Optimal adaptive designs for acute oral toxicity assessment |
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| Institution: | 1. Columbia, MO, USA;2. Baltimore, MD, USA;1. Student Research Committee, Department of Immunology, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran;2. Department of Immunology, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran;3. Immunoregulation Research Center, Shahed University, Tehran, Iran;4. Department of Immunology, Shahed University, Tehran, Iran;1. Xenon Pharmaceuticals, Burnaby, BC V5G 4W8, Canada;2. Genentech, South San Francisco, CA 94080, USA;1. The Department of Process Engineering, Stellenbosch University, Matieland 7602, South Africa;2. The Department of Electrical, Electronic and Computer Engineering, University of Pretoria, Pretoria 0002, South Africa;3. Anglo American Platinum Limited, Johannesburg, South Africa;1. Department of Chemistry, University of Leicester, LE1 7RH, UK;2. Chemical, Biological and Radiological Division, Dstl Porton Down, SP4 0JQ, UK;1. Faculty of Medicine, Memorial University of Newfoundland, 300 Prince Phillip Dr., St. John''s, NL A1B 3V6, Canada;2. School of Pharmacy, Memorial University of Newfoundland, 300 Prince Phillip Dr., St. John''s, NL A1B 3V6, Canada;3. Department of Dermatology, University of California San Francisco, N461 2340 Sutter Street, San Francisco, CA 94115, USA |
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| Abstract: | Acute oral toxicity studies are used to assess the toxicity to experimental animals of a single dose of the substance under investigation, assigning the substance to one of a number of classes. Animal welfare concerns have led to the development of three adaptive designs as alternatives to the traditional fixed sample design. These lead to reductions in the number of animals required in total and in the number exposed to lethal doses. In this paper, we show how designs can be constructed to optimise utility functions combining the need to classify correctly with the desire to use a small number of animals. Constrained optimal designs are also obtained in which no animal is exposed to a dose higher than that at which a death has been observed. The optimal designs lead to the correct classification with high probability whilst reducing the expected number of animal deaths relative to existing adaptive designs. |
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