G protein-coupled receptors (GPCRs) form one of the largest drug target families, reflecting their involvement in numerous pathophysiological processes. In this Review, we analyse drug discovery trends for the GPCR superfamily, covering compounds, targets and indications that have reached regulatory approval or that are being investigated in clinical trials. We find that there are 516 approved drugs targeting GPCRs, making up 36% of all approved drugs. These drugs act on 121 GPCR targets, one-third of all non-sensory GPCRs. Furthermore, 337 agents targeting 133 GPCRs, including 30 novel targets, are being investigated in clinical trials. Notably, 165 of these agents are approved drugs being tested for additional indications and novel agents are increasingly allosteric modulators and biologics. Remarkably, diabetes and obesity drugs targeting GPCRs had sales of nearly US $30 billion in 2023 and the numbers of clinical trials for GPCR modulators in the metabolic diseases, oncology and immunology areas are increasing strongly. Finally, we highlight the potential of untapped target-disease associations and pathway-biased signalling. Overall, this Review provides an up-to-date reference for the drugged and potentially druggable GPCRome to inform future GPCR drug discovery and development.

Depression is one of the most common psychiatric conditions resulting from a complex interaction of genetic, epigenetic and environmental factors. The present study aimed to identify independent genetic variants in the protein coding genes that associate with depression and to analyze their transcriptomic and methylation profile. Data from the GWAS Catalogue was used to identify independent genetic variants for depression. The identified genetic variants were validated in the UK Biobank cohort and used to calculate a genetic risk score for depression. Data was also used from publicly available cohorts to conduct transcriptome and methylation analyses. Eight SNPs corresponding to six protein coding genes (TNXB, NCAM1, LTBP3, BTN3A2, DAG1, FHIT) were identified that were highly associated with depression. These validated genetic variants for depression were used to calculate a genetic risk score that showed a significant association with depression (p <0.05) but not with co-morbid traits. The transcriptome and methylation analyses suggested nominal significance for some gene probes (TNXB- and NCAM1) with depressed phenotype. The present study identified six protein-coding genes associated with depression and primarily involved in inflammation (TNXB), neuroplasticity (NCAM1 and LTBP3), immune response (BTN3A2), cell survival (DAG1) and circadian clock modification (FHIT). Our findings confirmed previous evidence for TNXB- and NCAM1 in the pathophysiology of depression and suggested new potential candidate genes (LTBP3, BTN3A2, DAG1 and FHIT) that warrant further investigation.

AIM:Liver-expressed antimicrobial peptide 2 (LEAP2) dynamics in human plasma and its association with feeding behaviour remain poorly understood. Therefore, this study aims: (a) to investigate fasting LEAP2 in participants with normal weight or with overweight or mild obesity (OW/OB); (b) to study the association between fasting LEAP2 and anthropometric and metabolic traits, feeding behaviour, LEAP2 genetic variants and blood cell DNA methylation status; and (c) to ascertain postprandial changes in LEAP2 after high protein intake and the association with feeding behaviour and food intake. METHODS:Anthropometric and behavioural measures, genotyping, methylation profiling, plasma glucose and LEAP2 concentrations were assessed in 327 females and males. A subgroup of 123 participants received an ad libitum high-protein meal, and postprandial LEAP2 concentration and behavioural measures were assessed. RESULTS:LEAP2 concentration was higher in participants with OW/OB (p < 0.001) and in females (p < 0.001), and was associated with LEAP2 single nucleotide polymorphisms rs765760 (p = 0.012) and rs803223 (p = 0.019), but not with LEAP2 methylation status. LEAP2 concentration was directly related to glycaemia (p = 0.001) and fullness (p = 0.003) in participants with normal weight, whereas it was associated with body mass index (p = 0.018), waist circumference (p = 0.014) and motor impulsivity in participants with OW/OB (p = 0.005). A negative association with reward responsiveness was observed in participants with OW/OB (p = 0.023). LEAP2 concentration was inversely associated with food intake (p = 0.034) and decreased after a high-protein meal (p < 0.001), particularly in women (p = 0.002). CONCLUSION:Increased LEAP2 in participants with OW/OB is associated with behavioural characteristics of obesity. Our results show sexual dimorphism in LEAP2 concentration before and after food intake and highlight the role of LEAP2 in feeding regulation.

The object of the study were samples of fibers obtained from ultrahigh-molecular-weight polyethylene (UHMWP) gel with different multiplicity of stretching (λ) with a scatter (dispersion) of mechanical properties. It is shown that the dependence of strength (σb) on breaking elongation (ε) of the studied samples is expressed by the equation σb = σb0 + K·ε and appears as a series of variously inclined straight lines specific for each λ value, which converge upon extrapolation to ε = 0 to a single point (pole), the ordinate of which coincides with the strength of unoriented fiber (σb0). With increase of λ the straight lines representing the course of the relationships σb(ε) shift toward low ε values. The decrease of ε is compensated abundantly by increase of angular coefficient K, which determines the degree of effect of ε on σb.

IntroductionDepression is a major global burden with unclear pathophysiology and poor treatment outcomes. Diagnosis of depression continues to rely primarily on behavioral rather than biological methods. Investigating tools that might aid in diagnosing and treating early-onset depression is essential for improving the prognosis of the disease course. While there is increasing evidence of possible biomarkers in adult depression, studies investigating this subject in adolescents are lacking.MethodsIn the current study, we analyzed protein levels in 461 adolescents assessed for depression using the Development and Well-Being Assessment (DAWBA) questionnaire as part of the domestic Psychiatric Health in Adolescent Study conducted in Uppsala, Sweden. We used the Proseek Multiplex Neuro Exploratory panel with Proximity Extension Assay technology provided by Olink Bioscience, followed by transcriptome analyses for the genes corresponding to the significant proteins, using four publicly available cohorts.ResultsWe identified a total of seven proteins showing different levels between DAWBA risk groups at nominal significance, including RBKS, CRADD, ASGR1, HMOX2, PPP3R1, CD63, and PMVK. Transcriptomic analyses for these genes showed nominally significant replication of PPP3R1 in two of four cohorts including whole blood and prefrontal cortex, while ASGR1 and CD63 were replicated in only one cohort.DiscussionOur study on adolescent depression revealed protein-level and transcriptomic differences, particularly in PPP3R1, pointing to the involvement of the calcineurin pathway in depression. Our findings regarding PPP3R1 also support the role of the prefrontal cortex in depression and reinforce the significance of investigating prefrontal cortex-related mechanisms in depression.

The causes of depression are complex, and the current diagnosis methods rely solely on psychiatric evaluations with no incorporation of laboratory biomarkers in clinical practices. We investigated the stability of blood DNA methylation depression signatures in six different populations using six public and two domestic cohorts (n = 1942) conducting mega-analysis and meta-analysis of the individual studies. We evaluated 12 machine learning and deep learning strategies for depression classification both in cross-validation (CV) and in hold-out tests using merged data from 8 separate batches, constructing models with both biased and unbiased feature selection. We found 1987 CpG sites related to depression in both mega- and meta-analysis at the nominal level, and the associated genes were nominally related to axon guidance and immune pathways based on enrichment analysis and eQTM data. Random forest classifiers achieved the highest performance (AUC 0.73 and 0.76) in CV and hold-out tests respectively on the batch-level processed data. In contrast, the methylation showed low predictive power (all AUCs < 0.57) for all classifiers in CV and no predictive power in hold-out tests when used with harmonized data. All models achieved significantly better performance (>14% gain in AUCs) with pre-selected features (selection bias), with some of the models (joint autoencoder-classifier) reaching AUCs of up to 0.91 in the final testing regardless of data preparation. Different algorithmic feature selection approaches may outperform limma, however, random forest models perform well regardless of the strategy. The results provide an overview over potential future biomarkers for depression and highlight many important methodological aspects for DNA methylation-based depression profiling including the use of machine learning strategies.

This paper is devoted to solving the inverse problem (determining the parameters of a system of ordinary differential equations based on additional information determined at discrete points in time) and analyzing its solution for a mathematical model describing the dynamics of changes in the population and capital of two regions of the world. The inverse problem is reduced to the problem of minimizing the target functional and is solved by the method of differential evolution. A numerical method for solving direct and inverse problems is implemented. The developed method was tested on model and real data for countries such as Russia, China, India and the USA.

Trigeminal neuralgia (TN) is a severe facial pain disease of uncertain pathophysiology and unclear genetic background. Although recent research has reported a more important role of genetic factors in TN pathogenesis, few candidate genes have been proposed to date. The present study aimed to identify independent genetic variants in the protein-coding genes associated with TN. We focused on genes previously linked to TN based on the results of four proteomic studies conducted by our research team. The goal was to validate these findings on the genetic level to enhance our understanding of the role of genetics in TN. The study is based on the participants from UK Biobank cohort. Following quality control, 175 independent single nucleotide polymorphisms (SNPs) in 17 genes were selected. The study sample comprised of diagnosed TN cases (N = 555) and randomly matched controls (N = 6245) based on specific criteria. Two SNPs corresponding to C8B rs706484 [odds ratio (OR) (95

Major depressive disorder (MDD), commonly known as depression, affects over 300 million people worldwide as of 2018 and presents a wide range of clinical symptoms. The international clinical trials registry platform (ICTRP) introduced by WHO includes aggregated data from ClinicalTrials.gov and 17 other national registers, making it the largest clinical trial platform. Here we analysed data in ICTRP with the aim of providing comprehensive insights into clinical trials on depression. Applying a novel hidden duplicate identification method, 10,606 depression trials were identified in ICTRP, with ANZCTR being the largest non- ClinicalTrials.gov database at 1031 trials, followed by IRCT with 576 trials, ISRCTN with 501 trials, CHiCTR with 489 trials, and EUCTR with 351 trials. The top four most studied drugs, ketamine, sertraline, duloxetine, and fluoxetine, were consistent in both groups, but ClinicalTrials.gov had more trials for each drug compared to the non-ClinicalTrials.gov group. Out of 9229 interventional trials, 663 unique agents were identified, including approved drugs (74.5%), investigational drugs (23.2%), withdrawn drugs (1.8%), nutraceuticals (0.3%), and illicit substances (0.2%). Both ClinicalTrials.gov and non-ClinicalTrials.gov databases revealed that the largest categories were antidepressive agents (1172 in ClinicalTrials.gov and 659 in non-ClinicalTrials.gov) and nutrients, amino acids, and chemical elements (250 in ClinicalTrials.gov and 659 in non-ClinicalTrials.gov), indicating a focus on alternative treatments involving dietary supplements and nutrients. Additionally, 26 investigational antidepressive agents targeting 16 different drug targets were identified, with buprenorphine (opioid agonist), saredutant (NK2 antagonist), and seltorexant (OX2 antagonist) being the most frequently studied. This analysis addresses 40 approved drugs for depression treatment including new drug classes like GABA modulators and NMDA antagonists that are offering new prospects for treating MDD, including drug-resistant depression and postpartum depression subtypes.

Additional file 12: Table S4. Linkage disequilibrium for investigated SNPs based on the output from ( https://snipa.helmholtz-muenchen.de/snipa3/ ) with default settings. Only associations between investigated SNPs are shown. Column description provided by SNIPA: QRSID, Query SNP rsID; RSID, Proxy SNP rsID; RSALIAS, Proxy SNP alias rsID(s); CHR, Chromosome; POS1, Sentinel SNP Position; POS2, Proxy SNP Position; DIST, Distance; R2, LD r^2; D, LD D; DPRIME, LD D'; MAJOR, Proxy Allele A; MINOR, Proxy Allele B; MAF, Allele B Frequency; CMMB, Recombination Rate (CM/Mb); CM, Genetic distance (CM); COMPEFFECTS, Compressed functional annotation of Proxy SNP; TRAIT, Associated with trait (yes/no); CISEQTL, Associated with eQTL in cis (yes/no); TRANSEQTL, Associated with eQTL in trans (yes/no); GENES, Genes hit or close-by (distance 5 KB max); REGGENES, Potentially regulated genes (linked via promoter or enhancer); EQTLGENES, Genes linked via eQTL associations. Abbreviations: SNP, single nucleotide polymorphism.

BACKGROUND:Trigeminal neuralgia (TN) is a severe facial pain condition often associated with a neurovascular conflict. However, neuroinflammation has also been implicated in TN, as it frequently co-occurs with multiple sclerosis (MS). METHODS:We analysed protein expression levels of TN patients compared to MS patients and controls. Proximity Extension Assay technology was used to analyse the levels of 92 proteins with the Multiplex Neuro-Exploratory panel provided by SciLifeLab, Uppsala, Sweden. Serum and CSF samples were collected from TN patients before (n = 33 and n = 27, respectively) and after (n = 28 and n = 8, respectively) microvascular decompression surgery. Additionally, we included samples from MS patients (n = 20) and controls (n = 20) for comparison. RESULTS:In both serum and CSF, several proteins were found increased in TN patients compared to either MS patients, controls, or both, including EIF4B, PTPN1, EREG, TBCB, PMVK, FKBP5, CD63, CRADD, BST2, CD302, CRIP2, CCL27, PPP3R1, WWP2, KLB, PLA2G10, TDGF1, SMOC1, RBKS, LTBP3, CLSTN1, NXPH1, SFRP1, HMOX2, and GGT5. The overall expression of the 92 proteins in postoperative TN samples seems to shift towards the levels of MS patients and controls in both serum and CSF, as compared to preoperative samples. Interestingly, there was no difference in protein levels between MS patients and controls. CONCLUSIONS:We conclude that TN patients showed increased serum and CSF levels of specific proteins and that successful surgery normalizes these protein levels, highlighting its potential as an effective treatment. However, the similarity between MS and controls challenges the idea of shared pathophysiology with TN, suggesting distinct underlying mechanisms in these conditions. SIGNIFICANCE:This study advances our understanding of trigeminal neuralgia (TN) and its association with multiple sclerosis (MS). By analysing 92 protein biomarkers, we identified distinctive molecular profiles in TN patients, shedding light on potential pathophysiological mechanisms. The observation that successful surgery normalizes many protein levels suggests a promising avenue for TN treatment. Furthermore, the contrasting protein patterns between TN and MS challenge prevailing assumptions of similarity between the two conditions and point to distinct pathophysiological mechanisms.

The study aims at investigating a new fundamental property of infectious diseases with natural adaptive immunity that weakens over time—qualitative change (bifurcation) in the behavior of the “virus vs. human” system with an increase in contagiousness. Numerical experiments with a model of the COVID-19 epidemic in Moscow have demonstrated that when the reproduction number R0 is about 4, a qualitative change (bifurcation) occurs in the behavior of the virus–human system. Below this value, the long-term forecast tends toward undamped oscillations; above it, the forecast shows damped oscillations: the amplitudes of epidemic waves decrease gradually, with a constant, very high background level of morbidity that keeps natural immunity near 100%. To confirm this result analytically, we use an original modification of the Euler–Lotka renewal equation, which describes the dynamics of infected patients distributed by disease duration (time since infection) and accounts for immunity. To construct a bifurcation diagram, which illustrates the dependence of the equilibrium stability on the parameter R0, we linearize the equation in the vicinity of the equilibrium point and examine its numerical approximation (discrete form). This approximation can be interpreted as a Leslie model, with the matrix elements dependent on the parameter R0. By examining the roots of the corresponding Lotka polynomial, we can assess the stability of the equilibrium point and verify the basic assumption about the change in the properties of the system with increasing R0—about the transition from undamped oscillations to damped ones. For the bifurcation diagram, we use the functions obtained from the simulation of the COVID-19 epidemic in Moscow. However, observations of the epidemic in other cities and countries support the primary finding of our study regarding the attenuation of epidemic waves.

Modeling of the dynamics of the nickel concentration in soils, water, and bottom sediments of lakes, caused by emissions into the atmosphere from the Pechenga Nickel Plant (Kola Peninsula) over the entire period of its operation, is considered. The technology of balanced identification, which makes it possible based on mathematical description of inhomogeneous geochemical processes occurring in ecosystems to combine heterogeneous experimental data and to develop a computer model with an optimal balance of complexity and closeness to the data, is used. The results including estimates of the retrospective state of the simulated objects and a forecast of their dynamics until 2030 are presented and discussed. According to model calculations, the intensity of Ni accumulation in the soil was 2.4 and it was twice as high as in bottom sediments during the periods of maximum deposition (1980–2005). According to the forecast model, after the shutdown of the plant, Ni accumulation in bottom sediments will begin to decrease and Ni will slowly leach from the soil with an intensity of 0.2 mg/(m2 yr).

Nanoparticles are heterologous small composites that are usually between 1 and 100 nanometers in size. They are applied in many areas of medicine with one of them being drug delivery. Nanoparticles have a number of advantages as drug carriers which include reduced toxic effects, increased bioavailability, and their ability to be modified for specific tissues or cells. Due to the exciting development of nanotechnology concomitant with advances in biotechnology and medicine, the number of clinical trials devoted to nanoparticles for drug delivery is growing rapidly. Some nanoparticles, lipid-based types, in particular, played a crucial role in the developing and manufacturing of the two COVID-19 vaccines-Pfizer and Moderna-that are now being widely used. In this analysis, we provide a quantitative survey of clinical trials using nanoparticles during the period from 2002 to 2021 as well as the recent FDA-approved drugs (since 2016). A total of 486 clinical trials were identified using the clinicaltrials.gov database. The prevailing types of nanoparticles were liposomes (44%) and protein-based formulations (26%) during this period. The most commonly investigated content of the nanoparticles were paclitaxel (23%), metals (11%), doxorubicin (9%), bupivacaine and various vaccines (both were 8%). Among the FDA-approved nanoparticle drugs, polymeric (29%), liposomal (22%) and lipid-based (21%) drugs were the most common. In this analysis, we also discuss the differential development of the diverse groups of nanoparticles and their content, as well as the underlying factors behind the trends.

Additional file 17: Table S9. 9.1 The source publications of stress-related CpG sites. 9.2 CpG–CpG associations in the SKI. The models were calculated, using the standard implementation of linear regression. The covariates are specific for every cohort (see methods). The p values were adjusted with the false discovery rate method. The Stress CpG genes were obtained from Illumina annotation files for HumanMethylation450 and MethylationEPIC arrays. 9.3 CpG–CpG associations in the Grady sample. 9.4 CpG–CpG associations in the E-GEOD-41826 (glia) sample. 9.5 CpG–CpG associations in the GSE88890 (BA11) sample. 9.6 CpG–CpG associations in the GSE88890 (BA25) sample. 9.7 Pooled CpG–CpG associations that were nominally significant and detected more than once. 9.8 Pooled CpG–CpG associations that were nominally significant, were detected more than once, and had matching directions for beta coefficients.

Introduction: Clinical trials are the gold standard for testing new therapies. Databases like ClinicalTrials.gov provide access to trial information, mainly covering the US and Europe. In 2006, WHO introduced the global ICTRP, aggregating data from ClinicalTrials.gov and 17 other national registers, making it the largest clinical trial platform by June 2019. This study conducts a comprehensive global analysis of the ICTRP database and provides framework for large-scale data analysis, data preparation, curation, and filtering.Materials and methods: The trends in 689,793 records from the ICTRP database (covering trials registered from 1990 to 2020) were analyzed. Records were adjusted for duplicates and mapping of agents to drug classes was performed. Several databases, including DrugBank, MESH, and the NIH Drug Information Portal were used to investigate trends in agent classes.Results: Our novel approach unveiled that 0.5% of the trials we identified were hidden duplicates, primarily originating from the EUCTR database, which accounted for 82.9% of these duplicates. However, the overall number of hidden duplicates within the ICTRP seems to be decreasing. In total, 689 793 trials (478 345 interventional) were registered in the ICTRP between 1990 and 2020, surpassing the count of trials in ClinicalTrials.gov (362 500 trials by the end of 2020). We identified 4 865 unique agents in trials with DrugBank, whereas 2 633 agents were identified with NIH Drug Information Portal data. After the ClinicalTrials.gov, EUCTR had the most trials in the ICTRP, followed by CTRI, IRCT, CHiCTR, and ISRCTN. CHiCTR displayed a significant surge in trial registration around 2015, while CTRI experienced rapid growth starting in 2016.Conclusion: This study highlights both the strengths and weaknesses of using the ICTRP as a data source for analyzing trends in clinical trials, and emphasizes the value of utilizing multiple registries for a comprehensive analysis.

Biomarkers for early detection of breast cancer may complement population screening approaches to enable earlier and more precise treatment. The blood proteome is an important source for biomarker discovery but so far, few proteins have been identified with breast cancer risk. Here, we measure 2929 unique proteins in plasma from 598 women selected from the Karolinska Mammography Project to explore the association between protein levels, clinical characteristics, and gene variants, and to identify proteins with a causal role in breast cancer. We present 812 cis-acting protein quantitative trait loci for 737 proteins which are used as instruments in Mendelian randomisation analyses of breast cancer risk. Of those, we present five proteins (CD160, DNPH1, LAYN, LRRC37A2 and TLR1) that show a potential causal role in breast cancer risk with confirmatory results in independent cohorts. Our study suggests that these proteins should be further explored as biomarkers and potential drug targets in breast cancer.

The paper discusses modeling the dynamics of nickel concentration in soils, water, and the bottom sediments of lakes caused by atmospheric emissions from the Pechenganickel plant, Kola Peninsula, throughout its whole operation period. The applied technology of balanced identification makes it possible to use a mathematical description of heterogeneous geochemical processes in ecosystems to combine heterogeneous experimental data and build up a computer model with an optimal balance of its complexity and fitting quality of the data. The model is used to analyze the spatial and temporal variability of natural objects in the zone of distribution of atmospheric pollution (nickel) from the Pechenganickel plant. The paper presents and discusses results of this study, including estimates of the retrospective state of the simulated objects (before the start of the intense studies) and a forecast of their dynamics until 2030. According to the model calculations, the intensity of Ni accumulation in the soil and bottom sediments was 2.35 and 4.48 mg/(m2 year) during the maximum deposition periods (1980–2005), whereas the model predicts a decrease in the intensity of Ni accumulation in the bottom sediments (0.23 mg/(m2 year)) and slow Ni leaching from the soil (0.19 mg/(m2 year)) after the shutdown of the plant.

Background Understanding the role of circulating proteins in prostate cancer risk can reveal key biological pathways and identify novel targets for cancer prevention. Methods We investigated the association of 2,002 genetically predicted circulating protein levels with risk of prostate cancer overall, and of aggressive and early onset disease, using cis- pQTL Mendelian randomization (MR) and colocalization. Findings for proteins with support from both MR, after correction for multiple-testing, and colocalization were replicated using two independent cancer GWAS, one of European and one of African ancestry. Proteins with evidence of prostate-specific tissue expression were additionally investigated using spatial transcriptomic data in prostate tumor tissue to assess their role in tumor aggressiveness. Finally, we mapped risk proteins to drug and ongoing clinical trials targets. Results We identified 20 proteins genetically linked to prostate cancer risk (14 for overall [8 specific], 7 for aggressive [3 specific], and 8 for early onset disease [2 specific]), of which a majority were novel and replicated. Among these were proteins associated with aggressive disease, such as PPA2 [Odds Ratio (OR) per 1 SD increment = 2.13, 95% CI: 1.54-2.93], PYY [OR = 1.87, 95% CI: 1.43-2.44] and PRSS3 [OR = 0.80, 95% CI: 0.73-0.89], and those associated with early onset disease, including EHPB1 [OR = 2.89, 95% CI: 1.99-4.21], POGLUT3 [OR = 0.76, 95% CI: 0.67-0.86] and TPM3 [OR = 0.47, 95% CI: 0.34-0.64]. We confirm an inverse association of MSMB with prostate cancer overall [OR = 0.81, 95% CI: 0.80-0.82], and also find an inverse association with both aggressive [OR = 0.84, 95% CI: 0.82-0.86] and early onset disease [OR = 0.71, 95% CI: 0.68-0.74]. Using spatial transcriptomics data, we identified MSMB as the genome-wide top-most predictive gene to distinguish benign regions from high grade cancer regions that had five-fold lower MSMB expression. Additionally, ten proteins that were associated with prostate cancer risk mapped to existing therapeutic interventions. Conclusion Our findings emphasize the importance of proteomics for improving our understanding of prostate cancer etiology and of opportunities for novel therapeutic interventions. Additionally, we demonstrate the added benefit of in-depth functional analyses to triangulate the role of risk proteins in the clinical aggressiveness of prostate tumors. Using these integrated methods, we identify a subset of risk proteins associated with aggressive and early onset disease as priorities for investigation for the future prevention and treatment of prostate cancer.