Dr. Antonella Santuccione, Altoida - Studio Interview | LSI Europe ‘22


Dr. Antonella Santuccione

Dr. Antonella Santuccione

CMO, Altoida
Read Biography
Altoida is using technology to predict the onset of neurological diseases, such as Alzheimer's disease, before symptoms occur. The company's platform evaluates an individual's risk by interrogating 800 digital biomarkers collected through a digital assessment and augmented reality (AR).


Nick Talamantes  0:00  

Antonella, thank you so much for joining me for this interview here at LSI. Europe.


Antonella Santuccione  0:03  

Delighted Thank you


Nick Talamantes  0:04  

Tell me a little bit about Altoida


Unknown Speaker  0:06  

Altoida is a phenomenal journey trying to understand how we can use machine learning to basically diagnose recognize disease pattern referring to the brain, and how to manage patients at best, there is a profound science behind it. And I can say that before my role as a chief medical officer I was already serving advisor until around how to optimize the outcome of the science that the data scientists neuroscientists were doing. And what we do actually, we train a machine learning model to recognize certain type of disease pattern that are specific to Alzheimer disease, and mild cognitive impairment. So once the algorithm is trained to recognize how the disease phenotype, the digital phenotype of the disease look like it will tell you whether a patient coming in today will belong to that group or not, in theory, it can diagnose how the brain is functioning, it is analyzing basically the function of the brain in a 360 degree fashion, because within a 10 minutes test that you can do using your mobile device, you can assess how the brain is functioning, whether good or bad, and come up with a piece of information that will allow the doctor then to manage disease farther.


Nick Talamantes  1:25  

So is this using optical information or is it sort of a you're using


Unknown Speaker  1:29  

Optical information, we are using the sensors embedded in those devices to analyze for instance, micro tremors, how the patient or other person moves in space and time, we analyze the high movements, the speech and all those features are, which are many as you can imagine, it's not going to be only one we're coming up with several features that are then analyzed by the machine learning will come up in a result that consists of 13 cognitive domains that refer to certain types of skills, like for example, gait, or a special memory, or you know, the ability of our brain to learn the learning part, etc.


Nick Talamantes  2:13  

Identifying neuro degenerative diseases such as Alzheimer's early, obviously changes treatment outcomes, so how early will Altoida to help patients receive the care they need?


Unknown Speaker  2:27  

Well, I have to say that I am in the Alzheimer disease ecosystem, since I have started my journey within science and medicine. So what I can say is that what we learned that the earlier, the better. Time is brain, brain is time. The earlier you diagnose, the more you can do about your medical condition. This is in general true for anything for neurodegenerative diseases, it's particularly important because you nowadays, why we have some treatments that have proven to have an effect on cognition, such as the newly approved drug by FDA and the Kinomap. We also know that there are preventive measures that the patient can put in place to diminish the risk of having the disease progressing fast, or even delaying fully the onset of the disease, because maybe we can revert the narrative fully, but we can delay that onset. And this is why solution like Altoida can really early in time recognize those subtile changes when you are not functioning as you should, and which me as a doctor can't because I don't have the skills that a digital biomarkers might have. It is certainly the way to go forward. And there is another piece that we need to clarify. And this is how to implement those digital biomarkers not only in patient management and diagnostic, but also in clinical development. I strongly believe, based on my professional expertise, that several trials in brain and mental diseases have been failing. Because the readout was not accurate enough. It was not sensitive and specific enough, it was prone to biases, intra rater variability interrater reliability meaning the doctor assessing for cognition, for example, in Bucharest might have a different way of doing it in from from the doctor doing it in New York. So you have to do a lot of internal training and you know, standardized procedure when you outsource to a digital piece of biomarker that can have that precise readout that will be accurate enough to remove the human bias that we all carry then I think we will see a different outcome in in clinical trial developments. And this is not only true for Alzheimer, this is true for the whole as a sad spectrum of neuroscience indication where I think this is one of the major concern. I think we have successful readout in A disease area like oncology, like cardiovascular, you can measure the blood pressure very easily, right? And you can say for drunk brings it up and down what you do with the brain. I mean, I used to be a doctor in the ward, and I had to ask my patients about is sweda, suicidal ideation, why a person should respond to me as a young medical doctor back then, with a truthful piece of information. With me having just a few minutes to analyze all this, you know, if we outsource analysis of behavior of functionality of ideation to digital tools, or wearable devices, or even a piece of software as Altoida, then I think we have the right solution for the patients and for the doctor, because my frustration then was pretty high. It took me six months in one of the highest specialized medical center at least six months to diagnose Alzheimer's disease, and it was still a probabilistic type of diagnosis because the real one came post mortem, right. So you can imagine how tedious the journey is in in neurological disorders, we have up to two years to three years, depending on the medical indication, time needed to recognize the type of disease we're looking at. And this is true for multiple sclerosis for Alzheimer for Parkinson, for ILS, which means if we can have digital biomarkers implemented in diagnosing timing and managing the patient, this is where I want to see the revolution happening. 


Nick Talamantes  6:26  

So Altoida is not only early diagnostic, but it's almost like a co diagnostic that can be used to personalize management and therapy of patients, correct?


Antonella Santuccione  6:35  

Absolutely. Because we can also then measure and that's what we're doing with our partners with our external stakeholders that are knocking at the door and they're asking to use the tool, we're also measuring treatment response, we're also measuring the effect of a drug over time on cognition, majority executive function, etc. And this is an objective measure, an objective measure that can be easily done, because you can repeat the test. Actually, as many times as you want, there is a minimum learning effect, if not neglectful at all. And it allows you to have repetitive assessment, even daily, if you wish. Whereas the current scales, they are tedious to be performed, right? I mean, they take time you need a neuropsychological neuropsychologist, performing the test interpreting the results, here you have an immediate result on the spot, or whether your brain is functioning on whether the treatment that you're taking it's successful and how you're responding. I think that's phenomenal.


Nick Talamantes  7:34  

Absolutely. What's the first indication look like for Altoida? Are you working on developing digital biomarkers for multiple indications right now.


Antonella Santuccione  7:42  

So where we already are available as a 510 K exemption tool, it is being an adjunctive diagnostic aid to assess cognition, so we can already today measure the cognition and the functioning of the brain of an individual. This is about general, right. It's for cognition as an overall what we're now trying to do, we're trying to become a diagnostic tool for diseases like mild cognitive impairment and mild cognitive impairment due to Alzheimer disease. So the beauty of Altoida is that can tell you whether you have an impairment in cognition, like mild cognitive impairment, that can be due to several types of reasons, right, because you can have MCI due to depression to lack of sleep to problems with your titled functioning, you know, different types of dementia. But we are trying also to become a diagnostic tool to assess whether your mild cognitive impairment it's specifically due to Alzheimer, and how are we doing that? We are doing that by providing you with a result that will also assess whether you have amyloid or not in the brain. So the algorithm, the machine learning piece can recognize whether you have toxic protein that it is accumulating in the brain by interpreting the result of a digital phenotype. And again, I think this is phenomenal. And I believe to your question, that's why we are now targeting specifically Alzheimer this can be implemented in many other disease area. So we are exploring in our pipeline, also opportunity for Parkinson, for ILS for Antintin disease. I want to assess also ADHD in kids, we're going to get there.


Nick Talamantes  9:43  

That's exciting. You are a practicing physician than an advisor what brought you into the CMO role at Altoida?


Antonella Santuccione  9:51  

Well, exactly it is the fact that there was a co creation piece done even before having this specific role. I was So advising the company on how to improve the science. And, of course, a scientist must have the things you do pro bono. So I got passionate on what we were learning while this type of exercise was happening. And another things I've asked to the data scientists at Altoida I was, can your digital device can do machine learning? Tell me because I'm interested in sex and gender precision medicine, whether the tool can distinguish whether the person performing the test as a blue brain or a pink brain, meaning it's a man or a woman, of course without telling the machine learning beforehand that the gender of the individual and and that's what I thought I also did, you know, so what brought me there was certainly the science, the atmosphere, the people at Altoida, because I think at the end of the day, it is the team that makes an organization successful and the enthusiasm, the motivation of the people working to be successful in this journey. It is impressive. So if you ask me, how was your working day? For me, it doesn't feel like work. You know,


Nick Talamantes  11:05  

You have a great team at Altoida. And I'm extremely excited to continue to follow the developments that you guys are making in neurotech. Thank you so much for stopping by.


Antonella Santuccione  11:15  

Thank you


LSI USA '24 is filling fast. Secure your spot today to join Medtech and Healthtech leaders in Dana Point.


Share this video

Companies We Work With