, Volume 23, Issue 4, pp 265-280
Date: 23 Aug 2012

Drug-Induced Hypersensitivity

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Abstract

Drug-induced hypersensitivity is an adverse reaction, characterised by damaging immune-mediated responses, initiated by medicine given at therapeutic doses for prevention, diagnosis or treatment. Immune-mediated drug hypersensitivity accounts for 6–10% of the adverse drug reactions, which rank between the fourth and sixth leading causes of death in the US. With <10% of all adverse drug reactions reported, the magnitude of the problem is significant, with estimates of costs >$US30 billion annually in the US (1995 value). In addition, the costs of not determining the potential of a drug to produce hypersensitivity in the pre-clinical phase of drug development can be substantial. It has been estimated that the pre-clinical phase and clinical phase I, phase II and phase III costs are approximately $US6 million, $US12 million, $US12 million and $US100 million per drug, respectively (1999 values). It is important that investigational drugs with the potential to produce hypersensitivity reactions be identified as early in the development process as possible. Some adverse reactions to drugs can be avoided if drug-drug interactions are known or if there is a structure-activity relationship established. However, these methods are inadequate. Appropriate animal models of drug-induced hypersensitivity are needed, especially because hypersensitivity has been cited as the leading reason for taking drugs off the market.

It is of critical importance to be able to predict hypersensitivity reactions to drugs. Most anaphylactic reactions occur in atopic individuals. Similarly, patients who have experienced other hypersensitivity reactions are more likely to have recurrent reactions. Therefore, animal models should be considered that predispose the animal to the reaction, such as the use of appropriate adjuvants and species. Using known positive controls of varying strengths, the investigator can rank the reaction against the positive controls as standards. This approach might yield greater results in a shorter period of time than using novel models. For the greatest safety, use of well understood models that have been thoroughly validated is imperative.