1 00:00:14,510 --> 00:00:15,709 So I'm Helen McShane, 2 00:00:15,710 --> 00:00:25,370 and I'm the director of the NIHR Oxford Biomedical Research Centre that is hosting this Oxford Reads Kafka TB event for us this evening. 3 00:00:26,690 --> 00:00:33,800 The BRC represents a partnership between the University of Oxford and Oxford University Hospitals NHS Foundation Trust. 4 00:00:35,180 --> 00:00:41,060 Sorry. Excuse me. We're funded by the government to conduct translational research for patient benefit. 5 00:00:41,420 --> 00:00:49,100 That means developing new vaccines, new drugs, new diagnostic tests, new tools, all of which are designed to improve the health. 6 00:00:51,620 --> 00:00:55,550 And wealth of the patient, of of patients and the NHS. 7 00:00:56,840 --> 00:00:59,360 The BRC is organised into 15 themes, 8 00:00:59,630 --> 00:01:06,950 one of which is called life saving vaccines and one of which is called Modernising Medical Microbiology and Big Infection Data. 9 00:01:07,580 --> 00:01:13,520 The work of my research group sits within the vaccines theme, and the work of Phil's uh, 10 00:01:13,520 --> 00:01:18,140 group sits within the modest Modernising Medical Microbiology group. 11 00:01:18,680 --> 00:01:24,649 We both work on TB, so that's why we're here to talk to you today, because of course, the the whole, 12 00:01:24,650 --> 00:01:33,530 um, of this week's events are really focussed around the fact that Kafka died 100 years ago of TB. 13 00:01:34,190 --> 00:01:41,330 Um, uh, so that's that's why we're here today. I'm going to give a talk updating you on some of the progress in vaccine development. 14 00:01:41,690 --> 00:01:46,190 Uh, we're then going to hear from Amy about the lived experience of having tuberculosis. 15 00:01:46,490 --> 00:01:51,440 Uh, and then Phil is going to talk to us about, uh, some updates on diagnostic tests. 16 00:01:53,240 --> 00:02:02,420 So when I was thinking about this talk, uh, I found this quote, um, or this comment where that Kafka saw his TB as a liberation. 17 00:02:02,960 --> 00:02:11,560 I'm not quite sure I agree with that. Uh, and I'm quite sure that the 10.6 million, uh, people who developed TB in 2022, 18 00:02:11,570 --> 00:02:18,290 not to mention the 1.3 million people who died in 2022 of TB, would describe it as a liberation. 19 00:02:19,010 --> 00:02:23,390 TB remains a very significant global health problem around the world today. 20 00:02:24,080 --> 00:02:28,280 It is probably, again the leading cause, infectious cause of death. 21 00:02:28,280 --> 00:02:33,260 So I think it's overtaken Covid. Uh, and and got back into that number one position. 22 00:02:33,620 --> 00:02:39,019 Drug resistance is an increasing problem. About half a million people have drug resistant TB every year. 23 00:02:39,020 --> 00:02:40,790 And Phil will talk a little bit about that. 24 00:02:41,300 --> 00:02:49,970 The geographical overlap with the HIV epidemic, which makes people more susceptible to TB and the burden of latent infection, 25 00:02:49,970 --> 00:02:57,200 it's estimated that about a quarter of the world's population are latently infected with TB, and are at risk of reactivation. 26 00:02:57,470 --> 00:03:01,310 Should that latent, uh, should they become immunocompromised for any reason. 27 00:03:03,470 --> 00:03:11,420 TB is different to HIV and malaria. The other big what used to be called the big three pathogens in that we already have a vaccine, 28 00:03:11,420 --> 00:03:14,840 BCG, which is one of the world's most widely used vaccines. 29 00:03:15,110 --> 00:03:18,110 It's been given to over 3 billion people around the world. 30 00:03:19,040 --> 00:03:21,920 And BCG has been around for over 100 years. 31 00:03:22,340 --> 00:03:32,330 It was first given orally in 1921, and there have been many, many studies over the years looking at how well BCG works to protect people against TB. 32 00:03:32,810 --> 00:03:36,800 We know that when it's given at birth, as it is throughout most of the developing world, 33 00:03:37,100 --> 00:03:42,070 that it confers very good and reliable protection against disseminated TB. 34 00:03:42,080 --> 00:03:48,800 So that's TB that's spread outside of the lungs, particularly that spread to the to the brain, something we call TB meningitis. 35 00:03:49,880 --> 00:03:56,180 But what it really doesn't do very consistently is protect against lung disease, particularly in young adults. 36 00:03:56,210 --> 00:04:01,560 Uh, and adolescents. It's very variable, the protection it confers against lung disease. 37 00:04:01,580 --> 00:04:12,200 So the British MRC study conducted in this country in the 1950s shows the BCG was 80% protective, and that protected protection lasted for 20 years. 38 00:04:12,710 --> 00:04:18,320 In comparison, protection seen in, for example, India or Africa is close to zero. 39 00:04:19,880 --> 00:04:23,390 So one of the real challenges is what's underlying that variability. 40 00:04:23,990 --> 00:04:28,070 And how can we develop better vaccines that protect more universally. 41 00:04:28,640 --> 00:04:31,790 We know that giving BCG more than once doesn't make any difference. 42 00:04:31,790 --> 00:04:39,440 It doesn't make it any better. And there is the sort of slightly curious literature which suggests that BCG, when given to neonates, 43 00:04:39,680 --> 00:04:46,280 can protect against what we call all cause mortality, which means it reduces the deaths from things other than TB. 44 00:04:46,580 --> 00:04:54,170 Now that's a variable effect. And it's seen in some studies some of the times, but is quite interesting when you think about how BCG might be working. 45 00:04:55,820 --> 00:05:04,100 So this slide uh, which was taken from a meta analysis, looks at the reasons for the variability in protective efficacy of BCG. 46 00:05:04,670 --> 00:05:08,930 And what we see is that latitude is very important. 47 00:05:09,230 --> 00:05:14,030 So the closer you live to the equator, the less likely BCG is to work. 48 00:05:14,440 --> 00:05:18,700 And we think that's because of exposure to things called non tuberculose mycobacteria. 49 00:05:18,910 --> 00:05:24,400 So pathogens from the same family as TB that don't cause disease unless you're immunocompromised. 50 00:05:25,420 --> 00:05:32,590 We also see that if you give vaccines for the vaccine as a neonates rather than to older age groups, it's much better at protecting. 51 00:05:33,040 --> 00:05:37,690 And that's why W.H.O. policy is to give it ideally within the first seven days of life. 52 00:05:38,710 --> 00:05:44,110 Importantly, what we also see is the BCG strain doesn't seem to make any difference. So BCG, 53 00:05:44,110 --> 00:05:48,459 unlike most of the vaccines we use today, is not the same thing around the world. 54 00:05:48,460 --> 00:05:56,230 It's not clonal. There are lots of different strains of BCG, but there's very little evidence that although those strains are genetically different, 55 00:05:56,470 --> 00:06:01,510 they're probably not very different in how good they are at protecting against TB or not. 56 00:06:04,300 --> 00:06:08,440 So how do we put all this together and how do we design a better vaccine against TB? 57 00:06:08,650 --> 00:06:14,740 Well, because of that protection that BCG confers in the first few years of life against severe disease, 58 00:06:15,040 --> 00:06:19,630 really BCG in some shape or form has to stay part of any new regime. 59 00:06:20,140 --> 00:06:25,960 Um, and we know that you need to induce a very strong what we call cell mediated immune response. 60 00:06:26,080 --> 00:06:28,780 So this means relying on the T cells in your blood. 61 00:06:30,520 --> 00:06:39,160 So we know that, uh, all of the vaccines we give today, apart from BCG, work primarily via the induction of things called antibodies. 62 00:06:39,610 --> 00:06:45,130 Whereas TB is a different ballgame, hides inside cells, which means that antibodies can't get to it. 63 00:06:45,340 --> 00:06:51,880 So we actually need to induce the cell mediated arm of the immune response in order in order to develop a protective vaccine. 64 00:06:52,180 --> 00:06:59,950 And of course, we see that most obviously in patients with HIV, as their CD4 count comes down, the risk of getting TB goes up. 65 00:07:01,060 --> 00:07:04,690 Um, broadly speaking, there are two strategies being pursued in the field. 66 00:07:04,960 --> 00:07:12,790 The first is to say, well, let's leave BCG as it is a well-established part of the expanded program on immunisation led by W.H.O. 67 00:07:13,060 --> 00:07:20,530 And let's develop a second vaccine, a booster vaccine to be given at a later point in time to improve the efficacy of BCG. 68 00:07:21,550 --> 00:07:29,110 The alternative is to say, let's make a better BCG. And there are groups around the world genetically engineering either BCG or in fact, 69 00:07:29,110 --> 00:07:33,910 Mycobacterium tuberculosis, uh, in order to make a better strain of BCG. 70 00:07:34,150 --> 00:07:44,050 And of course, you could put the two together. So this slide illustrates the clinical development pipeline in the year 2000. 71 00:07:44,200 --> 00:07:48,160 So here were all the vaccines that were in clinical trials in the year 2000. 72 00:07:48,580 --> 00:07:54,790 No I haven't forgotten to paste the picture in. There were no new TB vaccines in clinical trials in the year 2000. 73 00:07:55,420 --> 00:08:00,790 Which really for me represents decades of neglect in in funding for this field. 74 00:08:01,060 --> 00:08:09,910 Uh, and I'm really just the idea that actually maybe we'd eradicated TB, whereas of course, we are very, very far from that, uh, that scenario. 75 00:08:10,600 --> 00:08:15,549 So fast forward to 2024, and at least we actually have a pipeline. 76 00:08:15,550 --> 00:08:18,820 Now, there are actually some vaccines in clinical trials. 77 00:08:19,270 --> 00:08:25,270 But, there's always a but with TB, if you look at this more carefully, uh, there are some problems with it. 78 00:08:25,270 --> 00:08:33,370 And there are two problems in particular. The first is, if I put up the pipeline from five years ago, you'd be hard pushed to see anything different. 79 00:08:33,790 --> 00:08:37,270 So it's a very stagnant pipeline. Things are not moving very quickly. 80 00:08:38,380 --> 00:08:43,990 The second problem with it is that it's a very thin pipeline, particularly on the left hand side. 81 00:08:46,300 --> 00:08:52,750 So for clinical development pipelines, we want a very rich early pipeline because of the attrition. 82 00:08:53,020 --> 00:09:00,490 Most things in early development fail. At best, probably 1 in 10 make it through to licensure and deployment. 83 00:09:00,970 --> 00:09:04,840 What that means is we need a huge number of things on the left hand side to make 84 00:09:04,840 --> 00:09:08,500 sure we actually have something we can pull through and make and use in real life. 85 00:09:09,280 --> 00:09:11,790 This is a pretty thin left hand side of the pipeline. 86 00:09:11,830 --> 00:09:20,440 So again, representing really lack of funding, uh, and sort of lack of, of global awareness of, of how pressing a problem this is. 87 00:09:21,760 --> 00:09:29,010 So I'm going to now just illustrate, uh, briefly, uh, a couple of vaccines that have made it through to clinical trials. 88 00:09:29,040 --> 00:09:33,429 So firstly, MVA, 85A, this was a vaccine my laboratory developed. 89 00:09:33,430 --> 00:09:38,560 It was the first new TB vaccine to go into clinical trials anywhere in the world in 2002. 90 00:09:39,040 --> 00:09:46,360 We conducted a very careful clinical development pipeline because it was the first vaccine, and we obviously wanted to be safe above everything else. 91 00:09:46,870 --> 00:09:53,439 And despite eight years of lots of clinical trials and lots of trials showing like this slide does here, um, 92 00:09:53,440 --> 00:09:57,879 that this vaccine was safe and was very potent at stimulating an immune response, 93 00:09:57,880 --> 00:10:04,030 at least when we tested it in this country, when we got to test it in 3000 babies in South Africa, 94 00:10:04,450 --> 00:10:09,580 We sadly saw that there was no difference between the vaccine arm and the placebo arm. 95 00:10:09,790 --> 00:10:13,960 So although the vaccine was safe in this trial and the babies were perfectly. 96 00:10:13,980 --> 00:10:20,790 Safe. There was no improvement in the rates of TB in the babies that got the vaccine compared with the placebo arm. 97 00:10:22,950 --> 00:10:26,280 Enormously disappointing results for us and for the field. 98 00:10:26,790 --> 00:10:31,680 But that's the price you pay for being the first. And we have to work out what we can learn from this study. 99 00:10:32,160 --> 00:10:35,820 We took an immune sample from every single baby in that study. 100 00:10:36,240 --> 00:10:39,600 So we went back to their samples taken at the beginning of the trial and said, 101 00:10:39,600 --> 00:10:43,980 what can we learn from those samples that will help us develop better vaccines? 102 00:10:44,490 --> 00:10:50,010 And what we learnt was two really important things. We learned that activated T cells. 103 00:10:50,160 --> 00:10:53,550 So T cells, if you like, that, are really primed and ready for action. 104 00:10:53,670 --> 00:11:00,749 All of them when were higher in the babies that went on to get TB compared with the controls. 105 00:11:00,750 --> 00:11:04,950 That didn't go on to get TB. What's really interesting, what's causing that activation. 106 00:11:05,490 --> 00:11:11,490 And we also found that the antigen specific response we were trying to induce with the vaccine was, 107 00:11:11,490 --> 00:11:15,780 in fact lower in the babies that went on to get TB compared with the controls. 108 00:11:16,050 --> 00:11:24,660 So perhaps if the vaccine had been a bit more potent and stimulated a stronger response, we might have seen a result that we had hoped to see. 109 00:11:26,340 --> 00:11:30,690 This is slide really is to remind me that serendipity plays an important part in science. 110 00:11:30,690 --> 00:11:36,360 So as part of the controls we do in our lab assays, we look at various, um, 111 00:11:36,510 --> 00:11:41,310 peptides or proteins from other pathogens, really just to check that the cells are alive. 112 00:11:42,090 --> 00:11:48,690 And as part of that, we had a, um, some peptides or some proteins from a virus called CMV, cytomegalovirus. 113 00:11:49,350 --> 00:11:55,889 When we looked at the responses to this, we found that the babies who were CMV infected in about a quarter of them were 114 00:11:55,890 --> 00:12:00,959 in the study were more likely to get TB than the CMV uninfected babies. 115 00:12:00,960 --> 00:12:04,590 And then we knew that. Why is that? Well, we don't know, but we're working on it. 116 00:12:04,950 --> 00:12:12,750 But actually, it reminds me that the infectious milia that people live in has an interaction with their risk of infectious diseases. 117 00:12:14,270 --> 00:12:16,850 So why is TB vaccine development so difficult? 118 00:12:16,880 --> 00:12:21,860 Well, the trials are very big and very expensive and take a long time, and I'll show you that in a minute. 119 00:12:22,280 --> 00:12:26,540 We don't know what the animal models mean in terms of predicting human efficacy. 120 00:12:27,650 --> 00:12:32,420 We don't have an immune correlate of protection, so we can't predict which vaccines will work. 121 00:12:33,080 --> 00:12:37,250 We don't know whether the whole organism vaccines or just a few proteins. 122 00:12:37,250 --> 00:12:41,660 There are 4000 to choose from. So how do we go about deciding, uh, is best? 123 00:12:42,500 --> 00:12:47,090 And we have a very, very clever pathogen that's really good at hiding from the immune system. 124 00:12:49,890 --> 00:12:57,030 So to end on a slightly more positive note, this is the most exciting result in the field in several decades. 125 00:12:57,300 --> 00:13:01,830 So this is a vaccine developed originally by GSK, now being developed by the Gates Foundation. 126 00:13:02,190 --> 00:13:11,040 3500 African adults who were already latently infected, vaccinated either with this vaccine called M72 or placebo. 127 00:13:11,490 --> 00:13:17,850 And unlike the curves in the MVA study, you can see that these are a bit further apart, which is what you want. 128 00:13:17,940 --> 00:13:21,030 And even more importantly, the vaccine arm is on the top. 129 00:13:21,240 --> 00:13:24,510 So about 50% protection seen in this study. 130 00:13:24,690 --> 00:13:28,229 So fantastic result for the field, but only 50%. 131 00:13:28,230 --> 00:13:34,500 We have a Covid vaccine that's nearly 100% effective. We have an HPV vaccine human papillomavirus vaccine. 132 00:13:34,620 --> 00:13:41,410 That's over 100%. We have to do better than this. That result needs repeating in a larger trial. 133 00:13:41,500 --> 00:13:45,150 And just to give you a sense of the challenge, we've got a trial that's just out. 134 00:13:45,170 --> 00:13:50,790 It's got 60 sites in Africa, Indonesia and Vietnam, 20,000 subjects. 135 00:13:50,970 --> 00:13:54,390 This trial is going to cost over half a billion dollars. 136 00:13:54,570 --> 00:13:59,860 We can't do many of these studies. Recruitment is underway and it's due to report in 2028. 137 00:13:59,880 --> 00:14:01,350 So you can see the problem. 138 00:14:03,330 --> 00:14:10,739 One of the things we're doing in my lab is to try and develop a human challenge, models to see if we can actually test vaccines, 139 00:14:10,740 --> 00:14:19,620 to see whether they work in a human challenge model where we deliberately give people an infection before we move to these large efficacy trials. 140 00:14:20,010 --> 00:14:23,850 We can do this in malaria. We can do it influenza. Why can't we do it in TB? 141 00:14:24,330 --> 00:14:28,650 So we started by giving people BCG by the skin route. 142 00:14:29,010 --> 00:14:34,500 And now we're doing this by aerosol directly into the lungs to mimic the natural route of infection. 143 00:14:35,100 --> 00:14:38,670 We think this kind of model will help us test new vaccines. 144 00:14:40,470 --> 00:14:46,270 This I'm going to skip over in the interest of time, but this just shows you the power of doing these kind of studies. 145 00:14:46,320 --> 00:14:51,930 We can look in the lungs at the kind of cells that go into the lungs after we've infected people. 146 00:14:52,080 --> 00:14:59,490 And that gives us new insights into immunity in people, in the target species, rather than in animal models that we don't know the relevance of. 147 00:15:01,230 --> 00:15:04,020 But really, just to finish with a slightly salutary note, 148 00:15:04,410 --> 00:15:11,850 and I'm very grateful to Barry Mannone from the Department of the Faculty of Modern Languages for drawing this to my attention. 149 00:15:12,360 --> 00:15:15,480 There is some evidence that Kafka may have been an anti-vaxxer. 150 00:15:16,200 --> 00:15:20,040 So, uh, he made friends with someone called Maurice Schnitzer. 151 00:15:20,460 --> 00:15:26,250 Um, and you can see here no vaccinations. Uh, record I recorded on his military conscription card. 152 00:15:26,680 --> 00:15:34,930 And there's his name from Doctor Franz Kafka. He's subscribing to this sort of, um, naturopath, uh, journal. 153 00:15:34,950 --> 00:15:40,770 So, um, the irony of all of this is, even if 100 years ago, 110 years ago, 154 00:15:40,860 --> 00:15:44,880 we had an effective vaccine against TB, I'm not sure Kafka would have had it. 155 00:15:45,780 --> 00:15:47,310 So that may not have altered history. 156 00:15:48,300 --> 00:15:54,390 So that, I hope, was just a little snapshot of of perhaps some optimism at the end of where we are with TB vaccine development. 157 00:15:54,870 --> 00:16:01,200 I'm now going to hand over to Amy, who's going to talk you through, really the lived experience of living with TB. 158 00:16:02,340 --> 00:16:24,020 Amy. 159 00:16:24,640 --> 00:16:28,110 160 00:16:29,330 --> 00:16:33,620 So Franz Kafka was just 40 years old when he sadly died from TB. 161 00:16:34,190 --> 00:16:36,560 Which Which is actually the age that I am now. 162 00:16:37,070 --> 00:16:42,950 So during my initial research into his life, I discovered that in addition to being a ferocious writer, 163 00:16:42,950 --> 00:16:46,370 he was also a lawyer and legally educated as I am too. 164 00:16:47,270 --> 00:16:53,630 However, unlike Kafka, I am extremely fortunate to be alive and able to share my story with you today. 165 00:16:54,110 --> 00:16:56,720 I just want to start by saying thank you for being here this evening. 166 00:16:57,350 --> 00:17:01,010 Um, there are a few things I hope you will take away from my personal experience. 167 00:17:01,880 --> 00:17:11,720 Firstly, please never forget that at the heart of this terrible disease are ordinary people from all walks of life with hopes, 168 00:17:11,990 --> 00:17:20,450 fears, worries, expectations, ambitions and dreams, all of whom deserve a chance at a future free from TB. 169 00:17:21,740 --> 00:17:28,880 Secondly, TB has never been eradicated in the UK and if I hear this one more time, I will scream literally. 170 00:17:29,390 --> 00:17:38,180 Um. In fact, confirmed cases of TB in the UK have risen sharply over the last few years since the Covid 19 pandemic. 171 00:17:38,720 --> 00:17:47,480 So, like many other infectious diseases, TB acts as a kind of magnifying glass, exposing existing fault lines and inequalities, 172 00:17:48,080 --> 00:17:55,040 especially in light of current challenges such as the cost of living crisis, housing shortages and poor living conditions, 173 00:17:55,430 --> 00:18:00,980 the hostile environment, major pressures in the NHS and global conflicts. 174 00:18:01,730 --> 00:18:08,630 It is more important than ever to be aware of TB and to understand the devastating consequences it can have. 175 00:18:10,100 --> 00:18:15,230 So I was a student in my late teens when I first developed symptoms of TB. 176 00:18:15,980 --> 00:18:23,660 And the most obvious symptom was a persistent cough or despite numerous courses of antibiotics, wouldn't go away. 177 00:18:24,170 --> 00:18:34,610 And next came a slow, insidious fatigue, unexplained weight loss, lack of appetite, and finally, night after night of waking up, covered in sweat. 178 00:18:35,180 --> 00:18:40,820 It was my friends, family, colleagues and even customers at the bar where I worked 179 00:18:40,850 --> 00:18:49,700 to know just how ill I was before I did. I mean, I was otherwise young and healthy, so I thought, how could I be seriously unwell? 180 00:18:50,180 --> 00:18:56,420 I thought these things happen to other people, not me. After nine months of visits to my GP, 181 00:18:57,050 --> 00:19:05,900 I came home for Christmas from university where I was studying law and my GP finally referred me to hospital for an initial chest x ray, 182 00:19:06,710 --> 00:19:17,130 suspecting I had asthma. I was given another course of antibiotics and a follow up chest x ray, and then I had nothing until two months later, 183 00:19:17,670 --> 00:19:24,960 when I received a phone call from the chest clinic at my local hospital in London, asking why I hadn't turned up for an appointment 184 00:19:25,230 --> 00:19:36,900 I didn't even know I had. So by now my condition had seriously deteriorated and I was in excruciating pain as my left lung had begun to collapse. 185 00:19:37,950 --> 00:19:43,980 My appointment was rescheduled for six weeks later. By this time it was Easter holidays. 186 00:19:44,550 --> 00:19:52,710 I had a bronchoscopy where a camera was inserted into my lungs, and the sample was taken to be tested, as well as three sputum samples. 187 00:19:54,350 --> 00:19:57,860 So by this time I had an inkling that I might have TB. 188 00:19:58,370 --> 00:20:05,510 After speaking to my GP, and when I asked the doctor who carried out the procedure if they thought I had TB, they said yes. 189 00:20:06,170 --> 00:20:15,580 And they told me that if they couldn't diagnose me, I could die. A few weeks later, I got a call from the TB nurse who finally diagnosed me with TB. 190 00:20:15,880 --> 00:20:21,850 She said to me, and this is an exact quote I remember: you have World War three going on inside you. 191 00:20:22,690 --> 00:20:30,310 It didn't actually come as a huge shock, and actually it was more of a relief, which might sound a bit strange, but I initially thought I had cancer. 192 00:20:31,090 --> 00:20:36,460 Um, so now I had the correct diagnosis and I could start the start the start treatment at last. 193 00:20:37,000 --> 00:20:44,320 So by now I weighed five and a half stone. Um, my hair was falling out and my left lung had completely collapsed. 194 00:20:46,000 --> 00:20:56,020 So when TB was first mentioned to me by my GP, I was so shocked because like many people in this country, I thought TB was no longer a threat. 195 00:20:56,560 --> 00:21:01,690 Besides, I'd had the BCG vaccine when I was 13, so I didn't think it was possible to catch it. 196 00:21:02,740 --> 00:21:04,870 I was completely ignorant about it. 197 00:21:05,230 --> 00:21:13,180 I didn't have any awareness of the havoc the disease was wreaking in other parts of the world and in certain affected communities here in the UK. 198 00:21:14,020 --> 00:21:19,360 Um, but, you know, I'm embarrassed to admit that now, but sadly, I'm far from being the only one. 199 00:21:20,500 --> 00:21:26,320 I suspect my GP didn't think of TB either, because I had no social or clinical risk factors. 200 00:21:26,680 --> 00:21:29,230 So I didn't fit the so-called typical profile. 201 00:21:30,130 --> 00:21:36,430 It's so important to remember that while some people are more at risk of TB, anyone who breathes can get TB. 202 00:21:37,360 --> 00:21:40,210 Secondly, with the public so focussed on Covid. 203 00:21:42,790 --> 00:21:50,110 Um, I am concerned that many will misstate their symptoms of TB for Covid and not seek medical help until a much later stage, 204 00:21:50,590 --> 00:21:56,860 leading to delays in diagnosis that could have serious consequences and increase the risk of further transmission. 205 00:21:57,790 --> 00:22:02,950 So by now I've been confined to my bed for about two months. 206 00:22:03,580 --> 00:22:09,280 I felt like a zombie. I had didn't even have the physical energy to move around my house. 207 00:22:09,820 --> 00:22:14,590 Eating was a huge challenge due to waves of nausea, and I couldn't stomach any food. 208 00:22:15,580 --> 00:22:23,590 To avoid being admitted to hospital and fed by tube, I was prescribed a short course of high dose steroids to stimulate my appetite. 209 00:22:25,630 --> 00:22:29,680 I then continue taking these long term to reduce the fluid in my left lung. 210 00:22:33,520 --> 00:22:43,660 So my close contacts were also screened, and I was so afraid of what would happen if I'd infected any of my family, friends and flatmates. 211 00:22:43,990 --> 00:22:48,340 I remember feeling at the time so much fear and guilt and worry. 212 00:22:48,970 --> 00:22:51,850 I thought, how would they react? Or would they blame me? 213 00:22:52,360 --> 00:22:59,560 Thankfully, nobody tested positive and my friends and family were very supportive and fairly level headed by this time. 214 00:22:59,950 --> 00:23:06,670 Most of them knew that TB was curable, so I spent a month in home isolation. 215 00:23:07,270 --> 00:23:11,680 Many of you may now have some idea of what this feels like thanks to thanks to Covid. 216 00:23:12,880 --> 00:23:17,680 Eventually, after a month or so, I noticed an improvement in my energy levels. 217 00:23:18,010 --> 00:23:24,639 Walking around the house felt easier, although I was still very short of breath and I found it difficult to wash my hair and cook meals. 218 00:23:24,640 --> 00:23:34,720 So my mum had to do all of these things for me. I hadn't left the house at all, and my family suggested taking a trip out in the car with a friend. 219 00:23:35,350 --> 00:23:40,780 I was not prepared for how I would feel being outside for the first time in about three months. 220 00:23:41,650 --> 00:23:49,540 As I stepped out of the car, the sound of the other cars, the voices of people around me and even birdsong were magnified in my ears. 221 00:23:49,930 --> 00:23:54,670 Everything felt too loud and too bright. I just wanted to get back to the safety of my bedroom. 222 00:23:55,570 --> 00:24:01,780 I was suddenly overwhelmed with panic and I felt dizzy. I got back in the car immediately and my friend drove me back home. 223 00:24:02,860 --> 00:24:04,930 I had no idea what was happening to me. 224 00:24:05,830 --> 00:24:12,490 Nobody had prepared me for this, but I now know that it's not unusual to experiences after a long period of isolation indoors. 225 00:24:13,450 --> 00:24:16,480 So the course of treatment for drug sensitive TB, 226 00:24:17,410 --> 00:24:25,510 and I should say this also actually includes non TB drugs as well that I took involve taking approximately 13 tablets a day for six months, 227 00:24:25,510 --> 00:24:30,070 although in my case it was extended to nine months. I hated those drugs. 228 00:24:31,060 --> 00:24:40,240 I once heard John Moore-Gillon, who was a top TB specialist at the time, described them as the size of horse tablets and he wasn't wrong either. 229 00:24:41,830 --> 00:24:46,360 The side effects were things like nausea and the most horrendous joint pains. 230 00:24:47,950 --> 00:24:55,330 So back then, TB patients had to pay for their prescriptions out of their own pockets as it wasn't classed as long term treatment. 231 00:24:56,620 --> 00:25:02,710 I wasn't well enough to work, and I came from a low income, working class family who were also struggling financially. 232 00:25:03,280 --> 00:25:10,780 Over Christmas, my dad died very suddenly within 24 hours after a short illness, and things have been really tough ever since. 233 00:25:11,770 --> 00:25:15,250 There was no additional financial support available for my university, 234 00:25:15,760 --> 00:25:20,260 and by this time most of the government funded hardship funds had been allocated. 235 00:25:20,860 --> 00:25:24,490 I wasn't eligible for any benefits because of my student status. 236 00:25:25,270 --> 00:25:30,880 I had to pay for treatment out of my remaining student loan and extend my overdraft to cover this period. 237 00:25:33,040 --> 00:25:41,050 Now, if someone in my situation, I had a roof over my head and family around me, was struggling to afford the cost of treatment. 238 00:25:41,470 --> 00:25:44,890 Can you imagine what it was like for homeless people? 239 00:25:45,310 --> 00:25:47,350 People would normally recourse to public funds. 240 00:25:47,650 --> 00:25:57,070 Refugees and asylum seekers living on £5 a week. To complete treatment in those circumstances would have been a miracle today. 241 00:25:57,100 --> 00:26:02,380 TB treatment is now free and everyone diagnosed with TB is exempt from charges. 242 00:26:03,430 --> 00:26:08,620 The problem is patients don't present with TB, they present with a collection of symptoms. 243 00:26:09,160 --> 00:26:15,940 So it is possible that people may be wrongly charged for treatment and with a hostile environment creeping into the NHS, 244 00:26:16,210 --> 00:26:20,680 this put some communities at risk of racism, discrimination and prejudice. 245 00:26:22,590 --> 00:26:30,960 Once I was no longer infectious, I was admitted to hospital for two weeks of intensive physiotherapy to try to inflate my left lung. 246 00:26:31,560 --> 00:26:33,090 Unfortunately, this didn't work, 247 00:26:33,240 --> 00:26:41,040 although my right lung did actually expand to help compensate as much as possible for the loss of function in the left lung. 248 00:26:41,280 --> 00:26:43,560 It's actually quite amazing what the body can do sometimes. 249 00:26:44,780 --> 00:26:54,590 So during this time I couldn't return to university to sit my interview exams, and later I made the decision to repeat my first year at university. 250 00:26:55,310 --> 00:27:03,290 Once I was back at uni in the autumn, I continue treatment and travel back to London regularly for follow up appointments with my TB specialist team. 251 00:27:04,340 --> 00:27:09,530 I started getting intermittent infections around this time, but otherwise all seemed to be going well. 252 00:27:09,950 --> 00:27:18,250 And after nine months I finished my treatment. About 2 or 3 months later, I noticed changes in my energy levels. 253 00:27:19,030 --> 00:27:24,010 I was sleeping a lot and genuinely not feeling very well. After coughing up quite a bit of blood, 254 00:27:24,460 --> 00:27:32,290 I ended up in A&E. They discovered my heart rate was 167 and as soon as I saw the doctors eyes widened, 255 00:27:32,620 --> 00:27:37,300 I knew what was coming next. They wanted to admit me to hospital for further tests. 256 00:27:38,470 --> 00:27:45,070 To cut a long story short. I ended up heading back to London to see my TB specialist team, and I was treated for a chest infection. 257 00:27:46,150 --> 00:27:51,520 I returned to university after the Easter holidays, but just a few weeks later, 258 00:27:52,060 --> 00:28:01,330 I was admitted to hospital in London with a Pseudomonas infection and an empyema after being rushed into it into the resuscitation area. 259 00:28:02,470 --> 00:28:11,590 This was when I was diagnosed with severe bronchiectasis, a lung condition that can cause infections in the lung due to excessive build-up of mucus. 260 00:28:11,920 --> 00:28:20,290 So TB is one of the underlying causes. If that wasn't bad enough, I was having a relapse of TB and treatment was restarted. 261 00:28:21,940 --> 00:28:29,919 It was also thought that the TB might be drug resistant. So it was an incredibly scary time. All the NHS 262 00:28:29,920 --> 00:28:33,249 workers who entered my room will face masks and PPE, 263 00:28:33,250 --> 00:28:41,410 which at that time was very bewildering and I was told by my TB nurse to prepare to be in hospital for a very long time. 264 00:28:43,170 --> 00:28:50,280 I had to make a very difficult and last minute decision to suspend my degree studies and take a period of intermission for 12 months. 265 00:28:51,750 --> 00:28:55,170 I had no idea how I was going to support myself financially. 266 00:28:56,160 --> 00:28:59,100 I still had student status, so I couldn't apply for benefits, 267 00:28:59,610 --> 00:29:06,900 but I couldn't access the government funded Access to Learning fund because it wasn't supposed to be relied upon as a sole means of income. 268 00:29:07,440 --> 00:29:11,400 So I ended up living off credit cards and my over my student overdraft. 269 00:29:12,120 --> 00:29:17,069 It wasn't long before I fell into debt. There is some good news 270 00:29:17,070 --> 00:29:25,430 believe it or not. I didn't have drug resistant TB, but I would need intensive daily physiotherapy and lung surgery. 271 00:29:25,440 --> 00:29:28,560 A pneumonectomy to me to remove my entire left lung. 272 00:29:29,880 --> 00:29:35,940 This was originally scheduled for January, but unfortunately yes, you guessed it, there was another twist to this story. 273 00:29:36,330 --> 00:29:39,450 I now had TB in my previously healthy right lung. 274 00:29:40,410 --> 00:29:47,010 The damage in my left lung was acting as a reservoir of infection, and it would need to be removed as soon as possible. 275 00:29:47,790 --> 00:29:55,590 So my plan to return to university from intermission was now in jeopardy, and I would need to consider my options and go through an appeal process, 276 00:29:56,340 --> 00:30:02,220 as I didn't have an automatic right to extend my intermission to cover the summer term or repeat the year. 277 00:30:03,900 --> 00:30:10,320 In May of that year, my lung surgery went ahead very successfully and my TB nurse said to me 278 00:30:11,040 --> 00:30:19,250 You can have your life back now. But life had already been on hold for far too long. 279 00:30:20,030 --> 00:30:23,930 I won my appeal and was able to return to university in September. 280 00:30:24,680 --> 00:30:30,860 In November, the treatment for the TB in my right lung ended for good and to focus my recovery from surgery. 281 00:30:32,030 --> 00:30:43,969 I signed up to run A5K race. Four months later, I didn't get medical clearance for that either, but I managed to raise £2,000 for TB alert, 282 00:30:43,970 --> 00:30:49,310 who supported me partway through my treatment journey and gave me a reason not to give up. 283 00:30:51,410 --> 00:30:56,540 So over the course of the academic year, my mental health began to seriously decline. 284 00:30:57,560 --> 00:31:03,140 I was having panic attacks, stress induced meltdowns, and experiencing low moods. 285 00:31:04,010 --> 00:31:10,880 I just stopped functioning altogether. Everything that happened to me over the last few years was starting to catch up with me. 286 00:31:11,780 --> 00:31:15,320 My credit cards and overdraft debts had spiralled out of control, 287 00:31:15,770 --> 00:31:20,810 and by the end of the summer term, I owed money to my university for accommodation rent. 288 00:31:21,350 --> 00:31:24,620 My registration for the following academic year was now at serious risk. 289 00:31:25,820 --> 00:31:32,420 There were so many people at my university, from the money advisor to the student at the students union, 290 00:31:32,420 --> 00:31:40,100 to my student advisor and the head of finance and many others who really came through for me and went above and beyond their jobs. 291 00:31:40,790 --> 00:31:44,750 They became my allies and I will never forget the support they gave to me. 292 00:31:45,990 --> 00:31:49,770 I always got the impression that my university wanted me to succeed. 293 00:31:50,280 --> 00:31:54,810 To cut another long story short, I did finish my degree, even though it took about six years. 294 00:31:56,400 --> 00:32:03,060 Eventually, I was diagnosed with depression and anxiety and started having therapy through my university. 295 00:32:04,020 --> 00:32:08,759 My GP said it was quite common for patients to experience both after having, and this is 296 00:32:08,760 --> 00:32:15,300 his words, come close to death. But I had no idea what the impact that TB would have on my mental health. 297 00:32:16,740 --> 00:32:20,040 Three years later, I was finally discharged from the clinic. 298 00:32:21,030 --> 00:32:29,130 There are a few things I want to highlight here that the TB treatment journey does not end when the patient takes their last pill. 299 00:32:29,700 --> 00:32:32,130 In my case, I still have ongoing breathlessness. 300 00:32:32,640 --> 00:32:39,870 I walk much more slowly than most people, and I have to pace myself and take regular breaks when doing simple household activities. 301 00:32:40,530 --> 00:32:45,780 It takes me weeks to recover from a chest infection and I have a rescue pack of antibiotics on standby. 302 00:32:46,410 --> 00:32:56,070 My right lung is now scarred. I have a permanent chronic cough and a croaky voice and to clear the excess mucus in my right lung. 303 00:32:56,460 --> 00:33:00,780 I do airway clearance exercises twice a day. This is my new normal. 304 00:33:03,280 --> 00:33:10,270 I often wonder how different my story would have been if there had been an effective TB vaccine available. 305 00:33:11,200 --> 00:33:21,010 A vaccine that could have prevented so much suffering and hardship for myself and for millions of people around the world. 306 00:33:21,790 --> 00:33:26,200 We all saw during the Covid pandemic that when there is a will, there is a way. 307 00:33:26,530 --> 00:33:32,379 I've had nine Covid 19 vaccines, but only one TB vaccine, the BCG, 308 00:33:32,380 --> 00:33:36,910 which isn't effective in teenagers and adults against the most common forms of the disease. 309 00:33:37,360 --> 00:33:43,420 I'm delighted by the speed of vaccine development and rollout for Covid 19, 310 00:33:43,720 --> 00:33:52,240 but I can't deny the anger and resentment I feel about TB and affected communities being left behind for so long. 311 00:33:52,600 --> 00:34:03,460 We deserve the same priority and investment. There is no doubt in my mind that if the burden of TB disease was spread more equally around the world, 312 00:34:04,000 --> 00:34:08,650 rather than being concentrated in low income countries, it would be a very different story. 313 00:34:09,040 --> 00:34:12,400 As far as funding is concerned. That speaks volumes. 314 00:34:14,090 --> 00:34:17,180 Ten years ago I was diagnosed in. 315 00:34:21,860 --> 00:34:27,440 Ten years ago, I was diagnosed with an inflammatory condition that affects both my joints and skin. 316 00:34:29,210 --> 00:34:35,870 My history of TB and long surgery meant that they first had to rule out TB in my bones as a differential diagnosis, 317 00:34:36,380 --> 00:34:41,900 and my reduced lung capacity meant that it was too risky to have the first line treatment choice. 318 00:34:42,380 --> 00:34:50,630 I am now severely immunosuppressed, which carries a serious risk of infection, including TB, and I'm back into the care of the chest clinic. 319 00:34:50,990 --> 00:34:55,550 Although with a different hospital trust. I'm monitored very carefully, 320 00:34:55,880 --> 00:35:00,530 by the head of the TB speciality and I am extremely grateful to be so well looked after. 321 00:35:01,370 --> 00:35:05,750 This means that the risk of catching TB again is never off the table. 322 00:35:07,670 --> 00:35:12,350 I experienced so much uncertainty and fear throughout this entire journey. 323 00:35:13,070 --> 00:35:19,820 To say that TB turned my life upside down is a huge understatement, but in some ways it was also a turning point for me. 324 00:35:20,420 --> 00:35:29,810 I've always said that TB politicised me and helped to spark an interest in TB advocacy and social justice, which has led my entire career so far. 325 00:35:30,530 --> 00:35:36,830 I am a member and chair of the TB action Group, the UK's only advocacy network for people affected by TB. 326 00:35:37,370 --> 00:35:44,870 I went from being adamant that I wanted to put this whole awful experience behind me forever and never think about TB again. 327 00:35:45,230 --> 00:35:48,800 But obviously that didn't happen because I wouldn't be here talking to you now. 328 00:35:49,970 --> 00:35:56,000 I just want to end by paying tribute to every TB survivor I've had the privilege to meet, 329 00:35:56,390 --> 00:36:03,980 either in person or online, from all over the UK, and even as far away as the US, India and South Africa. 330 00:36:04,670 --> 00:36:05,690 They know who they are. 331 00:36:06,590 --> 00:36:16,640 Their courage, friendship, solidarity and extraordinary activism inspire and motivate me to do better and to keep believing that progress is possible. 332 00:36:17,180 --> 00:36:21,230 Every single one of us has truly earned the label of TB survivor. 333 00:36:21,830 --> 00:36:38,650 Thank you for listening. Amy, that was absolutely extraordinary. 334 00:36:38,680 --> 00:36:45,330 Thank you for sharing such a very personal and incredibly moving experience with us all. 335 00:36:45,340 --> 00:36:48,880 That's really difficult to follow. Thank you very much. 336 00:36:49,660 --> 00:36:51,250 We now have our third speaker. 337 00:36:51,280 --> 00:36:58,299 There will be an opportunity to ask questions at the end, and all three of us will be outside milling around with the exhibition and drinks. 338 00:36:58,300 --> 00:37:04,000 But I'm going to hand over now to Philip, who's going to give us our third talk on TB diagnostics. 339 00:37:05,440 --> 00:37:09,580 Thank you, Helen. 340 00:37:09,760 --> 00:37:15,550 And thank you, Amy. Um. That's. 341 00:37:22,140 --> 00:37:29,760 So I'm afraid I'm a pacer. So I've got a microphone. So slight change of gear now. 342 00:37:30,330 --> 00:37:42,240 I'm going to talk about fairly recent advancements in diagnostics that hopefully could have helped Amy at least just a bit of that journey. 343 00:37:43,170 --> 00:37:48,959 Um, so as Helen said at the start, I work in the MMM unit, 344 00:37:48,960 --> 00:37:54,750 which is actually set in the top floor of the John Radcliffe Hospital, there's a few people here in the audience. 345 00:37:55,530 --> 00:38:04,020 And one of the things we do, there's quite a few things we do, is translating genetics into, um, clinical microbiology. 346 00:38:04,350 --> 00:38:07,320 And I'm going to talk about, uh, TB. 347 00:38:08,520 --> 00:38:15,270 So because we haven't had enough about TB, just I've come up with some minis I'm buying, I've found these in the literature. 348 00:38:15,270 --> 00:38:24,210 This and nice little snapshot. So just in case you didn't know, TB has killed more people than any other infectious disease ever. 349 00:38:25,080 --> 00:38:31,500 In fact, um, from genetics, we now know that TB is being with humans for tens of thousands of years. 350 00:38:32,340 --> 00:38:39,270 Um, and left Africa when the first humans migrated out of Africa about 70,000 years ago and moved with them around the world. 351 00:38:39,930 --> 00:38:46,920 And actually post industrialisation in Europe, it's estimated that about 20% of all deaths were caused by tuberculosis, 352 00:38:48,150 --> 00:38:52,590 and it's also been estimated in the last 200 years, about a billion people have died of TB. 353 00:38:52,800 --> 00:38:56,700 But we've sort of forgotten about it. Reminds me a bit of Covid. 354 00:38:57,330 --> 00:39:00,659 Um, you know, it's it's it's strange. 355 00:39:00,660 --> 00:39:04,230 It's it's been such a influence on human history. 356 00:39:05,700 --> 00:39:10,440 So we're here because Franz Kafka died in 1924 of laryngeal TB. 357 00:39:10,770 --> 00:39:16,349 Um, if you go to the exhibition, there's a bit more information. But he basically coughed up blood in 1917. 358 00:39:16,350 --> 00:39:21,420 So 77 years before he died, he was in and out of various sanatoriums. 359 00:39:21,930 --> 00:39:30,600 Um, the last one was near Vienna. Now, interestingly, the first, um, suggestion of, uh, sanatarium was by this guy, 360 00:39:30,600 --> 00:39:35,970 George Boddington in Sutton Coldfield in Birmingham, which is again curious because that's where I grew up. 361 00:39:36,120 --> 00:39:40,319 Anyway, that's another story, but TB is full of all these little peculiarities. 362 00:39:40,320 --> 00:39:46,580 And another one I like is the first randomised clinical trial was for TB. 363 00:39:46,590 --> 00:39:52,380 It was streptomycin in 1948. And what they found was that it was very successful. 364 00:39:52,560 --> 00:39:57,120 But quite quickly the, the the bacterium evolved the resistance. 365 00:39:57,510 --> 00:40:01,499 And to this day, as you've heard from Amy, we treat people, well 366 00:40:01,500 --> 00:40:05,250 I don't, but clinicians treat people with multiple antibiotics. 367 00:40:07,250 --> 00:40:13,550 So the current treatment, if you have regular susceptible TB is for drugs that you've probably never heard of, 368 00:40:14,210 --> 00:40:20,270 because they're not the sorts of things you get given when you normally go to the GP because they're pretty specific to TB 369 00:40:20,270 --> 00:40:26,930 usually, um, for six months, which is quite a long time, but it's necessary because of the disease. 370 00:40:28,520 --> 00:40:37,040 Now, resistance is obviously complicated because you're being treated with multiple drugs and there are various definitions, and it's fairly low 371 00:40:37,040 --> 00:40:42,920 globally, it's about 3% of new infections according to the W.H.O., are multi-drug resistant. 372 00:40:43,220 --> 00:40:48,200 But if you've been previously diagnosed or treated for TB, that rises to almost 20%. 373 00:40:48,980 --> 00:40:53,120 And obviously, you know, these proportions are going to rise as we keep using antibiotics. 374 00:40:53,780 --> 00:40:57,770 Um, and then this wasn't a problem that obviously Kafka had to face. 375 00:41:00,770 --> 00:41:04,220 The treating resistant TB is inevitably a lot more complicated. 376 00:41:04,760 --> 00:41:10,790 Um, there have been some new drugs come on in the last couple of years, which has been very helpful. 377 00:41:11,180 --> 00:41:17,960 And also then there have been some new regimens developed and approved and endorsed by the W.H.O., 378 00:41:18,380 --> 00:41:22,040 um, which is talked about on the right. That's from two years ago. 379 00:41:22,280 --> 00:41:28,580 So there's a new regimen for people who have multidrug resistant TB that's only six months long, 380 00:41:28,970 --> 00:41:33,140 which is a big improvement over the previous, possibly 18 months of treatment. 381 00:41:35,600 --> 00:41:46,850 So, as Amy said, often, uh, if you are suspected of having TB, you will give a sputum sample and that will be sent off for testing. 382 00:41:47,990 --> 00:41:53,540 And the two things are really looking for, uh, are there any mycobacterium tuberculosis in that sample? 383 00:41:54,260 --> 00:41:58,010 And if there are, what antibiotics could you be treated with? 384 00:42:00,040 --> 00:42:09,160 So the traditional way of doing this would be to culture the sample, say to grow it in some nutrients that it likes and the other bacteria don't. 385 00:42:10,600 --> 00:42:15,830 Um, and then I've got ahead of myself by one slide. 386 00:42:15,850 --> 00:42:22,440 Okay. So yeah. So throughout this I want to use a simile of, of imaging. 387 00:42:22,450 --> 00:42:27,969 So when we're looking at a sample in a clinical microbiology lab, 388 00:42:27,970 --> 00:42:32,650 what we're trying to see is which basically trying to capture the picture of what's in there, 389 00:42:32,740 --> 00:42:35,200 you know, uh, is there some TB, 390 00:42:35,200 --> 00:42:41,140 are there some other bacteria or other things that might have been in your upper respiratory tract when you gave the sample? 391 00:42:41,440 --> 00:42:45,550 You know, there's going to be a lot of human cells in there from you, you know, what's in there. 392 00:42:46,030 --> 00:42:52,870 And just like there are different ways of, you know, drawing a picture or capturing an image, there's different ways of looking inside these samples. 393 00:42:55,440 --> 00:42:57,630 So the the traditional way is to grow it. 394 00:42:57,720 --> 00:43:03,299 As I started to say, the problem with that, or the several problems with it, is it just takes a very long time. 395 00:43:03,300 --> 00:43:14,040 So it can take up to some 6 to 8 weeks to get a result back from the lab, because TB just grows so slowly and there's been some nice advances. 396 00:43:14,400 --> 00:43:21,570 Um, these magic tubes, um, because they glow when there's, when there's mycobacterium in there, 397 00:43:21,570 --> 00:43:23,549 So it's made it much easier. 398 00:43:23,550 --> 00:43:30,000 But fundamentally, you know, being told I'll give you a call in two months when the results come back, it's it's not good for anyone. 399 00:43:32,610 --> 00:43:39,670 So in an imaging simile this is a bit like, well, I have to paint the picture because I need you. 400 00:43:39,690 --> 00:43:46,889 It's very skilled. Um, I need some equipment, I need some reagents, I need some experience. 401 00:43:46,890 --> 00:43:55,230 It's a difficult job to to carry out these types of, uh, tests, and it fundamentally, it's going to take you quite a long time. 402 00:44:00,520 --> 00:44:07,420 So then genetics can play a role, and I'm skipping over lots of other ways of diagnosing here that I just don't have time to go into. 403 00:44:07,750 --> 00:44:15,310 Um, and focusing on, um, whole genome sequencing here, you still have to grow it for a bit, but then you can heat kill it. 404 00:44:15,760 --> 00:44:23,500 Take it outside the Cat 3 lab. I should say. You've got to do all this in a BSL 3 lab because TB, it's a very dangerous pathogen. 405 00:44:23,980 --> 00:44:31,209 Then you can put it on a genetic sequencer. That will just give you a whole bunch of fragments of genetic material and say, well, 406 00:44:31,210 --> 00:44:36,010 you work it out I've sort of done the hard bits, which hasn't really, but it gives you this problem. 407 00:44:36,670 --> 00:44:41,590 And then then you need to solve that problem. So that's when you now need a second speciality. 408 00:44:41,590 --> 00:44:44,280 Who hasn't been on the scene until now, which is a bioinformatician 409 00:44:44,290 --> 00:44:51,459 so someone who can handle genetic information and process it and they need to write, uh, what we usually call a pipeline. 410 00:44:51,460 --> 00:44:57,610 So a series of pieces of software that process those reads and hopefully give you the answers you're looking for. 411 00:44:58,510 --> 00:45:04,840 And that's often done on some local compute that you've squirrelled away somewhere in your hospital or public health body. 412 00:45:07,170 --> 00:45:13,050 So this is it's difficult, but it's a big improvement because you can do it in about a week. 413 00:45:13,710 --> 00:45:18,000 So that's a big acceleration from say, 6 to 8 weeks to a week. 414 00:45:18,420 --> 00:45:22,260 So that's that in itself is a is a huge advantage. 415 00:45:22,920 --> 00:45:26,220 Um, but as I'll come back to it's it's quite tough to do. 416 00:45:26,850 --> 00:45:30,620 So in my simile this is a little bit like, I think the invention of photography. 417 00:45:30,630 --> 00:45:34,920 So wow. You know we've now got large plate cameras. They're expensive. 418 00:45:35,700 --> 00:45:40,229 But you can take an image pretty quickly. But then you also need a thing called a dark room. 419 00:45:40,230 --> 00:45:43,770 And you need to get you need to go and buy a developer. You need to keep buying all these reagents. 420 00:45:43,770 --> 00:45:47,730 And you need to learn the skill of how to develop, um, photographs. 421 00:45:48,930 --> 00:45:52,170 So it's possible, but it's the kind of the preserve of the few. 422 00:45:52,830 --> 00:46:00,930 So it's done in high income countries, in, um, public health institutes, large tertiary research hospitals, that sort of thing. 423 00:46:02,430 --> 00:46:09,810 The good news is, England was the first country to implement this at the national public health level back in about 2018, 424 00:46:10,230 --> 00:46:15,500 and that was research that was done in the unit where I am now that got translated out into PHE, 425 00:46:15,570 --> 00:46:19,080 as it was then and the other home nations have followed suit. 426 00:46:19,830 --> 00:46:24,900 So that's really good news. Anyone who had a has a suspected mycobacterial infection. 427 00:46:25,290 --> 00:46:28,740 That sample have been sequenced since about that date. 428 00:46:29,580 --> 00:46:36,240 That won't have sped up the GP necessarily, but it will have sped up the process after the sample was sent off. 429 00:46:38,530 --> 00:46:44,679 I just want to spend a very quickly talk about why the genetics is challenging, because these sequences do seem like magic. 430 00:46:44,680 --> 00:46:50,620 But all they, as I said, all they do is they just give you bits of genetic material and say, right, you sort that out. 431 00:46:51,880 --> 00:46:56,530 Um, and it's a bit like doing a jigsaw puzzle with a few rather important differences. 432 00:46:56,540 --> 00:47:00,790 One is you might have up to 10 million pieces, which is going to make it a bit harder. 433 00:47:01,570 --> 00:47:06,430 The pieces will also overlap with each other. And you don't know how because the fragments are quite random. 434 00:47:07,160 --> 00:47:11,200 The some of them are going to have errors because the machines aren't perfect and you don't know which ones. 435 00:47:11,740 --> 00:47:21,470 Which is so hard. And because you've got, you know, you've almost certainly got multiple bacterial, um, pieces of DNA in there. 436 00:47:21,490 --> 00:47:24,219 It's a bit like having, you know, doing a jigsaw, 437 00:47:24,220 --> 00:47:30,730 or rather taking several jigsaws and mixing them up and then trying to just do one of them and not put in the wrong piece and that sort of thing. 438 00:47:31,390 --> 00:47:39,610 So it's it's very difficult. And bioinformatics is a, is a difficult, um, subject, so why bother. 439 00:47:39,640 --> 00:47:46,240 We've already said one of the key things is speed is we can do this much more quickly, which for TB is really important. 440 00:47:46,810 --> 00:47:50,230 Two other things. Um, accuracy. 441 00:47:50,260 --> 00:47:57,819 This is it's arguably more accurate, but that's difficult to prove because you're comparing it to something that is hard, 442 00:47:57,820 --> 00:48:04,030 which is culture and difficult because it's TB. And then the second one is epidemiology. 443 00:48:04,030 --> 00:48:08,710 And that's something you basically get for free with genetics because you've got the entire genome. 444 00:48:09,070 --> 00:48:14,560 So what do I mean by that. Well in Covid we got quite used to this idea of we can infer, uh, 445 00:48:15,550 --> 00:48:23,920 if two samples taken from two people are part of the same outbreak or cluster because the genomes of their pathogen are very similar. 446 00:48:24,460 --> 00:48:35,500 And you can do exactly the same thing with TB. And this was an example back from 2018, um, where they were able to show how, uh, 447 00:48:35,500 --> 00:48:42,670 patients with multi-drug resistant TB moved through Europe and caused these, um, separate outbreaks in different countries. 448 00:48:42,670 --> 00:48:48,370 And then, of course, you can do something about it and you can find these people and treat them and stop those outbreaks. 449 00:48:49,630 --> 00:48:57,459 Just to point out, the threshold they were using was only five, uh, snips are basically five letter changes in the whole genome. 450 00:48:57,460 --> 00:49:00,760 In the genome of TB is about 4.4 million letters long. 451 00:49:00,760 --> 00:49:05,379 So you've actually in doing that jigsaw puzzle, you've got to get it pretty accurate. 452 00:49:05,380 --> 00:49:10,990 Otherwise you can't do this sort of thing as well. So why is it so difficult? 453 00:49:11,000 --> 00:49:17,170 There's lots of reasons and I'm not going to talk about all of them. Um, I'm going to just talk about two of them. 454 00:49:17,170 --> 00:49:20,440 This is one, which is that actually, even if, 455 00:49:20,920 --> 00:49:27,430 even if you can do all that perfectly and you can work out what the genome of the pathogen that's infecting someone is, 456 00:49:27,430 --> 00:49:30,790 it's still quite hard to work out what antibiotics you should give them. 457 00:49:31,570 --> 00:49:35,860 So some work, um, led from our unit by Derek Crook. 458 00:49:35,860 --> 00:49:40,090 This ran for about six years. This was a major Wellcome project. 459 00:49:40,960 --> 00:49:50,470 Uh, they collected about 20,000 clinical samples of TB worldwide from, uh, memory, 11 different countries with 14 different laboratories. 460 00:49:51,250 --> 00:49:55,030 They were all sequenced. They all had a panel of antibiotics tested. 461 00:49:55,030 --> 00:49:58,210 And as a result, we did some really nice science. 462 00:49:59,560 --> 00:50:02,260 Um, it was picked up in the press a little as well. 463 00:50:02,590 --> 00:50:11,499 But actually the thing that I'm most proud of is that data set, along with some other historical samples and some extra samples we collected actually, 464 00:50:11,500 --> 00:50:18,700 immediately before the pandemic, that whole data set, we kind of did all the data cleaning, and it was handed over to the W.H.O. 465 00:50:19,240 --> 00:50:28,630 And that that indirectly led to them publishing their first catalogue of genetic mutations associated with resistance in TB. 466 00:50:29,290 --> 00:50:38,439 So that was back in June 2021. Uh, and in November last year, um, they've updated it with a slightly improved version. 467 00:50:38,440 --> 00:50:42,280 So that's really good. And that helps move us along this pathway. 468 00:50:43,570 --> 00:50:51,820 So the second thing I'm going to talk about, um, about why it's difficult is there's no standard way of determining the TB genome. 469 00:50:52,060 --> 00:50:57,910 It's basically informatics is hard and it's very difficult for labs to set up. 470 00:50:58,720 --> 00:51:07,660 So the story here starts in May 2021, when we started working with Oracle on a cloud based platform for Covid processing. 471 00:51:09,070 --> 00:51:12,850 And that has led to this thing we call GPAS, which I'm going to call second generation, 472 00:51:13,240 --> 00:51:21,400 where now the biomedical scientists can do the sequencing, but now they just basically drag and drop the file up into the cloud. 473 00:51:21,760 --> 00:51:26,980 It will process the genetics for them. And the, uh, report can come back in less than an hour. 474 00:51:28,240 --> 00:51:37,900 It's not speeding things up a much, just a day or two perhaps, but it makes it much, much simpler and straightforward to do this kind of work. 475 00:51:40,630 --> 00:51:44,950 So I'm just going to quickly go through some screenshots because I wasn't feeling brave enough to do this live. 476 00:51:45,760 --> 00:51:49,120 Um, this was actually some batches I was uploading last night. 477 00:51:49,330 --> 00:51:51,820 Um, I wasn't up at this time. The computer is doing me. 478 00:51:52,240 --> 00:51:59,200 Um, so if we just zoom in, they're all batches of 50 TBs and we go and look at this one because it kind of looks interesting. 479 00:52:00,340 --> 00:52:03,670 Um, this is all in the browser. So a lot of work is gone into this. 480 00:52:04,510 --> 00:52:11,200 Um, what we see is a lineage three TB. It looks pretty good from a kind of quality point of view I don't want to bore you, 481 00:52:11,440 --> 00:52:15,999 but it's definitely MDR, so it's resistant to three of the four first line drugs. 482 00:52:16,000 --> 00:52:18,700 So you do not want to be giving those drugs to that patient. 483 00:52:19,480 --> 00:52:26,500 Um, but healthily, it's resistant to uh, basically the second line drugs, the flow clean lines and so on. 484 00:52:27,430 --> 00:52:34,360 So the hope is that this information would help the clinician choose the appropriate treatment for that patient. 485 00:52:34,750 --> 00:52:41,500 And then you can see down the bottom, um, we get uh, some of the it's part of a cluster as well. 486 00:52:42,880 --> 00:52:51,880 So this kind of I think in my simile brings us up to about 1900, um, which is when Kodak released the Brownie. 487 00:52:51,880 --> 00:52:55,990 And the thing about the Brownie was the film was put inside a cartridge. 488 00:52:56,170 --> 00:53:02,200 And what that meant was you could take photos until your cartridge was all used up, 489 00:53:02,200 --> 00:53:07,630 but then you could send the film off to a lab to be processed, and then you got the photos back. 490 00:53:08,650 --> 00:53:13,900 And so you didn't need a darkroom anymore, and the cameras could be quite made, quite small and cheap. 491 00:53:13,900 --> 00:53:17,470 And I read, I didn't realise that the Brownie started off as a children's toy. 492 00:53:17,490 --> 00:53:26,290 I don't think genetics has. But anyway, um, and so it led to this kind of democratisation of photography. 493 00:53:27,700 --> 00:53:39,870 Um. So if we think back 100 years, um, and go and look in exhibition, there's some quite gruesome artefacts, um, showing, sort of illustrating, uh, 494 00:53:39,870 --> 00:53:43,620 Kafka's journey with TB, you know, 100 years later, 495 00:53:43,620 --> 00:53:50,250 I think we're hopefully on the verge of really beginning to use genetics to help us treat this disease better. 496 00:53:51,660 --> 00:53:56,300 Um, it's interesting thinking forward a 100 years. Um, um, sorry. 497 00:53:56,310 --> 00:54:01,950 Coming up, TB's probably still going to be here I'd expect, even with vaccines and everything. 498 00:54:01,950 --> 00:54:06,059 You know, latently it's in a quarter, a third of the world's lungs. 499 00:54:06,060 --> 00:54:10,290 So it's gonna you know, I don't think eradication is really on the cards. 500 00:54:10,830 --> 00:54:14,100 Will how we we've made it through the whole pandemic. 501 00:54:14,100 --> 00:54:18,140 Will we still be giving antibiotics? But we have to individually diagnose everyone. 502 00:54:18,150 --> 00:54:28,860 I really don't know. So it's just we're thinking on that. Um, a lot of people involved, um, in the past and a lot of this work. 503 00:54:28,860 --> 00:54:33,390 So that's just to show you just how many, I suppose it's all quite small writing. 504 00:54:33,990 --> 00:54:38,490 Um, and lots of, uh, funders and other peoples as well. 505 00:54:38,490 --> 00:54:41,700 So thank you very much. 506 00:54:44,020 --> 00:54:44,290