Current Topics in Medicinal Chemistry - Volume 22, Issue 15, 2022

Unlike the oxetane ring, which, as evidenced by numerous studies, is known to play an increasingly important role in medicinal chemistry, the thietane ring has thus far received comparatively limited attention. Nonetheless, a growing number of reports now indicate that this 4- membered ring heterocycle may provide opportunities in analog design. In the present review article, we discuss the possible use and utility of the thietane fragment in medicinal chemistry and provide an overview of its properties and recent applications with a focus on isosteric replacements.

Attention-deficit hyperactivity disorder is a neurodevelopmental disorder diagnosed primarily in children, although it is also present in adults. Patients present inattention, impulsivity, and hyperactivity symptoms that create difficulties in their daily lives. Pharmacological treatment with stimulants or non-stimulants is used most commonly to reduce ADHD symptoms. Although generally effective and safe, pharmacological treatments have different effects among patients, including lack of response and adverse reactions. The reasons for these differences are not fully understood, but they may derive from the highly diverse etiology of ADHD. Strategies to guide optimal pharmacological treatment selection based on individual patients’ physiological markers are being developed. In this review, we describe the main pharmacological ADHD treatments used and their main drawbacks. We present alternatives under study that would allow the customization of pharmacological treatments to overcome these drawbacks and achieve more reliable improvement of ADHD symptoms.

Background: The therapeutic options for neurobehavioral disorders are still limited, and in many cases, they lack a satisfactory balance between efficacy and side effects. Objectives: This work aims to review current evidence regarding the potential contribution of psychedelics and hallucinogens to the discovery of new drugs for treating different psychiatric disorders. Discussion: Ayahuasca/N,N-dimethyltryptamine (DMT), lysergic acid diethylamide (LSD), and psilocybin have evidence supporting their use in depression, and psilocybin and ayahuasca have also shown good results in treatment-resistant depression. In randomized controlled trials (RCTs) conducted with anxious patients, there were symptomatic improvements with psilocybin and LSD. Psilocybin diminished Yale-Brown Obsessive Compulsive Scale (Y-BOCS) scores in a small obsessive- compulsive disorder (OCD) sample. The evidence is less robust regarding substance use disorders, but it suggests a possible role for LSD and psilocybin in alcohol use disorders and for psilocybin in tobacco addiction. In a clinical setting, these substances seem to be safe and well-tolerated. Their mechanisms of action are not fully elucidated, but there seems to be a preponderant role of 5-hydroxytryptamine (5HT) 2A agonism, as well as connectivity changes within the default mode network (DMN) and amygdala and some other molecular modifications. Conclusion: The studies underlying the conclusions have small samples and are heterogeneous in their methods. However, the results suggest that the use of psychedelics and hallucinogens could be considered in some disorders. More studies are needed to reinforce their evidence as potential new drugs.

Objectives: The present work reviews current evidence regarding the contribution of machine learning to the discovery of new drug targets. Methods: Scientific articles from PubMed, SCOPUS, EMBASE, and Web of Science Core Collection published until May 2021 were included in this review. Results: The most significant areas of research are schizophrenia, depression and anxiety, Alzheimer´s disease, and substance use disorders. ML techniques have pinpointed target gene candidates and pathways, new molecular substances, and several biomarkers regarding psychiatric disorders. Drug repositioning studies using ML have identified multiple drug candidates as promising therapeutic agents. Conclusion: Next-generation ML techniques and subsequent deep learning may power new findings regarding the discovery of new pharmacological agents by bridging the gap between biological data and chemical drug information.

A complex neurobiological network drives the sleep-wake cycle. In addition, external stimuli, including stimulants or depressor drugs, also influence the control of sleep. Here we review the recent advances that contribute to the comprehensive understanding of the actions of stimulants and depressor compounds, such as alcohol and cannabis, in sleep regulation. The objective of this review is to highlight the neurobiological mechanism engaged by alcohol and cannabis in sleep control.

The sleep-wake cycle is the result of the activity of multiple neurobiological network interactions. The dreaming feature is one interesting sleep on that represents sensorial components, mostly visual perceptions, accompaniedby intense emotions. Further complexity has been added to the topic of the neurobiological mechanism of dream generation by the current data suggesting drugs' influence on dream generation. Here, we discuss the review of some of the neurobiological mechanisms of the regulation of dream activity, with special emphasis on the effects of stimulants on dreaming.