sky gate introduces DioMEDe Sciences, the first Web 3.0 integrated Biopharmaceutical company. Built on a Hub-and-spoke model, fully committed to a new therapeutic(s) development era. The company is focused on combinatorial approaches employing biological molecules and cell-based therapies to cure life-threatening rare pediatric solid tumors. Top 5% at Y Combinator Startup School and Top 100 at CEE Startup Challenge by Vestbee.
In the interview, dr. Richard Fox, the CEO of DioMEDe Sciences, provides us with the information:
✅ How using AI can help to cure rare pediatric cancer
✅ What is the background of the drug discovery process
✅ How can AI and machine learning be used in the decision-making process
✅ If a blockchain-based approach can be used in drug discovery
Who’s dr. Richard Fox?
Richard Fox is the CEO of DioMEDe Sciences.
Starting at the Fred Hutchinson cancer research center in the University of Washington, he worked on HIV-related research. Then working at Merck as a director of medical affairs for HIV Hep-C, Richard was involved in their infectious disease franchise launching a drug called Zepatier. Another breakout in his career was working with the startup of some biotech companies in Seattle. Later on, Richard moved to Europe and built biological molecules to treat solid tumors, like colorectal cancers. Currently Chief Scientific Officer w Seldovia Therapeutics and the owner of DioMEDe Sciences Company.
*Please, find the transcription of the interview below:
Sophia: I am Sophia, and today I’m joined by Dr. Richard Fox, the CEO of DioMEDe Sciences. So please take a moment to introduce yourself, dr. Fox.
Richard: Thank you, Sophia. I’m Richard Fox. I’m the CEO of DioMEDe Sciences. I started my career many years ago at the Fred Hutchinson cancer research center in the University of Washington. Most of that time spent there was focused on HIV cure-related research, human immunodeficiency virus. From there, I went to Merck, where I was involved in their infectious disease franchise as a director of medical affairs for HIV Hep-C. During that time, we commercially launched a drug called Suboxone. From there, I was involved in the startup of some biotech companies in Seattle, where we were looking at piece or element motifs, and then found myself in Europe, where we were involved in building biological molecules for the treatment of solid tumors like colorectal cancers. And now, I’m involved with people across Europe, where we are building the DioMEDe Sciences platform. Thank you.
Sophia: Okay. And what is the main focus of your research?
Richard: Today we’re focused on using deep technology tools, artificial intelligence, and machine learning to help guide our decision-making process and being able to build better biological therapies and cell-based therapies. The current focus is to build a suite of bio better molecules. These are molecules that are already developed or in the process of being developed and human clinical trials. This is a proof of concept for us to really leverage the technology platform to be able to build biological and cell therapies for our flagship programs, which are focused on rare pediatric solid tumors. And we are taking this approach because we believe that there’s two ways of doing science. The first way is to use existing tools and technologies to answer new questions. And the other approach is to use new technologies that are being created to answer existing questions. And we’re taking the latter approach where we’re building a suite of technology platforms that allow us to really validate that technology on existing disease states that allow us to translate molecules to the clinic quicker, safer, and hopefully more efficaciously. So thank you.
Sophia: Can you provide us with a brief background about the drug discovery process?
Richard: Yeah, sure. I’ve been doing this for a long time, and what I can say is that the drug discovery process is very long, it’s arduous, it’s difficult, and very expensive, with a very low success rate. So on average, it costs about $2.5 billion to build a drug from concept to completion in anywhere from six to 10 years. And it has a 5% success rate or a 95% failure rate. Oftentimes, these approaches use things like immunization of animals, and you then find a candidate molecule, you engineer aspects of what we call humanization into these molecules, which then alters the physiochemical characteristics of the molecule. You go back to the drawing board, rinse, wash, and repeat, so to speak. And then, three to five years later, you have a lead candidate molecule that’s ready for testing in humans. And you then execute your phase one clinical trial, you put it into humans, and all of a sudden you’re like, “oh my God, this drug has a toxicity issue or this frog is not doing, what it is that we expected it to do from the initial preclinical research.” And you’re back to the drawing. And just because drugs make it out of phase one trials, for example, that demonstrate safety, oftentimes they will fail by the time they get to phase three trials because they’re no better than drugs that either already exist or when we compare them to, for example, placebo controls. They don’t have the intended effect as far as being able to ameliorate or cure a disease. So, hopefully, that’s helpful in looking at the broad perspective of the drug discovery process without going into a lot of detail and nuance. Thank you.
Sophia: Are there other people doing similar things in this space?
Richard: Yeah, of course. And that’s a great question. You know, there are several other companies that are using artificial intelligence and machine learning-based approaches to build not only biological molecules or cell therapy-based molecules, or cell therapy-based approaches. I mean, but also in the small molecule space. And one of the things that is useful to understand is there’s a potential for predictive nature using these types of tools and really being able to test these approaches vigorously in the wet lab is critical to advancing how these approaches work. One of the key differentiators for our approaches compared to our colleagues and the artificial intelligence for drug discovery space is we are biologists and drug builders first, and we use these tools of artificial intelligence, machine learning, deep learning, natural language process to help guide our decision-making. We don’t view the black-box approach of this is going to just simply create molecules for us. So that’s really a significant value add for us that we understand the biology, we understand the science, we understand the disease space, and we build our drugs from the perspective of the mechanism of action. In addition to that, we’re introducing new technologies that are associated with blockchain-based approaches, and I’m sure we can touch on that.
Sophia: And for other technologies they are using in addition to artificial intelligence, and how does this add value to the work that you’re doing?
Richard: Yeah. So, as I alluded to, we use blockchain-based approaches. This is really useful for us because it allows us to work collaboratively with other entities in a very transparent manner. So everything that we do from a conceptual framework through the completion of a project gets associated with that blockchain, so there’s no opportunity for adulteration of data. This is a problem. And we’ve seen this in the industry where people have manipulated data to get a particular outcome. We believe that because the patient’s needs come first and I’m not the smartest person on the planet, I’m simply testing hypotheses and generating data, and analyzing that data to make a set of decisions. We follow the data, and we want our process to be unfettered, in addition to collaboration and transparency also with the regulatory bodies, right? So our blockchain can be immediately opened up and deployed to FDA, for example, or the EMA that be in the US food and drug administration or European medical agencies. It allows us to move things through those regulatory pathways more efficiently because of the transparency that’s involved. One of the other things that it does is it provides an ability to secure intellectual property or IP because we’re establishing these prior art positions. And most importantly, from my perspective, it provides access to people who normally wouldn’t have access to these types of projects, and it provides transparency to the patients that will eventually be the recipients of these new therapeutics moving forward. So that’s really the additional platform technologies that we’re deploying within DioMEDe side.
Sophia: This is really interesting. So to summarize it for our viewers, DioMEDe Sciences is the first web 3.0 company that is using a proprietary suite of platform technologies that allow you to build therapies, to treat kids with rare cancers. And because you are integrating these approaches, the web 3.0, you believe that this will democratize the research, provide transparency to the industry, and, most importantly, make therapies available to patients more safely and quicker than what has been the current state of the art.
Richard: Yeah, that’s a very nice summation. I would also like to add that what our blockchain provides also is robust quality assurance, quality control measures. And this provides a lot of value. And one way, one may ask, well, why would that provide value? We can take an example. That recently happened in the industry. So there was a contract drug manufacturing organization. We call them CDMOs. And they were making vaccines for COVID-19 for two different companies. So there’s company A, they have one type of vaccine, and company B has a different type of vaccine. And at one point in time, Company A came in, and they assessed some of their product for quality. They wanted to make sure that what they wanted to be in the vial of vaccine that’s going to be introduced into a patient didn’t have any impurities or something happened. And what they identified was that company B’s vaccine and company A’s vaccine, which are very different technologies actually got mixed together. And we, as a community, don’t know what would happen if people were given this mixture vaccine, and hopefully, it wouldn’t end up in people having something as severe as death. Right. But we don’t know this experiment hasn’t been done. And as a result, a million doses of COVID 19 vaccines were thrown out. Now, this has a really significant impact on the public. Because a million people didn’t have those doses of vaccine available. They were not able to get vaccinated with that material. And it also comes with a significant cost, and that these companies are paying for a drug to be manufactured that had to go on the trash. Now with our technology, we associate our programs with the blockchain at the very beginning of the process from the time that myself and other scientists at DioMEDe Sciences are thinking, “Hey, this is an approach that we should take to cure kids with cancer.” We begin to associate these with our blockchain. And we can manage and track all of the steps of the process from concept to commercialization, to include supply chain management and these types of quality assurance programs, where we would have known at the time that this contamination event occurred, where company A and company B’s product were being mixed together and could have stopped production at that point in time in real-time. Immediately. And instead of having to throw out a million doses, we probably would have reached out for guidance from our friends over at the regulatory bodies. And they may have said, okay, well take 50,000 doses from this side of production, 50,000 doses from this side of production, and we’ll throw out a hundred thousand doses, but that would’ve meant that 900,000 people would have been able to get vaccinated. And the cost of that material that found its way to the garbage, those million doses would have been one 10th of that. So in addition to, you know, these really powerful, predictive tools that allow us to try and make drugs and the computer faster, quicker, cheaper, safer. We also have the ability to track, manage and watch for quality assurance and quality control. So I’m sorry that I missed out on that point earlier, but I think it’s very useful for our listeners. So with that, I really appreciate you taking the time to meet with me today. Thank you very much. I’m Richard Fox. I’m the CEO of DioMEDe Sciences.
Sophia: Thank you, Dr. Fox. I appreciate you taking the time, and we really hope you are successful with this project.
Richard: Thank you. Bye-bye.
Sophia: Thank you. Bye.
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