How in silico drug development can improve patient outcomes [PODCAST]

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“In silico technologies also enable drug developers to perform, in theory, an unlimited number of tests using a wide range of variables that include age, sex, and the health status of each member of the virtual patient pool, compared to the more restrictive parameters of a traditional trial. By reducing the amount of trial and error involved in collecting data, drug developers can focus on therapies that prove to be more effective in treating a specific group of patients.”

Tanja Dowe is a health care executive.

She shares her story and discusses her KevinMD article, “The promise of in silico drug development to improve patient outcomes.”

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Hosted by Kevin Pho, MD, The Podcast by KevinMD shares the stories of the many who intersect with our health care system but are rarely heard from.

Transcript

Kevin Pho: Hi, and welcome to the show, where we share the stories of the many who intersect with our health care system but are rarely heard from. My name is Kevin Pho, founder and editor of KevinMD. Rate and review the show at KevinMD.com/rate. Subscribe at KevinMD.com/follow. Today on the show, we have Tanja Dowe. She is a health care executive, and her KevinMD article is titled “The promise of in silico drug development to improve patient outcomes.” Tanja, welcome to the show.

Tanja Dowe: Hi, Kevin. Thank you for having me.

Kevin Pho: We’ll get to the article in a little bit. First off, briefly share your story and journey to where you are today.

Tanja Dowe: So I grew up in Finland, which is one of the leaders in the digitalization of different industries, whether it be consumer industries or health care industries. My father was a cardiothoracic surgeon who was very passionate about his profession, and often over dinner tables, we would listen to his stories about his surgeries, his day at the hospital, and medicine. I grew interested in health care and how I could contribute to help patients.

I went to a university of technology to study microbiology and biochemical engineering, and right after graduating, I started my first company doing market research in the biotech industry. After that, I had a career in transaction and strategy consulting and life sciences, again trying to bring innovative new diagnostics or medical devices or treatments to patients. Six years ago, I joined a company in Switzerland developing oncology drugs, and I’m leading their fund investing in digital health startup companies.

Kevin Pho: So when it comes to that intersection between technology and health care, one of those technologies is in silico drug development. You talk about that and how that could improve patient outcomes. Now, before talking about your piece, tell us what in silico drug development is.

Tanja Dowe: Right. So as you can imagine, before an airplane goes up in the air, the engineers will always do a simulation on a computer screen. They will build technical models and simulate how the airplane will perform in the air. Now, in health and in pharmaceutical development in the past, it’s a very biological process where we’re developing drugs. We’re doing a lot of lab tests, we test in animals, and then we go to human trials, but we haven’t traditionally really simulated how drugs work on a computer screen.

And so in silico drug development, the way I see it, is really about building those disease models, building digital humans, and being able to simulate how drugs or treatments work in patients before we go to patients, so that we can predict the efficacy or toxicities and make sure that we only bring the best drugs that will most likely work into the human trials.

Kevin Pho: So talk more about that in your KevinMD article. Tell us what the benefits of those are.

Tanja Dowe: Yeah. So as I said, earlier in the drug development, you can really see whether the treatment shows a promise of efficacy and whether there seem to be toxicities based on complex molecular interactions. Another thing is that if we could reduce the amount of animal testing, it’s wonderful just from the moral and ethical perspective of saving animal lives. But at the same time, 90 percent of drugs, or more than 90 percent of drugs, that are tested in animals fail to pass human trials. If we build these models, we base them on human data, so human data-based models will predict the performance of the drug much better. So this is one thing.

There’s also, as you know, in clinical trials, you have the treatment arm and you have a control arm. It adds to the length of the drug development process and the cost when you have to have two groups, one group getting a potentially very promising drug and one staying on the standard of care or another treatment. Now, in these types of synthetic or in silico clinical trials, you may be able to reduce the number of patients required for the control arms. So again, this is something that will help bring better drugs to patients faster and maybe have a positive contribution to the cost problem of health care.

Kevin Pho: So when it comes to building those models where you can do some of this early-stage drug testing, tell us what goes into creating those computer models.

Tanja Dowe: So a lot of biological science. You have to understand interactions. You have to do a lot of research. You have to bring all the science from articles, from common knowledge, potentially from your own proprietary databases. You need to bring data; you bring knowledge onto a platform. You usually use something called mechanistic modeling. It’s a very mathematical concept, mechanistic modeling of how those interactions work. Today we use a lot of AI to build those models.

So you build that model, and it’s basically a picture of how the disease, how different parts of the disease or the interactions between molecules, lead to a certain trigger of a symptom. And if you bring a drug, you also model that drug compound, and you see on the screen how it interacts with those molecular interactions. Based on that, you can simulate humans and diseases in a very, very computational way.

Kevin Pho: So how commonly are we using it?

Tanja Dowe: We are, again, in the beginning of it, because I invest in innovative startups trying to solve big problems. Usually the field that I work in, and the kind of stories that I write, the articles that I write, is about very early innovation. But it is increasing, because today the pharmaceutical industry is also in trouble, because it’s not sustainable to bring high-cost drugs to market. We need to be able to squeeze the timeline and squeeze the cost.

I would say that the majority of the pharmaceutical companies are at the moment experimenting with these technologies, maybe side by side with the old methods, but we have already seen signs that these models work and that they can accelerate the development timelines. As well, the FDA, or in Europe the EMA, the regulators have started to acknowledge these methods and are developing ways so that they can also evaluate drugs that have been developed using some of these computational methods. So it is up and coming.

Kevin Pho: Now, are there any particular types of drugs that would benefit from this type of technology when it comes to doing trials?

Tanja Dowe: I would say that orphan diseases are, of course, one, because usually for orphan diseases, it’s difficult to find enough patient populations to test very different parameters in the patient. So being able to test different characteristics of patients and try to ensure the safety, this is one clear advantage.

Another one is cancer. Today we are going deeper and deeper into the molecular composition of cancer. We’re doing genetic testing to see what is the reason behind certain cancers, what’s the mutation. So we are going to more mutation-specific treatments. Again, when we go from general cancer, or a certain type of cancer, into the mutation-specific cancers, the patient population is smaller. And we need to make sure: Some of these drugs that we’re developing might work really well for certain types of cancers, or certain mutations, and not the others, and we might not be able to bring them to the market, to the patients, if we cannot test them in the right patient populations.

Kevin Pho: So as this technology matures, what do you see as a realistic pathway going forward?

Tanja Dowe: I would say that we should be able to reduce the timelines two to three or so, and bring down the cost of the drug development and have the drugs faster in the patients’ hands. It will take, I would say, five, and it always takes longer than you expect, so five years to get this more accepted and adopted. Maybe in 10 years, we start seeing the benefit of drugs that are entering the market, to patients, that are well targeted to the right patient population.

Kevin Pho: We’re talking to Tanja Dowe. She is a health care executive. Her KevinMD article is titled “The promise of in silico drug development to improve patient outcomes.” Tanja, you’re involved with a whole host of health technology startups. What other exciting health care technologies do we have to look forward to in the coming years?

Tanja Dowe: Right. So as I mentioned, I really like to be passionate about the big problems of health care and try to bring my contribution to solve them, whether it is cost, or whether it is the workload on clinicians, on doctors and nurses, having less hands available, and having an aging population with more and more severe diseases as we live longer. So whether we can bring AI more to support diagnosing, maybe earlier diagnosis of certain diseases, predicting the patient path, where the patient is going, how quickly the disease is going to progress, maybe we can help prevent some of these diseases also.

Another aspect that I’m really excited about is bringing tools to patients for them to take care of themselves, whether it is getting access to data, giving access to health care from remote places, or just having tools to help understand your symptoms and manage your symptoms. And remember, when you go to the doctor’s office and you hear about a diagnosis and he tells you what you should do, a big part of that conversation goes by, because you’re maybe in shock or you’re worried. You go home, and you have maybe some papers. But if you have an app that helps you with questions, if you have certain symptoms, it gives you advice. So this type of help for patients, I believe, is important and can really help with reducing the workload on health care professionals, giving the keys of their own health to the patients.

Kevin Pho: And my final question: What are some of your take-home messages that you want to leave with the KevinMD audience?

Tanja Dowe: Right. So I think that AI and computational methods and digital health sound very technical, but I really think that these engineering sciences are coming closer together with the biological sciences, and they can add value. But we need to work together. So we need to stay open to each other, listen to the engineering side, listen to the medical side, and remember that we have a common goal, which is to improve patient outcomes.

Kevin Pho: Tanja, thank you so much for sharing your time and insight. Thanks again for being on the show.

Tanja Dowe: Thank you, Kevin.

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