WEBVTT 1 00:00:00.080 --> 00:00:03.520 So if I ask you to picture a pharmaceutical factory, right, 2 00:00:03.919 --> 00:00:08.320 you probably think of, I don't know, miles of stainless 3 00:00:08.320 --> 00:00:09.000 steel pipes. 4 00:00:09.080 --> 00:00:12.279 Oh, definitely, huge vats, people in hazmat. 5 00:00:11.800 --> 00:00:16.320 Suits exactly like these massive, sprawling corporate campuses with sterile 6 00:00:16.359 --> 00:00:19.760 glass containers everywhere. But the reality is, if you want 7 00:00:19.760 --> 00:00:23.440 to visualize the world's most advanced pharmaceutical factories today, you 8 00:00:23.519 --> 00:00:26.960 actually need to look closer, like much much closer. 9 00:00:26.600 --> 00:00:30.559 Right, because the true heavy lifters of modern drug manufacturing 10 00:00:31.120 --> 00:00:34.640 aren't machines at all. They're microscopic living organisms. 11 00:00:34.799 --> 00:00:37.679 Yeah, which is just wild. Okay, let's unpack this. Today 12 00:00:37.759 --> 00:00:40.520 we are taking a deep dive into this incredible textbook 13 00:00:40.520 --> 00:00:44.880 Pharmaceutical Biotechnology by case Immamurthy and a Shoetosh car And 14 00:00:44.920 --> 00:00:46.359 I want to say right up front, if you are 15 00:00:46.359 --> 00:00:49.280 a pharmacy student, maybe a young professional just entering the field, 16 00:00:49.719 --> 00:00:52.159 or even just a you know, a dedicated self learner, 17 00:00:52.240 --> 00:00:55.359 our mission today is to really give you that comprehensive 18 00:00:55.399 --> 00:00:59.399 structural knowledge, Yeah, the foundational stoke exactly the backbone of 19 00:00:59.399 --> 00:01:03.960 a modern farm mycology career. Because we are tracking this 20 00:01:04.239 --> 00:01:08.319 massive philosophical shift, like how humanity went from forging in 21 00:01:08.359 --> 00:01:12.760 the woods for natural cures to literally coding them from scratch, and. 22 00:01:12.719 --> 00:01:16.040 It completely flips that standard industrial image on its head. 23 00:01:16.359 --> 00:01:18.799 I mean, we spent over a century building machines to 24 00:01:18.840 --> 00:01:21.760 manufacture our medicines, but the real breakthrough came when we 25 00:01:21.799 --> 00:01:25.040 realized we could, well, we could simply program biology to 26 00:01:25.120 --> 00:01:28.439 do the manufacturing for us. So we're going to explore 27 00:01:28.480 --> 00:01:31.680 the precise mechanisms of this shift today, and we won't 28 00:01:31.719 --> 00:01:34.000 just list what these bioprocesses are. We really want to 29 00:01:34.000 --> 00:01:36.920 look under the hood to understand exactly why they work. 30 00:01:37.079 --> 00:01:39.000 Yeah, the actual mechanic exactly. 31 00:01:39.159 --> 00:01:43.680 Now. A Hungarian agricultural economist named kark Riki actually coined 32 00:01:43.719 --> 00:01:46.319 the term biotechnology way back in nineteen nineteen. 33 00:01:46.400 --> 00:01:48.519 Wow, nineteen nineteen. I didn't realize it was that old. 34 00:01:48.599 --> 00:01:51.400 Yeah, because way back. But right now today, we are 35 00:01:51.400 --> 00:01:55.480 currently navigating what researchers call the third revolution in modern medicine, 36 00:01:55.560 --> 00:01:58.200 and it is entirely driven by molecular manipulation. 37 00:01:58.519 --> 00:01:59.599 The third Revolution. 38 00:01:59.680 --> 00:02:03.480 I love that framing, But you know, to understand how 39 00:02:03.519 --> 00:02:08.319 we are actively engineering these biological systems today, I feel 40 00:02:08.360 --> 00:02:11.280 like we first have to look at the historical blueprint, 41 00:02:11.400 --> 00:02:15.840 right which is basically just the human body's natural defense mechanism. 42 00:02:15.960 --> 00:02:16.120 Oh. 43 00:02:16.120 --> 00:02:21.639 Absolutely, the entire foundation of biopharmaceuticals is immunology. If you 44 00:02:21.680 --> 00:02:24.199 want to build a cellular factory, you have to study 45 00:02:24.199 --> 00:02:28.080 the one that nature perfected over millions of years. And 46 00:02:28.159 --> 00:02:30.719 the turning point in this specific field happened in eighteen 47 00:02:30.759 --> 00:02:34.439 ninety three. Okay, there was a scientist named Mechnikov, and 48 00:02:34.479 --> 00:02:39.039 he established what is known as the phagasitic doctrine phagactic doctrine, right, 49 00:02:39.080 --> 00:02:41.520 So he was looking through a microscope and he observed 50 00:02:41.520 --> 00:02:44.400 that specific cells in the body, these are phagocytes, were 51 00:02:44.439 --> 00:02:49.039 actively hunting, like they actually possess the ability to ingest, engulf, 52 00:02:49.080 --> 00:02:51.080 and destroy foreign biological materials. 53 00:02:51.159 --> 00:02:54.159 So he essentially what he basically caught the immune system 54 00:02:54.199 --> 00:02:56.800 in the act of eating cellular debris precisely. 55 00:02:56.960 --> 00:02:59.719 Yeah, and those foreign invaders, you know, they're antigens, so 56 00:02:59.719 --> 00:03:04.759 backa protozoa toxins Bekshinkov's observation really laid the groundwork for 57 00:03:04.879 --> 00:03:07.680 understanding immune bacteriolysis. 58 00:03:06.919 --> 00:03:09.479 Which is how the body clears out threats exactly. 59 00:03:10.000 --> 00:03:13.400 And once scientists understood those mechanics, the inevitable next question 60 00:03:13.520 --> 00:03:16.439 was just, well, how can we harness this, Like, how 61 00:03:16.439 --> 00:03:20.879 can we commandeer the body's ability to create specific antibodies 62 00:03:21.199 --> 00:03:23.199 that target very specific antigens. 63 00:03:23.240 --> 00:03:25.719 Okay, let me make sure I'm visualizing this correctly, because 64 00:03:25.719 --> 00:03:28.840 when I was reading the section on naturally occurring antibodies, 65 00:03:28.879 --> 00:03:32.280 like just in a standard immune response, they struck me 66 00:03:32.280 --> 00:03:36.039 as sort of a generalized, somewhat chaotic police force. 67 00:03:36.360 --> 00:03:37.840 Yeah, that's a good way to look at it, Like. 68 00:03:37.800 --> 00:03:40.280 They swarm an area and just do their best to 69 00:03:40.319 --> 00:03:42.800 clear out anything that looks vaguely like a threat. But 70 00:03:42.879 --> 00:03:47.400 then when we transition into modern targeted therapy specifically monoclonal 71 00:03:47.439 --> 00:03:52.479 antibodies or maths, that police force completely changes. Like, monoclonal 72 00:03:52.479 --> 00:03:56.280 antibodies are more like a team of highly trained specialized snipers, right, 73 00:03:56.560 --> 00:03:59.759 program to hit one exact specific molecular target. 74 00:04:00.080 --> 00:04:04.080 Nepper analogy captures the core utility of MAVs beautifully. I mean, 75 00:04:04.319 --> 00:04:08.199 monoclonal antibodies are identical immune cells that are all clones 76 00:04:08.199 --> 00:04:10.039 of a single unique parent cell. 77 00:04:10.159 --> 00:04:11.840 Right, They're all exact copies. 78 00:04:12.080 --> 00:04:16.279 Yes, And because they are identical clones, they bind to 79 00:04:16.360 --> 00:04:19.199 the exact same part of an antigen. With just this 80 00:04:19.519 --> 00:04:22.040 ruthless specificity. 81 00:04:21.240 --> 00:04:23.319 Which is what makes them so valuable exactly. 82 00:04:23.360 --> 00:04:28.160 It makes them incredibly powerful for diagnostics, imaging, and you know, 83 00:04:28.600 --> 00:04:32.120 targeted therapies like cancer treatments. But this is a big 84 00:04:32.160 --> 00:04:35.879 put But Samba Murthy in Car dedicate a really significant 85 00:04:35.920 --> 00:04:39.920 portion of the text to the rigorous limitations of producing 86 00:04:39.920 --> 00:04:41.399 thesememes naturally. 87 00:04:41.160 --> 00:04:43.519 Right, because reading through the textbook, it sounds like an 88 00:04:43.560 --> 00:04:46.720 absolute logistical nightmare to actually make these things. 89 00:04:46.720 --> 00:04:51.040 It is incredibly restrictive. There are basically three massive bottlenecks. First, 90 00:04:51.120 --> 00:04:52.839 you run into severe purity issues. 91 00:04:52.959 --> 00:04:54.720 Okay, how so, well, if. 92 00:04:54.560 --> 00:04:57.480 You attempt to use serum free media to grow these memes, 93 00:04:57.759 --> 00:05:00.240 you generally end up with antibody preparations of am much 94 00:05:00.240 --> 00:05:04.160 lower purity compared to say, those obtained from corresponding whole 95 00:05:04.199 --> 00:05:04.839 cell cultures. 96 00:05:04.920 --> 00:05:05.800 Wait, why is that? 97 00:05:06.120 --> 00:05:08.959 Because the environment is just too fragile without the supporting 98 00:05:09.000 --> 00:05:10.959 proteins that you naturally find in serum. 99 00:05:11.160 --> 00:05:13.319 Oh okay, so you have a purity problem right out 100 00:05:13.319 --> 00:05:13.680 of the gate. 101 00:05:13.839 --> 00:05:18.240 Yes. And then second, the methodology itself is exhaustingly labor intensive. 102 00:05:18.519 --> 00:05:19.199 I can imagine. 103 00:05:19.279 --> 00:05:23.879 Yeah, it requires meticulous, continuous hands on management to isolate 104 00:05:23.959 --> 00:05:25.600 and culture these specific clones. 105 00:05:25.639 --> 00:05:28.879 Without any contamination, right, because one mistake and the batch 106 00:05:28.920 --> 00:05:30.040 is ruined exactly. 107 00:05:30.519 --> 00:05:33.240 And then third and honestly, this is the most difficult 108 00:05:33.279 --> 00:05:37.560 biological constraint. To produce them properly in vivo, you require 109 00:05:37.800 --> 00:05:42.639 unconditionally pathogen free animals of extremely specific genotypes. 110 00:05:43.079 --> 00:05:46.399 Wow, which means you can't just run out and use 111 00:05:46.560 --> 00:05:47.759 a standard lab mouse. 112 00:05:48.000 --> 00:05:48.839 No, not at all. 113 00:05:48.879 --> 00:05:53.600 You need this genetically pristine, highly specific animal model housed 114 00:05:53.600 --> 00:05:55.439 in a totally flawless environment. 115 00:05:56.000 --> 00:05:58.079 So what does this all mean? Like, if natural. 116 00:05:57.759 --> 00:06:00.399 Antibody production is that fussy and the u uels are 117 00:06:00.399 --> 00:06:03.360 that limited, how on earth do we bypass those biological 118 00:06:03.360 --> 00:06:07.560 bottlenecks to get the sheer quantities we need for global medicine. 119 00:06:07.600 --> 00:06:10.439 Well, you have to stop relying on the natural process entirely. 120 00:06:10.639 --> 00:06:14.439 You basically move from isolating natural biology to actively hacking 121 00:06:14.439 --> 00:06:15.079 its source code. 122 00:06:15.199 --> 00:06:15.959 Hacking the code. 123 00:06:16.079 --> 00:06:18.319 Yeah, you move into recombinant DNA technology. 124 00:06:18.360 --> 00:06:21.759 Yeah. Okay, so we are jumping from isolating snipers to 125 00:06:21.839 --> 00:06:24.360 just straight up building them in a lab. Let's talk 126 00:06:24.360 --> 00:06:26.800 about how this works. Because the textbook is very clear 127 00:06:26.920 --> 00:06:32.839 about differentiating between historical natural genetic experiments and modern molecular 128 00:06:32.920 --> 00:06:35.879 manipulation like, for thousands of years, we've done things like 129 00:06:36.079 --> 00:06:39.240 cross breeding wheat or selectively breeding dogs. 130 00:06:39.160 --> 00:06:42.759 Right, But those historical methods are bound by taxonomic borders. 131 00:06:43.120 --> 00:06:46.439 You are relying on sexual compatibility or you know, specific 132 00:06:46.519 --> 00:06:51.680 virus host ranges, Nature's rules basically exactly. But modern recombinant 133 00:06:51.759 --> 00:06:56.959 DNA technology completely shatters those borders. We can now add, delete, 134 00:06:57.040 --> 00:07:00.680 or modify parts of the DNA molecule with atomic accuracy. 135 00:07:01.079 --> 00:07:04.439 We're talking about taking traits from completely unrelated organisms and 136 00:07:04.519 --> 00:07:05.319 just combining them. 137 00:07:05.360 --> 00:07:07.360 Okay, I really want to get into the physical mechanics 138 00:07:07.399 --> 00:07:10.120 of this. How are researchers actually cutting and pasting the 139 00:07:10.160 --> 00:07:10.879 code of life? 140 00:07:11.040 --> 00:07:11.160 Like? 141 00:07:11.399 --> 00:07:15.600 Physically, it relies heavily on biological vehicles called vectors, and 142 00:07:15.639 --> 00:07:16.920 the most common vectors. 143 00:07:16.600 --> 00:07:17.839 Are plasmids plasmids. 144 00:07:17.839 --> 00:07:22.319 Okay, yeah, A plasmid is a small circular DNA molecule 145 00:07:22.639 --> 00:07:25.879 that naturally occurs in bacteria. It is totally separate from 146 00:07:25.920 --> 00:07:29.920 the main bacterial chromosome and it replicates independently. So in 147 00:07:29.959 --> 00:07:34.079 the lab, researchers extract these plasmids and perform a process 148 00:07:34.120 --> 00:07:34.839 called splicing. 149 00:07:35.079 --> 00:07:39.360 Okay, the text mentions removing introns and joining exons. Can 150 00:07:39.399 --> 00:07:42.040 you sort of break down what that actual edit looks like. 151 00:07:42.680 --> 00:07:45.279 Sure, think of a raw strand of DNA like a 152 00:07:45.360 --> 00:07:48.680 rough draft of a document. It contains coding regions that 153 00:07:48.759 --> 00:07:51.000 actually build the protein. Those are the exons. 154 00:07:51.040 --> 00:07:52.800 Okay, exons are the good stuff. 155 00:07:52.560 --> 00:07:55.639 Right, But it's interrupted by non coding regions or introns, 156 00:07:55.839 --> 00:07:58.639 which are essentially just biological blank spaces. 157 00:07:58.319 --> 00:07:59.959 Like typos or extra spaces. 158 00:08:00.240 --> 00:08:03.600 Sort of. Yeah, So splicing involves biochemically cutting out those 159 00:08:03.639 --> 00:08:07.399 blank entrons and stitching the valuable exons together. You can 160 00:08:07.439 --> 00:08:09.759 take an exon from a human cell, spitch it together, 161 00:08:10.160 --> 00:08:14.079 and insert it directly into that bacterial plasmid. A landmark 162 00:08:14.120 --> 00:08:16.439 example of this from the textbook is Hopwood's work in 163 00:08:16.519 --> 00:08:17.360 nineteen eighty five. 164 00:08:17.480 --> 00:08:18.839 Oh the hybrid antibiotics. 165 00:08:18.920 --> 00:08:21.959 Right, Yes, Hopwood and his colleagues managed to take the 166 00:08:22.000 --> 00:08:26.279 genetic structural features from two altogether different metabolites and combine them. 167 00:08:26.399 --> 00:08:30.600 They created a completely new natural product, a hybrid antibiotic 168 00:08:30.879 --> 00:08:34.120 that did not exist anywhere in nature, and they did 169 00:08:34.120 --> 00:08:36.240 it simply by writing new genetic code. 170 00:08:36.480 --> 00:08:39.240 I have a very mechanical question here, though. Let's say 171 00:08:39.279 --> 00:08:43.360 we have successfully spliced this beautiful new piece of recombinant DNA. 172 00:08:43.840 --> 00:08:47.279 We have the perfect plasmid, how do we physically force 173 00:08:47.360 --> 00:08:51.399 it inside a target plant or cell without completely destroying 174 00:08:51.399 --> 00:08:53.799 the cell in the process, Because I mean, you can't 175 00:08:53.840 --> 00:08:56.159 just rub DNA on a plant leaf and expect it 176 00:08:56.200 --> 00:08:56.799 to absorb it. 177 00:08:57.000 --> 00:08:59.759 No, you definitely can't, and that is the central challenge 178 00:08:59.759 --> 00:09:03.200 of d delivery. The text details several methods that really 179 00:09:03.240 --> 00:09:06.639 showcase the sheer ingenuity of this field. Let's just compare 180 00:09:06.679 --> 00:09:10.519 two major delivery systems for plant cells. For example, first 181 00:09:10.639 --> 00:09:12.600 is agrobacteria mediated transfer. 182 00:09:12.720 --> 00:09:16.279 Okay, agrobacterium. This is essentially hijacking a natural infection. 183 00:09:16.039 --> 00:09:17.919 Right, that is a great way to put it. Yet, yeah, 184 00:09:18.000 --> 00:09:21.759 Agrobacterium naturally infects plants by injecting its own DNA into 185 00:09:21.799 --> 00:09:25.159 the plants cells. So scientists just replace the bacteria as 186 00:09:25.159 --> 00:09:28.919 harmful genes with the beneficial recombinant DNA. Well that's clever, 187 00:09:29.159 --> 00:09:31.960 it is, but it has very strict mechanical limitations, like 188 00:09:32.000 --> 00:09:34.600 what the DNA construct you are trying to deliver must 189 00:09:34.639 --> 00:09:38.720 be small, specifically less than fifty kilobase pairs in size. 190 00:09:39.279 --> 00:09:43.240 If it's too big, the delivery just fails entirely. Furthermore, 191 00:09:43.279 --> 00:09:45.879 it only works on a restricted range of plant species. 192 00:09:45.960 --> 00:09:48.679 Okay, so it's like having a delivery service that only 193 00:09:48.720 --> 00:09:52.759 accepts small envelopes and only delivers to specific zip codes exactly. 194 00:09:52.799 --> 00:09:56.279 But then the text introduces direct gene transfer methods and 195 00:09:56.320 --> 00:09:57.840 this one honestly blew my mind. 196 00:09:58.279 --> 00:09:59.120 The particle gun. 197 00:10:00.000 --> 00:10:02.679 You you to help me understand the physics of a microscopic 198 00:10:02.799 --> 00:10:03.639 DNA shotgun. 199 00:10:03.679 --> 00:10:06.639 It's amazing, right, The biolistic particle gun is exactly what 200 00:10:06.679 --> 00:10:09.519 it sounds like, and it represents a massive technological leap. 201 00:10:10.000 --> 00:10:14.600 Researchers take microscopic microprojectiles, usually these tiny beads of gold 202 00:10:14.679 --> 00:10:17.360 or tungsten, and they physically coat these beads in the 203 00:10:17.399 --> 00:10:22.320 recombinant DNA wait gold, yeah, gold beads. Then, using pressurized 204 00:10:22.360 --> 00:10:25.559 helium gas or even a small blank charge, they literally 205 00:10:25.559 --> 00:10:28.399 shoot these microprojectiles at the target tissue at high velocity. 206 00:10:28.440 --> 00:10:31.759 They are shooting gold bullets coated in DNA directly through 207 00:10:31.759 --> 00:10:32.279 the cell wall. 208 00:10:32.320 --> 00:10:35.360 That is insane, it is, And because the particles are 209 00:10:35.360 --> 00:10:39.320 so infinitesimally small, they pierce the cell wall and the 210 00:10:39.320 --> 00:10:43.080 membrane without actually killing the cell. The DNA is deposited 211 00:10:43.120 --> 00:10:46.320 inside and the real beauty of the particle gun is 212 00:10:46.320 --> 00:10:49.600 that there is no maximum sized limit for the DNA construction. 213 00:10:49.679 --> 00:10:52.720 Oh wow, and it works on all plant species. It 214 00:10:52.759 --> 00:10:57.039 completely removes the taxonomic and size barriers of the agrobacterium method. 215 00:10:57.120 --> 00:10:59.480 Okay, here's where it gets really interesting, you know, because 216 00:10:59.519 --> 00:11:03.519 once you can bypass those cellular barriers, you aren't just 217 00:11:03.600 --> 00:11:07.000 limited to engineering a better tomato. This leads us directly 218 00:11:07.000 --> 00:11:09.080 to the concept of molecular farming. 219 00:11:09.320 --> 00:11:13.519 Yes, molecular farming is where pharmaceutical biotechnology really starts to 220 00:11:13.519 --> 00:11:17.440 feel like science fiction. We're no longer just engineering microbes, 221 00:11:17.519 --> 00:11:19.360 we're engineering transgenic animals. 222 00:11:19.440 --> 00:11:23.159 Yeah, the book talks about engineering farm animals like sheep, pigs, cattle. 223 00:11:23.360 --> 00:11:24.960 Why are we doing that? Like, what's the goal? 224 00:11:25.159 --> 00:11:27.879 Because a cow or a sheep can become a living, 225 00:11:28.120 --> 00:11:32.200 breathing bioreactor. By inserting human genes into the genome of 226 00:11:32.240 --> 00:11:36.320 these farm animals, scientists can program them to produce highly valuable, 227 00:11:36.559 --> 00:11:41.200 complex pharmaceutical biochemicals inside the m Yes, and the absolute 228 00:11:41.320 --> 00:11:45.600 genius of molecular farming is that these biochemicals are excreted 229 00:11:45.759 --> 00:11:49.559 directly into the animal's milk, urine, or blood. 230 00:11:49.840 --> 00:11:51.200 That is just wow. 231 00:11:51.320 --> 00:11:54.039 You simply milk the transgenda sheep, and then you isolate 232 00:11:54.080 --> 00:11:56.879 and purify life saving drugs directly from that milk. 233 00:11:57.080 --> 00:11:59.720 It is staggering to think about, I mean, milking a 234 00:11:59.759 --> 00:12:02.840 sheep for medicine. But let's bring it back to a 235 00:12:02.840 --> 00:12:06.600 more traditional industrial scale for a moment. Whether we are 236 00:12:07.039 --> 00:12:10.519 engineering a specific strain of bacteria with a particle gun, 237 00:12:11.000 --> 00:12:13.639 or maybe we are just searching for a natural mutant 238 00:12:13.720 --> 00:12:17.759 microbe that produces a valuable acid, how do we find 239 00:12:17.840 --> 00:12:21.039 the perfect microscopic factory to begin with? Like I imagine 240 00:12:21.039 --> 00:12:23.639 researchers looking through thousands of soil samples, how do you 241 00:12:23.679 --> 00:12:26.279 find the one microscopic colony out of billions that is 242 00:12:26.320 --> 00:12:28.879 actually producing the amine or the enzyme you want? Right? 243 00:12:29.080 --> 00:12:31.799 That is the meticulous science of secondary screening. You are 244 00:12:31.840 --> 00:12:35.679 looking for a literal needle in a microbial haystack, so 245 00:12:35.720 --> 00:12:37.799 you have to force the microbe to reveal itself. 246 00:12:37.840 --> 00:12:38.080 Okay. 247 00:12:38.200 --> 00:12:41.279 Industrial researchers do this using a brilliant visual trick based 248 00:12:41.279 --> 00:12:46.200 on chemistry. They incorporate specific pH indicating dyes The text 249 00:12:46.279 --> 00:12:50.600 specifically mentions romothimol blue or neutral red into a highly 250 00:12:50.639 --> 00:12:53.000 buffered aggar nutrient medium Okay. 251 00:12:52.799 --> 00:12:54.679 Wait, let me see if I follow the mechanics of this. 252 00:12:55.120 --> 00:12:58.799 The agar plate is heavily buffered, meaning it chemically resists 253 00:12:59.000 --> 00:13:02.320 changes to its PA like it wants to stay neutral. 254 00:13:02.519 --> 00:13:05.879 Exactly, it fights to stain neutral. So you played out 255 00:13:05.879 --> 00:13:10.240 your diluted soil sample so that individual microbial colonies begin 256 00:13:10.320 --> 00:13:15.000 to grow across this buffered agar. Right, Most normal bacteria 257 00:13:15.159 --> 00:13:18.679 will grow, live and die without ever producing enough acid 258 00:13:18.840 --> 00:13:21.559 or amine to overcome that heavy buffer. 259 00:13:21.600 --> 00:13:23.120 So the dye remains unchanged. 260 00:13:23.240 --> 00:13:23.639 Correct. 261 00:13:23.720 --> 00:13:26.600 But if a specific colony is a high yield producer. 262 00:13:26.759 --> 00:13:28.879 Right, if you have a colony that is furiously pumping 263 00:13:28.919 --> 00:13:32.240 out massive amounts of an organic acid, it will physically 264 00:13:32.279 --> 00:13:36.039 overwhelm the buffer, but only in its immediate localized vicinity. 265 00:13:36.159 --> 00:13:38.480 Oh and because it breaks through the buffer, it radically 266 00:13:38.519 --> 00:13:40.559 drops the local pH which triggers the. 267 00:13:40.559 --> 00:13:44.879 Dye precisely, it induces a distinct, vivid color change in 268 00:13:44.879 --> 00:13:48.759 the dye, forming a highly visible halo right around that 269 00:13:48.879 --> 00:13:52.080 one specific colony. Wow, a researcher can pull a petri 270 00:13:52.240 --> 00:13:54.679 dish with ten thousand colonies on it out of an 271 00:13:54.720 --> 00:13:58.960 incubator and instantly see the one high yield producer because 272 00:13:59.000 --> 00:14:01.200 it's surrounded by a glowing halo of color. 273 00:14:01.399 --> 00:14:04.679 It's just such an elegant solution. They literally force the 274 00:14:04.720 --> 00:14:07.600 best microbes to flag themselves. So, okay, we find our 275 00:14:07.639 --> 00:14:09.759 perfect microbe. Now we need to make tons of this 276 00:14:09.919 --> 00:14:12.440 drug for the global market. How do we actually scale 277 00:14:12.440 --> 00:14:12.759 it up? 278 00:14:13.120 --> 00:14:16.639 So this is the domain of bioprocess technology and bioreactors. 279 00:14:17.440 --> 00:14:21.759 The text contrasts a few methods, notably submerged fermentation, which 280 00:14:21.799 --> 00:14:26.200 is growing cultures in massive liquid vats versus solid substrate fermentation. 281 00:14:26.759 --> 00:14:27.000 Yeah. 282 00:14:27.039 --> 00:14:30.000 Solid substrate fermentation really caught my eye because it happens 283 00:14:30.399 --> 00:14:33.039 in the absence or near absence of free water. How 284 00:14:33.039 --> 00:14:34.120 does that yield anything? 285 00:14:34.240 --> 00:14:37.279 Well, it essentially mimics the natural environment of fungi. A 286 00:14:37.320 --> 00:14:40.559 classic industrial application is the production of vital fungal enzymes, 287 00:14:40.840 --> 00:14:45.240 things like celluluses, amylases, and pectinases. You take a solid substrate, 288 00:14:45.639 --> 00:14:48.799 typically just agricultural waste like wheat bran or rice brand, 289 00:14:49.399 --> 00:14:51.480 and you spray it with a precise amount of water 290 00:14:51.519 --> 00:14:55.399 and nutrients and then grow filment as fungi directly on 291 00:14:55.440 --> 00:14:58.840 the surface of the solid brand. It's incredibly efficient for 292 00:14:58.919 --> 00:14:59.960 certain biologicals. 293 00:15:00.120 --> 00:15:02.480 What's fascinating here is that we aren't just using these 294 00:15:02.480 --> 00:15:06.159 scaled up microbes to produce simple acids or basic proteins, right, 295 00:15:06.600 --> 00:15:09.799 We are using them to do chemistry that human beings 296 00:15:09.840 --> 00:15:13.399 simply cannot replicate in a lab. The textbook calls this 297 00:15:13.559 --> 00:15:16.480 microbial transformation or biotransformation. 298 00:15:16.840 --> 00:15:20.240 Biotransformation is easily one of the most powerful tools in pharmacology. 299 00:15:20.480 --> 00:15:23.559 Let's look at the textbook's prime example steroids. 300 00:15:23.600 --> 00:15:24.480 Okay, steroids. 301 00:15:24.480 --> 00:15:28.159 Steroid molecules have a very rigid, multi ring structure. If 302 00:15:28.159 --> 00:15:31.159 a human chemist tries to attach an oxygen atom to 303 00:15:31.200 --> 00:15:34.559 one specific carbon atom on that complex ring structure, it 304 00:15:34.639 --> 00:15:38.600 requires dangerous chemicals, extreme heat, multiple steps, and they often 305 00:15:38.679 --> 00:15:41.559 end up altering other parts of the molecule entirely by accident. 306 00:15:41.639 --> 00:15:44.240 Right, it's just too blunt of an instrument exactly. 307 00:15:44.480 --> 00:15:48.279 But microbes have enzymes that operate with perfect stereo specificity. 308 00:15:49.120 --> 00:15:52.759 The text highlights the immobilized mycelium of a fungus called 309 00:15:52.879 --> 00:15:54.240 Curvularia lunata. 310 00:15:54.480 --> 00:15:55.679 Curvular lunata. 311 00:15:55.840 --> 00:15:59.159 Yes, researchers use this fungus to convert a compound called 312 00:15:59.159 --> 00:16:04.559 Reichstein's substance S, which is eleven deoxy cortisol directly into 313 00:16:04.559 --> 00:16:08.320 the highly valuable anti inflammatory steroid predness alone. 314 00:16:08.399 --> 00:16:11.600 So the fungus just performs the complex bioconversion in a 315 00:16:11.720 --> 00:16:13.679 single elegant. 316 00:16:13.320 --> 00:16:19.840 Step yes oxidation, reduction, hydrolysis. These fungi execute chemical wizardry 317 00:16:20.039 --> 00:16:23.240 at reen temperature that would cost millions to attempt synthetically. 318 00:16:23.399 --> 00:16:25.799 But there is a massive economic catch here that the 319 00:16:25.799 --> 00:16:28.600 textbook addresses, and we have to talk about it. Biotransformation 320 00:16:28.679 --> 00:16:32.320 is miraculous, sure, but pharmaceutical manufacturing is a business. If 321 00:16:32.360 --> 00:16:36.559 we are using these highly purified, perfect biological catalyst enzymes 322 00:16:36.600 --> 00:16:39.080 to do this work, we can't afford to lose them definitely, 323 00:16:39.080 --> 00:16:41.120 Not like if we just dump them into a standard 324 00:16:41.240 --> 00:16:44.360 batch operation, run the reaction once, and then flush the 325 00:16:44.399 --> 00:16:47.039 tank to harvest the drug. The cost of those lost 326 00:16:47.159 --> 00:16:49.639 enzymes would make the drug too expensive for anyone to buy. 327 00:16:49.879 --> 00:16:53.960 Yeah, the economics dictate the engineering here. Soluble enzymes used 328 00:16:53.960 --> 00:16:57.000 in standard batch operations are essentially single. 329 00:16:56.840 --> 00:16:58.759 Use, right, which is a huge waste. 330 00:16:58.799 --> 00:17:04.359 Exactly. Solution to this economic bottleneck is enzyme immobilization. 331 00:17:03.759 --> 00:17:07.200 Which basically means we are trapping the enzyme so it 332 00:17:07.200 --> 00:17:08.240 can't be flushed away. 333 00:17:08.400 --> 00:17:12.240 Exactly, you physically or chemically attach the active enzyme to 334 00:17:12.279 --> 00:17:16.319 a solid carrier matrix inside the bioreactor. So the raw 335 00:17:16.359 --> 00:17:20.160 substrate flows over the matrix, the complex cabical conversion happens, 336 00:17:20.359 --> 00:17:22.920 the finished drug falls out the other side. Yeah, but 337 00:17:22.960 --> 00:17:26.119 the enzyme remains firmly fixed in place, ready to catalyze 338 00:17:26.160 --> 00:17:26.960 the next molecule. 339 00:17:27.079 --> 00:17:28.079 Oh that's brilliant. 340 00:17:28.160 --> 00:17:30.440 Yeah, you can use the exact same enzyme continuously. 341 00:17:30.519 --> 00:17:34.279 That makes perfect logistical sense. But thinking as a pharmacy 342 00:17:34.319 --> 00:17:38.480 professional managing this, how do you mathematically calculate the efficiency 343 00:17:38.559 --> 00:17:41.279 of that immobilized enzyme? Like, how do you know when 344 00:17:41.319 --> 00:17:42.960 it's degrading or needs replacing. 345 00:17:43.119 --> 00:17:46.960 It comes down to enzyme kinetics, specifically the Michalis menton kinetics. 346 00:17:47.200 --> 00:17:51.359 The text focus is on the michaealis constant known as courama. Now, 347 00:17:51.400 --> 00:17:53.640 this is a concept that is often misunderstowed, so let's 348 00:17:53.680 --> 00:17:54.200 be precise. 349 00:17:54.240 --> 00:17:55.400 Okay, break it down for us. 350 00:17:55.640 --> 00:17:58.440 In an enzymatic reaction, the enzyme, which is E, binds 351 00:17:58.480 --> 00:18:02.720 to the substrate, which is to form an enzyme substrate complex. 352 00:18:02.799 --> 00:18:04.359 Right, the ES complex Right. 353 00:18:04.680 --> 00:18:09.440 The Makalis constant kalemen represents the specific substrate concentration at 354 00:18:09.440 --> 00:18:13.240 which the reaction achieves exactly half of its maximum velocity 355 00:18:13.480 --> 00:18:14.119 or vmax. 356 00:18:14.160 --> 00:18:17.599 Okay, wait, so it's essentially a measure of affinity. Then 357 00:18:17.759 --> 00:18:20.359 it measures how badly the enzyme wants to bind to 358 00:18:20.400 --> 00:18:20.960 the substrate. 359 00:18:21.240 --> 00:18:24.200 The math backs that up entirely. Yes, a lower CARAMU 360 00:18:24.279 --> 00:18:26.680 means the enzyme has a high affinity for the substrate, 361 00:18:27.119 --> 00:18:29.680 meaning it takes a very small concentration of the raw 362 00:18:29.759 --> 00:18:33.039 material to get the reaction running at half its maximum speed. 363 00:18:33.160 --> 00:18:33.759 Oh I see. 364 00:18:33.880 --> 00:18:36.839 By tracking the carara, industrial scientists can determine exactly how 365 00:18:36.880 --> 00:18:40.039 efficient their immobilized enzyme is and optimize the flow rate 366 00:18:40.039 --> 00:18:43.000 of the bioreactor to ensure the factory remains highly profitable. 367 00:18:43.079 --> 00:18:45.880 We are using high level calculus to track the affinity 368 00:18:45.920 --> 00:18:47.160 of a microscopic collision. 369 00:18:47.240 --> 00:18:49.359 It's just brilliant. But I want to. 370 00:18:49.279 --> 00:18:52.039 Push this into the modern era because we are talking 371 00:18:52.079 --> 00:18:55.680 about enzymes physically fixed to matrices. Are we starting to 372 00:18:55.720 --> 00:18:59.559 see a literal merger of these living biological principles with 373 00:18:59.720 --> 00:19:03.240 a uctual electronic hardware? Like are we putting living cells 374 00:19:03.359 --> 00:19:04.440 inside machines? 375 00:19:04.880 --> 00:19:07.039 We absolutely are, and it brings it to the field 376 00:19:07.039 --> 00:19:12.119 of biosensors. A biosensor physically integrates a living biological sensing 377 00:19:12.160 --> 00:19:14.480 element with an electrochemical transducer. 378 00:19:14.680 --> 00:19:18.039 Yeah, I was reading the section detailing the hypoxenthene biosensor. 379 00:19:18.079 --> 00:19:20.279 Can you walk us through the mechanism of how a 380 00:19:20.319 --> 00:19:23.640 living cell actually translates its biology into electricity. 381 00:19:23.720 --> 00:19:28.599 It's fascinating. The hypoxanthine biosensor utilizes a specific immobilized strain 382 00:19:28.680 --> 00:19:33.440 of bacteria Brevobacterium ammoniagenes. These living bacteria are trapped right 383 00:19:33.440 --> 00:19:37.720 against an electronic sensor. When the target compound hypoxenthene is introduced, 384 00:19:38.000 --> 00:19:39.680 the bacteria naturally metabolize it. 385 00:19:39.720 --> 00:19:40.359 They just eat it. 386 00:19:40.480 --> 00:19:42.599 Yes, they eat it, and as a byproduct of that 387 00:19:42.640 --> 00:19:46.680 specific metabolic reaction, they release compounds like hydrogen peroxide or 388 00:19:46.720 --> 00:19:47.680 specific ions. 389 00:19:47.839 --> 00:19:48.000 Ah. 390 00:19:48.359 --> 00:19:53.640 The electrochemical transducer detects that specific chemical byproduct and instantly 391 00:19:53.720 --> 00:19:55.880 converts it into a measurable electrical current. 392 00:19:55.960 --> 00:19:59.519 So the bacteria act as the processing chip. They detect 393 00:19:59.559 --> 00:20:02.759 the chemical, produce a byproduct, and the hardware turns that 394 00:20:02.839 --> 00:20:04.480 byproduct into data. 395 00:20:04.319 --> 00:20:04.920 On a screen. 396 00:20:05.279 --> 00:20:08.000 Exactly. And if we connect this to the bigger picture, 397 00:20:08.440 --> 00:20:12.519 this convergence of living cells and digital hardware leads directly 398 00:20:12.559 --> 00:20:15.400 to the ultimate frontier bioinformatics. 399 00:20:15.480 --> 00:20:20.200 And in silicobiology in silico like silicon microchips, meaning we 400 00:20:20.279 --> 00:20:23.559 are simulating biology entirely on computers. 401 00:20:23.119 --> 00:20:26.240