Kari Bennett: We’re at Biotech Showcase in San Francisco and I have the pleasure of speaking with Erica Smith, VP of Business Development at Tools4Patient.
Kari Bennett: Hello Erica.
Erica Smith: Good morning.
Kari Bennett: Could you tell us a bit about Tools4Patient?
Erica Smith: Absolutely.
Erica Smith: Tools4Patient was founded by a group of industry veterans that had been running clinical trials for decades, and they had the lofty ambition of trying to tackle some of the issues that make clinical trials run more slowly and less effectively than they should be. The overall goal of the company is to accelerate the launch of much needed drugs to patients.
Erica Smith: The company was founded in 2013 and we launched our first product, Placebell, at the middle of last year.
Kari Bennett: Tell me a bit more about how this idea emerged.
Erica Smith: Our group set out to develop predictive algorithms to optimize the conduct of clinical trials. The first issue that we decided to focus on was the placebo effect.
Erica Smith: The placebo effect has confounded drug development for decades. And while many people have put lots of great efforts into trying to understand and manage this, there’ve been very few gains in managing the impact of the placebo effect in clinical trials over the last 30 years.
Erica Smith: Our team, which includes clinical pharmacologists, data scientists, and statisticians, set out to develop a more sophisticated data-driven scientific approach to minimize the impact of the placebo effect on clinical trials. And the end result of all of those efforts was the product Placebell.
Kari Bennett: That’s an interesting concept. Can you tell us a bit more about Placebell?
Erica Smith: Absolutely.
Erica Smith: Placebell is a predictive algorithm that takes into consideration many of the factors that the scientific community have found to be associated with the placebo response over the last several decades. These factors include things like personality traits. Include patient characteristics, things like how long a patient has had a certain disease, how many medications the patient may be taking for that disease, as well as demographics. The age and gender of the patient, maybe the geography of the patient.
Erica Smith: Our algorithm takes all of these data, combines it into the algorithm, produces one single number, and that number describes the expected magnitude of that patient’s placebo response. This number can be incorporated into the statistical analysis at the end of the trial and reduce the impact of the placebo effect on the trial data.
Kari Bennett: That seems simple enough. Is there a downside?
Erica Smith: There really is no downside to using this technology. The beauty of this approach is that the only change that is made to the conduct of the clinical trial is the inclusion of our questionnaire to measure traits of personality that are associated with the placebo response, at the beginning of the study. It’s only given to patients once. And with this simple change, we can explain double digits of variants in the data, so when this is incorporated in the statistical analysis, we can actually improve the ability to discriminate between placebo treated patients and drug treated patients.
Erica Smith: Importantly, this decreases the risk of failing to pursue a drug that actually is efficacious, but does not increase the risk of pursuing a drug that’s not efficacious. So the risk of the trial is very minimal. Ultimately, we are just trying to help drug developers make decisions more effectively and more efficiently.
Kari Bennett: You mentioned that Placebell is disease specific, so which diseases are you working in?
Erica Smith: In order to be optimally applied, Placebell needs to be calibrated in each disease using a subset of placebo patients. We have initiated trials and completed some trials in areas like pain, osteoarthritis and Parkinson’s disease, but we recognize that this could be broadly applied in many indications, in many therapeutic areas.
Erica Smith: It can be used for things like depression, schizophrenia, epilepsy, inflammatory diseases, women’s health, dermatology, and a wide variety of therapeutic areas, and we’re actively working to expand this.
Kari Bennett: How do you intend to expand the indications?
Erica Smith: Calibrating Placebell in new indications is very straightforward. All we really need is access to patients in clinical trials, in that indication, and the ability to administer our questionnaire.
Erica Smith: We are doing this by entering into strategic partnerships and collaborations with companies working in these indications and are structuring these to have significant financial and strategic advantages. These advantages include early access to the technology, include the ability to have input into the factors that are being examined, and the endpoints that are being modeled with the technology, as well as significant financial advantages.
Erica Smith: Using this method, the resulting model can be robustly applied in any and all clinical trials that that sponsor runs in the future. And as well, they can gain significant insight and benefit even in the trial that is being used for calibration of the model.
Erica Smith: We’re really intent for this to be very advantageous for our collaborating sponsors throughout the entire process.
Kari Bennett: Well, with such minimal risk and so many benefits, I can see how this could be very desirable for potential partners.
Kari Bennett: If someone is interested in partnering with you, what’s the best way for them to find you?
Erica Smith: Yep. The best way for them to reach us is to find our website at www.tools4patient.com. That’s tools, the number four, patient.com. There’s a link on the website to contact us and we’d be happy to talk to anyone who wants to learn more.
Kari Bennett: Thank you very much.
Erica Smith: Thank you.