Feb. 7, 2022

April Zambelli-Weiner PhD

In this episode of Life Science success I interviewed April Zambelli-Weiner PhD.  April is the CEO of TTi Health Research & Economics.  At TTi they provide life cycle clinical and economic research and consulting services to guide strategic...


In this episode of Life Science success I interviewed April Zambelli-Weiner PhD.  April is the CEO of TTi Health Research & Economics.  At TTi they provide life cycle clinical and economic research and consulting services to guide strategic decisions from pre-market development through commercialization and growth, enabling companies to demonstrate and evolve their product’s true differentiated value story.

Please check out our Life Science Success Resources.  You will find tools that will support growing companies and books for authors I have interviewed.  

Transcript

April Zambelli-Weiner

[00:00:00]

 

Don Davis PhD, MBA: Welcome to the life science success podcast. For those of you who are new here, my name is Don. I'm a consultant in the life science space. I help companies manage complexity and increase performance today. I'm going to be joined by April Zambelli-Weiner, and a welcome April.

April Zambelli-Weiner PhD: Thank you, Don. Thanks for having me.

Oh,

Don Davis PhD, MBA: you're welcome. So can you tell the listeners just a little bit about.

April Zambelli-Weiner PhD: Sure. Sure. Uh, [00:01:00] so I'll try to be short. I like to talk, you'll see that. Um, so I am the founder and CEO of TTI health research and economics, and we are a strategic market access consultancy, and, uh, contract research organization is.

Personally, I'm an epidemiologist by training. So my background is an epidemiological methods, clinical research in genomics, and I have a really broad backgrounds, um, in research from bench research to field research, to running a data. Core facility at Hopkins. And so I left Hopkins. I left academia really looking to take that toolkit and apply it to a very wide range of problems in healthcare.

I saw so much need. Um, and I wanted to have a more diverse, applied experience. Um, I also was really frustrated with, um, Silos data silos, but other silos, um, and the [00:02:00] L the slow speed of translation. So my passion is really translational research, and that's why I love what I do, because part of what I do is say, how do we make resources?

Impactful and work for business outcomes and move the needle and actually get these programs and drugs and technologies to the patients who need them. So sort of in a nutshell, um, that's, that's what we do. And we're very focused in the med tech, um, life science space. We're, we're also a federal contractor.

We work for CDC and NIH and we run big studies and things like that. But in terms of our commercial activity, it's really focused on helping to bring. High-impact emerging technologies to market.

Don Davis PhD, MBA: Yeah. So that's a good transition to my first question. So how does your organization help companies through pre-market development through commercials?

April Zambelli-Weiner PhD: Sure. So, you know, it usually and, well, I'll back up by saying, I'm [00:03:00] going to try to be really practical. I like to just be very practical with my insights and recommendation. So people come to us with a problem. Um, and that problem can really vary because we work with companies across the life cycle. So at different phases, we do work with a lot of early stage companies, but the kinds of problems they might come.

For, you know, maybe they're unsure about their optimal go-to-market strategy or they need help getting investment. Um, they're unsure how to drive reimbursement. Um, they need to know how to generate evidence, but they don't know what or how. Um, sometimes it's even very more tactical and on the operation side, like they might just need data analytics support.

Um, but they need a more market facing company. They want someone who understands how to translate research to the marketplace in a way that they need to receive it. So usually there's a point of entry that's a very specific need. Um, but it, we almost always because [00:04:00] we're strategic consultancy, you know, sort of ask sure.

If you want to buy a health economic model, we can do that model for you. And we can tell you what kind of model we think you need, but we really want to know what your business outcomes are that you're driving towards. Do you really know what your optimal path is? Um, so that's the strategic part of what we do.

So as you can imagine, services really range from go to market strategy, investor decks, full market access, roadmaps, regulatory reimbursement landscape, EV evidence, generation, or Epogen. So building health, economic models, designing and implementing clinical economic studies. Value communication, dossiers backpacks, all those communication products.

So it really does span the gamut. And, um, a big portion of our, um, work is programmatic. So where we're wrapping our arms around a company and we're taking them the whole way to whatever that may be, [00:05:00] whether it's exit or, you know, scaling or. So

Don Davis PhD, MBA: you could, so do you work with companies that are, it sounds to me like you work with companies that are extremely early stage, even somebody that's, you know, getting ready to, um, to launch a drug as well.

April Zambelli-Weiner PhD: Yes. Um, we have a few select pharma clients, so we're, there's a lot of companies that do what we do in the pharma space. And that's a more, I call it commoditized marketplace, right? Because the market access pathway is a little more straightforward. For a drug. Um, so you've had all these, and then the regulatory pathway, you have all these CRS kind of crop up around that.

For anyone in the life sciences or med tech, we know that this pathway is entirely different and nuanced and difficult. And so, um, that's really where our focus is. We do a lot of companion diagnostics and things. So we end up getting into that space sometimes that way as well. Um, [00:06:00] but we do work with companies of all sizes.

Of course, startups make up the majority of the marketplace. So we work with a lot of them, but we also work with a lot of growth stage companies who are. You know, coming to America, um, you know, maybe they have approval or sales outside of the U S and want to come here. Um, and we also work with companies who are based here and have, have, um, stumbled.

So their revenue is underperforming, you know, they're not tracking and they're getting a lot of pressure. They need to get to exit. And so we come in and it's. You know, what can we do to solve this problem? Because usually it's a failure to get positive coverage or reimbursement, and that's something that we deal with.

Don Davis PhD, MBA: It's interesting that you say that. Cause I know I've, I've talked to somebody in Europe previously and they had said, you know, we're, we're interested in moving our headquarters to the United States. Um, we want to, uh, be on either the east coast or the west [00:07:00] coast. And I was. That's a big, that's a big decision by itself.

So you probably need to make that decision first and then you can kind of follow along with the other. So, um, yeah. So what shifts have you seen in the economic models and strategic direction in healthcare economics?

April Zambelli-Weiner PhD: So there's a lot going on in this space, obviously. Especially with all the shifts from COVID.

And I know we're going to probably get into that. How could we, how could we not, um, some of the real practical shifts that I will, um, talk to companies about that we're seeing in terms of health economics, number one is early requirement from India. So, what we're seeing is investors are requiring more health economics earlier to support the value prop and product pricing.

And to demonstrate that there [00:08:00] is in fact, a viable reimbursement pathway, um, even though with emerging technologies like AI there's, uh, there's there's money flowing overall, there's less money going to early rounds in, in med tech. And so you've got more companies competing for less dollars. You know, investors are, um, savvier about this they're hedging risk more, and they expect to see that there's a defendable economic value story.

So we do a lot of pitch decks. We revise pitch decks. We help companies with this. And a lot of what we do is add that component because it really can't be like your CFO doing a back of the envelope calculation. That's just not going to cut it anymore. So that's one, I think another one is inclusion of a real-world data and I'll even add the adjective local real-world.

So a lot of what I talk about is that things are getting very localized in terms of value prop. [00:09:00] And this represents a challenge because our market is so diverse. You know, we have such diversity in payers, health systems. Um, and so you've got a lot of people to satisfy and a lot of people with different ideas about what value means.

And so overall though, what we're seeing is, you know, it used to be, you did a health economic model and there was this expectation. It was sort of more of the pharma model of populating it with clinical trial data. And now, you know, you'll see even FDA and, and different agencies, but especially FDA recognizing the value of real world data.

Um, a lot of studies showing that real-world studies. Are as effective at answering certain questions as clinical trials. Um, so populating these health economic models with real-world data. And w when we say real-world data, what we mean is really not collected in a controlled setting, like a clinical trial, but in the real world as healthcare is delivered.

Um, so it's more reflective of [00:10:00] reality, and that is something that we're doing a lot more. And also designing models. So that payers and health systems can populate the inputs, um, with things that reflect their own patient population, because they want to know how this is going to impact their bottom line and their population outcomes, not some theorized, uh, base case of, of just, you know, something else, which I think is really reflective of where everything is going.

It's probably a thread that's going to. Go through our whole conversation today, just in terms of the move to, to AI and personalized medicine or precision medicine and pharmacogenomics and localized and treating subpopulations and treating the patient in front of us, everything is getting micro. You know, we've had a very macro aggregated approach to health care over the past 50 years, and it helped us solve some important high level problems, but it's also created a lot of.

And [00:11:00] it's that one size fits all. Well, one size does not fit all and we're finally at the place. Um, and this is where I'll avoid pontificating, but I I'm, I'm like a geek, I'm a data geek. I'm a geek about healthcare history, you know, and I can get on a tangent, but you know, the last, the third industrial revolution was about data, just producing data.

Right. And so we had all. Data that that became valuable, but we didn't really know what to do with it. Um, and the fourth industrial revolution, which we're entering, or some might say we're in, is really about realizing that promise of data. And so that's really exciting. And I, I won't even, I won't even go down there, but localized, real world.

And then the last thing. Yeah, go ahead. Let me just going to say,

Don Davis PhD, MBA: I have a couple of questions related to the localized data. So I know from my experience as well, the moment that you go to let's just say Asia. [00:12:00] Right? So, um, now if I wanted to release a companion diagnostic in Japan, they're going to expect, you know, sort of.

What are the models show that, that you're going to be able to impact here locally? What data do you already have? You know, have you studied our population? Do you know what's going to happen? So you guys helped to provide a bit of that, you know, outlook as well?

April Zambelli-Weiner PhD: Oh, absolutely. Absolutely. And I think, you know, that's a little bit of where I was going with with, uh, um, data tangent, which is, um, We're still a long way off from true interoperability and, and device companies or people having access to all the different data streams and, and them coming together to really demonstrate, improve their value prop.

So we're very much still in this space of needing. What data even exists and how do I pull it together? That's a big part of what we do. You know, [00:13:00] what is it that we need to address? What is it that we need to show? Where can we get that data in a cost-effective way? How do we combine it and bring it together in a study or a model or otherwise?

So, yeah, absolutely because. You can't show up with, and you know, this is the issue in AI, too, right? I mean, you can't take this and say it applies to that. That's the old model. That's not where we're going anymore. Thankfully, because I really believe, I really believe that's one big cause of our low value.

I don't believe, I know it's a cause of our low value healthcare in this country, um, is that we're taking models. Developed in clinical trial populations, which are really narrow and really well-defined for a particular reason. And we're applying that to all comers and it doesn't apply. It doesn't work.

And our clinical guidelines are based on this. And we have many, many examples of, you know, where this is falling down.

Don Davis PhD, MBA: And I know recently I, [00:14:00] in one of the interviews that I did recently with, uh, um, Focused on liquid biopsy does similar sort of topic came up that, you know, look, I mean, cancer is not one cancer, isn't the same, you know, even throughout its lifetime.

So, you know, it shifts, shifts over time. So, um, yeah, the next question from my side would be, uh, you know, so in terms of, um, underserved populations, how are underserved populations even defined right now in, in the models that you guys have.

April Zambelli-Weiner PhD: Yeah. So underserved populations are really. From our perspective.

And I think the general perspective, you know, vulnerable, vulnerable populations who face barriers to healthcare. So that's underserved right there. They're not getting access to the health coverage and the basic health care services that they need. And these are typically members of minority populations or groups who [00:15:00] experienced health disparities.

And I, I think everyone, you know, won't be any surprise to anyone. Um, African-American Latino populations, refugees, individuals with disabilities, um, individuals with low health literacy, or just a general lack of, you know, familiarity with our healthcare system in this country. And I think until you've worked with some of these populations, you know, I had the privilege of doing studies in, um, refugee populations in this country.

Um, you don't, you know, it's hard to fully grasp even just how many layers to this there really is. Um, so, you know, there's also just basic social determinants of health stuff, you know, access to transportation and that kind of stuff. So that's how I would define underserved population.

Don Davis PhD, MBA: Okay. Yeah, my, uh, it's, it's interesting because, uh, I grew up in Northwest Indiana and, um, you know, we definitely saw individuals who, you know, had never been to a [00:16:00] doctor.

Very, very seldom had been to a doctor, you know, even. Um, and so, you know, I could only imagine somebody that doesn't have access, just wouldn't show up in a lot of the things, a lot of the data that we have to be able to provide for these sorts of.

April Zambelli-Weiner PhD: Yes. And that's, what's really exciting. Um, really, I mean, I've worked in health equity and health disparities, a good bit of my career.

It's always been a layer to the work that I do, um, and what I'm passionate about. And, you know, it's just really exciting to be working in this field in this time, because we're really seeing these new technologies come that are addressing these issues. And, you know, we're seeing all of these technologies that allow people to get access in new and different ways.

Um, not having to show up whether it's, you know, at home delivery versus showing up for an injection or whether it's a remote monitoring technology, you know, the, those types of things. So it, it is a exciting time.

Don Davis PhD, MBA: And, uh, [00:17:00] Look at data models, or you even think about the future with artificial intelligence, how do, how do you envision that we could prevent bias from creeping into, um, those models?

April Zambelli-Weiner PhD: Yeah, so AI, there's a lot, there's a lot to talk about there. Um, and I I'll get really excited about this. So let's, let's talk about bias first since you brought it up. So let's talk about bias. So, um, It might be good to just do a little primmer. I don't know how many people, you know, who watch this are listening are, are, you know, really into AI.

Maybe some people are joining for the first time, the conversation, but. When we talk about bias and AI products, from my perspective, we're talking about very established research problems, so old problems within the context of very new complex products and those old problems are internal validity and external.[00:18:00]

So when we talk about research, these are the two sources of bias that bias can impact. So what is internal validity? That is just essentially, are we measuring what we think we are, um, is that relationship really causal, those kinds of questions? Are we adequately controlling for confounding? I'll give an example because that's often easiest.

So if you are wanting to include in your model or your algorithm, a measure of, um, medical needs, Medical need and you are utilizing healthcare costs as a surrogate or a measure of need. Are you really measuring? And you could just skim the surface of that and be like, yep, that sounds good. That's a good measure.

But truthfully, it's not a good measure. Um, and there's a lot of things that impact health care costs. So obviously minority populations who have access issues are going to have lower costs, even if they have [00:19:00] insurance and don't have access issues, they historically have lower costs. So you will actually be missing some of the people with greatest need by using that surrogate measure and therefore, introducing.

And to, into the algorithm or the model. So that's an example. And then external validity is really do the results apply in different settings. And so this is the issue of the data in the AI, right? Like the underlying data that the model has been trained on and developed. And so do the results apply in different settings.

Um, and you know, the answer is really often, no, they don't. Um, so. That's sort of the, the overarching issues. Um, I don't know how much you want to dive into the different kinds of AI products and machine learning and all of that in terms of what's happening in the landscape, but happy to, if you want to.

Don Davis PhD, MBA: Yeah. I'd love to, I'd love to hear your thoughts in terms of, you know, where you think things are going next, uh, from an AI perspective. [00:20:00]

April Zambelli-Weiner PhD: Yeah, absolutely. So. Let's let's talk a little bit about some definitional things with AI. So AI products can have different purposes. They can look really different. Um, there's like a lot, a big range.

So diagnosis, triaging, um, treatment, predicting outcomes, predicting adverse events. And they can be just an algorithm. They can draw data from an EMR. They can combine genomic data with other types of data. They can use imaging, they can pull data from a biopsy. Um, so there's a lot of different types of AI products and that means there'll be regulated differently and they'll have different reimbursement pathways.

So I think. Right at the beginning, there's a lot of confusion, um, about the pathways for these products. Um, and then they can have different approaches, so different uses they can be rules-based or machine learning. [00:21:00] So rules-based is just what it says. Right? It's based on something established like clinical guidelines or practice guidelines, something in the literature.

What we hear a lot about most is machine learning and without getting into. Supervised unsupervised and I'll I'll impress. Um, I think the most important thing is there's kind of two times two types of ML algorithms there's locked and there's adaptive and locked is just what it says. If patient a comes in with these characteristics and the algorithm says high risk of cancer.

If patient becomes in six months later with the same characteristics, the algorithm will say high risk of cancer. So same input, same out, but that's a locked algorithm, but the one that has everyone most concerned, and that there's a lot of activity in the marketplace. And especially at FDA. Is continuous learning or adaptive algorithms.

And so those are the ones that change and iterate as new data comes in. [00:22:00] So in that case, patient B comes in six months later, they may not be routed to the same outcome or the same decision point. And so we can all pretty easily deduce that there's some concerns and risks and risk management that needs to happen around that.

Because there can be patient safety issues. And then from a regular regulatory standpoint, how do you monitor that? How do you, um, you know, free submit, when do you need to resubmit updates and things like that? Um, but I would say overall, the risks really fall into three buckets and we kind of touched on them, the data underlying the algorithm.

The nature of the algorithm, the cell itself, because we, as people can introduce cognitive bias into the algorithms, um, and then how the product, you know, is deployed. So,

Don Davis PhD, MBA: yeah, it's funny. Cause I've spoken to, to dusty my Jim Dar and several others with regards to AI and, and um, [00:23:00] you know, what, what their thoughts are, you know, around this and this whole idea of, you know, Not having a black box, but having something that you know exactly what it's doing when it's doing it so that you can kind of know whether or not, if it's doing it in the right way, is, is something that I know has been stressed, you know, overall in our discussions as well.

Just to that, you know, look turning, turning over a computer and having it, just do stuff. Probably not wise, but, um, being there and kind of, you know, double checking the results and saying, yeah, I agree. Or, you know, I agree, but I would make this adjustment along the way makes a lot of sense to a lot of people.

April Zambelli-Weiner PhD: Absolutely. And I, and I think what's, what's a little comforting to me and, and maybe other people, um, is as a data scientist. I mean, it's, it's really not different from anything we've been doing before in terms of developing models. I mean, we've prior to all of this, Uh, an [00:24:00] analogous problem that could be solved with AI now is we have a lot of outcomes in healthcare that are not easily studied.

They're not well-defined, they don't have codes associated with them, but we have to study them nonetheless. And so we've had situations. Um, an example would be, um, anastomotic leaks in, in like bariatric surgery. So the potential for a leak on the staple line, and then you have risk of sepsis and all kinds of downturn, you know, downstream sequelae.

And so that's not, how do you define a leak? Um, how do you define a leak in administrative claims data? Right. There's no code for leak. Um, and so what we had to do was build algorithms, essentially predictive algorithms in those datasets. To predict leak. And then we had to validate them back on a clinical dataset and show that they actually predicted leak in an existing dataset.

So it's the same types of concepts that need to be applied to these [00:25:00] products. And I guess I would say at the end of the day, um, the ways to really prevent bias are. Um, kind of straightforward, um, probably easier said than, than implemented, but important on the less have diversity in the dataset that the algorithm is trained on.

Um, it's concerning, right? I mean, I read a study on imaging technologies. It was like a 20, 20 JAMA study and I'm forgetting the author's name, but, um, They looked at like 56 studies that underlied, um, imaging algorithms and 70% of the popular patient population underlying those studies came from three states, California, Massachusetts, and New York and 34 states of the U S were not represented in any of these.

And so that's concerning, right? Right. That's concerning. So I think, you know, diversity or the flip side of that, and this is something I, you know, we [00:26:00] advise clients anyway, is focus, right? Focus on a very particular population or a particular use case. Don't at least to start. Don't try to be all things to all people everywhere.

And if you narrow that way, then you won't get in, in as much trouble. And then make sure you're using unbiased variables, you know, estimates, definitions, models. I mean, we've worked with various AI companies and data scientists are not necessarily healthcare people. Right. They don't necessarily, right.

This is the same thing we ran into in academia. You know, when you're running a data analysis core facility and the clinical researchers come in and it's like, okay, we need to bridge this gap. Right. Um, you need to understand, you know, I get the clinical perspective. But the data perspective is you need to understand what you're measuring you.

Can't, you don't just throw anything into a model, um, right. There needs to be an understanding of what we're building and what we're doing. So I think, um, unfortunately we see a lot of [00:27:00] that, just not enough thought put into the measures, um, and how we're measuring things. Yeah, yeah. Or

Don Davis PhD, MBA: you could definitely wind up with a model that has inherent bias just due to the fact that the way it was constructed.

So yeah, for sure. So to shift gears a little. What, uh, what shifts do you think will happen, uh, in the market due to.

April Zambelli-Weiner PhD: So I think, yeah, I think overall, um, what we're seeing is a much, much needed disruption to the inertia, um, that normally characterizes our market. And I think we're going to see, um, an exponential speed historically, our exponential increase. Compared to historicals in the speed of adoption for these technologies, especially ones that can drive big bottom line changes.

And we do a lot of budgetary impact models. That's what health systems want to see. It's what payers want to see. Um, tell me, and, you know, a lot of people don't [00:28:00] know this, but a lot of payers, um, you know, they have bottom line numbers. If you can't move the needle by a certain amount, they're not even interested in looking at your technology.

And so I'm excited by this. It's a paradigm shift, but what I'm also excited about is getting to see these budgetary impact models for these emerging tech. Um, I'm seeing bigger impacts than I've seen in a long time. And it makes sense because it's exciting. I mean, all of a sudden you can route people and say all of these people.

You know, can be treated up front this way and avoid all of these costly hospitalizations down term. All of these people don't need this expensive, extra workup over here. Um, and when you start multiplying that by the numbers, when you're talking about diabetes and cancer, I mean, you're seeing big impacts to the bottom line.

So I think there's this incredible potential. To address the whole quadruple aim of, of healthcare with these [00:29:00] emerging technologies. But there are huge hurdles in the marketplace, huge, huge hurdles. Um, I could do a whole other, you know, episode on market access barriers for, you know, AI and, and, and laboratory companies.

Yeah.

Don Davis PhD, MBA: I mean, at least from my perspective, right? I mean, I, what I'm seeing a lot of investment go to right now. Um, so we've seen. A huge amount of investment go to, uh, companies like free gnome or they're working on, you know, personalized diagnostics, uh, in next generation sequencing. Um, you know, were there, uh, lots of, lots of dollars going that way.

There also a lot of dollars for sure. Going towards. COVID route, uh, as well. Um, but I've seen major slowdowns and I mean, I was just talking to somebody yesterday about anti-microbial, uh, an antimicrobial drug that they're working on. [00:30:00] Um, I've had previous conversations with somebody else about an, uh, an epileptic drug and those guys.

I mean, they're having trouble, a lot of trouble finding money. And, um, it's because a lot of these dollars are being sort of redirected. I would say to, um, you know, to other things inside of COVID now, are you seeing the same thing on the AI side, then

April Zambelli-Weiner PhD: we'll see a lot of dollars seeing a lot of interest in a lot of dollars.

I think the, the what's, what's interesting. We're seeing a lot of overseas dollars coming for those technologies. And I think especially, um, east Asia and I think what we're, but what's what I'm seeing actually is it's almost, um, I don't wanna say unfortunate. That's not the right word, but it's, it's.

Allowing companies to make moves before they fully thought things through and they're stumbling. And I like to remind people there's a 98% failure rate for digital technologies. [00:31:00] You know, you can join the bucket if you want. My job is to make sure you don't, but it's an uphill battle. And you know, there's some imploding happening because.

There, you know, jumping the gun and, and we're seeing this across the board. I mean, one of the trends that I'm really seeing, I don't know if you're seeing this as well, but, um, we have a lot of new players entering the marketplace. We have a lot of people coming from other health to other tech, um, direct to consumer, and they're entering this regulated market for the first time.

And you've got really, really smart people, but they're either coachable and willing to admit what they don't know or they're not. And what I always say to people is. My job, what we say is I'm not going to let you do anything stupid, but I can't stop you. So if you want to do something stupid, please go ahead.

And I just don't want to be, you know, attached to

Don Davis PhD, MBA: that.

April Zambelli-Weiner PhD: [00:32:00] Right. You know, but you've got a lot of smart people. It's sort of the equivalent of the patient walking into the doctor's office and saying, I know what I have because they've done all their internet reasons. Right. These are really smart people. Um, and they, they can put, kind of pull the pieces together, but they don't understand the nuance.

And I'll give you an example. I had, uh, had a company, um, it was a, um, you know, a precision diagnostics company, um, backed by some of the largest players in this space. Um, had a lot of possible applications. They were going to go screening for. Gosh, liver was like, I think it was liver. Um, and they had this whole go-to-market strategy, which we basically had to do.

Which just fell, fell apart like this, because I said, you know, it basically had like, um, regulatory approval FDA, and then just a little short arrow down to CMS reimbursement [00:33:00] and, you know, like, boom, like it's going to happen, which, you know, we all know, but, but the bigger point was, I said, you know, You do realize that CMS is statutorily unable to pay for a screening technology, unless it's a carve out.

Right? Like, and you don't have a carve-out, you're not breast, you're not colon. That's how we got Cologuard guard, but it's not going to be you. I'm sorry. You know? Um, um, and, and it was just no, because they, you know, they hadn't gotten that deep basically. Um, and, and they shouldn't, they shouldn't, that's what you have experts for.

Don Davis PhD, MBA: But it also kind of goes back to, I mean, cause I, you know, so I've done this whole, um, sort of personal analysis of what happened with there and dose, right. Um, as an example, right? I mean, you know, not to say that anybody here is associated with them, but the, um, the, the whole thought that you have, you have somebody that has a good idea.

I mean, [00:34:00] certainly if you could run over to. Tests with, you know, just a small sample of blood. It would be a great idea. However, Um, this is where you kind of have to rely on the experts to say, will I ever find enough signal to run 200 tests out of this little, tiny amount of blood? And I think, you know, at least the industry experts that I know they had said, you know, look, it's just, it's not possible today.

What they were trying to do is. Not not possible at a large scale today. So, you know, how do, how do you move one inch closer to that would be a great sort of next step that I could see people taking, but it's this, this whole idea of not relying on the experts and getting a bunch of people that have that have opinions, but they're not experts in the field.

They just kind of, you know, come in and say, yeah, this sounds like a great idea. And you know, the investors in Theranos, whenever you. Kind of the overall example, all of their investors, a good [00:35:00] majority of their investors had no clue what they were getting themselves into. And they were just relying on, you know, people that really didn't have a, have a strong opinion.

And so that relying on the experts who comes critical, um,

April Zambelli-Weiner PhD: you know, like yourself, and I think that's a great, you know, Warning, you know, a cautionary tale, obviously, but I'm telling you, I still see this a lot and now it's become like a thing with our team. Like I'm getting Theranose vibes, right? This is what they say.

We say to each other, I'm getting sick because. You know, all of these companies want to go the LDT route because they think it's easier than being regulated by FDA. And first of all, if FDA wants to regulate you, they will. And they might come in and do it. But there's all this other issue and policy problems with LDTs that these companies don't have a clue what they're stepping into.

They think they're going to do rebates. They think they're going to do all these things. [00:36:00] They're violating stark law, they're violating the anti-kickback statute and you know what? The government is getting nasty and aggressive about this. You know, they're filing civil charges, criminal charges, you know, you can't mess around with, um, CMS and coding and all of this.

So it's a, it's a hornet's nest and you should not be trying to navigate it yourself. It really, you know, um, it's, it's an ever-changing.

Don Davis PhD, MBA: And I personally have seen the positive effects of having a lab developed test. Initially, and then having that kind of migrate to, Hey, look, I want to develop a more personalized diagnostic or I want to, you know, do additional things, but, you know, to your point to, to try and circumvent something and having that be your market strategy is probably not the right way of doing it instead.

It's, you know, Hey, look, let's, let's try and work with, you know, individuals that actually could get this thing, [00:37:00] you know, studied well and used, uh, you know, in a smart

April Zambelli-Weiner PhD: way. Right, right. Because, you know, it's all those downstream implications and that's where you really have to have the experts. Um, again, you know, I, um, I mean, look, I'm an entrepreneur myself, so I, you know, I point the finger at myself first, you know, the shiny object syndrome, all of those things, you know, we're, we're all guilty of, of these things, but the issue is, you know, for example, just continuing with the LDTs, um, You know, under the reimbursement pathway, you know, if you're going to go LDT, you've got to establish clinical utility.

So if you're going to go to multiplex or you're going to go somewhere like that, you've got to have clinical utility or it's a no go, or you've got to go private payer road show and. What they don't understand what the private payer road show, um, like, you know, my favorite is when it's like, we're going to have this payer, this payer and this payer, and that [00:38:00] equals this amount of market share and we'll put Salesforce around it and it's.

No, no. And no, none of those payers are going to open their doors to you. You're not going to get even credentialed. You have nothing to offer them, you know? So there's so much nuance to this again, that I think if I could just harp on anything, I just, I love to get very far upstream with people and just educate about, you know, the complexity.

To your point and what you do as well, that just the complexity of this pathway. Right. And get this advice up front. So you know what you're dealing with. So, you know, the landscape.

Don Davis PhD, MBA: Yeah. So that you don't, I mean, the other thing that I've seen as well, especially in the personal personalized diagnostic market is that people get so far a field.

Now, all of a sudden they're like, wow, I spent all this money. I've done all this research now for me to go backwards and do it all over again, you know, to get something else. And it's like, well, that's why, [00:39:00] that's why, at least in my mind, you want to rely on people that, that know this journey a little bit better before you get too far a field and then have to spend a lot of money that you've already spent.

April Zambelli-Weiner PhD: Exactly. And, you know, we have both of those stories and I'm sure you do as well, but just from. From a study perspective. Um, you know, we've had the situations where, you know, people have listened and. It's it's fueled investment. It's fueled valuation. It's taken them to the next level. And then we have the stories where they come and they're like, eh, I don't want to spend that much.

You know, they really have a cost mindset instead of an ROI mindset and they don't wanna spend that much. So they do three bad studies and now they're in an even worse spot than they were. And it's like help us. And that's where you need the methodology. Right? You need the people who can come in and say, look, you need 300.

Hold up your value prop and you have not brought these specific three things together in the same study yet. So once you [00:40:00] do it will work. Um, and, and you know, that kind of thing. So yeah, absolutely. Absolutely. I mean, don't have your, C-suite write the protocol, please.

Don Davis PhD, MBA: I mean, the other thing I've seen work out to people's benefit is we're meeting with regulators. You know, when you're ready meeting with regulators early to get their input before you get again, way too far, um, that cause they can point out things that they've seen, they know what they're looking for in your data and, and things like that.

I mean, I've, I've seen that benefit people as well in terms of meeting early and just saying, Hey, look, here's what we got. Here's what we're thinking. Absolutely. Yeah.

April Zambelli-Weiner PhD: We always recommend early engagement with agencies, um, as much as possible. And I think that, but I, but I think this is another area. If you don't mind me just jumping in.

Cause I'm all, I'm all about, you know, let's, let's call out the barriers and avoid [00:41:00] them. Um, you know, it sounds easier than it is. It sounds really simple. And w we run into this sometimes, um, You know, I just, I just had a client. It was in the AI space to, um, very complex, a lot of regulatory ambiguity, a lot of reimbursement ambiguity, and we're, we're laying out the landscape and we're saying, look, you know, the first step is, is, uh, meeting with FDA.

Um, and we need to talk about the pros and cons of doing that in, in a certain way versus another. Um, and it's, and they're responding. So your recommendation is a meeting with FDA. When we already know it's a five, 10 K submission. And I was like, oh, I didn't realize you were an expert in it. But I was like, um, you know, I didn't say this, but really showcasing your ignorance because, um, number one, you have regulatory ambiguity.

We've already checked with agency contacts. We already know you're at risk of being class three. [00:42:00] Or, you know, similarly severe, um, requiring a lot of documentation, whatever you want to, you want to talk about, but, but the other point is you don't just have a meeting with FDA. This is not just something that happens.

You have to talk about. Right. You know? Right. Do we do it informally? Do we do it formally? How do we want to do it? And so again, just kind of, there's so much nuance to it. Really walk in and

Don Davis PhD, MBA: get yourself in a lot of trouble. Yes, sir. So there were three questions that I like to ask every guest, April, what inspires you?

April Zambelli-Weiner PhD: What inspires me? Um, I would say like hands down right now, our frontline healthcare workers. I mean, every time I think about what they have gone through and continue to go through, I am just humbled by the selflessness, the call to serve. And I think how can you not be inspired by this? It's really like the best of humanity.

So [00:43:00] kudos, kudos to all of them out there.

Don Davis PhD, MBA: That's for sure. And, um, I mean, I given everything that they've gone through already and, you know, continue to go through, especially in this COVID environment, for sure. They are, they're an absolute inspiration. What concerns you.

April Zambelli-Weiner PhD: Policy. I do a lot of policy work, a fair amount of policy work.

And, um, I'm really concerned about the great need in healthcare and the inability for policy to keep up with the needs and demands of the marketplace. And I mean that at all levels, I mean, I mean it, at the local, you know, the payer level, the national payer level, the national federal level. I have a lot of opportunity to review different things through my policy work I'm on the HIMS public policy committee and other, other, um, activities and non-profit activities I do, but like I just, not long ago, the cures 2.0 bill that was introduced in November, just as an example, I'm reviewed and provided some [00:44:00] commentary to that.

And you know, there's a lot of goodness in there, right? I mean, the entire point of the bill is to fuel medical innovation. And there's a lot of goodness, but there's also a lot that concerns me. One of the things that's just has stuck with me is we shouldn't have to wait another six years for a report on how genetic testing will improve precision medicine and reduce health health.

Right. We shouldn't have to wait six years to know that information. We already have that information available right now. So it's just, you know, policy needs to change and adapt to meet the needs of the health of healthcare. And the innovation is there, but we've got this chasm between the innovation culture and the bureaucracy and the inertia of, of policy and of our market.

Like more than ever.

Don Davis PhD, MBA: So it's funny that you said that because, I mean, that's initially where my mind went earlier, whenever you were talking about, you know, just some of the, some of the overall shifts that you've seen in the [00:45:00] healthcare market, because I. I honestly thought coming through COVID and, you know, vaccines getting approved and things like that.

I thought it would've spurred some sort of dramatic change that I certainly am not seeing the result of yet. Right. So I'm not, you know, we, we, we may have gotten to vaccine approval pretty quickly, but at the same time, the key question in the back of my mind is what more could be done, uh, kind of across the board along the same.

So, yeah, I agree. Yeah.

April Zambelli-Weiner PhD: Yeah. Um, I think we're just at the precipice, right? We're just at the precipice. So we we've seen the disruption. We've seen. You know, crisis has a way of, of showing the cracks and putting the pressure on right. To solve those cracks. So we see the cracks. Um, and we're just beginning, we're just beginning, you know, I mean, there's good stuff with tele-health, right.

I mean, it's good to see tele-health extended [00:46:00] so that, you know, we're seeing some disruption to that and, and we've seen some new codes applied. And so there have been some opportunities for new technology to progress, but. I agree with you

Don Davis PhD, MBA: and what excites you.

April Zambelli-Weiner PhD: So in general change excites me. So, I mean, to your point, I mean, it's kind of apropos, I think, um, I think we are seeing it.

I think it's, I feel very motivated to try to push that change as much as I can from the top down and from the bottom up. Um, Moving to a new preventative technology enabled rapid learning model of healthcare. I mean, it is time. There are things that I have been talking about for 20, 30 years and many others, um, that, um, that are finally getting up-ended.

Um, you know, are finally, we're allowed to question these things. So it's the beginning and I'm excited for, you know, what's. [00:47:00]

Don Davis PhD, MBA: Yeah, it's funny. Cause I was at, I was at GE healthcare decades ago in decades. Um, the, I remember the healthcare CEO at that point in time, starting to talk about personalized maps.

And we're starting to really see personalized medicine now, but of course, I mean, the way we talked about it was almost like it was around the corner. Right. And, um, and, and it just takes a while if it seems like, but at the same time, it's an exciting environment to be a part of and just kind of help with the transitions overall, you know, in the market, even though it may take us longer than, than what we'd all hope.

Right. That, that. Today or tomorrow, but it might be decades from now that, that we actually see these things come to fruition, but it is the work that of today that actually puts that in place.

April Zambelli-Weiner PhD: Absolutely. And I think this is where AI and these new technologies are going to help us speed that process up.

And so that's, that's [00:48:00] exciting, right? Because we have new ways of synthesizing all of this information and making it actionable. So hopefully we shorten that time as we go forward.

Don Davis PhD, MBA: So if people want to try and get in touch with you, or want to learn more about your company, where could they best.

April Zambelli-Weiner PhD: Sure. Um, obviously we have a website, um, it's TTI, hyphen research.com.

We just redid it. So it's a pretty comprehensive resource, um, for different companies at different stages. Um, so I would, I would say there were also obviously on LinkedIn, um, and our emails are on the website. So if anyone wants, you can sign up for our newsletter there or reach out to me, um,

Don Davis PhD, MBA: April Zambelli-Weiner.

Thank you so much for being a guest on the life science success podcast. I appreciate your time.

April Zambelli-Weiner PhD: Thank you so much. It's been a lot of fun. Thanks, Don. All right. Thanks.[00:49:00] .