Databases of ligand-binding pockets and protein-ligand interactions

Many research groups and institutions have created a variety of databases curating experimental and predicted data related to protein-ligand binding. The landscape of available databases is dynamic, with new databases emerging and established databases becoming defunct. Here, we review the current state of databases that contain binding pockets and protein-ligand binding interactions. We have compiled a list of such databases, fifty-three of which are currently available for use. We discuss variation in how binding pockets are defined and summarize pocket-finding methods. We organize the fifty-three databases into subgroups based on goals and contents, and describe standard use cases. We also illustrate that pockets within the same protein are characterized differently across different databases. Finally, we assess critical issues of sustainability, accessibility and redundancy.

Revealing Comprehensive Food Functionalities and Mechanisms of Action through Machine Learning

Foods possess a range of unexplored functionalities; however, fully identifying these functions through empirical means presents significant challenges. In this study, we have proposed an in silico approach to comprehensively predict the functionalities of foods, encompassing even processed foods. This prediction is accomplished through the utilization of machine learning on biomedical big data. Our focus revolves around disease-related protein pathways, wherein we statistically evaluate how the constituent compounds collaboratively regulate these pathways. The proposed method has been employed across 876 foods and 83 diseases, leading to an extensive revelation of both food functionalities and their underlying operational mechanisms. Additionally, this approach identifies food combinations that potentially affect molecular pathways based on interrelationships between food functions within disease-related pathways. Our proposed method holds potential for advancing preventive healthcare.

The atypical antipsychotics and sexual dysfunction: a pharmacovigilance-pharmacodynamic study

Background

Atypical antipsychotics (AAPs)-induced sexual dysfunction (SD) is a frequent issue in clinical practice, often underestimated by clinicians and not extensively researched. The current study aimed to quantify the strength of association between the use of different AAPs and SD using real-world data from the FDA Adverse Event Reporting System (FAERS), as well as investigate the receptor mechanisms that are involved.

Methods

Data from the FAERS database from the first quarter of 2004 to the third quarter of 2023 were queried through OpenVigil 2.1. Disproportionality analysis was estimated using the reporting odds ratio (ROR) and information component (IC) methods, and linear regression was used to investigate the relationship between ROR and receptor occupancy which was estimated using in vitro receptor binding profiles.

Results

Our analysis yielded 4839 reports that co-mentioned AAP and SD events, and the findings revealed statistical associations between 12 AAPs and SD. The highest signal value was identified for iloperidone reporting retrograde ejaculation with iloperidone (ROR = 832.09, ROR025 = 552.77; IC = 9.58, IC025 = 6.36), followed by compulsive sexual behavior with aripiprazole (ROR = 533.02, ROR025 = 435.90; IC = 7.30, IC025 = 5.97), and psychosexual disorder for aripiprazole (ROR = 145.80, ROR025 = 109.57; IC025 = 6.47, IC025 = 4.86). Different characteristics of the SD side effects in each AAPs were discovered after further data mining. Regression analysis revealed potential effects for receptor occupancy of D2, D3, and 5-HT1A receptors on ROR. However, no significant correlation persisted following sensitivity analyses.

Conclusion

This is the first study to investigate the AAP-SD associations by using FAERS. In this study, we report for the first time a significant association between aripiprazole and SD based on real-world data. The study suggests that different AAPs have varying levels of association with SD, and the D2, D3, and 5-HT1A receptor occupancy may contribute to potential mechanisms. The findings of this study warrant further validation of more studies and clinical causality assessment.

Psilocybin reduces heroin seeking behavior and modulates inflammatory gene expression in the nucleus accumbens and prefrontal cortex of male rats

Preclinical and human studies indicate psilocybin may reduce perseverant maladaptive behaviors, including nicotine and alcohol seeking. Such studies in the opioid field are lacking, though opioids are involved in more >50% of overdose deaths. Psilocybin is an agonist at the serotonin 2A receptor (5-HT2AR), a well-documented target for modulation of drug seeking, and evidence suggests 5-HT2AR agonists may dampen motivation for opioids. We sought to investigate the therapeutic efficacy of psilocybin in mediating cessation of opioid use and maintenance of long-lasting abstinence from opioid seeking behavior in a rat model of heroin self-administration (SA). Psilocybin or 5-HT2AR antagonists ketanserin and volinanserin were administered systemically to rats prior to SA of 0.075 mg/kg/infusion of heroin, or relapse following forced abstinence. Psilocybin did not alter heroin taking, but a single exposure to 3.0 mg/kg psilocybin 4-24 hours prior to a relapse test blunted cue-induced heroin seeking. Conversely, 5-HT2AR antagonists exacerbated heroin relapse. To begin to elucidate mechanisms of psilocybin, drug-naïve rats received psilocybin and/or ketanserin, and tissue was collected from the prefrontal cortex (PFC), a region critical for drug seeking and responsive to psilocybin, 24 hours later for RNA-sequencing. 3.0 mg/kg psilocybin regulated ~2-fold more genes in the PFC than 1.0 mg/kg, including genes involved in the cytoskeleton and cytokine signaling. Ketanserin blocked >90% of psilocybin-regulated genes, including the IL-17a cytokine receptor, Il17ra. Psychedelic compounds have reported anti-inflammatory properties, and therefore we performed a gene expression array to measure chemokine/cytokine molecules in the PFC of animals that displayed psilocybin-mediated inhibition of heroin seeking. Psilocybin regulated 4 genes, including Il17a, and a subset of genes correlated with relapse behavior. Selective inhibition of PFC IL-17a was sufficient to reduce heroin relapse. We conclude that psilocybin reduces heroin relapse and highlight IL-17a signaling as a potential downstream pathway of psilocybin that also reduces heroin seeking.

Opioid modulation of prefrontal cortex cells and circuits

Several neurochemical systems converge in the prefrontal cortex (PFC) to regulate cognitive and motivated behaviors. A rich network of endogenous opioid peptides and receptors spans multiple PFC cell types and circuits, and this extensive opioid system has emerged as a key substrate underlying reward, motivation, affective behaviors, and adaptations to stress. Here, we review the current evidence for dysregulated cortical opioid signaling in the pathogenesis of psychiatric disorders. We begin by providing an introduction to the basic anatomy and function of the cortical opioid system, followed by a discussion of endogenous and exogenous opioid modulation of PFC function at the behavioral, cellular, and synaptic level. Finally, we highlight the therapeutic potential of endogenous opioid targets in the treatment of psychiatric disorders, synthesizing clinical reports of altered opioid peptide and receptor expression and activity in human patients and summarizing new developments in opioid-based medications. This article is part of the Special Issue on "PFC circuit function in psychiatric disease and relevant models".

Genome-wide association analyses identify 95 risk loci and provide insights into the neurobiology of post-traumatic stress disorder

Post-traumatic stress disorder (PTSD) genetics are characterized by lower discoverability than most other psychiatric disorders. The contribution to biological understanding from previous genetic studies has thus been limited. We performed a multi-ancestry meta-analysis of genome-wide association studies across 1,222,882 individuals of European ancestry (137,136 cases) and 58,051 admixed individuals with African and Native American ancestry (13,624 cases). We identified 95 genome-wide significant loci (80 new). Convergent multi-omic approaches identified 43 potential causal genes, broadly classified as neurotransmitter and ion channel synaptic modulators (for example, GRIA1, GRM8 and CACNA1E), developmental, axon guidance and transcription factors (for example, FOXP2, EFNA5 and DCC), synaptic structure and function genes (for example, PCLO, NCAM1 and PDE4B) and endocrine or immune regulators (for example, ESR1, TRAF3 and TANK). Additional top genes influence stress, immune, fear and threat-related processes, previously hypothesized to underlie PTSD neurobiology. These findings strengthen our understanding of neurobiological systems relevant to PTSD pathophysiology, while also opening new areas for investigation.

Structural insights into the unexpected agonism of tetracyclic antidepressants at serotonin receptors 5-HT1eR and 5-HT1FR

Serotonin [5-hydroxytryptamine (5-HT)] acts via 13 different receptors in humans. Of these receptor subtypes, all but 5-HT1eR have confirmed roles in native tissue and are validated drug targets. Despite 5-HT1eR's therapeutic potential and plausible druggability, the mechanisms of its activation remain elusive. To illuminate 5-HT1eR's pharmacology in relation to the highly homologous 5-HT1FR, we screened a library of aminergic receptor ligands at both receptors and observe 5-HT1eR/5-HT1FR agonism by multicyclic drugs described as pan-antagonists at 5-HT receptors. Potent agonism by tetracyclic antidepressants mianserin, setiptiline, and mirtazapine suggests a mechanism for their clinically observed antimigraine properties. Using cryo-EM and mutagenesis studies, we uncover and characterize unique agonist-like binding poses of mianserin and setiptiline at 5-HT1eR distinct from similar drug scaffolds in inactive-state 5-HTR structures. Together with computational studies, our data suggest that these binding poses alongside receptor-specific allosteric coupling in 5-HT1eR and 5-HT1FR contribute to the agonist activity of these antidepressants.

Microdosing psychedelics and the risk of cardiac fibrosis and valvulopathy: Comparison to known cardiotoxins

Though microdosing psychedelics has become increasingly popular, its long-term effects on cardiac health remain unknown. Microdosing most commonly involves ingesting sub-threshold doses of lysergic acid diethylamide (LSD), psilocybin, or other psychedelic drugs 2-4 times a week for at least several weeks, but potentially months or years. Concerningly, both LSD and psilocybin share structural similarities with medications which raise the risk of cardiac fibrosis and valvulopathy when taken regularly, including methysergide, pergolide, and fenfluramine. 3,4-Methylenedioxymethamphetamine, which is also reportedly used for microdosing, is likewise associated with heart valve damage when taken chronically. In this review, we evaluate the evidence that microdosing LSD, psilocybin, and other psychedelics for several months or more could raise the risk of cardiac fibrosis. We discuss the relationship between drug-induced cardiac fibrosis and the 5-HT2B receptor, and we make recommendations for evaluating the safety of microdosing psychedelics in future studies.

DIRECTEUR: transcriptome-based prediction of small molecules that replace transcription factors for direct cell conversion

MOTIVATION

Direct reprogramming (DR) is a process that directly converts somatic cells to target cells. Although DR via small molecules is safer than using transcription factors (TFs) in terms of avoidance of tumorigenic risk, the determination of DR-inducing small molecules is challenging.

RESULTS

Here we present a novel in silico method, DIRECTEUR, to predict small molecules that replace TFs for DR. We extracted DR-characteristic genes using transcriptome profiles of cells in which DR was induced by TFs, and performed a variant of simulated annealing to explore small molecule combinations with similar gene expression patterns with DR-inducing TFs. We applied DIRECTEUR to predicting combinations of small molecules that convert fibroblasts into neurons or cardiomyocytes, and were able to reproduce experimentally verified and functionally related molecules inducing the corresponding conversions. The proposed method is expected to be useful for practical applications in regenerative medicine.

AVAILABILITY AND IMPLEMENTATION

The code and data are available at the following link: https://github.com/HamanoLaboratory/DIRECTEUR.git.

Standigm ASK™: knowledge graph and artificial intelligence platform applied to target discovery in idiopathic pulmonary fibrosis

Standigm ASK™ revolutionizes healthcare by addressing the critical challenge of identifying pivotal target genes in disease mechanisms-a fundamental aspect of drug development success. Standigm ASK™ integrates a unique combination of a heterogeneous knowledge graph (KG) database and an attention-based neural network model, providing interpretable subgraph evidence. Empowering users through an interactive interface, Standigm ASK™ facilitates the exploration of predicted results. Applying Standigm ASK™ to idiopathic pulmonary fibrosis (IPF), a complex lung disease, we focused on genes (AMFR, MDFIC and NR5A2) identified through KG evidence. In vitro experiments demonstrated their relevance, as TGFβ treatment induced gene expression changes associated with epithelial-mesenchymal transition characteristics. Gene knockdown reversed these changes, identifying AMFR, MDFIC and NR5A2 as potential therapeutic targets for IPF. In summary, Standigm ASK™ emerges as an innovative KG and artificial intelligence platform driving insights in drug target discovery, exemplified by the identification and validation of therapeutic targets for IPF.

Genetic architecture distinguishes tinnitus from hearing loss

Tinnitus is a heritable, highly prevalent auditory disorder treated by multiple medical specialties. Previous GWAS indicated high genetic correlations between tinnitus and hearing loss, with little indication of differentiating signals. We present a GWAS meta-analysis, triple previous sample sizes, and expand to non-European ancestries. GWAS in 596,905 Million Veteran Program subjects identified 39 tinnitus loci, and identified genes related to neuronal synapses and cochlear structural support. Applying state-of-the-art analytic tools, we confirm a large number of shared variants, but also a distinct genetic architecture of tinnitus, with higher polygenicity and large proportion of variants not shared with hearing difficulty. Tissue-expression analysis for tinnitus infers broad enrichment across most brain tissues, in contrast to hearing difficulty. Finally, tinnitus is not only correlated with hearing loss, but also with a spectrum of psychiatric disorders, providing potential new avenues for treatment. This study establishes tinnitus as a distinct disorder separate from hearing difficulties.

Network-Based Methods and Their Applications in Drug Discovery

Drug discovery is time-consuming, expensive, and predominantly follows the "one drug → one target → one disease" paradigm. With the rapid development of systems biology and network pharmacology, a novel drug discovery paradigm, "multidrug → multitarget → multidisease", has emerged. This new holistic paradigm of drug discovery aligns well with the essence of networks, leading to the emergence of network-based methods in the field of drug discovery. In this Perspective, we initially introduce the concept and data sources of networks and highlight classical methodologies employed in network-based methods. Subsequently, we focus on the practical applications of network-based methods across various areas of drug discovery, such as target prediction, virtual screening, prediction of drug therapeutic effects or adverse drug events, and elucidation of molecular mechanisms. In addition, we provide representative web servers for researchers to use network-based methods in specific applications. Finally, we discuss several challenges of network-based methods and the directions for future development. In a word, network-based methods could serve as powerful tools to accelerate drug discovery.

Uncovering the Oxidative Stress Mechanisms and Targets in Alzheimer's Disease by Integrating Phenotypic Screening Data and Polypharmacology Networks

Background

The oxidative stress hypothesis is challenging the dominant position of amyloid-β (Aβ) in the field of understanding the mechanisms of Alzheimer's disease (AD), a complicated and untreatable neurodegenerative disease.

Objective

The goal of the present study was to uncover the oxidative stress mechanisms causing AD, as well as the potential therapeutic targets and neuroprotective drugs against oxidative stress mechanisms.

Methods

In this study, a systematic workflow combining pharmacological experiments and computational prediction was proposed. 222 drugs and natural products were collected first and then tested on SH-SY5Y cells to obtain phenotypic screening data on neuroprotection. The preliminary screening data were integrated with drug-target interactions (DTIs) and multi-scale biomedical data, which were analyzed with statistical tests and gene set enrichment analysis. A polypharmacology network was further constructed for investigation.

Results

340 DTIs were matched in multiple databases, and 222 cell viability ratios were calculated for experimental compounds. We identified significant potential therapeutic targets based on oxidative stress mechanisms for AD, including NR3C1, SHBG, ESR1, PGR, and AVPR1A, which might be closely related to neuroprotective effects and pathogenesis. 50% of the top 14 enriched pathways were found to correlate with AD, such as arachidonic acid metabolism and neuroactive ligand-receptor interaction. Several approved drugs in this research were also found to exert neuroprotective effects against oxidative stress mechanisms, including beclometasone, methylprednisolone, and conivaptan.

Conclusion

Our results indicated that NR3C1, SHBG, ESR1, PGR, and AVPR1A were promising therapeutic targets and several drugs may be repurposed from the perspective of oxidative stress and AD.

Trajectories of sentiment in 11,816 psychoactive narratives

OBJECTIVE

Can machine learning (ML) enable data-driven discovery of how changes in sentiment correlate with different psychoactive experiences? We investigate by training models directly on text testimonials from a diverse 52-drug pharmacopeia.

METHODS

Using large language models (i.e. BERT) and 11,816 publicly-available testimonials, we predicted 28-dimensions of sentiment across each narrative, and then validated these predictions with adjudication by a clinical psychiatrist. BERT was then fine-tuned to predict biochemical and demographic information from these narratives. Lastly, canonical correlation analysis linked the drugs' receptor affinities with word usage, revealing 11 statistically-significant latent receptor-experience factors, each mapped to a 3D cortical Atlas.

RESULTS

These methods elucidate a neurobiologically-informed, sequence-sensitive portrait of drug-induced subjective experiences. The models' results converged, revealing a pervasive distinction between the universal psychedelic heights of feeling in contrast to the grim, mundane, and personal experiences of addiction and mental illness. Notably, MDMA was linked to "Love", DMT and 5-MeO-DMT to "Mystical Experiences" and "Entities and Beings", and other tryptamines to "Surprise", "Curiosity" and "Realization".

CONCLUSIONS

ML methods can create unified and robust quantifications of subjective experiences with many different psychoactive substances and timescales. The representations learned are evocative and mutually confirmatory, indicating great potential for ML in characterizing psychoactivity.

The 5-HT7 receptor system as a treatment target for mood and anxiety disorders: A systematic review

BACKGROUND

Preclinical animal and preliminary human studies indicate that 5-HT7 antagonists have the potential as a new treatment approach for mood and anxiety disorders. In this systematic review, we aimed to review the relationship between the 5-HT7 receptor system and mood and anxiety disorders, and to explore the pharmacology and therapeutic potential of medications that target the 5-HT7 receptor for their treatment.

METHODS

Medline, Cochrane Library, EMBASE, PsycINFO databases, the National Institute of Health website Clinicaltrials.gov, controlled-trials.com, and relevant grey literature were used to search for original research articles, and reference lists of included articles were then hand searched.

RESULTS

Sixty-four studies were included in the review: 52 animal studies and 12 human studies. Studies used a variety of preclinical paradigms and questionnaires to assess change in mood, and few studies examined sleep or cognition. Forty-four out of 47 (44/47) preclinical 5-HT7 modulation studies identified potential antidepressant effects and 20/23 studies identified potential anxiolytic effects. In clinical studies, 5/7 identified potential antidepressant effects in major depressive disorder, 1/2 identified potential anxiolytic effects in generalized anxiety disorder, and 3/3 identified potential antidepressant effects in bipolar disorders.

CONCLUSION

While there is some evidence that the 5-HT7 receptor system may be a potential target for treating mood and anxiety disorders, many agents included in the review also bind to other receptors. Further research is needed using drugs that bind specifically to 5-HT7 receptors to examine treatment proof of concept further.

Exploring DrugCentral: from molecular structures to clinical effects

DrugCentral, accessible at https://drugcentral.org , is an open-access online drug information repository. It covers over 4950 drugs, incorporating structural, physicochemical, and pharmacological details to support drug discovery, development, and repositioning. With around 20,000 bioactivity data points, manual curation enhances information from several major digital sources. Approximately 724 mechanism-of-action (MoA) targets offer updated drug target insights. The platform captures clinical data: over 14,300 on- and off-label uses, 27,000 contraindications, and around 340,000 adverse drug events from pharmacovigilance reports. DrugCentral encompasses information from molecular structures to marketed formulations, providing a comprehensive pharmaceutical reference. Users can easily navigate basic drug information and key features, making DrugCentral a versatile, unique resource. Furthermore, we present a use-case example where we utilize experimentally determined data from DrugCentral to support drug repurposing. A minimum activity threshold t should be considered against novel targets to repurpose a drug. Analyzing 1156 bioactivities for human MoA targets suggests a general threshold of 1 µM: t = 6 when expressed as - log[Activity(M)]). This applies to 87% of the drugs. Moreover, t can be refined empirically based on water solubility (S): t = 3 - logS, for logS < - 3. Alongside the drug repurposing classification scheme, which considers intellectual property rights, market exclusivity protections, and market accessibility, DrugCentral provides valuable data to prioritize candidates for drug repurposing programs efficiently.

Structural Insights into the Unexpected Agonism of Tetracyclic Antidepressants at Serotonin Receptors 5-HT1eR and 5-HT1FR

Serotonin (5-hydroxytryptamine, 5-HT) acts via 13 different receptors in humans. Of these receptor subtypes, all but 5-HT1eR have confirmed roles in native tissue and are validated drug targets. Despite 5-HT1eR's therapeutic potential and plausible druggability, the mechanisms of its activation remain elusive. To illuminate 5-HT1eR's pharmacology in relation to the highly homologous 5-HT1FR, we screened a library of aminergic receptor ligands at both receptors and observe 5-HT1e/1FR agonism by multicyclic drugs described as pan-antagonists at 5-HT receptors. Potent agonism by tetracyclic antidepressants mianserin, setiptiline, and mirtazapine suggests a mechanism for their clinically observed anti-migraine properties. Using cryoEM and mutagenesis studies, we uncover and characterize unique agonist-like binding poses of mianserin and setiptiline at 5-HT1eR distinct from similar drug scaffolds in inactive-state 5-HTR structures. Together with computational studies, our data suggest that these binding poses alongside receptor-specific allosteric coupling in 5-HT1eR and 5-HT1FR contribute to the agonist activity of these antidepressants.

GWAS Meta-Analysis of Suicide Attempt: Identification of 12 Genome-Wide Significant Loci and Implication of Genetic Risks for Specific Health Factors

OBJECTIVE

Suicidal behavior is heritable and is a major cause of death worldwide. Two large-scale genome-wide association studies (GWASs) recently discovered and cross-validated genome-wide significant (GWS) loci for suicide attempt (SA). The present study leveraged the genetic cohorts from both studies to conduct the largest GWAS meta-analysis of SA to date. Multi-ancestry and admixture-specific meta-analyses were conducted within groups of significant African, East Asian, and European ancestry admixtures.

METHODS

This study comprised 22 cohorts, including 43,871 SA cases and 915,025 ancestry-matched controls. Analytical methods across multi-ancestry and individual ancestry admixtures included inverse variance-weighted fixed-effects meta-analyses, followed by gene, gene-set, tissue-set, and drug-target enrichment, as well as summary-data-based Mendelian randomization with brain expression quantitative trait loci data, phenome-wide genetic correlation, and genetic causal proportion analyses.

RESULTS

Multi-ancestry and European ancestry admixture GWAS meta-analyses identified 12 risk loci at p values <5×10-8. These loci were mostly intergenic and implicated DRD2, SLC6A9, FURIN, NLGN1, SOX5, PDE4B, and CACNG2. The multi-ancestry SNP-based heritability estimate of SA was 5.7% on the liability scale (SE=0.003, p=5.7×10-80). Significant brain tissue gene expression and drug set enrichment were observed. There was shared genetic variation of SA with attention deficit hyperactivity disorder, smoking, and risk tolerance after conditioning SA on both major depressive disorder and posttraumatic stress disorder. Genetic causal proportion analyses implicated shared genetic risk for specific health factors.

CONCLUSIONS

This multi-ancestry analysis of suicide attempt identified several loci contributing to risk and establishes significant shared genetic covariation with clinical phenotypes. These findings provide insight into genetic factors associated with suicide attempt across ancestry admixture populations, in veteran and civilian populations, and in attempt versus death.

Discovery of 95 PTSD loci provides insight into genetic architecture and neurobiology of trauma and stress-related disorders

Posttraumatic stress disorder (PTSD) genetics are characterized by lower discoverability than most other psychiatric disorders. The contribution to biological understanding from previous genetic studies has thus been limited. We performed a multi-ancestry meta-analysis of genome-wide association studies across 1,222,882 individuals of European ancestry (137,136 cases) and 58,051 admixed individuals with African and Native American ancestry (13,624 cases). We identified 95 genome-wide significant loci (80 novel). Convergent multi-omic approaches identified 43 potential causal genes, broadly classified as neurotransmitter and ion channel synaptic modulators (e.g., GRIA1, GRM8, CACNA1E ), developmental, axon guidance, and transcription factors (e.g., FOXP2, EFNA5, DCC ), synaptic structure and function genes (e.g., PCLO, NCAM1, PDE4B ), and endocrine or immune regulators (e.g., ESR1, TRAF3, TANK ). Additional top genes influence stress, immune, fear, and threat-related processes, previously hypothesized to underlie PTSD neurobiology. These findings strengthen our understanding of neurobiological systems relevant to PTSD pathophysiology, while also opening new areas for investigation.

Mechanisms and molecular targets surrounding the potential therapeutic effects of psychedelics

Psychedelics, also known as classical hallucinogens, have been investigated for decades due to their potential therapeutic effects in the treatment of neuropsychiatric and substance use disorders. The results from clinical trials have shown promise for the use of psychedelics to alleviate symptoms of depression and anxiety, as well as to promote substantial decreases in the use of nicotine and alcohol. While these studies provide compelling evidence for the powerful subjective experience and prolonged therapeutic adaptations, the underlying molecular reasons for these robust and clinically meaningful improvements are still poorly understood. Preclinical studies assessing the targets and circuitry of the post-acute effects of classical psychedelics are ongoing. Current literature is split between a serotonin 5-HT2A receptor (5-HT2AR)-dependent or -independent signaling pathway, as researchers are attempting to harness the mechanisms behind the sustained post-acute therapeutically relevant effects. A combination of molecular, behavioral, and genetic techniques in neuropharmacology has begun to show promise for elucidating these mechanisms. As the field progresses, increasing evidence points towards the importance of the subjective experience induced by psychedelic-assisted therapy, but without further cross validation between clinical and preclinical research, the why behind the experience and its translational validity may be lost.

2B Determined: The Future of the Serotonin Receptor 2B in Drug Discovery

The cardiotoxicity associated with des-ethyl-dexfenfluramine (norDF) and related agonists of the serotonin receptor 2B (5-HT2B) has solidified the receptor's place as an "antitarget" in drug discovery. Conversely, a growing body of evidence has highlighted the utility of 5-HT2B antagonists for the treatment of pulmonary arterial hypertension (PAH), valvular heart disease (VHD), and related cardiopathies. In this Perspective, we summarize the link between the clinical failure of fenfluramine-phentermine (fen-phen) and the subsequent research on the role of 5-HT2B in disease progression, as well as the development of drug-like and receptor subtype-selective 5-HT2B antagonists. Such agents represent a promising class for the treatment of PAH and VHD, but their utility has been historically understudied due to the clinical disasters associated with 5-HT2B. Herein, it is our aim to examine the current state of 5-HT2B drug discovery, with an emphasis on the receptor's role in the central nervous system (CNS) versus the periphery.

G protein-coupled receptors in neurodegenerative diseases and psychiatric disorders

Neuropsychiatric disorders are multifactorial disorders with diverse aetiological factors. Identifying treatment targets is challenging because the diseases are resulting from heterogeneous biological, genetic, and environmental factors. Nevertheless, the increasing understanding of G protein-coupled receptor (GPCR) opens a new possibility in drug discovery. Harnessing our knowledge of molecular mechanisms and structural information of GPCRs will be advantageous for developing effective drugs. This review provides an overview of the role of GPCRs in various neurodegenerative and psychiatric diseases. Besides, we highlight the emerging opportunities of novel GPCR targets and address recent progress in GPCR drug development.

Investigation of Anti-Alzheimer's Mechanisms of Sarsasapogenin Derivatives by Network-Based Combining Structure-Based Methods

Alzheimer's disease (AD), a neurodegenerative disease with no cure, affects millions of people worldwide and has become one of the biggest healthcare challenges. Some investigated compounds play anti-AD roles at the cellular or the animal level, but their molecular mechanisms remain unclear. In this study, we designed a strategy combining network-based and structure-based methods together to identify targets for anti-AD sarsasapogenin derivatives (AAs). First, we collected drug-target interactions (DTIs) data from public databases, constructed a global DTI network, and generated drug-substructure associations. After network construction, network-based models were built for DTI prediction. The best bSDTNBI-FCFP_4 model was further used to predict DTIs for AAs. Second, a structure-based molecular docking method was employed for rescreening the prediction results to obtain more credible target proteins. Finally, in vitro experiments were conducted for validation of the predicted targets, and Nrf2 showed significant evidence as the target of anti-AD compound AA13. Moreover, we analyzed the potential mechanisms of AA13 for the treatment of AD. Generally, our combined strategy could be applied to other novel drugs or compounds and become a useful tool in identification of new targets and elucidation of disease mechanisms. Our model was deployed on our NetInfer web server (http://lmmd.ecust.edu.cn/netinfer/).

Investigation of the Entry Pathway and Molecular Nature of σ1 Receptor Ligands

The σ1 receptor (σ1-R) is an enigmatic endoplasmic reticulum resident transmembrane protein implicated in a variety of central nervous system disorders and whose agonists have neuroprotective activity. In spite of σ1-R’s physio-pathological and pharmacological importance, two of the most important features required to fully understand σ1-R function, namely the receptor endogenous ligand(s) and the molecular mechanism of ligand access to the binding site, have not yet been unequivocally determined. In this work, we performed molecular dynamics (MD) simulations to help clarify the potential route of access of ligand(s) to the σ1-R binding site, on which discordant results had been reported in the literature. Further, we combined computational and experimental procedures (i.e., virtual screening (VS), electron density map fitting and fluorescence titration experiments) to provide indications about the nature of σ1-R endogenous ligand(s). Our MD simulations on human σ1-R suggested that ligands access the binding site through a cavity that opens on the protein surface in contact with the membrane, in agreement with previous experimental studies on σ1-R from Xenopus laevis. Additionally, steroids were found to be among the preferred σ1-R ligands predicted by VS, and 16,17-didehydroprogesterone was shown by fluorescence titration to bind human σ1-R, with significantly higher affinity than the prototypic σ1-R ligand pridopidine in the same essay. These results support the hypothesis that steroids are among the most important physiological σ1-R ligands.

BEAR: A Novel Virtual Screening Method Based on Large-Scale Bioactivity Data

Data-driven drug discovery exploits a comprehensive set of big data to provide an efficient path for the development of new drugs. Currently, publicly available bioassay data sets provide extensive information regarding the bioactivity profiles of millions of compounds. Using these large-scale drug screening data sets, we developed a novel in silico method to virtually screen hit compounds against protein targets, named BEAR (Bioactive compound Enrichment by Assay Repositioning). The underlying idea of BEAR is to reuse bioassay data for predicting hit compounds for targets other than their originally intended purposes, i.e., "assay repositioning". The BEAR approach differs from conventional virtual screening methods in that (1) it relies solely on bioactivity data and requires no physicochemical features of either the target or ligand. (2) Accordingly, structurally diverse candidates are predicted, allowing for scaffold hopping. (3) BEAR shows stable performance across diverse target classes, suggesting its general applicability. Large-scale cross-validation of more than a thousand targets showed that BEAR accurately predicted known ligands (median area under the curve = 0.87), proving that BEAR maintained a robust performance even in the validation set with additional constraints. In addition, a comparative analysis demonstrated that BEAR outperformed other machine learning models, including a recent deep learning model for ABC transporter family targets. We predicted P-gp and BCRP dual inhibitors using the BEAR approach and validated the predicted candidates using in vitro assays. The intracellular accumulation effects of mitoxantrone, a well-known P-gp/BCRP dual substrate for cancer treatment, confirmed nine out of 72 dual inhibitor candidates preselected by primary cytotoxicity screening. Consequently, these nine hits are novel and potent dual inhibitors for both P-gp and BCRP, solely predicted by bioactivity profiles without relying on any structural information of targets or ligands.

Utility of preclinical models in the study of psilocybin - A comprehensive review

Interest in the therapeutic potential of psilocybin across a broad range of neuropsychiatric disorders is rapidly expanding. Despite promising clinical data and tremendous public enthusiasm, complimentary basic and translational studies - which are critical for advancing our understanding of psilocybin's biological effects and promoting innovation - have been relatively few. As with all work involving the study of complex neuropsychopharmacology, the search for deeper understanding of biological mechanisms, and the need for nuanced behavioral analyses in the context of both normal and diseased states, the roles for preclinical models are clear. A systematic search of the literature identified 57 articles involving the study of psilocybin in preclinical rodent models. A comprehensive review and thematic analysis identified 4 broad areas of investigation - pharmacology, toxicity, effects on disease models, and molecular mechanisms - with pharmacology studies accounting for the majority. Though these papers represent a still remarkably small body of literature, several important conclusions can already be drawn, and several areas of high priority for future work can be identified.

Aripiprazole: examining the clinical implications of D2 affinity

Schizophrenia is a prevalent psychiatric illness, which causes significant financial and social burden on the population overall. The development of second generation antipsychotics, such as Aripiprazole, Risperidone, and Paliperidone, has changed treatment practice for many psychiatrists. Aripiprazole has extremely high binding affinity for the dopamine D2 receptor, which is the receptor thought to be responsible for the antipsychotic effect, although Aripiprazole is not the most potent of the second generation antipsychotics. In theory, Aripiprazole could displace or outcompete other, more potent antipsychotics, prompting decreased antipsychotic effect. We describe a proposed case of this phenomenon, Ms. A. We describe how Aripiprazole may have caused a worsening of psychiatric symptoms by blocking the antipsychotic effects of Paliperidone due to its strong binding affinity for the D2 receptor. Aripiprazole has a high affinity for the D2 receptor, but may have a lesser reduction of psychotic symptoms compared to other antipsychotics. Prescribers should be aware of this potential interaction and carefully consider initiating long-acting injectable forms of Aripiprazole to avoid this phenomenon.

Revisiting PFA-mediated tissue fixation chemistry: FixEL enables trapping of small molecules in the brain to visualize their distribution changes

Various small molecules have been used as functional probes for tissue imaging in medical diagnosis and pharmaceutical drugs for disease treatment. The spatial distribution, target selectivity, and diffusion/excretion kinetics of small molecules in structurally complicated specimens are critical for function. However, robust methods for precisely evaluating these parameters in the brain have been limited. Herein, we report a new method termed "fixation-driven chemical cross-linking of exogenous ligands (FixEL)," which traps and images exogenously administered molecules of interest (MOIs) in complex tissues. This method relies on protein-MOI interactions and chemical cross-linking of amine-tethered MOI with paraformaldehyde used for perfusion fixation. FixEL is used to obtain images of the distribution of the small molecules, which addresses selective/nonselective binding to proteins, time-dependent localization changes, and diffusion/retention kinetics of MOIs such as the scaffold of PET tracer derivatives or drug-like small molecules.

Shared and Distinct Brain Regions Targeted for Immediate Early Gene Expression by Ketamine and Psilocybin

Psilocybin is a psychedelic with therapeutic potential. While there is growing evidence that psilocybin exerts its beneficial effects through enhancing neural plasticity, the exact brain regions involved are not completely understood. Determining the impact of psilocybin on plasticity-related gene expression throughout the brain can broaden our understanding of the neural circuits involved in psychedelic-evoked neural plasticity. In this study, whole-brain serial two-photon microscopy and light sheet microscopy were employed to map the expression of the immediate early gene, c-Fos, in male and female mice. The drug-induced c-Fos expression following psilocybin administration was compared to that of subanesthetic ketamine and saline control. Psilocybin and ketamine produced acutely comparable elevations in c-Fos expression in numerous brain regions, including anterior cingulate cortex, locus coeruleus, primary visual cortex, central and basolateral amygdala, medial and lateral habenula, and claustrum. Select regions exhibited drug-preferential differences, such as dorsal raphe and insular cortex for psilocybin and the CA1 subfield of hippocampus for ketamine. To gain insights into the contributions of receptors and cell types, the c-Fos expression maps were related to brain-wide in situ hybridization data. The transcript analyses showed that the endogenous levels of Grin2a and Grin2b predict whether a cortical region is sensitive to drug-evoked neural plasticity for both ketamine and psilocybin. Collectively, the systematic mapping approach produced an unbiased list of brain regions impacted by psilocybin and ketamine. The data are a resource that highlights previously underappreciated regions for future investigations. Furthermore, the robust relationships between drug-evoked c-Fos expression and endogenous transcript distributions suggest glutamatergic receptors as a potential convergent target for how psilocybin and ketamine produce their rapid-acting and long-lasting therapeutic effects.

Probing the antidepressant potential of psilocybin: integrating insight from human research and animal models towards an understanding of neural circuit mechanisms

Interest in the therapeutic potential of serotonergic psychedelic compounds including psilocybin has surged in recent years. While human clinical research suggests psilocybin holds promise as a rapid and long-lasting antidepressant, little is known about how its acute mechanisms of action mediate enduring alterations in cognition and behavior. Human neuroimaging studies point to both acute and sustained modulation of functional connectivity in key cortically dependent brain networks. Emerging evidence in preclinical models highlights the importance of psilocybin-induced neuroplasticity and alterations in the prefrontal cortex (PFC). Overviewing research in both humans and preclinical models suggests avenues to increase crosstalk between fields. We review how acute modulation of PFC circuits may contribute to long-term structural and functional alterations to mediate antidepressant effects. We highlight the potential for preclinical circuit and behavioral neuroscience approaches to provide basic mechanistic insight into how psilocybin modulates cognitive and affective neural circuits to support further development of psilocybin as a promising new treatment for depression.

Therapeutic Implications of microRNAs in Depressive Disorders: A Review

MicroRNAs are hidden players in complex psychophysical phenomena such as depression and anxiety related disorders though the activation and deactivation of multiple proteins in signaling cascades. Depression is classified as a mood disorder and described as feelings of sadness, loss, or anger that interfere with a person’s everyday activities. In this review, we have focused on exploration of the significant role of miRNAs in depression by affecting associated target proteins (cellular and synaptic) and their signaling pathways which can be controlled by the attachment of miRNAs at transcriptional and translational levels. Moreover, miRNAs have potential role as biomarkers and may help to cure depression through involvement and interactions with multiple pharmacological and physiological therapies. Taken together, miRNAs might be considered as promising novel therapy targets themselves and may interfere with currently available antidepressant treatments.

The dilemma of polypharmacy in psychosis: is it worth combining partial and full dopamine modulation?

Antipsychotic polypharmacy in psychotic disorders is widespread despite international guidelines favoring monotherapy. Previous evidence indicates the utility of low-dose partial dopamine agonist (PDAs) add-ons to mitigate antipsychotic-induced metabolic adverse effects or hyperprolactinemia. However, clinicians are often concerned about using PDAs combined with high-potency, full dopaminergic antagonists (FDAs) due to the risk of psychosis relapse. We, therefore, conducted a literature review to find studies investigating the effects of combined treatment with PDAs (i.e. aripiprazole, cariprazine and brexpiprazole) and FDAs having a strong D 2 receptor binding affinity. Twenty studies examining the combination aripiprazole - high-potency FDAs were included, while no study was available on combinations with cariprazine or brexpiprazole. Studies reporting clinical improvement suggested that this may require a relatively long time (~11 weeks), while studies that found symptom worsening observed this happening in a shorter timeframe (~3 weeks). Patients with longer illness duration who received add-on aripiprazole on ongoing FDA monotherapy may be at greater risk for symptomatologic worsening. Especially in these cases, close clinical monitoring is therefore recommended during the first few weeks of combined treatment. These indications may be beneficial to psychiatrists who consider using this treatment strategy. Well-powered randomized clinical trials are needed to derive more solid clinical recommendations.

Visualizing classification of drugs used in psychotic disorders: A 'subway map' representing mechanisms, established classes and informal categories

Drugs used to treat psychotic disorders ('antipsychotics') have been widely used in psychiatry since the introduction of chlorpromazine in the mid-1950s. The categorization of these drugs evolved in a piecemeal way, relying initially on grouping by chemical structure (e.g. phenothiazines, butyrophenones), then by epoch of introduction (e.g. first generation ('conventional') vs second generation ('atypical')). As psychopharmacological expertise has advanced, it has become possible to quantify affinities for each drug in this class for relevant receptors including dopamine D2, 5HT2A, 5HT2C, histamine H1 and others. However, until the recent emergence of a new generation of agents known collectively as dopamine D2 receptor partial agonists (e.g. aripiprazole, brexpiprazole and cariprazine), there had been little reference in drug classification to specific pharmacological properties. An overview of data on receptor affinities across multiple drugs and receptor types would permit categorization according to binding affinities and putative pharmacological mechanisms. In this paper, we have attempted to construct a 'subway map' of 32 drugs used for treatment of psychotic disorders. This design allows a visualization of both the historical classifications by structure and epoch of introduction, and of the binding affinities for key receptors based on appraisal of scientific literature. The map represents a step towards categorization by mechanism, allowing prescribers and patients to understand which drugs share common biological features and the extent to which drugs may have similarities and differences in their mechanisms. In addition, this approach may encourage more logical groupings of drugs to be used in systematic reviews and meta-analyses.

Profiling prediction of nuclear receptor modulators with multi-task deep learning methods: toward the virtual screening

Nuclear receptors (NRs) are ligand-activated transcription factors, which constitute one of the most important targets for drug discovery. Current computational strategies mainly focus on a single target, and the transfer of learned knowledge among NRs was not considered yet. Herein we proposed a novel computational framework named NR-Profiler for prediction of potential NR modulators with high affinity and specificity. First, we built a comprehensive NR data set including 42 684 interactions to connect 42 NRs and 31 033 compounds. Then, we used multi-task deep neural network and multi-task graph convolutional neural network architectures to construct multi-task multi-classification models. To improve the predictive capability and robustness, we built a consensus model with an area under the receiver operating characteristic curve (AUC) = 0.883. Compared with conventional machine learning and structure-based approaches, the consensus model showed better performance in external validation. Using this consensus model, we demonstrated the practical value of NR-Profiler in virtual screening for NRs. In addition, we designed a selectivity score to quantitatively measure the specificity of NR modulators. Finally, we developed a freely available standalone software for users to make profiling predictions for their compounds of interest. In summary, our NR-Profiler provides a useful tool for NR-profiling prediction and is expected to facilitate NR-based drug discovery.

The chemical induction of synaesthesia

OBJECTIVE

Preliminary research suggests that experiences resembling synaesthesia are frequently reported under the influence of a diverse range of chemical substances although the incidence, chemical specificity, and characteristics of these effects are poorly understood.

METHODS

Here we surveyed recreational drug users and self-reported developmental synaesthetes regarding their use of 28 psychoactive drugs from 12 different drug classes and whether they had experienced synaesthesia under the influence of these substances.

RESULTS

The drug class of tryptamines exhibited the highest incidence rates of drug-induced synaesthesia in controls and induction rates of novel forms of synaesthesia in developmental synaesthetes. Induction incidence rates in controls were strongly correlated with the corresponding induction and enhancement rates in developmental synaesthetes. In addition, the use of lysergic acid diethylamide (LSD) was the strongest predictor of drug-induced synaesthesia in both controls and developmental synaesthetes. Clear evidence was observed for a clustering of synaesthesia-induction rates as a function of drug class in both groups, denoting non-random incidence rates within drug classes. Sound-colour synaesthesia was the most commonly observed type of induced synaesthesia. Further analyses suggest the presence of synaesthesia-prone individuals, who were more likely to experience drug-induced synaesthesia with multiple drugs.

CONCLUSIONS

These data corroborate the hypothesized link between drug-induced synaesthesia and serotoninergic activity, but also suggest the possibility of alternative neurochemical pathways involved in the induction of synaesthesia. They further imply that the induction and modulation of synaesthesia in controls and developmental synaesthetes share overlapping mechanisms and that certain individuals may be more susceptible to experiencing induced synaesthesia with different drugs.

Investigation of the mechanism of Shen Qi Wan prescription in the treatment of T2DM via network pharmacology and molecular docking

Shen Qi Wan (SQW) prescription has been used to treat type 2 diabetes mellitus (T2DM) for thousands of years, but its pharmacological mechanism is still unclear. The network pharmacology method was used to reveal the potential pharmacological mechanism of SQW in the treatment of T2DM in this study. Nine core targets were identified through protein-protein interaction (PPI) network analysis and KEGG pathway enrichment analysis, which were AKT1, INSR, SLC2A1, EGFR, PPARG, PPARA, GCK, NOS3, and PTPN1. Besides, this study found that SQW treated the T2DM through insulin resistance (has04931), insulin signaling pathway (has04910), adipocytokine signaling pathway (has04920), AMPK signaling pathway (has04152) and FoxO signaling pathway (has04068) via ingredient-hub target-pathway network analysis. Finally, molecular docking was used to verify the drug-target interaction network in this research. This study provides a certain explanation for treating T2DM by SQW prescription, and provides a certain angle and method for researchers to study the mechanism of TCM in the treatment of complex diseases.

Supplementary information

The online version contains supplementary material available at 10.1007/s40203-022-00124-2.

A narrative synthesis of research with 5-MeO-DMT

BACKGROUND

5-Methoxy-N,N-dimethyltryptamine (5-MeO-DMT) is a naturally occurring, short-acting psychedelic tryptamine, produced by a variety of plant and animal species. Plants containing 5-MeO-DMT have been used throughout history for ritual and spiritual purposes. The aim of this article is to review the available literature about 5-MeO-DMT and inform subsequent clinical development.

METHODS

We searched PubMed database for articles about 5-MeO-DMT. Search results were cross-checked against earlier reviews and reference lists were hand searched. Findings were synthesised using a narrative synthesis approach. This review covers the pharmacology, chemistry and metabolism of 5-MeO-DMT, as well epidemiological studies, and reported adverse and beneficial effects.

RESULTS

5-MeO-DMT is serotonergic agonist, with highest affinity for 5-HT1A receptors. It was studied in a variety of animal models, but clinical studies with humans are lacking. Epidemiological studies indicate that, like other psychedelics, 5-MeO-DMT induces profound alterations in consciousness (including mystical experiences), with potential beneficial long-term effects on mental health and well-being.

CONCLUSION

5-MeO-DMT is a potentially useful addition to the psychedelic pharmacopoeia because of its short duration of action, relative lack of visual effects and putatively higher rates of ego-dissolution and mystical experiences. We conclude that further clinical exploration is warranted, using similar precautions as with other classic psychedelics.

The GG genotype of the serotonin 4 receptor genetic polymorphism, rs1345697, is associated with lower remission rates after antidepressant treatment: Findings from the METADAP cohort

BACKGROUND

Pharmacological studies have yielded valuable insights into the role of the serotonin 4 receptor (HTR4) in major depressive episodes (MDE) and response to antidepressant drugs (AD). A genetic association has been shown between HTR4 and susceptibility to mood disorders. Our study aims at assessing the association between the HTR4 genetic polymorphism, rs1345697, and improvement in depressive symptoms and remission after antidepressant treatment in MDE patients.

METHODS

492 depressed patients from the METADAP cohort were treated prospectively for 6 months with ADs. The clinical outcomes according to HTR4 rs1345697 were compared after 1 (M1), 3 (M3), and 6 (M6) months of treatment. Mixed-effects logistic regression and adjusted linear models assessed the association between rs1345697 and 17-item Hamilton Depression Rating Scale (HDRS) score improvement and response/remission.

RESULTS

Over the 6 months of treatment, mixed-effects regressions showed lower improvements in HDRS scores (Coefficient=1.52; Confident Interval (CI) 95% [0.37-2.67]; p = 0.009) and lower remission rates (Odds Ratio=2.0; CI95% [1.0-4.1]; p = 0.05) in GG homozygous patients as compared to allele A carriers.

LIMITATIONS

The major limitations of our study are the uncertainty of the rs1345697 effect on HTR4 function, the substantial drop-out rate, and the fact that analysis is not based on randomization between polymorphism groups.

CONCLUSIONS

In our study, patients who were homozygous carriers of the variant G of the HTR4 rs1345697 had lower depressive symptoms improvement and 2-fold lower remission rates after antidepressant treatment as compared to allele A carriers. Randomization study should be done to confirm these results.

wSDTNBI: a novel network-based inference method for virtual screening

In recent years, the rapid development of network-based methods for the prediction of drug-target interactions (DTIs) provides an opportunity for the emergence of a new type of virtual screening (VS), namely, network-based VS. Herein, we reported a novel network-based inference method named wSDTNBI. Compared with previous network-based methods that use unweighted DTI networks, wSDTNBI uses weighted DTI networks whose edge weights are correlated with binding affinities. A two-pronged approach based on weighted DTI and drug-substructure association networks was employed to calculate prediction scores. To show the practical value of wSDTNBI, we performed network-based VS on retinoid-related orphan receptor γt (RORγt), and purchased 72 compounds for experimental validation. Seven of the purchased compounds were confirmed to be novel RORγt inverse agonists by in vitro experiments, including ursonic acid and oleanonic acid with IC50 values of 10 nM and 0.28 μM, respectively. Moreover, the direct contact between ursonic acid and RORγt was confirmed using the X-ray crystal structure, and in vivo experiments demonstrated that ursonic acid and oleanonic acid have therapeutic effects on multiple sclerosis. These results indicate that wSDTNBI might be a powerful tool for network-based VS in drug discovery.

The IUPHAR/BPS guide to PHARMACOLOGY in 2022: curating pharmacology for COVID-19, malaria and antibacterials

The IUPHAR/BPS Guide to PHARMACOLOGY (GtoPdb; www.guidetopharmacology.org) is an open-access, expert-curated database of molecular interactions between ligands and their targets. We describe expansion in content over nine database releases made during the last two years, which has focussed on three main areas of infection. The COVID-19 pandemic continues to have a major impact on health worldwide. GtoPdb has sought to support the wider research community to understand the pharmacology of emerging drug targets for SARS-CoV-2 as well as potential targets in the host to block viral entry and reduce the adverse effects of infection in patients with COVID-19. We describe how the database rapidly evolved to include a new family of Coronavirus proteins. Malaria remains a global threat to half the population of the world. Our database content continues to be enhanced through our collaboration with Medicines for Malaria Venture (MMV) on the IUPHAR/MMV Guide to MALARIA PHARMACOLOGY (www.guidetomalariapharmacology.org). Antibiotic resistance is also a growing threat to global health. In response, we have extended our coverage of antibacterials in partnership with AntibioticDB.

Machine learning approaches for drug combination therapies

Drug combination therapy is a promising strategy to treat complex diseases such as cancer and infectious diseases. However, current knowledge of drug combination therapies, especially in cancer patients, is limited because of adverse drug effects, toxicity and cell line heterogeneity. Screening new drug combinations requires substantial efforts since considering all possible combinations between drugs is infeasible and expensive. Therefore, building computational approaches, particularly machine learning methods, could provide an effective strategy to overcome drug resistance and improve therapeutic efficacy. In this review, we group the state-of-the-art machine learning approaches to analyze personalized drug combination therapies into three categories and discuss each method in each category. We also present a short description of relevant databases used as a benchmark in drug combination therapies and provide a list of well-known, publicly available interactive data analysis portals. We highlight the importance of data integration on the identification of drug combinations. Finally, we address the advantages of combining multiple data sources on drug combination analysis by showing an experimental comparison.

Assessment of antidepressant-like effects of dextromethorphan on differential reinforcement of low-rate 72-s performance in rats

The effectiveness of ketamine for treatment-resistant depression along with several other clinical advantages, such as rapid onset and reduced adverse effects associated with serotonin transporter inhibition, has garnered interest in other similar acting psychedelics as novel antidepressant drugs. The antitussive dextromethorphan exhibits glutamate N-methyl-d-aspartate receptor antagonism, sigma-1 receptor agonism, and serotonin reuptake inhibition, which has exhibited antidepressant effects in limited human studies and animal models. The present study sought to further examine dextromethorphan using a differential reinforcement of low-rate 72-s schedule, which can be used to screen antidepressant drugs, in male and female rats. The tricyclic antidepressant drug imipramine and the psychostimulant d-amphetamine also were examined. Sex differences were not shown for baseline performance or for the drugs tested. Further, performance did not differ between the estrus and diestrus stages. Dextromethorphan alone and with quinidine produced an antidepressant-like effect by reducing the number of responses emitted, increasing the number of reinforcers earned, and shifting inter-response times to the right, although significant response suppression occurred at these doses. An antidepressant-like effect was shown with imipramine, but d-amphetamine increased the number of responses emitted and did not affect the number of reinforcers earned. The present findings provide additional support for antidepressant effects produced by dextromethorphan.

Rediscovering Psilocybin as an Antidepressive Treatment Strategy

There has recently been a renewal of interest in psychedelic research on the use of psilocybin in psychiatric treatment and, in particular, for the treatment of major depressive disorder (MDD). Several state-of-the-art studies have provided new insight into the mechanisms of action of psilocybin and its therapeutic potential. Nevertheless, many questions remain unanswered. With this review, we provide an overview of the current state of research on the potential mechanisms of psilocybin, its antidepressant potential, and the associated risks and adverse effects, to provide an update on a controversial topic discussed in psychopharmacology. A database search was conducted in Medline including articles on psilocybin over the period of the last 20 years. Despite the promising progress in understanding the mechanisms of psilocybin, the exact antidepressive mechanism and the role of the psychedelic experience remain elusive. The studies included in this review found high treatment effect sizes for psilocybin as an antidepressant. However, the results must be regarded as preliminary due to several limitations. Although the current studies observed no severe adverse events, several questions regarding safety and utility remain and must be subject of future research.

Revisiting the Molecular Interactions between the Tumor Protein TCTP and the Drugs Sertraline/Thioridazine

TCTP protein is a pharmacological target in cancer and TCTP inhibitors such as sertraline have been evaluated in clinical trials. The direct interaction of TCTP with the drugs sertraline and thioridazine has been reported in vitro by SPR experiments to be in the ∼30-50 μM K_d range (Amson et al. Nature Med 2012), supporting a TCTP-dependent mode of action of the drugs on tumor cells. However, the molecular details of the interaction remain elusive although they are crucial to improve the efforts of on-going medicinal chemistry. In addition, TCTP can be phosphorylated by the Plk-1 kinase, which is indicative of poor prognosis in several cancers. The impact of phosphorylation on TCTP structure/dynamics and binding with therapeutical ligands remains unexplored. Here, we combined NMR, TSA, SPR, BLI and ITC techniques to probe the molecular interactions between TCTP with the drugs sertraline and thioridazine. We reveal that drug binding is much weaker than reported with an apparent ∼mM K_d and leads to protein destabilization that obscured the analysis of the published SPR data. We further demonstrate by NMR and SAXS that TCTP S46 phosphorylation does not promote tighter interaction between TCTP and sertraline. Accordingly, we question the supported model in which sertraline and thioridazine directly interact with isolated TCTP in tumor cells and discuss alternative modes of action for the drugs in light of current literature.

Loxapine in patient with clozapine-resistant psychosis

Clozapine is recognized as the drug of choice for treatment-refractory schizophrenia, but use may be limited because of strict monitoring requirements and adverse effects including severe neutropenia, seizures, and myocarditis. Loxapine is a first-generation antipsychotic with similarities to clozapine in both structure and receptor binding. This case describes a 57-year-old male with a history of severe paranoid schizophrenia despite treatment with clozapine and other psychotropic agents, who experienced clinical improvement after a cross titration from clozapine to loxapine. Loxapine may be a reasonable alternative in patients with treatment-refractory schizophrenia who do not tolerate or respond to clozapine.

Genome-wide association study of more than 40,000 bipolar disorder cases provides new insights into the underlying biology

Bipolar disorder is a heritable mental illness with complex etiology. We performed a genome-wide association study of 41,917 bipolar disorder cases and 371,549 controls of European ancestry, which identified 64 associated genomic loci. Bipolar disorder risk alleles were enriched in genes in synaptic signaling pathways and brain-expressed genes, particularly those with high specificity of expression in neurons of the prefrontal cortex and hippocampus. Significant signal enrichment was found in genes encoding targets of antipsychotics, calcium channel blockers, antiepileptics and anesthetics. Integrating expression quantitative trait locus data implicated 15 genes robustly linked to bipolar disorder via gene expression, encoding druggable targets such as HTR6, MCHR1, DCLK3 and FURIN. Analyses of bipolar disorder subtypes indicated high but imperfect genetic correlation between bipolar disorder type I and II and identified additional associated loci. Together, these results advance our understanding of the biological etiology of bipolar disorder, identify novel therapeutic leads and prioritize genes for functional follow-up studies.

Discovery of Natural Products Targeting NQO1 via an Approach Combining Network-Based Inference and Identification of Privileged Substructures

NAD(P)H:quinone oxidoreductase 1 (NQO1) has been shown to be a potential therapeutic target for various human diseases, such as cancer and neurodegenerative disorders. Recent advances in computational methods, especially network-based methods, have made it possible to identify novel compounds for a target with high efficiency and low cost. In this study, we designed a workflow combining network-based methods and identification of privileged substructures to discover new compounds targeting NQO1 from a natural product library. According to the prediction results, we purchased 56 compounds for experimental validation. Without the assistance of privileged substructures, 31 compounds (31/56 = 55.4%) showed IC₅₀ < 100 μM, and 11 compounds (11/56 = 19.6%) showed IC₅₀ < 10 μM. With the assistance of privileged substructures, the two success rates were increased to 61.8 and 26.5%, respectively. Seven natural products further showed inhibitory activity on NQO1 at the cellular level, which may serve as lead compounds for further development. Moreover, network analysis revealed that osthole may exert anticancer effects against multiple cancer types by inhibiting not only carbonic anhydrases IX and XII but also NQO1. Our workflow and computational methods can be easily applied in other targets and become useful tools in drug discovery and development.

A computational method for drug sensitivity prediction of cancer cell lines based on various molecular information

Determining sensitive drugs for a patient is one of the most critical problems in precision medicine. Using genomic profiles of the tumor and drug information can help in tailoring the most efficient treatment for a patient. In this paper, we proposed a classification machine learning approach that predicts the sensitive/resistant drugs for a cell line. It can be performed by using both drug and cell line similarities, one of the cell line or drug similarities, or even not using any similarity information. This paper investigates the influence of using previously defined as well as two newly introduced similarities on predicting anti-cancer drug sensitivity. The proposed method uses max concentration thresholds for assigning drug responses to class labels. Its performance was evaluated using stratified five-fold cross-validation on cell line-drug pairs in two datasets. Assessing the predictive powers of the proposed model and three sets of methods, including state-of-the-art classification methods, state-of-the-art regression methods, and off-the-shelf classification machine learning approaches shows that the proposed method outperforms other methods. Moreover, The efficiency of the model is evaluated in tissue-specific conditions. Besides, the novel sensitive associations predicted by this model were verified by several supportive evidence in the literature and reliable database. Therefore, the proposed model can efficiently be used in predicting anti-cancer drug sensitivity. Material and implementation are available at https://github.com/fahmadimoughari/CDSML.