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Two-start design within a Sephadex inflammatory model – A means to generate reliable ED50data whilst significantly reducing the number of animals used

Pulmonary Pharmacology & Therapeutics
DOI: 10.1016/j.pupt.2012.03.001
  • Drug Discovery
  • Drug Optimization
  • Glucocorticoids
  • Pulmonary Inflammation
  • Rats
  • Sephadex
  • Biology
  • Design
  • Medicine


Abstract Pulmonary inflammation disorders represent a major healthcare burden, and novel anti-inflammatory agents are critically needed for the treatment of patients unresponsive to current therapies. In vivo animal models play a key role in the preclinical assessment of novel anti-inflammatory compounds. The implementation of streamlined in vivo experimental designs that are time-and cost-efficient, while keeping animal usage low, is a key consideration for drug optimization programs. The Sephadex rat model of pulmonary inflammation captures many pathophysiologic characteristics of clinical asthma and allergy, such as eosinophilic infiltration andinterstitial edema. Using the in vivo Sephadex model, we compared two different study designs that were implemented to screen and select two novel candidate drugs for a drug discovery project. The traditional one-start design, which utilizes few dose-testing groups with many animals per group, was used to select the first candidate drug. Due to tight timelines, the selection process for the second candidate drug had to be optimized, leading to the development of the novel two-start design, an approach whereby dose ranges are optimized in two experimental phases. Here we show that both study designs were comparable in their generation of robust median effective dose values for selected candidate drugs, as represented by similar confidence interval ratios. However, implementation of the two-start design resulted in approximately 50% fewer animals and 50% less time taken to assess the efficacy of an equal number of compounds compared with the one-start design. These results demonstrate that the two-start design is a more efficient experimental approach, and its widespread implementation in drug optimization programs will impact upon the selection process for candidate drugs with regards to time, cost, and animal usage.

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