There is growing concern about the poor quality and lack of repeatability of many pre-clinical experiments involving laboratory animals. According to one estimate as much as $28 billion is wasted annually in the USA alone in such studies. A decade ago the FDA's "Critical path" white paper noted that "The traditional tools used to assess product safety-animal toxicology and outcomes from human studies-have changed little over many decades and have largely not benefited from recent gains in scientific knowledge. The inability to better assess and predict product safety leads to failures during clinical development and, occasionally, after marketing." Repeat-dose 28-days and 90-days toxicity tests in rodents have been widely used as part of a strategy to assess the safety of drugs and chemicals but their repeatability and power to detect adverse effects have not been formally evaluated.The guidelines (OECD TG 407 and 408) for these tests specify the dose levels and number of animals per dose but do not specify the strain of animals which should be used. In practice, almost all the tests are done using genetically undefined "albino" rats or mice in which the genetic variation, a major cause of inter-individual and strain variability, is unknown and uncontrolled. This chapter suggests that a better strategy would be to use small numbers of animals of several genetically defined strains of mice or rats instead of the undefined animals used at present. Inbred strains are more stable providing more repeatable data than outbred stocks. Importantly their greater phenotypic uniformity should lead to more powerful and repeatable tests. Any observed strain differences would indicate genetic variation in response to the test substance, providing key data. We suggest that the FDA and other regulators and funding organizations should support research to evaluate this alternative.
Keywords: Drug development; Experimental design; Factorial experimental designs; Inbred strains; Preclinical development; Signal/noise ratio; Statistical analysis; Statistics; Toxicity testing.