“Functional Drug Testing and AI ML for Treatment Decisions”

l you throw at them.

“Functional Drug Testing and AI/ML for Treatment Decisions” (Noah Berlow and Diana Azzam) [#18] Noah Berlow: In many ways I'm in agreement.

Some of the other work that I've done has been on showing the difference between sequential combinations or simultaneous combinations using the AI to find a combination and then testing that ex vivo on a couple of cell models derived from the same type of cancer, showing the combination can essentially stop tumor regrowth.

But at the same time, we're showing in a practical setting, when you give the drugs at the same time, the effect is that the cancer cells go away and don't come back. Which of course is the key end goal. We have heard concerns about toxicity from treating physicians regarding the kinds of drug combinations you are recommending. How did you overcome those concerns?

Diane Azzam: I have not really seen toxicity concerns because our patients haven’t had other options. We look at the drugs’ concentrations in the blood and use Cmax. (Cmax is the highest concentration of a drug in the blood after a dose is given.) In the case of the osteosarcoma patient, they administered the drugs in a rapid sequence - a few weeks.

In the case of the rhabdomyosarcoma patient, we had seen in functional testing that one of the drugs (vincristine) was stronger as a single agent, so they administered that first, then the other two in a rapid sequence. You're working off fresh tissue. Can you run one functional test, then come up with a new hypothesis, and run functional testing again without getting a new biopsy?

Is the tissue still viable for testing after 48 hours? Diana Azzam: We get a second shot. That's what happened in all the cases, because we get the fresh sample, we process it, we use what we need for drug testing, and we freeze them down. If we need to go back and test combinations, we're throwing those out fresh and testing within a few days.

It's very helpful because sometimes doctors look at the single agent data, and say, “I want you to go back and test these combinations.”, and that's what we've done. It's been very helpful for the doctor.

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“Functional Drug Testing and AI/ML for Treatment Decisions” (Noah Berlow and Diana Azzam) [#18] Meeting Notes Diana Azzam: I'm an assistant professor at Florida International University. My PhD focused on

are program, treatment, product or other course of action that might affect your health.

“Functional Drug Testing and AI/ML for Treatment Decisions” (Noah Berlow and Diana Azzam) [#18] Meeting Notes Diana Azzam: I'm an assistant professor at Florida International University. My PhD focused on ovarian cancer stem cells and then my postdoc focused on high throughput drug discovery.

My research interests at FIU include implementation of functional precision medicine in clinical trials to guide individualized treatments in advanced cancer patients, and a major focus of my lab is to understand the resistance of cancer stem cells and how they play a role in metastasis.

“Functional Drug Testing and AI/ML for Treatment Decisions” (Noah Berlow and Diana Azzam) [#18] Noah Berlow: I have a couple roles. Right now I'm most accurately serving as founder and CTO of First Ascent Biomedical. My background is Al engineering, computer science, and math. I did my PhD in electrical engineering.

I got involved in pediatric cancer research using artificial intelligence machine learning to start solving some real big challenges in that space. I did my postdoc in bioinformatics and molecular biology. I eventually started getting more involved in the sort of day-to-day laboratory side of things.

All of that was happening at the Children's Cancer Therapy and Development Institute, where I am an assistant member. My main focus is on First Ascent, which is bringing functional precision medicine and AI analysis for individualized treatment options into the clinic for patients in need.

Diana and I are collaborating to put together this entire pipeline to use functional precision medicine to guide individualized treatments. Together we are taking a biopsy sample from a patient's cancer, doing a rapid culture and drug sensitivity testing protocol, using Diana's technology that she's developed over the past decade. Functional drug testing is side one.

Side two is taking the same tissue and sending it for DNA and RNA sequencing analysis. Where I come in is taking both of those data sets and ingesting them into the AI/ML engine that I've also been researching and building over the past decade. From the drug testing data and the sequencing data, we can better understand the weaknesses underlying a patient's tumor.

Another way of saying that is we take all the drugs that are working, and all the drugs that aren't, to understand the mechanisms that are really driving drug sensitivity.

“Functional Drug Testing and AI/ML for Treatment Decisions” (Noah Berlow and Diana Azzam) [#18] we need to target this gene, and we can find the right drug already available in the clinic that best fits that need. Our goal is to deliver all of this data within two weeks, making this as

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“Functional Drug Testing and AI/ML for Treatment Decisions” (Noah Berlow and Diana Azzam) [#18] we need to target this gene, and we can find the right drug already available in the clinic that best fits that need. Our goal is to deliver all of this data within two weeks, making this as clinically usable as possible because that turnaround is absolutely critical to meeting the needs of patients.

We've already done a lot of this work in the clinic already. Diana Azzam: The goal is to be able to implement this in the clinic, and we have two feasibility studies, both in pediatric and adult cancer patients, in collaboration with Nicklaus Children's Hospital and Cleveland Clinic Florida , to test whether this is feasible. Can we recommend treatments in a clinically actionable timeframe?

And if these treatments are recommended, how do the patients respond? This is a seven-year-old girl with metastatic rhabdomyosarcoma, a particularly difficult-to-treat cancer. She has been through multiple treatments. None of them were effective. We received a piece of her tumor. We confirmed we had the right cells by looking at the different markers of rhabdomyosarcoma.

We tested our panel of drugs and multiple drug combinations for the patient. We were able to deliver this data to the doctor and the molecular tumor board in about one week. Brad Power: I helped Kasey Altman, a young woman with alveolar rhabdomyosarcoma, with her hackathon. I learned that rhabdomyosarcoma responds to chemo, but just about nothing else.

So if you find more chemo drugs, you're probably going to delay progression, but you're not really going to come up with a durable response. Is that correct?

“Functional Drug Testing and AI/ML for Treatment Decisions” (Noah Berlow and Diana Azzam) [#18] Diana Azzam: We found chemotherapy drugs that were effective, and we also found targeted drugs. For example, for this patient we found: ●dasatinib , (Dasatinib is in a class of medications called kinase inhibitors.

It works by blocking the action of an abnormal protein that signals cancer cells to multiply.), ●HDAC inhibitors (Histone DeACetylase inhibitors are in a class of anti-cancer agents that play important roles in epigenetic or non-epigenetic regulation, inducing death, apoptosis, and cell cycle arrest in cancer cells.

), ●lenalidomide (Lenalidomide is in a class of medications called immunomodulatory agents. It works by helping the bone marrow to produce normal blood cells and by killing abnormal cells in the bone marrow.), and ●an mTOR inhibitor. (mTOR inhibitors are a class of drugs that inhibit the mechanistic Target of Rapamycin.

) One of the challenges about recommending treatments is whether doctors have access to these drugs. With children we customize the library of drugs based on what's available in the pharmacy at Nicklaus Children's Hospital. In this case we wanted to treat the patient quickly.