#455: Is animal testing in drug development on its way out?

we all do want to remove away from animal testing but we also realize it's not an overnight thing we can reduce study animals by 30 to 50 percent which is a step in the right direction through computer algorithms machine learning algorithms you can then do the comparisons with the treated animal how would i as a patient get to a stage of trusting it how would a regulator get to the stage of trusting it over 20 studies in every single time the conclusion did not change hey ready delighted to be joined this week by steve balera chief scientific officer for safety assessment and toxicology at charles river laboratories which is one of the largest cro's in the world and a company that almost every new drug will pass through on its way from the lab to human trials you , Steve spent nearly two decades at charles river uh before that decade in pharma at pfizer and bristom off squib toxicologist by training background in cancer research and is now responsible for scientific conduct of safety studies that determine whether or not a molecule is safe enough to ever reach a patient uh he's also at the center of one of the biggest shifts happening in drug development which we're going to talk about which is the move away from animal testing towards virtual and human relevant models it's funny i I've got Claude to do this intro, and it now says, which makes him a fascinating person to talk to, because he's helping lead that change from inside a company built in part on the very thing it's now trying to replace. What an AI intro that is. Steve, welcome to HealthTech Podcast. How are you doing, sir? Good. Thanks, James. Pleasure to be here. Glad to talk about science. It's going to be fun. Whereabouts are you speaking to us from today, Steve? Where are you based? So I'm based actually in Reno, Nevada. Oh, nice. In the desert. Yeah, I'm an East Coaster by the United States. By birth and had an opportunity presented itself with Charles River, I moved out to Reno, Nevada. And to me, my wife and I still say, can you believe we live in Reno, Nevada? Who in the wildest dreams would ever think of that? That might be awesome. But where's your heart then? Where are you? Where are you? Where are you brought up? With my career, I've lived in so many different places. We moved on average every four to five years. This is the longest I've been anywhere. So we've been out here in Reno for about 16 years now, which is very bizarre. And like most people who are fascinated by television, you know, I've got friends and family up in Montana. And I always tell my wife, we should move to Montana. And she's like, be great if you go. You can go. But I'm staying here. Oh, nice. But I do like it out here. The weather is beautiful. Oh, I bet. If I could show you the outside, my front yard sits right at the base of the Sierra Nevadas. The ski areas are 12 minutes away. So make a big plug for Reno. If you're a big skier, you can work at Charles River in Reno. You're nine minutes from Mount Rose Ski Area. Glorious. Glorious. What a plug for hiring that is. So Steve, I'm looking forward to this. We obviously have people from all across the health tech space. And I use health tech very broadly there. And so to speak to someone in pharma, in a CRO that's spearheading something relatively new in that industry, it's going to be quite an interesting chat. But before we get into that stuff, I want to learn a bit more about you. So where is your career taking you? How did you begin? How did you rise the ranks? I mean, it's a heck of a title you've now got in a heck of an industry. But what does that ascent look like? How does one become Steve Ballera? My favorite subject. So, yeah, I've had a very interesting career. Like most scientists did my undergraduate. So I did my I'm actually originally from just outside of Buffalo, New York, went to school at a small private Catholic college for Canisius College, did my graduate work at the University of Connecticut. So if you're a big basketball fan, college basketball fan, UConn is very well known. For both men's and women's basketball, where I got my my master's and my Ph.D. And I'm actually a I'm actually a protein biochemist by training. And we shared a grant with a toxicology group that was working on acetaminophen. So I know more about acetaminophen than probably most people would care to know about. And I did. Those that speak English, by the way, I'm just going to throw that in there. OK, yes, the paracetamol. So when we shared this grant, I did. I did sit in on a toxicology course and I was like toxicology. This is the most boring discipline ever. Who would want to be a toxicologist? Funny how fate has it now that I part of one of the world's largest toxicology organizations on the planet. Fate has a funny way of laughing at you. So so I did my graduate work on acetaminophen, looking at proteins that bound acetaminophen. And then I did my postdoctoral fellow at the University. At the University of Wisconsin at the actually fit the McCartle Laboratory for Cancer Research, which which has the privilege of having a number of Nobel laureates there. So I was in very fine company working in that in Dr. Henry Pito's laboratory. So I did my postdoc there and then I was hired into Park Davis Pharmaceuticals. So I was brought in as an investigative investigative toxicologist, basically was brought in to work on. of that you have to develop a whole different skill set you have to develop really how do you make change how do you have impact without having direct report lines so you know leading people through influence so it's a very different set of skills and i've had to hone that over the years to kind of figure out how do you get things done when you can't just tell people because you report to me you need to do this so it's a very it's been it's a different like i said different skill set so can i ask you about that then how to i mean the obvious question is how do you lead people through influence when you can't literally tell them what to do i think a lot of it is one tool that i've found is asking questions okay you know you never throw anybody under the bus but you can ask them questions and ask them to kind of provide the rationale and they may say aha you're right i didn't really think of that maybe i ought to consider x y and z so it is a very different way of working with people and it's a very different way of working with people and it's a very different way of working with people and it's a very different way of working with people and it's a very different how does one get the attention of those above them in order to keep getting the promotion getting higher up in the organization being able to make more impact is it simply doing the job is it more than that again nature abhors a vacuum you know it's volunteering it's delivering so for those that i guess you know don't live in preclinical what what is a chief scientific officer for safety a week month look like what are the responsibilities it sounds like multiple countries multiple time zones from a very practical element what what is the job it's kind of a jack of all trades job in the sense of and again i've been allowed to kind of create my own role and and and one of the things that attracted me and it's kept me in the contract world as opposed to pharma is every day is different every day brings new challenges my job i kind of divide into like three buckets one is definitely looking at our division strategy and science and being defender of the faith of science you know saying well that's a great business thing but scientifically that may not be the best direction and as charles river is a very scientific animal welfare based company is making sure that you know i'm kind of the conscience of that science and it's not everything is a business decision we all take it's just reminding people of that so that's one part is this the strategy the science where do we go what are the technologies we need to bring in what do we need to what scientific uh strengths do we need to be current those things and then that's one bucket and the other bucket would be is really i liaison with a lot of our large global pharma clients figuring out them how do i help them how do i help them get their drugs to patients faster um that's a lot of what i do on a daily basis that's a lot of what i do on a daily basis that's a lot of what i do on a daily basis that's Then, of course, I work internally closely with operations about harmonization of processes, optimization of processes, making sure that we have captured the best data, have low error rates, and bring the best science and data to our clients. So it's kind of an everyday is some combination of that. I love that. Sorry, I'm just finishing writing notes here because there's lots I want to ask you about here. But firstly, the first bucket, essentially defending the science in the organization. For a scientist, that must be a wonderful job, a wonderful part of the job, because actually, I think so many people that I talk to on here that are scientists have to very much battle with the commercialization side of things. And of course, you'll have an appreciation for that. And of course, you're optimizing operations in part for commercial reason, because, you know, the organization can have more impact and all that sort of stuff. But I guess to be able to go to work. And actually, I think it's important to be able to go to work. And I think it's important to be able to go to work. interesting like choke point where every drug will basically pass through from that vantage point where's where's the real bottleneck in getting a molecule from from discovery to the clinic like what what actually is the biggest bottleneck that you're seeing currently time interesting i mean that's it and i say time i mean if you're still running a if you're running your clinical trial you still need to see most people are supporting it with a one-month study so you can't you can't run a one-month study in 21 days you still got to do it it's a month six-month study is six months so what we've been focusing on is how do you start studies faster and then how do you report them out faster which also leads to a conundrum of and i had a conversation with um i've created my own advisory board and i had dinner with them at the american college of toxicology last year and i sat down with them and i said how do we balance as a cro you want us to go faster you want reports you want data faster how do you balance that with quality so quality is you know i'll use an old advertising line quality is job one from both from an execution of study point of view to accuracy and reports how do you balance that if you're taking all our time away because you want it faster and that is a big trend in industry right now how do you go faster how do you do that how do you balance quality with science or as quality with speed i should say i mean it's a great question and actually so so let's say uh i'm gonna say that nobody had an answer for me well i was gonna ask you you know when in that case when a company does come to you they've got limited cash they've got a lot of money they've got a lot of money they've got a lot of you know a regulatory clock that's ticking away what what is the conversation that you have with them in that scenario because i imagine it happens fairly regularly so that's where it kind of you know well i'll say the word ai starts to play how long did we get 23 minutes 23 minutes without saying i know we can we can talk on ai for the rest of the 23 minutes but ai starts to become part of that conversation and right now i think ai to me i we call it i call it ai assistant decision making we know that ai is not perfect but it definitely can give like our scientists a something to think about a heads up a a check on what the data is saying to them have they missed something that maybe the ai picked up of course they have to verify it and things of that nature but i see that's where ai is going to help our scientists save time ai can help with quality so that people don't have to do a lot of qc that if it's validated and under the regulation of qc it's going to save time and it's going to save time and it's going to save time and it's going to save time and it's going to save time so i'm familiar with the fda cfr regulations you can talk about having validated systems that if the ai and the computer system says that this data is accurate nobody has to question it anymore so that saves time combine that with ai and assisted uh ai assisted decision making you can start to save time still can't you know ai will help with pathology you know maybe they don't have to read every slide the ai can say hey that's normal you can start to save time you can start to save time you can start to save time don't look at that but this there's something funny happening here you need to look at that and when you talk to a pathologist the number they always tell me is about 80 of the slides have no change so then if the ai can say focus on 20 that'll give them a time savings so that's where i think time and quality will become we won't have to worry about quality because the ai will help with that and it'll give us that speed are you starting to see ai having an impact there or is that something that you're looking at and planning for i'm not sure i'm not sure i'm not sure i'm not sure i'm not sure i'm not sure i'm not sure i'm not sure i'm not sure i'm not sure i'm not sure now in the hope that it's going to whereabouts are you on that timeline i think we're at the beginning um we are seeing i would say in the last couple months we are starting to see uh a bit of nervousness around ai now we have companies coming to us like hey you're not allowed to use ai on our data and so what we do is we have a conversation with them it's like so if i have a reporting tool that can cut your reporting time in half you don't want me to develop that tool they're like oh no we don't mean that well your your language you're sending to us says no ai yeah we're starting to have these conversations and i think it's it's natural it's a natural response to what things are happening you know they don't want us taking their data and putting into i won't some software i won't name anyone and then just sending it out to the worldwide net for to look at it but if we can develop tools behind our firewalls to help write reports faster bring better qc yeah then there are like oh okay that's okay but we have to have those conversations absolutely right and that is the sensible patient conversation patient is in time conversation nuanced conversation that definitely needs to happen which sounds great i mean the devil's in the detail with that isn't it because it is complex a lot has to do with the speed of adoption i think we just have to be careful with what we're doing and like we're i was we've been talking with our legal team a lot and talking with clients a lot about the language we want to put into master service agreements and the lawyers all excited because this is a brand new field you know how often do they get to start thinking about language and rules and laws and things around what you put into contracts because this is all new space we're all inventing it because it's coming to us fast and furious and not everyone is comfortable with you know ai for administrative tasks ai for prediction ai for discovery ai for this they're all a little different and they all have different i'll say rules and regulations around definitely definitely and look you're a scientist by background so you're of the evidence-based community where you want to know something works we're also in health care we you know this the stuff that we're part of eventually reaches patients and again exactly we have internally this alarm that goes off whenever anything new like this comes along and threatens our processes we all you and i and everyone that's got you know clinical or health care or scientific backgrounds will go is this safe exactly completely appropriate that things are slower it's completely appropriate that there's more conversations that we we indulge in that nuance to try and find you know the devil in the detail that you know we we want to get to the right answer not necessarily the fastest most efficient answer because of what it might miss because in our industry that that ends up causing a lot more problems than going slightly quicker did cause us a slightly better line on the pnl or whatever at the end of the day it's all about patient safety it's all about patient safety and nobody wants to jeopardize that whether it's as you said the pnl is better it's faster if patient safety is jeopardized then we're not doing our job right exactly that exactly that great so i want to talk about what you just mentioned previously actually about animal testing and this obviously being incredibly important to your company and looking at what you're part of specifically you like very important to you as well with virtual control groups so could you could you just give us the lie of the land in terms of the industry at the moment and where we are with animal testing of of drugs and medications and things and i guess where we're moving to and your role in that again there's a lot of caution a lot of confusion a lot of what ifs what what about this nobody wants to slow down their drug getting to patients so people are asking lots of questions and they're not sure what they're doing and they're not sure what they're doing and they're not sure what the answers are i think the prevailing opinion in the industry is is yes we all do want to remove away from animal testing we also see that happening we want that to happen but we also realize it's not an overnight thing yeah not everything can be done in a 96 well plate a 364 well plate it cannot be done so but where we see things happening is making better decisions to put better compounds through so you don't waste animals that way you can help make decisions on you know investigative work is this the right comp you know thing using again the word nams is being used a lot um there's your new approach methodologies those of us who are kind of in this area and talking about it it's not new i mean companies have been working on in-vitro screens for decades to pick better compounds to develop we've been working for decades to pick better compounds to develop we've done in vitro testing or assays as i said about you know i was brought in as an investigative toxicologist we were using in vitro methods to elucidate mechanisms on toxicity so nams in the regulated space i call that investigative toxicology the stuff in the pre the non the the pre-clinical or the pre-ind phase that's just picking better candidates so we've been doing it for years what we're doing is creating better assays that pick better compounds more predictive of the human condition and again helping us develop drugs that are better safer for patients so that's all new uh it's that's not new it's it's just it's an evolution and that's not a revolution it's an evolution of what we've been doing forever so those are the kinds of things and so the industry is looking for ways to do that um but yes i think what will be happening for at least the near term and i don't know i'm not going to define near term is we will be doing in conjunction animal testings with some kind of in vitro for a period of time and any way we can reduce animals whether it's using one sex using one species, weight of evidence arguments, to do that is the way where I see things happening for the near future. And part of that is what we kind of mentioned earlier is virtual control groups. So for those who don't know what we're talking about, it's kind of been done in the clinic for years, where you use historical control data to compare to a treated group. So the idea would be is one day to replace all the control animals on a study with data from previously collected data and use those as comparators to your treated groups. Where the industry is heading in the short term is what we're calling a hybrid model. And there's a reason for this. So a hybrid model. So on a typical four-week toxicology study, we use 10 male, 10 female rats. Where we see. Where we see the industry going for the near term is reducing each group by half. So five live animals, five virtual animals. And there's a reason for not going to zero. One is in talking with regulatory agencies, experts in the field, that everyone's concerned about, I'll say, genetic drift of the animals in your database. So if you're going to continue to repopulate that database, five animals, 10 animals is better to repopulate, but five will allow us to keep them alive. So that's one aspect of it. One, having animals on study allows you to have animals in the room as your treated group to control for procedural changes. Things that happen while you're actually executing on the study. Also, it happens that there are infections, diseases that come in with animals. Having those live controls as comparators, but we've reduced, we can reduce study animals by at least control animals by 30 to 50%, which is a step in the right direction. And then using that virtual data with live animal data, and then through computer algorithms, machine learning algorithms, you can then do the comparisons with the treated animals. So you still get the same scientific quality, the same set of data. You're using matched animals to the study data, to the treated animals. So that you can still do these comparisons. It's a step in the right direction. It is. It's a huge step in the right direction. I mean, you know, 30 to 50% reduction. I mean, that's, that's huge numbers. I mean, any, anything in the, you know, drug discovery drug to market processes that would give you a 30 to 50% uplift on anything would be, you know, considered almost impossible. So I yeah, I think it's obviously a very large step in the right direction. I'm intrigued, though. Oh, like, how do I mean, think about it even from even if I put myself in the in the shoes of a patient, right? I'm a patient, this new drug has come through this type of process. And let's say I'm, I'm part of the trial. I'm part of the human trial. Now I've got a, you know, I've got a condition, there's a trial out is to use this for the first time in humans, and I'm part of that trial. I'm just, I'm just thinking this through of, you know, how would? How would I as a patient get to a stage of trusting it? How would a regulator get to the stage of trusting it? And what does that? What does that look like? Do you guys think about that? Like what? How we get to that future of trust? Because it's all trust, right? In healthcare, like all of this stuff is just trust. It is. It's just the gap in trust from it works in an animal. Now we're going to use it in a bigger animal. Now we're going to use it in a human. I can plot that the trust of Yeah, this has worked on the sort of digital versions. And now we're going to put into humans. It's an interesting one, I think. It's the science again, scientific approach. So like for when we started on this journey with virtual control groups, what we did is we took old studies. First, we took more and work with clients as well. So we took over 20 studies. And we said, let's let's use our outcomes. And we said, let's use our outcomes, let's use our algorithm to pick animals out of our database. And then we're going to reanalyze the study using these virtual control animals. And every single time, the conclusions did not change. So that gave us confidence that what we're doing is right. Now we're also running virtual study, we're running studies with animals as a parallel group to compare. So we're not compromising the study, because we're still doing it the kind of I'll say the traditional way. Yeah. But and then we reanalyze the data with concurrent study. So we did retrospective analysis first. Now we're doing what we call concurrent analysis, or prospective analysis, where we run the study as normal. But then we also do a comparison with a virtual group to see what happens in real time. And those are panning out. And then we've been in discussions with the FDA saying, what do you think of our approach? What do you think and getting their feedback? And they're like, yeah, we want to see some now we want to see some people submit data to us so we can get familiar with it. And we've talked to regulators and educating them on this approach. So they know what to expect. And of course, there's an international consortium that's working on virtual controls as well that we're part of. So there's a lot of different avenues that we're looking to make people more comfortable with this approach. There are, of course, there are naysayers. But you know, we're working on like, what, why is this not comfortable with you? You know, I was gonna say, I'm interested in that, actually, the what, what's the argument of the naysayers? Or what's the position of the naysayers, the ones that are the loudest, I guess, what are they? Some of them, it's about, well, it's not an exact match. You know, it's, it's like, okay, it's, you know, you know, how we advance in anything. If you think about to ensure that we have the best animals to use as comparators from a virtual point of view. So we're doing all the right things. And it's sometimes it's just, yes, you know, some people agree, disagree, agree to disagree, no matter what. Absolutely. That is life, Steve. That's kind of it. Yes. That is life. You mentioned the concept of genetic drift. Could you explain that again for me? Because I don't think I grasped that when you mentioned it the first time. It's not a, to me, it's not a big risk, but it is a risk that we need to address. So if you think about the rats, and the rats we use, or the larger animals we use on studies, over time, they could have, be different over time, just through genetic, you know, evolution and things like that. So we want to make sure that our database is as current as possible. So that's why we want to keep feeding the database. Now, when we talk about, like, Charles River rats, we have what's called an international global standard, which is a breeding program to make sure that there isn't drift. But that it says, so the naysayers may say, well, the animals that are today, they're not drifting. They're not drifting. They're drifting. They're drifting. They're drifting. They're drifting. They're drifting. They're drifting. They're drifting. They're drifting. They're drifting. They're drifting. They're drifting. They're drifting. They're different than the animals 10 years ago. What are you going to do about that? Well, our database is current. We only use it for three to five years. We're not using animals that are 10, 15 years old. We're using more, more current animals from our database, which gets back to using the hybrid model so we can keep feeding the database. The next thing I wanted to chat to you about, I'm just going to pull this up here because it's something I spotted when I was looking you up. I saw a press release that you guys are developing virtual control groups with Sanofi. That specifically, that project specifically, I guess, why them and what does success look like over the next year, two years with this project? Could you tell me a bit more about it if you're able to? Sanofi was one of the first people to say, "Yes, you can go public with working with us on virtual controls." We are working with, I would say we're working with 10 to 15 global pharmas. Wow. Okay. They just haven't been as public about it with us. Sure. I think we have a manuscript coming out with another major global pharma company. I know it's either in press or being ready to be submitted. That'll be, again, another public acknowledgement of a collaboration. It was really the early days of doing the perspective and concurrent analysis with them, so things of that nature. We're working with a client, a couple of clients right now where we are going to get away from the parallel and actually run studies with virtual controls that would be submitted to the agencies. To start giving agencies data so they can start getting familiar with it. Got it. Got it. We are moving forward. We are moving forward quite a bit. It's just not everyone wants to go public right away. In terms of phasing out animal testing, I guess completely in inverted commas, if indeed that is the aim or as a society, if we decide that is the aim, are virtual control groups, do you think and feel that they're a stepping stone to getting to that reality or are they the destination? I think it is a stepping stone. Eventually, virtual controls would go away if, let's just say, animal testing goes away. But in talking with some clients, they all still feel that at least in the near term, again, I'm not going to define near term, but you can imagine a world in the future where we run a lot of in vitro or NAMS assay, and maybe one confirmatory study in an animal model just to say let's do it do a check just to make sure that because you think about the complexity of a human a complexity of an animal you can't answer everything in a 96 wild dish or an instrument so just as a last you know just as a last check to make sure you're not missing something you know one one study maybe where we end up so we're not headed to fully in silico then not not any not i'll say in my career or pretty much any i mean we're talking it's going to take decades to develop some of this stuff yeah understood understood if i can ask who owns the data interesting question so as a cro we do not own the data yeah our clients own their own data so we work with clients and that's kind of where the ai's discussion start coming in is how can we use your data to build better models to help you can you release data can you let us use data to develop models we're working on a consortium now of a couple large pharma clients where maybe we get them to combine let their let us use their data to help develop tools that'll help them but there's kind of a you know of course there's a competitive advantage of having your own data or not mixing your data with someone else's okay a couple more questions on on the control groups before um before i let you go i'm just thinking this through now um and what would happen if the and this may have happened i don't know but a virtual control group um and a live control disagree does one bad result kind of set the whole concept back or how how is that i mean has that happened is that is there a plan for that if it would you expect it to for any reason i would expect again it comes back to making sure you're picking the right cohort of animals that match the study so going into the database so we actually have an algorithm that helps determine if a study would be an virtual control would be an option for the study so there are some studies you may have a vehicle or you may have something in your study design that says you know what we don't have the right data in our database to find the proper match maybe there's a lot of endpoints on the study that we don't have in our database so they may not be an option for the study or like i said exotic i mean you asked about changes over time i remember when uh back in the day some of the vehicles you know hydroxymethyl cellulose was an exotic vehicle today with biologics there are things that are so complex and so novel from companies that vehicles have changed over time so could you have a vehicle that has a that that that they're using that we don't have in our database that would make it that you couldn't use use virtual control animals so we're trying to be as again going through it stepwise going at the beginning like is this study a candidate for virtual control and there will be instances where we say no and we'll have to use a full cohort of animals finally like are the are the powers that be the the regulators or even politically i'm not asking you to comment politically too much but are they are they pulling for this is this is this the direction of travel is this an environment where you feel that you've got green lights and and you're feeling the pull from this or do you feel like you're on you know to some extent pushing this uphill i wouldn't say no i would say is they are open to it but then again prove to me that this is not going to jeopardize patients yeah because that that's what ultimately they're that's the ultimate goal is if you to what we're talking about earlier is if the virtual control like only per day it was only if we did the retrospective analysis and only worked five out 30 times we did it then you might say this is not the right direction yeah so because there might be times where you're you're missing significant findings or the opposite of your fear create you're saying something is significant when it's not which could impact how you run a clinical trial or a drug a drug falling out of development that's perfectly good so i think they're open to it but prove that it it's going to be equal or better than what we have today and that we don't jeopardize patient safety i understand um that's a that's yeah that's a great answer um and before i let you go steve one one final question would be what are you what are you excited about at the moment so is there is there a moment that you've had recently is there a result that you've had recently is the a project that's going on currently is there what what's what's exciting you or has excited you uh at the moment or recently uh there's been a couple projects at charles river that have been very very satisfying um there's a couple there's at least two things that jump out into my mind is let's talk about both i'll talk about both it's really the impact that one is the impact where i should say both are showing impact of what we have on patients so when i was the head of talks here in reno which wasn't that long ago but we would do like a all hands meeting and part of that meeting was kind of like a scientific corner and they brought in a one of our employees a family member um had ms and talked about how one of the drugs we worked on had changed her life dramatically i don't think there was a dry eye in the audience and that's a real example of the impact we're having every day um that's one that really stood always stands out to me and the second is there was a project um there was a a child in boston who had something called batten's disease a rare genetic disease patients probably less than five in the world and we developed a customized therapy from kind of starting the studies to when she received her first dose like 12 weeks we partnered internally with our sites we partnered with the fda to get this drug tested and approved and into into a patient patient in record time and it did slow down the pro the progression of her disease quite a bit so that really stands out to me as a wow look at the impact we we have and i mean as and when you're in pharma you know the compounds you work on you know the fate one company if the drug made it to market you got a lucite plaque with the label in it as showed that you worked on it in a cro you don't always get that direct connect of what happened to the drug afterward so we're going to see what happens so when we have these examples and clients have been very good about sharing progress of the compounds we work on and again one of the things we do at the company is track how many fda-approved drugs we've worked on it's that showing that it's the satisfaction where you can sit in your living room and watch tv and go we worked on that one we worked on that one we worked on that one and having that satisfaction so those are the things that really stick out to me in working in this business because in a pharma company you only see your compounds in charles river we see your compounds yes and it's um it's people isn't it and i think that's the thing i've got a friend that uh runs a health tech company and and actually made a point of this in fact i've got more than one that have now done this um and they they they work with a hospital to essentially make sure that all of their staff can spend time actually seeing their technology work on site in a hospital to give them that to just just to give them an appreciation of what that technology is actually doing both for staff and for patients actually um and i know that for for them it's one of the more powerful motivators for people because it actually just humanizes what they do and i think that's the thing with pharma it's it's difficult because of you know things that get said and and people's feeling a collision's feelings towards pharma and all the rest of it like we know this is all public but at the end of the day life-saving drugs are being created and as you say you can have a unique indication for a rare disease you can mobilize a lot of people very quickly to work on that because there's a very real story five people around the world and this is just one of them but that's going to happen for that one person and it um yeah it's i i know amongst you know people that i know in in um the the mood from clinical care that people have this moment of realization of like oh hold on a minute like this is actually part of the world that does an incredibly useful job um and it can be quite emotive like when i imagine when when you're seeing those results and it's great that you get to be part of that it's a great motivator for our people and our clients have been very good about coming in and we've had them like make videos and things that we show to our people like thank you for helping us develop this drug and the impact it's had or they'll come in and give a seminar to a staff and say here's our portfolio you worked on all these compounds thank you very much here's where they are here's the impact they're having having impatience. So things like that. Yeah, it's great. It's great. It's great. I think closing that loop, closing that loop on communication is, um, I mean, I'm in, I'm in the comms world. That's, uh, I'm, I'm biased obviously, but closing the loop with communication is one of the most powerful things you can do. I always appreciate it, Steve, when, when I've given an intro to someone or to two people, and at least one of them comes back to me and tells me how the meeting went. I just, I just think that's such a, it's such a lovely loop closer. I'm like, ah, it was really nice. And now they're doing a piece of work together. And now that, that, that, that, that thing that took me 10 seconds has now created something in the world. It's just a very nice, very nice feeling to close that loop, let alone hearing that some, something that you worked on ended up, um, dramatically changing someone's health and their life. So, um, yes, more of that, more of that from everyone listening. Um, Steve's been absolutely pleasure having you on. Thank you so much. I'd say the same thing. It's been a pleasure talking with you. Glad you reached out and, um, I really enjoy the time. If, um, if people want your work, uh, Charles River's work, um, what's the best way for them to, to find out or to ask you a question? LinkedIn, email Charles River. We have, you know, through our public page, there's ways to get in contact with people at Charles River and they can route the information to me. Amazing. Thank you so much for your time. And I'm sure we'll catch up in future. Great. Thank you. Subtitles by the Amara.org community