November 6, 2017


Andrew B.:  Hi, I’m Andy Burrows of PCT TV and I’m here today with Dominique Demolle who’s the co-founder and CEO of Tools4Patient. Welcome.

Dominique D.:  Thank you very much.

Andrew B.:   So you have a background within pharma, 20 years there as a sponsor and then became a service provider. What brought you to this new project and co-founding Tools4Patients?

Dominique D.: Okay. I don’t think really there was a difference from being in the sponsor company or provider company. I think that at the end of the day it’s all about direct development. When we moved to Tools4Patient and setting up Tools4Patient, our main objective was to help with clinical research and maybe address development of compound in a different way, more probably with mathematical type of technology. And really this is what we are aiming to, is to help predicting our patients may answer to a treatment, to a drug or the reverse, might not answer to the treatment or to the drug.

Dominique D.:               So that was the decision-making process and ultimately with putting on market innovative drug for sponsor company that we support and for the patients who are waiting for that.

Andrew B.:                    Great. And you’re proposing an industrial application for sponsors who face placebo effect challenges, a big challenge for a lot of people. With the placebo response characterization approach?

Dominique D.:               Yes indeed. When we were in the sponsor industry and also as a CEO providing advice on the development of new compounds, we have been facing this placebo challenge for decades, almost. And we say, “Okay, could we benefit from all the academic work that has been done by great people, scientific people, and use this information in a way that it could be implemented in a clinical trial?” So we start with this idea of being able to characterize for each patient, what would be his or her placebo response waiting scale, let’s say.

Dominique D.:               And this interpretive area where the placebo effect is very pronounced, will innovatively help your statistical analysis. And this is what we’re aiming to.

Andrew B.:                    It’s really interesting. And how do you see this technology being implemented across other clinical trial activities?

Dominique D.:               In fact, at a technology to a clinical trial, you have to come with something simple. Because as you know, clinical development, the clinical protocols, have become tremendously complex. A lot of observations made on the patient. So you cannot even complicate more the life of the investigator or of the patient.

Dominique D.:               So one of our critical success factor was to have a technology that would be easy of use for the clinical team. And so basically it’s about a psychological questionnaire that is going to be administered to a patient just before he receives the first dose of the protocol. And after that, all the other data that we need to process the mathematical algorithms and predict the placebo response, are anyway collected in any, if not all, clinical studies. So meaning the demographic data, your disease intensity, for example, or your medical history.

Dominique D.:               So for the study team it’s only about a questionnaire. But at the end of the day we put the data from that questionnaire with data that is collected anyway in the study protocol. So simple. Yeah, the word is simple.

Andrew B.:                    Yeah, it definitely feels like there’s technologies, there’s a lot of exciting opportunities, but there’s a lot challenges still as well.

Dominique D.:               There are a lot of challenges. I think that, of course, it appears simple to the user. Behind the scene, first of all, for the first application it’s four years of research with a lot of different type of expertise. Mathematicians are working with us. Of course, some disease specialists to have the most pertinent question. It’s about a very high-tech statistics as well. So the work, four years of pleuridisciplinary team, to achieve the first release towards a placebo for placebo response characterization.

Andrew B.:                    That’s great. Well, thank you very much for your time today.

Dominique D.:               Thank you very much to you.


VP, Business Development

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