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0:22: Today we're diving into something really, really fascinating.
0:25: It's this intersection, right, between biotechnology and artificial intelligence.
0:30: Yeah, and how they're basically teaming up to revolutionize drug discovery.
0:33: It's a pretty groundbreaking stuff.
0:36: Totally.
0:36: So the mission today, what we want to do is really unpack how these two fields are converging, because traditional drug development.
0:43: Oh, it really is.
0:44: You're talking high cost, incredibly long timelines, like over a decade sometimes and honestly a really high failure rate.
0:50: It's a huge challenge.
0:51: Exactly.
0:52: So we want to give you a kind of shortcut, a way to understand how combining biotech, genomics, proteomics, all that with AI like machine learning and deep learning is tackling these exact problems.
1:03: Right, because Biotech gives us these incredible tools to peer inside biology, generating just vast amounts of data, mountains of it.
1:11: Yeah, and AI is what lets us actually make sense of it all.
1:15: It can process and interpret this biological data in ways we just couldn't before, uncovering insights that were, well, basically hidden.
1:22: OK, so let's start peeling back the layers.
1:25: Genomics and transcriptomics, we hear these terms a lot.
1:29: Break those down for us in the context of finding new drugs.
1:32: Sure, so genomics, that's the study of the whole genome, like the complete genetic instruction manual of an organism, the entire blueprint exactly.
1:40: And we use technologies like next gen sequencing, NGS, or whole genome sequencing to find variations in genes that might be linked to diseases.
1:49: OK, so that's what's written.
1:50: What about transcriptomics then?
1:52: Transcriptomics is more about what's actually being used from that man.
1:55: All at any given time.
1:57: It looks at all the RNA transcripts, the copies made from the genes.
2:00: This tells us about gene expression patterns.
2:03: Like, are certain genes switched on more, up regulated or switched off down regulated in a disease?
2:10: That activity level helps us pinpoint potential drug targets.
2:14: Right.
2:14: So you're looking for genes that are acting differently in disease states.
2:18: It helps narrow down where we might want to intervene with the drug.
2:21: Can you give us a solid example?
2:22: Where has this approach really worked?
2:24: Cancer genomics is a huge one.
2:26: We can sequence tumors and find specific mutations in genes known to drive cancer like oncogenes, EGFR, CAS, BRAF, or in tumor suppressor genes like TP53, which normally put the brakes on cell growth.
2:41: And finding those mutations actually leads to treatments.
2:44: Absolutely.
2:45: Drugs have been developed that specifically target cells with those mutations.
2:48: Think.
2:49: Jainnib, for instance.
2:50: It's been a gearing changer for certain non-small cell lung cancer patients who have specific EGFR mutations.
2:56: That's a direct result of understanding the genomics.
2:59: That really drives home the idea of personalized medicine, doesn't it?
3:02: Tailoring the treatment to the individuals specific genetic profile.
3:06: Exactly.
3:06: And that ties into pharmacogenomics too, which is studying how your genes affect your response to drugs.
3:12: So predicting who will benefit, or maybe who will have side effect.
3:16: Yeah, making treatments safer and more effective based on your genetics.
3:20: And we have amazing tools now like CRISPR Cas 9 for gene editing, like the molecular scissors, kind of, yeah.
3:26: It lets us precisely edit genes in the lab to study their function and really confirm if they're good drug targets.
3:32: And on the transcriptomic side, RNA se is a key technique to analyze those gene expression levels and discover new biomarkers for diseases.
3:40: OK, that covers the gene level pretty well.
3:43: But what about the next step?
3:44: The proteins that the genes code for and the metabolism, proteomics and metabolomics, right.
3:50: So proteomics moves downstream to look at the proteins themselves, the actual machinery of the cell.
3:54: It's a large scale study because proteins do most of the work.
3:58: They're the effectors basically.
3:59: Exactly.
4:00: Understanding them is crucial for figuring out disease mechanisms.
4:04: We use techniques like mass spectrometry MS to identify and quantify proteins.
4:09: This helps find protein biomarkers like indicators of disease, precisely or track progression.
4:15: Another technique is 2D gel electrophoresis, which helps separate out different protein forms or modifications.
4:22: Yeah, but heallomics, that sounds like it's about metabolism.
4:24: It is.
4:24: It's the analysis of all the small molecules, the metabolites produced during metabolism.
4:30: Think sugars, fats, amino acids, the end products and intermediates.
4:34: Yeah.
4:35: Studying these gives us a snapshot of the cell's metabolic state.
4:39: It's great for finding biomarkers for things like diabetes, heart disease, cancer, and also for understanding how a drug actually affects the body by looking at changes in metabolic pathways.
4:50: OK, concrete examples again.
4:52: Where are proteomics and metabolomics making a difference?
4:55: Well, take Alzheimer's disease.
4:56: Proteomics was key.
4:57: Identifying those abnormal protein clumps like beta amyloid and tau, which are now major targets for drugs and diagnostics.
5:04: Exactly.
5:04: And in cancer, metabolomics helped uncover something called the Warburg effect, this altered way cancer cells process energy, right?
5:11: They metabolize differently.
5:13: Yeah, and that different metabolism is now being looked at as a potential weakness, a target for specific cancer drugs.
5:19: The level of detail is just incredible.
5:22: OK, let's shift gears a bit to CRISPR again and gene editing more broadly.
5:27: You mentioned it for validation, but it feels like it could do much more.
5:30: Oh, it's truly revolutionary.
5:31: CRISPR Cas 9 and others like Talons and ZFNs let us make really precise changes to the genome.
5:38: It opens doors for studying gene function in ways we couldn't before, validating targets, and even developing gene therapies.
5:45: Gene therapy actually correcting the faulty gene.
5:47: That's the ultimate goal for many genetic diseases.
5:49: The idea is to fix the root cause.
5:52: CRISPR works using a guide RNA that directs the Cas 9 enzyme, the scissors, to a specific spot in the DNA.
5:58: It makes a cut, and then the Cells' natural repair systems can be harnessed to delete, insert, or modify the gene.
6:04: So how is this being used right now in the drug pipeline?
6:07: Target validation is a big one.
6:08: You can knock out a gene in cells or an animal model using CRISPR.
6:12: Just switch it off, right, and see if that prevents or reverses the disease signs.
6:17: If it does, that gene becomes a much more confident drug target.
6:21: Makes sense.
6:21: And then there's gene therapy development itself, aiming to correct mutations, causing diseases like cystic fibrosis or sickle cell anemia.
6:30: It's still early days for many applications, but the potential is huge.
6:34: What about in cancer research specifically?
6:36: It's being used.
6:37: Extensively there.
6:38: Creating knockout models of ontogenes or tumor suppressors lets researchers study cancer mechanisms and find vulnerabilities.
6:46: And a really exciting area is engineering immune cells, like CARI therapy.
6:50: Exactly.
6:51: That involves gene editing.
6:52: But also researchers are using CRISPR to say knock out the PD1 gene in T cells.
6:57: PD1 acts like a break on the immune response.
7:00: Removing it can basically supercharge the T cells to attack cancer more effectively, leading to new immunotherapies.
7:06: Wow, OK.
7:07: From that very precise editing, let's zoom out to something that sounds like the opposite.
7:11: High throughput screening or HTS.
7:14: What's that about?
7:15: HTS is all about speed and scale.
7:17: It's a way to conduct millions of chemical or biological tests very rapidly.
7:21: Millions.
7:22: How?
7:23: Through automation and miniaturization.
7:25: Instead of a scientist manually testing one compound, robotic systems test thousands or millions in parallel using tiny amounts of substances in special plates.
7:34: So you're throwing Tons of potential drug candidates at a target to see what sticks.
7:39: Pretty much, yeah.
7:40: You're screening vast libraries of compounds to find initial hits compounds that show some desired activity against your target like inhibiting an enzyme involved in a disease, and technology makes this feasible.
7:52: Absolutely.
7:53: Automated liquid handling robots dispense tiny precise amounts of reagents and compounds into Microplates plates with 384 or even 1,536 little wells.
8:05: Microfluidics using tiny channels also allows for high throughput with minimal materials.
8:10: It dramatically accelerates the early stages of discovery, finding that needle in the haystack, but much faster.
8:16: Exactly.
8:16: It lets you sift through enormous chemical diversity quickly.
8:19: OK, another fascinating area is synthetic biology.
8:22: This sounds like Building life.
8:25: How does that fit into drug discovery?
8:26: It's not quite building life from scratch, but more like applying engineering principles to biology, designing and building new biological parts, devices, systems, engineering biological systems for new jobs, right, for new applications, including developing.
8:42: Or producing drugs, the core idea is creating biological functions that don't exist in nature or enhancing existing ones.
8:49: What are the tools for this kind of biological engineering?
8:52: Things like gene circuits, these are engineered networks of genes designed to control how other genes are expressed, maybe in response to a specific signal.
9:00: And metabolic engineering where you rewire an organism's metabolism, rewire it by modifying genes involved in metabolic pathways to make the organism produce more of something useful like a drug molecule or maybe even produce a molecule it wouldn't normally make.
9:16: Can you give examples where synthetic biology is making drugs.
9:20: A classic one is engineering yeast.
9:22: Scientists redesigned yeast metabolic pathways to produce artemisinin, which is a really important anti-malarial drug that was originally extracted from a plant.
9:31: So using yeast is a tiny drug factory.
9:34: Basically, yes, much more efficient and scalable.
9:37: CRT cell therapy, which we mentioned, is another great example.
9:40: You're engineering a patient's immune cells with the synthetic receptor, the CAR, to make them target cancer.
9:46: That's cure synthetic biology in action and insulin production too, right?
9:50: Yeah, engineering bacteria to produce human insulin revolutionized diabetes treatment decades ago.
9:56: That was an early massive success for this kind of approach.
9:59: This is all incredibly powerful biology, but you mentioned earlier the data generated is immense.
10:06: That's where AI really steps in, isn't it?
10:08: Absolutely.
10:09: AI is the game changer for handling the sheer volume and complexity of data coming from all these biotech approaches.
10:15: So it's the analysis engine.
10:17: It's the analysis engine, the pattern finder, the prediction tool.
10:19: It uses machine learning, deep learning, natural language processing, all these techniques to analyze data sets from genomics, proteomics, HDS, clinical trials, scientific literature, you name it.
10:30: Extracting meaning from the noise.
10:32: Exactly building models of biological systems, predicting how drugs might interact with targets, identifying potential candidates, things that would take humans' lifetimes, or just be impossible otherwise.
10:45: What are some specific AI approaches being used?
10:48: Well, there's supervised learning.
10:50: You train the AI on data that's already labeled like this compound was toxic, this one.
10:55: Wasn't so it learns to predict outcomes like drug activity, toxicity, or side effects.
11:00: Then there's unsupervised learning, which is great for finding hidden patterns in data where you don't have labels.
11:06: Think clustering patients into subgroups based on their gene expression, maybe revealing different forms of a disease, finding structures we didn't know were there.
11:14: Right.
11:14: And reinforcement learning is also emerging where the AI learns through trial and error, getting Feedback to optimize tasks like designing the structure of a new drug molecule.
11:24: Fascinating.
11:25: Can you point to some real world applications of AI in the drug pipeline now?
11:29: Definitely.
11:30: AI is heavily used in pharmacokinetic and pharmacodynamic modeling PKPD.
11:35: That's basically simulating what the body does to the drug and what the drug does to the body.
11:39: ADME absorption, distribution, metabolism, excretion, exactly that.
11:44: AI models help predict how a drug will behave, helping to figure out optimal dosing and flag potential side effects way earlier.
11:52: Toxicity prediction is another big one, using AI to analyze preclinical data to spot warning signs.
11:58: Catching failures before they happen in expensive trials.
12:01: That's the goal.
12:02: AI models are also being built to simulate how diseases like cancer or Alzheimer's progress over time.
12:08: This helps understand the disease better and predict how patients might respond to treatments.
12:12: Any specific companies or platforms using AI effectively?
12:15: Sure, there are many now.
12:17: One example mentioned sometimes is Deep 6 AI.
12:20: They use AI to analyze electronic health records, EHRs, to find patients who Match the criteria for specific clinical trials much faster than manual searching.
12:29: Speeding up recruitment, which is often a huge bottleneck, a massive bottleneck.
12:33: AI helps streamline that process significantly.
12:35: So we've seen the power of biotech tools and the analytical might of AI separately.
12:42: Now, the core of this, how does putting them together create something more powerful, the synergy.
12:48: This is really where the paradigm shift happens.
12:50: Biotechnology generates the high resolution biological data.
12:54: The fuel, exactly.
12:56: Genomics gives us the genetic variations, transcriptomics, the expression patterns, proteomics, the protein landscape.
13:03: CRISPR screens identify essential genes.
13:06: It's a fire hose of information, and AI is the engine that burns that feel.
13:10: You could put it that way.
13:12: AI, specifically machine learning, digests these huge Omics data sets to pinpoint the critical genes, proteins, or pathways driving a disease.
13:20: Natural language processing, NLP can scan millions of research papers, patents, trial results, stuff no human could read comprehensively, right, to pull out potential drug targets or relevant biological connections, then predictive AI models can assess how druggable a target is, like, based on its structure or function, is it likely we can develop a drug against it?
13:39: So AI helps prioritize where to focus efforts.
13:42: Definitely.
13:43: And AI enables things like drug repurposing.
13:46: Finding new uses for existing drugs by analyzing vast data sets for unexpected biological effects.
13:53: Some platforms specialize in this.
13:55: Finding hidden treasures and medicines.
13:58: Kind of futuristic, AI models are being used for drug design new drug molecules from scratch.
14:07: A novel chemical structures predicted to have the right to and be effective.
14:12: It's a huge leap.
14:13: And this integration also helps later on in clinical trials.
14:17: Yes, by improving patient selection, predicting responses, maybe even designing better trial protocols.
14:23: Companies like Genentech, for example, have reportedly used AI combined with HTS data to find novel immuno-oncology targets leading to new drug classes.
14:32: It's about making the whole process smarter and more efficient.
14:34: It sounds almost too good to be true.
14:36: What are the roadblocks, the challenges in making this biotech AI integration work smoothly?
14:41: Oh, there are definitely challenges, big ones.
14:43: Data is probably number one.
14:45: Getting enough high quality, well annotated data is hard.
14:48: Garbage in, garbage out for the AI.
14:50: Precisely.
14:51: We also need better ways to integrate different types of data genomic, proteomic, clinical, and standardize the formats so AI can use them together effectively.
15:00: And the AI itself.
15:02: Is it always easy to understand?
15:04: Not always.
15:05: Some models, especially deep learning, can be black boxes.
15:09: They give you an answer, a prediction, but it's hard to see why they reach that conclusion.
15:13: In medicine, you really want to understand the biological reasoning, that interpretability issue exactly.
15:19: Then there are regulatory hurdles.
15:21: Agencies are still figuring out how to evaluate drugs developed using AI, and of course data privacy is a huge concern when you're dealing with sensitive patient health information.
15:30: So lots to navigate.
15:31: Looking ahead then, what are the next steps?
15:34: Where is this field heading?
15:35: Well, a big focus is on improving data quality and sharing making more good data available.
15:40: Developing more transparent, explainable AI models is also critical, so we trust their outputs more.
15:45: Moving away from the black box.
15:47: Hopefully, yes, or at least understanding it better.
15:50: Creating hybrid models that blend AI's power with traditional biological knowledge and methods is another promising direction.
15:57: both worlds potentially and underpinning all of this is the need for really strong collaboration between the biologists, the chemists, the clinicians, and the AI experts.
16:06: They need to speak the same language.
16:07: Breaking down silos.
16:08: Absolutely.
16:09: And finally, we need clear adaptable regulatory frameworks that can keep pace with the technology, ensuring safety and efficacy without stifling innovation.
16:19: So wrapping it all up, the key takeaway is the synergy is real.
16:23: Combining biotechnology's ability to generate deep biological insights with AI's power to analyze and predict is fundamentally changing drug discovery.
16:31: It promises faster, cheaper, more accurate, and more personalized medicines.
16:35: It's a genuine revolution in progress.
16:38: Techdailly.AI, your source for technical information.
16:41: It really feels like this integration.
16:43: AI and biotech.
16:44: It's not just an incremental improvement.
16:46: It's like you said, a fundamental shift in how we approach health and medicine.
16:50: Yeah, the aha moment, I think, is seeing how all these different pieces, gene editing, sequencing, AI algorithms, robotics are all clicking together to solve problems that seemed almost insurmountable just a decade or two ago.
17:03: So for you listening, what part of this really grabs you?
17:07: Is it the promise of medicines tailored just for you based on your genes, or maybe the sheer speed AI brings to designing new drugs, or perhaps the ethical side of using these powerful tools?
17:17: Lots to think about.
17:18: Definitely.
17:19: And this deep dive, it's really just scratching the surface.
17:21: If you want to go deeper, maybe look into research on explainable AI specifically for drug discovery, or check out the cutting edge of using organoids, those mini organs grown in labs combined with AI.
17:32: That's a whole other facet.
17:34: Indeed, lots more to explore there.
17:35: TechDaily.AI, your source for technical information.
17:39: This deep dive was sponsored by Stonefly, your trusted solution provider and advisor in enterprise storage, backup, disaster recovery, hyperconverged and VMware, Hyper-V, Proxox cluster, AI servers, and public and private cloud.
17:52: Check out stoneFly.com or email your project requirements to sales at stonefly.com.