Harnessing associative learning paradigms to optimize drug treatment

Abstract

Continuous treatment with drugs is an inevitable prerequisite for many clinical conditions, such as chronic inflammatory diseases, pain, or depression. However, the amount of adverse side effects induced by opioids, antidepressants, or immunosuppressive drugs urges the need for developing alternative or supportive treatment strategies. In this context, conditioned pharmacological effects, obtained by means of associative learning, have been successfully implemented as controlled drug-dose reduction strategies to maintain and strengthen the efficacy of medical treatments. Such approaches have been proven effective in experimental animals, healthy subjects, and patient populations. Thus, a systematic use of conditioned pharmacological effects should be seriously considered as a supportive treatment option to optimize pharmacological treatment effects for the patients benefit.

Keywords: Pavlovian conditioned drug effects; associative learning; extinction; placebo; treatment optimization.