WEBVTT 1 00:00:00.080 --> 00:00:04.480 A:middle L:100% S:80% We are developing a computational model for human neural tube closure. 2 00:00:04.920 --> 00:00:07.000 A:middle L:100% S:80% With this model we think we can improve... 3 00:00:07.120 --> 00:00:09.680 A:middle L:100% S:80% the prediction of human developmental toxicity. 4 00:00:16.360 --> 00:00:18.720 A:middle L:100% S:80% VOICE OVER: In order to improve human chemical... 5 00:00:18.840 --> 00:00:23.680 A:middle L:100% S:80% and pharmaceutical safety assessment we need a fresh look at the way we work. 6 00:00:23.840 --> 00:00:26.080 A:middle L:100% S:80% The classical way of safety assessment... 7 00:00:26.200 --> 00:00:29.400 A:middle L:100% S:80% relies heavily on the use of laboratory animals... 8 00:00:29.520 --> 00:00:32.640 A:middle L:100% S:80% assuming that they were miniature human beings. 9 00:00:32.760 --> 00:00:38.280 A:middle L:100% S:80% However, humans may respond differently to toxic substances than animals. 10 00:00:38.400 --> 00:00:40.960 A:middle L:100% S:80% PIERSMA: We believe that toxicity testing should be improved... 11 00:00:41.080 --> 00:00:44.200 A:middle L:100% S:80% to achieve a better risk assessment in humans. 12 00:00:44.360 --> 00:00:49.880 A:middle L:100% S:80% We can do that by combining human data with new in silico as well as in vitro tools... 13 00:00:50.000 --> 00:00:53.120 A:middle L:100% S:80% to get a complete picture of the toxicity of chemicals. 14 00:00:53.240 --> 00:00:57.280 A:middle L:100% S:80% VOICE OVER: RIVM is working on a new approach for human safety assessment... 15 00:00:57.400 --> 00:00:59.360 A:middle L:100% S:80% based on ontologies. 16 00:00:59.560 --> 00:01:03.800 A:middle L:100% S:80% Ontologies describe the biology of human physiology and disease... 17 00:01:03.960 --> 00:01:07.080 A:middle L:100% S:80% starting at the level of genes and molecules. 18 00:01:07.440 --> 00:01:10.280 A:middle L:100% S:80% PIERSMA: Based on our knowledge of physiology and disease... 19 00:01:10.600 --> 00:01:13.360 A:middle L:100% S:80% we can actually describe what happens when a compound... 20 00:01:13.480 --> 00:01:18.320 A:middle L:100% S:80% affects the system all the way to the disease that emerges which is at the end. 21 00:01:18.640 --> 00:01:22.560 A:middle L:100% S:80% We can describe this process in so-called adverse outcome pathways... 22 00:01:22.760 --> 00:01:25.040 A:middle L:100% S:80% in which we describe all the different elements... 23 00:01:25.160 --> 00:01:29.480 A:middle L:100% S:80% that lead from the initial event to the adverse outcome in the end. 24 00:01:29.680 --> 00:01:33.680 A:middle L:100% S:80% In order to build ontologies, we need extensive data mining. 25 00:01:33.800 --> 00:01:37.200 A:middle L:100% S:80% The data mining will help us delineate the AOPs... 26 00:01:37.400 --> 00:01:42.120 A:middle L:100% S:80% starting from the exposure situation to the disease state. 27 00:01:42.400 --> 00:01:45.640 A:middle L:100% S:80% And within the ontologies and within the AOPs... 28 00:01:45.760 --> 00:01:48.960 A:middle L:100% S:80% we will then be able to define what are the critical points... 29 00:01:49.080 --> 00:01:52.280 A:middle L:100% S:80% that we need to address in terms of a testing system. 30 00:01:52.480 --> 00:01:58.200 A:middle L:100% S:80% So we will design a battery of test systems that take care of all these different points... 31 00:01:58.320 --> 00:02:00.040 A:middle L:100% S:80% and then with an in silico model... 32 00:02:00.160 --> 00:02:02.920 A:middle L:100% S:80% we will translate the results of these in vitro models... 33 00:02:03.040 --> 00:02:05.480 A:middle L:100% S:80% into a prediction of toxicity. 34 00:02:05.640 --> 00:02:09.160 A:middle L:100% S:80% Using AOPs, we will be able to understand... 35 00:02:09.280 --> 00:02:13.600 A:middle L:100% S:80% how neural tube defects come about and how compounds affect the system. 36 00:02:14.080 --> 00:02:17.200 A:middle L:100% S:80% VOICE OVER: One of the most prominent neural tube-related... 37 00:02:17.320 --> 00:02:20.800 A:middle L:100% S:80% malformations in human embryos is spina bifida. 38 00:02:21.160 --> 00:02:22.880 A:middle L:100% S:80% Using the ontology approach... 39 00:02:23.000 --> 00:02:26.920 A:middle L:100% S:80% RIVM is working on a three-dimensional computer model... 40 00:02:27.040 --> 00:02:30.720 A:middle L:100% S:80% of neural tube closure in the developing embryo. 41 00:02:30.840 --> 00:02:32.720 A:middle L:100% S:80% PIERSMA: We have a detailed understanding... 42 00:02:32.840 --> 00:02:36.120 A:middle L:100% S:80% of how the neural tube is formed in embryogenesis. 43 00:02:36.520 --> 00:02:38.960 A:middle L:100% S:80% This allows us to describe the ontology... 44 00:02:39.080 --> 00:02:44.000 A:middle L:100% S:80% to extract the AOPs and to define what test systems we need... 45 00:02:44.120 --> 00:02:46.800 A:middle L:100% S:80% to test the effects of compounds on the system. 46 00:02:47.040 --> 00:02:49.160 A:middle L:100% S:80% And this combined with in silico models... 47 00:02:49.280 --> 00:02:53.120 A:middle L:100% S:80% will allow us to do a hazard and risk assessment of chemicals... 48 00:02:53.240 --> 00:02:56.280 A:middle L:100% S:80% using this novel, human-based system.