IUPHAR review - Data-driven computational drug repurposing approaches for opioid use disorder.

Opioid Use Disorder (OUD) is a chronic and relapsing condition characterized by the misuse of opioid drugs, causing significant morbidity and mortality in the United States. Existing medications for OUD are limited, and there is an immediate need to discover treatments with enhanced safety and efficacy. Drug repurposing aims to find new indications for existing medications, offering a time-saving and cost-efficient alternative strategy to traditional drug discovery. Computational approaches have been developed to further facilitate the drug repurposing process. In this paper, we reviewed state-of-the-art data-driven computational drug repurposing approaches for OUD and discussed their advantages and potential challenges. We also highlighted promising repurposed candidate drugs for OUD that were identified by computational drug repurposing techniques and reviewed studies supporting their potential mechanisms of action in treating OUD.

Examining the Potential of ChatGPT on Biomedical Information Retrieval: Fact-Checking Drug-Disease Associations.

Large language models (LLMs) such as ChatGPT have recently attracted significant attention due to their impressive performance on many real-world tasks. These models have also demonstrated the potential in facilitating various biomedical tasks. However, little is known of their potential in biomedical information retrieval, especially identifying drug-disease associations. This study aims to explore the potential of ChatGPT, a popular LLM, in discerning drug-disease associations. We collected 2694 true drug-disease associations and 5662 false drug-disease pairs. Our approach involved creating various prompts to instruct ChatGPT in identifying these associations. Under varying prompt designs, ChatGPT's capability to identify drug-disease associations with an accuracy of 74.6-83.5% and 96.2-97.6% for the true and false pairs, respectively. This study shows that ChatGPT has the potential in identifying drug-disease associations and may serve as a helpful tool in searching pharmacy-related information. However, the accuracy of its insights warrants comprehensive examination before its implementation in medical practice.

Potential effect of antidepressants on remission from cocaine use disorder - A nationwide matched retrospective cohort study.

BACKGROUND: Cocaine use disorder (CUD) is a significant public health issue for which there is no Food and Drug Administration-approved pharmacotherapy. Depressive disorders are common psychiatric comorbidity amongst individuals with CUD. METHODS: A retrospective cohort study was conducted among 161,544 patients diagnosed with CUD and depression to evaluate the effectiveness of 13 antidepressants on CUD remission. For any antidepressant found to be associated with CUD remission that had an additional indication, we conducted an additional analysis to evaluate the effectiveness of the candidate drug in patients with CUD with that indication. We then analyzed publicly genomic and functional databases to identify potential explanatory mechanisms of action of the candidate drug in the treatment of CUD. RESULTS: Among these antidepressants, bupropion was associated with higher rates of CUD remission compared to propensity-score matched patients prescribed other antidepressants: hazard ratio (HR) and 95% confidence interval (CI) 1.57 (95% CI: 1.27-1.94). Bupropion is also approved for smoking cessation. We identified CUD patients with co-occurring nicotine dependence and observed that patients prescribed bupropion displayed a higher rate of CUD remission compared to matched individuals prescribed other drugs for nicotine dependence: 1.38 (95% CI: 1.11-1.71). Genetic and functional analyses revealed that bupropion interacts with four protein-encoding genes (COMT, DRD2, SLC6A3, and SLC6A4) which are also associated with CUD and targets CUD-associated pathways including serotonergic synapses, cocaine addiction, and dopaminergic synapses. CONCLUSIONS: Our findings suggest that bupropion might be considered a treatment for improving CUD remission in patients with CUD and co-occurring depression or nicotine dependence.

Drug repurposing for reducing the risk of cataract extraction in patients with diabetes mellitus: integration of artificial intelligence-based drug prediction and clinical corroboration.

Diabetes mellitus (DM) increases the incidence of age-related cataracts. Currently, no medication is approved or known to delay clinical cataract progression. Using a novel approach based on AI, we searched for drugs with potential cataract surgery-suppressing effects. We developed a drug discovery strategy that combines AI-based potential candidate prediction among 2650 Food and Drug Administration (FDA)-approved drugs with clinical corroboration leveraging multicenter electronic health records (EHRs) of approximately 800,000 cataract patients from the TriNetX platform. Among the top-10 AI-predicted repurposed candidate drugs, we identified three DM diagnostic ICD code groups, such as cataract patients with type 1 diabetes mellitus (T1DM), type 2 diabetes mellitus (T2DM), or hyperglycemia, and conducted retrospective cohort analyses to evaluate the efficacy of these candidate drugs in reducing the risk of cataract extraction. Aspirin, melatonin, and ibuprofen were associated with a reduced 5-, 10-, and 20-year cataract extraction risk in all types of diabetes. Acetylcysteine was associated with a reduced 5-, 10-, and 20-year cataract extraction risk in T2DM and hyperglycemia but not in T1DM patient groups. The suppressive effects of aspirin, acetylcysteine, and ibuprofen waned over time, while those of melatonin became stronger in both genders. Thus, the four repositioned drugs have the potential to delay cataract progression in both genders. All four drugs share the ability to directly or indirectly inhibit cyclooxygenase-2 (COX-2), an enzyme that is increased by multiple cataractogenic stimuli.

Integrin ß3-Mediated Cell Senescence Associates with Gut Inflammation and Intestinal Degeneration in Models of Alzheimer's Disease.

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by memory loss and personality changes that ultimately lead to dementia. Currently, 50 million people worldwide suffer from dementia related to AD, and the pathogenesis underlying AD pathology and cognitive decline is unknown. While AD is primarily a neurological disease of the brain, individuals with AD often experience intestinal disorders, and gut abnormalities have been implicated as a major risk factor in the development of AD and relevant dementia. However, the mechanisms that mediate gut injury and contribute to the vicious cycle between gut abnormalities and brain injury in AD remain unknown. In the present study, a bioinformatics analysis was performed on the proteomics data of variously aged AD mouse colon tissues. We found that levels of integrin ß3 and ß-galactosidase (ß-gal), two markers of cellular senescence, increased with age in the colonic tissue of mice with AD. The advanced artificial intelligence (AI)-based prediction of AD risk also demonstrated the association between integrin ß3 and ß-gal and AD phenotypes. Moreover, we showed that elevated integrin ß3 levels were accompanied by senescence phenotypes and immune cell accumulation in AD mouse colonic tissue. Further, integrin ß3 genetic downregulation abolished upregulated senescence markers and inflammatory responses in colonic epithelial cells in conditions associated with AD. We provide a new understanding of the molecular actions underpinning inflammatory responses during AD and suggest integrin ß3 may function as novel target mediating gut abnormalities in this disease.

Repurposing ketamine to treat cocaine use disorder: integration of artificial intelligence-based prediction, expert evaluation, clinical corroboration and mechanism of action analyses.

BACKGROUND AND AIMS: Cocaine use disorder (CUD) is a significant public health issue for which there is no Food and Drug Administration (FDA) approved medication. Drug repurposing looks for new cost-effective uses of approved drugs. This study presents an integrated strategy to identify repurposed FDA-approved drugs for CUD treatment. DESIGN: Our drug repurposing strategy combines artificial intelligence (AI)-based drug prediction, expert panel review, clinical corroboration and mechanisms of action analysis being implemented in the National Drug Abuse Treatment Clinical Trials Network (CTN). Based on AI-based prediction and expert knowledge, ketamine was ranked as the top candidate for clinical corroboration via electronic health record (EHR) evaluation of CUD patient cohorts prescribed ketamine for anesthesia or depression compared with matched controls who received non-ketamine anesthesia or antidepressants/midazolam. Genetic and pathway enrichment analyses were performed to understand ketamine's potential mechanisms of action in the context of CUD. SETTING: The study utilized TriNetX to access EHRs from more than 90 million patients world-wide. Genetic- and functional-level analyses used DisGeNet, Search Tool for Interactions of Chemicals and Kyoto Encyclopedia of Genes and Genomes databases. PARTICIPANTS: A total of 7742 CUD patients who received anesthesia (3871 ketamine-exposed and 3871 anesthetic-controlled) and 7910 CUD patients with depression (3955 ketamine-exposed and 3955 antidepressant-controlled) were identified after propensity score-matching. MEASUREMENTS: EHR analysis outcome was a CUD remission diagnosis within 1 year of drug prescription. FINDINGS: Patients with CUD prescribed ketamine for anesthesia displayed a significantly higher rate of CUD remission compared with matched individuals prescribed other anesthetics [hazard ratio (HR) = 1.98, 95% confidence interval (CI) = 1.42-2.78]. Similarly, CUD patients prescribed ketamine for depression evidenced a significantly higher CUD remission ratio compared with matched patients prescribed antidepressants or midazolam (HR = 4.39, 95% CI = 2.89-6.68). The mechanism of action analysis revealed that ketamine directly targets multiple CUD-associated genes (BDNF, CNR1, DRD2, GABRA2, GABRB3, GAD1, OPRK1, OPRM1, SLC6A3, SLC6A4) and pathways implicated in neuroactive ligand-receptor interaction, cAMP signaling and cocaine abuse/dependence. CONCLUSIONS: Ketamine appears to be a potential repurposed drug for treatment of cocaine use disorder.

Fragment-based deep molecular generation using hierarchical chemical graph representation and multi-resolution graph variational autoencoder.

Graph generative models have recently emerged as an interesting approach to construct molecular structures atom-by-atom or fragment-by-fragment. In this study, we adopt the fragment-based strategy and decompose each input molecule into a set of small chemical fragments. In drug discovery, a few drug molecules are designed by replacing certain chemical substituents with their bioisosteres or alternative chemical moieties. This inspires us to group decomposed fragments into different fragment clusters according to their local structural environment around bond-breaking positions. In this way, an input structure can be transformed into an equivalent three-layer graph, in which individual atoms, decomposed fragments, or obtained fragment clusters act as graph nodes at each corresponding layer. We further implement a prototype model, named multi-resolution graph variational autoencoder (MRGVAE), to learn embeddings of constituted nodes at each layer in a fine-to-coarse order. Our decoder adopts a similar but conversely hierarchical structure. It first predicts the next possible fragment cluster, then samples an exact fragment structure out of the determined fragment cluster, and sequentially attaches it to the preceding chemical moiety. Our proposed approach demonstrates comparatively good performance in molecular evaluation metrics compared with several other graph-based molecular generative models. The introduction of the additional fragment cluster graph layer will hopefully increase the odds of assembling new chemical moieties absent in the original training set and enhance their structural diversity. We hope that our prototyping work will inspire more creative research to explore the possibility of incorporating different kinds of chemical domain knowledge into a similar multi-resolution neural network architecture.

Spontaneous time-reversal symmetry breaking in twisted double bilayer graphene.

Twisted double bilayer graphene (tDBG) comprises two Bernal-stacked bilayer graphene sheets with a twist between them. Gate voltages applied to top and back gates of a tDBG device tune both the flatness and topology of the electronic bands, enabling an unusual level of experimental control. Metallic states with broken spin and valley symmetries have been observed in tDBG devices with twist angles in the range 1.2-1.3°, but the topologies and order parameters of these states have remained unclear. We report the observation of an anomalous Hall effect in the correlated metal state of tDBG, with hysteresis loops spanning hundreds of mT in out-of-plane magnetic field (B⊥) that demonstrate spontaneously broken time-reversal symmetry. The B⊥ hysteresis persists for in-plane fields up to several Tesla, suggesting valley (orbital) ferromagnetism. At the same time, the resistivity is strongly affected by even mT-scale values of in-plane magnetic field, pointing to spin-valley coupling or to a direct orbital coupling between in-plane field and the valley degree of freedom.

KG-Predict: A knowledge graph computational framework for drug repurposing.

The emergence of large-scale phenotypic, genetic, and other multi-model biochemical data has offered unprecedented opportunities for drug discovery including drug repurposing. Various knowledge graph-based methods have been developed to integrate and analyze complex and heterogeneous data sources to find new therapeutic applications for existing drugs. However, existing methods have limitations in modeling and capturing context-sensitive inter-relationships among tens of thousands of biomedical entities. In this paper, we developed KG-Predict: a knowledge graph computational framework for drug repurposing. We first integrated multiple types of entities and relations from various genotypic and phenotypic databases to construct a knowledge graph termed GP-KG. GP-KG was composed of 1,246,726 associations between 61,146 entities. KG-Predict then aggregated the heterogeneous topological and semantic information from GP-KG to learn low-dimensional representations of entities and relations, and further utilized these representations to infer new drug-disease interactions. In cross-validation experiments, KG-Predict achieved high performances [AUROC (the area under receiver operating characteristic) = 0.981, AUPR (the area under precision-recall) = 0.409 and MRR (the mean reciprocal rank) = 0.261], outperforming other state-of-art graph embedding methods. We applied KG-Predict in identifying novel repositioned candidate drugs for Alzheimer's disease (AD) and showed that KG-Predict prioritized both FDA-approved and active clinical trial anti-AD drugs among the top (AUROC = 0.868 and AUPR = 0.364).

A knowledge graph-based disease-gene prediction system using multi-relational graph convolution networks.

Identifying disease-gene associations is important for understanding molecule mechanisms of diseases, finding diagnostic markers and therapeutic targets. Many computational methods have been proposed to predict disease related genes by integrating different biological databases into heterogeneous networks. However, it remains a challenging task to leverage heterogeneous topological and semantic information from multi-source biological data to enhance disease-gene prediction. In this study, we propose a knowledge graph-based disease-gene prediction system (GenePredict-KG) by modeling semantic relations extracted from various genotypic and phenotypic databases. We first constructed a knowledge graph that comprised 2,292,609 associations between 73,358 entities for 14 types of phenotypic and genotypic relations and 7 entity types. We developed a knowledge graph embedding model to learn low-dimensional representations of entities and relations, and utilized these embeddings to infer new disease-gene interactions. We compared GenePredict-KG with several state-of-the-art models using multiple evaluation metrics. GenePredict-KG achieved high performances [AUROC (the area under receiver operating characteristic) = 0.978, AUPR (the area under precision-recall) = 0.343 and MRR (the mean reciprocal rank) = 0.244], outperforming other state-of-art methods.

Increased systemic RNA oxidative damage and diagnostic value of RNA oxidative metabolites during Shigella flexneri-induced intestinal infection.

BACKGROUND: Shigella flexneri (S. flexneri) is a major pathogen causing acute intestinal infection, but the systematic oxidative damage incurred during the course of infection has not been investigated. AIM: To investigate the incurred systemic RNA oxidative damage and the diagnostic value of RNA oxidative metabolites during S. flexneri-induced intestinal infection. METHODS: In this study, a Sprague-Dawley rat model of acute intestinal infection was established by oral gavage with S. flexneri strains. The changes in white blood cells (WBCs) and cytokine levels in blood and the inflammatory response in the colon were investigated. We also detected the RNA and DNA oxidation in urine and tissues. RESULTS: S. flexneri infection induced an increase in WBCs, C-reactive protein, interleukin (IL)-6, IL-10, IL-1ß, IL-4, IL-17a, IL-10, and tumor necrosis factor α (TNF-α) in blood. Of note, a significant increase in urinary 8-oxo-7,8-dihydroguanosine (8-oxo-Gsn), an important marker of total RNA oxidation, was detected after intestinal infection (P = 0.03). The urinary 8-oxo-Gsn level returned to the baseline level after recovery from infection. In addition, the results of a correlation analysis showed that urinary 8-oxo-Gsn was positively correlated with the WBC count and the cytokines IL-6, TNF-α, IL-10, IL-1ß, and IL-17α. Further detection of the oxidation in different tissues showed that S. flexneri infection induced RNA oxidative damage in the colon, ileum, liver, spleen, and brain. CONCLUSION: Acute infection induced by S. flexneri causes increased RNA oxidative damage in various tissues (liver, spleen, and brain) and an increase of 8-oxo-Gsn, a urinary metabolite. Urinary 8-oxo-Gsn may be useful as a biomarker for evaluating the severity and prognosis of infection.

The influence of hepatitis B virus infection in pregnant women.

We carried out the investigation to evaluate hepatitis B virus (HBV) infection status and the influence of HBV infection in pregnant women in Tianjin of China. We founded that the prevalence of HBsAg was 3.77% (69/1829). 88.57% (1620/1829) pregnant women conducted HBsAg screening in last pregnancy. Spontaneous abortion and premature delivery did not show significant differences between HBV infected and uninfected pregnant women. But ALT and AST levels were significantly higher in infected women. And 56.65% of participants (997/1760) were anti-HBs positive. In conclusion, HBsAg prevalence was moderate in pregnant women in this region, which was consistent with the total population in western Pacific regions. And HBV infection did not influence spontaneous abortion and premature delivery. But the HBsAg screening was conducted mostly in the last pregnancy. Early screening and intervention were suggested in pregnant women within countries of moderately endemic regions.

Systemic RNA oxidation can be used as a biomarker of infection in challenged with Vibrio parahaemolyticus.

More and more evidence support the concept that RNA oxidation plays a substantial role in the progress of multiple diseases; however, only a few studies have reported RNA oxidation caused by microbial pathogens. Urinary 8-oxo-7,8-dihydroguanosine (8-oxo-Gsn) and 8-oxo-7,8-dihydro-2'-deoxyguanosine (8-oxo-dGsn), which are broadly used as indicators of oxidative damage of RNA and DNA, were analyzed in this study to determine which can be used as a biomarker of infection in challenged with Vibrio parahaemolyticus (V. parahaemolyticus). In this work, 24 specific-pathogen-free (SPF) male SD rats were randomly divided into two groups: an infection group and a phosphate-buffered saline (PBS) control group. Our results proved that 8-oxo-Gsn rather than 8-oxo-dGsn was significantly increased after challenged with V. parahaemolyticus in urine and tissue samples of SD rats compared with the PBS control group. Simultaneously, white blood cells (WBCs) counts, intestinal inflammation and inflammatory factors (including CRP, IL-6, IL-1ß, TNF-α, IL-10, and IL-17A) were also increased sharply. Which has more clinical value is that the trend of urinary 8-oxo-Gsn was consistent with WBCs, intestinal inflammation and all kinds of inflammatory factors. More importantly is that urinary 8-oxo-Gsn of infection group was positively correlated with WBCs and various inflammatory cytokines. In a word, our results demonstrated that as a systemic RNA oxidation biomarker, we hope 8-oxo-Gsn can be used as a biomarker of the severity of microbial pathogens infection, rather than a specific biomarker of microbial pathogens infection.

Oxidative DNA and RNA damage and their prognostic values during Salmonella enteritidis-induced intestinal infection in rats.

Emerging evidence suggests that microbial pathogens may induce oxidative stress in infected hosts. The aim of the present study was to investigate the relationship between changes in oxidative stress and intestinal infection with and without antibiotic treatment in animal models. Sprague-Dawley (SD) rats were divided into three groups: rats infected with Salmonella enterica serovar Enteritidis (S. enteritidis), rats infected with S. enteritidis followed by norfloxacin treatment, and the control group. To evaluate oxidative stress changes, levels of 8-oxo-7,8-dihydroguanosine (8-oxo-Gsn) and 8-oxo-7,8-dihydro-2-deoxyguanosine (8-oxo-dGsn), which represented oxidative damage to RNA and DNA, respectively, were analysed in urine and tissue samples. In urine, the level of 8-oxo-Gsn increased significantly after oral exposure to S. enteritidis (p ≤ 0.001) and returned to baseline after recovery. Notably, norfloxacin treatment decreased the level of 8-oxo-Gsn in urine significantly (p = 0.001). Changes of 8-oxo-Gsn measured in tissues from the small intestine, colon, liver and spleen were consistent with 8-oxo-Gsn measured in urine. Our study suggested that 8-oxo-Gsn in urine may serve as a highly sensitive biomarker for evaluating the severity of S. enteritidis infection and the effectiveness of antibiotic treatment against infection.

Comparison of the Target Values Established Using a Robust Statistical Method and Roche's Calibration Values.

BACKGROUND: In EQA schemes, results are usually assessed against a target value. METHODS: Outliers in each result were deleted using a robust statistical method. Mean was used as a target value if trimmed results were normally distributed; otherwise, median was used. Differences between target value and Roche's calibration value were calculated. Rates of acceptable performance evaluated by target value and Roche's calibration value were determined. RESULTS: Target values of P, GLU, urea, UA, TP, ALB, TC, TG, ALT, AST, ALP, AMY, LDH, GGT, FE, and HBDH were 1.70 mmol/L, 11.0 mmol/L, 16.8 mmol/L, 307 µmol/L, 54.0 g/L, 38.1 g/L, 4.19 mmol/L, 1.46 mmol/L, 97.7 U/L, 98.2 U/L, 206 U/L, 180 U/L, 242 U/L, 115 U/L, 35.6 µmol/L, and 255 U/L, respectively. Differences between target values and Roche's calibration values were -3.45 to 1.48%. Rates of acceptable performance evaluated by target values and Roche's calibration values were 96.5 - 100.0% and 93.0 - 100.0%, respectively. CONCLUSIONS: Target values established using a robust statistical method and Roche's calibration values were comparable.

Comparison of traditional, trimmed traditional and robust Youden charts.

BACKGROUND: Traditional Youden chart can be distorted by non-normally distributed data. Here, an optimized Youden chart was developed and compared with the traditional and trimmed traditional Youden charts. METHODS: The urea concentrations were determined by 28 laboratories to provide data for the construction of Youden charts. Normality of these data was tested. Outliers were excluded prior to construction of the trimmed traditional Youden chart. Non-robust and robust estimators were computed to construct the traditional and robust Youden charts, respectively. Robust between-laboratory z-score (ZBi) and within-laboratory z-score (ZWi) were obtained to assess whether or not these charts can reasonably present the urea results. Expected outcomes were the points related to acceptable (|ZBi| and |ZWi| ≤ 2), questionable (2 < |ZBi| < 3 and/or 2 < |ZWi| < 3), and unacceptable (|ZBi| and/or |ZWi| ≥ 3) results fall inside, on/near, and outside the ellipse, respectively. RESULTS: Only the data from lot 201111 are non-normally distributed. Five- and 2-pair outliers are excluded from the data of lots 201111 and 201112, respectively. The concordance rates of the traditional, trimmed traditional and robust Youden charts are 87.1%, 92.9%, and 94.3%, respectively. CONCLUSIONS: Among the three charts, the robust Youden chart presents the urea results best.

[An epidemiologic study of diabetes mellitus in employees of Baotou Iron & Steel Company].

OBJECTIVE: To investigate prevalence of diabetes mellitus (DM), impaired glucose tolerance (IGT), impaired fasting glucose (IFG) in Baotou Iron & Steel Company and to provide scientific evidence for prevention and intervention of DM. METHODS: Prevalence of DM was studied in 20 221 workers aged 20 years and over (male 15 124 and female 5 097), with the criteria set by the American Diabetes Association (ADA, 1997), in Baotou Iron & Steel Company. RESULTS: Prevalence of DM, IGT and IFG was 3.22%, 3.48% and 2.09%, respectively in 20 221 subjects, adjusted for age, which increased with age, body mass index and waist to hip ratio, and 56.30% of them were newly-diagnosed. Family history of DM and overweight correlated to prevalence of DM, IGT and IFG, which was higher in mental workers than that in physical laborers. The lower level of education, the higher prevalence of DM, IGT and IFG. Prevalence of DM, IGT and IFG in the workers working under high-temperature condition had no significantly difference with that in the control group. Mean blood pressure was significantly higher in persons with DM, IGT and IFG than that in the normal controls. CONCLUSIONS: Age, overweight, obesity, family history of DM, mental work, low level of education, and history of gestation with a huge fetus all were risk factors for DM, IGT and IFG. There is no significant impact of high temperature environment on prevalence of DM, IGT and IFG.