Abstract: OBJECTIVE: To identify specific Chinese medicines (CMs) that may benefit patients with gastroesophageal reflux disease (GERD), and explore the action mechanism. METHODS: Domestic and foreign literature on the treatment of GERD with CMs was searched and selected from China National Knowledge Infrastructure, China Science and Technology Journal Database, Wanfang Database, and PubMed from October 1, 2011 to October 1, 2021. Data from all eligible articles were extracted to establish the database of CMs for GERD. Apriori algorithm of data mining techniques was used to analyze the rules of herbs selection and core Chinese medicine formulas were identified. A system pharmacology approach was used to explore the action mechanism of these medicines. RESULTS: A total of 278 prescriptions for GERD were analyzed, including 192 CMs. Results of Apriori algorithm indicated that Evodiae Fructus and Coptidis Rhizoma were the highest confidence combination. A total of 32 active ingredients and 66 targets were screened for the treatment of GERD. Enrichment analysis showed that the mechanisms of action mainly involved pathways in cancer, fluid shear stress and atherosclerosis, advanced glycation end product (AGE), the receptor for AGE signaling pathway in diabetic complications, bladder cancer, and rheumatoid arthritis. CONCLUSION: Evodiae Fructus and Coptidis Rhizoma are the core drugs in the treatment of GERD and the potential mechanism of action of these medicines includes potential target and pathways.

Abstract: The emergence of bacterial resistance has motivated researchers to discover new antibacterial agents. Nowadays, fluoroquinolones keep their status as one of the essential classes of antibacterial agents. The new generations of fluoroquinolones are valuable therapeutic tools with a spectrum of activity, including Gram-positive, Gram-negative, and atypical bacteria. This review article surveys the design of fluoroquinolone hybrids with other antibacterial agents or active compounds and underlines the new hybrids' antibacterial properties. Antibiotic fluoroquinolone hybrids have several advantages over combined antibiotic therapy. Thus, some challenges related to joining two different molecules are under study. Structurally, the obtained hybrids may contain a cleavable or non-cleavable linker, an essential element for their pharmacokinetic properties and mechanism of action. The design of hybrids seems to provide promising antibacterial agents helpful in the fight against more virulent and resistant strains. These hybrid structures have proven superior antibacterial activity and less susceptibility to bacterial resistance than the component molecules. In addition, fluoroquinolone hybrids have demonstrated other biological effects such as anti-HIV, antifungal, antiplasmodic/antimalarial, and antitumor activity. Many fluoroquinolone hybrids are in various phases of clinical trials, raising hopes that new antibacterial agents will be approved shortly.

Abstract: BACKGROUND: Polypharmacy increases the risk of potential drug-drug interactions (pDDIs). This retrospective analysis was conducted to detect pDDIs and adverse drug reactions (ADRs) among older adults with psychiatric disorder, and identify pDDIs with clinical significance. METHODS: A retrospective analysis was carried out based on the medical records of older adults with psychiatric disorders. Data on demographic characteristics, substance abuse, medical history, and medications were extracted. The Lexi-Interact online database was used to detect pDDIs. The minimal clinically important difference (MCID) was set as the change in the Treatment Emergent Symptom Scale (TESS) score between admission and discharge. The median and interquartile ranges were used for continuous variables, and frequencies were calculated for dichotomous variables. Poisson regression was implemented to determine the factors influencing the number of ADR types. The influencing factors of each ADR and the clinical significance of the severity of the ADR were analysed using binary logistic regression. P < 0.05 was considered statistically significant. RESULTS: A total of 308 older adults were enrolled, 171 (55.52%) of whom had at least 1 pDDI. Thirty-six types of pDDIs that should be avoided were found, and the most frequent pDDI was the coadministration of lorazepam and olanzapine (55.5%). A total of 26 ADRs induced by pDDIs were identified, and the most common ADR was constipation (26.05%). There was a 9.4 and 10.3% increase in the number of ADR types for each extra medical diagnosis and for each extra drug, respectively. There was a 120% increase in the number of ADR types for older adults hospitalized for 18-28 days compared with those hospitalized for 3-17 days. There was an 11.1% decrease in the number of ADR types for each extra readmission. The length of hospitalization was a risk factor for abnormal liver function (P < 0.05). The use of a large number of drugs was a risk factor for gastric distress (P < 0.05) and dizziness and fainting (P < 0.05). None of the four pDDIs, including coadministrations of olanzapine and lorazepam, quetiapine and potassium chloride, quetiapine and escitalopram, and olanzapine and clonazepam, showed clinical significance of ADR severity (P > 0.05). CONCLUSIONS: pDDIs are prevalent in older adults, and the rate is increasing. However, many pDDIs may have no clinical significance in terms of ADR severity. Further research on assessing pDDIs, and possible measures to prevent serious ADRs induced by DDIs is needed to reduce the clinical significance of pDDIs.

Abstract: BACKGROUND: Heart failure (HF) is the end stage of the development of heart disease, whose prognosis is poor. The previous research of our team indicated that the formulae containing Aconiti Lateralis Radix Praeparata and Lepidii Semen Descurainiae Semen (ALRP-LSDS) could inhibit myocardial hypertrophy, inhibit cardiomyocyte apoptosis, delay myocardial remodeling (REM), and improve the prognosis of patients with HF effectively. In order to explore the mechanism of ALRP-LSDS for the treatment of HF, a combined approach of network pharmacology and molecular docking was conducted. METHODS: Public database TCMSP was used to screen the active compounds of ALRP-LSDS. The targets of screened active compounds were obtained from the TCMSP database and predicted using the online analysis tool PharmMapper. The targets of HF were obtained from 6 databases including GeneCards, OMIM, DrugBank, TTD, PharmGKB, and DisGeNET. Protein-protein interaction and enrichment analysis were performed, respectively, by STRING and Metascape online tools after merging the targets of active compounds and HF. Cytoscape software was used to conduct networks. Finally, molecular docking was performed by Vina to verify the correlation between key targets and active compounds. RESULTS: Final results indicated that the active compounds including beta-sitosterol, isorhamnetin, quercetin, kaempferol, and (R)-norcoclaurine, the targets including AKT1, CASP3, and MAPK1 might be the main active compounds and key targets of ALRP-LSDS for the treatment of HF separately. The binding ability of AKT1 to the main active compounds was better compared with the other 2 key targets, which means it might be more critical. The pathways including AGE-RAGE signaling pathway in diabetic complications, Pathways in cancer, and Fluid shear stress and atherosclerosis might play important roles in the treatment of HF with ALRP-LSDS. In general, ALRP-LSDS could inhibit cardiomyocyte apoptosis, delay REM, and improve cardiac function through multicompound, multitarget, and multipathway, which contributes to the treatment of HF. CONCLUSIONS: Based on the combined approach of network pharmacology and molecular docking, this study screened out the main active compounds, key targets, and main pathways of ALRP-LSDS for the treatment of HF, and revealed its potential mechanisms, providing a theoretical basis for further research.

Abstract: BACKGROUND: G-protein coupled receptors (GPCRs) sense and transmit extracellular signals into the intracellular machinery by regulating G proteins. GPCR malfunctions are associated with a variety of signaling-related diseases, including cancer and diabetes; at least a third of the marketed drugs target GPCRs. Thus, characterization of their signaling and regulatory mechanisms is crucial for the development of effective drugs. RESULTS: In this study, we developed a machine learning model to identify GPCR agonists and antagonists. We designed two-step prediction models: the first model identified the ligands binding to GPCRs and the second model classified the ligands as agonists or antagonists. Using 990 selected subset features from 5270 molecular descriptors calculated from 4590 ligands deposited in two drug databases, our model classified non-ligands, agonists, and antagonists of GPCRs, and achieved an area under the ROC curve (AUC) of 0.795, sensitivity of 0.716, specificity of 0.744, and accuracy of 0.733. In addition, we verified that 70% (44 out of 63) of FDA-approved GPCR-targeting drugs were correctly classified into their respective groups. CONCLUSIONS: Studies of ligand-GPCR interaction recognition are important for the characterization of drug action mechanisms. Our GPCR-ligand interaction prediction model can be employed in the pharmaceutical sciences for the efficient virtual screening of putative GPCR-binding agonists and antagonists.

Abstract: Pleiotropy, which refers to the ability of different mutations on the same gene to cause different pathological effects in human genetic diseases, is important in understanding system-level biological diseases. Although some biological experiments have been proposed, still little is known about pleiotropy on gene-gene dynamics, since most previous studies have been based on correlation analysis. Therefore, a new perspective is needed to investigate pleiotropy in terms of gene-gene dynamical characteristics. To quantify pleiotropy in terms of network dynamics, we propose a measure called in silico Pleiotropic Scores (sPS), which represents how much a gene is affected against a pair of different types of mutations on a Boolean network model. We found that our model can identify more candidate pleiotropic genes that are not known to be pleiotropic than the experimental database. In addition, we found that many types of functionally important genes tend to have higher sPS values than other genes; in other words, they are more pleiotropic. We investigated the relations of sPS with the structural properties in the signaling network and found that there are highly positive relations to degree, feedback loops, and centrality measures. This implies that the structural characteristics are principles to identify new pleiotropic genes. Finally, we found some biological evidence showing that sPS analysis is relevant to the real pleiotropic data and can be considered a novel candidate for pleiotropic gene research. Taken together, our results can be used to understand the dynamics pleiotropic characteristics in complex biological systems in terms of gene-phenotype relations.

Abstract: Monkeypox is an emerging epidemic of concern. The disease is caused by the monkeypox virus and an increasing global incidence with a 2022 outbreak that has spread to Europe amid the COVID-19 pandemic. The new outbreak is associated with novel, previously undiscovered mutations and variants. Currently, the US Food and Drug Administration (FDA) approved poxvirus treatment involves the use of tecovirimat. However, there is otherwise limited pharmacopoeia and research interest in monkeypox. In this study, virtual screening and molecular dynamics were employed to explore the potential repurposing of multiple drugs previously approved by the FDA or other jurisdictions for other applications. Several drugs are predicted to tightly bind to viral proteins, which are crucial in viral replication, including molecules which show high potential for binding the monkeypox D13L capsid protein, whose inhibition has previously been demonstrated to suppress viral replication.

Abstract: The 3-phosphoinositide-dependent protein kinase 1 (PDPK1) has a significant role in cancer progression and metastasis as well as other inflammatory disorders, and has been proposed as a promising therapeutic target for several malignancies. In this work, we conducted a systematic virtual screening of natural compounds from the IMPPAT database to identify possible PDPK1 inhibitors. Primarily, the Lipinski rules, ADMET, and PAINS filter were applied and then the binding affinities, docking scores, and selectivity were carried out to find effective hits against PDPK1. Finally, we identified two natural compounds, 11-Hydroxytephrosin and Torosaflavone A, bearing substantial affinity with PDPK1. Both compounds showed drug-likeness as predicted by the ADMET analysis and their physicochemical parameters. These compounds preferentially bind to the ATP-binding pocket of PDPK1 and interact with functionally significant residues. The conformational dynamics and complex stability of PDPK1 with the selected compounds were then studied using interaction analysis and molecular dynamics (MD) simulations for 100 ns. The simulation results revealed that PDPK1 forms stable docked complexes with the elucidated compounds. The findings show that the newly discovered 11-Hydroxytephrosin and Torosaflavone A bind to PDPK1 in an ATP-competitive manner, suggesting that they could one day be used as therapeutic scaffolds against PDPK1-associated diseases including cancer.

Abstract: Non-human animal studies outline precise mechanisms of central mu-opioid regulation of pain, stress, affiliation and reward processing. In humans, pharmacological blockade with non-selective opioid antagonists such as naloxone and naltrexone is typically used to assess involvement of the mu-opioid system in such processing. However, robust estimates of the opioid receptor blockade achieved by opioid antagonists are missing. Dose and timing schedules are highly variable and often based on single studies. Here, we provide a detailed analysis of central opioid receptor blockade after opioid antagonism based on existing positron emission tomography data. We also create models for estimating opioid receptor blockade with intravenous naloxone and oral naltrexone. We find that common doses of intravenous naloxone (0.10-0.15 mg/kg) and oral naltrexone (50 mg) are more than sufficient to produce full blockade of central MOR (>90% receptor occupancy) for the duration of a typical experimental session (~60 min), presumably due to initial super saturation of receptors. Simulations indicate that these doses also produce high KOR blockade (78-100%) and some DOR blockade (10% with naltrexone and 48-74% with naloxone). Lower doses (e.g., 0.01 mg/kg intravenous naloxone) are estimated to produce less DOR and KOR blockade while still achieving a high level of MOR blockade for ~30 min. The models and simulations form the basis of two novel web applications for detailed planning and evaluation of experiments with opioid antagonists. These tools and recommendations enable selection of appropriate antagonists, doses and assessment time points, and determination of the achieved receptor blockade in previous studies.

Abstract: BACKGROUND AND OBJECTIVES: Previous studies have highlighted antidiabetic drugs as repurposing candidates for Alzheimer disease (AD), but the disease-modifying effects are still unclear. METHODS: A 2-sample mendelian randomization study design was applied to examine the association between genetic variation in the targets of 4 antidiabetic drug classes and AD risk. Genetic summary statistics for blood glucose were analyzed using UK Biobank data of 326,885 participants, whereas summary statistics for AD were retrieved from previous genome-wide association studies comprising 24,087 clinically diagnosed AD cases and 55,058 controls. Positive control analysis on type 2 diabetes mellitus (T2DM), insulin secretion, insulin resistance, and obesity-related traits was conducted to validate the selection of instrumental variables. RESULTS: In the positive control analysis, genetic variation in sulfonylurea targets was associated with higher insulin secretion, a lower risk of T2DM, and an increment in body mass index, waist circumference, and hip circumference, consistent with drug mechanistic actions and previous trial evidence. In the primary analysis, genetic variation in sulfonylurea targets was associated with a lower risk of AD (odds ratio [OR] = 0.38 per 1 mmol/L decrement in blood glucose, 95% CI 0.19-0.72, p = 0.0034). These results for sulfonylureas were largely unchanged in the sensitivity analysis using a genetic variant, rs757110, that has been validated to modulate the target proteins of sulfonylureas (OR = 0.35 per 1 mmol/L decrement in blood glucose, 95% CI 0.15-0.82, p = 0.016). An association between genetic variations in the glucagon-like peptide 1 (GLP-1) analogue target and a lower risk of AD was also observed (OR = 0.32 per 1 mmol/L decrement in blood glucose, 95% CI 0.13-0.79, p = 0.014). However, this result should be interpreted with caution because the positive control analyses for GLP-1 analogues did not comply with a weight-loss effect as shown in previous clinical trials. Results regarding other drug classes were inconclusive. DISCUSSION: Genetic variation in sulfonylurea targets was associated with a lower risk of AD, and future studies are warranted to clarify the underlying mechanistic pathways between sulfonylureas and AD.