Predicting new molecular targets for known drugs

Polypharmacology prediction: the long road toward comprehensively anticipating small-molecule selectivity to de-risk drug discovery

INTRODUCTION

Small molecules often bind to multiple targets, a behavior termed polypharmacology. Anticipating polypharmacology is essential for drug discovery since unknown off-targets can modulate safety and efficacy - profoundly affecting drug discovery success. Unfortunately, experimental methods to assess selectivity present significant limitations and drugs still fail in the clinic due to unanticipated off-targets. Computational methods are a cost-effective, complementary approach to predict polypharmacology.

AREAS COVERED

This review aims to provide a comprehensive overview of the state of polypharmacology prediction and discuss its strengths and limitations, covering both classical cheminformatics methods and bioinformatic approaches. The authors review available data sources, paying close attention to their different coverage. The authors then discuss major algorithms grouped by the types of data that they exploit using selected examples.

EXPERT OPINION

Polypharmacology prediction has made impressive progress over the last decades and contributed to identify many off-targets. However, data incompleteness currently limits most approaches to comprehensively predict selectivity. Moreover, our limited agreement on model assessment challenges the identification of the best algorithms - which at present show modest performance in prospective real-world applications. Despite these limitations, the exponential increase of multidisciplinary Big Data and AI hold much potential to better polypharmacology prediction and de-risk drug discovery.

Discovering Potential in Non-Cancer Medications: A Promising Breakthrough for Multiple Myeloma Patients

MM is a common type of cancer that unfortunately leads to a significant number of deaths each year. The majority of the reported MM cases are detected in the advanced stages, posing significant challenges for treatment. Additionally, all MM patients eventually develop resistance or experience relapse; therefore, advances in treatment are needed. However, developing new anti-cancer drugs, especially for MM, requires significant financial investment and a lengthy development process. The study of drug repurposing involves exploring the potential of existing drugs for new therapeutic uses. This can significantly reduce both time and costs, which are typically a major concern for MM patients. The utilization of pre-existing non-cancer drugs for various myeloma treatments presents a highly efficient and cost-effective strategy, considering their prior preclinical and clinical development. The drugs have shown promising potential in targeting key pathways associated with MM progression and resistance. Thalidomide exemplifies the success that can be achieved through this strategy. This review delves into the current trends, the challenges faced by conventional therapies for MM, and the importance of repurposing drugs for MM. This review highlights a noncomprehensive list of conventional therapies that have potentially significant anti-myeloma properties and anti-neoplastic effects. Additionally, we offer valuable insights into the resources that can help streamline and accelerate drug repurposing efforts in the field of MM.

Computational Frontiers in Aptamer-Based Nanomedicine for Precision Therapeutics: A Comprehensive Review

In the rapidly evolving landscape of nanomedicine, aptamers have emerged as powerful molecular tools, demonstrating immense potential in targeted therapeutics, diagnostics, and drug delivery systems. This paper explores the computational features of aptamers in nanomedicine, highlighting their advantages over antibodies, including selectivity, low immunogenicity, and a simple production process. A comprehensive overview of the aptamer development process, specifically the Systematic Evolution of Ligands by Exponential Enrichment (SELEX) process, sheds light on the intricate methodologies behind aptamer selection. The historical evolution of aptamers and their diverse applications in nanomedicine are discussed, emphasizing their pivotal role in targeted drug delivery, precision medicine and therapeutics. Furthermore, we explore the integration of artificial intelligence (AI), machine learning (ML), Internet of Things (IoT), Internet of Medical Things (IoMT), and nanotechnology in aptameric development, illustrating how these cutting-edge technologies are revolutionizing the selection and optimization of aptamers for tailored biomedical applications. This paper also discusses challenges in computational methods for advancing aptamers, including reliable prediction models, extensive data analysis, and multiomics data incorporation. It also addresses ethical concerns and restrictions related to AI and IoT use in aptamer research. The paper examines progress in computer simulations for nanomedicine. By elucidating the importance of aptamers, understanding their superiority over antibodies, and exploring the historical context and challenges, this review serves as a valuable resource for researchers and practitioners aiming to harness the full potential of aptamers in the rapidly evolving field of nanomedicine.

Predicting therapeutic and side effects from drug binding affinities to human proteome structures

Evaluation of the binding affinities of drugs to proteins is a crucial process for identifying drug pharmacological actions, but it requires three dimensional structures of proteins. Herein, we propose novel computational methods to predict the therapeutic indications and side effects of drug candidate compounds from the binding affinities to human protein structures on a proteome-wide scale. Large-scale docking simulations were performed for 7,582 drugs with 19,135 protein structures revealed by AlphaFold (including experimentally unresolved proteins), and machine learning models on the proteome-wide binding affinity score (PBAS) profiles were constructed. We demonstrated the usefulness of the method for predicting the therapeutic indications for 559 diseases and side effects for 285 toxicities. The method enabled to predict drug indications for which the related protein structures had not been experimentally determined and to successfully extract proteins eliciting the side effects. The proposed method will be useful in various applications in drug discovery.

Topological regression as an interpretable and efficient tool for quantitative structure-activity relationship modeling

Quantitative structure-activity relationship (QSAR) modeling is a powerful tool for drug discovery, yet the lack of interpretability of commonly used QSAR models hinders their application in molecular design. We propose a similarity-based regression framework, topological regression (TR), that offers a statistically grounded, computationally fast, and interpretable technique to predict drug responses. We compare the predictive performance of TR on 530 ChEMBL human target activity datasets against the predictive performance of deep-learning-based QSAR models. Our results suggest that our sparse TR model can achieve equal, if not better, performance than the deep learning-based QSAR models and provide better intuitive interpretation by extracting an approximate isometry between the chemical space of the drugs and their activity space.

Indigenous wisdom of a Kwatha to treat NASH: An insight into the mechanism

ETHNOPHARMACOLOGICAL RELEVANCE

Nonalcoholic fatty liver disease (NAFLD) is the most common severe liver disease globally, progressing further into nonalcoholic steatohepatitis (NASH) and hepatocellular carcinoma (HCC). Vasaguduchyadi Kwatha (VK) is an Ayurvedic formulation traditionally used to treat liver diseases and other metabolic complications. This study is an ethnopharmacological approach to unravel this indigenous remedy.

AIM OF THE STUDY

We aimed to discover the probable mechanism of action of VK against NASH in this study, using network pharmacology, molecular docking, in vitro study, and preclinical investigation.

METHODS AND RESULTS

Among the 55 components identified, 10 were confirmed based on mass, elution charecteristics, MS/MS analysis data, and fragmentation rules. Computational study indicated 92 targets involved in the central pathways of NASH, out of which only 15 targets and 9 VK constituents have significant docking scores. In vitro and in vivo analysis results showed that VK significantly reduces weight gain and improves insulin sensitivity, dyslipidemia, steatohepatitis and overall histological features of NASH compared to saroglitazar (SGZR).

CONCLUSION

Our detailed study yielded three signalling pathways related to NASH on which VK has maximum effect, bringing up a probable alternative treatment for NASH.

Drug repurposing for parasitic protozoan diseases

Protozoan parasites are major hazards to human health, society, and the economy, especially in equatorial regions of the globe. Parasitic diseases, including leishmaniasis, malaria, and others, contribute towards majority of morbidity and mortality. Around 1.1 million people die from these diseases annually. The lack of licensed vaccinations worsens the worldwide impact of these diseases, highlighting the importance of safe and effective medications for their prevention and treatment. However, the appearance of drug resistance in parasites continuously affects the availability of medications. The demand for novel drugs motivates global antiparasitic drug discovery research, necessitating the implementation of many innovative ways to maintain a continuous supply of promising molecules. Drug repurposing has come out as a compelling tool for drug development, offering a cost-effective and efficient alternative to standard de novo approaches. A thorough examination of drug repositioning candidates revealed that certain drugs may not benefit significantly from their original indications. Still, they may exhibit more pronounced effects in other disorders. Furthermore, certain medications can produce a synergistic effect, resulting in enhanced therapeutic effectiveness when given together. In this chapter, we outline the approaches employed in drug repurposing (sometimes referred to as drug repositioning), propose novel strategies to overcome these hurdles and fully exploit the promise of drug repurposing. We highlight a few major human protozoan diseases and a range of exemplary drugs repurposed for various protozoan infections, providing excellent outcomes for each disease.

DReAmocracy: A Method to Capitalise on Prior Drug Discovery Efforts to Highlight Candidate Drugs for Repurposing

In the area of drug research, several computational drug repurposing studies have highlighted candidate repurposed drugs, as well as clinical trial studies that have tested/are testing drugs in different phases. To the best of our knowledge, the aggregation of the proposed lists of drugs by previous studies has not been extensively exploited towards generating a dynamic reference matrix with enhanced resolution. To fill this knowledge gap, we performed weight-modulated majority voting of the modes of action, initial indications and targeted pathways of the drugs in a well-known repository, namely the Drug Repurposing Hub. Our method, DReAmocracy, exploits this pile of information and creates frequency tables and, finally, a disease suitability score for each drug from the selected library. As a testbed, we applied this method to a group of neurodegenerative diseases (Alzheimer's, Parkinson's, Huntington's disease and Multiple Sclerosis). A super-reference table with drug suitability scores has been created for all four neurodegenerative diseases and can be queried for any drug candidate against them. Top-scored drugs for Alzheimer's Disease include agomelatine, mirtazapine and vortioxetine; for Parkinson's Disease, they include apomorphine, pramipexole and lisuride; for Huntington's, they include chlorpromazine, fluphenazine and perphenazine; and for Multiple Sclerosis, they include zonisamide, disopyramide and priralfimide. Overall, DReAmocracy is a methodology that focuses on leveraging the existing drug-related experimental and/or computational knowledge rather than a predictive model for drug repurposing, offering a quantified aggregation of existing drug discovery results to (1) reveal trends in selected tracks of drug discovery research with increased resolution that includes modes of action, targeted pathways and initial indications for the investigated drugs and (2) score new candidate drugs for repurposing against a selected disease.

Drug repurposing for regenerative medicine and cosmetics: Scientific, technological and economic issues

Regenerative medicine and cosmetics are currently two outstanding fields for drug discovery. Although many pharmaceutical products for regenerative medicine and cosmetics have received approval by official agencies, several challenges are still needed to overcome, especially financial and time issues. As a result, drug repositioning, which is the usage of previously approved drugs for new treatment, stands out as a promising approach to tackle these problems. Recently, increasing scientific evidence is collected to demonstrate the applicability of this novel method in the field of regenerative medicine and cosmetics. Experts in drug development have also taken advantage of novel technologies to discover new candidates for repositioning purposes following computational approach, one of two main approaches of drug repositioning. Therefore, numerous repurposed candidates have obtained approval to enter the market and have witnessed financial success such as minoxidil and fingolimod. The benefits of drug repositioning are undeniable for regenerative medicine and cosmetics. However, some aspects still need to be carefully considered regarding this method including actual effectiveness during clinical trials, patent regulations, data integration and analysis, publicly unavailable databases as well as environmental concerns and more effort are required to overcome these obstacles.

Testing the predictive power of reverse screening to infer drug targets, with the help of machine learning

Estimating protein targets of compounds based on the similarity principle-similar molecules are likely to show comparable bioactivity-is a long-standing strategy in drug research. Having previously quantified this principle, we present here a large-scale evaluation of its predictive power for inferring macromolecular targets by reverse screening an unprecedented vast external test set of more than 300,000 active small molecules against another bioactivity set of more than 500,000 compounds. We show that machine-learning can predict the correct targets, with the highest probability among 2069 proteins, for more than 51% of the external molecules. The strong enrichment thus obtained demonstrates its usefulness in supporting phenotypic screens, polypharmacology, or repurposing. Moreover, we quantified the impact of the bioactivity knowledge available for proteins in terms of number and diversity of actives. Finally, we advise that developers of such approaches follow an application-oriented benchmarking strategy and use large, high-quality, non-overlapping datasets as provided here.

DeepARV: ensemble deep learning to predict drug-drug interaction of clinical relevance with antiretroviral therapy

Drug-drug interaction (DDI) may result in clinical toxicity or treatment failure of antiretroviral therapy (ARV) or comedications. Despite the high number of possible drug combinations, only a limited number of clinical DDI studies are conducted. Computational prediction of DDIs could provide key evidence for the rational management of complex therapies. Our study aimed to assess the potential of deep learning approaches to predict DDIs of clinical relevance between ARVs and comedications. DDI severity grading between 30,142 drug pairs was extracted from the Liverpool HIV Drug Interaction database. Two feature construction techniques were employed: 1) drug similarity profiles by comparing Morgan fingerprints, and 2) embeddings from SMILES of each drug via ChemBERTa, a transformer-based model. We developed DeepARV-Sim and DeepARV-ChemBERTa to predict four categories of DDI: i) Red: drugs should not be co-administered, ii) Amber: interaction of potential clinical relevance manageable by monitoring/dose adjustment, iii) Yellow: interaction of weak relevance and iv) Green: no expected interaction. The imbalance in the distribution of DDI severity grades was addressed by undersampling and applying ensemble learning. DeepARV-Sim and DeepARV-ChemBERTa predicted clinically relevant DDI between ARVs and comedications with a weighted mean balanced accuracy of 0.729 ± 0.012 and 0.776 ± 0.011, respectively. DeepARV-Sim and DeepARV-ChemBERTa have the potential to leverage molecular structures associated with DDI risks and reduce DDI class imbalance, effectively increasing the predictive ability on clinically relevant DDIs. This approach could be developed for identifying high-risk pairing of drugs, enhancing the screening process, and targeting DDIs to study in clinical drug development.

PT-Finder: A multi-modal neural network approach to target identification

Efficient target identification for bioactive compounds, including novel synthetic analogs, is crucial for accelerating the drug discovery pipeline. However, the process of target identification presents significant challenges and is often expensive, which in turn can hinder the drug discovery efforts. To address these challenges machine learning applications have arisen as a promising approach for predicting the targets for novel chemical compounds. These methods allow the exploration of ligand-target interactions, uncovering of biochemical mechanisms, and the investigation of drug repurposing. Typically, the current target identification tools rely on assessing ligand structural similarities. Herein, a multi-modal neural network model was built using a library of proteins, their respective sequences, and active inhibitors. Subsequent validations showed the model to possess accuracy of 82 % and MPRAUC of 0.80. Leveraging the trained model, we developed PT-Finder (Protein Target Finder), a user-friendly offline application that is capable of predicting the target proteins for hundreds of compounds within a few seconds. This combination of offline operation, speed, and accuracy positions PT-Finder as a powerful tool to accelerate drug discovery workflows. PT-Finder and its source codes have been made freely accessible for download at https://github.com/PT-Finder/PT-Finder.

Neurochemical and Neurophysiological Effects of Intravenous Administration of N,N-dimethyltryptamine in Rats

N,N-dimethyltryptamine (DMT) is a serotonergic psychedelic that is being investigated clinically for the treatment of psychiatric disorders. Although the neurophysiological effects of DMT in humans are well-characterized, similar studies in animal models as well as data on the neurochemical effects of DMT are generally lacking, which are critical for mechanistic understanding. In the current study, we combined behavioral analysis, high-density (32-channel) electroencephalography, and ultra-high-performance liquid chromatography-tandem mass spectrometry to simultaneously quantify changes in behavior, cortical neural dynamics, and levels of 17 neurochemicals in medial prefrontal and somatosensory cortices before, during, and after intravenous administration of three different doses of DMT (0.75 mg/kg, 3.75 mg/kg, 7.5 mg/kg) in male and female adult rats. All three doses of DMT produced head twitch response with most twitches observed after the low dose. DMT caused dose-dependent increases in serotonin and dopamine levels in both cortical sites along with a reduction in EEG spectral power in theta (4-10 Hz) and low gamma (25-55 Hz), and increase in power in delta (1-4 Hz), medium gamma (65-115), and high gamma (125-155 Hz) bands. Functional connectivity decreased in the delta band and increased across the gamma bands. In addition, we provide the first measurements of endogenous DMT in these cortical sites at levels comparable to serotonin and dopamine, which together with a previous study in occipital cortex, suggests a physiological role for endogenous DMT. This study represents one of the most comprehensive characterizations of psychedelic drug action in rats and the first to be conducted with DMT.

AI is a viable alternative to high throughput screening: a 318-target study

High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery.

Application of artificial intelligence and machine learning in drug repurposing

The purpose of drug repurposing is to leverage previously approved drugs for a particular disease indication and apply them to another disease. It can be seen as a faster and more cost-effective approach to drug discovery and a powerful tool for achieving precision medicine. In addition, drug repurposing can be used to identify therapeutic candidates for rare diseases and phenotypic conditions with limited information on disease biology. Machine learning and artificial intelligence (AI) methodologies have enabled the construction of effective, data-driven repurposing pipelines by integrating and analyzing large-scale biomedical data. Recent technological advances, especially in heterogeneous network mining and natural language processing, have opened up exciting new opportunities and analytical strategies for drug repurposing. In this review, we first introduce the challenges in repurposing approaches and highlight some success stories, including those during the COVID-19 pandemic. Next, we review some existing computational frameworks in the literature, organized on the basis of the type of biomedical input data analyzed and the computational algorithms involved. In conclusion, we outline some exciting new directions that drug repurposing research may take, as pioneered by the generative AI revolution.

Orphan G protein-coupled receptors: the ongoing search for a home

G protein-coupled receptors (GPCRs) make up the largest receptor superfamily, accounting for 4% of protein-coding genes. Despite the prevalence of such transmembrane receptors, a significant number remain orphans, lacking identified endogenous ligands. Since their conception, the reverse pharmacology approach has been used to characterize such receptors. However, the multifaceted and nuanced nature of GPCR signaling poses a great challenge to their pharmacological elucidation. Considering their therapeutic relevance, the search for native orphan GPCR ligands continues. Despite limited structural input in terms of 3D crystallized structures, with advances in machine-learning approaches, there has been great progress with respect to accurate ligand prediction. Though such an approach proves valuable given that ligand scarcity is the greatest hurdle to orphan GPCR deorphanization, the future pairings of the remaining orphan GPCRs may not necessarily take a one-size-fits-all approach but should be more comprehensive in accounting for numerous nuanced possibilities to cover the full spectrum of GPCR signaling.

Exploring Novel Antidepressants Targeting G Protein-Coupled Receptors and Key Membrane Receptors Based on Molecular Structures

Major Depressive Disorder (MDD) is a complex mental disorder that involves alterations in signal transmission across multiple scales and structural abnormalities. The development of effective antidepressants (ADs) has been hindered by the dominance of monoamine hypothesis, resulting in slow progress. Traditional ADs have undesirable traits like delayed onset of action, limited efficacy, and severe side effects. Recently, two categories of fast-acting antidepressant compounds have surfaced, dissociative anesthetics S-ketamine and its metabolites, as well as psychedelics such as lysergic acid diethylamide (LSD). This has led to structural research and drug development of the receptors that they target. This review provides breakthroughs and achievements in the structure of depression-related receptors and novel ADs based on these. Cryo-electron microscopy (cryo-EM) has enabled researchers to identify the structures of membrane receptors, including the N-methyl-D-aspartate receptor (NMDAR) and the 5-hydroxytryptamine 2A (5-HT2A) receptor. These high-resolution structures can be used for the development of novel ADs using virtual drug screening (VDS). Moreover, the unique antidepressant effects of 5-HT1A receptors in various brain regions, and the pivotal roles of the α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR) and tyrosine kinase receptor 2 (TrkB) in regulating synaptic plasticity, emphasize their potential as therapeutic targets. Using structural information, a series of highly selective ADs were designed based on the different role of receptors in MDD. These molecules have the favorable characteristics of rapid onset and low adverse drug reactions. This review offers researchers guidance and a methodological framework for the structure-based design of ADs.

Molecular glues of the regulatory ChREBP/14-3-3 complex protect beta cells from glucolipotoxicity

The Carbohydrate Response Element Binding Protein (ChREBP) is a glucose-responsive transcription factor (TF) that is characterized by two major splice isoforms (α and β). In acute hyperglycemia, both ChREBP isoforms regulate adaptive β-expansion; however, during chronic hyperglycemia and glucolipotoxicity, ChREBPβ expression surges, leading to β-cell dedifferentiation and death. 14-3-3 binding to ChREBPα results in its cytoplasmic retention and concomitant suppression of transcriptional activity, suggesting that small molecule-mediated stabilization of this protein-protein interaction (PPI) via molecular glues may represent an attractive entry for the treatment of metabolic disease. Here, we show that structure-based optimizations of a molecular glue tool compound led not only to more potent ChREBPα/14-3-3 PPI stabilizers but also for the first time cellular active compounds. In primary human β-cells, the most active compound stabilized the ChREBPα/14-3-3 interaction and thus induced cytoplasmic retention of ChREBPα, resulting in highly efficient β-cell protection from glucolipotoxicity while maintaining β-cell identity. This study may thus not only provide the basis for the development of a unique class of compounds for the treatment of Type 2 Diabetes but also showcases an alternative 'molecular glue' approach for achieving small molecule control of notoriously difficult targetable TFs.

Synthetic surprise as the foundation of the psychedelic experience

Psychedelic agents, such as LSD and psilocybin, induce marked alterations in consciousness via activation of the 5-HT2A receptor (5-HT2ARs). We hypothesize that psychedelics enforce a state of synthetic surprise through the biased activation of the 5-HTRs system. This idea is informed by recent insights into the role of 5-HT in signaling surprise. The effects on consciousness, explained by the cognitive penetrability of perception, can be described within the predictive coding framework where surprise corresponds to prediction error, the mismatch between predictions and actual sensory input. Crucially, the precision afforded to the prediction error determines its effect on priors, enabling a dynamic interaction between top-down expectations and incoming sensory data. By integrating recent findings on predictive coding circuitry and 5-HT2ARs transcriptomic data, we propose a biological implementation with emphasis on the role of inhibitory interneurons. Implications arise for the clinical use of psychedelics, which may rely primarily on their inherent capacity to induce surprise in order to disrupt maladaptive patterns.

Making Sense of Psychedelics in the CNS

For centuries, ancient lineages have consumed psychedelic compounds from natural sources. In the modern era, scientists have since harnessed the power of computational tools, cellular assays, and behavioral metrics to study how these compounds instigate changes on molecular, cellular, circuit-wide, and system levels. Here, we provide a brief history of psychedelics and their use in science, medicine, and culture. We then outline current techniques for studying psychedelics from a pharmacological perspective. Finally, we address known gaps in the field and potential avenues of further research to broaden our collective understanding of physiological changes induced by psychedelics, the limits of their therapeutic capabilities, and how researchers can improve and inform treatments that are rapidly becoming accessible worldwide.

Progress interrogating TRPMPZQ as the target of praziquantel

The drug praziquantel (PZQ) has served as the long-standing drug therapy for treatment of infections caused by parasitic flatworms. These encompass diseases caused by parasitic blood, lung, and liver flukes, as well as various tapeworm infections. Despite a history of clinical usage spanning over 4 decades, the parasite target of PZQ has long resisted identification. However, a flatworm transient receptor potential ion channel from the melastatin subfamily (TRPMPZQ) was recently identified as a target for PZQ action. Here, recent experimental progress interrogating TRPMPZQ is evaluated, encompassing biochemical, pharmacological, genetic, and comparative phylogenetic data that highlight the properties of this ion channel. Various lines of evidence that support TRPMPZQ being the therapeutic target of PZQ are presented, together with additional priorities for further research into the mechanism of action of this important clinical drug.

Large-scale comparison of machine learning methods for profiling prediction of kinase inhibitors

Conventional machine learning (ML) and deep learning (DL) play a key role in the selectivity prediction of kinase inhibitors. A number of models based on available datasets can be used to predict the kinase profile of compounds, but there is still controversy about the advantages and disadvantages of ML and DL for such tasks. In this study, we constructed a comprehensive benchmark dataset of kinase inhibitors, involving in 141,086 unique compounds and 216,823 well-defined bioassay data points for 354 kinases. We then systematically compared the performance of 12 ML and DL methods on the kinase profiling prediction task. Extensive experimental results reveal that (1) Descriptor-based ML models generally slightly outperform fingerprint-based ML models in terms of predictive performance. RF as an ensemble learning approach displays the overall best predictive performance. (2) Single-task graph-based DL models are generally inferior to conventional descriptor- and fingerprint-based ML models, however, the corresponding multi-task models generally improves the average accuracy of kinase profile prediction. For example, the multi-task FP-GNN model outperforms the conventional descriptor- and fingerprint-based ML models with an average AUC of 0.807. (3) Fusion models based on voting and stacking methods can further improve the performance of the kinase profiling prediction task, specifically, RF::AtomPairs + FP2 + RDKitDes fusion model performs best with the highest average AUC value of 0.825 on the test sets. These findings provide useful information for guiding choices of the ML and DL methods for the kinase profiling prediction tasks. Finally, an online platform called KIPP ( https://kipp.idruglab.cn ) and python software are developed based on the best models to support the kinase profiling prediction, as well as various kinase inhibitor identification tasks including virtual screening, compound repositioning and target fishing.

Safety, tolerability, pharmacodynamic and wellbeing effects of SPL026 (dimethyltryptamine fumarate) in healthy participants: a randomized, placebo-controlled phase 1 trial

Background

Due to their potential impact on mood and wellbeing there has been increasing interest in the potential of serotonergic psychedelics such as N,N-dimethyltryptamine (DMT) in the treatment of major depressive disorder (MDD).

Aim

The aim of Part A of this study was to evaluate the safety, tolerability, pharmacokinetics (PK) and pharmacodynamic (PD) profile of escalating doses of SPL026 (DMT fumarate) in psychedelic-naïve healthy participants to determine a dose for administration to patients with MDD in the subsequent Phase 2a part of the trial (Part B: not presented in this manuscript).

Methods

In the Phase 1, randomized, double-blind, placebo-controlled, parallel-group, single dose-escalation trial, psychedelic-naïve participants were randomized to placebo (n = 8) or four different escalating doses [9, 12, 17 and 21.5 mg intravenously (IV)] of SPL026 (n = 6 for each dose) together with psychological support from 2 therapy team members. PK and acute (immediately following dosing experience) psychometric measures [including mystical experience questionnaire (MEQ), ego dissolution inventory (EDI), and intensity rating visual analogue scale (IRVAS)] were determined. Additional endpoints were measured as longer-term change from baseline to days 8, 15, 30 and 90. These measures included the Warwick and Edinburgh mental wellbeing scale and Spielberger's state-trait anxiety inventory.

Results

SPL026 was well tolerated, with an acceptable safety profile, with no serious adverse events. There was some evidence of a correlation between maximum plasma concentration and increased IRVAS, MEQ, and EDI scores. These trends are likely to require confirmation in a larger sample size. Using the analysis of the safety, tolerability, PD, PK results, doses of 21.5 mg SPL026 were the most likely to provide an intense, tolerated experience.

Conclusion

Based on the data obtained from this part of the trial, a dose of 21.5 mg SPL026 given as a 2-phase IV infusion over 10 min (6 mg/5 min and 15.5 mg/5 min) was selected as the dose to be taken into patients in Part B (to be presented in a future manuscript).Clinical trial registration:www.clinicaltrials.gov, identifier NCT04673383; https://www.clinicaltrialsregister.eu, identifier 2020-000251-13; https://www.isrctn.com/, identifier ISRCTN63465876.

Serotonin 2A Receptor (5-HT2AR) Agonists: Psychedelics and Non-Hallucinogenic Analogues as Emerging Antidepressants

Psychedelics make up a group of psychoactive compounds that induce hallucinogenic effects by activating the serotonin 2A receptor (5-HT2AR). Clinical trials have demonstrated the traditional psychedelic substances like psilocybin as a class of rapid-acting and long-lasting antidepressants. However, there is a pressing need for rationally designed 5-HT2AR agonists that possess optimal pharmacological profiles in order to fully reveal the therapeutic potential of these agonists and identify safer drug candidates devoid of hallucinogenic effects. This Perspective provides an overview of the structure-activity relationships of existing 5-HT2AR agonists based on their chemical classifications and discusses recent advancements in understanding their molecular pharmacology at a structural level. The encouraging clinical outcomes of psychedelics in depression treatment have sparked drug discovery endeavors aimed at developing novel 5-HT2AR agonists with improved subtype selectivity and signaling bias properties, which could serve as safer and potentially nonhallucinogenic antidepressants. These efforts can be significantly expedited through the utilization of structure-based methods and functional selectivity-directed screening.

Predicting drug-protein interactions by preserving the graph information of multi source data

Examining potential drug-target interactions (DTIs) is a pivotal component of drug discovery and repurposing. Recently, there has been a significant rise in the use of computational techniques to predict DTIs. Nevertheless, previous investigations have predominantly concentrated on assessing either the connections between nodes or the consistency of the network's topological structure in isolation. Such one-sided approaches could severely hinder the accuracy of DTI predictions. In this study, we propose a novel method called TTGCN, which combines heterogeneous graph convolutional neural networks (GCN) and graph attention networks (GAT) to address the task of DTI prediction. TTGCN employs a two-tiered feature learning strategy, utilizing GAT and residual GCN (R-GCN) to extract drug and target embeddings from the diverse network, respectively. These drug and target embeddings are then fused through a mean-pooling layer. Finally, we employ an inductive matrix completion technique to forecast DTIs while preserving the network's node connectivity and topological structure. Our approach demonstrates superior performance in terms of area under the curve and area under the precision-recall curve in experimental comparisons, highlighting its significant advantages in predicting DTIs. Furthermore, case studies provide additional evidence of its ability to identify potential DTIs.

Current trends and future prospects of drug repositioning in gastrointestinal oncology

Gastrointestinal (GI) cancers comprise a significant number of cancer cases worldwide and contribute to a high percentage of cancer-related deaths. To improve survival rates of GI cancer patients, it is important to find and implement more effective therapeutic strategies with better prognoses and fewer side effects. The development of new drugs can be a lengthy and expensive process, often involving clinical trials that may fail in the early stages. One strategy to address these challenges is drug repurposing (DR). Drug repurposing is a developmental strategy that involves using existing drugs approved for other diseases and leveraging their safety and pharmacological data to explore their potential use in treating different diseases. In this paper, we outline the existing therapeutic strategies and challenges associated with GI cancers and explore DR as a promising alternative approach. We have presented an extensive review of different DR methodologies, research efforts and examples of repurposed drugs within various GI cancer types, such as colorectal, pancreatic and liver cancers. Our aim is to provide a comprehensive overview of employing the DR approach in GI cancers to inform future research endeavors and clinical trials in this field.

Predicting drug-target interactions using matrix factorization with self-paced learning and dual similarity information

BACKGROUND

Drug repositioning (DR) refers to a method used to find new targets for existing drugs. This method can effectively reduce the development cost of drugs, save time on drug development, and reduce the risks of drug design. The traditional experimental methods related to DR are time-consuming, expensive, and have a high failure rate. Several computational methods have been developed with the increase in data volume and computing power. In the last decade, matrix factorization (MF) methods have been widely used in DR issues. However, these methods still have some challenges. (1) The model easily falls into a bad local optimal solution due to the high noise and high missing rate in the data. (2) Single similarity information makes the learning power of the model insufficient in terms of identifying the potential associations accurately.

OBJECTIVE

We proposed self-paced learning with dual similarity information and MF (SPLDMF), which introduced the self-paced learning method and more information related to drugs and targets into the model to improve prediction performance.

METHODS

Combining self-paced learning first can effectively alleviate the model prone to fall into a bad local optimal solution because of the high noise and high data missing rate. Then, we incorporated more data into the model to improve the model's capacity for learning.

RESULTS

Our model achieved the best results on each dataset tested. For example, the area under the receiver operating characteristic curve and the precision-recall curve of SPLDMF was 0.982 and 0.815, respectively, outperforming the state-of-the-art methods.

CONCLUSION

The experimental results on five benchmark datasets and two extended datasets demonstrated the effectiveness of our approach in predicting drug-target interactions.

MPTN: A message-passing transformer network for drug repurposing from knowledge graph

Drug repurposing (DR) based on knowledge graphs (KGs) is challenging, which uses knowledge graph reasoning models to predict new therapeutic pathways for existing drugs. With the rapid development of computing technology and the growing availability of validated biomedical data, various knowledge graph-based methods have been widely used to analyze and process complex and novel data to discover new indications for given drugs. However, existing methods need to be improved in extracting semantic information from contextual triples of biomedical entities. In this study, we propose a message-passing transformer network named MPTN based on knowledge graph for drug repurposing. Firstly, CompGCN is used as precoder to jointly aggregate entity and relation embeddings. Then, to fully capture the semantic information of entity context triples, the message propagating transformer module is designed. The module integrates the transformer into the message passing mechanism and incorporates the attention weight information of computing entity context triples into the entity embedding to update the entity embedding. Next, the residual connection is introduced to retain information as much as possible and improve prediction accuracy. Finally, MPTN utilizes the InteractE module as the decoder to obtain heterogeneous feature interactions in entity and relation representations and predict new pathways for drug treatment. Experiments on two datasets show that the model is superior to the existing knowledge graph embedding (KGE) learning methods.

LSTM-SAGDTA: Predicting Drug-target Binding Affinity with an Attention Graph Neural Network and LSTM Approach

INTRODUCTION

Drug development is a challenging and costly process, yet it plays a crucial role in improving healthcare outcomes. Drug development requires extensive research and testing to meet the demands for economic efficiency, cures, and pain relief.

METHODS

Drug development is a vital research area that necessitates innovation and collaboration to achieve significant breakthroughs. Computer-aided drug design provides a promising avenue for drug discovery and development by reducing costs and improving the efficiency of drug design and testing.

RESULTS

In this study, a novel model, namely LSTM-SAGDTA, capable of accurately predicting drug-target binding affinity, was developed. We employed SeqVec for characterizing the protein and utilized the graph neural networks to capture information on drug molecules. By introducing self-attentive graph pooling, the model achieved greater accuracy and efficiency in predicting drug-target binding affinity.

CONCLUSION

Moreover, LSTM-SAGDTA obtained superior accuracy over current state-of-the-art methods only by using less training time. The results of experiments suggest that this method represents a highprecision solution for the DTA predictor.

Frentizole, a Nontoxic Immunosuppressive Drug, and Its Analogs Display Antitumor Activity via Tubulin Inhibition

Antimitotic agents are one of the more successful types of anticancer drugs, but they suffer from toxicity and resistance. The application of approved drugs to new indications (i.e., drug repurposing) is a promising strategy for the development of new drugs. It relies on finding pattern similarities: drug effects to other drugs or conditions, similar toxicities, or structural similarity. Here, we recursively searched a database of approved drugs for structural similarity to several antimitotic agents binding to a specific site of tubulin, with the expectation of finding structures that could fit in it. These searches repeatedly retrieved frentizole, an approved nontoxic anti-inflammatory drug, thus indicating that it might behave as an antimitotic drug devoid of the undesired toxic effects. We also show that the usual repurposing approach to searching for targets of frentizole failed in most cases to find such a relationship. We synthesized frentizole and a series of analogs to assay them as antimitotic agents and found antiproliferative activity against HeLa tumor cells, inhibition of microtubule formation within cells, and arrest at the G2/M phases of the cell cycle, phenotypes that agree with binding to tubulin as the mechanism of action. The docking studies suggest binding at the colchicine site in different modes. These results support the repurposing of frentizole for cancer treatment, especially for glioblastoma.

Intrahippocampal injection of a selective blocker of NMDA receptors containing the GluN2B subunit, Ro25-6981, increases glutamate neurotransmission and induces antidepressant-like effects

Major depressive disorder (MDD) is a serious public health problem, as it is the most common psychiatric disorder worldwide. Antidepressant drugs increase adult hippocampal neurogenesis, which is required to induce some behavioral effects of antidepressants. Adult-born granule cells in the dentate gyrus (DG) and the glutamate receptors subunits 2 (GluN2B) subunit of N-methyl-D-aspartate (NMDA) ionotropic receptors play an important role in these effects. However, the precise neurochemical role of the GluN2B subunit of the NMDA receptor on adult-born GCs for antidepressant-like effects has yet to be elucidated. The present study aims to explore the contribution of the GluN2B-containing NMDA receptors in the ventral dentate gyrus (vDG) to the antidepressant drug treatment using a pharmacological approach. Thus, (αR)-(4-hydroxyphenyl)-(βS)-methyl-4-(phenylmethyl)-1-piperidinepropanol (Ro25-6981), a selective antagonist of the GluN2B subunit, was acutely administered locally into the ventral DG (vDG, 1 μg each side) following a chronic fluoxetine (18 mg/kg/day) treatment-known to increase adult hippocampal neurogenesis-in a mouse model of anxiety/depression. Responses in a neurogenesis-dependent task, the novelty suppressed feeding (NSF), and neurochemical consequences on extracellular glutamate and gamma-aminobutyric acid (GABA) levels in the vDG were measured. Here, we show a rapid-acting antidepressant-like effect of local Ro25-6981 administration in the NSF independent of fluoxetine treatment. Furthermore, we revealed a fluoxetine-independent increase in the glutamatergic transmission in the vDG. Our results suggest behavioral and neurochemical effects of GluN2B subunit independent of serotonin reuptake inhibition.

New insight into gut microbiota-derived metabolites to enhance liver regeneration via network pharmacology study

We intended to identify favourable metabolite(s) and pharmacological mechanism(s) of gut microbiota (GM) for liver regeneration (LR) through network pharmacology. We utilized the gutMGene database to obtain metabolites of GM, and targets associated with metabolites as well as LR-related targets were identified using public databases. Furthermore, we performed a molecular docking assay on the active metabolite(s) and target(s) to verify the network pharmacological concept. We mined a total of 208 metabolites in the gutMGene database and selected 668 targets from the SEA (1,256 targets) and STP (947 targets) databases. Finally, 13 targets were identified between 61 targets and the gutMGene database (243 targets). Protein-protein interaction network analysis showed that AKT1 is a hub target correlated with 12 additional targets. In this study, we describe the potential microbe from the microbiota (E. coli), chemokine signalling pathway, AKT1 and myricetin that accelerate LR, providing scientific evidence for further clinical trials.

An overview of recent advances and challenges in predicting compound-protein interaction (CPI)

Compound-protein interactions (CPIs) are critical in drug discovery for identifying therapeutic targets, drug side effects, and repurposing existing drugs. Machine learning (ML) algorithms have emerged as powerful tools for CPI prediction, offering notable advantages in cost-effectiveness and efficiency. This review provides an overview of recent advances in both structure-based and non-structure-based CPI prediction ML models, highlighting their performance and achievements. It also offers insights into CPI prediction-related datasets and evaluation benchmarks. Lastly, the article presents a comprehensive assessment of the current landscape of CPI prediction, elucidating the challenges faced and outlining emerging trends to advance the field.

A novel efficient drug repurposing framework through drug-disease association data integration using convolutional neural networks

Drug repurposing is an exciting field of research toward recognizing a new FDA-approved drug target for the treatment of a specific disease. It has received extensive attention regarding the tedious, time-consuming, and highly expensive procedure with a high risk of failure of new drug discovery. Data-driven approaches are an important class of methods that have been introduced for identifying a candidate drug against a target disease. In the present study, a model is proposed illustrating the integration of drug-disease association data for drug repurposing using a deep neural network. The model, so-called IDDI-DNN, primarily constructs similarity matrices for drug-related properties (three matrices), disease-related properties (two matrices), and drug-disease associations (one matrix). Then, these matrices are integrated into a unique matrix through a two-step procedure benefiting from the similarity network fusion method. The model uses a constructed matrix for the prediction of novel and unknown drug-disease associations through a convolutional neural network. The proposed model was evaluated comparatively using two different datasets including the gold standard dataset and DNdataset. Comparing the results of evaluations indicates that IDDI-DNN outperforms other state-of-the-art methods concerning prediction accuracy.

A framework for considering prior information in network-based approaches to omics data analysis

For decades, molecular biologists have been uncovering the mechanics of biological systems. Efforts to bring their findings together have led to the development of multiple databases and information systems that capture and present pathway information in a computable network format. Concurrently, the advent of modern omics technologies has empowered researchers to systematically profile cellular processes across different modalities. Numerous algorithms, methodologies, and tools have been developed to use prior knowledge networks (PKNs) in the analysis of omics datasets. Interestingly, it has been repeatedly demonstrated that the source of prior knowledge can greatly impact the results of a given analysis. For these methods to be successful it is paramount that their selection of PKNs is amenable to the data type and the computational task they aim to accomplish. Here we present a five-level framework that broadly describes network models in terms of their scope, level of detail, and ability to inform causal predictions. To contextualize this framework, we review a handful of network-based omics analysis methods at each level, while also describing the computational tasks they aim to accomplish.

Antifungal activity of selective serotonin reuptake inhibitors against Cryptococcus spp. and their possible mechanism of action

Fungal infections caused by Cryptococcus spp. pose a threat to health, especially in immunocompromised individuals. The available arsenal of drugs against cryptococcosis is limited, due to their toxicity and/or lack of accessibility in low-income countries, requiring more therapeutic alternatives. Selective serotonin reuptake inhibitors (SSRIs), through drug repositioning, are a promising alternative to broaden the range of new antifungals against Cryptococcus spp. This study evaluates the antifungal activity of three SSRIs, sertraline, paroxetine, and fluoxetine, against Cryptococcus spp. strains, as well as assesses their possible mechanism of action. Seven strains of Cryptococcus spp. were used. Sensitivity to SSRIs, fluconazole, and itraconazole was evaluated using the broth microdilution assay. The interactions resulting from combinations of SSRIs and azoles were investigated using the checkerboard assay. The possible action mechanism of SSRIs against Cryptococcus spp. was evaluated through flow cytometry assays. The SSRIs exhibited in vitro antifungal activity against Cryptococcus spp. strains, with minimum inhibitory concentrations ranging from 2 to 32 μg/mL, and had synergistic and additive interactions with azoles. The mechanism of action of SSRIs against Cryptococcus spp. involved damage to the mitochondrial membrane and increasing the production of reactive oxygen species, resulting in loss of cellular viability and apoptotic cell death. Fluoxetine also was able to cause significant damage to yeast DNA. These findings demonstrate the in vitro antifungal potential of SSRIs against Cryptococcus spp. strains.

Artificial Intelligence/Machine Learning-Driven Small Molecule Repurposing via Off-Target Prediction and Transcriptomics

The process of discovering small molecule drugs involves screening numerous compounds and optimizing the most promising ones, both in vitro and in vivo. However, approximately 90% of these optimized candidates fail during trials due to unexpected toxicity or insufficient efficacy. Current concepts with respect to drug-protein interactions suggest that each small molecule interacts with an average of 6-11 targets. This implies that approved drugs and even discontinued compounds could be repurposed by leveraging their interactions with unintended targets. Therefore, we developed a computational repurposing framework for small molecules, which combines artificial intelligence/machine learning (AI/ML)-based and chemical similarity-based target prediction methods with cross-species transcriptomics information. This repurposing methodology incorporates eight distinct target prediction methods, including three machine learning methods. By using multiple orthogonal methods for a "dataset" composed of 2766 FDA-approved drugs targeting multiple therapeutic target classes, we identified 27,371 off-target interactions involving 2013 protein targets (i.e., an average of around 10 interactions per drug). Relative to the drugs in the dataset, we identified 150,620 structurally similar compounds. The highest number of predicted interactions were for drugs targeting G protein-coupled receptors (GPCRs), enzymes, and kinases with 10,648, 4081, and 3678 interactions, respectively. Notably, 17,283 (63%) of the off-target interactions have been confirmed in vitro. Approximately 4000 interactions had an IC50 of <100 nM for 1105 FDA-approved drugs and 1661 interactions had an IC50 of <10 nM for 696 FDA-approved drugs. Together, the confirmation of numerous predicted interactions and the exploration of tissue-specific expression patterns in human and animal tissues offer insights into potential drug repurposing for new therapeutic applications.

The G protein biased serotonin 5-HT2A receptor agonist lisuride exerts anti-depressant drug-like activities in mice

There is now evidence from multiple Phase II clinical trials that psychedelic drugs can exert long-lasting anxiolytic, anti-depressant, and anti-drug abuse (nicotine and ethanol) effects in patients. Despite these benefits, the hallucinogenic actions of these drugs at the serotonin 2A receptor (5-HT2AR) limit their clinical use in diverse settings. Activation of the 5-HT2AR can stimulate both G protein and β-arrestin (βArr) -mediated signaling. Lisuride is a G protein biased agonist at the 5-HT2AR and, unlike the structurally-related lysergic acid diethylamide (LSD), the drug does not typically produce hallucinations in normal subjects at routine doses. Here, we examined behavioral responses to lisuride, in wild-type (WT), βArr1-knockout (KO), and βArr2-KO mice. In the open field, lisuride reduced locomotor and rearing activities, but produced a U-shaped function for stereotypies in both βArr lines of mice. Locomotion was decreased overall in βArr1-KOs and βArr2-KOs relative to wild-type controls. Incidences of head twitches and retrograde walking to lisuride were low in all genotypes. Grooming was decreased in βArr1 mice, but was increased then decreased in βArr2 animals with lisuride. Serotonin syndrome-associated responses were present at all lisuride doses in WTs, but they were reduced especially in βArr2-KO mice. Prepulse inhibition (PPI) was unaffected in βArr2 mice, whereas 0.5 mg/kg lisuride disrupted PPI in βArr1 animals. The 5-HT2AR antagonist MDL100907 failed to restore PPI in βArr1 mice, whereas the dopamine D2/D3 antagonist raclopride normalized PPI in WTs but not in βArr1-KOs. Clozapine, SCH23390, and GR127935 restored PPI in both βArr1 genotypes. Using vesicular monoamine transporter 2 mice, lisuride reduced immobility times in tail suspension and promoted a preference for sucrose that lasted up to 2 days. Together, it appears βArr1 and βArr2 play minor roles in lisuride's actions on many behaviors, while this drug exerts anti-depressant drug-like responses without hallucinogenic-like activities.

Design and Synthesis of l-1'-Homologated Adenosine Derivatives as Potential Anti-inflammatory Agents

Inflammatory responses are fundamental protective warning mechanisms. However, in certain instances, they contribute significantly to the development of several chronic diseases such as cancer. Based on previous studies of truncated 1'-homologated adenosine derivatives, l-nucleosides and their nucleobase-modified quinolone analogues were designed, synthesized, and evaluated for anti-inflammatory activities. The target molecules were synthesized via the key intramolecular cyclization of monotosylate and Mitsunobu condensation from the natural product, d-ribose. All compounds tested and showed potent anti-inflammatory activities, as indicated by their inhibition of LPS-induced IL-1β secretion from the RAW 264.7 macrophages. Gene expressions of pro-inflammatory cytokines showed that all compounds, except 3a and 3b, significantly reduced LPS-induced IL-1β and IL-6 mRNA expressions. The half-maximal inhibitory concentrations (IC50) of 2g and 2h against IL-1β were 1.08 and 2.28 μM, respectively. In contrast, only 2d, 2g, and 3d effectively reversed LPS-induced TNFα mRNA expression. Our mechanistic study revealed that LPS-induced phosphorylation of NF-κB was significantly downregulated by all compounds tested, providing evidence that the NF-κB signaling pathway is involved in their anti-inflammatory activities. Among the compounds tested, 2g and 2h had the most potent anti-inflammatory effects, as shown by the extent of decrease in pro-inflammatory gene expression, protein secretion, and NF-κB phosphorylation. These findings suggest that the l-truncated 1'-homologated adenosine skeleton and its nucleobase-modified analogues have therapeutic potential as treatments for various human diseases by mediating inflammatory processes.

Ataluren prevented bone loss induced by ovariectomy and aging in mice through the BMP-SMAD signaling pathway

Both estrogen deficiency and aging may lead to osteoporosis. Developing novel drugs for treating osteoporosis is a popular research direction. We screened several potential therapeutic agents through a new deep learning-based efficacy prediction system (DLEPS) using transcriptional profiles for osteoporosis. DLEPS screening led to a potential novel drug examinee, ataluren, for treating osteoporosis. Ataluren significantly reversed bone loss in ovariectomized mice. Next, ataluren significantly increased human bone marrow-derived mesenchymal stem cell (hBMMSC) osteogenic differentiation without cytotoxicity, indicated by the high expression index of osteogenic differentiation genes (OCN , BGLAP, ALP, COL1A, BMP2, RUNX2). Mechanistically, ataluren exerted its function through the BMP-SMAD pathway. Furthermore, it activated SMAD phosphorylation but osteogenic differentiation was attenuated by BMP2-SMAD inhibitors or small interfering RNA of BMP2. Finally, ataluren significantly reversed bone loss in aged mice. In summary, our findings suggest that the DLEPS-screened ataluren may be a therapeutic agent against osteoporosis by aiding hBMMSC osteogenic differentiation.

DGDTA: dynamic graph attention network for predicting drug-target binding affinity

BACKGROUND

Obtaining accurate drug-target binding affinity (DTA) information is significant for drug discovery and drug repositioning. Although some methods have been proposed for predicting DTA, the features of proteins and drugs still need to be further analyzed. Recently, deep learning has been successfully used in many fields. Hence, designing a more effective deep learning method for predicting DTA remains attractive.

RESULTS

Dynamic graph DTA (DGDTA), which uses a dynamic graph attention network combined with a bidirectional long short-term memory (Bi-LSTM) network to predict DTA is proposed in this paper. DGDTA adopts drug compound as input according to its corresponding simplified molecular input line entry system (SMILES) and protein amino acid sequence. First, each drug is considered a graph of interactions between atoms and edges, and dynamic attention scores are used to consider which atoms and edges in the drug are most important for predicting DTA. Then, Bi-LSTM is used to better extract the contextual information features of protein amino acid sequences. Finally, after combining the obtained drug and protein feature vectors, the DTA is predicted by a fully connected layer. The source code is available from GitHub at https://github.com/luojunwei/DGDTA .

CONCLUSIONS

The experimental results show that DGDTA can predict DTA more accurately than some other methods.

A pilot study of cerebral metabolism and serotonin 5-HT2A receptor occupancy in rats treated with the psychedelic tryptamine DMT in conjunction with the MAO inhibitor harmine

Rationale: The psychedelic effects of the traditional Amazonian botanical decoction known as ayahuasca are often attributed to agonism at brain serotonin 5-HT2A receptors by N,N-dimethyltryptamine (DMT). To reduce first pass metabolism of oral DMT, ayahuasca preparations additionally contain reversible monoamine oxidase A (MAO-A) inhibitors, namely β-carboline alkaloids such as harmine. However, there is lacking biochemical evidence to substantiate this pharmacokinetic potentiation of DMT in brain via systemic MAO-A inhibition. Objectives: We measured the pharmacokinetic profile of harmine and/or DMT in rat brain, and tested for pharmacodynamic effects on brain glucose metabolism and DMT occupancy at brain serotonin 5-HT2A receptors. Methods: We first measured brain concentrations of harmine and DMT after treatment with harmine and/or DMT at low sub-cutaneous doses (1 mg/kg each) or harmine plus DMT at moderate doses (3 mg/kg each). In the same groups of rats, we also measured ex vivo the effects of these treatments on the availability of serotonin 5-HT2A receptors in frontal cortex. Finally, we explored effects of DMT and/or harmine (1 mg/kg each) on brain glucose metabolism with [18F]FDG-PET. Results: Results confirmed that co-administration of harmine inhibited the formation of the DMT metabolite indole-3-acetic acid (3-IAA) in brain, while correspondingly increasing the cerebral availability of DMT. However, we were unable to detect any significant occupancy by DMT at 5-HT2A receptors measured ex vivo, despite brain DMT concentrations as high as 11.3 µM. We did not observe significant effects of low dose DMT and/or harmine on cerebral [18F]FDG-PET uptake. Conclusion: These preliminary results call for further experiments to establish the dose-dependent effects of harmine/DMT on serotonin receptor occupancy and cerebral metabolism.

A suite of engineered mice for interrogating psychedelic drug actions

Psychedelic drugs like lysergic acid diethylamide (LSD) and psilocybin have emerged as potentially transformative therapeutics for many neuropsychiatric diseases, including depression, anxiety, post-traumatic stress disorder, migraine, and cluster headaches. LSD and psilocybin exert their psychedelic effects via activation of the 5-hydroxytryptamine 2A receptor (HTR2A). Here we provide a suite of engineered mice useful for clarifying the role of HTR2A and HTR2A-expressing neurons in psychedelic drug actions. We first generated Htr2a-EGFP-CT-IRES-CreERT2 mice (CT:C-terminus) to independently identify both HTR2A-EGFP-CT receptors and HTR2A-containing cells thereby providing a detailed anatomical map of HTR2A and identifying cell types that express HTR2A. We also generated a humanized Htr2a mouse line and an additional constitutive Htr2A-Cre mouse line. Psychedelics induced a variety of known behavioral changes in our mice validating their utility for behavioral studies. Finally, electrophysiology studies revealed that extracellular 5-HT elicited a HTR2A-mediated robust increase in firing of genetically-identified pyramidal neurons--consistent with a plasma membrane localization and mode of action. These mouse lines represent invaluable tools for elucidating the molecular, cellular, pharmacological, physiological, behavioral, and other actions of psychedelic drugs in vivo.

Overfit deep neural network for predicting drug-target interactions

Drug-target interactions (DTIs) prediction is an important step in drug discovery. As traditional biological experiments or high-throughput screening are high cost and time-consuming, many deep learning models have been developed. Overfitting must be avoided when training deep learning models. We propose a simple framework, called OverfitDTI, for DTI prediction. In OverfitDTI, a deep neural network (DNN) model is overfit to sufficiently learn the features of the chemical space of drugs and the biological space of targets. The weights of trained DNN model form an implicit representation of the nonlinear relationship between drugs and targets. Performance of OverfitDTI on three public datasets showed that the overfit DNN models fit the nonlinear relationship with high accuracy. We identified fifteen compounds that interacted with TEK, a receptor tyrosine kinase contributing to vascular homeostasis, and the predicted AT9283 and dorsomorphin were experimentally demonstrated as inhibitors of TEK in human umbilical vein endothelial cells (HUVECs).

Discovery and In Vitro Characterization of SPL028: Deuterated N,N-Dimethyltryptamine

The psychedelic N,N- dimethyltryptamine (DMT) is in clinical development for the treatment of major depressive disorder. However, when administered via intravenous infusion, its effects are short-lived due to rapid clearance. Here we describe the synthesis of deuterated analogues of DMT with the aim of prolonging the half-life and decreasing the clearance rate while maintaining similar pharmacological effects. The molecule with the greatest degree of deuteration at the α-carbon (N,N-D2-dimethyltryptamine, D2-DMT) demonstrated the longest half-life and intrinsic clearance in hepatocyte mitochondrial fractions when compared with DMT. The in vitro receptor binding profile of D2-DMT was comparable to that of DMT, with the highest affinity at the 5-HT1A, 5-HT2A, and 5-HT2C receptors. D2-DMT was therefore the preferred candidate to consider for further evaluation.

Molecular and structural insights into the 5-HT2C receptor as a therapeutic target for substance use disorders

Substance use disorder (SUD) is a chronic condition, with maintained abuse of a substance leading to physiological and psychological alterations and often changes in cognitive and social behaviours. Current therapies include psychotherapy coupled with medication; however, high relapse rates reveal the shortcomings of these therapies. The signalling, expression profile, and neurological function of the serotonin 2C receptor (5-HT2C receptor) make it a candidate of interest for the treatment of SUD. Recently, psychedelics, which broadly act at 5-HT2 receptors, have indicated potential for the treatment of SUD, implicating the 5-HT2C receptor. The modern psychedelic movement has rekindled interest in the 5-HT2C receptor, resulting in many new studies, especially structural analyses. This review explores the structural, molecular and cellular mechanisms governing 5-HT2C receptor function in the context of SUD. This provides the basis of the preclinical and clinical evidence for their role in SUD and highlights the potential for future exploration.

The risk of chronic psychedelic and MDMA microdosing for valvular heart disease

Psychedelic microdosing is the practice of taking very low doses of psychedelic substances, typically over a longer period of time. The long-term safety of chronic microdosing is relatively uncharacterized, but valvular heart disease (VHD) has been proposed as a potential risk due to activation of the serotonin 5-HT2B receptor. However, this risk has not yet been comprehensively assessed. This analysis searched for all relevant in vitro, animal, and clinical studies related to the VHD risk of lysergic acid diethylamide (LSD), psilocybin, mescaline, N,N-dimethyltryptamine (DMT), and the non-psychedelic 3,4-methylenedioxymethamphetamine (MDMA). All five compounds and some metabolites could bind to the 5-HT2B receptor with potency equal to or greater than that of the 5-HT2A receptor, the primary target of psychedelics. All compounds were partial agonists at the 5-HT2B receptor with the exception of mescaline, which could not be adequately assessed due to low potency. Safety margins relative to the maximum plasma concentrations from typical microdoses were greater than known valvulopathogens, but not without potential risk. No animal or clinical studies appropriately designed to evaluate VHD risk were found for the four psychedelics. However, there is some clinical evidence that chronic ingestion of full doses of MDMA is associated with VHD. We conclude that VHD is a potential risk with chronic psychedelic microdosing, but further studies are necessary to better define this risk.

Improving Compound-Protein Interaction Prediction by Self-Training with Augmenting Negative Samples

Identifying compound-protein interactions (CPIs) is crucial for drug discovery. Since experimentally validating CPIs is often time-consuming and costly, computational approaches are expected to facilitate the process. Rapid growths of available CPI databases have accelerated the development of many machine-learning methods for CPI predictions. However, their performance, particularly their generalizability against external data, often suffers from a data imbalance attributed to the lack of experimentally validated inactive (negative) samples. In this study, we developed a self-training method for augmenting both credible and informative negative samples to improve the performance of models impaired by data imbalances. The constructed model demonstrated higher performance than those constructed with other conventional methods for solving data imbalances, and the improvement was prominent for external datasets. Moreover, examination of the prediction score thresholds for pseudo-labeling during self-training revealed that augmenting the samples with ambiguous prediction scores is beneficial for constructing a model with high generalizability. The present study provides guidelines for improving CPI predictions on real-world data, thus facilitating drug discovery.

Applications and Potential of In Silico Approaches for Psychedelic Chemistry

Molecular-level investigations of the Central Nervous System have been revolutionized by the development of computational methods, computing power, and capacity advances. These techniques have enabled researchers to analyze large amounts of data from various sources, including genomics, in vivo, and in vitro drug tests. In this review, we explore how computational methods and informatics have contributed to our understanding of mental health disorders and the development of novel drugs for neurological diseases, with a special focus on the emerging field of psychedelics. In addition, the use of state-of-the-art computational methods to predict the potential of drug compounds and bioinformatic tools to integrate disparate data sources to create predictive models is also discussed. Furthermore, the challenges associated with these methods, such as the need for large datasets and the diversity of in vitro data, are explored. Overall, this review highlights the immense potential of computational methods and informatics in Central Nervous System research and underscores the need for continued development and refinement of these techniques and more inclusion of Quantitative Structure-Activity Relationships (QSARs).

A Brief Review on the Potential of Psychedelics for Treating Alzheimer's Disease and Related Depression

Alzheimer's disease (AD), the most common form of senile dementia, is poised to place an even greater societal and healthcare burden as the population ages. With few treatment options for the symptomatic relief of the disease and its unknown etiopathology, more research into AD is urgently needed. Psychedelic drugs target AD-related psychological pathology and symptoms such as depression. Using microdosing, psychedelic drugs may prove to help combat this devastating disease by eliciting psychiatric benefits via acting through various mechanisms of action such as serotonin and dopamine pathways. Herein, we review the studied benefits of a few psychedelic compounds that may show promise in treating AD and attenuating its related depressive symptoms. We used the listed keywords to search through PubMed for relevant preclinical, clinical research, and review articles. The putative mechanism of action (MOA) for psychedelics is that they act mainly as serotonin receptor agonists and induce potential beneficial effects for treating AD and related depression.