Optimized design and analysis of preclinical intervention studies in vivo

Recent reports have called into question the reproducibility, validity and translatability of the preclinical animal studies due to limitations in their experimental design and statistical analysis. To this end, we implemented a matching-based modelling approach for optimal intervention group allocation, randomization and power calculations, which takes full account of the complex animal characteristics at baseline prior to interventions. In prostate cancer xenograft studies, the method effectively normalized the confounding baseline variability, and resulted in animal allocations which were supported by RNA-seq profiling of the individual tumours. The matching information increased the statistical power to detect true treatment effects at smaller sample sizes in two castration-resistant prostate cancer models, thereby leading to saving of both animal lives and research costs. The novel modelling approach and its open-source and web-based software implementations enable the researchers to conduct adequately-powered and fully-blinded preclinical intervention studies, with the aim to accelerate the discovery of new therapeutic interventions.

[1]  I. Cuthill,et al.  Reporting : The ARRIVE Guidelines for Reporting Animal Research , 2010 .

[2]  L. Hutchinson,et al.  High drug attrition rates—where are we going wrong? , 2011, Nature Reviews Clinical Oncology.

[3]  Jeffrey H Silber,et al.  Optimal multivariate matching before randomization. , 2004, Biostatistics.

[4]  Daniel Normolle,et al.  Bayesian Hierarchical Changepoint Methods in Modeling the Tumor Growth Profiles in Xenograft Experiments , 2010, Clinical Cancer Research.

[5]  F. Collins,et al.  Policy: NIH plans to enhance reproducibility , 2014, Nature.

[6]  Chi-Ping Day,et al.  Preclinical Mouse Cancer Models: A Maze of Opportunities and Challenges , 2015, Cell.

[7]  Mallika Singh,et al.  Modeling and predicting clinical efficacy for drugs targeting the tumor milieu , 2012, Nature Biotechnology.

[8]  Ulrich Dirnagl,et al.  Distinguishing between Exploratory and Confirmatory Preclinical Research Will Improve Translation , 2014, PLoS biology.

[9]  Gregory R Pond,et al.  Trends in the application of dynamic allocation methods in multi-arm cancer clinical trials , 2010, Clinical trials.

[10]  Xinyi Xu,et al.  Optimal Nonbipartite Matching and Its Statistical Applications , 2011, The American statistician.

[11]  Shah Ebrahim,et al.  Data dredging , bias , or confounding They can all get you into the BMJ and the Friday papers , 2002 .

[12]  Malcolm Macleod,et al.  Why animal research needs to improve , 2011, Nature.

[13]  M. Gillman,et al.  Whole Animal Experiments Should Be More Like Human Randomized Controlled Trials , 2013, PLoS biology.

[14]  Joshua A. Bittker,et al.  Harnessing Connectivity in a Large-Scale Small-Molecule Sensitivity Dataset. , 2015, Cancer discovery.

[15]  T. Laajala,et al.  Castration induces up-regulation of intratumoral androgen biosynthesis and androgen receptor expression in an orthotopic VCaP human prostate cancer xenograft model. , 2014, The American journal of pathology.

[16]  J. Hoenig,et al.  Statistical Practice The Abuse of Power: The Pervasive Fallacy of Power Calculations for Data Analysis , 2001 .

[17]  D. Fergusson,et al.  A meta-analysis of threats to valid clinical inference in preclinical research of sunitinib , 2015, eLife.

[18]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[19]  Andrew Gelman,et al.  Data Analysis Using Regression and Multilevel/Hierarchical Models , 2006 .

[20]  D. Moher,et al.  CONSORT 2010 explanation and elaboration: updated guidelines for reporting parallel group randomised trials. , 2012, International journal of surgery.

[21]  I. Cuthill,et al.  Improving Bioscience Research Reporting: The ARRIVE Guidelines for Reporting Animal Research † , 2012, Osteoarthritis and Cartilage.

[22]  Katie Lidster,et al.  Two Years Later: Journals Are Not Yet Enforcing the ARRIVE Guidelines on Reporting Standards for Pre-Clinical Animal Studies , 2014, PLoS biology.

[23]  Jennifer Couzin-Frankel,et al.  When mice mislead. , 2013, Science.

[24]  Jukka Corander,et al.  Improved Statistical Modeling of Tumor Growth and Treatment Effect in Preclinical Animal Studies with Highly Heterogeneous Responses In Vivo , 2012, Clinical Cancer Research.

[25]  Falk Hildebrand,et al.  Inflammation-associated enterotypes, host genotype, cage and inter-individual effects drive gut microbiota variation in common laboratory mice , 2013, Genome Biology.

[26]  J P Matts,et al.  Randomization in clinical trials: conclusions and recommendations. , 1988, Controlled clinical trials.

[27]  Brendan McCane,et al.  Distance functions for categorical and mixed variables , 2008, Pattern Recognit. Lett..

[28]  T. Treasure,et al.  Minimisation: the platinum standard for trials? , 1998, BMJ.

[29]  E. Sugar,et al.  Reporting of preclinical tumor-graft cancer therapeutic studies , 2012, Cancer biology & therapy.

[30]  D. Cressey.  UK funders demand strong statistics for animal studies , 2015, Nature.

[31]  S. Perrin.  Preclinical research: Make mouse studies work , 2014, Nature.

[32]  I. Cockburn,et al.  The Economics of Reproducibility in Preclinical Research , 2015, PLoS biology.

[33]  S. Reardon.  A mouse’s house may ruin experiments , 2016, Nature.

[34]  J. Gower.  A General Coefficient of Similarity and Some of Its Properties , 1971 .

[35]  A. Gelman,et al.  Data Analysis Using Regression and Multilevel/Hierarchical Models: Sample size and power calculations , 2006 .

[36]  V. Carey,et al.  Mixed-Effects Models in S and S-Plus , 2001 .

[37]  N. Schork,et al.  Generalized genomic distance-based regression methodology for multilocus association analysis. , 2006, American journal of human genetics.

[38]  James M. Eales,et al.  RIPOSTE: a framework for improving the design and analysis of laboratory-based research , 2015, eLife.

[39]  S. Lazic,et al.  A call for transparent reporting to optimize the predictive value of preclinical research , 2012, Nature.

[40]  G. Pond,et al.  Statistical issues in the use of dynamic allocation methods for balancing baseline covariates , 2011, British Journal of Cancer.

[41]  D. Howells,et al.  Can Animal Models of Disease Reliably Inform Human Studies? , 2010, PLoS medicine.

[42]  J. Grimshaw,et al.  Threats to Validity in the Design and Conduct of Preclinical Efficacy Studies: A Systematic Review of Guidelines for In Vivo Animal Experiments , 2013, PLoS medicine.

[43]  J. Eisen,et al.  Open Science and Reporting Animal Studies: Who's Accountable? , 2014, PLoS biology.

[44]  Dario A Gutierrez,et al.  What have we really learned about macrophage recruitment to adipose tissue? , 2014, Endocrinology.

[45]  S. Ebrahim,et al.  Data dredging, bias, or confounding , 2002, BMJ : British Medical Journal.

[46]  Tony R. Martinez,et al.  Improved Heterogeneous Distance Functions , 1996, J. Artif. Intell. Res..

[47]  C. Begley,et al.  Drug development: Raise standards for preclinical cancer research , 2012, Nature.

[48]  J. Geisler,et al.  IRINI: random group allocation of multiple prognostic factors. , 2011, Contemporary clinical trials.

[49]  Zheng Su.  Optimal Allocation of Prognostic Factors in Randomized Preclinical Animal Studies , 2011 .