Скачать презентацию mily Sena Gillian Currie Hanna Vesterinen Kieren Egan Скачать презентацию mily Sena Gillian Currie Hanna Vesterinen Kieren Egan

2fe391f3df51ccca480dd51d6cf40300.ppt

  • Количество слайдов: 26

mily Sena, Gillian Currie, Hanna Vesterinen, Kieren Egan, Nicki Sherratt, Cristina Fonseca, Zsannet Bahor, mily Sena, Gillian Currie, Hanna Vesterinen, Kieren Egan, Nicki Sherratt, Cristina Fonseca, Zsannet Bahor, Jing Liao, heo Hirst, Kim Wever, Hugo Pedder, Katerina Kyriacopoulou, Julija Baginskaite, Ye Ru, Stelios Serghiou, Aaron c. Lean, Catherine Dick, Tracey Woodruff, Patrice Sutton, Andrew Thomson, Aparna Polturu, Sarah Ma. Cann, Gillian ead, Joanna Wardlaw, Rustam Salman, Joseph Frantzias, Robin Grant, Paul Brennan, Ian Whittle, Andrew Rice, osie Moreland, Nathalie Percie du Sert, Paul Garner, Lauralyn Mc. Intyre, Gregers Wegener, Lindsay Thomson, David owells, Ana Antonic, Tori O’Collins, Uli Dirnagl, H Bart van der Worp, Philip Bath, Mharie Mc. Rae, Stuart Allan, Ian arshall, Xenios Mildonis, Konstantinos Tsilidis, Orestis Panagiotou, John Ioannidis, Peter Batchelor, David Howells, anne Jansen of Lorkeers, Geoff Donnan, Peter Sandercock, Emily Sena, Gillian Currie, Hanna Vesterinen, Kieren gan, Nicki Sherratt, Cristina Fonseca, Zsannet Bahor, Jing Liao, Theo Hirst, Kim Wever, Hugo Pedder, Katerina yriacopoulou, Julija Baginskaite, Ye Ru, Stelios Serghiou, Aaron Mc. Lean, Catherine Dick, Tracey Woodruff, Patrice utton, Andrew Thomson, Aparna Polturu, Sarah Ma. Cann, Gillian Mead, Joanna Wardlaw, Rustam Salman, Joseph antzias, Robin Grant, Paul Brennan, Ian Whittle, Andrew Rice, Rosie Moreland, Nathalie Percie du Sert, Paul arner, Lauralyn Mc. Intyre, Gregers Wegener, Lindsay Thomson, David Howells, Ana Antonic, Tori O’Collins, Uli rnagl, H Bart van der Worp, Philip Bath, Mharie Mc. Rae, Stuart Allan, Ian Marshall, Xenios Mildonis, Konstantinos silidis, Orestis Panagiotou, John Ioannidis, Peter Batchelor, David Howells, Sanne Jansen of Lorkeers, Geoff Donnan, eter Sandercock, Emily Sena, Gillian Currie, Hanna Vesterinen, Kieren Egan, Nicki Sherratt, Cristina Fonseca, Malcolm Macleod sannet Bahor, Jing Liao, Theo Hirst, Kim Wever, Hugo Pedder, Katerina Kyriacopoulou, Julija Baginskaite, Ye Ru, elios Serghiou, Aaron Mc. Lean, Catherine Dick, Tracey Woodruff, Patrice Sutton, Andrew Thomson, Aparna Polturu, Professor of Neurology and Translational Neurosciences arah Ma. Cann, Gillian Mead, Joanna Wardlaw, Rustam Salman, Joseph Frantzias, Robin Grant, Paul Brennan, Ian hittle, Andrew Rice, Rosie Moreland, Nathalie Percie duof Edinburgh University Sert, Paul Garner, Lauralyn Mc. Intyre, Gregers Wegener, ndsay Thomson, David Howells, Ana Antonic, Tori O’Collins, Uli Dirnagl, H Bart van der Worp, Philip Bath, Mharie and c. Rae, Stuart Allan, Ian Marshall, Xenios Mildonis, Konstantinos Tsilidis, Orestis Panagiotou, John Ioannidis, Peter atchelor, David Howells, Sanne Jansen of Lorkeers, Geoff Donnan, Peter Sandercock, Emily Sena, Gillian Currie, anna Vesterinen, Kieren. Honorary Sherratt, Cristina Fonseca, Zsannet Bahor, Jing Liao, Theo Hirst, Kim Wever, Egan, Nicki Consultant Neurologist, NHS Forth Valley ugo Pedder, Katerina Kyriacopoulou, Julija Baginskaite, Ye Ru, Stelios Serghiou, Aaron Mc. Lean, Catherine Dick, acey Woodruff, Patrice Sutton, Andrew Thomson, Aparna Polturu, Sarah Ma. Cann, Gillian Mead, Joanna Wardlaw, ustam Salman, Joseph Frantzias, Robin. Approach to Meta-Analysis Andrew Rice, Rosie Moreland, Nathalie Collaborative Grant, Paul Brennan, Ian Whittle, and Review of ercie du Sert, Paul Garner, Lauralyn Mc. Intyre, Gregers Wegener, Lindsay Thomson, David Howells, Ana Antonic, Animal Data from Experimental Studies ori O’Collins, Uli Dirnagl, H Bart van der Worp, Philip Bath, Mharie Mc. Rae, Stuart Allan, Ian Marshall, Xenios ldonis, Konstantinos Tsilidis, Orestis Panagiotou, John Ioannidis, Peter Batchelor, David Howells, Sanne Jansen of orkeers, Geoff Donnan, Peter Sandercock, Emily Sena, Gillian Currie, Hanna Vesterinen, Kieren Egan, Nicki Sherratt, istina Fonseca, Zsannet Bahor, Jing Liao, Theo Hirst, Kim Wever, Hugo Pedder, Katerina Kyriacopoulou, Julija CAMARADES: Catherine Dick, Tracey to translational medicine aginskaite, Ye Ru, Stelios Serghiou, Aaron Mc. Lean, Bringing evidence Woodruff, Patrice Sutton, Andrew Animal models for human disease How can we improve translation of effects to humans?

Disclosures • Member of UK Home Office Animals in Science Committee • Member, Commission Disclosures • Member of UK Home Office Animals in Science Committee • Member, Commission for Human Medicines, UK MHRA • Have sought and will seek funding for work in this area CAMARADES: Bringing evidence to translational medicine

I am not in the office at the moment. Send any work to be I am not in the office at the moment. Send any work to be translated. CAMARADES: Bringing evidence to translational medicine

Winner of the 2012 Ignoble Prize for Neuroscience CAMARADES: Bringing evidence to translational medicine Winner of the 2012 Ignoble Prize for Neuroscience CAMARADES: Bringing evidence to translational medicine

Web based analysis tools Deighton et al 2010 Figure 2: Bootstrap of 1000 sets Web based analysis tools Deighton et al 2010 Figure 2: Bootstrap of 1000 sets of 13 random proteins. A frequency distribution of the IPA scores from 1000 randomly generated sets of 13 proteins. The median IPA score is 16. Only 16 of the 1000 random sets have a score of 30 or greater. CAMARADES: Bringing evidence to translational medicine

10 -120 M 10 -60 M CAMARADES: Bringing evidence to translational medicine 10 -120 M 10 -60 M CAMARADES: Bringing evidence to translational medicine

Why preclinical trials fail to translate Animal studies mislead if their conclusions are confounded Why preclinical trials fail to translate Animal studies mislead if their conclusions are confounded by bias because • of how the experiment were done • of how the data were analysed • incomplete data are available • the wrong sample size • the wrong model CAMARADES: Bringing evidence to translational medicine

How the experiment was done … • 12 graduate psychology students • 5 day How the experiment was done … • 12 graduate psychology students • 5 day experiment: rats in T maze with dark arm alternating at random, and the dark arm always reinforced • 2 groups – “Maze Bright” and “Maze dull” Group Day 1 Day 2 Day 3 Day 4 Day 5 “Maze bright” 1. 33 1. 60 2. 83 3. 26 “Maze dull” 0. 72 1. 10 2. 23 1. 83 Δ +0. 60 +0. 50 +0. 37 +1. 00 +1. 43 Rosenthal and Fode (1963), Behav Sci 8, 183 -9 CAMARADES: Bringing evidence to translational medicine

Evidence from various neuroscience domains … Stroke Alzheimer´s disease Multiple Sclerosis Parkinson´s disease CAMARADES: Evidence from various neuroscience domains … Stroke Alzheimer´s disease Multiple Sclerosis Parkinson´s disease CAMARADES: Bringing evidence to translational medicine

Risk of bias in animal studies Infarct Volume – 11 publications, 29 experiments, 408 Risk of bias in animal studies Infarct Volume – 11 publications, 29 experiments, 408 animals – Improved outcome by 44% (35 -53%) Efficacy è • Randomisation Blinded conduct of experiment Blinded assessment of outcome Macleod et al, 2008 CAMARADES: Bringing evidence to translational medicine

The scale of the problem CAMARADES 2671 CAMARADES: Bringing evidence to translational medicine The scale of the problem CAMARADES 2671 CAMARADES: Bringing evidence to translational medicine

The scale of the problem Pub. MED 2000 CAMARADES: Bringing evidence to translational medicine The scale of the problem Pub. MED 2000 CAMARADES: Bringing evidence to translational medicine

The scale of the problem RAE 1173 “an outstanding contribution to the internationally excellent The scale of the problem RAE 1173 “an outstanding contribution to the internationally excellent position of the UK in biomedical science and clinical/translational research. ” “impressed by the strength within the basic neurosciences that were returned …particular in the areas of behavioural, cellular and molecular neuroscience” 1173 publications using non human animals, published in 2009 or 2010, from 5 leading UK universities Rand Blind I/E SSC CAMARADES: Bringing evidence to translational medicine

Reporting of randomisation across 3 datasets, 2009 -10 CAMARADES: Bringing evidence to translational medicine Reporting of randomisation across 3 datasets, 2009 -10 CAMARADES: Bringing evidence to translational medicine

Reporting of risk bias items by decile of journal impact factor CAMARADES: Bringing evidence Reporting of risk bias items by decile of journal impact factor CAMARADES: Bringing evidence to translational medicine

Are multiply positive studies plausible? • Assume a publication describes 6 experiments • Assume Are multiply positive studies plausible? • Assume a publication describes 6 experiments • Assume the mechanistic hypothesis is correct • Assume individual experiments, using different cohorts, are powered at 50% • Then – p(at least 1 outcome positive) = 98% – p(at least 2 outcomes positive)= 89% – p(3 outcomes positive) = 66% –… – p(6 outcomes positive) = 2% CAMARADES: Bringing evidence to translational medicine

Probability of at least n positive studies 0 1 2 3 4 5 6 Probability of at least n positive studies 0 1 2 3 4 5 6 6 studies 100% 98% 89% 66% 35% 12% 3% 12 studies 100% 98% 93% 81% 61% CAMARADES: Bringing evidence to translational medicine

Publication bias 20% - 32% n expts Estimated unpublished Reported efficacy Corrected efficacy Stroke Publication bias 20% - 32% n expts Estimated unpublished Reported efficacy Corrected efficacy Stroke – infarct volume 1359 214 31. 3% 23. 8% EAE - neurobehaviour 1892 505 33. 1% 15. 0% EAE – inflammation 818 14 38. 2% 37. 5% EAE – demyelination 290 74 45. 1% 30. 5% EAE – axon loss 170 46 54. 8% 41. 7% AD – Water Maze 80 15 0. 688 sd 0. 498 sd AD – plaque burden 632 154 0. 999 sd 0. 610 sd CAMARADES: Bringing evidence to translational medicine

Small group sizes and publication bias conspire together Simulation: 1000 studies Complete publication bias Small group sizes and publication bias conspire together Simulation: 1000 studies Complete publication bias (anything p>0. 05 unpublished) True effect size 10, SD 10 Number of animals per group % of studies published 5 10 15 20 30% 54% 76% 86% CAMARADES: Bringing evidence to translational medicine

Validation of animal models Clot busting treatment for stroke CAMARADES: Bringing evidence to translational Validation of animal models Clot busting treatment for stroke CAMARADES: Bringing evidence to translational medicine

Hypothermia: How powerful is the treatment in animals? 101 publications 222 experiments 3256 animals Hypothermia: How powerful is the treatment in animals? 101 publications 222 experiments 3256 animals 43. 5% protection (40. 1 -47. 0) CAMARADES: Bringing evidence to translational medicine

What is the quality of that evidence? CAMARADES: Bringing evidence to translational medicine What is the quality of that evidence? CAMARADES: Bringing evidence to translational medicine

What is the range of evidence? duration of ischaemia favours hypothermia delay to treatment What is the range of evidence? duration of ischaemia favours hypothermia delay to treatment presence of hypertension favours control favours hypothermia favours control depth of hypothermia CAMARADES: Bringing evidence to translational medicine

Criterion Euro. HYP-1 Knowledge translation table Animal data Euro. HYP-1 >40% improvement in outcome Criterion Euro. HYP-1 Knowledge translation table Animal data Euro. HYP-1 >40% improvement in outcome Powered to detect 7% improvement in outcome Efficacy maintained in high quality studies Randomised, blinded outcome assessment, intensely monitored Yes, but >35% improvement in adjusted outcome Registered What is the range of evidence? Good: sex, duration, delay to treatment, intensity, hypertension, reperfusion Patients >18 yo with acute ischaemic stroke, NIHSS 6 to 20, treated within 6 hrs What are the conditions of maximum efficacy? Temperature dependent: otherwise robust across dimensions Target 34 -35°C How powerful is the treatment? What is the quality of evidence? Is there evidence of a publication bias? CAMARADES: Bringing evidence to translational medicine

How can we increase the chances of translational success? • Systematically review the available How can we increase the chances of translational success? • Systematically review the available data • Conduct further in vivo experiments if indicated • Design your clinical trial accordingly • Develop tools to allow rapid, living systematic reviews CAMARADES: Bringing evidence to translational medicine

If you are planning a systematic review or meta-analysis of animal data, CAMARADES are If you are planning a systematic review or meta-analysis of animal data, CAMARADES are here to help: malcolm. [email protected] ac. uk CAMARADES: Bringing evidence to translational medicine