Reverse Translation for Assessment of Confidence in Animal Models of Multiple Sclerosis for Drug Discovery

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Excerpt

Aging Western societies are facing an increasing prevalence of chronic inflammatory and degenerative disorders for which no adequate treatment exists. High investments by the drug development industry into preclinical research have produced a stream of new targets and sophisticated new therapies. However, only for a disappointingly few, estimated at less than 10% in some disease areas, have promising effects been observed in animal models that could be reproduced in the clinic.1 A main cause of the high attrition is the poor predictive validity (see Box 1) for clinical success of the animal models that are currently used in the pipeline selection of drug candidates. Frustration about the high frequency of costly failures has stimulated the search for novel human‐based in vitro technologies for preclinical research, including cell and organ culture systems (induced pluripotent stem cells, organ‐on‐a‐chip, 3D cultures) derived from patients or even direct research in patients.2
We believe that moving away from animal disease models will not be the solution, as most diseases are too complex for in vitro modeling. Moreover, pathological processes in vulnerable tissues, such as the human brain, cannot be directly examined in patients. A wiser approach may be to invest in the improvement of the predictive validity (see Box 1) of animal models by a critical analysis of the reasons why forward translation (see Box 1) of promising therapies from the animal model to the clinic failed (Figure1). We posit here that because of their close proximity to humans, disease models in nonhuman primates (NHPs) are especially useful in such a reverse translation (see Box 1) strategy, as the drug that failed in the clinic can be retested in the animal model. We will use examples from our own research, autoimmune‐mediated inflammatory disease (AIMID), to illustrate how important information gained from reverse translation analysis of failed and successful treatments can guide the improvement of an NHP model for MS.

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