Trials with Maya Z

  Hello and welcome to the next episode of Trials with Maya Z, brought to you by TrialHub, a data intelligence platform that helps clinical research organizations and sponsors plan clinical trials. This podcast is about how we can make clinical trials more successful and patient-friendly. Friendly, I am your host Maya C and in every episode, I will be interviewing a leading expert from various industries in order to discuss some of the major challenges and brainstorm how we can solve them.

Let's get started.

Hello everyone and welcome to another episode of Trials with Maya Z. I'm Maya, I already have my coffee and my guest already has his coffee and we're ready to enjoy a nice coffee meeting, a virtual one, in order to record our conversation and share with you our perspectives on the topic on clinical trials and how we can make them better. I'm very delighted to have Moe Alsumidaie here today with me, who is the chief editor of Clinical Trial Vanguard, and has a lot of great experiences. Moe, please introduce yourself.

Hello, everyone. A pleasure to meet you. My name is Moe Alsumidaie. I have worked in the clinical trials industry for around 20 years now, starting my career at Stanford Medical Center, working at Genentech and Roche and Abbott Vascular, as well as Mount Sinai, before founding Annex Clinical Corporation back in 2011, I founded it as Chief Data Scientist.

Doing computer data science research, still doing that, and I partnered with my colleague, Krishma Shah, back in 2015 to start up Clinibiz, which is an integrated research organization, and, we run study sites across the globe. And have done international government consulting, most recently assisting the Thai government with building and establishing research infrastructure and cohesive research infrastructure in the country.

Making them more competitive in the global landscape. And also consulting with e-clinical technology companies. Krishma Shah, I'm very proud of her. She made the Forbes 30 under 30, with a company featured in India. And, recently I started up The Clinical Trial Vanguard after being with applied clinical trials as a contributing columnist for 13 years.

And I started The Clinical Trial Vanguard as chief editor, writing about innovative topics in clinical trials and really spreading awareness to push innovation forward in clinical research.

You have such a rich background. And you mentioned Stanford, but then again, was there anything in particular that brought you to clinical research more? I wonder, was there anything that really defined your career in clinical research?

Absolutely. Well, I can tell you, I must say that I am privileged that my family is a medical family and my father is a researcher and a physician. And so a lot of that started from there, you know, I've always been around patients when I was younger, my father has always explained to me what it's like to treat patients and I've always had a passion for helping people, especially patients in need.

And so that's where my passion really started. My mother's side of the family is entrepreneurs mostly. And. So being an entrepreneur in clinical trials is, I gotta say, one of the toughest things anyone can ever do because it's a very tough industry to make it. However, a lot of it has to do with trust and relationships that you have in the industry.

And so having worked in a variety of different areas in the industry, that's really where my inspiration comes from.

So probably three or four episodes in a row, I'm hearing the word trust, maybe even more, but because I'm still remembering the last few episodes that I recorded - trust was the number one word that everyone was saying was essential in this industry. I wonder, is it because we don't have it?

Or is it because trust is essential for everything we do? But anyway, I don't want to go into that topic because I know that there is one very particular topic that you're very passionate about, and you're speaking loud about, and you're innovating in space, and this is decentralized clinical trials.

Decentralized clinical trials boomed, especially after COVID. They existed before that, but let's say we got the official label after COVID. But now, There are more and more people saying, Hey, this momentum died. Is this really the case? What's your perception of that? And how do you see the market right now?

So people are saying that momentum is dying, right? In the conferences, I'm hearing it all over. In fact, we're currently running a survey right now to evaluate that.

Hmm.

However, we don't have the results yet. Um,

Such a shame.

But

there is talk there's talk, right? And where there's smoke, there's fire. But I think what is really essential for us is to really focus on where the future is, right?

We can all talk about today. But I think that it's important to focus on what regulators and biopharma companies are saying about decentralized clinical trials. So, recently at SCOPE, Rob Goodwin from Pfizer was having quite an extensive discussion around innovation and clinical trials and what's really interesting is from 2019 until like 2022, they were able to reduce the cycle time performance by removing three years in three years. So for instance, we're looking at 8.5 years to first in human to approval planning cycles reduced from 8.5 years down to 6.3 years in 2022 from 2019 to 2022. And so, a lot of what Goodwin was talking about, and Goodwin, by the way, is senior VP and head of clinical development operations at Pfizer. And some of the things he was saying is that Pfizer is making public commitments. And for him saying, but we're making public commitments, we have to commit to them.

And so what Pfizer is really committing to is, you know, when we look at some of the things they're saying is they really want to push decentralized trials forward. For example, telehealth, home health, community partners, flexible sample collection, Apps, wearables, and sensors, as well as partnering with retail pharmacies and retail locations to enhance clinical trials. Those are all signs that DCTs are going to be moving forward. Another thing that's really interesting is that the FDA is really pushing this as well. When you take a look at what the FDA is saying, especially, and even EMA. You're seeing a lot of movement and a lot of discussion from regulators around decentralized clinical trials, some of the challenges that they could potentially face, even when you take a look at their guidance documents that they've recently created.

A lot of those things are being discussed right now and are really being pushed forward. And the FDA's push really comes from number one, the quality of your data. And number two, patient inclusion, meaning reaching out to those various communities. And enhancing diversity in your clinical trials, especially in the United States, to FDA, you know, diversity doesn't mean going to different countries.

Yeah.

It needs to focus on the U S population, and the diverse population in the U S inclusion there. And so, you know, there was like, over at DTR, the Decentralized Clinical Trial Alliance, headed up by Craig Lipset hosted their conference about DCTs and, if the FDA was there having roundtables talking about the potential for FDA to redefine clinical trials, uh, how to make them more patient-centric and efficient at the adaptable trial frameworks and how this approach really aligns with the broader transformation of health care delivery and treatment modalities.

Yeah, I think actually it was Craig Lipset if I'm not mistaken, but I think he was the one who said how come decentralized trials are like going down, where, not going down, like declining, where actually, we just, Started seeing like decentralized as the normal clinical trial.

So now almost every clinical trial has some sort of a decentralized component, whether we put the label decentralized or just a clinical trial, I guess that's not declining. It's actually just massive adoption of the market. So I personally think that right now what we've experienced is that suddenly we moved away from this early phase of adoption where everyone is out there jumping.

Oh, I want to try new things and I want to have all the decentralized clinical trial components at the same time for every single clinical trial. Now it's more about, okay, This is where I can benefit because this is my trial design. This is my schedule. This is actually how I can benefit to make the patient experience better to improve the quality of the data.

And so maybe there are fewer components for a trial that are more related to the centralized way of monitoring and supporting patients. Yes, that doesn't mean that that's not a form of a decentralized clinical trial. So, yeah, I agree with you.

I mean, there are 3 components to it, right? You've got fully decentralized trials means the entire trial is using decentralized methodologies. Then you've got hybrid trials, where you're fundamentally changing the trial's design by combining in-person visits with remote digital technologies designed from the start to be mixed.

And then you have DCT elements in conventional studies, where you've got primarily traditional trials augmented by, or enhanced by specific decentralized technologies or methods. I

Exactly. Exactly. And, that's one thing that we are always getting wrong. And I actually want to hear your opinion on that. I kind of feel throughout my career in clinical research, going to these conferences, going like through these different phases, of course, blockchain and the centralized now AI.

We always focus on the technology, the solution is sometimes we forget what's the problem we tried to solve. So Moe, do you think that's really the case and how can we overcome this jump into what's trendy and really focus back on the challenges that we are trying to solve?

Hmm.

I think it really depends on what regulators are saying. What's trendy or not, right? What's trendy is, what people are talking about, what's the latest trend in terms of buzzwords that you hear around the industry, right? What's real is when you got regulator backing behind it. But what's particularly interesting about decentralized trials Is that regulators are making it concerted effort to really push this forward. I mean, back when RBQM or RBM back in the day, we used to call it risk-based monitoring, regulators were behind that, right? But they weren't really out there promoting it like they are with what I'm seeing with decentralized trials. So, you know, with regulators with RBM or RBQM now, risk-based quality management, you are seeing it in guidance documents and in the ICH guidance.

But like you have ICHE6R2, R3, which define frameworks for risk-based quality management. However, you didn't see this concerted effort like you're seeing in decentralized clinical trials, where regulators are going to these conferences, speaking about this, speaking about really pushing the industry to adopt these methodologies.

This is something in my career I have never seen before with regulators doing this. And so, I think that's the difference here between what's real and what's not. What are regulators pushing? What are they not pushing? I think that's how you can define between what's real and what isn't.

But in this situation, with let's say AI, are you expecting that the regulators will Go out and speak about, hey, everyone, implement AI to speed up your processes? Is that what you're expecting to happen? Or maybe there's something that I don't see here.

This is an interesting question because the birthing of decentralized trials came out of the pandemic when it was a necessity, right? Pushed by regulators by telling the industry, okay, we have an emergency here. You need to figure out how to work with your studies. AI is different because AI is coming from the private industry and is penetrating biopharma companies and is not coming from regulators.

Right? So, for example, you're looking at in silico, right? In-silico-based simulations, where you can essentially simulate clinical trials through artificial intelligence. There's a company called InSilico Trials, and we can now visualize, you know, when we take a look at certain aspects, for instance, modeling your trial, taking a look at different patient populations, this company was able to save a pharma company, like, 30 million euros.

And reducing the drug development timelines by, like, 3 years, right? But those are all things where the private industry is now pushing pharma to utilize these technologies. However, you know, when we take a look at operationalizing these technologies, You can still use FDA guidance, right?

You have to validate the technologies. How do you validate it? Those are all open, right? nonetheless, think that the FDA and the regulators will be forced to generate guidance documents around this. So what's interesting is there's been a publication called 'Towards Good Simulation Practice'.

So, so it really, it was just released actually, like, in February or March of this year. And it really talks about the best practice for the use of computational modeling and simulation in the regulatory process for biomedical products. And so we're seeing these, you know, these publications come out, which are from what I understand, supported by FDA, supported by regulators, to push this forward.

So I think there's a difference. DCTs are being pushed actively by regulators. Whereas AI is, you know, regulators going to have to catch up to it. So I think there's a nuanced difference here.

Yeah, a different dynamic. I think you were spot on when you said it's coming from the private industry. It actually is coming from mainly the investors pushing the industry, the pharma companies, the biotech companies do things more efficiently, do it faster, capital in investment, like in RnD and so on and so forth, not less capital, but more efficiently.

Utilizing this capital. So, I guess that's the private industry that you're speaking about. That means that they see this pressing need for improvement and AI may be the technology that can actually speed up things.

 But I wonder if we go back to what are the challenges?

Because as we discussed, technology can help you solve some challenges. But which are these pressing challenges that the life sciences industry really experiences today? And do you think that AI has any role in actually solving that? From your perspective, what are these top pressing challenges? And what's the role of AI there?

I think one of the biggest challenges is number one identifying new markets. So for example, you can identify subgroups where you can target specific patient populations, where if you're using real-world evidence, real-world data, you can identify them and then generate therapies that can address those specific patient populations because it's a very crowded space right now.

You know, there's a medication for pretty much every indication we know.

Becasue there are also many gaps within these indications. Right? And I think I can really help with, spotting some of those indications, for instance, I'm not a physician, but I'm just giving you a hypothetical example.

Let's say that there's diabetes, right? And a subgroup of the population in Africa does not respond the same way that even ethnic minorities in the U. S. respond to this diabetes drug. And then you start seeing that in the medical records that they're prescribed this particular medication, but they're not responding to it.

And so that unveils an opportunity to address an unmet medical need, right? That's 1 example. Another example is, of course, you know, the handmaid's tale old maid tale I don't know what the colloquial saying is, but it's the ability to increase the chances of success of your medical product and identifying whether your studies going to fail or not beforehand before even investing millions of dollars into your clinical trials.

And I think that's where AI can really help. And we're seeing that already being done. And so what's really cool and interesting about AI now is that it opens a market that was previously inaccessible to many executives, many researchers, many innovators, where they can now access and predict clinical trials.

And evaluates whether their medical products are going to work on patient populations before going into clinical trials. And so we're really reducing the life cycle of drug development and also opening opportunities. So I anticipate that the field will be burgeoning and booming with novel technologies.

We're gonna say novel technologies, but rather novel biopharmaceutical companies that are in the startup mode that can now have much higher chances of success. For Big Pharma, we're looking at scaled cost savings and efficiency, right? I mean, typically what happens with those smaller companies, is they end up getting acquired when they hit phase 3 by Big Pharma because they have the budget to run those big studies.

Right. But maybe it'll get to a point where those smaller pharma companies won't need the funding of big pharma to run those clinical trials because they have a good idea. And when we're talking about the future of clinical trials, we're looking at, for instance, digital health technologies or DHTs.

Um, AbbVie recently reported in one of their, they were speaking at SCOPE, about some case studies where they were using DHTs that significantly reduced the number of patients needed to achieve their studies outcome using DHTs, and there are opportunities, in so many disease indications and therapeutic indications, where these technologies can be used.

Psychiatry is a big one. And CNS is a huge one because some of the traditional methods, like the battery tests that we use, right, to evaluate and analyze patients, imagine now being able to analyze patients' depression moods by evaluating

the way they look into an application, evaluating eye movement, evaluating facial expressions and engagement, evaluating intonations in your vocal cords, which can all be done with AI nowadays. That can be a lot more precise in evaluating the subtle differences in improvement compared to those traditional battery tests, which tend to be pretty highly subjective despite the standardization that comes with it.

AI can really fine-tune that. Maybe previously previous drugs that were not approved can now be approved because the traditional tests, and battery tests were not able to detect those subtle changes in differences in the patients. So I do see that there are definitely big opportunities here to be approached.

So, yeah.

But, Moe, I have an interesting question. You mentioned these digital biomarkers and I also love the whole idea and I'm very convinced that this is a completely new approach to diagnosing and monitoring patients and their disease progression. But if you have to think of the adoption of these digital biomarkers.

Do you see that happening first in health care and then in clinical research? Or do you think clinical research should lead the way to the adoption of digital biomarkers? How do you see this happening?

So, it's interesting because I recently took a course by the DiMe society, which is the, I need to take a look at what they actually stand for.

Yeah, I don't know the abbreviation.

Digital medicine society, the digital medicine society. Yeah, so they actually recently released a course called 'Building Fit for Purpose'. Sensor-based digital health technology is a crash course, and it's actually really interesting.

If you go on to our website, The Clinical Trial Vanguard and you do a search for DiMe, you'll be able to find a summary that I wrote on it. And it's actually targeted at healthcare professionals. However, these approaches can also be applied to clinical trials. And so they created a way in which you can validate your digital health technology or DHT in clinical research, where I interpreted it as clinical research.

We have to verify your device. You have to do a usability validation. You have to do an analytical validation, then a clinical validation. And then they have this entire, V3 plus modularity approach where you can evaluate the DHT. And so what 1 thing going back to what AbbVie was saying is that you know, the way they've been able to successfully integrate DHTs into their clinical trials is to engage regulators early.

So a lot of, you can actually conduct, you can create your own DHT in a clinical trial. As long as you go through the validation process and prove that it works, then you can approach regulators. And say, Hey, listen, you know, we don't need to use this battery test. We've done a validation on this device.

Here are the specifications. Here's the validation of this device and see what the FDA says, right? Because this validation device is not only validated scientifically but also accepted by patients. If you follow this framework. And so, the FDA is all about the patient and making things a lot easier for the patient.

So if you can demonstrate that and you offer the plus that it's more patient-centric, more patient friendly. They're much more likely to say, yes, you can use this validation in your clinical trial or invalidated device to measure your outcome in this clinical trial.

Yeah, that sounds like really cool. I really hope that we can adopt quicker these digital biomarkers. I guess that there will be a lot of like challenges regarding like what device actually you have and like how exactly you're using that. But then again, that comes with really interesting promise.

And thanks for sharing that. Where do you see the future of clinical trials? You kept repeating the future of clinical research, the future of clinical trials. Do you have this ideal world in your head where clinical research should go to?

Well, I think what's going to happen is we're first going to see digital twins.

Nice. Yeah.

So you're essentially running simulations in clinical trials in humans, but you're not using humans. You're using digital twins. So which is essentially patient-specific data to create highly tailored treatment plans for patients.

And so, like, the data driven approach, as I was mentioning earlier, can really, enhance the prediction of those responses and reduce the likelihood of adverse effects. So, for example, like digital twins have been used in oncology to simulate various chemo regimens to identify the most effective and least toxic combinations for a patient's cancer profile.

And so you're bringing personalized medicine into clinical trials. As my generation, I'm a millennial. And as we get older, you know, and we become elderly, we're not going to want to go to the doctor's office and sit there for 3 hours. I've been part of, I was a participant in clinical trials before.

And it was awful. I gotta say, it's such a big waste of time. Sitting there for three hours with the site sort of not knowing what to do, trying to figure things out, sitting there filling out a bunch of forms when I could have been doing all of this at home, right? You know, this was when I was living in California.

I had to drive all the way to Sacramento, like an hour to visit the site to do these assessments. And it took like, you know, like almost an entire day to do a study visit. People don't have time for that anymore. I don't, right? And so I see the trials coming to us, like where healthcare has gone, where my medications are being delivered to my home.

Like my groceries are and so I see also the use of sensor data being integrated. So this is what's really cool and interesting. I recently saw this presentation by who is it? His name is Dr. Michael Snyder, who runs the Snyder lab at Stanford Medical Center. And what's really cool is that he's doing a lot of research around personalized biomarkers.

So, for example, the average human temperature is 98. 6, right? But the true average is 97. 7 with a notable spread between individuals. Right. And so what is a fever for one person might mean a fever for one person and not for another, right? And so this omics approach, when you take a look at another, example he provided in there, like, he was talking about, some of the strategies, like with biomarkers discovered through omics.

You can, for instance, identify pre-disease. So for instance, a set of biomarkers for, evaluating, like the example he gave is okay, here's my norm, but something was acting strange. Like his heart rate was going up.

Yeah.Yeah.

He was on the plane. And his smartwatch oximeter picked up unusual changes in his physiological data.

For example, like a significant drop in blood oxygen levels and an unexpected increase in the heart rate while he was on the airplane. And it remained well after he landed. And so something was amiss. He went and got tested and he had Lyme disease. And so these changes in the baseline from your baseline, I think really help with understanding vitals.

So rather, when it comes to clinical trials, like, so like rather than falling off the stairs and because you're dizzy from a medical product, right? And that potentially being an adverse event and detecting it, like, before patients even feel a symptom, can see those changes in those omics in those digital biomarkers and be able to report the adverse events before it's unsafe for the patient.

So I see less adverse events, assessments being done at the patient's home or close to the patient's home, for example, at retail pharmacies, or even at local health care providers that I've got my reservations about local health care providers because operationally It's kind of hairy when it comes to compliance and monitoring all of that, but, scalable, locations that are close and convenient to patient's homes.

I see things being delivered to me. I see consenting being done remotely through digital devices. So yeah, that's where I envision the future of clinical trials. They're safer. They're more patient-friendly. They work around our schedules, around our lives. And also being able to just, where biomarkers, like, digital biomarkers that could detect issues beforehand, the blood samples would be taken, like microsampling is now, I know Theranos was sort of in the news for like falsifying data, but one of Dr. Snyder's studies with micro sampling, they use this technique to provide samples and then analyze like for a broad range of like health indicators. And one of the discoveries they made was this approach to early detection of diseases like lymphoma and precancerous conditions, which were identified before any of the symptoms were noticeable by the patients.

And so pre-symptomatic detection through microsampling, that's going to be the future also. Like you don't need to go to the core lab and get five, 10, or two of blood taken from you.

Yeah. That's a very good thing. I can envision the end point will be completely different in the clinical trials and that's all awesome. Thank you so much for sharing with me and my, network your vision. And I just wonder, you mentioned. A lot of things actually, and they all sound incredible, but I wonder if you have to choose one thing that will have the biggest impact on clinical trial success today, what will that be? From your perspective today? Yeah,

Running study sites and running studies with pharma companies, I can tell you the biggest thing, the biggest challenge that we face, One of the things that I've noticed is patient friendliness and putting yourself in the patient's shoes, because it's really surprising to me, having worked at the site side, working all the way at the sponsor, managing studies, and coming back to engaging with patients, I can tell you that sponsors still don't involve patients, it's clear to me when certain studies have not involved patients in their design.

And it really it's hard when you're a study site and the patients are disrespected almost, like, for example, there was this one study we were running, it was a neurology trial and the PRO device for children, by the way, is for pediatrics was this old clunky device. I'm like, everyone's using smartphones and why do we have to use these clunky devices?

So not only were they hard to use, but they were malfunctioning on top of that. And so, you know, our coordinator would call and we get a call from a patient and the patient's mother or the parent saying, well, I don't know how to use this thing. It's not working. It's connected to the wifi, but it's not functioning.

It's not prompting me for the questions. And so, what the patient said is that this sponsor does not care about us. They don't care about us because this is not how we want to do the study. And they dropped out. That's what I mean by being disrespected. Another example, like we're recruiting patients for this red disease trial and the study sites are just not as engaged.

And so when we make the referral over, we hand them through the process and we're with the patient. But the study sites have other priorities and they're not walking the patient through the process. So, the patient gets lost. Insurance coverage doesn't kick in. And then their window of opportunity, to enroll in the trial expires. And so here's the patient desperately seeking, therapeutic options, but no longer is able to qualify for the trial because the workflows are not streamlined.

yeah,

Those are things that I think the sponsors really need to think about is put yourself in the patient's shoes. Where they're coming from, and understand not only the complaints that you're hearing from the sites, but also listen to your patients because you're ultimately serving your patients.

Yeah, that's actually the disconnect. I would say disconnect. You mentioned disrespected. I would say disconnection to reality. Because your reality right now, like the sponsor's reality in most cases is provided by the doctors, by the investigators, let's say, sometimes it's even worse, like by, by the QP leader, the one behind the publication, but the reality is the reality, the life and the daily schedules and activities and pains and challenges that the patients actually go through, of course, the site as well.

So, yeah, I hear your words and actually you. I would say again, patience disrespected. That was powerful. And thank you very much for sharing that. Thank you everyone very much for sharing everything today. Actually, it's been an incredible conversation. A lot of things that I learned as well.

I'll try and I hope you can help me collect these different links and publications that you mentioned in the course that you mentioned, because I'm pretty sure that a lot of people would like to dive into these publications and materials as well. Thank you for making the time and also thank you for not just naming the challenges and the future but also that you're working towards this future through your work through your Chief Editor role as well. So yeah, I appreciate it.

Thank you, Maya. And my hope is that through this publication, we can really push the industry forward. I've been doing this for 20 years trying to push the envelope forward. My hope is that with The Clinical Trial Vanguard, we can really make people's voices heard and make those innovations really resonate with the industry.

It's one thing to have a regulator speak at a conference that not everyone goes to, but it's another to have it disseminated widely so that everyone from the top to the bottom can understand what's really happening

Yeah. And like you said for that, we need trust and collaboration.

Thank you. It's been a pleasure.

My pleasure. Maya, thank you for hosting.

 Hope you enjoyed listening to Trials with Maya Z. If you're interested to hear more about how clinical trials can serve patients globally, subscribe to the podcast on Spotify, Apple Podcasts, and Google Podcasts. Have a great day.