protein hormone, in the case of immune components. GATC’s platform looks at proteins, molecules, and other interventions that can affect a biotarget. The model we create goes back to the pharma partner where they take on digital validations and cellular validations and plug them back into the human model. When a pharma partner uses GATC, they go straight from not knowing much about a target to having a really good place to start and a set of specific tools to use. Sending the information back and forth allows us to aid them in the clinical trial component to help differentiate patient responses and minimize side effects.
Jeff: On the first project we worked with Liquid Biosciences, in less than a couple of months, we were able to assess biomarkers (potential targets) and identify new molecules that could eventually become the basis for a treatment or drug for that particular disease state. We are in the process of patenting these new molecules. In this example, we believed that we saved our partner at least 20% in development time. Under the old model, Liquid Biosciences would identify the biomarkers and the pharma partner would go back into the lab to trial-and-error their way through proteins, compounds, or molecules to try to find something that was effective.
Q: What therapeutic treatments can be positively impacted by this, and can you share an example?
Ian: The biggest impact will be in hormonal imbalances, immune challenges, and in identifying key triggers to these for earlier prevention. While the core of that is being able to save time and money, this also allows us to address what we haven’t been able to do before from a pharmaceutical standpoint. For example, we identified an addiction molecule for a drug company that gave biopsied tissue data to Liquid Biosciences. They then were able to identify a probable group of targets and an equation that represented a trigger point. We took that information and incorporated it to create a quantitative shift in the biomarkers and the underlying factors involved with addiction in general. We took one addictive model, which was a stimulant-based addiction, and tracked the underlying mechanistic actions of addiction. We found a unique way to address addiction from a mechanistic approach. From there, we were able to grab a component of known molecules and recombine them in a way to achieve a high predictive affinity and create a shift in biotargets to remodel the limbic system and erase the damage of addiction. A lot of times in addiction you are looking at stopping a craving. What we need to do is treat it by remodeling the damaged part of the brain; restructuring the brain with a more specified mechanism. This is a very different way of approaching addiction and we believe these molecules will be able to do it. It’s a combination of molecular signaling and immune-driven mechanisms that ultimately create the shift.
Jeff: Addiction is difficult to treat and has a lot of moving parts. It’s not a simple disease model. Our platform’s ability to think in a biological context gives us a leg up in being able to address these things in a unique, non-linear manner.
Q: What role can the GATC and Liquid Biosciences technologies play in personalized medicine?
Ian: The goal of our company is to turn this into a personalized approach. I think, in time, this will become the way we do medicine – analyzing these key biomarkers on the individual to find the perfect molecule for that specific individual. In our Perfect Molecule Program we’ve started looking at predictive modeling for the individual based off the genome and expressome to determine a one-to-one relationship. Right now, it’s a one to many, but we’re narrowing it down to groups of people. We are currently working towards using our technology to treat the individual, essentially making the perfect drug for the individual. Our ultimate goal is to have a complete one-to-one relationship between disease prediction and intervention or treatment per patient. This is a major step in that direction.
Q: In addition to identifying new molecules, what are other capabilities for your technology?
Jeff: Liquid Biosciences and GATC are stand-alone companies that also operate independently of our joint venture. Combined, what we are doing is really unique and revolutionary. Aside from GATC’s work in drug discovery and development, we have developed and are currently selling consumer-focused DNA test kits that provide personalized reports for specific disease states for health issues. Current, GATC produces a Viral Immunity Platform™ (VIP) that can tell an individual’s risk, response and after effects of viral infections, like COVID-19. We also offer a detailed Health & Wellness platform and a Depression/Anxiety platform and just announced our Diabetes platform. In the near future, we will also introduce a Cardiac Risk platform.
Ian: There are two uses for our technology: detect a disease earlier, or before it starts, and treat it or prevent it better. We are detecting faster, earlier, and treating better. We have a non- obvious way of approaching a disease. We can look at a disease with less bias, which allows us to find a less common route to treating a disease.
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