Abstract: BACKGROUND: Efforts to minimize harms from opioid drug interactions may be hampered by limited evidence on which drugs, when taken concomitantly with opioids, result in adverse clinical outcomes. OBJECTIVE: To identify signals of opioid drug interactions by identifying concomitant medications (precipitant drugs) taken with individual opioids (object drugs) that are associated with unintentional traumatic injury DESIGN: We conducted pharmacoepidemiologic screening of Optum Clinformatics Data Mart, identifying drug interaction signals by performing confounder-adjusted self-controlled case series studies for opioid+precipitant pairs and injury. SETTING: Beneficiaries of a major United States-based commercial health insurer during 2000-2015 PATIENTS: Persons aged 16-90 years co-dispensed an opioid and >/=1 precipitant drug(s), with an unintentional traumatic injury event during opioid therapy, as dictated by the case-only design EXPOSURE: Precipitant-exposed (vs. precipitant-unexposed) person-days during opioid therapy. OUTCOME: Emergency department or inpatient International Classification of Diseases discharge diagnosis for unintentional traumatic injury. We used conditional Poisson regression to generate confounder adjusted rate ratios. We accounted for multiple estimation via semi-Bayes shrinkage. RESULTS: We identified 25,019, 12,650, and 10,826 new users of hydrocodone, tramadol, and oxycodone who experienced an unintentional traumatic injury. Among 464, 376, and 389 hydrocodone-, tramadol-, and oxycodone-precipitant pairs examined, 20, 17, and 16 (i.e., 53 pairs, 34 unique precipitants) were positively associated with unintentional traumatic injury and deemed potential drug interaction signals. Adjusted rate ratios ranged from 1.23 (95 % confidence interval: 1.05-1.44) for hydrocodone+amoxicillin-clavulanate to 4.21 (1.88-9.42) for oxycodone+telmisartan. Twenty (37.7 %) of 53 signals are currently reported in a major drug interaction knowledgebase. LIMITATIONS: Potential for reverse causation, confounding by indication, and chance CONCLUSIONS: We identified previously undescribed and/or unappreciated signals of opioid drug interactions associated with unintentional traumatic injury. Subsequent etiologic studies should confirm (or refute) and elucidate these potential drug interactions.

Abstract: Biodegradation is the breakdown of complex organic compounds into simpler molecules like carbon dioxide and water by microorganisms like bacteria and fungi. Biodegradation studies of pharmaceuticals are initially done to assess which pharmaceuticals are persistent in the environment. Whole pharmaceuticals or their metabolites are excreted from the human body via urine or fecal matter after administration. These go into the Wastewater Treatment Plants (WWTP) and are later released into the environment with the treated wastewater. Recent studies have reported a number of pharmaceuticals in the ecosystem and the effects of these on non-target species has become an issue of environmental concern. The biodegradation studies of eight pharmaceuticals were carried out in this research. The choice of pharmaceuticals was based on the most commonly prescribed medications at the University of Zambia (UNZA) Clinic in seven therapeutic groups: anti-hypertensives, antibiotic, antimalarial drugs, anti-tuberculosis, antihelminthics, antifungals and antiretroviral drugs. The biodegradability tests were carried out using a modified carbon dioxide evolution method (modified Sturm test). The inoculum was derived from the secondary effluent of the UNZA WWTP plant and Dextrose monohydrate was used as a system control. Using this guideline, the system control, dextrose monohydrate biodegraded 77 +/- 0.270% in seven days. All the pharmaceuticals except ciprofloxacin were found to be non-biodegradable: Atenolol degraded 6.8 +/- 0.026%, ketoconazole degraded 1.0 +/- 0.003%, isoniazid/rifampicin degraded 0.8 +/- 0.003%, mebendazole degraded 13.0 +/- 0.050%, nevirapine degraded 1.3 +/- 0.005%, pen-v degraded 1.0 +/- 0.004% and quinine sulfate degraded 1.8 +/- 0.008%. Ciprofloxacin showed a negative carbon dioxide evolution and it was noted that bacteria were not viable as the drug proved to be very potent against bacterial strains in the inoculum used.

Abstract: Aging is the dominant risk factor for most chronic diseases. Development of antiaging interventions offers the promise of preventing many such illnesses simultaneously. Cellular stress resistance is an evolutionarily conserved feature of longevity. Here, we identify compounds that induced resistance to the superoxide generator paraquat (PQ), the heavy metal cadmium (Cd), and the DNA alkylator methyl methanesulfonate (MMS). Some rescue compounds conferred resistance to a single stressor, while others provoked multiplex resistance. Induction of stress resistance in fibroblasts was predictive of longevity extension in a published large-scale longevity screen in Caenorhabditis elegans, although not in testing performed in worms and flies with a more restricted set of compounds. Transcriptomic analysis and genetic studies implicated Nrf2/SKN-1 signaling in stress resistance provided by two protective compounds, cardamonin and AEG 3482. Small molecules identified in this work may represent attractive tools to elucidate mechanisms of stress resistance in mammalian cells.

Abstract: BACKGROUND: Though currently approved immunotherapies, including chimeric antigen receptor T cells and checkpoint blockade antibodies, have been successfully used to treat hematological and some solid tumor cancers, many solid tumors remain resistant to these modes of treatment. In solid tumors, the development of effective antitumor immune responses is hampered by restricted immune cell infiltration and an immunosuppressive tumor microenvironment (TME). An immunotherapy that infiltrates and persists in the solid TME, while providing local, stable levels of therapeutic to activate or reinvigorate antitumor immunity could overcome these challenges faced by current immunotherapies. METHODS: Using lentivirus-driven engineering, we programmed human and murine macrophages to express therapeutic payloads, including Interleukin (IL)-12. In vitro coculture studies were used to evaluate the effect of genetically engineered macrophages (GEMs) secreting IL-12 on T cells and on the GEMs themselves. The effects of IL-12 GEMs on gene expression profiles within the TME and tumor burden were evaluated in syngeneic mouse models of glioblastoma and melanoma and in human tumor slices isolated from patients with advanced gastrointestinal malignancies. RESULTS: Here, we present a cellular immunotherapy platform using lentivirus-driven genetic engineering of human and mouse macrophages to constitutively express proteins, including secreted cytokines and full-length checkpoint antibodies, as well as cytoplasmic and surface proteins that overcomes these barriers. GEMs traffic to, persist in, and express lentiviral payloads in xenograft mouse models of glioblastoma, and express a non-signaling truncated CD19 surface protein for elimination. IL-12-secreting GEMs activated T cells and induced interferon-gamma (IFNgamma) in vitro and slowed tumor growth resulting in extended survival in vivo. In a syngeneic glioblastoma model, IFNgamma signaling cascades were also observed in mice treated with mouse bone-marrow-derived GEMs secreting murine IL-12. These findings were reproduced in ex vivo tumor slices comprised of intact MEs. In this setting, IL-12 GEMs induced tumor cell death, chemokines and IFNgamma-stimulated genes and proteins. CONCLUSIONS: Our data demonstrate that GEMs can precisely deliver titratable doses of therapeutic proteins to the TME to improve safety, tissue penetrance, targeted delivery and pharmacokinetics.

Abstract: Increased blood sugar levels in prolonged diabetes lead to secondary complications such as retinopathy, neuropathy, and nephropathy, which gradually end in death. Synthesis of nano-phytomedicines from active phytoconstituents for novel emerging applications in the field of pharmaceuticals is of huge interest among researchers. In the present investigation, encapsulated ellagic acid (NEA) was synthesized at four different concentrations (0.2%, 0.3%, 0.4%, 0.5%) using ZnO nanoparticles as encapsulating agent. The surface morphology (fiber-like structures) of the nanoparticles were determined by scanning electron microscopy (SEM) and particle size (161-297 nm) and zeta potential (- 54.9-38.4 mV) were determined by dynamic light scattering technique. Further, the alpha-glucosidase and aldose reductase enzymes were significantly inhibited by the 0.4% of NEA compared to the other concentrations which strengthened our studies in overcoming the secondary complications of diabetes. The interaction analysis between ellagic acid and insulin receptor found Hit 1 among 10 executed G score and energy of - 5.76, - 4.63 kcal/mol and formed polar bond with Arg 113 with - 1.175 A distance. The residues Arg115, Lys116, Phe118, Ile115, Arg1131, Arg1155, Ile1157, Lys1165 and Phe1186 were found in ligand-protein interactions. ADME/T analysis of hit 1 was within the acceptable range without any toxic functional groups, providing a framework for developing novel therapeutics.

Abstract: Historically, the main classes of drug targets have been receptors, enzymes, ion channels and transporters. However, owing largely to the rise of antibody-based therapies in the past two decades, soluble protein ligands such as inflammatory cytokines have become an increasingly important class of drug targets. In this Review, we analyse drugs targeting ligands that have reached clinical development at some point since 1992. We identify 291 drugs that target 99 unique ligands, and we discuss trends in the characteristics of the ligands, drugs and indications for which they have been tested. In the last 5 years, the number of ligand-targeting drugs approved by the FDA has doubled to 34, while the number of clinically validated ligand targets has doubled to 22. Cytokines and growth factors are the predominant types of targeted ligands (70%), and inflammation and autoimmune disorders, cancer and ophthalmological diseases are the top therapeutic areas for both approved agents and agents in clinical studies, reflecting the central role of cytokine and/or growth factor pathways in such diseases.

Abstract: The accumulation of various types of drug informatics data and computational approaches for drug repositioning can accelerate pharmaceutical research and development. However, the integration of multi-dimensional drug data for precision repositioning remains a pressing challenge. Here, we propose a systematic framework named PIMD to predict drug therapeutic properties by integrating multi-dimensional data for drug repositioning. In PIMD, drug similarity networks (DSNs) based on chemical, pharmacological, and clinical data are fused into an integrated DSN (iDSN) composed of many clusters. Rather than simple fusion, PIMD offers a systematic way to annotate clusters. Unexpected drugs within clusters and drug pairs with a high iDSN similarity score are therefore identified to predict novel therapeutic uses. PIMD provides new insights into the universality, individuality, and complementarity of different drug properties by evaluating the contribution of each property data. To test the performance of PIMD, we use chemical, pharmacological, and clinical properties to generate an iDSN. Analyses of the contributions of each drug property indicate that this iDSN was driven by all data types and performs better than other DSNs. Within the top 20 recommended drug pairs, 7 drugs have been reported to be repurposed. The source code for PIMD is available at https://github.com/Sepstar/PIMD/.

Abstract: BACKGROUND: Checkpoint targets play a key role in tumor-mediated immune escape and therefore are critical for cancer immunotherapy. Unfortunately, there is a lack of bioinformatics resource that compile all the checkpoint targets for translational research and drug discovery in immuno-oncology. METHODS: To this end, we developed checkpoint therapeutic target database (CKTTD), the first comprehensive database for immune checkpoint targets (proteins, miRNAs and LncRNAs) and their modulators. A scoring system was adopted to filter more relevant targets with high confidence. In addition, a few biological databases such as Oncomine, Drugbank, miRBase and Lnc2Cancer database were integrated into CKTTD to provide an in-depth information. Moreover, we computed and provided ligand-binding site information for all the targets which may support bench scientists for drug discovery efforts. RESULTS: In total, CKTTD compiles 105 checkpoint protein targets, 53 modulators (small-molecules and antibody), 30 miRNAs and 18 LncRNAs in cancer immunotherapy with validated experimental evidences curated from 10 649 literatures via an enhanced text-mining system. CONCLUSIONS: In conclusion, the CKTTD may serve as a useful platform for the research of cancer immunotherapy and drug discovery. The CKTTD database is freely available to public at http://www.ckttdb.org/.

Abstract: Cancer patient classification using predictive biomarkers for anti-cancer drug responses is essential for improving therapeutic outcomes. However, current machine-learning-based predictions of drug response often fail to identify robust translational biomarkers from preclinical models. Here, we present a machine-learning framework to identify robust drug biomarkers by taking advantage of network-based analyses using pharmacogenomic data derived from three-dimensional organoid culture models. The biomarkers identified by our approach accurately predict the drug responses of 114 colorectal cancer patients treated with 5-fluorouracil and 77 bladder cancer patients treated with cisplatin. We further confirm our biomarkers using external transcriptomic datasets of drug-sensitive and -resistant isogenic cancer cell lines. Finally, concordance analysis between the transcriptomic biomarkers and independent somatic mutation-based biomarkers further validate our method. This work presents a method to predict cancer patient drug responses using pharmacogenomic data derived from organoid models by combining the application of gene modules and network-based approaches.

Abstract: The coronavirus disease-19 (COVID-19) outbreak that is caused by a highly contagious severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has become a zoonotic pandemic, with approximately 24.5 million positive cases and 8.3 lakhs deaths globally. The lack of effective drugs or vaccine provoked the research for drug candidates that can disrupt the spread and progression of the virus. The identification of drug molecules through experimental studies is time-consuming and expensive, so there is a need for developing alternative strategies like in silico approaches which can yield better outcomes in less time. Herein, we selected transmembrane protease serine 2 (TMPRSS2) enzyme, a potential pharmacological target against SARS-CoV-2, involved in the spread and pathogenesis of the virus. Since 3D structure is not available for this protein, the present study aims at homology modelling and validation of TMPRSS2 using Swiss-model server. Validation of the modelled TMPRSS2 using various online tools confirmed model accuracy, topology and stereochemical plausibility. The catalytic triad consisting of Serine-441, Histidine-296 and Aspartic acid-345 was identified as active binding site of TMPRSS2 using existing ligands. Molecular docking studies of various drugs and phytochemicals against the modelled TMPRSS2 were performed using camostat as a standard drug. The results revealed eight potential drug candidates, namely nafamostat, meloxicam, ganodermanontriol, columbin, myricetin, proanthocyanidin A2, jatrorrhizine and baicalein, which were further studied for ADME/T properties. In conclusion, the study unravelled eight high affinity binding compounds, which may serve as potent antagonists against TMPRSS2 to impact COVID-19 drug therapy.