Harsh Thakkar (00:01)
All right, we're live on another episode of Life Sciences 360. And today we're gonna be going into the world of pharmacometrics. the guest I have today is Mark Sheets. He is the Associate Dean of Research at the College of Pharmacy at Midwestern University. And he's going to be talking about pharmacometrics, all the different things. If you've never heard of this term, don't worry about it. We're gonna start slow, we're gonna ask some...

set the context as to what this is and how it helps bring new treatments or modify existing treatment plans. And we're gonna go into deep into all the other facets of pharmacometrics. So let's dive in and have a chat with Mark. Welcome to the show, Mark.

Marc Scheetz (00:47)
Great. Thanks so much for having me, Harsh. I'm excited to be here.

Harsh Thakkar (00:51)
Yeah, for someone who's just clicked on this episode, maybe they know what pharmacometrics is, maybe they don't know it. What do you have to say to them? Why should they stick around for the next 30 minutes?

Marc Scheetz (01:05)
Yeah, absolutely. I think that's a very fair question. So pharmacometrics is sort of a newer name that we've given to some older science that people may have heard about, which is pharmacokinetics and pharmacodynamics. And really what pharmacometrics seeks to do is to start to blend all of those things together, and then really use them in real time to make decisions for patients. We use those to look back, as we'll talk about, we try to have a

Harsh Thakkar (01:20)
Hmm.

Marc Scheetz (01:35)
a good looking glass back and then we try to also try to look forward as well.

Harsh Thakkar (01:41)
Hmm. And some of the stuff that you're working on, right? Like you've been in this space, you are a professor, you're constantly, you know, reading and keeping up with all the research and all the trends that are happening in this space. When did you realize that this pharmacometrics and the other supporting areas, this was a field

that was going to change the field of medicine for the greater good.

Marc Scheetz (02:16)
Yeah, absolutely. So, you know, as a pharmacist, I've really always been drawn to data and really using data to try to figure out how can we do things better? How can we take advantage of what we know to then better predict the future? I would say that pretty early on in my career, I started to see that there

there is an immense capability of some of these predictive models for being able to help us rather than just using every individual person's intuition, right? So that having a model, having some way of guiding us that is based on prior events and what we know about the predictors of those prior events.

to make our decisions in the future. And really that's a lot better than just trying to treat the patient in front of you really without data and without any additional support.

Harsh Thakkar (03:25)
Right, yeah, I could see how that's helpful, right, is having the data, not only just having any data, but having the right amount of data at the right time, and then also having the skill set to analyze that data and use it to make adjustments or modifications to that patient's plan, or if it's in clinical research for any of that. So that is really important.

So when we talk about pharmacometrics and how it can help tailor different treatments for a patient or for somebody who's not in life sciences, what is an unexpected way that pharmacometrics can be helpful to them? Can you make it more relatable to them?

Marc Scheetz (04:19)
Sure, absolutely. So let's just take the example of you needing to take any drug that you might need when you come into the hospital setting. If you find yourself in a very unfortunate situation that you're septic, that you potentially have a bacteria in your blood, and it's this medical emergency that is treatable with something like antibiotics. Really, the status quo.

Harsh Thakkar (04:27)
Hmm.

Marc Scheetz (04:48)
What's going on even at many places today is that you show up at the hospital and they give you a dose they give you a dose of an antibiotic that doesn't vary Really much across the patient population They're going to give you the same dose whether you weigh 300 pounds or you weigh 100 pounds There really isn't that much difference between the dose that you might receive But just as you would expect

Harsh Thakkar (05:09)
and

Marc Scheetz (05:17)
You as an individual, you've lived your life, you know that you are not a mean. You are not simply the average person in looking at an entire population. You're an individual and you are likely to act like yourself moving forward rather than to do something like regress to a mean. So a lot of what the older data, a lot of what the older science was, was to find

Harsh Thakkar (05:40)
Hmm.

Marc Scheetz (05:47)
the best average dose for a population of patients and to treat patients with that. The problem with that is it's not always timely. We might miss, we might miss high. If we miss high, we can end up with some toxicities. We might miss low. If we miss low, then we don't give enough of the drug and you may not be able to treat the infection that you're presenting with. So really what pharmacometrics is aiming to do

Harsh Thakkar (05:56)
Hmm.

Marc Scheetz (06:15)
is really to use the best looking glass back. looking in the rear view mirror to figure out what has worked best for people over time, according to certain classifiers, things like what their renal function might look like, things like what their body size is. But then, even beyond that, we know that if you were to create a group of patients that look just like you, patients that are the same size, patients that have the same renal function,

there is still quite a bit of variability. And that variability for some of these antibiotics or many other drugs, you know, is in the 10 to 100 fold range. So what we're trying to do is we're trying to get a better idea of what the right dose is for the individual patient, not just the patient that happens to weigh 80 kilograms and has good kidney function.

Harsh Thakkar (06:53)
Mm.

Yeah, that's such an important distinction that you made is that there is no differentiation in the dose based on either the weight of the patient or any other attributes, right? where they are or what, I mean, maybe there is some, like I talk to my family in India and sometimes they're like, yeah, if we get allergy, we get like this drug.

And then I tell them, but we don't have that here. We get this one. So yeah, maybe in countries and population, there might be a little bit difference, but at the end of the day, we're all still getting like sort of the same kind of drug. And what I want to ask you, and this is a question that I'm sure you've got hundreds of times before, is what you are sort of explaining is how can we get the data or get

information about that patient's genetic makeup or whatever's going on in their body and then use some of it to see if they're still going to take this one size fits all drug or do they need something different, right? And it's almost like we're going, I don't want to say the word, but we're going in this term of personalized medicine. And when I say that, people probably hear it and they're like, wow, that sounds really nice. Personalized, right? So every everything is personalized, but

for you as an expert in this space, what are some of the roadblocks to making that a reality and how as the industry are we trying to overcome them?

Marc Scheetz (08:46)
Yeah, absolutely. So there's been a lot of talk about personalized medicine, precision medicine. And a lot of the talk about personalized medicine, precision medicine has really focused on genotypes. So is there something about a patient's genetic background that we can measure, that we can go to the lab and send a sample and say that this person has

Harsh Thakkar (08:55)
Hmm.

Marc Scheetz (09:14)
this certain type of genetic background, and then this is what we expect to happen with the drug. Now, where I hope we get to is that personalized medicine really starts to link up with something that we're calling in our field called precision dosing. Now, personalized medicine is using a lot of these genetic factors. They might say something like a person metabolizes a drug faster because of their certain genetics.

Harsh Thakkar (09:30)
Hmm.

Marc Scheetz (09:40)
their genetic background, their genetic makeup. And that absolutely is true. So you have some number of people, you can even just think about it in terms of nutrition. give the same, well, you give two different individuals the same number of calories for the next six months, and the weight gain will be different. There's something that's very different about those two individuals.

Harsh Thakkar (09:56)
Yeah.

Marc Scheetz (10:07)
beyond just the fact that you've given them the exact same foods over that exact same time. And so what precision dosing does is it really attempts to give equal exposures to the various patients after measuring things like concentration. So we look at how much drug is in the blood, how much drug is at a site that we're interested in looking at, and then we try to make sure that we get

those concentrations, those exposures to where we actually need them. Whenever I talk about using genotypes and how precision medicine can help, I like to say, and I think I've probably stolen this from somebody and I really wish I could attribute it better because I don't remember where I first heard it, but precision medicine, so the genotype of a drug, really gets you to the stadium.

Harsh Thakkar (10:41)
Mm.

Marc Scheetz (11:02)
but precision dosing gets you to the seat. Now, what that basically means is you can get to the general correct area by knowing what a patient's genotype is. Are they a fast metabolizer or are they a slow metabolizer? Is that drug being cleared quickly? Is it being cleared slowly? But it really doesn't get you beyond that. So even once you know something like if they're a fast metabolizer or a slow metabolizer, you still have about 10 full differences usually.

Harsh Thakkar (11:05)
Mmm.

Marc Scheetz (11:32)
between patients in the exposures that they might achieve. So it's really nice to be able to use some of these measurements from individual patients to treat them as individuals. But really both fields do need to come together. Precision dosing works best after we have some data about the patient. So things that we have measured and we know how that patient is going to respond.

Some of the other things that we're hoping to gain information on really help us with the first dose. So before we've ever given a patient a dose, how are they likely to respond? So pharmacometrics really is at this point where we're able to predict how a patient will respond to a drug at the population level, even before you take it. However, the most powerful feature is that once that individual data is collected,

Harsh Thakkar (12:13)
Mmm.

Marc Scheetz (12:27)
we have a much better idea of how to help that individual in the future. And that's changing the way that we're treating patients.

Harsh Thakkar (12:35)
Right. Yeah. And I can resonate with everything. And that quote that you said, I'll be sure to go back and recap that. that makes the big takeaway there is you can get close enough, but you're not going to be at the right, exactly at the right amount. And that's why, or the precise location. So that's super helpful.

I know that you're working a lot in the infectious disease space. Do you want to share with us, like, what are you currently up to or any trends or insights from your work that you want to talk about?

Marc Scheetz (13:19)
Yeah, absolutely. I think we have a lot of very exciting things going on in the lab. you know, I'm always indebted to the postdocs and the technicians and all the people that we're working with that really enabled me to be here on this podcast with you talking about the great things because they're in the lab, they're doing the work. really, my lab is really focused in two different areas right now. One of the major studies that we have going on

Harsh Thakkar (13:37)
Hmm.

Marc Scheetz (13:47)
Kevin Downs and some collaborators at Children's Hospital of Philadelphia is using something that is a unique collection tool to be able to get extremely small samples of blood in our young children that are in multiple organ dysfunction syndrome. And so we're trying to use very small samples of blood and some of this predictive modeling to figure out the best doses for children that require antibiotics that are really in dire need.

the type of patients that if they don't get the right dose, things may not go very well for that child. So very excited about that work. We're enrolling from about 14 pediatric ICUs around the country, and we'll be sharing some of those data coming up at actually at ID week, which is just around the corner here. In fact, my next meeting is to meet with our postdoc and to...

Harsh Thakkar (14:40)
Okay.

Marc Scheetz (14:44)
finalize our model. It's already starting to look pretty good, but to finalize our model for some of those antibiotics. We're looking at six different antibiotics in that study. And then another half of the lab is working on some translational type aims. I've gotten really interested about antibiotic toxicity. And when I trained and we put together these population models, the way to

make the models work best was to give larger and larger doses because we were really concerned about getting enough antibiotic to the patient to be able to treat the infection. And that served us well for such a very long time. The problem is, that the bacteria now have acquired quite a bit of resistance and you have to give more and more of those antibiotics for them to be effective. So now we're starting to run into toxicity concerns. So

Harsh Thakkar (15:36)
Hmm.

Marc Scheetz (15:42)
The other sort of half of our lab is looking at antibiotic toxicity, toxicology, and we're trying to think of ways that we can give the antibiotics in a smarter way. So to make the exposures good enough to treat the infection, but not high enough to cause toxicity, as well as some pharmacologic ways. We're working with some really bright medicinal chemists to see if we can sort of steer the antibiotics away from the places that they're causing.

the toxicities.

Harsh Thakkar (16:13)
Okay. And, for, for somebody that is currently taking any of these medications or antibiotics, and maybe they are, they've also faced this, challenge of, I'm not seeing the same effect now. Maybe, you know, it's, it's the resistance. so resistance to, to antibiotics definitely is, is a concern, is a challenge. I've seen that in the past with some of the stuff I've taken. but.

Like, is there like one solution that works for all types of antibiotic resistance? Or are you researching on what are the different ways and then based on what that patient is taking, maybe there is one way to help them.

Marc Scheetz (17:00)
Yeah, absolutely. I wish there was one way and I wish that there was a single discovery that we could award somebody a Nobel and say that problem is solved. Unfortunately, it's really this constellation of many problems that are combined together. We, the collective we, the scientific community will test bacteria in the lab and see how much of an antibiotic is required to

Harsh Thakkar (17:04)
Yeah.

Marc Scheetz (17:29)
inhibit its growth, and we call that the minimum inhibitory concentration. And so we already have that at hospitals, really around the country and worldwide that's being used. But there is this complex interplay, even beyond how much antibiotic in a test tube needs to be used to inhibit a bacteria's growth. We're learning that the bacterial genome also plays a role as well. so

Harsh Thakkar (17:55)
Mmm.

Marc Scheetz (17:56)
that the numbers that we get out of the lab there really do need to be mixed with some of the genotypes from these bacteria. I think that's a really exciting part of the field is that we, as with each step we take closer where we get more and more information, we realize that there's still more to learn. And so while we're getting better with many of these treatment options, it's still really a, you know,

excellent field for our young scientists to go in. I don't think this is going to be a problem that's going to be solved in my lifetime, but I think we're going to solve a lot of the problems. And so we look forward to the younger generations entering the field, really trying to reinvigorate the antibiotic markets and research. There's been a little bit of a disincentive in the sense that in the antibiotic development field,

Harsh Thakkar (18:31)
Hmm.

Marc Scheetz (18:52)
There's a disincentive for companies to really pursue antibiotics. And we're looking at lots of different ways, again, we, the collective we, the societal we, of how do we get companies interested in creating antibiotics, even though patients may only take them for a couple days of their lives. They are life-changing drugs and we need to treat them that way.

Harsh Thakkar (19:16)
Yeah, that's really helpful.

I'm gonna pause here because I have a train running close to my house.

Marc Scheetz (19:26)
Yeah, of course.

Harsh Thakkar (19:29)
I'm sure you can hear that.

Yeah, okay

thing is done.

All right, so where was I? OK, yeah, I know what I was going to ask you.

Okay, so you've talked a lot about the importance of data, having the data at the right time earlier on in the episode. And I can't help but ask you with everything that's going on in the world of artificial intelligence and machine learning, I want to go at the intersection of artificial intelligence, machine learning and pharmacometrics. So when you're looking at that intersection,

being an expert in who is going to be impacted by these technologies, what do you think is one good thing or maybe one bad thing that would happen with the integration of AI ML and pharmacometrics space?

Marc Scheetz (20:54)
Yeah, absolutely. So AI is definitely the buzzword. Machine learning is definitely the buzzword. And I think we're all really excited about it. I think we're very interested to see where this is going to start having tangible gains in the field. And I think there are going to be applications like in model finding, how do we find the right models faster? How do we get those in front of our patients more quickly?

How do we send the right data to the right person at the right time? So even having the right answer simply isn't enough, right? I think we just saw this even with the weather prediction tools. And if you asked me before this recent hurricane came through, who is most likely going to be impacted? I would have said the coast, of course. And we're seeing that that's not true.

The weather models were predicting that early on, but that wasn't communicated effectively. And so it didn't translate into sort of the meaningful change that is needed, even though the models were doing a really good job. And so I think AI is going to help us there. It's going to help us create models faster. It's going to help us get the models in front of the right people. And it's really going to enable people to make decisions.

The way I sort of view all of this is the way that I sort of view taking a flight. Now, so many of these decisions that need to be made are extremely complex. And we know that in flights, we already have drones that are capable of self-flying on their own. But if you were to take a plane today, it's going to be flown by a pilot and a co-pilot. We don't even trust a single pilot for our commercial planes.

Harsh Thakkar (22:27)
Hmm.

Marc Scheetz (22:51)
So we have this pilot and this co-pilot. And even with all of the great things that AI does for the pilot and the co-pilot, the pilot is still landing the plane. They say that if you or I were on a commercial aircraft and the pilots became incapacitated, there's a really low chance that you or I would be able to land that plane if we hadn't had significant training in doing so.

And so I think that's where AI is going to help us. It's really going to take away 95 % of the noise. It's going to take away many of these things that are extremely dangerous, have the potential for extreme danger. It's going to make many of those things extremely mundane so that the clinicians can focus on the most important aspects of care so that they can maybe have three choices in front of them and decide which way to move forward for the patient.

So think it's going to allow a more direct focus for the clinicians at the bedside.

Harsh Thakkar (23:52)
Mm-hmm.

Yeah. That is, I've talked about AI with a bunch of people on the podcast, because it is, I mean, almost every field in life sciences has some impact and something is going on with AI. But that example of the airplane and the pilot, co-pilot, that was really good. That's one example that I'm going to remember for a long time, because I've said this to many other people.

even clients, consulting clients that I work with, because we help them, you know, implement different technologies and tools. And I always tell them like, Hey, AI is going to take away the 80 % of the stuff that you don't like doing. But what you're doing today is you want to focus on the 20%, but because of, you know, your inefficiencies in your process or

not having the right tools or not understanding how to manage different aspects of your job or whatever it is, you're probably spending a lot of time doing the 80 % that you don't want to do. And AI will take some of that burden off you, and it will give you more time to do the 20 % that you actually want to do, which is to think, make decisions, run different experiments, analyze the data, what it's saying, do it again.

You know, that kind of stuff. And AI is not gonna do it all by itself. You're still gonna have to give it some strategy that, hey, this is how we're gonna do it. Now run the numbers, run the data and tell me what you see. And that example is a really good one. I'm gonna use that and I'll give you credit wherever I use it. But that was really good.

Marc Scheetz (25:38)
Absolutely.

Harsh Thakkar (25:39)
So you mentioned about, you're excited about the future of the different, about the different, the newer professionals that are coming into the industry, the new clinical pharmacists that are going through, doing a lot of the research. What's one trend that you have seen that you're most excited about? Because I'm assuming that education and research today is

is vastly different than how it was 20, 30 years ago. So what are you most excited about?

Marc Scheetz (26:18)
Absolutely. mean, I think it's really easy to look at the world and get depressed. There are so many things that are going on that are trends that we want to go in a different way. But there's so much on the other hand that really is truly exciting. I'm really excited about the way that we're starting to use computer software in many of these cases where when I started my career,

Harsh Thakkar (26:31)
Hmm.

Marc Scheetz (26:46)
the idea of having some sort of software calculate a dose, calculate an exposure, calculate what was going to happen for the patient. Those were sort of viewed as, they're a crutch, they're an interesting talking piece, but they aren't really ready for prime time yet. And as software and electronics have

really become the tools that the younger generation are coming out with. They don't even remember a time when they didn't have email. So it's already really deeply ingrained in them. And they're willing to assess the software and the predictive capabilities of the software and really embrace them when they work. Whenever I talk to people about using software to calculate doses,

Harsh Thakkar (27:26)
Hmm.

Marc Scheetz (27:44)
I really do get a range of opinions of how is that possible? I can't possibly do a better job than I can do as a clinician with X number of years under my belt. our clinicians are brilliant. Our brains really are. We can talk about Bayesian math in a little bit, but our brains really operate on these Bayesian principles. They take the past history and they see what's in front of them and then they use that to make a prediction.

about what is best, what's the best decision for the future to go the way they want the future to go. And when we talk about our young clinicians, they just are a little bit more ready to adopt some of these new softwares. The other example I really like to talk about is thinking about some of these dosing softwares and precision dosing very similar to our navigation tools that we use. Now,

Harsh Thakkar (28:18)
Yeah.

Hmm.

Marc Scheetz (28:39)
I'm old enough, I have enough gray hair that I had a hard notebook atlas for the United States in my glove box when I had my first car. Whenever you took your first journey, you were on a road trip, you had to have that atlas with you. You had to know if I'm in the middle of Kentucky and the highway shuts down, how can I get to where I'm going? And that was just...

Everybody had one. And then they came out with some personal navigation software that used satellites and some of the early ones. I was an early adopter and I had one of those. It was almost a mini TV size on my dashboard. But what that did is allowed for a real time look using the data that you were putting in. So where you were, your speed, how fast you were going and other traffic.

Harsh Thakkar (29:21)
Hmm.

Marc Scheetz (29:36)
And it helped to come up with a new plan for you. Now, early on in that technology, I mean, I think we called it getting quested, getting map quested, where the idea was that what it had selected for you wasn't really that appropriate because it split some hairs and then it wasn't using the best or real-time data to update the answers.

Harsh Thakkar (29:39)
Hmm.

Yep.

Marc Scheetz (30:02)
Today, you'll be really hard pressed to find a single person that doesn't use their phone to aid them in navigation. It really has been adopted by many people. It has been crowd shared, so lots of data go into bettering these models. And then on the developer side, they've made much better models. And in the end, it results in a much better product for everybody.

At the end of the day, there is not a single person that says that their decisions are completely driven by AI. They're not driven by AI. They get augmented support. So they're driving and they're on the interstate and they get a notice that says there may be a better route available. But then the driver has to use their past experiences to think, well, that road closure?

Harsh Thakkar (30:39)
Hmm.

Hmm.

Marc Scheetz (30:59)
likely to continue or is it going to be temporary and I'm not going to change the way that I'm going. But in the end, the driver is in the driver's seat and they're making the decisions. And I see the younger generation just have a higher percentage of people that are okay with using that input, that software input. And I think that's something to be really encouraged about that the field has gotten much better. It's really exciting. And people are starting to adopt these tools.

Harsh Thakkar (31:19)
Yeah.

Yeah, I have to agree with you. And as you were talking about this, I was reflecting back on when I was in high school or school, even back in India when I was in school, we weren't allowed calculators in class. So a lot of the math I learned to do was by... And I know people who were listening to this and who are in those countries will relate to this. When we had a test or...

you know, any kind of math or some kind of test, we had to actually show, write and show how we calculated something. So it was calculus or statistics or whatever, we had to actually scribble and say, this is this, this is how I got this. And then we had to explain, but then came, you know, just basic calculators, then came the fancy scientific calculators. And at some point, by the time I was in college, I think we were allowed to get the, have the scientific calculators in the classroom.

But like now people don't even have calculators. Like they just have laptops and literally or phone and everything or iPad and everything as their fingertips is available. So yeah, it's definitely interesting to see how technology is getting more and more sort of a way of life, right? Like even if you think about this other example where

people who said, I'm not gonna use a smartphone. I'm fine with the phone that I have. It works great. I can call people. I don't need all these apps. But then you look at the number of phones that have been sold in the past five years, and more and more people are just saying, this is nice. It's great to have all these apps. I could use this app for reading, this app for something else. So we're slowly seeing, not just in life sciences, but...

technology is becoming more more embedded. ultimately, like you mentioned the point, the decision still is, you know, whoever's holding the phone or whoever's holding that technology to decide, you know, what road to take based on what they're seeing in amount of data they're seeing.

Marc Scheetz (33:46)
Absolutely. Absolutely. And that excites me. I think that we still will have people that get PhDs in being able to take apart the black box because that is absolutely needed. And we do need to push the fields forward. However, every user does not need to know how Google is coming up with the best directions for us. They just need to use the system and then decide.

Harsh Thakkar (33:49)
Yeah.

Yeah.

ride.

Marc Scheetz (34:14)
if it's making the right decisions for them.

Harsh Thakkar (34:17)
Yep, yep. And I also feel, I don't know what's your take on this, but I also feel that because technology is embedded so deeply in everything that we do, it's also making it easy to fail, right? So if you're building a new app, you're writing code or you're doing some research,

Technology has made it easy for you to go out there, experiment your idea, put it in a forum, put it online, have 50 other people comment on it, tell you it's great, it's not great, launch that startup, be part of some incubator that's gonna help you launch that startup, and if not, you just fail and you start again. And the time and the effort that it takes to build something

tinker with a few softwares or write some code and experiment an idea, that time is getting shorter and shorter. So I feel like for people that are in research or that are trying to build companies, software companies, it's becoming much easier for them to fail, learn, and then apply what they failed at and then go back again. What are your thoughts on that?

Marc Scheetz (35:33)
Yeah, absolutely. I think that's, we see that same pattern repeatedly with technology and that we have these very linear ramps where things get faster, better, cheaper in very linear fashion until we have that sort of logarithmic jump where things change. There's another advancement.

But absolutely, I think that we're able to conduct our experiments more quickly. dosing software that I use to put our models together and test the models, the advances on the computer that I'm using to speak to you today are greater than what my mentors were using when they would write a grant to be able to use NASA software back in the day.

Harsh Thakkar (36:31)
Right. Yep.

Marc Scheetz (36:32)
that we've gotten to a point where technology has improved a lot of the old techniques that we have and they can be applied in a much better and much more rapid fashion.

Harsh Thakkar (36:48)
Yeah, I completely agree with you. Listen, this has been a really great conversation. loved the way you explain things, the way you give examples and analogies. It really makes it easier for somebody who hasn't heard these terms or it's maybe too much of technical stuff for them. It makes it easy for them to understand what's going on.

And I think it comes for you naturally since you're interacting with students and talking to people at all level, it becomes easy. So thank you for doing all of that. Do you wanna share like where people can learn more about you or the research or the work that you're doing? What's the best way for people to connect with you?

Marc Scheetz (37:34)
Yeah, absolutely. So you can always feel free to get ahold of me by my university email and at Midwestern.edu. have our faculty profiles. I'm also still active on Twitter and my handle there is ID Pharmacometrics and I can give you the spelling of that for your listeners here. Those are probably the two main ways to get in touch with

to get in touch with me, been really excited about all of the various collaborations that we have around the country, around the world. podcasts such as these really enable us to connect with those researchers. so even the things that were some of the hard challenges for humanity when the pandemic hit, now it's very easy to communicate on.

these teleconferences and in the past that wasn't nearly as possible. You had to fly to another country and present data and then meet colleagues in that manner. And now we're able to connect with people in a very rapid fashion. So I thank you for the opportunity to speak with you today and to be able to reach your listeners and would love to connect with anybody that's interested in precision dosing and how we can marry that up with precision medicine.

really start to solve these multitude of problems that I think can improve patient care.

Harsh Thakkar (39:10)
Thank you, thank you, I appreciate it. And for the listeners and viewers, if you enjoyed this conversation, be sure to subscribe or check out some of our other videos, our other topics. We've discussed this in the past with a few other guests, and we've also had some very other interesting, really interesting topics. So be sure to check out and be sure to subscribe so that you don't miss out on the next episodes. Thank you, Mark, and have a great week ahead.

Marc Scheetz (39:36)
Thank you so much, Harsh. Really appreciate it.

Harsh Thakkar (39:38)
Thanks.