Patient-Centric Approach To Characterizing Placebo response

Transcript

Good morning and good afternoon everyone, depending on your location. My name is Erica Smith. I’m the VP of Business Development at Tools4Patient, and I’d like to welcome you to a webinar entitled A Patient-Centric Approach to Characterizing the Individual Placebo Response. Questions can be submitted using the chat function on the lower right side of your screen, and please note that any questions we don’t get to today will be addressed by email within the next 24 hours. So now, I’d like to introduce Doctor Dominique Demolle, CEO of Tools4Patient, who will be presenting today’s webinar.

Thank you very much, Erica. So, good morning. Good afternoon, everybody. The presentation of today is about the characterization of the placebo response. But, from the perspective of taking the maximum advantage of patient characterization, and patient-centric approach. We are going to cover briefly the placebo effect in clinical drug development, as we suppose that many of you are very familiar with this problem when conducting clinical trial. We will cover why we have used machine learning to predict placebo response. In fact, evidence we could have on the clinical trials, clinical drug development. And we will complete the session by summary, and take away messages before answering your questions. As we all know, unfortunately as we speak still today, about one-third of the failure happening in phase three may be still related to the placebo effect. In fact this phenomena happens in many different indication reviews. See that in the next few slides. And of course, the immediate associated, we could call “side effect”, is to delay the drug on the market and access to innovative drugs to the patients.

So, as mentioned, this phenomena could happen in a variety of disease, and indications. Of course the most well known are CNS. I mean depression for more than two decades has been extremely well known for a huge placebo effect phenomena. But, this is observed also in respiratory disease, in immunology, in inflammatory disease. And day after day, since we have created the company, we are contacted by sponsor regarding many of the different indications, like the mythology of calmology for example. So, it’s a huge and complex phenomena. Now, why is it complex? It’s multifactorial. So, by nature, when you give a placebo drug to a patient, you may elicit a clinical improvement, so called the placebo response. This might be related to different type of things. The first thing is the patient is entering a trial. Per se, this impacts the way the patient is going to answer to the placebo.

Of course, study bias may influence the error on your efficacy measurement, it’s also a source of noise. The natural evolution of the disease. The genetic, a few years ago, some genes have been associated to the placebo response. Regression to the mean, a huge effect on the placebo response. And finally what the academician called the true placebo effect, which is the one that is really, uniquely related to the patient. So, if you say placebo response, at the end it induce variance. And really this is this variance, it will be about during this presentation. This variance that could inflate your standard error, and of course jeopardize your true evaluation of the treatment effect. There might be, also, bias. For example, you may, in small side clinical studies have an imbalanced distribution of possible responders, non-responders in your study. That is also, unfortunately, a source of difficulties when comparing the different CD arms.

So, could the uniqueness of the patient help to characterize this placebo phenomena, and really take advantage of this information for your CD analysis going to the patient? I mean, clearly since the time this placebo effect has started to be characterized, in pain for example, where we know it’s of two phenomena. It’s related to the opioid and the dopamine pathways. So there is clearly a pharmacological path we associated with clinical improvements, where pain decreases after placebo intake. It’s extremely evident to understand that it’s something unique to the patient. I mean, each brain is different from each other. And also, because the the trigger to induce the activation of the biased pathway, maybe, for example, related to the psychology of the patient. And if you take that into account, so the anatomical property of a patient plus things like the personality of the patient, it becomes very evident that it’s a key part of the puzzle. So that it’s not only about that, it’s also about the medical history, about the disease intensity, about the demography. A lot of work has been done in the literature to try to identify the source of the type of patient-specific feature, or characteristics that are related to the placebo effect.

Unfortunately, it is a lot of information. And if you would like to take all those different components feature of a patient into account, this would be too many, and you would not know which ones are really the most pertinent one. In particular, if you think about the psychological traits of personality, I mean you would have to submit your patient to a lot of quite different type of questionnaire. So, clearly not optimal. This is why, in fact, we felt about at the very beginning when we created the company, about the ideal approach. Ad what, as characteristics, it should have to be really usable in a clinical trial setting. So, okay, we have a tremendous amount of data describing a patient. But at the same time we could, we should take advantage of that for that to be easy of use, and really adding a minimal burden on the patients or the staff knowing that the clinical studies are already sufficiently complex enough.

So metadata, an approach that has to be easy to implement, and at the end of the day we have to address the variance. So the idea we have, was could we from this set of different features come to as single covariate. And from now, I’m going to speak about the placebo covariate. Meaning, really, for a patient, is he or is she plus or minus on placebo responders. And this would be a composite covariate. Why? Because we will take into account all those different characteristics of a patient, and we use that to a single value which will be his or her placebo covariate. So, to achieve that goal, one of the only means was to use machine learning to predict the placebo response. And this is what we are going to see in the next few slides. So what we did when we started with the development of this technology about five years ago, we conducted our own clinical trials, and I’m going to show it on the data of those very early studies.

So, we conducted our own clinical trials, and we have collected patient data. Including, evidently, some psychological data in the homemade psychological questionnaire. And based on the information collected, we have developed models, algorithms, to produce this famous placebo covariates. So, really thinking about the previous slides we have seen, is taking the characteristics of a patient to come to a single value, which is the placebo covariate, for that to be used in future analysis. Why? Also, to come to a single covariate. You may be aware that there is a guidance in Europe from the EMA that specify that only a few covariates should be included in the primary analysis.