利用联想学习范式优化药物治疗。

Harnessing associative learning paradigms to optimize drug treatment.

作者信息

Hadamitzky Martin, Schedlowski Manfred

机构信息

Institute of Medical Psychology and Behavioral Immunobiology, Center for Translational Neuro- and Behavioral Sciences (C-TNBS), University Hospital Essen, University of Duisburg-, Essen, Germany.

Institute of Medical Psychology and Behavioral Immunobiology, Center for Translational Neuro- and Behavioral Sciences (C-TNBS), University Hospital Essen, University of Duisburg-, Essen, Germany; Department of Clinical Neuroscience, Osher Center for Integrative Medicine, Karolinska Institutet, 171 77 Stockholm, Sweden.

出版信息

Trends Pharmacol Sci. 2022 Jun;43(6):464-472. doi: 10.1016/j.tips.2022.03.002. Epub 2022 Mar 31.

DOI:10.1016/j.tips.2022.03.002

PMID:35369993

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.

摘要

对于许多临床病症，如慢性炎症性疾病、疼痛或抑郁症，持续药物治疗是不可避免的前提条件。然而，阿片类药物、抗抑郁药或免疫抑制药物所引发的大量副作用促使人们需要开发替代或辅助治疗策略。在此背景下，通过联想学习获得的条件性药理效应已成功作为可控药物剂量减少策略得以实施，以维持和增强药物治疗的疗效。此类方法已在实验动物、健康受试者及患者群体中被证明有效。因此，应认真考虑系统运用条件性药理效应作为辅助治疗选择，以优化药物治疗效果，造福患者。