The role of neuropeptides in drug and ethanol abuse: Medication targets for drug and alcohol use disorders

Neurotensin and Alcohol Use Disorders: Towards a Pharmacological Treatment

Harmful alcohol use is responsible for a group of disorders collectively named alcohol use disorders (AUDs), according to the DSM-5 classification. The damage induced by alcohol depends on the amount, time, and consumption patterns (continuous and heavy episodic drinking). It affects individual global well-being and social and familial environments with variable impact. Alcohol addiction manifests with different degrees of organ and mental health detriment for the individual, exhibiting two main traits: compulsive drinking and negative emotional states occurring at withdrawal, frequently causing relapse episodes. Numerous individual and living conditions, including the concomitant use of other psychoactive substances, lie in the complexity of AUD. Ethanol and its metabolites directly impact the tissues and may cause local damage or alter the homeostasis of brain neurotransmission, immunity scaffolding, or cell repair biochemical pathways. Brain modulator and neurotransmitter-assembled neurocircuitries govern reward, reinforcement, social interaction, and consumption of alcohol behaviors in an intertwined manner. Experimental evidence supports the participation of neurotensin (NT) in preclinical models of alcohol addiction. For example, NT neurons in the central nucleus of the amygdala projecting to the parabrachial nucleus strengthen alcohol consumption and preference. In addition, the levels of NT in the frontal cortex were found to be lower in rats bred to prefer alcohol to water in a free alcohol-water choice compared to wild-type animals. NT receptors 1 and 2 seem to be involved in alcohol consumption and alcohol effects in several models of knockout mice. This review aims to present an updated picture of the role of NT systems in alcohol addiction and the possible use of nonpeptide ligands modulating the activity of the NT system, applied to experimental animal models of harmful drinking behavior mimicking alcohol addiction leading to health ruin in humans.

NeuroPred-FRL: an interpretable prediction model for identifying neuropeptide using feature representation learning

Neuropeptides (NPs) are the most versatile neurotransmitters in the immune systems that regulate various central anxious hormones. An efficient and effective bioinformatics tool for rapid and accurate large-scale identification of NPs is critical in immunoinformatics, which is indispensable for basic research and drug development. Although a few NP prediction tools have been developed, it is mandatory to improve their NPs' prediction performances. In this study, we have developed a machine learning-based meta-predictor called NeuroPred-FRL by employing the feature representation learning approach. First, we generated 66 optimal baseline models by employing 11 different encodings, six different classifiers and a two-step feature selection approach. The predicted probability scores of NPs based on the 66 baseline models were combined to be deemed as the input feature vector. Second, in order to enhance the feature representation ability, we applied the two-step feature selection approach to optimize the 66-D probability feature vector and then inputted the optimal one into a random forest classifier for the final meta-model (NeuroPred-FRL) construction. Benchmarking experiments based on both cross-validation and independent tests indicate that the NeuroPred-FRL achieves a superior prediction performance of NPs compared with the other state-of-the-art predictors. We believe that the proposed NeuroPred-FRL can serve as a powerful tool for large-scale identification of NPs, facilitating the characterization of their functional mechanisms and expediting their applications in clinical therapy. Moreover, we interpreted some model mechanisms of NeuroPred-FRL by leveraging the robust SHapley Additive exPlanation algorithm.