ANIMAL EXPERIMENTS IN THE ERA OF TRANSLATIONAL MEDICINE. WHAT WOULD THEY BE?

Currently, laboratory animals are used in biomedical research to test hypotheses about the mechanisms of development and progression of the diseases, to search the new targets for pharmacotherapy, to test the safety and efficacy of new drugs. However, the results of experimental studies, with the established practice of their planning and performing, do not provide a sufficient level of evidence to go to performance of clinical trials. Key concepts, that allow the analysis of the current situation and outline the way out of the crisis, are the concepts of internal and external validity and bias. This review briefly describes the main types of systematic errors that reduce the validity of experimental research. Tools, that improve the translational potential of experimental studies, are systematic reviews and meta-analyzes, international guidelines and checklists, as well as the «reverse» translation of failed clinical trials. Guidelines and checklists that help researchers to assess the compliance of the planned and ongoing research to «gold standard», should be implemented at various levels, starting with the grantmaking organizations, bioethics committees and editorial boards of scientific journals. Pre-registration of experimental works and access of a wide range of researchers to the primary data after publication should make the research process more open and therefore objective. The above approaches might help to overcome the crisis of translatability of experimental results in practice and regain the trust of the general public to the results of biomedical experimental studies.

1. Cook DJ, Tymianski M. Translating promising preclinical neuroprotective therapies to human stroke trials. Expert Rev Cardiovasc Ther. 2011; 9(4):433-449.

2. Arrowsmith J. Trial watch: Phase II failures: 2008-2010. Nat Rev Drug Discov. 2011; 10(5):328-329.

3. Collins F. Of Mice, Men, and Medicine. http://directorsblog.nih.gov/2013/02/19/of-mice-men-and-medicine/ (19 February 2013).

4. Chalmers I, Glasziou P. Avoidable waste in the production and reporting of research evidence. Lancet. 2009; 374(9683): 86-89.

5. Freedman LP, Gibson MC. The impact of preclinical irreproducibility on drug development. Clin Pharmacol Ther. 2015; 97(1):16-18.

6. Begley CG, Ellis LM. Drug development: Raise standards for preclinical cancer research. Nature. 2012; 483(7391):531-533.

7. Herper M. The Truly Staggering Cost Of Inventing New Drugs http://www.forbes.com/sites/matthewherper/2012/02/10/the-truly-staggering-cost-of-inventing-new-drugs/ (10 February 2012).

8. Deloitte and Thomson Reuters. Measuring the return from pharmaceutical innovation: weathering the storm: Annual Report. London: Deloitte LLP, 2013.

9. van der Worp HB, Howells DW, Sena ES et al. Can animal models of disease reliably inform human studies? PLoS Med. 2010; 7(3):e1000245.

10. Shcherbak NS, Galagudza MM. Experimental models of ischemic stroke. Bulletin of Federal Almazov Medical Research Centre. 2011; 3:39-46. In Russian [Щербак Н. С., Галагудза М. М. Экспериментальные модели ишемического инсульта. Бюллетень Федерального Центра сердца, крови и эндокринологии им. В. А. Алмазова. 2011; 3:39-46].

11. Galagudza MM, Kostareva AA. Experimental models of restrictive cardiomyopathy. Regional Haemodynamics and Microcirculation. 2015; 14(3):14-21. In Russian [Галагудза М. М., Костарева А. А. Экспериментальные модели рестриктивной кардиомиопатии. Регионарное кровообращение и микроциркуляция. 2015; 14(3):14-21].

12. Fisher M, Feuerstein G, Howells DW et al. Update of the stroke therapy academic industry roundtable preclinical recommendations. Stroke. 2009; 40(6):2244-2250.

13. Galagudza ММ, Nekrasova MK, Syrenskii АV et al. Cardioprotective effect of anti-ischemic and metabolic preconditioning in experiment. Russian Journal of Physiology. 2006; 92(3):284-291. In Russian [Галагудза М. М., Некрасова М. К., Сыренский А. В. и др. Устойчивость миокарда к ишемии и эффективность ишемического прекондиционирования при экспериментальном сахарном диабете // Российский физиологический журнал им. И. М. Сеченова. 2006; 92(3):284-291].

14. Chavalarias D, Ioannidis JP. Science mapping analysis characterizes 235 biases in biomedical research. J Clin Epidemiol. 2010; 63(11):1205-1215.

15. Ericsson AC, Davis JW, Spollen W et al. Effects of vendor and genetic background on the composition of the fecal microbiota of inbred mice. PLoS One. 2015; 10(2):e0116704.

16. Treuting PM, Clifford CB, Sellers RS et al. Of mice and microflora: considerations for genetically engineered mice. Vet Pathol. 2012; 49(1):44-63.

17. Borshcev YuYu., Minasian SM, Burovenko IYu et al. Influence of probiotic strain E. faecium L3 on myocardial tolerance to ischemia-reperfusion injury in the model of antibiotic-induced intestinal dysbiosis. Russian Journal of Physiology. 2016; 102(11):1323-1332. In Russian [Борщев Ю. Ю., Минасян С. М., Буровенко И. Ю. и др. Влияние пробиотического штамма E. faecium L3 на устойчивость миокарда к ишемии-реперфузии в модели антибиотик-индуцированного дисбиоза кишечника. Российский физиологический журнал им. И. М. Сеченова. 2016; 102(11):1323-1332].

18. Altman DG, Bland JM. How to randomise. BMJ. 1999; 319(7211):703-704.

19. Pedder H, Vesterinen HM, Macleod MR et al. Systematic review and meta-analysis of interventions tested in animal models of lacunar stroke. Stroke. 2014; 45(2):563-570.

20. Simon MM, Greenaway S, White JK et al. A comparative phenotypic and genomic analysis of C57BL/6J and C57BL/6N mouse strains. Genome Biol. 2013; 14(7):R82.

21. Hirst JA, Howick J, Aronson JK et al. The need for randomization in animal trials: an overview of systematic reviews. PLoS One. 2014; 9(6):e98856.

22. Kilkenny C, Parsons N, Kadyszewski E et al. Survey of the quality of experimental design, statistical analysis and reporting of research using animals. PLoS One. 2009; 4(11):e7824.

23. O'Connor AM, Sargeant JM. Critical appraisal of studies using laboratory animal models. ILAR J. 2014; 55(3):405-417.

24. Hurst JL, West RS. Taming anxiety in laboratory mice. Nat Methods. 2010; 7(10):825-826.

25. Sorge RE, Martin LJ, Isbester KA et al. Olfactory exposure to males, including men, causes stress and related analgesia in rodents. Nat Methods. 2014; 11(6):629-632.

26. Minnerup J, Wersching H, Diederich K et al. Methodological quality of preclinical stroke studies is not required for publication in high-impact journals. J Cereb Blood Flow Metab. 2010; 30(9):1619-1624.

27. ter Riet G, Korevaar DA, Leenaars M et al. Publication bias in laboratory animal research: a survey on magnitude, drivers, consequences and potential solutions. PLoS One. 2012; 7(9):e43404.

28. Peters JL, Sutton AJ, Jones DR et al. A systematic review of systematic reviews and meta-analyses of animal experiments with guidelines for reporting. J Environ Sci Health B. 2006; 41(7):1245-1258.

29. Nuffield Council on Bioethics. The ethics of research involving animals. London: Nuffield Council on Bioethics, 2005.

30. Korevaar DA, Hooft L, ter Reit G. Systematic reviews and meta-analyses of preclinical studies: publication bias in laboratory animal experiments. Lab Anim. 2011; 45(4):225-230.

31. Leenaars M. Systematic reviews of preclinical animal studies: current state of affairs. Webinar. http://www.youtube.com/watch?v=FxdRlJ1RfWY (19 June 2014).

32. van Luijk J, Bakker B, Rovers MM et al. Systematic reviews of animal studies; missing link in translational research? PLoS One. 2014; 9(3):e89981.

33. Higgins JP, Altman DG, Gotzsche PC et al. The Cochrane Collaboration’s tool for assessing risk of bias in randomised trials. BMJ. 2011; 343:d5928.

34. Hooijmans CR, Rovers MM, de Vries RB et al. SYRCLE's risk of bias tool for animal studies. BMC Med Res Methodol. 2014; 14:43.

35. Evans I, Thornton H, Chalmers I. Testing treatments: better research for better healthcare. London: The British Library, 2006.

36. Hooijmans CR, Leenaars M, Ritskes-Hoitinga M. A gold standard publication checklist to improve the quality of animal studies, to fully integrate the Three Rs, and to make systematic reviews more feasible. Altern Lab Anim. 2010; 38(2):167-182.

37. Altman DG. Better reporting of randomised controlled trials: the CONSORT statement. BMJ. 1996; 313(7057):570-571.

38. Kilkenny C, Browne WJ, Cuthill IC et al. Improving bioscience research reporting: the ARRIVE guidelines for reporting animal research. PLoS Biol. 2010; 8(6):e1000412.

39. Muhlhausler BS, Bloomfield FH, Gillman MW. Whole animal experiments should be more like human randomized controlled trials. PLoS Biol. 2013; 11(2):e1001481.

40. Scudamore CL, Soilleux EJ, Karp NA et al. Recommendations for minimum information for publication of experimental pathology data: MINPEPA guidelines. J Pathol. 2016; 238(2):359-367.

41. Al-Shahi Salman R, Beller E, Kagan J et al. Increasing value and reducing waste in biomedical research regulation and management. Lancet. 2014; 383(9912):176-185.

42. Johnson VE. Revised standards for statistical evidence. Proc Natl Acad Sci U S A. 2013; 110(48):19313-19317.

43. Bailey KR. Detecting fabrication of data in a multicenter collaborative animal study. Control Clin Trials. 1991; 12(6):741-752.

44. Lefer DJ, Bolli R. Development of an NIH consortium for preclinicAl AssESsment of CARdioprotective therapies (CAESAR): a paradigm shift in studies of infarct size limitation. J Cardiovasc Pharmacol Ther. 2011; 16(3-4):332-339.

45. Jones SP, Tang XL, Guo Y et al. The NHLBI-sponsored Consortium for preclinicAl assESsment of cARdioprotective therapies (CAESAR): a new paradigm for rigorous, accurate, and reproducible evaluation of putative infarct-sparing interventions in mice, rabbits, and pigs. Circ Res. 2015; 116(4):572-586.

46. Ioannidis JP, Khoury MJ. Assessing value in biomedical research: the PQRST of appraisal and reward. JAMA. 2014; 312(5):483-484.

47. Ledford H. Translational research: the full cycle. Nature. 2008; 453(7197):843-845.

48. 't Hart BA. Reverse translation of failed treatments can help improving the validity of preclinical animal models. Eur J Pharmacol. 2015; 759:14-18.

49. Sequist LV, Bell DW, Lynch TJ et al. Molecular predictors of response to epidermal growth factor receptor antagonists in non-small-cell lung cancer. J Clin Oncol. 2007; 25(5):587-595.

50. McGonigle P, Ruggeri B. Animal models of human disease: challenges in enabling translation. Biochem Pharmacol. 2014; 87(1):162-171.