1 00:00:02,780 --> 00:00:08,750 OK. Good evening and welcome to the first meeting of trafficking in. 2 00:00:08,750 --> 00:00:13,040 My name is Michael. Allow me to get a quick comment. 3 00:00:13,040 --> 00:00:25,370 And I am the coordinator of the project got out of the heat of the announcement of these new interdisciplinary seminars. 4 00:00:25,370 --> 00:00:34,950 I am delighted to see so many of you today. Thank you all for finding the time to join us for the very first day of access media scrum. 5 00:00:34,950 --> 00:00:39,500 Before starting, I would like to thank those institutions and people. 6 00:00:39,500 --> 00:00:42,380 Without some done these projects, enough will be possible. 7 00:00:42,380 --> 00:00:47,630 The work on institutional strategic support grants and the joint venture funds for countries 8 00:00:47,630 --> 00:00:53,600 supporting their projects that we saw that put us in such a wonderful venue for us together, 9 00:00:53,600 --> 00:00:58,020 for our discussions and are all in Canada, 10 00:00:58,020 --> 00:01:07,520 so pretty handle all the captures books that directness and the complexity of the issues that we will explore in these meetings. 11 00:01:07,520 --> 00:01:14,480 My gratitude goes also to all your colleagues in the UK and the world who have supported my project in various ways, 12 00:01:14,480 --> 00:01:22,190 especially thanks to my team, my Japanese police department today for his invaluable and continues mentorship to report. 13 00:01:22,190 --> 00:01:31,430 I have been following my academic and artistic journey and to align my clients because he asked new directions. 14 00:01:31,430 --> 00:01:37,400 Thank you also to my daddy for accepting my invitation to co-host each event. 15 00:01:37,400 --> 00:01:43,220 Madalena is an apology registrar in our London hospitals, as stated in the programme. 16 00:01:43,220 --> 00:01:51,440 Please note that the seminar will be recorded and made available on the university podcast website and that forecast. 17 00:01:51,440 --> 00:01:57,650 We will also see to take a piece of paper on which you can write down the name and email address, 18 00:01:57,650 --> 00:02:08,450 and if you would like to be added to the mailing list, I now have the pleasure and honour to introduce today's speaker just and track 19 00:02:08,450 --> 00:02:12,680 the official confirmation on medecine at the National Department of Medicine, 20 00:02:12,680 --> 00:02:20,720 University of Oxford. Professor von Trier is co-director of the Oxford Martin Programme on Affordable Magazines, 21 00:02:20,720 --> 00:02:28,550 and that's been made Oxford's The Challenge set up for innovation just to be being a pioneer, 22 00:02:28,550 --> 00:02:36,350 as well as an innovative idea discovery of new drugs for many cancer, metabolic and neuropsychiatric diseases. 23 00:02:36,350 --> 00:02:41,270 Setting up a first image system for Travis County, Texas, 24 00:02:41,270 --> 00:02:49,170 in addition to his eminent academic profile chapter leader the more than 300 invited lectures across the world. 25 00:02:49,170 --> 00:02:54,080 Buncha is an expert in several fundamental charitable research funding bodies, 26 00:02:54,080 --> 00:02:58,990 as well as an advisor for many biotech and pharma giant to solving problems. 27 00:02:58,990 --> 00:03:04,160 In, people follow one of the top innovators, he said. 28 00:03:04,160 --> 00:03:08,510 I really thought that it could be no just another. 29 00:03:08,510 --> 00:03:18,950 Take a seminar series that encourages us to receive and perhaps even present AI concepts of illness and cancellation. 30 00:03:18,950 --> 00:03:22,040 What do medicine accomplish and have in common? 31 00:03:22,040 --> 00:03:31,790 In what sense, and to what extent is translation using context as different as the transfer meaning from one language or media to the other? 32 00:03:31,790 --> 00:03:36,560 The concept of a knowledge foundation and the process of protein synthesis? 33 00:03:36,560 --> 00:03:45,210 How we did not. So the standard of translation help us advance in each area and we see that as well as in clinical research. 34 00:03:45,210 --> 00:03:51,830 In today's talk, Jasper, shall we tackle these questions from the perspective of the next of kin? 35 00:03:51,830 --> 00:04:02,030 Chief Scientist ActionScript Southampton, lead researcher Closer to possibly sustainable scientific advancements and with ethics, 36 00:04:02,030 --> 00:04:08,300 he will explain to us why we are masters at translating that science easily medical 37 00:04:08,300 --> 00:04:14,750 patients and we said yes to which measures we can take in order to improve translation. 38 00:04:14,750 --> 00:04:36,710 So please join me in welcoming transformative. Is it possible to get the projector off, do you think that? 39 00:04:36,710 --> 00:04:43,820 Well, ladies and gentlemen, good evening. Thank you very much for coming. It's nice of you to give up your evenings to listen to me. 40 00:04:43,820 --> 00:04:56,850 That's sort. So the title I gave this talk was we are not very good at translating lab science or data science. 41 00:04:56,850 --> 00:05:00,240 It's a New Mexico. 42 00:05:00,240 --> 00:05:15,760 So when I think of translation, I think that maybe at least three definitions we talk about translating into medicine, rather that it's a need for a. 43 00:05:15,760 --> 00:05:18,210 We're looking to talk about this. 44 00:05:18,210 --> 00:05:34,290 We also talk about taking life science based science that we do in cells and tissues or organs or animals and translating that into action. 45 00:05:34,290 --> 00:05:45,540 So showing that what we live in animal overextended or at is also seeing humans in the cage in healthy individuals and patients we talked 46 00:05:45,540 --> 00:05:59,730 about that is translating science from the bench to that people also talk about translating knowledge and creating benefits for industry, 47 00:05:59,730 --> 00:06:04,470 creating benefit for society, creating benefit for the economy. 48 00:06:04,470 --> 00:06:13,170 So that could be a form of creating new companies, new jobs, helping the local economy, et cetera, et cetera. 49 00:06:13,170 --> 00:06:19,410 The pizza, the maestro that so I will talk about these last two. 50 00:06:19,410 --> 00:06:26,280 So in terms of drug discovery, let me just share with you what I worry about. 51 00:06:26,280 --> 00:06:33,750 I worry that as a community. So when I say community, I mean us as academic scientists, 52 00:06:33,750 --> 00:06:41,700 but also I mean scientists working inside biotechs or scientists working inside pharmaceutical companies. 53 00:06:41,700 --> 00:06:48,150 We as a community are not producing enough new medicines. 54 00:06:48,150 --> 00:06:58,080 I also worry that when we do produce a new medicine, frankly, it is becoming increasingly unaffordable. 55 00:06:58,080 --> 00:07:05,400 I also worry that in biomedical research, there is massive duplication. 56 00:07:05,400 --> 00:07:14,760 Many academic labs, many industry labs, they all work on the same, few ideas in parallel and in secret. 57 00:07:14,760 --> 00:07:19,500 And most of those ideas are destined for failure. 58 00:07:19,500 --> 00:07:31,450 And I also worry that when I talk to my colleagues in the industry, they say that 50, 60, 70 percent of academic literature, they cannot reproduce. 59 00:07:31,450 --> 00:07:38,160 So these are the major problems. Let me kick off, just think about it. 60 00:07:38,160 --> 00:07:43,500 What do patients, what do patients carers? 61 00:07:43,500 --> 00:07:53,430 What do relatives? What do health care providers expect from us and what they fund our research? 62 00:07:53,430 --> 00:07:58,590 They fund it, not the people I make and produce papers and become more famous. 63 00:07:58,590 --> 00:08:08,010 That's not the intention they are giving us funding because they want us to help patients help society. 64 00:08:08,010 --> 00:08:15,660 Now what do they expect from us? Now I think what they want is they want more novel medicines. 65 00:08:15,660 --> 00:08:21,060 They want more effective medicines. They want more affordable medicines. 66 00:08:21,060 --> 00:08:31,260 And they want to quickly. Let me just take each of those in place for a three month assignment. 67 00:08:31,260 --> 00:08:42,150 So in the UK, in the next 12 months, three hundred and fifty thousand people will get diagnosed with cancer. 68 00:08:42,150 --> 00:08:48,330 That is one thousand people every day. That's one person every 90 seconds. 69 00:08:48,330 --> 00:08:53,400 Half of us in this room during our lifetime will have a diagnosis of cancer. 70 00:08:53,400 --> 00:09:00,570 In the next 12 months, 14 million people on the planet will get diagnosed with cancer. 71 00:09:00,570 --> 00:09:09,540 I worry a lot about dementia in the UK today, we have 850000 people with dementia. 72 00:09:09,540 --> 00:09:14,280 In 2050, that number will be 2.1 million. 73 00:09:14,280 --> 00:09:19,770 We are going to have to 60 the size of Berlin with dementia. 74 00:09:19,770 --> 00:09:26,220 The average cost of caring for a dementia patient today is thirty two thousand pounds a year. 75 00:09:26,220 --> 00:09:33,510 That's to the tax package. And that's despite the fact that two thirds of the cost is ruled by the relatives. 76 00:09:33,510 --> 00:09:40,020 So if you add that up, we are spending twenty six billion pounds a year just looking after dementia. 77 00:09:40,020 --> 00:09:41,880 Thank you. 78 00:09:41,880 --> 00:09:51,810 All of us who need to more than 80 years of age, one in six of us will have to, which we not come up with a new treatment for dementia since 2002. 79 00:09:51,810 --> 00:10:00,150 And that treatment is purely a symptomatic treatment that works in the first few months of the diagnosis maybe 12 months, 18 months. 80 00:10:00,150 --> 00:10:03,110 And then after that, it stops working. 81 00:10:03,110 --> 00:10:14,210 We desperately need new treatments for dementia, and I don't think we're even close to having something that is effective and safe in patients. 82 00:10:14,210 --> 00:10:24,690 And this is an area that the industry is ploughed over the past three decades, several tens of billions of dollars of research funding. 83 00:10:24,690 --> 00:10:34,260 Mental health. It's a massive talking point across this university, across all universities, across societies across the world. 84 00:10:34,260 --> 00:10:42,360 It's estimated that a quarter of adults during their lifetime some sort of mental health episode. 85 00:10:42,360 --> 00:10:50,710 It's estimated across Europe, probably 20 percent of people have some sort of depressive episode at any one time. 86 00:10:50,710 --> 00:10:56,010 We probably got 83 million people across Europe with some sort of mental health condition. 87 00:10:56,010 --> 00:11:07,170 And I had a horrific figure recently that if you take a 15 year old girls in the UK out of every 150, one of them will be in Iraq. 88 00:11:07,170 --> 00:11:13,410 These are horrific figures. A.J. We've got ageing societies across the planet. 89 00:11:13,410 --> 00:11:21,630 In the next 20 years, we are going to have a doubling in the number of pensioners with no health conditions. 90 00:11:21,630 --> 00:11:31,050 It's going to be a 180 per cent increase in patients with cancer, a 180 percent that's almost triple. 91 00:11:31,050 --> 00:11:35,610 There's going to be a 120 percent decrease in patients with diabetes. 92 00:11:35,610 --> 00:11:43,590 So more than double. These are major challenges. Yeah, these are common diseases. 93 00:11:43,590 --> 00:11:50,680 Now, if you talk about rare diseases across the planet, if you add up all the rare diseases there, 94 00:11:50,680 --> 00:11:59,760 seven thousand diseases, if you add up all the patients, that's about 350 million patients across the planet. 95 00:11:59,760 --> 00:12:08,140 30 percent of those kids do not reach the age of five. It takes anywhere between six and eight years to get a diagnosis. 96 00:12:08,140 --> 00:12:15,390 But would you believe it that 95 percent of those individuals? Absolutely no treatment whatsoever. 97 00:12:15,390 --> 00:12:21,710 Can you imagine being a parent of a child where there is no. 98 00:12:21,710 --> 00:12:28,640 Anti-Microbial resistance in this country, Sammy Davis is now headed Trinity in Cambridge. 99 00:12:28,640 --> 00:12:37,130 Former chief medical officer For the past five, six, seven years, she's been highlighting what a crisis we have in terms of. 100 00:12:37,130 --> 00:12:45,530 We're becoming resistant to existing antibiotics. And she estimated that today, if we at the moment, 101 00:12:45,530 --> 00:12:56,930 about 700000 people across the world are dying because they are resistant to existing antibiotics in 2050, that number is estimated to be in. 102 00:12:56,930 --> 00:13:00,740 And in the O'Neill report, which was published four or five years ago, 103 00:13:00,740 --> 00:13:07,700 he said that if we don't come up with a new generation antibiotics, then by 2050, it's the cost of the planet. 104 00:13:07,700 --> 00:13:14,190 One hundred trillion in GDP. Massive challenges. 105 00:13:14,190 --> 00:13:23,130 Let me just share with you the story from those of the patient, the patient's mother. 106 00:13:23,130 --> 00:13:30,000 So about four years ago, I gave a public lecture of a sudden people living in the Months Institute. 107 00:13:30,000 --> 00:13:35,070 At the end of this lecture, this lady came up to me and she said, Professor answer. 108 00:13:35,070 --> 00:13:39,040 My daughter died earlier this year with a brain tumour. 109 00:13:39,040 --> 00:13:45,220 If you want to study that brain, this is where you can get it for that, she handed me this slip of paper a little. 110 00:13:45,220 --> 00:13:50,800 This paper was that her daughter's name, the date of birth, the day she died. 111 00:13:50,800 --> 00:14:02,620 And what frightens me after this, my wife paid me up and I said to my wife, I just know a complete stranger offered me the great deal. 112 00:14:02,620 --> 00:14:08,890 This is how desperate parents carers are. 113 00:14:08,890 --> 00:14:14,840 We desperately need new treatments. We need more effective treatments. 114 00:14:14,840 --> 00:14:19,630 They might think, Well, why do you say to help all the drugs that are out there affect it? 115 00:14:19,630 --> 00:14:29,380 Well, in 2015, there was a publication where they looked at the treatments we had at the time, the solid tumours. 116 00:14:29,380 --> 00:14:37,840 So at the time, we had 71 treatments with solid tumours, and they looked to see how much benefit these treatments provide. 117 00:14:37,840 --> 00:14:47,950 And the conclusion of this paper was that of the 71 treatments, the average increase in progression free survival was two point five months. 118 00:14:47,950 --> 00:14:55,210 The average increase in overall survival was 2.1 months and also send people in treatment. 119 00:14:55,210 --> 00:15:00,640 They concluded that only 30 of them have clinically meaningful efficacy, 120 00:15:00,640 --> 00:15:08,710 so less than half the drugs that are out there are clinically meaningful at the pace we need for effective treatments. 121 00:15:08,710 --> 00:15:20,440 We also need more affordable treatments. So again, this publication in 2014, they looked at in the UK in the year 2000, 122 00:15:20,440 --> 00:15:27,280 we had sixty nine treatments for cancer of those 50 with cytotoxic agents. 123 00:15:27,280 --> 00:15:31,330 The average duration of treatment was 181 days. 124 00:15:31,330 --> 00:15:42,670 The average cost of the treatment was three thousand pounds and in the two thousand three thousand that estimated to be about 20 percent of our GDP. 125 00:15:42,670 --> 00:15:53,110 They did the same analysis in 2013, so more than a decade later, by now, we had a further sixty three drugs for treating cancer. 126 00:15:53,110 --> 00:15:57,400 The average duration of treatment was not two hundred sixty three days. 127 00:15:57,400 --> 00:16:05,710 So it's gone up from urging you wants to invest. But the average cost of the treatment was now thirty five thousand pounds a year. 128 00:16:05,710 --> 00:16:09,610 It's gone up from three thousand thirty five dollars. 129 00:16:09,610 --> 00:16:21,250 In 2013, 14, thirty five thousand was estimated to be 140 percent of our GDP per capita, up from 20 percent to 40 percent. 130 00:16:21,250 --> 00:16:34,720 And that trend, frankly, is continuing. Last year, Novartis had a drug approved by the FDA for a rare condition. 131 00:16:34,720 --> 00:16:46,330 Now this is a gene therapy. So it's a single injection, and the cost of that single injection is two point one to five billion dollars. 132 00:16:46,330 --> 00:16:54,160 So more than two million dollars for a single injection. Now, even in the U.S., they will not be able to afford it. 133 00:16:54,160 --> 00:17:03,310 You know, in the U.K., I guarantee the NHS will never pay for it and leave aside countries like China or India or in Africa or South America. 134 00:17:03,310 --> 00:17:07,960 We need more affordable drugs and of course, we need to quickly. 135 00:17:07,960 --> 00:17:13,990 Tomorrow, it's too late for these patients. They want the drugs that we need more novel drugs. 136 00:17:13,990 --> 00:17:20,190 We need more effective drugs. We need more food because then we need to quickly. 137 00:17:20,190 --> 00:17:30,340 The problem with the way that the drug discovery today, frankly, is too costly, it's too risky and it's too slow. 138 00:17:30,340 --> 00:17:41,520 So let me just take each of those two calls. So in 2012, Forbes did an analysis where they looked at the number of these pharmaceutical companies. 139 00:17:41,520 --> 00:17:46,320 They looked to see how much money they spent a lot of money over a five year period. 140 00:17:46,320 --> 00:17:50,390 And they used to see that finite period. How many drugs did they launch? 141 00:17:50,390 --> 00:17:54,630 I mean, they are true. They divided one number by the other. 142 00:17:54,630 --> 00:18:01,410 Then they came up with an average cost of new treatment. Now that's analysis for AstraZeneca. 143 00:18:01,410 --> 00:18:09,480 The average cost of the new drug was 11 and a half billion dollars, 11 and a half billion dollars. 144 00:18:09,480 --> 00:18:15,000 The best in that analysis was after, but even they were about three and a half billion dollars. 145 00:18:15,000 --> 00:18:23,640 Now, most people accept today that the average cost of a new drug is somewhere between three and four billion dollars. 146 00:18:23,640 --> 00:18:29,310 Bear in mind to run this whole university with the thirty nine colleges and all 147 00:18:29,310 --> 00:18:33,930 the divisions and all the departments and all salaries and all the infrastructure, 148 00:18:33,930 --> 00:18:41,850 it's probably about two billion pounds. So that one of the cheap calls to launch one of these drugs. 149 00:18:41,850 --> 00:18:47,730 But bear in mind, many of these drugs that were launched were not truly novel drugs. 150 00:18:47,730 --> 00:18:52,200 Many of them are what we would call me tubes of new formulations. 151 00:18:52,200 --> 00:18:58,800 And that's not what we need. We need absolutely new treatments for some of these conditions. 152 00:18:58,800 --> 00:19:08,790 So it's too costly. It's also too risky. So again, in 2013, there was a publication where they looked at. 153 00:19:08,790 --> 00:19:18,670 So in the year 2002, across the world, we had five hundred and twenty nine molecules in development for cancer. 154 00:19:18,670 --> 00:19:27,990 So volume development, we read that they were either in phase one study or phase two study for phase two study five of the infection. 155 00:19:27,990 --> 00:19:39,030 They looked to see a decade later in 2013, what happened to the five minutes ideology that they found 45 of them made it to the market. 156 00:19:39,030 --> 00:19:46,440 Ninety five was development, but 389 molecules were terminated. 157 00:19:46,440 --> 00:19:56,580 So we took it as a community. We took three hundred eighty nine molecules into the clinic, into patients and their families. 158 00:19:56,580 --> 00:20:04,620 Can you imagine how much money we spent on those molecules? Can you imagine how much is also how many people's careers went into that? 159 00:20:04,620 --> 00:20:12,010 They can't, even importantly, imagine how many patients were exposed to those molecules before we? 160 00:20:12,010 --> 00:20:16,780 This process is too risky in the same study they looked at, 161 00:20:16,780 --> 00:20:23,320 if a molecule is in phase one for cancer, what's the probability it will make it to the market? 162 00:20:23,320 --> 00:20:28,990 The answer is seven and a half percent less than one in 10 molecules in phase one. 163 00:20:28,990 --> 00:20:34,870 The cancer makes the little they look to see if a molecule is in phase three for cancer. 164 00:20:34,870 --> 00:20:41,020 So these are the final registration studies. What's the probability we'll make it to the market? 165 00:20:41,020 --> 00:20:49,330 The answer is three percent. Only one in three molecules in phase three for cancer makes it to the market. 166 00:20:49,330 --> 00:20:53,800 It is too risky. It's also too slow. 167 00:20:53,800 --> 00:20:59,470 A couple of my colleagues, Stephane Bancel, Michael Silver, a few years ago published a paper. 168 00:20:59,470 --> 00:21:06,910 They looked to see, where did we come up with data that a particular target could be useful in the lab? 169 00:21:06,910 --> 00:21:11,390 And then how long did it take to take that idea into the clinic? 170 00:21:11,390 --> 00:21:18,670 And the answer was anywhere between six years to 30 years, three, zero, three decades. 171 00:21:18,670 --> 00:21:24,610 This process is too costly, it's too risky and it's too slow. 172 00:21:24,610 --> 00:21:33,910 So what this industry, this community employs some of the smartest people on the planet to have access to great technologies. 173 00:21:33,910 --> 00:21:39,730 They have access to great collaborators in the sector. Why is it so difficult? 174 00:21:39,730 --> 00:21:47,640 I think there's a number of reasons, and I would group the group them into making scientific challenges and then maybe organisational challenges. 175 00:21:47,640 --> 00:21:58,150 So let me just take each of those into to scientific. First of all, I do not think the many diseases in the country. 176 00:21:58,150 --> 00:22:03,310 We have a very good understanding of the molecular causes. 177 00:22:03,310 --> 00:22:11,020 Frankly, if I take a patient with schizophrenia or depression or Alzheimer's, there's nobody on the planet. 178 00:22:11,020 --> 00:22:17,680 You could tell me the molecular causes of the phenotype and not one individual. 179 00:22:17,680 --> 00:22:23,050 This is a major problem. These diseases are incredibly heterogeneous. 180 00:22:23,050 --> 00:22:28,900 If we took a hundred patients with Alzheimer's, they would all be different different ages, 181 00:22:28,900 --> 00:22:33,820 different sects, different way, different ethnic backgrounds, different diets, et cetera, et cetera. 182 00:22:33,820 --> 00:22:39,850 But importantly, their symptoms would be completely different. Some of them would have just forgotten where their keys are. 183 00:22:39,850 --> 00:22:43,960 Others will not even recognise that case. Some will be aggressive. 184 00:22:43,960 --> 00:22:53,800 Some will be depressed. Some will be anxious. Some will be agitated, etc. These are incredibly heterogeneous diseases. 185 00:22:53,800 --> 00:22:59,140 We also find many of these diseases. We do not have good biomarkers by biomarkers, 186 00:22:59,140 --> 00:23:08,380 only readouts that we can use in the clinic to assess if a new molecule is effective in the clinical study in Alzheimer's. 187 00:23:08,380 --> 00:23:13,420 You can't say to the patient, though. Is your memory better today than it was last week? 188 00:23:13,420 --> 00:23:19,420 Well, you can't say to a depressed patient, are you less depressed today than you were last month? 189 00:23:19,420 --> 00:23:28,390 We need that survival. We there are also many molecules, drugs that we don't even know how they work. 190 00:23:28,390 --> 00:23:32,620 Paracetamol, acetaminophen, we've all taken it. 191 00:23:32,620 --> 00:23:37,420 Probably today across the planet, 100 million people took paracetamol. 192 00:23:37,420 --> 00:23:44,740 We do not know how the pharmaceutical works. We go into the mode of action we don't know the science of. 193 00:23:44,740 --> 00:23:49,800 So if you don't know how existing drugs work, how can you design better? 194 00:23:49,800 --> 00:23:53,550 And then I'm afraid, animal models now, many of us use them. 195 00:23:53,550 --> 00:24:04,170 Some of you probably use them. I'm afraid I do not believe we will ever, ever have an animal model that truly recapitulate clinical disease. 196 00:24:04,170 --> 00:24:10,350 We will never have an animal model that predicts schizophrenia in the clinical depression in the clinical Alzheimer's, 197 00:24:10,350 --> 00:24:18,240 and I appreciate they have their uses. Know we need to get a sense of some sort of in vivo activity. 198 00:24:18,240 --> 00:24:22,290 We need to get some sort of idea of what the side effects may be. 199 00:24:22,290 --> 00:24:27,990 But I tell you, just because something works in an animal model does not mean it will work. 200 00:24:27,990 --> 00:24:35,220 I have seen many things work beautifully in animal models, and we take that into the clinic and. 201 00:24:35,220 --> 00:24:44,650 The major scientific challenges, then there are organisational challenges, and I touch all these at the stuff. 202 00:24:44,650 --> 00:24:49,990 At the moment, many academics, many scientists in biotech. 203 00:24:49,990 --> 00:24:55,690 Many scientists in the pharmaceutical industry, they all work on the same few ideas, 204 00:24:55,690 --> 00:25:03,760 the same futile society, molecular biology, and they do this in parallel and they do this in secret. 205 00:25:03,760 --> 00:25:07,810 They all read the publication in Nature and then they start working on it. 206 00:25:07,810 --> 00:25:14,080 They all read the same publications that go to the same conferences. They talk to the same opinion leaders. 207 00:25:14,080 --> 00:25:20,740 They go back to their labs and start working on exactly the same idea in parallel, in secret. 208 00:25:20,740 --> 00:25:27,370 Now we know that most of those ideas, when we translate them from the lab into the clinic, 209 00:25:27,370 --> 00:25:32,300 the failure rate is more than eight times of 10 nine out of 10. 210 00:25:32,300 --> 00:25:35,470 I've heard figures as high as 95 percent. 211 00:25:35,470 --> 00:25:47,200 95 percent of our life is from the lab do not translate into the clinic, so you can imagine if you've got 20 companies doing exactly the same thing. 212 00:25:47,200 --> 00:25:52,120 If one of them fails, the other 19 are likely to fail. 213 00:25:52,120 --> 00:25:57,640 The way we're doing drug discovery today, we are wasting a lot of the money. 214 00:25:57,640 --> 00:26:00,160 We're wasting a lot of people's careers. 215 00:26:00,160 --> 00:26:11,450 But importantly, we're exposing patients to molecules that other people or other organisations already know are destined to fail. 216 00:26:11,450 --> 00:26:16,520 This is the consequence of competitive science. 217 00:26:16,520 --> 00:26:28,430 So major challenge, and then I also try to reproduce that there have been some high profile publications from companies like Bio and Jet, 218 00:26:28,430 --> 00:26:35,540 where they said 50, 60, 70 percent of academic reviews. 219 00:26:35,540 --> 00:26:38,750 Now there's no point people like us getting defensive about it. 220 00:26:38,750 --> 00:26:46,670 We just need to do something about the major scientific organisational challenges. 221 00:26:46,670 --> 00:26:51,890 So it's very easy to identify the problems. So what's the solution? 222 00:26:51,890 --> 00:27:00,530 What are we going to do to practise? So let me just share with you what we've been doing in office that over the past 12 years, 223 00:27:00,530 --> 00:27:07,910 I came back for the 22nd of January 2018, so almost exactly 12 years. 224 00:27:07,910 --> 00:27:15,890 So in the past 12 years, what we've been doing is maybe four things. 225 00:27:15,890 --> 00:27:20,880 Firstly, we've decided that we're only going to work out completely normal, 226 00:27:20,880 --> 00:27:29,300 like normal genes, normal proteins, genes and proteins that nobody else is working on. 227 00:27:29,300 --> 00:27:42,200 There's no other publications, et cetera. So we work on these, and what we do is we generate novel tools to reach the genes and proteins. 228 00:27:42,200 --> 00:27:46,790 We purify the human protein. I'm not interested in rats and mice. 229 00:27:46,790 --> 00:27:52,850 We build biophysical biochemical assays. We work out the structure of that protein. 230 00:27:52,850 --> 00:27:58,160 We generate small molecule inhibitors and we generate bodies. 231 00:27:58,160 --> 00:28:06,650 So we work on novel genes and we generate novel tools, and we do this to drive innovation. 232 00:28:06,650 --> 00:28:12,260 But what we've also done is we've pooled resources to share risk. 233 00:28:12,260 --> 00:28:17,000 So currently, we're working with nine large pharmaceutical companies. 234 00:28:17,000 --> 00:28:22,820 Each of these companies has given us five million euros of funding over a five year period. 235 00:28:22,820 --> 00:28:29,450 We're also getting funding now from certain patient groups for Alzheimer's Research UK. 236 00:28:29,450 --> 00:28:32,890 Four years ago, it's 10 million pounds six months ago. 237 00:28:32,890 --> 00:28:41,670 Getting this notifiable because the Wellcome Trust over the past 12 years has given us close to 60 million pounds into our lab, 238 00:28:41,670 --> 00:28:49,930 we were pooling all of these resources to share with. 239 00:28:49,930 --> 00:28:59,200 The third thing we do, and this is probably what makes this the most unique, all of these tools that we generate and these tools are high quality, 240 00:28:59,200 --> 00:29:07,570 not because we're clever, they're high quality because we're tapping into the resources, the expertise of these nine large pharmaceutical companies. 241 00:29:07,570 --> 00:29:15,010 We have access to their whole phone collection of how we develop assays, their high throughput screening, etc., etc. 242 00:29:15,010 --> 00:29:21,700 So what makes this unique is that these tools, which are high quality, we make them freely available, 243 00:29:21,700 --> 00:29:27,550 we give them away to anybody in academia, anybody in biotech may putting in fun. 244 00:29:27,550 --> 00:29:37,780 But the reason we do that is because we believe that's the best thing we can do to facilitate science and therefore facilitate drug discovery. 245 00:29:37,780 --> 00:29:42,700 Now, of course, the consequence of that is that you can imagine every academic who comes into my 246 00:29:42,700 --> 00:29:46,540 office wants to collaborate with us because they know we've got their secrets. 247 00:29:46,540 --> 00:29:50,770 We'll share all of our new and all of our expertise and all of our reagents. 248 00:29:50,770 --> 00:29:55,900 That transparency creates a lot of trust, which is great for collaboration. 249 00:29:55,900 --> 00:30:05,380 It's great for science and it's great for drug discovery. So we're not collaborating with more than 300 academics across all of these labs. 250 00:30:05,380 --> 00:30:09,460 Take these novel tools and then they test them with whatever they are. 251 00:30:09,460 --> 00:30:17,740 It could be a model for cancer or a model of diabetes, or some rare disease or some dementia model or whatever. 252 00:30:17,740 --> 00:30:23,110 And then, of course, academics all they care about is publishing, so they publish this data. 253 00:30:23,110 --> 00:30:28,720 They take the high quality novel tool they test in there and say they publish it. 254 00:30:28,720 --> 00:30:39,040 This is a way of crowdsourcing science, and the full thing we do is that all of our data are all of our knowledge, all of our reagents. 255 00:30:39,040 --> 00:30:47,890 We share them with the world media, so we get a signal at the club months while we're writing the manuscript because in those 12 months 256 00:30:47,890 --> 00:30:54,430 there could be people out there trying to do something that we've already done that would be a waste. 257 00:30:54,430 --> 00:30:58,950 So if I were using it immediately, we're trying to reduce duplication and waste. 258 00:30:58,950 --> 00:31:09,010 So the four things who resources to share with working novel areas generate novel, high quality tools to drive innovation, 259 00:31:09,010 --> 00:31:18,370 make everything freely available to crowdsourced science, and release everything immediately to reduce duplication of the way to. 260 00:31:18,370 --> 00:31:27,850 Let me share with you some of the things that we're now doing. So we've been building lots of links with patient groups. 261 00:31:27,850 --> 00:31:35,260 The patient groups are already very powerful and in future years they're going to become even more powerful. 262 00:31:35,260 --> 00:31:45,280 They are already telling the government where to spend their research dollars, but we're keen to work with patient groups for two reasons. 263 00:31:45,280 --> 00:31:53,560 One, they can help us get patient material, so I'm not interested in testing on molecules in our own walls. 264 00:31:53,560 --> 00:31:58,720 I want to test for the cancer cells in patients or immune cells from patients. 265 00:31:58,720 --> 00:32:04,180 I think that's a much better way to come up with new targets to block this cycle. 266 00:32:04,180 --> 00:32:10,540 But also, if we generate the molecule and I think it's going to be useful, let's say Huntington's disease. 267 00:32:10,540 --> 00:32:14,730 Well, I give you the best. Scientists on the planet are in Huntington's disease. 268 00:32:14,730 --> 00:32:23,290 You can imagine this charity does that, so they will give that molecule to those respective labs to try and accelerate the science. 269 00:32:23,290 --> 00:32:26,770 So getting closer to the patient groups. 270 00:32:26,770 --> 00:32:37,120 The second thing we're doing is we've built this dementia institute and in this institute we're focussing on completely pathways. 271 00:32:37,120 --> 00:32:43,480 I'm not interested in working on amyloid and I'm not interested in working on trial because frankly, 272 00:32:43,480 --> 00:32:52,330 the global community has been doing that for 30 years, and we've done 13 or 14 phase three clinical trials in Alzheimer's. 273 00:32:52,330 --> 00:32:57,460 And every single one is there. So we need to move into new areas. 274 00:32:57,460 --> 00:33:01,630 So we're looking at the role of like a and looking at the role of inflammation. 275 00:33:01,630 --> 00:33:06,750 We're looking for the role of epigenetics. We're looking at the role of metabolic pathways, cetera. 276 00:33:06,750 --> 00:33:12,820 So we're trying to come up with completely new approaches to continue to match. 277 00:33:12,820 --> 00:33:18,730 The third thing we're doing is we're now trying to build across the UK a national 278 00:33:18,730 --> 00:33:26,590 initiative to accelerate new therapeutics for multimorbidity associated with ageing. 279 00:33:26,590 --> 00:33:32,180 So we're all aware that elderly patients don't have one disease. They normally have half a dozen diseases. 280 00:33:32,180 --> 00:33:37,750 This is a bit of cancer of the cardiovascular systems compromise that people with compromised. 281 00:33:37,750 --> 00:33:41,950 They have respiratory problems, they have frailty, et cetera, et cetera. 282 00:33:41,950 --> 00:33:50,150 Now we believe that there are pathways that affect multiple morbidity associated with ageing. 283 00:33:50,150 --> 00:33:58,210 And so we're trying to identify targets all those pathways in an attempt to get one drug to treat all of the walk. 284 00:33:58,210 --> 00:34:03,760 This is a completely new approach. This is not something that the pharmaceutical industry is doing. 285 00:34:03,760 --> 00:34:06,490 This is not something that biotech to do. 286 00:34:06,490 --> 00:34:15,160 This is something that's going to be incredibly risky because it's going to involve working with lots of different clinicians at the moment in pharma. 287 00:34:15,160 --> 00:34:22,460 What happens is you come up with a drug for diabetes and a separate one for Alzheimer's and a separate one for cancer. 288 00:34:22,460 --> 00:34:30,880 Here we're not talking about coming up with something, but we're going to measure lots of different readouts in a particular way. 289 00:34:30,880 --> 00:34:35,140 But we now have the technologies that allow us to do that. 290 00:34:35,140 --> 00:34:39,610 So you now when there are companies like, say, the market based in Boulder, 291 00:34:39,610 --> 00:34:45,130 Colorado, where they can take a tiny sample of blood and they're not so good, 292 00:34:45,130 --> 00:34:53,980 but they can measure 5000 different proteins, the $200 the TV is doubling, measuring 10000 in three years. 293 00:34:53,980 --> 00:35:02,620 It will be the whole plan because these platforms have become cheaper, higher throughput, Blackpool and faster. 294 00:35:02,620 --> 00:35:07,390 Then of course, you've got wearables, devices, you know, these wearable devices. 295 00:35:07,390 --> 00:35:11,860 Now you can measure heart rate, blood pressure, respiratory rate, 296 00:35:11,860 --> 00:35:21,640 lots of other parameters continuously 24-7 longitudinally non-invasively noninvasively cheap. 297 00:35:21,640 --> 00:35:28,030 We can generate tens of thousands of data points on each particular patient. 298 00:35:28,030 --> 00:35:36,130 And then, of course, we now got things like air and machine learning to help just put all that data together to make sense of it. 299 00:35:36,130 --> 00:35:42,910 So we're trying to build this national effort. We're working with the University of Dundee because they have lots of chemistry. 300 00:35:42,910 --> 00:35:49,870 We're working with the Medicines Discovery Catapult in Manchester because they have access to patient groups and see our rows. 301 00:35:49,870 --> 00:35:55,660 We're working with the University of Birmingham because they've got access to a catchment of six million patients, 302 00:35:55,660 --> 00:36:05,740 and we're working with the crick because they've got lots of cool biology. So these four centres in Oxford are pulling this national effort together. 303 00:36:05,740 --> 00:36:14,320 We're also having discussions with the Wellcome Trust about generating tools for the whole of the human genome. 304 00:36:14,320 --> 00:36:23,230 So you're aware that in humans there's 20000 to 30000 different proteins, each of those proteins could be a drug target. 305 00:36:23,230 --> 00:36:28,090 Now at the moment, the drugs that are out there probably hit about, let's say, 306 00:36:28,090 --> 00:36:34,420 a thousand of these targets that if you look at what the biomedical community is working on with the land, 307 00:36:34,420 --> 00:36:44,230 they're probably looking at another two thousand. But there's probably ten twelve fourteen thousand genes for which we have no tools. 308 00:36:44,230 --> 00:36:49,540 We have no protein, we have no structure, we have no evidence that we have built an antibody. 309 00:36:49,540 --> 00:36:54,580 So we're talking about how we can create an international consortium to generate 310 00:36:54,580 --> 00:37:02,220 tools for the whole of the human genome in an attempt to accelerate discovery. 311 00:37:02,220 --> 00:37:09,690 So let me sum up now, I think at the moment in biomedical science, 312 00:37:09,690 --> 00:37:18,090 there's too much competition, too much secrecy, too much duplication and too much waste. 313 00:37:18,090 --> 00:37:27,090 What we're trying to do is we're trying to bring together lots of clinicians, lots of academics, lots of pharma companies, lots of patient groups, 314 00:37:27,090 --> 00:37:35,550 lots of funders to work together to come up with completely Typekit new ways 315 00:37:35,550 --> 00:37:41,640 of treating disease in patients new de-risked targets for treating disease. 316 00:37:41,640 --> 00:37:48,170 If we do this, we believe it will be good for industry, obviously, but it'll also be good for patients. 317 00:37:48,170 --> 00:37:58,200 It'll also be good for society and the economy. What we're trying to do is to create a new ecosystem for drug discovery and ecosystem, 318 00:37:58,200 --> 00:38:05,430 which I hope will generate more novel drugs more quickly, more effectively. 319 00:38:05,430 --> 00:38:11,340 But I hope that these drugs will also be more of. 320 00:38:11,340 --> 00:38:24,360 Now, this whole seminar series that Martin sets out, the rough translation and the humanities, et cetera, and I think the market for the coffee, 321 00:38:24,360 --> 00:38:37,380 the one of the things that we need are colleagues in humanities to do to help us in this regard is that we need people to write about this, 322 00:38:37,380 --> 00:38:44,220 to talk about it. I knew I could write plays, write books, write articles, etc. 323 00:38:44,220 --> 00:38:49,440 We need to get people out there to appreciate what a crisis. 324 00:38:49,440 --> 00:38:59,190 We have an anti-microbial resistance, what a crisis we have and people have, what a crisis we have in dementia. 325 00:38:59,190 --> 00:39:03,920 This is the only way we're going to start putting more focus on. 326 00:39:03,920 --> 00:39:10,200 And you know, what's been amazing to me in the past, let's not exceed four years. 327 00:39:10,200 --> 00:39:21,420 It's the power of celebrity. If I think of David Attenborough three years ago, nobody was really talking about plastic pollution. 328 00:39:21,420 --> 00:39:30,000 He does this one programme, and now you've got kids in the holidays cleaning up beaches and everybody's talking about. 329 00:39:30,000 --> 00:39:39,600 Four years ago, people weren't really talking about mental health outcomes Prince William and Prince Harry and Stephen Fry, 330 00:39:39,600 --> 00:39:44,640 and now people are talking about it. This is the power of celebrity. 331 00:39:44,640 --> 00:39:53,080 This is the power of storytelling. This is the power of poetry. And so we need to help with what you and your colleagues. 332 00:39:53,080 --> 00:39:56,490 So, ladies and gentlemen, thank you very much. Thank you for sharing with us. 333 00:39:56,490 --> 00:40:17,960 I hope that was of interest in. Thank you so much just for such a comprehensive talk and nice and thought-provoking show that that fast and awful, 334 00:40:17,960 --> 00:40:26,150 awful fact to be pleading that I actually see no need for conflict or anything. 335 00:40:26,150 --> 00:40:30,270 And I totally agree with you that we're going to be mine, he said. 336 00:40:30,270 --> 00:40:35,270 Then back to the same stories. Let's see if we have not done any one of those photos or stories. 337 00:40:35,270 --> 00:40:45,080 The story got quite a lot of DNA, it says we hopefully as humanity is going to also connect the diagnostic and therapy as well as, 338 00:40:45,080 --> 00:40:48,620 you know, a documentation of it. 339 00:40:48,620 --> 00:40:51,980 So thank you so much. It was such an inspiring look. 340 00:40:51,980 --> 00:41:01,850 And I like I never like to ask this audience whether there are any immediate responses to Chelsea's talk. 341 00:41:01,850 --> 00:41:16,300 Uh, yes, it course. Enjoyed it very much was because it recalls a time I spent in the electronics industry and I had a very similar problem. 342 00:41:16,300 --> 00:41:22,930 And open innovation suddenly appeared on the scene. Just like you, you're trying to do it here. 343 00:41:22,930 --> 00:41:31,900 But the missing ingredient in your field seems to be the collaboration between companies in the electronics area. 344 00:41:31,900 --> 00:41:41,500 Things like roadmaps for the next generation of CDs and DVDs, or cover televisions or whatever they appeared. 345 00:41:41,500 --> 00:41:48,460 And you've got a lot of collaboration between the companies that used to fiercely compete with each other. 346 00:41:48,460 --> 00:41:55,120 So this open innovation model accelerated products to market by a factor of two. 347 00:41:55,120 --> 00:42:08,380 I suspect, is anything like that happening in the pharmaceutical and drug discovery at the request. 348 00:42:08,380 --> 00:42:22,030 It is a great question. I think the community is getting better, but I don't think it's as advanced as your community, if you like by, 349 00:42:22,030 --> 00:42:28,120 I mean, certainly we've now got nine follow companies working together, but this is the very early stage. 350 00:42:28,120 --> 00:42:32,590 I mean, one of the things that worries me like the leader in your field. 351 00:42:32,590 --> 00:42:42,760 I mean, sort of there are many farmers, you know, now there's 200 drugs out there for cancer and there are many companies. 352 00:42:42,760 --> 00:42:47,740 They are just sticking combinations together with a couple of them, 353 00:42:47,740 --> 00:42:53,270 just either because they've got them in their own portfolio or they're partnering with another company. 354 00:42:53,270 --> 00:42:59,680 They've got their asset and they just try it and see that it's not a rational way to do it. 355 00:42:59,680 --> 00:43:05,950 You know, at the end of the day, you know, if I was a patient or a carer of a patient and I thought, 356 00:43:05,950 --> 00:43:10,420 you know, we were doing this speculative experiments in patients, you know, 357 00:43:10,420 --> 00:43:17,320 I would both be happy, etc. So we do need to come up with a rational, 358 00:43:17,320 --> 00:43:25,600 more efficient way of doing this because I think this combination will be effective, but it needs to be well thought through. 359 00:43:25,600 --> 00:43:29,800 And I think we have a lot to learn from people like. 360 00:43:29,800 --> 00:43:33,430 So we try and sort of we make that happen. 361 00:43:33,430 --> 00:43:41,680 I mean, the lessons you've learnt, the challenges you went through, some of that we need to communicate those to the community that I work. 362 00:43:41,680 --> 00:43:45,820 Yeah. So thanks for us. Thank you so much. 363 00:43:45,820 --> 00:44:00,220 If there anyone else? Yes. All right. 364 00:44:00,220 --> 00:44:05,950 So you mentioned earlier that the example of Alzheimer's disease research and how 30 365 00:44:05,950 --> 00:44:11,170 years of research was spent on kind of spent research and nothing really came of it. 366 00:44:11,170 --> 00:44:20,120 Can you tell us more about what happened there and the mistakes made there have any lessons for other subjects or. 367 00:44:20,120 --> 00:44:25,690 Yeah, I'm we're in the master's course in clinical neuroscience and we've heard this story many times, 368 00:44:25,690 --> 00:44:31,120 but no one can really tell us what exactly this happened. 369 00:44:31,120 --> 00:44:39,400 So it's a great question that we sort of, you know, we started working on that amyloid in, let's say, the late 90s. 370 00:44:39,400 --> 00:44:45,050 And so the hypothesis was that this protein unalloyed accumulates in the brain. 371 00:44:45,050 --> 00:44:54,100 As a consequence of this accumulation, you get the generation, you get cognitive decline, you get dementia. 372 00:44:54,100 --> 00:45:00,970 So what everybody's been trying to do for the past three decades is either stop the synthesis of that 373 00:45:00,970 --> 00:45:09,640 protein or increase its breakdown or increase its removal from the brain gets clearance from the brain. 374 00:45:09,640 --> 00:45:16,660 And frankly, after 30 years, we know we still got positive clinical data. 375 00:45:16,660 --> 00:45:23,560 I can share with you a story, and I think he was probably about 2014 15. 376 00:45:23,560 --> 00:45:34,090 I was at a meeting that was organised by the New York Academy, the FDA and the NIH, and they wanted a bunch of us. 377 00:45:34,090 --> 00:45:42,070 I think there's about 30 of us to think about a clinical trial in Alzheimer's for prevention. 378 00:45:42,070 --> 00:45:48,400 So at the time, the thinking was that the treatments we've been giving in these clinical studies, it was just too late, 379 00:45:48,400 --> 00:45:54,820 you know, so all these clinical trials were done when the patients demonstrated some sort of cognitive decline. 380 00:45:54,820 --> 00:45:58,090 And then after that, of course, they just spiralled down. 381 00:45:58,090 --> 00:46:06,400 But of course, we now know that you can detect amyloid in the brains of patients maybe 10 to 15 years before they show any symptoms. 382 00:46:06,400 --> 00:46:14,500 So the idea was to do a prevention trial. Now, of course, if you do a prevention trial, the trial is going to be much longer. 383 00:46:14,500 --> 00:46:20,140 It's going to require a lot more patience and it's going to be a lot more expensive. 384 00:46:20,140 --> 00:46:26,650 So already one of the phase three clinical studies that Libby did cost 750 million dollars to do, 385 00:46:26,650 --> 00:46:31,540 one of these prevention trials would be a multi-billion dollar experiment. 386 00:46:31,540 --> 00:46:37,240 But you know, the biggest challenge is that and I believe it's fair to say, 387 00:46:37,240 --> 00:46:45,640 even with all the clinical trials that are being done, you know, these are expensive experiments. 388 00:46:45,640 --> 00:46:50,770 A lot of that data is not available to us. 389 00:46:50,770 --> 00:47:00,730 So, you know, either the data is not published if either the publish or it's not published quickly enough or it's not published in enough detail. 390 00:47:00,730 --> 00:47:07,510 So basically, people are doing the next experiment with that before they learnt from the previous failures, etc. 391 00:47:07,510 --> 00:47:11,410 And that's something that we need to try and change. 392 00:47:11,410 --> 00:47:16,570 Now, I think things are getting better. Industry is publishing more of that data. 393 00:47:16,570 --> 00:47:24,460 But you know, it's good knowing that all the cold ban didn't work because what you need to know is what dose did you give? 394 00:47:24,460 --> 00:47:29,710 What exposures did you get? What were the biomarkers you Typekit you select the patients. 395 00:47:29,710 --> 00:47:32,890 Which centres did you do the trial, all of that sort of stuff. 396 00:47:32,890 --> 00:47:49,960 So there's so much detail that you need to understand and compare these studies, and that's that's the big challenge. 397 00:47:49,960 --> 00:47:56,620 You've spoken a lot about the development of science in the masses of data and sharing resources. 398 00:47:56,620 --> 00:48:03,520 And I just wondered what your opinion was on preregistration and how that may potentially influence collaboration between 399 00:48:03,520 --> 00:48:11,110 different industries that fit into types of studies of investigators that be around before the data is actually being produced. 400 00:48:11,110 --> 00:48:21,550 So sorry, I thought that what you're saying is that the drug is taken to the market and and then you tested it, 401 00:48:21,550 --> 00:48:24,300 lots of patients, and that's when you get final approval. 402 00:48:24,300 --> 00:48:31,330 So as in like before you start carrying out your studies of the drug, you pre-register your hypothesis. 403 00:48:31,330 --> 00:48:37,650 And how you aim to carry out study is something that's starting to be done in the field of psychology. 404 00:48:37,650 --> 00:48:45,580 And it's that sort of setting up the precepts that you aim to produce how they can influence. 405 00:48:45,580 --> 00:48:48,130 I think that that could only help. 406 00:48:48,130 --> 00:48:58,060 I mean, if what you're saying is if I work in a lady or Pfizer and I'm planning on doing a clinical trial in Alzheimer's, I share that protocol. 407 00:48:58,060 --> 00:49:06,340 I share what I intend to do. I get people in the industry, in the academic community to critique it, whatever. 408 00:49:06,340 --> 00:49:07,810 I think that can only help. 409 00:49:07,810 --> 00:49:19,090 But the challenge, of course, is that, you know, a lot of these companies are competitive, and if they're working on the same target, 410 00:49:19,090 --> 00:49:38,970 then they're not necessarily going to want to share the trial design or details of that molecule, etc. So I'm not sure how we have to stop this. 411 00:49:38,970 --> 00:49:45,060 Yeah, I was wondering what your advice would be to young people who haven't yet differentiated or 412 00:49:45,060 --> 00:49:52,800 specialised in any particular sector and kind of what sector do you think is most ripe for? 413 00:49:52,800 --> 00:49:57,990 I guess change or disruption along the pipeline of transition? 414 00:49:57,990 --> 00:50:11,070 So I mean, the one thing I would say is that I encourage all my academic colleagues to go work in the industry because I think you learn so much. 415 00:50:11,070 --> 00:50:18,030 You learn to speak the language. You understand what the issues are, what the priorities are. 416 00:50:18,030 --> 00:50:25,110 You grow your network. And if you do come back into academia that you will continue to work together, et cetera. 417 00:50:25,110 --> 00:50:34,020 So I think I would encourage all of you youngsters because you're all going to live to the age of 100 and you're going to have multiple careers, 418 00:50:34,020 --> 00:50:38,880 just move around and get lots of experiences. 419 00:50:38,880 --> 00:50:44,310 Spend a bit of time in academia, a bit of time in industry, maybe a bit of time and a charity, 420 00:50:44,310 --> 00:50:47,880 a bit of time with a venture capitalists or whatever, et cetera, 421 00:50:47,880 --> 00:50:48,810 et cetera, 422 00:50:48,810 --> 00:50:57,330 and understand each other's perspective because I think the only way we're going to succeed in this game is if we break down into these silos. 423 00:50:57,330 --> 00:51:11,280 This is a team sport and you know, I, you know, I don't like people in academia criticising people in industry and vice versa, 424 00:51:11,280 --> 00:51:17,340 etc. You know, I genuinely believe that people in the industry have tried to do the best they can, 425 00:51:17,340 --> 00:51:27,350 but we all have our own constraints and our own priorities, etc., etc. So we just need to understand each other's perspective, but just work together. 426 00:51:27,350 --> 00:51:31,940 The yes, of course, this is also a problem of land. 427 00:51:31,940 --> 00:51:40,910 Yes, and we're going to speak each other's language. You would hope on the phone the device would be in line with these people. 428 00:51:40,910 --> 00:51:54,630 How do you envisage, if at all, eye rolling on the minds of people in the city and specifically in the development of new drugs? 429 00:51:54,630 --> 00:52:00,750 Well, I'm not quite sure, but for her to answer that question, let me try this way. 430 00:52:00,750 --> 00:52:12,010 You know, I love working with patient groups and patient groups because they are just desperate for a new case. 431 00:52:12,010 --> 00:52:19,350 You know, they do. They don't want to hear excuses. They don't want to hear people like me say, Oh, 432 00:52:19,350 --> 00:52:32,130 it's the regulators or the hurdles are too high or they just want the drugs and they have this absolute razor sharp focus and we just have. 433 00:52:32,130 --> 00:52:37,860 And so I think, you know, it's very easy as an academic just to make excuses. 434 00:52:37,860 --> 00:52:42,120 As long as we produce a few pages and get a few problems, it's all fine. 435 00:52:42,120 --> 00:52:49,050 But, you know, I genuinely think we are here not just to produce papers, 436 00:52:49,050 --> 00:53:00,990 but it is very much to translate as it translates and to create benefits for patients, the society for the industry and for the economy, etc., etc. 437 00:53:00,990 --> 00:53:08,220 You know, we work in a very privileged environment, you know, sort of we're surrounded by awesome people. 438 00:53:08,220 --> 00:53:14,460 We attract the best students and the best researchers. We have access to wonderful infrastructure. 439 00:53:14,460 --> 00:53:19,410 We have lots of research funding. We have great convening power. 440 00:53:19,410 --> 00:53:22,410 Our alumni network is global. 441 00:53:22,410 --> 00:53:31,860 My God, if we call them the to probably this problem of West, they the something that very much both the sciences and humanities and showing. 442 00:53:31,860 --> 00:53:36,960 We need to do something to demonstrate to businesses, can we take some more questions? 443 00:53:36,960 --> 00:53:45,240 I can, probably. Yeah, that's all right. I was wondering what you think the role of government and policy could be in? 444 00:53:45,240 --> 00:53:50,020 Taking this process and having more transparent. 445 00:53:50,020 --> 00:54:03,460 You know, I I think I think many of these problems that we're trying to tackle global problems, the global challenges. 446 00:54:03,460 --> 00:54:08,410 And I think they require critical mass. 447 00:54:08,410 --> 00:54:18,430 They require lots of innovation, entrepreneurship risk Typekit doing things that nobody's even dreamt of, global sorts of etc. 448 00:54:18,430 --> 00:54:24,010 I think it involves people working with individuals from other disciplines. 449 00:54:24,010 --> 00:54:28,150 You know, I often say to colleagues, many said in the next 10, 450 00:54:28,150 --> 00:54:35,470 20 years is going to get completely transformed, not necessarily by biologists or chemists, 451 00:54:35,470 --> 00:54:44,170 but war by engineers and computational scientists and materials scientists and data people, et cetera, et cetera. 452 00:54:44,170 --> 00:54:49,090 They're going to completely transform this area. And so we need to work with other disciplines. 453 00:54:49,090 --> 00:54:58,480 We need to work with other institutions. We need to work with all the stakeholders, you know, industry patient groups, regulators, funders. 454 00:54:58,480 --> 00:55:07,090 You know, one of the things I you know, I believe the funders need to do is and I think they started doing it. 455 00:55:07,090 --> 00:55:13,360 I think one of the great things that David Cameron did do was set up this dementia research 456 00:55:13,360 --> 00:55:19,230 institute in the U.K. So this was two hundred and fifty million pounds into dementia research. 457 00:55:19,230 --> 00:55:25,270 And I understand from talking to John Bell is like. He got to be another 500 million coming in that area. 458 00:55:25,270 --> 00:55:32,900 But I think that's the sort of scale of funding we need to tackle some of these problems, but it needs to be done. 459 00:55:32,900 --> 00:55:40,060 A lot of these things, you know, even in Oxford, we don't have all the expertise, the resources, et cetera, et cetera. 460 00:55:40,060 --> 00:55:46,750 We have to work with each other. I have to. We have some we have time for some questions. 461 00:55:46,750 --> 00:55:56,320 What are your items like paper and another voice of humanity? 462 00:55:56,320 --> 00:56:04,390 Actually, two questions that they're sort of combined. He said that you're sharing immediately the results of your research and work. 463 00:56:04,390 --> 00:56:14,680 Could you say how actually to do it, if not through publishing papers and articles and send it before making another question? 464 00:56:14,680 --> 00:56:24,160 Sort of combined with this one and more towards the actual translation of the very basic understanding of the work. 465 00:56:24,160 --> 00:56:29,200 You mentioned that you work closely with the patients organisations. 466 00:56:29,200 --> 00:56:34,000 Do you actually make any effort? And I know it might not be crucial in your work, 467 00:56:34,000 --> 00:56:42,550 but you actually make any effort to translate your scientific what's called meta language into common words 468 00:56:42,550 --> 00:56:51,460 that would be understood by patients who are essentially involved and interested in the results of your work. 469 00:56:51,460 --> 00:56:59,620 So the first one actually rapidly descended on me, so we talked about a lot of data. 470 00:56:59,620 --> 00:57:04,570 We share our tools immediately, we publish it on our website. 471 00:57:04,570 --> 00:57:12,220 So that's the way we can simulate it in terms of the language with patient groups. 472 00:57:12,220 --> 00:57:18,760 You know, it's interesting one of the things I learnt when I talked to some of these patient representatives. 473 00:57:18,760 --> 00:57:26,500 You know, it is incredible how much they know about the science. You know, they have read every single paper like they can get ahold of. 474 00:57:26,500 --> 00:57:33,130 And that's a sign of desperation. And so we it, we have to do that, to be honest. 475 00:57:33,130 --> 00:57:38,380 But the one thing I am convinced of, though, is slightly related. 476 00:57:38,380 --> 00:57:48,820 Comment is, you know, often at the moment when, for example, a patient with Parkinson's or outside is goes to see their GP. 477 00:57:48,820 --> 00:57:53,200 Now they may see their GP every three months to ten minutes or something like this, 478 00:57:53,200 --> 00:58:05,140 etc. You cannot expect the GP to have a very good understanding of that patient's symptoms, et cetera, has to be resolved. 479 00:58:05,140 --> 00:58:11,740 But I tell you, the people who really have a good understanding is usually the carers. 480 00:58:11,740 --> 00:58:21,680 Often their spouses do you know? And so we need to listen to that thinking if the project projects just yes. 481 00:58:21,680 --> 00:58:25,100 Yeah, yeah, yeah, you're definitely right. 482 00:58:25,100 --> 00:58:32,800 I think there are the humanities can do a lot to get rid of a few misconceptions that are on both sides. 483 00:58:32,800 --> 00:58:36,220 Oh, sorry, yeah. Misconceptions that are on both sides. 484 00:58:36,220 --> 00:58:40,300 I mean, the scientists believing in total objectivity or, you know, 485 00:58:40,300 --> 00:58:47,860 aspiring to some executive definitive solutions that theoretically would resolve problems for everybody 486 00:58:47,860 --> 00:58:58,510 in the same way and have the same degree of efficacy and also the expectations of patients from science. 487 00:58:58,510 --> 00:59:06,120 I think but both sides should become more aware of their limits and of how much more they can do by, 488 00:59:06,120 --> 00:59:16,390 you know, helping one another in ways that are not directly connected with markets, the results. 489 00:59:16,390 --> 00:59:24,010 So I think that's something the humanities is indeed entitled to do, but it's difficult. 490 00:59:24,010 --> 00:59:27,520 For example, eight years ago, if I may just mention something personal, 491 00:59:27,520 --> 00:59:33,580 I was very interested in Alzheimer's in observing how linguistic decay happened in Alzheimer's. 492 00:59:33,580 --> 00:59:43,150 Suffer suffer. In that particular case, it was my father and I got some money to do some some sort of research academy in New York, 493 00:59:43,150 --> 00:59:50,380 which then developed into something very literary how emissions work in literary and literary works. 494 00:59:50,380 --> 00:59:55,300 And I was surrounded by neurologists, and of course, I could not believe my luck. 495 00:59:55,300 --> 01:00:01,750 But they probably want to understand what I meant by coming from a literary point of view. 496 01:00:01,750 --> 01:00:05,800 So, you know, I tried to make myself as understandable as possible, 497 01:00:05,800 --> 01:00:18,430 but there's a lot that we could do and absolutely glad that our and authors are trying hard to translate medicine into literature and literature to, 498 01:00:18,430 --> 01:00:30,370 of course, to take it as humanities into into the scientific visions of I don't know how that can be possible, but I just want to thank you for it, 499 01:00:30,370 --> 01:00:40,480 for prompting us to want to write about it and make people more aware of of how vast the, you know, the questions still are. 500 01:00:40,480 --> 01:00:46,960 But it's exciting for us human beings who suffer from all kinds of diseases. 501 01:00:46,960 --> 01:00:52,270 Well, let me just share this with you. You said so. 502 01:00:52,270 --> 01:01:02,200 About three or four years ago, I was asked to go to a charity event in London where they were trying to raise money for Alzheimer's research. 503 01:01:02,200 --> 01:01:08,650 So this event was organised by a young musician who played the violin, 504 01:01:08,650 --> 01:01:22,090 and he was telling us that he was one of 10 grandchildren and he played his grandfather's violin and his grandfather had just died. 505 01:01:22,090 --> 01:01:34,720 And that's what prompted him to organise this event. And his grandmother would could not remember the names of the other nine grandchildren. 506 01:01:34,720 --> 01:01:44,110 But if she could remember his name and he was convinced it was purely because he played her husband's violin. 507 01:01:44,110 --> 01:01:51,730 I've also heard, you know, some of the musicians, either sometimes you go into these sort of care homes, et cetera. 508 01:01:51,730 --> 01:01:57,430 But some of these poor individuals, they're just sort of sitting on a chair and just staring at the wall, etc. 509 01:01:57,430 --> 01:02:08,200 And but if you go in and you see the solemn little place of music that they loved when they were 20 years old or 30 years old, 510 01:02:08,200 --> 01:02:13,690 they simply get up and start dancing. The facial expression just completely changed. 511 01:02:13,690 --> 01:02:18,200 So there's something about music, which. 512 01:02:18,200 --> 01:02:32,600 I don't understand it. I a certain degree you want to to me that this is going on and they maybe this number one question, I do see that and a. 513 01:02:32,600 --> 01:02:39,380 Do I? Yes, please. So this has made a question. 514 01:02:39,380 --> 01:02:45,920 The humanities people here. It strikes me that one of the most important things that we sometimes don't translate 515 01:02:45,920 --> 01:02:54,020 to the general public as scientists is the the idea that but to some degree, 516 01:02:54,020 --> 01:02:58,910 the low-hanging fruit in pharmacology ought to be dealt with. 517 01:02:58,910 --> 01:03:03,050 So it comes back to you. What about expectations? 518 01:03:03,050 --> 01:03:10,220 So, you know, we're about 20 metres away from where Howard Florey spent the last three years of his life having developed penicillin. 519 01:03:10,220 --> 01:03:16,730 So the penicillin is a sort of the poster child really in terms of pharmacology. 520 01:03:16,730 --> 01:03:21,800 A single drug massively effective against its target and killing it. 521 01:03:21,800 --> 01:03:30,950 And saving millions of lives and vaccines similarly massively effective, saving millions of unnecessary premature deaths. 522 01:03:30,950 --> 01:03:37,340 It may be the case that pharmacology has no equivalent drug for Alzheimer's. 523 01:03:37,340 --> 01:03:44,870 Has that lucrative drug for even those cancers that we really do understand quite well and clean, clear cell reading cell carcinoma. 524 01:03:44,870 --> 01:03:53,660 We know is driven by a mutation in a gene that encodes a protein that is in the potent machine that destroys other proteins. 525 01:03:53,660 --> 01:03:59,870 We know that. But you know, the smart money says that it's probably unlikely to be a drug or target. 526 01:03:59,870 --> 01:04:03,740 So, so maybe there isn't a drug that will do the same thing for set for that particular treatment. 527 01:04:03,740 --> 01:04:12,020 So do you think one of the biggest problems and challenges we face as a community and this goes probably further along 528 01:04:12,020 --> 01:04:20,540 the room is in trying to help everyone on the planet to understand the pharmacology may not have all the answers. 529 01:04:20,540 --> 01:04:23,940 I mean, let me answer this in two ways. 530 01:04:23,940 --> 01:04:31,250 I think you're right that sometimes as scientists, we like to be touched and always especially to fund the research, 531 01:04:31,250 --> 01:04:35,930 and we need to be careful not to need to be responsible for it. 532 01:04:35,930 --> 01:04:46,460 The second thing is that I do think patients want and their carers want hope that is important. 533 01:04:46,460 --> 01:04:59,510 The third thing is that, you know, I fully accept the disease like Alzheimer's, which you could argue is massively affected by environment. 534 01:04:59,510 --> 01:05:06,740 It probably started when the individual was conceived, and it's been building up over the past 50, 535 01:05:06,740 --> 01:05:12,860 60, 70 years, etc. So it's a very complex, multifactorial disease. 536 01:05:12,860 --> 01:05:23,360 I fully accept that. I I also I worry that I mean, I've never worked in the antibiotic resistance. 537 01:05:23,360 --> 01:05:32,630 But, you know, I always used to think that if you've got a bug in the dish and you've got a volunteer killer kills that bug in the dish, 538 01:05:32,630 --> 01:05:36,350 that we should be able to translate that into the clinic. 539 01:05:36,350 --> 01:05:41,780 You know, if you get the right concentration in the blood that you should kill the bugs in patients, etcetera. 540 01:05:41,780 --> 01:05:47,630 But clearly, it's not the case so that we run into mechanisms or rather resistance, et cetera, et cetera. 541 01:05:47,630 --> 01:05:55,120 So you're right. And the. 542 01:05:55,120 --> 01:06:03,670 If I thought that I've done this without my thank you so much right now, 543 01:06:03,670 --> 01:06:12,010 you are set to make space for a more risky environment where science is a minefield, getting back on a journey together. 544 01:06:12,010 --> 01:06:20,410 And I would like to thank you for being here and to all members of the cabinet who took part in tonight's discussion. 545 01:06:20,410 --> 01:06:28,570 I just want to remind you that our next meeting will be on Monday, the 17th of seven next Monday week. 546 01:06:28,570 --> 01:06:39,070 Five. It always it, and I'm happy you anticipated that the topic of Professor Green has to be the crucifixion of rather 547 01:06:39,070 --> 01:06:47,290 peter more planes and religious imagery in two competing narratives about the Cochrane collaboration. 548 01:06:47,290 --> 01:07:04,630 So thank you for being here. Thank you. 549 01:07:04,630 --> 01:07:13,914 I know.