WEBVTT 00:00:00.000 --> 00:00:02.839 All right, so today we're diving into drug discovery. 00:00:03.359 --> 00:00:06.620 And more specifically, how scientists actually 00:00:06.620 --> 00:00:10.939 validate a target. Yeah, it's like, you know, 00:00:11.019 --> 00:00:12.500 before you build a house, you got to make sure 00:00:12.500 --> 00:00:14.599 you have like a solid foundation, right? Exactly. 00:00:14.640 --> 00:00:16.420 You don't want to spend years developing a drug 00:00:16.420 --> 00:00:18.910 that's aimed at the wrong thing. And you've brought 00:00:18.910 --> 00:00:20.750 in some really interesting research on this. 00:00:20.910 --> 00:00:23.570 Oh, yeah. From like classic lab techniques to 00:00:23.570 --> 00:00:26.769 AI. To AI, exactly. And all of it burrows down 00:00:26.769 --> 00:00:30.890 to this one key question. How do scientists confirm 00:00:30.890 --> 00:00:33.850 that a specific molecule is actually involved 00:00:33.850 --> 00:00:36.850 in a disease? Right. Because it's not enough 00:00:36.850 --> 00:00:39.549 to just kind of have a hunch. No, not at all. 00:00:39.590 --> 00:00:41.969 You need evidence. Hard evidence. Hard evidence 00:00:41.969 --> 00:00:45.939 to avoid wasting time and resources. So let's 00:00:45.939 --> 00:00:48.119 start with the traditional methods. Okay, I'm 00:00:48.119 --> 00:00:49.560 seeing kind of like two main approaches here 00:00:49.560 --> 00:00:51.520 genetic and chemical Yeah, those are the two 00:00:51.520 --> 00:00:55.200 big ones So genetic methods so genetic methods 00:00:55.200 --> 00:00:58.140 involve manipulating genes to see what happens 00:00:58.140 --> 00:01:00.340 like actually like tweaking the blueprint of 00:01:00.340 --> 00:01:02.320 a cell Yeah, think of it like you're an engineer 00:01:02.320 --> 00:01:05.120 and you're tinkering with the design So one of 00:01:05.120 --> 00:01:08.000 the classic techniques is the knockout study 00:01:08.000 --> 00:01:11.180 the knockout study where scientists essentially 00:01:11.180 --> 00:01:14.500 silence a gene. So they just like turn it off. 00:01:14.579 --> 00:01:16.280 They turn it off and then they see what happens. 00:01:16.540 --> 00:01:18.099 And from that, they could tell that that gene 00:01:18.099 --> 00:01:20.780 was doing. Exactly. It's like removing a cog 00:01:20.780 --> 00:01:23.840 from a machine. You take out a specific gene 00:01:23.840 --> 00:01:27.400 and you observe what stops working. OK, so it 00:01:27.400 --> 00:01:30.159 helps you pinpoint what goes wrong in a disease 00:01:30.159 --> 00:01:32.280 when that gene isn't functioning properly. Right. 00:01:32.340 --> 00:01:35.879 Our sources talk about PEP2 knockout mouse models. 00:01:36.120 --> 00:01:38.599 PEP2. Yeah, they've been really helpful in understanding 00:01:38.599 --> 00:01:41.700 how the PEP2 transporter affects drug absorption. 00:01:41.950 --> 00:01:43.950 Interesting, so like how well a drug gets into 00:01:43.950 --> 00:01:46.739 the body? Precisely. OK. But I imagine there 00:01:46.739 --> 00:01:48.620 are limitations to these genetic techniques. 00:01:48.780 --> 00:01:51.319 Oh, absolutely. Even with powerful tools like 00:01:51.319 --> 00:01:53.180 CRISPR, there's always a chance of what we call 00:01:53.180 --> 00:01:55.620 off -target effects. Off -target. Off -target 00:01:55.620 --> 00:01:57.159 effects. It's like you're trying to edit one 00:01:57.159 --> 00:02:00.000 specific gene. OK. But you accidentally end up 00:02:00.000 --> 00:02:02.920 editing another gene. Oh, so you get like misleading 00:02:02.920 --> 00:02:05.359 results. Exactly. So you always have to be careful 00:02:05.359 --> 00:02:07.840 with the interpretation. It's like needing a 00:02:07.840 --> 00:02:09.659 double check system just to make sure you didn't 00:02:09.659 --> 00:02:12.159 accidentally delete something important. Exactly. 00:02:12.419 --> 00:02:14.599 Now let's move on to chemical meth. Chemical 00:02:14.599 --> 00:02:17.479 methods. These use something called tool compounds, 00:02:17.740 --> 00:02:20.659 which are basically small molecules designed 00:02:20.659 --> 00:02:23.400 to interact with the target of interest. So each 00:02:23.400 --> 00:02:27.740 tool compound is designed for a specific target. 00:02:27.939 --> 00:02:30.379 Yeah, like a key fitting into a lock. Key and 00:02:30.379 --> 00:02:33.539 a lock, OK. So by observing the effects of blocking 00:02:33.539 --> 00:02:36.800 or activating the target with these keys, researchers 00:02:36.800 --> 00:02:38.939 can kind of tease out its role in the disease. 00:02:39.229 --> 00:02:41.250 It's like seeing what happens when you throw 00:02:41.250 --> 00:02:43.270 a wrench into the machinery. Precisely. And, 00:02:43.270 --> 00:02:45.289 you know, tool compounds are also used in lead 00:02:45.289 --> 00:02:47.550 optimization. Lead optimization. So that's where 00:02:47.550 --> 00:02:50.969 researchers are actually fine -tuning promising 00:02:50.969 --> 00:02:53.870 drug candidates. Right. They're assessing things 00:02:53.870 --> 00:02:56.610 like metabolic stability. Metabolic stability. 00:02:56.710 --> 00:02:59.210 How quickly a compound gets broken down in the 00:02:59.210 --> 00:03:01.490 body. OK. So does it hang around long enough 00:03:01.490 --> 00:03:04.530 to do its job or? Exactly. Or does it disappear? 00:03:04.669 --> 00:03:06.169 Right. If it disappears too quickly, there's 00:03:06.169 --> 00:03:08.300 no point in developing it further. Makes sense. 00:03:08.479 --> 00:03:12.240 So these tool compounds, do they always behave 00:03:12.240 --> 00:03:15.759 perfectly? Sadly, no. No. Sometimes it can be 00:03:15.759 --> 00:03:18.659 a little bit tricky. Tricky how so? Well, sometimes 00:03:18.659 --> 00:03:21.199 they interact with other molecules besides the 00:03:21.199 --> 00:03:23.659 intended target. OK. And that can lead to false 00:03:23.659 --> 00:03:25.500 positives. So it's like the key fitting into 00:03:25.500 --> 00:03:28.319 the wrong lock. Exactly. You might think you've 00:03:28.319 --> 00:03:30.099 found the right target, but you're actually looking 00:03:30.099 --> 00:03:32.539 at something else entirely. So how do researchers 00:03:32.539 --> 00:03:34.759 avoid getting tricked by these false positives? 00:03:35.080 --> 00:03:37.599 It all comes back to rigorous experimentation. 00:03:37.960 --> 00:03:41.419 You know, using multiple methods to really confirm 00:03:41.419 --> 00:03:44.039 a target's role. OK, so it's a gathering evidence 00:03:44.039 --> 00:03:46.080 from different sources. Exactly. You want to 00:03:46.080 --> 00:03:48.699 build a really strong case. Yeah, you don't want 00:03:48.699 --> 00:03:51.259 to jump to conclusions. No, definitely not. And 00:03:51.259 --> 00:03:53.479 that's actually a perfect segue into the world 00:03:53.479 --> 00:03:56.580 of AI -driven target validation. Ooh, now we're 00:03:56.580 --> 00:04:00.080 talking. I've heard so much about how AI is revolutionizing 00:04:00.080 --> 00:04:02.500 drug discovery. It's pretty amazing. Imagine 00:04:02.500 --> 00:04:05.099 having this super -powered research assistant 00:04:05.099 --> 00:04:08.840 that can analyze massive data sets. Like a supercomputer. 00:04:09.080 --> 00:04:11.860 Way faster than any human could. And what kind 00:04:11.860 --> 00:04:13.580 of data are we talking about here? Oh, everything, 00:04:13.759 --> 00:04:16.240 you know, genomic information, protein structures, 00:04:16.800 --> 00:04:18.980 scientific literature, all of it. Wow. And so 00:04:18.980 --> 00:04:23.060 AI can sift through all of that and find potential 00:04:23.060 --> 00:04:25.680 targets. Exactly. And it can even predict how 00:04:25.680 --> 00:04:28.800 drugs might interact with those targets. So could 00:04:28.800 --> 00:04:31.459 it actually help us avoid those accidental delete 00:04:31.459 --> 00:04:34.040 moments we talked about earlier? In a way, yes. 00:04:34.339 --> 00:04:37.660 AI can flag potential issues early on. That's 00:04:37.660 --> 00:04:39.360 incredible. It must save researchers so much 00:04:39.360 --> 00:04:41.240 time and effort. Oh, absolutely. Our sources 00:04:41.240 --> 00:04:44.420 mention AtomWise. AtomWise. Yeah, and their AtomNet 00:04:44.420 --> 00:04:47.079 platform. AtomNet. It's a really great example 00:04:47.079 --> 00:04:49.779 of how AI is changing the game. So how does it 00:04:49.779 --> 00:04:52.600 work? Well, AtomNet uses deep learning to sift 00:04:52.600 --> 00:04:55.220 through enormous chemical libraries. OK. And 00:04:55.220 --> 00:04:57.519 it looks for molecules that could bind to species. 00:04:57.420 --> 00:04:59.600 specific targets. So it's like a matchmaking 00:04:59.600 --> 00:05:01.839 service for drugs and targets. Exactly. And they've 00:05:01.839 --> 00:05:04.160 even used it to identify potential drug candidates 00:05:04.160 --> 00:05:07.480 for neglected tropical diseases. Oh, wow. So 00:05:07.480 --> 00:05:09.920 AI is giving these diseases a fighting chance. 00:05:09.980 --> 00:05:11.899 It really is. It's a really exciting development. 00:05:12.180 --> 00:05:15.420 But even with all of his promise, AI and drug 00:05:15.420 --> 00:05:18.589 discovery... is still in its early stages, right? 00:05:18.649 --> 00:05:20.730 It's right. So we still need those traditional 00:05:20.730 --> 00:05:22.810 methods for validation. Absolutely. It's about 00:05:22.810 --> 00:05:25.050 using the best of both worlds. I like the best 00:05:25.050 --> 00:05:27.610 of both worlds. AI can provide really powerful 00:05:27.610 --> 00:05:31.209 insights and accelerate the process. but we still 00:05:31.209 --> 00:05:33.949 need that rigorous experimental validation. Exactly, 00:05:34.430 --> 00:05:36.110 to confirm those findings and make sure we're 00:05:36.110 --> 00:05:38.550 on the right track. This is all so fascinating. 00:05:38.730 --> 00:05:41.750 It really highlights the complexity and the ingenuity 00:05:41.750 --> 00:05:43.990 involved in drug development. Absolutely. And 00:05:43.990 --> 00:05:46.230 we've only just scratched the surface. Oh, I 00:05:46.230 --> 00:05:48.089 bet there's so much more to uncover. Oh, yeah. 00:05:48.269 --> 00:05:50.629 Maybe we should pause here, let our listeners 00:05:50.629 --> 00:05:53.569 absorb all this. That's a good idea. Take a break, 00:05:53.589 --> 00:05:58.860 and we'll be right back. Okay, so we're back 00:05:58.860 --> 00:06:01.819 and we were just talking about how AI is really 00:06:01.819 --> 00:06:03.660 like shaking things up in the world of target 00:06:03.660 --> 00:06:05.779 validation Yeah, it's really exciting to see 00:06:05.779 --> 00:06:08.500 how fast things are moving. Yeah, it is. But 00:06:08.500 --> 00:06:11.620 um, you know, even AI can't solve every problem, 00:06:11.620 --> 00:06:13.759 right? That's right. It can help us find those 00:06:13.759 --> 00:06:16.819 targets, but Doesn't mean drug development is 00:06:16.819 --> 00:06:19.129 suddenly easy Right, like there are still challenges. 00:06:19.470 --> 00:06:21.310 Big challenges, and one of the biggest is what 00:06:21.310 --> 00:06:23.750 happens during the metabolism process. Oh yeah, 00:06:23.850 --> 00:06:25.310 you're talking about those reactive metabolites. 00:06:25.490 --> 00:06:28.490 Exactly, even when a drug binds perfectly to 00:06:28.490 --> 00:06:31.370 its target, things can still go wrong. And these 00:06:31.370 --> 00:06:33.290 metabolites, they can cause some serious problems, 00:06:33.470 --> 00:06:36.209 right? Oh yeah, from drug reactions to liver 00:06:36.209 --> 00:06:39.709 damage, even DNA damage. That's scary. So it's 00:06:39.709 --> 00:06:41.839 not just about finding the right target. No, 00:06:41.939 --> 00:06:44.000 it's also about what happens to the drug once 00:06:44.000 --> 00:06:46.720 it's in the body. And like how it breaks down. 00:06:47.399 --> 00:06:49.079 Exactly. Researchers need to figure out if a 00:06:49.079 --> 00:06:51.060 drug is going to be metabolized into something 00:06:51.060 --> 00:06:53.519 harmful. That sounds like a super complicated 00:06:53.519 --> 00:06:55.980 puzzle. It is. It's like trying to predict all 00:06:55.980 --> 00:06:58.939 the possible moves in a chess game. So how do 00:06:58.939 --> 00:07:01.600 scientists even begin to predict and mitigate 00:07:01.600 --> 00:07:03.519 these risks? Well, there are a few different 00:07:03.519 --> 00:07:07.050 strategies. One approach is to design drugs that 00:07:07.050 --> 00:07:09.629 are less likely to form those reactive metabolites. 00:07:09.670 --> 00:07:11.449 So it's like designing drugs that are more body 00:07:11.449 --> 00:07:14.069 -friendly. Exactly. And AI can actually play 00:07:14.069 --> 00:07:16.649 a role here, too. Really? Yeah. Some AI algorithms 00:07:16.649 --> 00:07:19.050 can actually predict the metabolic fate of a 00:07:19.050 --> 00:07:21.529 drug candidate. Wow. So it's like having a sneak 00:07:21.529 --> 00:07:23.550 peek into the future. That's a great way to put 00:07:23.550 --> 00:07:26.889 it. It helps researchers identify potential problems 00:07:26.889 --> 00:07:30.949 early on. But even with AI... I'm guessing scientists 00:07:30.949 --> 00:07:33.350 still need to do a lot of real world testing. 00:07:33.689 --> 00:07:35.490 Oh, absolutely. Prediction is just the first 00:07:35.490 --> 00:07:38.089 step. They use a combination of in vitro and 00:07:38.089 --> 00:07:41.050 in vivo models. OK, what does that mean? So in 00:07:41.050 --> 00:07:45.310 vitro means they're testing the drug outside 00:07:45.310 --> 00:07:48.089 of a living organism, like in a test tube. OK. 00:07:48.670 --> 00:07:50.670 And in vivo means they're testing it in a living 00:07:50.670 --> 00:07:52.819 organism, like an animal model. Got it. So they're 00:07:52.819 --> 00:07:54.819 basically testing it at different levels of complexity. 00:07:55.040 --> 00:07:57.920 Exactly. It's like a dress rehearsal before the 00:07:57.920 --> 00:07:59.160 main performance. Yeah, you want to make sure 00:07:59.160 --> 00:08:01.019 everything works properly before you test it 00:08:01.019 --> 00:08:03.120 on humans. Exactly. And speaking of complexity, 00:08:03.220 --> 00:08:05.620 we haven't even talked about protein interactions. 00:08:05.879 --> 00:08:07.600 Oh, yeah. Those are super important, too, right? 00:08:07.720 --> 00:08:10.540 Absolutely. Remember, proteins rarely act alone. 00:08:10.600 --> 00:08:12.459 They're constantly interacting with each other. 00:08:12.519 --> 00:08:14.379 They're like little social butterflies. Yeah, 00:08:14.379 --> 00:08:17.079 exactly. They form these intricate networks that 00:08:17.079 --> 00:08:20.699 influence cellular behavior. So even if a drug 00:08:20.699 --> 00:08:24.259 hits its intent, target perfectly, it could still 00:08:24.259 --> 00:08:26.220 trigger unintended consequences elsewhere in 00:08:26.220 --> 00:08:28.199 the network. Exactly. It's like throwing a pebble 00:08:28.199 --> 00:08:30.620 into a pond. Oh yeah, the ripple spread out. 00:08:30.620 --> 00:08:33.299 Exactly. Affecting the whole ecosystem. So understanding 00:08:33.299 --> 00:08:35.519 those protein interactions is really crucial. 00:08:35.860 --> 00:08:38.639 So how do scientists go about mapping out these 00:08:38.639 --> 00:08:41.279 intricate protein networks? Well, they use a 00:08:41.279 --> 00:08:44.360 combination of experimental techniques and computational 00:08:44.360 --> 00:08:47.779 approaches. One common method is called yeast 00:08:47.779 --> 00:08:51.019 -to -hybrid screening. Yeast -to -hybrid screening. 00:08:51.139 --> 00:08:53.299 I remember reading about that. Yeah, it helps 00:08:53.299 --> 00:08:55.399 scientists figure out which proteins interact 00:08:55.399 --> 00:08:57.700 with each other. So it's like solving a cellular 00:08:57.700 --> 00:08:59.779 puzzle. Exactly. And there are other techniques, 00:08:59.960 --> 00:09:03.059 too, like co -immuno precipitation and mass spectrometry. 00:09:03.340 --> 00:09:05.759 They all sound so complicated. They can be, but 00:09:05.759 --> 00:09:07.840 they all help identify the different protein 00:09:07.840 --> 00:09:09.940 partners that interact with a target. But I bet 00:09:09.940 --> 00:09:11.879 all of that data can be really overwhelming. 00:09:12.059 --> 00:09:13.799 Oh, it definitely is. That's where computational 00:09:13.799 --> 00:09:15.840 biology comes in. OK, so how does that help? 00:09:16.220 --> 00:09:18.399 Well, scientists use sophisticated algorithms 00:09:18.399 --> 00:09:22.120 to analyze these huge data sets and create detailed 00:09:22.120 --> 00:09:24.299 maps of the molecular networks. So it's like 00:09:24.299 --> 00:09:26.519 having a Google Maps for the cellular world. 00:09:26.669 --> 00:09:29.970 Exactly. And those maps can show how a drug might 00:09:29.970 --> 00:09:33.070 affect not just its target, but also its target's 00:09:33.070 --> 00:09:35.070 friends and their friends and so on. It's like 00:09:35.070 --> 00:09:38.090 a chain reaction. Exactly. And it helps researchers 00:09:38.090 --> 00:09:40.509 understand the bigger picture and anticipate 00:09:40.509 --> 00:09:42.690 potential ripple effects. It's pretty amazing 00:09:42.690 --> 00:09:45.970 to realize that cells are such complex ecosystems. 00:09:46.269 --> 00:09:48.669 It is. And it highlights why drug development 00:09:48.669 --> 00:09:50.980 is such a challenging endeavor. But as we learn 00:09:50.980 --> 00:09:53.580 more about these cellular processes, we get closer 00:09:53.580 --> 00:09:55.899 to developing safer and more effective therapies. 00:09:56.159 --> 00:09:58.679 Absolutely. So where do we go from here? What 00:09:58.679 --> 00:10:01.320 does the future hold for target validation? Yeah. 00:10:01.320 --> 00:10:03.340 What's next? I think we're going to see even 00:10:03.340 --> 00:10:06.840 more integration of AI and computational biology 00:10:06.840 --> 00:10:09.740 into every stage of drug development. So those 00:10:09.740 --> 00:10:11.580 technologies are going to become even more powerful. 00:10:11.700 --> 00:10:14.379 Exactly. And that means scientists can predict 00:10:14.379 --> 00:10:17.399 and prioritize targets with even greater accuracy. 00:10:17.539 --> 00:10:19.419 So it's all about working smarter, not harder. 00:10:19.940 --> 00:10:22.580 Precisely. And we're also seeing the rise of 00:10:22.580 --> 00:10:25.600 new approaches like phenotypic screening. Phenotypic 00:10:25.600 --> 00:10:27.620 screening that rings a bell. Yeah. It's a different 00:10:27.620 --> 00:10:30.659 way of looking at target validation. So remind 00:10:30.659 --> 00:10:32.919 me, how does that work again? So instead of starting 00:10:32.919 --> 00:10:36.340 with a specific target in mind, you start by 00:10:36.340 --> 00:10:39.879 observing the effects of a drug on cells or organisms. 00:10:40.659 --> 00:10:42.519 So you're kind of working backwards. Exactly. 00:10:42.559 --> 00:10:44.899 You see a desired effect and then you try to 00:10:44.899 --> 00:10:46.940 figure out how it happened. It's like being a 00:10:46.940 --> 00:10:49.600 detective searching for clues. That's a great 00:10:49.600 --> 00:10:51.759 analogy. And you know what? Phenotypic screening 00:10:51.759 --> 00:10:54.679 has actually led to the discovery of drugs that 00:10:54.679 --> 00:10:56.659 are on the market today. Really? So it's a proven 00:10:56.659 --> 00:10:59.360 approach. It is. It's been really valuable, especially 00:10:59.360 --> 00:11:01.980 when those traditional target -based approaches 00:11:01.980 --> 00:11:04.639 have hit a wall. It's really encouraging to know 00:11:04.639 --> 00:11:06.659 that scientists are always exploring new ways 00:11:06.659 --> 00:11:09.299 to solve these complex puzzles. It is. And I 00:11:09.299 --> 00:11:11.120 think we're just scratching the surface of what's 00:11:11.120 --> 00:11:13.419 possible. Yeah. As our understanding of cellular 00:11:13.419 --> 00:11:17.159 processes grows, so too will our ability to develop. 00:11:17.340 --> 00:11:19.779 even more effective in targeted therapies? Absolutely. 00:11:19.940 --> 00:11:23.600 All right, so we're back. And we've been on this 00:11:23.600 --> 00:11:26.879 deep dive into target validation. And it's definitely 00:11:26.879 --> 00:11:29.360 been ajourner, right? It has. It's clear that 00:11:29.360 --> 00:11:32.399 this is like a really critical step in drug discovery. 00:11:32.620 --> 00:11:34.399 Yeah, you could say it's the foundation of the 00:11:34.399 --> 00:11:35.940 whole process. Yeah, like you were saying before, 00:11:35.960 --> 00:11:37.840 if you're building a skyscraper, you got to make 00:11:37.840 --> 00:11:40.019 sure that foundation is solid. Exactly. You don't 00:11:40.019 --> 00:11:42.159 want to cut corners when it comes to target validation. 00:11:42.580 --> 00:11:44.419 And we've seen how researchers are using all 00:11:44.419 --> 00:11:47.200 these different tools to do it. from traditional 00:11:47.200 --> 00:11:53.279 lab techniques to AI to, you know, a deep understanding 00:11:53.279 --> 00:11:55.919 of how cells work. It's really amazing to see 00:11:55.919 --> 00:11:58.100 how it all comes together. It's a really cool 00:11:58.100 --> 00:12:00.480 blend of science and technology. But I think 00:12:00.480 --> 00:12:02.120 throughout our conversation there's been this 00:12:02.120 --> 00:12:04.820 one theme that keeps coming up. Oh yeah, what's 00:12:04.820 --> 00:12:07.059 that? The importance of context. Context, okay. 00:12:07.159 --> 00:12:09.340 It's not enough to just find a target and hit 00:12:09.340 --> 00:12:11.059 it with a drug. Right, it's like you need to 00:12:11.059 --> 00:12:12.940 see the bigger picture. Exactly, like what's 00:12:12.940 --> 00:12:15.139 the target's role in the cell? How does it interact 00:12:15.139 --> 00:12:19.379 with other molecules? And even how might different 00:12:19.379 --> 00:12:21.259 patients respond differently? Right, because 00:12:21.259 --> 00:12:23.179 everyone's different. Yeah, exactly. So it's 00:12:23.179 --> 00:12:25.580 about appreciating the complexity of biology 00:12:25.580 --> 00:12:28.759 and understanding that there's rarely a simple 00:12:28.759 --> 00:12:31.320 solution. Right, like those protein networks 00:12:31.320 --> 00:12:33.320 we talked about. Oh, yeah. Those are a great 00:12:33.320 --> 00:12:35.580 example. They're so intricate and interconnected. 00:12:35.799 --> 00:12:37.940 Yeah, disrupting those connections can have a 00:12:37.940 --> 00:12:40.620 domino effect. Even if a drug is doing what it's 00:12:40.620 --> 00:12:43.049 supposed to be doing. Exactly. And that's why 00:12:43.049 --> 00:12:45.309 researchers are really starting to focus on systems 00:12:45.309 --> 00:12:48.210 biology. Systems biology. Yeah, it's this holistic 00:12:48.210 --> 00:12:51.370 approach that considers the whole system. Okay, 00:12:51.409 --> 00:12:53.710 so instead of looking at just one protein, you're 00:12:53.710 --> 00:12:55.950 looking at the entire network. Exactly. It's 00:12:55.950 --> 00:12:57.649 like the difference between looking at a single 00:12:57.649 --> 00:12:59.750 tree and then stepping back and seeing the whole 00:12:59.750 --> 00:13:03.090 forest. That's a great analogy. So systems biology 00:13:03.090 --> 00:13:06.250 helps us see how a drug might affect not just 00:13:06.250 --> 00:13:09.129 its target, but also like everything around it. 00:13:09.269 --> 00:13:12.409 Right. It allows us to anticipate potential side 00:13:12.409 --> 00:13:15.029 effects and develop therapies that are more targeted. 00:13:15.309 --> 00:13:17.210 And speaking of targeted therapies, we also talked 00:13:17.210 --> 00:13:19.389 about personalized medicine. Oh, yeah, that's 00:13:19.389 --> 00:13:21.450 super exciting. It's like the idea that treatments 00:13:21.450 --> 00:13:24.250 can be tailored to each individual patient. Exactly. 00:13:24.330 --> 00:13:26.590 Based on their genetic makeup. So we can predict 00:13:26.590 --> 00:13:30.330 how they might respond to a drug or even identify 00:13:30.330 --> 00:13:32.730 potential risks. It's like moving away from this 00:13:32.730 --> 00:13:35.330 one size fits all approach to medicine. Yeah, 00:13:35.470 --> 00:13:37.529 and creating treatments that are truly customized. 00:13:37.750 --> 00:13:39.990 Right. It's about finding the right drug for 00:13:39.990 --> 00:13:42.330 the right patient. This has been a really eye 00:13:42.330 --> 00:13:44.070 -opening conversation. We've covered so much 00:13:44.070 --> 00:13:46.769 ground. We have. It's been great. But I think 00:13:46.769 --> 00:13:49.590 as we wrap up our deep dive into target validation, 00:13:50.450 --> 00:13:52.529 there's one question I keep coming back to. OK, 00:13:52.629 --> 00:13:55.309 what's that? What does all of this mean for, 00:13:55.309 --> 00:13:57.879 like, the average person? Right, why should they 00:13:57.879 --> 00:14:01.019 care about this really complex science? Exactly. 00:14:01.080 --> 00:14:03.379 Well, the answer is actually pretty simple. Target 00:14:03.379 --> 00:14:06.259 validation impacts all of our lives. How so? 00:14:06.320 --> 00:14:08.419 Think about all the medications we take every 00:14:08.419 --> 00:14:11.360 day. For everything. For everything. Headaches, 00:14:11.539 --> 00:14:13.820 heart disease, infections, you name it. So those 00:14:13.820 --> 00:14:15.879 scientists working in labs, they're the ones 00:14:15.879 --> 00:14:18.120 who make those medications possible. Exactly. 00:14:18.360 --> 00:14:20.779 They're the unsung heroes of our health. It's 00:14:20.779 --> 00:14:23.639 a powerful reminder that science isn't just about 00:14:23.639 --> 00:14:25.919 knowledge. No, it's about making a difference 00:14:25.919 --> 00:14:28.669 in the world. and target validation is a huge 00:14:28.669 --> 00:14:30.970 part of that. It really is. It's about developing 00:14:30.970 --> 00:14:34.230 drugs that are more effective, safer, and more 00:14:34.230 --> 00:14:36.830 personalized. It's about taking those scientific 00:14:36.830 --> 00:14:39.289 discoveries and turning them into real benefits 00:14:39.289 --> 00:14:42.250 for people. Absolutely. This has been an absolutely 00:14:42.250 --> 00:14:44.429 fascinating deep dive. I've learned so much. 00:14:44.590 --> 00:14:47.190 Me too. It's been a pleasure. And for our listeners 00:14:47.190 --> 00:14:49.460 who want to learn even more, We've got links 00:14:49.460 --> 00:14:51.120 to some of the research articles we've discussed 00:14:51.120 --> 00:14:53.120 in the show notes. Yeah, definitely check those 00:14:53.120 --> 00:14:54.820 out if you're interested in learning more about 00:14:54.820 --> 00:14:57.059 this really important topic. And with that, we'll 00:14:57.059 --> 00:14:59.220 wrap up this episode of the Deep Dive. Thanks 00:14:59.220 --> 00:15:02.059 for joining us. And until next time, stay curious.