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Introduction: Cocaine use disorder is a chronic disease with severe consequences and a high relapse rate. There is a critical need to explore the factors influencing relapse in order to achieve more efficient treatment outcomes. Furthermore, there is a great need for easy-to-measure, repeatable, and valid biomarkers that can predict treatment response or relapse. Methods: We reviewed the available literature on the Pubmed database concerning the biomarkers associated with relapse in CUD, including central nervous system-derived, genetic, immune, oxidative stress, and “other” biomarkers. Results: Fifty-one articles were included in our analysis. Twenty-five imaging brain anatomic and function assessment studies, mostly using fMRI, examined the role of several structures such as the striatum activity in abstinence prediction. There were fewer studies assessing the use of neuropsychological factors, neurotrophins, or genetic/genomic factors, immune system, or oxidative stress measures to predict abstinence. Conclusion: Several biomarkers have been shown to have predictive value. Prospective studies using combined multimodal assessments are now warranted.

Optimal classification of the response to lithium (Li) is crucial in genetic and biomarker research. This proof of concept study aims at exploring whether different approaches to phenotyping the response to Li may influence the likelihood of detecting associations between the response and genetic markers. We operationalized Li response phenotypes using the Retrospective Assessment of Response to Lithium Scale (i.e., the Alda scale) in a sample of 164 cases with bipolar disorder (BD). Three phenotypes were defined using the established approaches, whilst two phenotypes were generated by machine learning algorithms. We examined whether these five different Li response phenotypes showed different levels of statistically significant associations with polymorphisms of three candidate circadian genes (RORA, TIMELESS and PPARGC1A), which were selected for this study because they were plausibly linked with the response to Li. The three original and two revised Alda ratings showed low levels of discordance (misclassification rates: 8–12%). However, the significance of associations with circadian genes differed when examining previously recommended categorical and continuous phenotypes versus machine-learning derived phenotypes. Findings using machine learning approaches identified more putative signals of the Li response. Established approaches to Li response phenotyping are easy to use but may lead to a significant loss of data (excluding partial responders) due to recent attempts to improve the reliability of the original rating system. While machine learning approaches require additional modeling to generate Li response phenotypes, they may offer a more nuanced approach, which, in turn, would enhance the probability of identifying significant signals in genetic studies.