<p>Science faces a reproducibility crisis, and public trust in science declines when large clinical trials, which had been qualified by promising preclinical studies, fail. While some clinical trial designs may have been inadequate, preclinical assessments of disease interventions might have lacked key elements of rigor such as treatment concealment, randomization, blinded outcomes, prespecified and adequate sample sizes, and models including comorbidities. Here, to demonstrate feasibility and practicality of enhanced rigor in preclinical assessment, we designed a six-laboratory network that implemented rigorous study elements, using acute ischemic stroke for demonstration. This network enrolled 2,615 rodents in 5 different models and implemented a multistage, multiarm statistical design that sequentially eliminated candidate interventions during interim analyses. The methods included centralized intervention packaging, randomization, data quality assessment and data archiving. Blinded analysis of 9,274 video-recorded behavioral tasks and 3,652 magnetic resonance images were evaluated. All tools and protocols are presented and could be adapted to preclinical assessment in other disease areas.</p>

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Methods for randomized, blinded, controlled evaluation of putative disease interventions in multilaboratory, preclinical assessment networks

  • Jessica Lamb,
  • Karisma Nagarkatti,
  • Marcio A. Diniz,
  • Ryan Cabeen,
  • Monica Estrada,
  • Karen L. Crawford,
  • Andre Rogatko,
  • Sungjin Kim,
  • Cenk Ayata,
  • David C. Hess,
  • Mohammad Badruzzaman Khan,
  • Rakesh B. Patel,
  • Mariia Kumskova,
  • Enrique C. Leira,
  • Anil K. Chauhan,
  • Kazi Akhter,
  • Ken Arai,
  • Ali Arbab,
  • Jaroslaw Aronowski,
  • Brooklyn Avery,
  • Hannah Beatty,
  • Adnan Bibic,
  • Frank Blixt,
  • Ligia Boisserand,
  • Marian Cabrera-Ayala,
  • Suyi Cao,
  • Anjali Chauhan,
  • Valina Dawson,
  • Kris Dhandapani,
  • Nirav Dhanesha,
  • Sebastian Diaz,
  • Taylan Erodogan,
  • Andrew Goh,
  • Ali Herman,
  • Shuning Huang,
  • Fahmeed Hyder,
  • Takahiko Imai,
  • Emma Imakavar,
  • Xuyan Jin,
  • Conor Johnson,
  • Pradip Kamat,
  • Senthilkumar Karuppagounder,
  • Raymond Koehler,
  • Ewa Kulikowicz,
  • Javier Labastida,
  • Steven Lannon,
  • Siyue Li,
  • Eng Lo,
  • Joe Mandeville,
  • Michael Maniskas,
  • Louise McCullough,
  • Andreia Morais,
  • Diego Motales-Scheihing,
  • Steward Niefert,
  • Mohammad Nisar,
  • Lidiya Obertas,
  • Tao Qin,
  • Basavaraju G. Sanganahalli,
  • Lauren Sansing,
  • Yanrong Shi,
  • Shahneela Siddiqui,
  • Cameron Smith,
  • Guanghua Sun,
  • Brijesh Sutariya,
  • W. Taylor Kimberly,
  • Dan Thedens,
  • Shun-Mug Ting,
  • Klaus van Leyen,
  • Peter Van Zijl,
  • Sofia Velazquez,
  • Jun Wang,
  • Nicholas Wilder,
  • Kristofer Wood,
  • Jiadi Xu,
  • Lili Yu,
  • Steve Zeiler,
  • Patrick Lyden

摘要

Science faces a reproducibility crisis, and public trust in science declines when large clinical trials, which had been qualified by promising preclinical studies, fail. While some clinical trial designs may have been inadequate, preclinical assessments of disease interventions might have lacked key elements of rigor such as treatment concealment, randomization, blinded outcomes, prespecified and adequate sample sizes, and models including comorbidities. Here, to demonstrate feasibility and practicality of enhanced rigor in preclinical assessment, we designed a six-laboratory network that implemented rigorous study elements, using acute ischemic stroke for demonstration. This network enrolled 2,615 rodents in 5 different models and implemented a multistage, multiarm statistical design that sequentially eliminated candidate interventions during interim analyses. The methods included centralized intervention packaging, randomization, data quality assessment and data archiving. Blinded analysis of 9,274 video-recorded behavioral tasks and 3,652 magnetic resonance images were evaluated. All tools and protocols are presented and could be adapted to preclinical assessment in other disease areas.