News Article | November 11, 2016
ReportsnReports.com adds "Polycystic Kidney Disease - Pipeline Review, H2 2016" to its store providing comprehensive information on the therapeutics under development for Polycystic Kidney Disease (Respiratory), complete with analysis by stage of development, drug target, mechanism of action (MoA), route of administration (RoA) and molecule type. The guide covers the descriptive pharmacological action of the therapeutics, its complete research and development history and latest news and press releases. Complete report on H2 2016 pipeline review of Polycystic Kidney Disease with 29 market data tables and 16 figures, spread across 87 pages is available at http://www.reportsnreports.com/reports/743483-polycystic-kidney-disease-pipeline-review-h2-2016.html . Polycystic kidney disease (PKD) is a disorder in which clusters of cysts develop primarily within kidneys. Polycystic kidney disease symptoms may include high blood pressure, back or side pain, headache, blood in urine, frequent urination and kidney failure. The predisposing factors include age and family history. Treatment includes antihypertensive drugs and diuretics. Companies discussed in this Polycystic Kidney Disease Pipeline Review, H2 2016 report include Angion Biomedica Corp., Aptevo Therapeutics Inc, DiscoveryBiomed, Inc., Endocyte, Inc., IC-MedTech, Inc., Ipsen S.A. , Kadmon Corporation, LLC, ManRos Therapeutics, Metabolic Solutions Development Company, LLC, NovaTarg Therapeutics, Inc, Otsuka Holdings Co., Ltd. and XORTX Pharma Corp. Drug profiles mentioned in this research report are (ascorbic acid + menadione), ANG-3070, CIM-2, CR-8, DBM-43H11, Drugs for Polycystic Kidney Disease, EC-0371, JP-153, lanreotide acetate, menadione sodium bisulfite, MSDC-0160, MSDC-0602, oxypurinol, pyrimethamine, Small Molecule to Inhibit EnaC and CFTR for Cystic Fibrosis, Diarrhea and Autosomal Dominant Polycystic Kidney Disease, Small Molecules to Activate AMPK for Polycystic Kidney Disease, Small Molecules to Activate Somatostatin Receptor Type 4 for Polycystic Kidney Disease, Acromegaly and Neuroendocrine Tumors, Small Molecules to Inhibit Hsp90 for Polycystic Kidney Disease, STA-2842, tesevatinib tosylate, TNFR x TWEAKR and tolvaptan. The Polycystic Kidney Disease (Genetic Disorders) pipeline guide also reviews of key players involved in therapeutic development for Polycystic Kidney Disease and features dormant and discontinued projects. The guide covers therapeutics under Development by Companies /Universities /Institutes, the molecules developed by Companies in Pre-Registration, Phase III, Phase II, Phase I, Preclinical and Discovery stages are 1, 1, 2, 1, 11 and 2 respectively for Similarly, the Universities portfolio in Preclinical and Discovery stages comprises 2 and 2 molecules, respectively for Polycystic Kidney Disease. Polycystic Kidney Disease (Genetic Disorders) pipeline guide helps in identifying and tracking emerging players in the market and their portfolios, enhances decision making capabilities and helps to create effective counter strategies to gain competitive advantage. The guide is built using data and information sourced from Global Markets Direct’s proprietary databases, company/university websites, clinical trial registries, conferences, SEC filings, investor presentations and featured press releases from company/university sites and industry-specific third party sources. Additionally, various dynamic tracking processes ensure that the most recent developments are captured on a real time basis. Another newly published market research report titled on Kidney Transplant Rejection - Pipeline Review, H2 2016 provides comprehensive information on the therapeutic development for Kidney Transplant Rejection, complete with comparative analysis at various stages, therapeutics assessment by drug target, mechanism of action (MoA), route of administration (RoA) and molecule type, along with latest updates, and featured news and press releases. It also reviews key players involved in the therapeutic development for Kidney Transplant Rejection and special features on late-stage and discontinued projects. The report enhances decision making capabilities and help to create effective counter strategies to gain competitive advantage. It strengthens R&D pipelines by identifying new targets and MOAs to produce first-in-class and best-in-class products. Companies Involved in Therapeutics Development are Alexion Pharmaceuticals Inc, Amgen Inc., Amyndas Pharmaceuticals LLC, Angion Biomedica Corp., Apellis Pharmaceuticals Inc, Astellas Pharma Inc., Bio-inRen, Biogen Inc, Catalyst Biosciences, Inc., Corline Biomedical AB, CSL Limited, Digna Biotech, S.L., GlaxoSmithKline Plc, Grifols, S.A., Hansa Medical AB, Kyowa Hakko Kirin Co., Ltd., Mabtech Limited, Magnus Life Ltd, Noorik Biopharmaceuticals AG, Novartis AG, Opsona Therapeutics Limited, OSE Immunotherapeutics, Pharmicell Co., Ltd., Pharming Group N.V., Prolong Pharmaceuticals, LLC, Quark Pharmaceuticals, Inc., Shire Plc and Tiziana Life Sciences Plc. Kidney Transplant Rejection Pipeline market research report of 174 pages is available at http://www.reportsnreports.com/reports/743419-kidney-transplant-rejection-pipeline-review-h2-2016.html . ReportsnReports.com is your single source for all market research needs. 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News Article | May 10, 2017
Patients who were included in the study all had Goodpasture disease and fulfilled the following key diagnostic criteria: (1) serum anti-α3(IV)NC1 IgG by enzyme-linked immunosorbent assay (ELISA), (2) linear IgG staining of the GBM and (3) necrotizing and crescentic glomerulonephritis. HLA-DR15 typing of patients was done by monoclonal antibody staining (BIH0596, One Lambda) and flow cytometry. Blood from HLA-typed healthy humans was collected via the Australian Bone Marrow Donor Registry. HLA-DR15, HLA-DR1 and HLA-DR15/DR1 donors were molecularly typed and were excluded if they expressed DQB1*03:02, which is potentially weakly associated with susceptibility to anti-GBM disease2. Studies were approved by the Australian Bone Marrow Donor Registry and Monash Health Research Ethics Committees, and informed consent was obtained from each individual. Mouse MHCII deficient, DR15 transgenic mice and mouse MHCII deficient, DR1 transgenic mice were derived from existing HLA transgenic colonies and intercrossed so that they were on the same background as previously described4. The background was as follows: 50% C57BL/10, 43.8% C57BL/6, 6.2% DBA/2; or with an Fcgr2b−/− background: 72% C57BL/6, 25% C57BL/10 and 3% DBA/2. To generate mice transgenic for both HLA-DR15 and HLA-DR1, mice transgenic for either HLA-DR15 or HLA-DR1 were intercrossed. FcγRIIb intact HLA transgenic mice and cells were used for all experiments, except those in experimental Goodpasture disease, where Fcgr2b−/− HLA transgenic strains were used. While DR15+ mice readily break tolerance to α3(IV)NC1 when immunized with human α3 or mouse α3 , renal disease is mild4. As genetic changes in fragment crystallizable (Fc) receptors have been implicated in the development of nephritis in rodents and in humans18, Fcgr2b−/− HLA transgenic strains were used when end organ injury was an important endpoint. For in vitro experiments, cells from either male or female mice were used. For in vivo experiments both male and female mice were used, for immunization aged 8–12 weeks and for the induction of experimental Goodpasture disease aged 8–10 weeks. Experiments were approved by the Monash University Animal Ethics Committee (MMCB2011/05 and MMCB2013/21). HLA-DR15-α3 and HLA-DR1-α3 were produced in High Five insect cells (Trichoplusia ni BTI-Tn-5B1-4 cells, Invitrogen) using the baculovirus expression system essentially as described previously for HLA-DQ2/DQ8 proteins19, 20. Briefly, synthetic DNA (Integrated DNA Technologies, Iowa, USA) encoding the α- and β-chain extracellular domains of HLA-DR15 (HLA-DR1A*0101, HLA-DRB1*15:01), HLA-DR1 (HLA-DR1A*0101, HLA-DRB1*01:01) and the α3 peptide were cloned into the pZIP3 baculovirus vector19, 20. To promote correct pairing, the carboxy (C) termini of the HLA-DR15 and HLA-DR1 α- and β-chain encoded enterokinase cleavable Fos and Jun leucine zippers, respectively. The β-chains also encoded a C-terminal BirA ligase recognition sequence for biotinylation and a poly-histidine tag for purification. HLA-DR15-α3 and HLA-DR1-α3 were purified from baculovirus-infected High Five insect cell supernatants through successive steps of immobilized metal ion affinity (Ni Sepharose 6 Fast-Flow, GE Healthcare), size exclusion (S200 Superdex 16/600, GE Healthcare) and anion exchange (HiTrap Q HP, GE Healthcare) chromatography. For crystallization, the leucine zipper and associated tags were removed by enterokinase digestion (Genscript, New Jersey, USA) further purified by anion exchange chromatography, buffer exchanged into 10 mM Tris, pH 8.0, 150 mM NaCl and concentrated to 7 mg ml−1. Purified HLA-DR15-α3 and HLA-DR1-α3 proteins were buffer exchanged into 10 mM Tris pH 8.0, biotinylated using BirA ligase and tetramers assembled by addition of Streptavidin-PE (BD Biosciences) as previously described19. In mice, 107 splenocytes or cells from kidneys were digested with 5 mg ml−1 collagenase D (Roche Diagnostics, Indianapolis, Indiana, USA) and 100 mg ml−1 DNase I (Roche Diagnostics) in HBBS (Sigma-Aldrich) for 30 min at 37 °C, then filtered, erythrocytes lysed and the CD45+ leukocyte population isolated by MACS using mouse CD45 microbeads (Miltenyi Biotec); they were then surface stained with Pacific Blue-labelled anti-mouse CD4 (BD), antigen-presenting cell (APC)-Cy7-labelled anti-mouse CD8 (BioLegend) and 10 nM PE-labelled tetramer. Cells were then incubated with a Live/Dead fixable Near IR Dead Cell Stain (Thermo Scientific), permeabilized using a Foxp3 Fix/Perm Buffer Set (BioLegend) and stained with Alexa Fluor 647-labelled anti-mouse Foxp3 antibody (FJK16 s). To determine Vα2 and Vβ6 usage, cells were stained with PerCP/Cy5.5 anti-mouse Vα2 (B20.1, Biolegend) and antigen-presenting cell labelled anti-mouse Vβ6 (RR4-7, Biolegend). For each mouse a minimum of 100 cells were analysed. The tetramer+ gate was set on the basis of the CD8+ population. In humans, 3 × 107 white blood cells were surface stained with BV510-labelled anti-human CD3 (BioLegend), Pacific Blue-labelled anti-human CD4 (BioLegend), PE-Cy7-labelled anti-human CD127 (BioLegend), FITC-labelled anti-human CD25 (BioLegend) and 10 nM PE-labelled tetramer. Then, cells were incubated with a Live/Dead fixable Near IR Dead Cell Stain (Life Technologies), permeabilized using a Foxp3 Fix/Perm Buffer Set (BioLegend) and stained with Alexa Fluor 647-labelled anti-human Foxp3 antibody (150D). The tetramer+ gate was set on the basis of the CD3+CD4− population. As validation controls, we found that HLA-DR1-α3 tetramer+ cells did not bind to HLA-DR1-CLIP tetramers (data not shown). The human α3 peptide (GWISLWKGFSF), the mouse α3 peptide (DWVSLWKGFSF) and control OVA peptide (ISQAVHAAHAEINEAGR) were synthesized at >95% purity, confirmed by high-performance liquid chromatography (Mimotopes). Recombinant murine α3(IV)NC1 was generated using a baculovirus system21 and recombinant human α3(IV)NC1 expressed in HEK 293 cells22. The murine α3(IV)NC1 peptide library, which consists of 28 20-amino-acid long peptides overlapping by 12 amino acids, was synthesized as a PepSet (Mimotopes). To measure peptide specific recall responses, IFN-γ and IL-17A ELISPOTs and [3H]thymidine proliferation assays were used (Mabtech for human ELISPOTs and BD Biosciences for mouse ELISPOTs). To measure pro-inflammatory responses of HLA-DR15-α3 tetramer+ CD4+ T cells in patients with Goodpasture disease, HLA-DR15-α3 tetramer+ CD4+ T cells were enumerated then isolated from peripheral blood mononuclear cells of patients with Goodpasture disease (frozen at the time of presentation) by magnetic bead separation (Miltenyi Biotec) then co-cultured at a frequency of 400 HLA-DR15-α3 tetramer+ CD4+ T cells per well with 2 × 106 HLA-DR15-α3 tetramer-depleted mitomycin C-treated white blood cells and stimulated with either no antigens, α3 (10 μg ml−1) or whole recombinant human α3(IV)NC1 (10 μg ml−1) in supplemented RPMI media (10% male AB serum, 2 mM l-glutamine, 50 μM 2-ME, 100 U ml−1 penicillin and 0.1 mg ml−1 streptomycin) (Sigma-Aldrich). Cells were cultured for 18 h at 37 °C, 5% CO and the data expressed as numbers of IFN-γ or IL-17A spots per well. To measure pro-inflammatory responses of HLA-DR15-α3 tetramer+ CD4+ T cells in DR15+ transgenic mice, HLA-DR15-α3 tetramer+ CD4+ T cells were enumerated then isolated from pooled spleen and lymph node cells of DR15+ transgenic mice, immunized with mouse α3 10 days previously by magnetic bead separation. They were then co-cultured at a frequency of 400 HLA-DR15-α3 tetramer+ CD4+ T cells per well with 106 HLA-DR15-α3 tetramer-depleted mitomycin C-treated white blood cells and stimulated with either no antigens, mouse α3 (10 μg ml−1), human α3 (10 μg ml−1), whole recombinant mα3(IV)NC1 (10 μg ml−1) or whole recombinant hα3(IV)NC1 (10 μg ml−1) in supplemented RPMI media (10% FCS, 2 mM l-glutamine, 50 μM 2-ME, 100 U ml−1 penicillin and 0.1 mg ml−1 streptomycin). Cells were cultured for 18 h at 37 °C, 5% CO and the data expressed as numbers of IFN-γ or IL-17A spots per well. To determine the immunogenic portions of α3(IV)NC1, mice were immunized subcutaneously with peptide pools (containing α3 amino acids 1–92, 81–164, or 153–233; 10 μg per peptide per mouse), the individual peptide or in some experiments mα3 at 10 μg per mouse in Freund’s complete adjuvant (Sigma-Aldrich). Draining lymph node cells were harvested 10 days after immunization and stimulated in vitro (5 × 105 cells per well) with no antigen, peptide (10 μg ml−1) or whole α3(IV)NC1 (10 μg ml−1) in supplemented RPMI media (10% FCS, 2 mM l-glutamine, 50 μM 2-ME, 100 U ml−1 penicillin and 0.1 mg ml streptomycin). For [3H]thymidine proliferation assays, cells were cultured in triplicate for 72 h with [3H]thymidine added to culture for the last 16 h. To measure human α3 - or mouse α3 -specific responses in CD4+ T cells from naive transgenic mice or blood of healthy humans, we used a modification of a previously published protocol23. One million CD4+ T cells were cultured with 106 mitomycin-treated CD4-depleted splenocytes for 8 days in 96-well plates with or without 100 μg ml−1 of human α3 or mouse α3 . T cells were depleted from mouse cultures by sorting out CD4+CD25+ and in humans by sorting out CD4+CD25hiCD127lo cells using antibodies and a cell sorter. Cytokine secretion was detected in the cultured supernatants by cytometric bead array (BD Biosciences) or ELISA (R&D Systems). To determine proliferation, magnetically separated CD4+ T cells were labelled with CellTrace Violet (CTV; Thermo Scientific) before culture. To measure the expansion of T cells, mice were immunized with 100 μg of α3 emulsified in Freund’s complete adjuvant, then boosted 7 days later in Freund’s incomplete adjuvant. Draining lymph node cells were stained with the HLA-DR15-α3 tetramer, CD3, CD4, CXCR5, PD-1, CD8 and Live/Dead Viability dye. To determine the potency of HLA-DR1-α3 tetramer+ T cells, 106 cells per well of CD4+CD25− T effectors isolated by CD4+ magnetic beads and CD25− cell sorting from naive DR15+DR1+ mice were co-cultured with CD4+CD25+ T cells with or without depletion of HLA-DR1-α3 tetramer+ T cells from DR1+ mice at different concentrations: 0, 12.5 × 103, 25 × 103, 50 × 103 and 100 × 103 cells per well in the presence of 106 CD4-depleted mitomycin C-treated spleen and lymph node cells from DR15+DR1+mice in supplemented RPMI media (10% FCS, 2 mM l-glutamine, 50 μM 2-ME, 100 U ml−1 penicillin and 0.1 mg ml−1 streptomycin) containing 100 μg ml−1 of mouse α3 . To determine proliferation, the CD4+CD25− T effector cells were labelled with CTV before culture. Cells were cultured in triplicate for 8 days in 96-well plates. HLA transgenic mice, on an Fcgr2b−/− background, were immunized with 100 μg of α3 or mα3 subcutaneously on days 0, 7 and 14, first in Freund’s complete, and then in Freund’s incomplete, adjuvant. Mice were killed on day 42. Albuminuria was assessed in urine collected during the last 24 h by ELISA (Bethyl Laboratories) and expressed as milligrams per micromole of urine creatinine. Blood urea nitrogen and urine creatinine were measured using an autoanalyser at Monash Health. Glomerular necrosis and crescent formation were assessed on periodic acid-Schiff (PAS)-stained sections; fibrin deposition using anti-murine fibrinogen antibody (R-4025) and DAB (Sigma); CD4+ T cells, macrophages and neutrophils were detected using anti-CD4 (GK1.5), anti-CD68 (FA/11) and anti-Gr-1 (RB6-8C5) antibodies. The investigators were not blinded to allocation during experiments and outcome assessment, except in histological and immunohistochemical assessment of kidney sections. To deplete regulatory T cells, mice were injected intraperitoneally with 1 mg of an anti-CD25 monoclonal antibody (clone PC61) or rat IgG (control) 2 days before induction of disease. In these experiments, mice were randomly assigned to receive control or anti-CD25 antibodies. Individual DR15-α3 -specific CD4+ T cells were sorted into wells of a 96-well plate. Multiplex single-cell reverse transcription and PCR amplification of TCR CDR3α and CDR3β regions were performed using a panel of TRBV- and TRAV-specific oligonucleotides, as described24, 25. Briefly, mRNA was reverse transcribed in 2.5 μl using a Superscript III VILO cDNA Synthesis Kit (Thermo Fisher Scientific, Waltham, Massachusetts, USA) (containing 1× Vilo reaction mix, 1× superscript RT, 0.1% Triton X-100), and incubated at 25 °C for 10 min, 42 °C for 120 min and 85 °C for 5 min. The entire volume was then used in a 25 μl first-round PCR reaction with 1.5 U Taq DNA polymerase, 1× PCR buffer, 1.5 mM MgCl , 0.25 mM dNTPs and a mix of 25 mouse TRAV or 40 human TRAV external sense primers and a TRAC external antisense primer, along with 19 mouse TRBV or 28 human TRBV external sense primers and a TRBC external antisense primer (each at 5 pmol μl−1), using standard PCR conditions. For the second-round nested PCR, a 2.5 μl aliquot of the first-round PCR product was used in separate TRBV- and TRAV-specific PCRs, using the same reaction mix described above; however, a set of 25 mouse TRAV or 40 human TRAV internal sense primers and a TRAC internal antisense primer, or a set of 19 mouse TRBV or 28 human TRBV internal sense primers and a TRBV internal antisense primer, were used. Second-round PCR products were visualized on a gel and positive reactions were purified with ExoSAP-IT reagent. Purified products were used as template in sequencing reactions with internal TRAC or TRBC antisense primers, as described. TCR gene segments were assigned using the IMGT (International ImMunoGeneTics) database26. In mouse experiments, three mice were pooled per HLA and the number of sequences obtained were as follows. For TRAV: DR15, n = 81; DR1 n = 84; for TRBV: DR15, n = 100; DR1 n = 87; for TRAJ: DR15, n = 81; DR1 n = 84; and for TCR beta joining (TRBJ): DR15, n = 100; DR1 n = 87. Red-blood-cell-lysed splenocytes from DR1+ and DRB15+DR1+ mice were sorted on the basis of surface expression of CD4 and CD25 and being either DR1-α3 tetramer positive or negative into three groups: (1) CD4+CD25−HLA-DR1-α3 tetramer− T cells; (2) CD4+CD25+HLA-DR1-α3 tetramer− T cells; and (3) CD4+CD25+HLA-DR1-α3 tetramer+ T cells. A minimum of 1,000 cells were sorted. Immediately after sorting, the RNA was isolated and complementary DNA (cDNA) generated using a Cells to Ct Kit (Ambion) followed by a preamplification reaction using Taqman Pre Amp Master Mix (Applied Biosystems), which preamplified the following cDNAs: Il2ra, Foxp3, Ctla4, Tnfrsf18, Il7r, Sell, Pdcd1, Entpd1, Cd44, Tgfb3, Itgae, Ccr6, Lag3, Lgals1, Ikzf2, Tnfrsf25, Nrp1, Il10. The preamplified cDNA was used for RT–PCR reactions in duplicate using Taqman probes for the aforementioned genes. Each gene was expressed relative to 18S, logarithmically transformed and presented as a heat map. The Epstein-Barr-virus-transformed human B lymphoblastoid cell lines IHW09013 (SCHU, DR15-DR51-DQ6) and IHW09004 (JESTHOM, DR1-DQ5) were maintained in RPMI (Invitrogen) supplemented with 10% FCS, 50 IU ml−1 penicillin and 50 μg ml−1 streptomycin. Confirmatory tissue typing of these cells was performed by the Victorian Transplantation and Immunogenetics Service. The B-cell hybridoma LB3.1 (anti-DR) was grown in RPMI-1640 with 5% FCS at 37 °C and secreted antibody purified using protein A sepharose (BioRad). HLA-DR-presented peptides were isolated from naive DR15+Fcgr2b+/+ or DR1+Fcgr2b+/+ mice. Spleens and lymph nodes (pooled from five mice in each group) or frozen pellets of human B lymphoblastoid cell lines (triplicate samples of 109 cells) were cryogenically milled and solubilized as previously described12, 27, cleared by ultracentrifugation and MHC peptide complexes purified using LB3.1 coupled to protein A (GE Healthcare). Bound HLA complexes were eluted from each column by acidification with 10% acetic acid. The eluted mixture of peptides and HLA heavy chains was fractionated by reversed-phase high-performance liquid chromatography as previously described10. Peptide-containing fractions were analysed by nano-liquid chromatography–tandem mass spectrometry (nano-LC–MS/MS) using a ThermoFisher Q-Exactive Plus mass spectrometer (ThermoFisher Scientific, Bremen, Germany) operated as described previously10. LC–MS/MS data were searched against mouse or human proteomes (Uniprot/Swissprot v2016_11) using ProteinPilot software (SCIEX) and resulting peptide identities subjected to strict bioinformatic criteria including the use of a decoy database to calculate the false discovery rate28. A 5% false discovery rate cut-off was applied, and the filtered data set was further analysed manually to exclude redundant peptides and known contaminants as previously described29. The mass spectrometry data have been deposited in the ProteomeXchange Consortium via the PRIDE30 partner repository with the data set identifier PXD005935. Minimal core sequences found within nested sets of peptides with either N- or C-terminal extensions were extracted and aligned using MEME (http://meme.nbcr.net/meme/), where motif width was set to 9–15 and motif distribution to ‘one per sequence’31. Graphical representation of the motif was generated using IceLogo32. Crystal trials were set up at 20 °C using the hanging drop vapour diffusion method. Crystals of HLA-DR15-α3 were grown in 25% PEG 3350, 0.2 M KNO and 0.1 M Bis-Tris-propane (pH 7.5), and crystals of HLA-DR1-α3 were grown in 23% PEG 3350, 0.1 M KNO , and 0.1 M Bis-Tris-propane (pH 7.0). Crystals were washed with mother liquor supplemented with 20% ethylene glycol and flash frozen in liquid nitrogen before data collection. Data were collected using the MX1 (ref. 33) and MX2 beamlines at the Australian Synchrotron, and processed with iMosflm and Scala from the CCP4 program suite34. The structures were solved by molecular replacement in PHASER35 and refined by iterative rounds of model building using COOT36 and restrained refinement using Phenix37 (see Extended Data Table 2 for data collection and refinement statistics). No statistical methods were used to predetermine sample size. For normally distributed data, an unpaired two-tailed t-test (when comparing two groups). For non-normally distributed data, non-parametric tests (Mann–Whitney U-test for two groups or a Kruskal–Wallis test with Dunn’s multiple comparison) were used. Statistical analyses, except for TCR usage, was by GraphPad Prism (GraphPad Software). For each TCR type/region (TRAV, TRBV, TRAJ, TRBJ), we compared the TCR distribution (frequencies of different TCRs) between DR15 and DR1 using Fisher’s exact test. This was applied both to mice and to human samples. The P values associated with those TCR distributions are indicated above the pie-charts. To correct for multiple testing for individual TCRs, we used Holm’s method. *P < 0.05, **P < 0.01, ***P < 0.001. The data that support the findings of this study are available from the corresponding authors upon request. Self-peptide repertoires have been deposited in the Proteomics Identifications Database archive with the accession code PXD005935. Structural information has been deposited in the Protein Data Bank under accession numbers 5V4M and 5V4N.
Agency: European Commission | Branch: H2020 | Program: RIA | Phase: SC1-PM-01-2016 | Award Amount: 15.04M | Year: 2017
The complex interactions between genetic and non-genetic factors produce heterogeneities in patients as reflected in the diversity of pathophysiology, clinical manifestations, response to therapies, disease development and progression. Yet, the full potential of personalized medicine entails biomarker-guided delivery of efficient therapies in stratified patient populations. MultipleMS will therefore develop, validate, and exploit methods for patient stratification in Multiple Sclerosis, a chronic inflammatory disease and a leading causes of non-traumatic disability in young adults, with an estimated cost of 37 000 per patient per year over a duration of 30 years. Here we benefit from several large clinical cohorts with multiple data types, including genetic and lifestyle information. This in combination with publically available multi-omics maps enables us to identify biomarkers of the clinical course and the response to existing therapies in a real-world setting, and to gain in-depth knowledge of distinct pathogenic pathways setting the stage for development of new interventions. To create strategic global synergies, MultipleMS includes 21 partners and covers not only the necessary clinical, biological, and computational expertise, but also includes six industry partners ensuring dissemination and exploitation of the methods and clinical decision support system. Moreover, the pharmaceutical industry partners provide expertise to ensure optimal selection and validation of clinically relevant biomarkers and new targets. Our conceptual personalized approach can readily be adapted to other immune-mediated diseases with a complex gene-lifestyle background and broad clinical spectrum with heterogeneity in treatment response. MultipleMS therefore goes significantly beyond current state-of-the-art thereby broadly affecting European policies, healthcare systems, innovation in translating big data and basic research into evidence-based personalized clinical applications.
Agency: European Commission | Branch: FP7 | Program: CP-FP | Phase: HEALTH-2007-2.4.3-4 | Award Amount: 3.94M | Year: 2008
Obesity represents the major risk factor for the cardiometabolic syndrome, which is an epidemic disease that generates a severe global socio-economic burden for the public health systems. Enhanced production of proinflammatory adipocytokines by expanded adipose tissue is now considered as a key event in the pathogenesis of this syndrome. This process involves i) the systemic release of adipokines, preferentially by visceral abdominal fat and ii) the paracrine, adipokine-mediated crosstalk between periorganic fat and different organs including skeletal and cardiac muscle. Members of the ADAPT consortium have pioneered this novel view of adipose tissue as an active endocrine organ. However, there is very limited knowledge if adipokines and their downstream signalling pathways may represent drugable targets potentially opening new avenues to combat the devastating complications linked to obesity and the cardiometabolic syndrome. Therefore, the major goal of this project is to identify novel or existing adipocytokines as drug targets that could be used to reverse obesity-associated inflammation and adverse reactions related to excess fat, as outlined in the work programme. For this purpose the mustidisciplinary ADAPT consortium has been formed which integrates basic and clinical science, bioinformatics, in silico drug design and the specific expertise of a large pharmaceutical company. To reach the objectives, a stepwise strategy will be used including i) the identification of novel adipocytokines and the cellular sources and regulation of adipokine production, ii) the analysis of intraorgan crosstalk within adipose tissue which plays a pivotal role in adipose tissue inflammation, iii) the assessment of interorgan crosstalk with a focus on skeletal and cardiac muscle and the role of brown fat and iv) the pharmacological and clinical evaluation of adipokines as drug targets and potential biomarkers.
News Article | December 6, 2016
This report studies Cancer Angiogenesis Inhibitor in Global market, especially in North America, Europe, China, Japan, Southeast Asia and India, focuses on top manufacturers in global market, with Production, price, revenue and market share for each manufacturer, covering Biocon Allergan CASI Pharmaceuticals Novartis Pfizer Roche Celgene Corporation Levolta Pharmaceuticals Philogen Mabtech Market Segment by Regions, this report splits Global into several key Regions, with production, consumption, revenue, market share and growth rate of Cancer Angiogenesis Inhibitor in these regions, from 2011 to 2021 (forecast), like North America Europe China Japan Southeast Asia India Split by product type, with production, revenue, price, market share and growth rate of each type, can be divided into Type I Type II Type III Split by application, this report focuses on consumption, market share and growth rate of Cancer Angiogenesis Inhibitor in each application, can be divided into Application 1 Application 2 Application 3 Global Cancer Angiogenesis Inhibitor Market Research Report 2016 1 Cancer Angiogenesis Inhibitor Market Overview 1.1 Product Overview and Scope of Cancer Angiogenesis Inhibitor 1.2 Cancer Angiogenesis Inhibitor Segment by Type 1.2.1 Global Production Market Share of Cancer Angiogenesis Inhibitor by Type in 2015 1.2.2 Type I 1.2.3 Type II 1.2.4 Type III 1.3 Cancer Angiogenesis Inhibitor Segment by Application 1.3.1 Cancer Angiogenesis Inhibitor Consumption Market Share by Application in 2015 1.3.2 Application 1 1.3.3 Application 2 1.3.4 Application 3 1.4 Cancer Angiogenesis Inhibitor Market by Region 1.4.1 North America Status and Prospect (2011-2021) 1.4.2 Europe Status and Prospect (2011-2021) 1.4.3 China Status and Prospect (2011-2021) 1.4.4 Japan Status and Prospect (2011-2021) 1.4.5 Southeast Asia Status and Prospect (2011-2021) 1.4.6 India Status and Prospect (2011-2021) 1.5 Global Market Size (Value) of Cancer Angiogenesis Inhibitor (2011-2021) 7 Global Cancer Angiogenesis Inhibitor Manufacturers Profiles/Analysis 7.1 Biocon 7.1.1 Company Basic Information, Manufacturing Base and Its Competitors 7.1.2 Cancer Angiogenesis Inhibitor Product Type, Application and Specification 188.8.131.52 Type I 184.108.40.206 Type II 7.1.3 Biocon Cancer Angiogenesis Inhibitor Production, Revenue, Price and Gross Margin (2015 and 2016) 7.1.4 Main Business/Business Overview 7.2 Allergan 7.2.1 Company Basic Information, Manufacturing Base and Its Competitors 7.2.2 Cancer Angiogenesis Inhibitor Product Type, Application and Specification 220.127.116.11 Type I 18.104.22.168 Type II 7.2.3 Allergan Cancer Angiogenesis Inhibitor Production, Revenue, Price and Gross Margin (2015 and 2016) 7.2.4 Main Business/Business Overview 7.3 CASI Pharmaceuticals 7.3.1 Company Basic Information, Manufacturing Base and Its Competitors 7.3.2 Cancer Angiogenesis Inhibitor Product Type, Application and Specification 22.214.171.124 Type I 126.96.36.199 Type II 7.3.3 CASI Pharmaceuticals Cancer Angiogenesis Inhibitor Production, Revenue, Price and Gross Margin (2015 and 2016) 7.3.4 Main Business/Business Overview 7.4 Novartis 7.4.1 Company Basic Information, Manufacturing Base and Its Competitors 7.4.2 Cancer Angiogenesis Inhibitor Product Type, Application and Specification 188.8.131.52 Type I 184.108.40.206 Type II 7.4.3 Novartis Cancer Angiogenesis Inhibitor Production, Revenue, Price and Gross Margin (2015 and 2016) 7.4.4 Main Business/Business Overview 7.5 Pfizer 7.5.1 Company Basic Information, Manufacturing Base and Its Competitors 7.5.2 Cancer Angiogenesis Inhibitor Product Type, Application and Specification 220.127.116.11 Type I 18.104.22.168 Type II 7.5.3 Pfizer Cancer Angiogenesis Inhibitor Production, Revenue, Price and Gross Margin (2015 and 2016) 7.5.4 Main Business/Business Overview 7.6 Roche 7.6.1 Company Basic Information, Manufacturing Base and Its Competitors 7.6.2 Cancer Angiogenesis Inhibitor Product Type, Application and Specification 22.214.171.124 Type I 126.96.36.199 Type II 7.6.3 Roche Cancer Angiogenesis Inhibitor Production, Revenue, Price and Gross Margin (2015 and 2016) 7.6.4 Main Business/Business Overview 7.7 Celgene Corporation 7.7.1 Company Basic Information, Manufacturing Base and Its Competitors 7.7.2 Cancer Angiogenesis Inhibitor Product Type, Application and Specification 188.8.131.52 Type I 184.108.40.206 Type II 7.7.3 Celgene Corporation Cancer Angiogenesis Inhibitor Production, Revenue, Price and Gross Margin (2015 and 2016) 7.7.4 Main Business/Business Overview 7.8 Levolta Pharmaceuticals 7.8.1 Company Basic Information, Manufacturing Base and Its Competitors 7.8.2 Cancer Angiogenesis Inhibitor Product Type, Application and Specification 220.127.116.11 Type I 18.104.22.168 Type II 7.8.3 Levolta Pharmaceuticals Cancer Angiogenesis Inhibitor Production, Revenue, Price and Gross Margin (2015 and 2016) 7.8.4 Main Business/Business Overview 7.9 Philogen 7.9.1 Company Basic Information, Manufacturing Base and Its Competitors 7.9.2 Cancer Angiogenesis Inhibitor Product Type, Application and Specification 22.214.171.124 Type I 126.96.36.199 Type II 7.9.3 Philogen Cancer Angiogenesis Inhibitor Production, Revenue, Price and Gross Margin (2015 and 2016) 7.9.4 Main Business/Business Overview 7.10 Mabtech 7.10.1 Company Basic Information, Manufacturing Base and Its Competitors 7.10.2 Cancer Angiogenesis Inhibitor Product Type, Application and Specification 188.8.131.52 Type I 184.108.40.206 Type II 7.10.3 Mabtech Cancer Angiogenesis Inhibitor Production, Revenue, Price and Gross Margin (2015 and 2016) 7.10.4 Main Business/Business Overview
Vallhov H.,Karolinska Institutet |
Kupferschmidt N.,Uppsala University |
Gabrielsson S.,Karolinska Institutet |
Paulie S.,Mabtech Ab |
And 3 more authors.
Small | Year: 2012
Alum is the most frequently used adjuvant today, primarily inducing Th2 responses. However, Th1-type responses are often desirable within immune therapy, and therefore the development of new adjuvants is greatly needed. Mesoporous silica particles with a highly ordered pore structure have properties that make them very interesting for future controlled drug delivery systems, such as controllable particle and pore size; they also have the ability to induce minor immune modulatory effects, as previously demonstrated on human-monocyte-derived dendritic cells (MDDCs). In this study, mesoporous silica particles are shown to be efficiently engulfed by MDDCs within 2 h, probably by phagocytic uptake, as seen by confocal microscopy and transmission electron microscopy. A co-culture protocol is developed to evaluate the capability of MDDCs to stimulate the development of naïve CD4+ T cells in different directions. The method, involving ELISpot as a readout system, demonstrates that MDDCs, after exposure to mesoporous silica particles (AMS-6 and SBA-15), are capable of tuning autologous naïve T cells into different effector cells. Depending on the size and functionalization of the particles added to the cells, different cytokine patterns are detected. This suggests that mesoporous silica particles can be used as delivery vehicles with tunable adjuvant properties, which may be of importance for several medical applications, such as immune therapy and vaccination. Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Jahnmatz M.,Swedish Institute for Communicable Disease Control |
Kesa G.,Mabtech AB |
Netterlid E.,Swedish Institute for Communicable Disease Control |
Buisman A.-M.,National Institute for Public Health and the Environment |
And 2 more authors.
Journal of Immunological Methods | Year: 2013
B-cell responses after infection or vaccination are often measured as serum titers of antigen-specific antibodies. Since this does not address the aspect of memory B-cell activity, it may not give a complete picture of the B-cell response. Analysis of memory B cells by ELISpot is therefore an important complement to conventional serology. B-cell ELISpot was developed more than 25. years ago and many assay protocols/reagents would benefit from optimization. We therefore aimed at developing an optimized B-cell ELISpot for the analysis of vaccine-induced human IgG-secreting memory B cells. A protocol was developed based on new monoclonal antibodies to human IgG and biotin-avidin amplification to increase the sensitivity. After comparison of various compounds commonly used to in vitro-activate memory B cells for ELISpot analysis, the TLR agonist R848 plus Interleukin (IL)-2 was selected as the most efficient activator combination. The new protocol was subsequently compared to an established protocol, previously used in vaccine studies, based on polyclonal antibodies without biotin avidin amplification and activation of memory B-cells using a mix of antigen, CpG, IL-2 and IL-10. The new protocol displayed significantly better detection sensitivity, shortened the incubation time needed for the activation of memory B cells and reduced the amount of antigen required for the assay. The functionality of the new protocol was confirmed by analyzing specific memory B cells to five different antigens, induced in a limited number of subjects vaccinated against tetanus, diphtheria and pertussis. The limited number of subjects did not allow for a direct comparison with other vaccine studies. Optimization of the B-cell ELISpot will facilitate an improved analysis of IgG-secreting B cells in vaccine studies. © 2013 Elsevier B.V.