How Do Pharma and Digital Health Converge in 2024? (Amir Lahav)

[00:00:01] Dear listeners, welcome to Faces of Digital Health, a podcast about digital health and how health care systems around the world adopt technology with me, Tjasa Zaits

[00:00:14] In 2023, in silicone medicine, a biotech company developing medications with a heavy reliance on AI used AI to develop an experimental drug for the incurable lung disease idiopatic pulmonary fibrosis

[00:00:31] The treatment is in mid-stage trials in the US and China with some results expected in early 2025

[00:00:40] Biotech is one of the fields that has been using generative AI years before changegpt brought the technology to the public view

[00:00:50] Latest tech is essential in drug development

[00:00:54] However, the convergence of digital health and pharma seems less clear

[00:01:01] We've seen many ideas around how pharma should or could use digital health

[00:01:06] Around 2015, when digital health apps started gaining popularity, it seemed that all pharma companies were trying to figure out how they could use digital health apps, so they financed accelerators, incubators one after the other

[00:01:26] In the last few years, we've seen many notorious cases where partnerships between tech companies and pharma failed

[00:01:34] For example, assuming unicorn, proteins that designed digital sensors equipped fields when bankrupt in 2019 after Otsuka pharmaceuticals pulled out of a funding ground

[00:01:50] Peer therapeutics the guiding star in the digital therapeutics space the leader in FDA cleared prescription digital therapeutics, partnered with Novartis but in the end the company filed for bankruptcy in 2023

[00:02:06] So where is pharma in relation to digital health and digital therapeutics?

[00:02:13] This is the topic of today's episode discussed with Amir Lahau Digital Health Innovation Advisor for pharma and medtech from the US

[00:02:24] We discussed where is pharma in relation to digitalization, AI and digital health

[00:02:31] He shared his thoughts about the impact of AI on biotech, the state of decentralized clinical trials and potential of tech for improved drug development

[00:02:43] Enjoy the show, and if you haven't yet, make sure to subscribe to the show wherever you get your podcast

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[00:03:13] Now let's dive in

[00:03:32] Amir Lahau, thank you so much for joining the faces of digital health

[00:03:36] For a discussion about biotech and AI biotech and digital health

[00:03:41] The two fields are converging a lot, they also differ a lot

[00:03:46] I guess biotech is much more science based and you as someone who is working very closely with pharma clients

[00:03:52] And with medtech companies as an advisor, as an expert, you know the trends, you are aware of what's happening in the field

[00:04:01] Thank you so much for having me, I'm really excited to talk to you

[00:04:05] I obviously need to start with AI because biotech is actually among the areas that have been in the AI space for the longest

[00:04:13] And what I'm wondering as a warmup question is what are some of the groundbreaking things in 2023 that you saw?

[00:04:24] I see personally 2 trends of the use of artificial intelligence in biotech and pharma

[00:04:32] The first one has to do with the drug development process

[00:04:36] We know that it takes sometimes 8 to 10 years to bring drug to market

[00:04:41] It's a really long time, especially when there are terminal diseases and people die and they wait for the drug

[00:04:48] Most often than not, the drug is not getting approved, so a lot of those investments actually doesn't actually go very well

[00:04:55] There is a huge trend in the industry now to develop drugs not based on actual experiment in laboratories

[00:05:02] But based on deep learning, machine learning, even the chemical ingredients and the pharma kinetic of the drugs is being curated by an algorithm by AI

[00:05:17] We can shorten the time to deliver drug to market by almost half

[00:05:21] It's gonna be much cheaper and much faster to bring the drug to market

[00:05:25] And that is a huge trend, that if it's really going to be adopted, we will see a huge difference

[00:05:32] I don't know if you're gonna see it honestly in 2024, but it's building up

[00:05:37] And I know of at least several big pharma companies, including Pfizer for example, that are using those techniques

[00:05:46] That are advanced AI to develop new drugs and test them, which means from the algorithm, right away to phase one or phase 2

[00:05:55] You don't actually have to go through the laboratory to in vitro, in vivo, in enamel and all this kind of process re-usually

[00:06:03] That's a huge trend, and I'm really curious what the future stores for us when it comes to something like that

[00:06:11] I was just reading earlier that basically, maybe it's limited to oncology, but less than 4% of the drugs that enter phase 1 clinical trials

[00:06:24] Ever get approved in the end, so that's 96% of the drugs don't make it phase 1

[00:06:33] So I think that's quite a significant number

[00:06:37] And that's a really good segue for maybe talking about another trend of using AI in the pharmaceutical industry

[00:06:44] This has nothing to do with the process of drug development, but this has to do with the actual measurements that we take in a clinical trial to determine if the drug is effective or not

[00:06:57] If the treatment group is significantly better than the control group

[00:07:03] You mentioned a high percentage of clinical trials fail, one reason could be the drug, the compound is not working, the mechanism of fiction

[00:07:13] Suck, it doesn't work, so we have to rethink

[00:07:17] But there is also a very good possibility, in some cases, that the drug is actually perfectly fine

[00:07:25] The measurements suck, we are looking to measure an improvement in someone's health, but the resolution and the tools that we use to measure it are not good enough

[00:07:37] Sometimes we measure it, for example, only at the hospital, we bring the patients to the hospital

[00:07:43] And we assume, say, this is like a 12-month clinical trial, the patients come in baseline, 3 months, 6 months, 9 months, 12 months

[00:07:53] What you get eventually is snapshots

[00:07:56] Snapshots of what the patient is feeling, whatever you measure in the clinic, it doesn't matter

[00:08:03] But think about it, the real health conditions of a patient is not a snapshot, it's a continuous

[00:08:12] And there are a lot of changes that happen between those study visits, between baseline to 3 months, between 3 months to 6 months, we are not there

[00:08:22] We are not there to measure if actually something good happens

[00:08:25] I've seen many trials where patients come to the clinic and he had a really bad day, or he didn't sleep well the night before

[00:08:32] And that affects a lot of the measurements, and you get biased measurements that are not representative of what the patient is really feeling

[00:08:40] If we were home, with the patients, where most of the health changes actually occur

[00:08:48] And we were there using wearable devices, using passive sensors, using clinical surveillance technology at the home environment

[00:08:57] Including even smartphones and mobile apps that can measure voice and motion and video, and a lot of things that can be analyzed by artificial intelligence

[00:09:08] When you deal with the data that is captured in the clinic, between us, it's not a very complicated data

[00:09:17] Say you came to the clinic to draw blood, or to get an MRI, it's very straightforward, you get the results

[00:09:22] But when you deal with data that is continuously measured throughout the 12 months period of the trial

[00:09:29] You get to that niche of AI that is beyond the human ability to analyze, you get to a huge amount of data

[00:09:41] And even if you look at the data, you are like what the heck I am, I don't know what to do with it

[00:09:47] I need an algorithm, I need something, I need a scene-eye dog to help me because I get blind by seeing so many numbers

[00:09:55] And this is where AI can help with analyzing continuous data, or more frequent data, it doesn't have to be continuous

[00:10:05] And that is collected in clinical trials, not just during the traditional study visits

[00:10:12] What that data is going to do, it will increase the power

[00:10:17] To the point that you don't need so many patients in a clinical trial to show significant difference between the treatment group

[00:10:26] And the control group, because you have almost hundred times more data point

[00:10:32] If you don't need so many patients, it means the time, the text to do the trial is much shorter

[00:10:38] Maybe the budget is obviously lower, but most importantly you get the resolution that you need in order to capture changes in somebody's health

[00:10:48] I can give you an example if you want from oncology

[00:10:52] I'll ask you a question actually, if that's okay

[00:10:56] In oncology trials, in oncology is not my saying "I'm in the normal, muscular where this is"

[00:11:01] In oncology trials, what is the most common primary endpoint in a clinical trial that we measure?

[00:11:10] I actually don't know

[00:11:13] So there are usually two that are most common, very intuitive

[00:11:20] One is tumor size

[00:11:24] We give one group of patients a drug, the other one gets placebo

[00:11:29] We measure the two more size, if the true tumor as a cash rank

[00:11:33] That's generally a good thing in medicine

[00:11:36] The other thing we measure is survival

[00:11:38] Those people tend to die if our drug can give them, I don't know, 18 more months of life

[00:11:44] Maybe for you and me it's nothing but for them, these are 18 more months of memories with their families

[00:11:51] That could be important and drugs get approved for them

[00:11:55] The point that I'm trying to make is that there are so many oncology trials where

[00:12:01] The drug was approved because the clinical trial results show that the tumor was much smaller in the treatment group

[00:12:13] But at the end of the day, if you ask the patients, how do you feel?

[00:12:17] How do you feel?

[00:12:20] They say, not good

[00:12:22] I feel like my life is in hell

[00:12:25] I still have no energy, I don't want to get out of the house

[00:12:29] My fatigue level is incredible, I have headaches

[00:12:33] Yes, the tumor has shrunk, but the patient doesn't feel good

[00:12:38] So I'm asking FDA in this case

[00:12:42] Step is really the whole idea of a clinical trial. Is it to prove that the drug is effective,

[00:12:48] that the mechanism of action is working? Okay, if that's the goal, yes, that's perfect.

[00:12:55] But that's not a goal. The goal is to prove that the treatment is effective. It doesn't

[00:13:00] matter if the clinical trial met the primary endpoint, it matters at the end of the day

[00:13:05] how the patient feels. How does that have to do with AI? It has to do with all these measurements

[00:13:11] that I was referring before with wearable devices, for example. With smartphone apps that you

[00:13:17] can measure continuously at home. Without those measurements, you will not know if the

[00:13:23] patient is actually feeling good or bad. There's definitely been a lot of progress made in

[00:13:29] terms of the patient reported outcomes, how you actually collect them and while the survival

[00:13:36] rate for many cancers hasn't really improved like the quality of life with new drugs has

[00:13:41] and that's basically the biggest impact that I've seen in drug development so the quality

[00:13:47] of life of patients that improve. So it's not just about survival, it's also about actually

[00:13:53] having a life as you weren't describing. Exactly. The fact that we should focus on quality

[00:14:00] of life, that's not the innovative part. That FDA is aware of it. The innovative part is

[00:14:08] patient reported outcome, subjective reports of patient being asked by a questionnaire.

[00:14:14] How did you feel over the last two weeks? And I need to think, "How did I feel over

[00:14:19] the last two weeks?" And I rated, "Okay, seven here from zero to 10, I put five. This

[00:14:25] is not a quantitative objective measure of health. This is a very subjective evaluation

[00:14:32] of sick patients who are is given some rating. People have very different pain threshold

[00:14:39] and a lot of biases. It's almost embarrassing to me that in 2024, we decide whether the

[00:14:48] drug is effective or not, simply based on these subjective measurements. Whether you

[00:14:54] have equipment with sensors that you can measure fatigue in a very precise way, in

[00:15:02] a very objective way. That, in my opinion, is the innovative part that we need to see

[00:15:08] more in a clinical trial. I see trend, I see pharma companies are deploying more and more

[00:15:15] measurements like that, continuous, more frequent measurements that needs AI and machine learning

[00:15:22] to be analyzed in order to make those patient reported outcome more stronger. And we can

[00:15:30] build trust in those measurements as opposed to just subjective questions.

[00:15:35] You talk to a lot about innovative approaches to measuring various patient data related

[00:15:44] to treatments. And we've been talking about remote or virtual or at home clinical trials

[00:15:51] for years, and what you were describing is the regular setting in the hospital. So what

[00:15:57] I really wonder is how much do you see that virtual clinical trials are already used?

[00:16:05] Because I still am hearing a lot of the same problems in drug development, in clinical

[00:16:11] trial design, in patient recruitment, and challenges related to that.

[00:16:17] I think that the concept of virtual clinical trial, decentralized clinical trials, thanks,

[00:16:24] I was looking for that story.

[00:16:26] People have, I think, used that too much of a buzzword. To be honest, every clinical trial

[00:16:33] should have decentralized elements, should include more data that is being collected

[00:16:41] virtually at the home environment in order to capture the reality of the patients. And

[00:16:47] not just snapshot in the clinic. Yes, there are a lot of advantages to do remote clinical

[00:16:53] trial or decentralized, but I'm not suggesting that drug trials should be completely virtual.

[00:17:00] There are things that you cannot chip an MRI machine to the home environment, right?

[00:17:05] You will not send somebody to do by a muscle biopsy at the home. There are things that

[00:17:09] needs to be at the hospital. Sometimes the drug delivery itself needs to be at the hospital.

[00:17:15] So decentralization is more of a mindset that should be adapted by pharmaceutical companies

[00:17:24] when they design the protocol to enrich the measurement and include more measurement

[00:17:30] at home and allow patients, if possible, to do things remotely without bringing them to

[00:17:36] the clinic if it's absolutely unnecessary. That's at least my perspective on the decentralized

[00:17:43] trial.

[00:17:44] If you look at the conversions of digital health and pharma more broadly, I still remember

[00:17:52] in 2015-16, here in Europe, like in Germany, there were so many accelerators after basically

[00:18:02] bears, grants for apps was established. It seemed that every pharma company wanted to

[00:18:07] have an accelerator, and it also wasn't quite clear what exactly do they want to do with

[00:18:11] digital health. They supported some companies and then in the following years, some accelerators

[00:18:17] became more targeted and they basically the pharma companies chose specific areas that

[00:18:24] they were researching and were looking for startups that were developing specific solutions

[00:18:28] for those therapeutic areas. So where do you see that pharma is today in relation to digital

[00:18:35] health?

[00:18:36] Yeah, I think it's a really interesting perspective that you bring. You're absolutely right. The

[00:18:40] digital health surged around this time in 2014, 2015, 2016, and a lot of pharma. So it

[00:18:48] was almost like a mini pandemic of digital health if you want to the point that I think

[00:18:55] it was done in many ways without much strategy. That resulted in a lot of companies developing

[00:19:03] accelerators, developing sharp tanks, developing center for excellence in digital health.

[00:19:09] It become to the point that a lot of big pharma had so many people with digital intertitles

[00:19:15] that you even day within the company. They didn't know what the other person is doing.

[00:19:21] There was so many duplicative efforts and inefficiencies and the need to digitize or

[00:19:29] digitize the way pharma behaves that becomes like almost a mission, but the strategy was

[00:19:35] not there. And that resulted in a lot of crashes. We saw many crashes in Novartis a couple of

[00:19:42] years ago recently at Biogen, Pfizer shutting down some of those big kingdoms that they

[00:19:49] built for digital health. And now they're scaling down after realizing, you know what?

[00:19:57] Maybe we need to strategize this a little bit better and see what works and what doesn't

[00:20:03] work and how we use our resources more thoughtfully. Yes, we can develop an app that will be a

[00:20:11] companion app for this new drug. Before that, people do it because it was sexy, it was attractive,

[00:20:18] it was cool, it was new. Maybe even it could increase adherence and infections would want

[00:20:27] to use the Pfizer drug because it comes with an app and they will prefer that over a drug

[00:20:33] from there. But now it's not a big deal. I think pharmaceuticalians should realize that

[00:20:39] technology is there, but without the stickiness, without engagement properties that are built

[00:20:47] in very wisely into those apps, patients will download them, but will not use them. And without

[00:20:54] analytical, very sophisticated, analytical, infrastructure that needs to be in the back

[00:21:01] end to analyze the data that is collected by those apps. If you don't have that, you

[00:21:07] basically build a house without foundation. And this is where I see a lot of people coming

[00:21:13] back to that sort of smaller team strategy in a way learning from their own mistakes.

[00:21:21] What are some of the things that you found, perhaps, inspiring or good use cases in terms

[00:21:28] of the digital health strategies that you see in pharma? Because I think like the whole

[00:21:34] fact that pharma is scaling down with the examples that you mentioned. Biogen closed

[00:21:41] their digital health section last year in 2023. We had also various other examples of pharma

[00:21:49] supporting digital health companies that either went bankrupt or just somebody would plug.

[00:21:55] And that's discouraging for the digital health industry because the demands for clinical

[00:22:02] trials, for evidence that you need for something digital to be approved is rising. And the

[00:22:09] money that you need to actually bring all that together is really hard to get. And pharma

[00:22:15] is an industry that actually has that capital to be able to support endeavors such as data

[00:22:20] gathering and clinical trials on top of having a lot of experiences in how to actually conduct

[00:22:27] a clinical trial that startup might not even have. So what do you see that the whole kind

[00:22:34] of boiling down of the excitement, let's call it like that, will do to digital health?

[00:22:42] I see it very differently. I don't think that there is a lack of interest or some sort of

[00:22:47] something that impede on this excitement and mission. The scaling down does not reflect

[00:22:54] the lack of interest or importance. It simply means in my opinion efficiency. I'm in touch

[00:23:01] with a lot of pharmaceutical companies that I'm either advising to or sometimes have advisory

[00:23:08] board position with tech companies that sell to pharma. I don't see anything close to lack

[00:23:13] of interest. What I see is that they have a smaller team that are more strategically

[00:23:20] moving the needle in order to make it right. The biogen shutting down the digital health

[00:23:28] does not reflect in my opinion any trend in the industry because I see so many clinical

[00:23:34] trials. In fact, deep or in digital health technologies. I see FDA. When I in 2015, I

[00:23:44] remember FDA question, a static protocol that in in in in lower muscular disease that included

[00:23:52] a wearable device to measure stride lengths and stride velocity to see if it's a nor the

[00:23:59] generative disease, do shared muscular dystrophy, FDA question, why? What the heck, why do we

[00:24:06] need it? Now in 2024, 2023, if you design a study for the share muscular dystrophy, then

[00:24:14] you want to measure disease progression and you do not include a wearable device FDA will

[00:24:20] question. So I think even at the regulatory level, we see so many guidelines, yes, sometimes

[00:24:27] those guidelines are a little vague and there's some confusion and we do fly the plan as

[00:24:34] we build it. There's a lot of learning, but I really think this is more efficiencies and

[00:24:40] not a reflection of the field being going the wrong direction. Digital therapeutics is

[00:24:46] a totally different area. If you refer before to bankrupt or company that had to shut down

[00:24:53] computer a therapeutic, that is a different category. Digital therapeutics has its own

[00:25:00] opportunities and challenges, but if we just talk about digital health technologies in

[00:25:07] healthcare or in clinical trials and the use of AI, I see it booming and not wielding.

[00:25:13] Yeah, I was just thinking that maybe the general expectation is too high in terms of how many

[00:25:21] solutions need to succeed, which brings us back to those less than 40.

[00:25:25] 4% of drugs that actually go from being eligible to start the phase one click or trial and actually getting the approval.

[00:25:34] Maybe we just have to expect a similar metrics in digital health.

[00:25:38] You asked a really important question before, what trends do I see with these new smaller teams of digital health across the industry?

[00:25:48] I can tell you what I see. Part of it is what I try to bring to pharmaceutical team when I talk to them, and I be in a thought partner to help them decide how to improve their digital measurements in their trial to improve and increase likelihood to improve efficacy in the trial.

[00:26:09] The first one, the first trend, people in the digital health industry are collaborating across the pharmaceutical industry - share knowledge in a non-competitive fashion.

[00:26:23] I've personally participated in several forums like that last year, where people coming from all the pharmaceutical companies sitting together and talking about what failed and why.

[00:26:37] They want that these lessons were learned, so we don't burden patients again.

[00:26:43] We don't share the secrets of how to develop the compound of the drug.

[00:26:48] This remains completely separate, but we do share a lot about the methods, the analytical methods, the digital technologies, the logistic how to deploy it.

[00:26:59] I see a huge trend about companies not being afraid to share what didn't work.

[00:27:05] The other thing that I see and maybe - I don't see it much, but I'd like to see - this has to do with airplanes and long runways.

[00:27:14] We talked about how it takes about almost 10 years to bring drug to mind, that's a long run.

[00:27:20] It means that somebody at the company leadership is willing to put this end deep in the pocket, commit to a huge budget, a long-term view.

[00:27:31] And during this 10-year period, we expect to fail several times, but eventually, we'll make it.

[00:27:40] My experience with digital health is that the leadership in the pharmaceutical industry, the executive leadership, they don't have such patience for a digital health solution.

[00:27:54] They don't have long runways. They can give you a year or two years. Show me that it works. If it doesn't work, okay, budget is cut.

[00:28:07] Goodbye. And I think this is something that I'd like to see being changed, so that those leadership understand that it takes several more years, and the way to do it - at least the way I recommend pharma companies to do it -

[00:28:22] you have a phase zero, you have a phase one. These are amazing opportunities to piggyback on those opportunities, to pilot some of your digital health solution, say you have an app that measures cognitive function and you have a clinical trial in Haalton.

[00:28:42] You don't know how this app that measure cognitive function is going to work. Is it going to be validated? Will you use it in phase three?

[00:28:50] Okay, don't wait to phase three. Use it in the very early phases to pilot it, so you have an opportunity to fix things, to change things, to build trust in that product so that when it graduates to that phase three trial, phase four, you have a digital solution that really went through the process of validation, and you get what you want.

[00:29:17] Or no, you have an opportunity to have and go, no go decision.

[00:29:22] What inspires you most in terms of the progress that has been made in pharma, in biotech, either related to the scientific progress and related to technology, big data, secondary use of data, and AI?

[00:29:40] I think that's one of the most common things that I hear, pharma discussing, is precision medicine, the increase in targeted therapies, the hope that basically you're going to get the exactly appropriate drug for your specific condition, genetic variation, etc.

[00:29:59] So, there's no shortage of ideas about the possibilities, but the actual progress is somewhere else. So I wonder what's your perspective on where it is, like where precision medicine is?

[00:30:13] Two things inspire me, or I would say I look forward to see how it evolves. I'm really curious to see. One has to do with the actual development of the drug, the fact that you and I may have the same disease, but the AI algorithm will create, curate a different drug for each of us.

[00:30:33] Remember, even drugs that are approved by FDA, they're based on results of a clinical trial. Not every patient in the clinical trial behave the same. There are significant amount of patients in the clinical trial.

[00:30:48] That drug didn't actually work on them. It's just that the majority of the patients, it worked, so that's why the drug is approved.

[00:30:56] But AI can benefit us by answering the question of why it didn't work for those individuals in the clinical trials.

[00:31:06] What do we need to change in the formula of this drug to make it workable for everybody, so it's effective, and that curation in the individualized precision medicine, this is something that I'm super excited about.

[00:31:22] And hopefully, that well, maybe not in my lifetime, but maybe a little later. The other thing that I'm really excited about precision medicine is a lot about the drug itself, but the way it is administered, and the holistic view of managing yourself and manage your disease.

[00:31:40] AI that guides you in every aspect of your life in real time and give you feedback can really help you live better, make smarter choices, and you almost have the virtual friends that guide you, invisible friends that guide you, how to manage your life and manage your disease.

[00:32:01] I feel that also developing quite a lot in relation to medications and pharma is pharma kugenomics, but I don't really often hear that kugenomics would be tied into clinical trials.

[00:32:15] I do see it, though, in the mental health psychiatry space. We all know about depression and other more advanced diseases like schizophrenia and post-COVID.

[00:32:28] A lot of people talk about depression much more openly, because it becomes like a second pandemic. They are not afraid to say, "I'm taking anti-depressant."

[00:32:38] But if you ever take an anti-depressant, you know that those psychiatrists are not magicians.

[00:32:45] They use a lot of trial and error, try this, maybe try this combination. They send you home with different options, you come back and you are your own doctor. You tell them how you feel.

[00:32:57] That's exactly why in psychiatry they develop this genetic profiling that you can go through that step and it will tell you which drugs are more likely to be effective on you.

[00:33:10] Not everybody does it, because it's obviously not reimbursable by insurance at this point.

[00:33:15] But I think that it should, because it will minimize the trial and error to the minimum possible and people can start feeling better, much quicker.

[00:33:24] Not putting chemicals in their body, there's no chance to help them anyway. I haven't seen it much in others, but I might just not know enough about it.

[00:33:33] Yeah, even though we already have an existing knowledge about pharmacogenomics, there's still a lot that's not covered.

[00:33:42] And in a way, I'm surprised that there's not more research related to pharmacogenomics that would automatically include it in clinical trials.

[00:33:55] Even when you're very picky with the patients that you include in clinical trials, that you would also add that component, not as a research criteria, but just as a research component,

[00:34:09] a way of getting to know the patients that you chose more specifically, so you would know more about the genetic makeup of the patients that potentially didn't respond to the treatment. Does that make sense?

[00:34:22] I think we're not utilizing the potential of these genetic profiling and genetic data. There's so much data that is collected in clinical trials,

[00:34:32] but it stays within the clinical trial context, whether if we had access it, we can monetize and expand this data and put all of that in one bucket and let those deep learning algorithms give us some insights,

[00:34:48] we will know much more than the existing data that we have. Is that your point?

[00:34:54] Not quite, but it's I'm glad we're having this conversation when it comes to genetic testing.

[00:35:03] You'll hear it's getting cheaper, it's getting more affordable, more people can get it, but it's still not utilized for clinical trials.

[00:35:13] Why is genetic testing not automatically included in clinical trials, so you would expand the field of pharmacogenomics because you would now know more about the drugs and the genetic of patients.

[00:35:29] So genetic profiling should become part of every clinical trial database because this is the only way we can draw empirical connections between the treatment effect and this genetic data, right?

[00:35:45] And we can learn so next time we do a better job or have a better inclusion exclusion criteria of patients in the clinical trial and not waste patient's time if we know they have no choice to benefit from this trial.

[00:35:58] You're right, it is not part of every clinical trial and that is probably a cost issue I would imagine.

[00:36:08] What do you anticipate most in 2024 and beyond in terms of the future development of biotech and medtech?

[00:36:19] Biotech and medtech pharma and medtech are very different worlds, that's what I can tell you.

[00:36:25] Pharma is slow, they have a lot of SOPs, they are risk-averse, they're all about innovation as long as you don't change it.

[00:36:36] But medtech is fast, agile is sometimes overselling, they want to look for shortcut, they look for business opportunities.

[00:36:46] Sometimes I feel like these are big two mountains, the pharma mountain and the medtech mountain, there's a lot of ego sometimes at the top of the mountain and in the middle between these two mountains, like in nature, there's a valley.

[00:37:02] And I call this valley, the valley of miso protone.

[00:37:06] There are a lot of really cool and innovative solutions that could really bring value to patients and help patients feel better, that are end up in this valley because of lack of communication between these two worlds that are so different.

[00:37:24] I think we need to do a better job teaching medtech, how to talk to pharma, teaching pharma, how to work with medtech, so that valley will only have some water stream and not miso control!

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