WEBVTT 1 00:00:00.208 --> 00:00:01.751 - Alex, welcome. 2 00:00:01.751 --> 00:00:04.796 Just talk me through the big challenges 3 00:00:04.796 --> 00:00:07.382 facing the biotech industry at the moment. 4 00:00:07.382 --> 00:00:10.135 - So besides the obvious challenges 5 00:00:10.135 --> 00:00:11.594 of the economic downturn, 6 00:00:11.594 --> 00:00:14.681 funding rounds being a bit more challenging, 7 00:00:14.681 --> 00:00:16.725 I think the bigger picture 8 00:00:16.725 --> 00:00:19.060 of what we are really seeing as a challenge 9 00:00:19.060 --> 00:00:21.604 is that the way 10 00:00:21.604 --> 00:00:25.191 we're currently doing biotech drug development, 11 00:00:25.191 --> 00:00:30.196 and the way we're focusing our efforts in treating diseases 12 00:00:30.488 --> 00:00:33.283 is very reactive. 13 00:00:33.283 --> 00:00:37.954 So healthcare is not really healthcare, 14 00:00:37.954 --> 00:00:38.955 it's more sick care. 15 00:00:38.955 --> 00:00:40.040 Once you have a symptom, 16 00:00:40.040 --> 00:00:41.374 you kind of get it treated, 17 00:00:41.374 --> 00:00:46.379 and that leads to a lot of inefficiencies. 18 00:00:46.546 --> 00:00:49.716 So what we are really trying to do 19 00:00:49.716 --> 00:00:52.469 is become more proactive, 20 00:00:52.469 --> 00:00:54.512 and by that, 21 00:00:54.512 --> 00:00:58.308 really preventing the huge cost burden 22 00:00:58.308 --> 00:01:02.395 that comes as we are treating diseases by their symptoms. 23 00:01:02.395 --> 00:01:05.272 - What are the ways to become proactive? 24 00:01:05.272 --> 00:01:10.278 - So that is the big challenge really because right now, 25 00:01:10.487 --> 00:01:14.783 the healthcare systems are not designed to be proactive. 26 00:01:14.783 --> 00:01:15.909 And in order to do that, 27 00:01:15.909 --> 00:01:17.952 we actually need to generate a lot more data 28 00:01:17.952 --> 00:01:20.497 to demonstrate that being proactive 29 00:01:20.497 --> 00:01:22.415 actually makes a difference. 30 00:01:22.415 --> 00:01:27.128 And also the ways we can be proactive, 31 00:01:27.128 --> 00:01:31.758 we need to really target the underlying causes of disease 32 00:01:31.758 --> 00:01:33.259 rather than the symptoms. 33 00:01:33.259 --> 00:01:38.264 So what we need to do is really identify the right patients 34 00:01:38.765 --> 00:01:40.934 before they actually develop the diseases, 35 00:01:40.934 --> 00:01:42.685 so presymptomatically. 36 00:01:42.685 --> 00:01:44.646 We need to do more screening. 37 00:01:44.646 --> 00:01:46.397 We're doing that in cancer already, 38 00:01:46.397 --> 00:01:50.318 and I think that's kind of the role model 39 00:01:50.318 --> 00:01:51.486 of what we need to follow 40 00:01:51.486 --> 00:01:52.779 with a lot of the different 41 00:01:52.779 --> 00:01:55.156 other age-related and chronic diseases, 42 00:01:55.156 --> 00:01:57.242 where we need to be screening 43 00:01:57.242 --> 00:02:00.370 and identifying markers 44 00:02:00.370 --> 00:02:03.373 that can suggest that someone is on their trajectory 45 00:02:03.373 --> 00:02:04.874 to developing a disease, 46 00:02:04.874 --> 00:02:09.128 and interfere then with the right treatments. 47 00:02:09.128 --> 00:02:11.548 And those treatments still need to be developed. 48 00:02:11.548 --> 00:02:13.800 - Where are the opportunities for investment? 49 00:02:13.800 --> 00:02:16.427 - So that's exactly what we are currently focusing on. 50 00:02:16.427 --> 00:02:18.805 So we are developing therapeutics 51 00:02:18.805 --> 00:02:22.725 that are targeting the underlying biology of aging, 52 00:02:22.725 --> 00:02:26.312 which is a main risk factor and probably the main cause 53 00:02:26.312 --> 00:02:29.482 of the development of age-related chronic diseases. 54 00:02:29.482 --> 00:02:33.945 So we are looking at the biology of aging 55 00:02:33.945 --> 00:02:35.613 and the sense of mechanisms 56 00:02:35.613 --> 00:02:40.451 that are associated with the aging process. 57 00:02:40.451 --> 00:02:43.329 And we want to interfere with these mechanisms 58 00:02:43.329 --> 00:02:47.041 so that we can actually prevent development of disease. 59 00:02:48.585 --> 00:02:52.130 One example would be developing drugs 60 00:02:52.130 --> 00:02:54.465 that can deal with protein aggregates. 61 00:02:54.465 --> 00:02:55.508 So protein aggregates, 62 00:02:55.508 --> 00:02:58.386 we all know that about Alzheimer's disease, 63 00:02:58.386 --> 00:03:01.806 they lead to cellular dysfunction. 64 00:03:01.806 --> 00:03:04.726 And if we can deal with the protein aggregates, 65 00:03:04.726 --> 00:03:05.852 we can actually prevent 66 00:03:05.852 --> 00:03:08.688 the development of Alzheimer's disease. 67 00:03:08.688 --> 00:03:11.149 So that's one of the examples. 68 00:03:11.149 --> 00:03:14.068 - What do you see as the future of medicine? 69 00:03:15.653 --> 00:03:17.989 - So the future of medicine will be exactly that. 70 00:03:17.989 --> 00:03:19.866 I envision that 71 00:03:19.866 --> 00:03:23.536 people will, while they're still healthy, 72 00:03:23.536 --> 00:03:24.829 go and see their physicians, 73 00:03:24.829 --> 00:03:26.331 get a full checkup, 74 00:03:26.331 --> 00:03:29.083 get their genetic background checked, 75 00:03:29.083 --> 00:03:31.211 but also all of their biomarkers 76 00:03:31.211 --> 00:03:33.504 associated with lifestyle, nutrition. 77 00:03:33.504 --> 00:03:37.091 And based on those biomarkers, 78 00:03:37.091 --> 00:03:40.720 physicians will be able to tell where they are 79 00:03:40.720 --> 00:03:43.598 in that trajectory of developing certain diseases. 80 00:03:43.598 --> 00:03:46.851 And there will be treatments available 81 00:03:46.851 --> 00:03:51.189 that can be applied before someone develops a symptom, 82 00:03:51.189 --> 00:03:53.566 and really keep people healthy. 83 00:03:53.566 --> 00:03:54.859 - Alex, thank you so much.