Standardizing definitions and reporting guidelines for the infertility core outcome set : an international consensus development study

Acknowledgments We would like to thank the consensus development meeting participants and colleagues at the Cochrane Gynaecology and Fertility Group, University of Auckland, New Zealand. Funding This research was funded by the Catalyst Fund, Royal Society of New Zealand, Auckland Medical Research Fund and Maurice and Phyllis Paykel Trust. The funder had no role in the design and conduct of the study, the collection, management, analysis or interpretation of data or manuscript preparation. Siladitya Bhattacharya was supported by the University of Auckland Foundation Seelye Travelling Fellowship. B.W.M. is supported by a National Health and Medical Research Council Practitioner Fellowship (GNT1082548) This article has not been externally peer reviewed. This article has been published simultaneously in Fertility and SterilityPeer reviewedPublisher PD

The Genetic Landscape and Epidemiology of Phenylketonuria

Phenylketonuria (PKU), caused by variants in the phenylalanine hydroxylase (PAH) gene, is the most common autosomal-recessive Mendelian phenotype of amino acid metabolism. We estimated that globally 0.45 million individuals have PKU, with global prevalence 1:23,930 live births (range 1:4,500 [Italy]-1:125,000 [Japan]). Comparing genotypes and metabolic phenotypes from 16,092 affected subjects revealed differences in disease severity in 51 countries from 17 world regions, with the global phenotype distribution of 62% classic PKU, 22% mild PKU, and 16% mild hyperphenylalaninemia. A gradient in genotype and phenotype distribution exists across Europe, from classic PKU in the east to mild PKU in the southwest and mild hyperphenylalaninemia in the south. The c.1241A gt G (p.Tyr414Cys)-associated genotype can be traced from Northern to Western Europe, from Sweden via Norway, to Denmark, to the Netherlands. The frequency of classic PKU increases from Europe (56%) via Middle East (71%) to Australia (80%). Of 758 PAH variants, c.1222C gt T (p.Arg408Trp) (22.2%), c.1066-11G gt A (IVS10-11G gt A) (6.4%), and c.782G gt A (p.Arg261Gln) (5.5%) were most common and responsible for two prevalent genotypes: p.[Arg408Trp];[Arg408Trp] (11.4%) and c.[1066-11G gt A];[1066-11G gt A] (2.6%). Most genotypes (73%) were compound heterozygous, 27% were homozygous, and 55% of 3,659 different genotypes occurred in only a single individual. PAH variants were scored using an allelic phenotype value and correlated with pre-treatment blood phenylalanine concentrations (n = 6,115) and tetrahydrobiopterin loading test results (n = 4,381), enabling prediction of both a genotype-based phenotype (88%) and tetrahydrobiopterin responsiveness (83%). This study shows that large genotype databases enable accurate phenotype prediction, allowing appropriate targeting of therapies to optimize clinical outcome

Adjuvant hyperthermic intraperitoneal chemotherapy (HIPEC) in patients with colon cancer at high risk of peritoneal carcinomatosis; the COLOPEC randomized multicentre trial

Background: The peritoneum is the second most common site of recurrence in colorectal cancer. Early detection of peritoneal carcinomatosis (PC) by imaging is difficult. Patients eventually presenting with clinically apparent PC have a poor prognosis. Median survival is only about five months if untreated and the benefit of palliative systemic chemotherapy is limited. Only a quarter of patients are eligible for curative treatment, consisting of cytoreductive surgery and hyperthermic intraperitoneal chemotherapy (CR/HIPEC). However, the effectiveness depends highly on the extent of disease and the treatment is associated with a considerable complication rate. These clinical problems underline the need for effective adjuvant therapy in high-risk patients to minimize the risk of outgrowth of peritoneal micro metastases. Adjuvant hyperthermic intraperitoneal chemotherapy (HIPEC) seems to be suitable for this purpose. Without the need for cytoreductive surgery, adjuvant HIPEC can be performed with a low complication rate and short hospital stay. Methods/Design: The aim of this study is to determine the effectiveness of adjuvant HIPEC in preventing the development of PC in patients with colon cancer at high risk of peritoneal recurrence. This study will be performed in the nine Dutch HIPEC centres, starting in April 2015. Eligible for inclusion are patients who underwent curative resection for T4 or intra-abdominally perforated cM0 stage colon cancer. After resection of the primary tumour, 176 patients will be randomized to adjuvant HIPEC followed by routine adjuvant systemic chemotherapy in the experimental arm, or to systemic chemotherapy only in the control arm. Adjuvant HIPEC will be performed simultaneously or shortly after the primary resection. Oxaliplatin will be used as chemotherapeutic agent, for 30 min at 42-43 degrees C. Just before HIPEC, 5-fluorouracil and leucovorin will be administered intravenously. Primary endpoint is peritoneal disease-free survival at 18 months. Diagnostic laparoscopy will be performed routinely after 18 months postoperatively in both arms of the study in patients without evidence of disease based on routine follow-up using CT imaging and CEA. Discussion: Adjuvant HIPEC is assumed to reduce the expected 25 % absolute risk of PC in patients with T4 or perforated colon cancer to a risk of 10 %. This reduction is likely to translate into a prolonged overall survival

Developing a core outcome set for future infertility research : An international consensus development study

STUDY QUESTION: Can a core outcome set to standardize outcome selection, collection and reporting across future infertility research be developed? SUMMARY ANSWER: A minimum data set, known as a core outcome set, has been developed for randomized controlled trials (RCTs) and systematic reviews evaluating potential treatments for infertility. WHAT IS KNOWN ALREADY: Complex issues, including a failure to consider the perspectives of people with fertility problems when selecting outcomes, variations in outcome definitions and the selective reporting of outcomes on the basis of statistical analysis, make the results of infertility research difficult to interpret. STUDY DESIGN, SIZE, DURATION: A three-round Delphi survey (372 participants from 41 countries) and consensus development workshop (30 participants from 27 countries). PARTICIPANTS/MATERIALS, SETTING, METHODS: Healthcare professionals, researchers and people with fertility problems were brought together in an open and transparent process using formal consensus science methods. MAIN RESULTS AND THE ROLE OF CHANCE: The core outcome set consists of: viable intrauterine pregnancy confirmed by ultrasound (accounting for singleton, twin and higher multiple pregnancy); pregnancy loss (accounting for ectopic pregnancy, miscarriage, stillbirth and termination of pregnancy); live birth; gestational age at delivery; birthweight; neonatal mortality; and major congenital anomaly. Time to pregnancy leading to live birth should be reported when applicable. LIMITATIONS, REASONS FOR CAUTION: We used consensus development methods which have inherent limitations, including the representativeness of the participant sample, Delphi survey attrition and an arbitrary consensus threshold. WIDER IMPLICATIONS OF THE FINDINGS: Embedding the core outcome set within RCTs and systematic reviews should ensure the comprehensive selection, collection and reporting of core outcomes. Research funding bodies, the Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) statement, and over 80 specialty journals, including the Cochrane Gynaecology and Fertility Group, Fertility and Sterility and Human Reproduction, have committed to implementing this core outcome set. STUDY FUNDING/COMPETING INTEREST(S): This research was funded by the Catalyst Fund, Royal Society of New Zealand, Auckland Medical Research Fund and Maurice and Phyllis Paykel Trust. The funder had no role in the design and conduct of the study, the collection, management, analysis or interpretation of data, or manuscript preparation. B.W.J.M. is supported by a National Health and Medical Research Council Practitioner Fellowship (GNT1082548). S.B. was supported by University of Auckland Foundation Seelye Travelling Fellowship. S.B. reports being the Editor-in-Chief of Human Reproduction Open and an editor of the Cochrane Gynaecology and Fertility group. J.L.H.E. reports being the Editor Emeritus of Human Reproduction. J.M.L.K. reports research sponsorship from Ferring and Theramex. R.S.L. reports consultancy fees from Abbvie, Bayer, Ferring, Fractyl, Insud Pharma and Kindex and research sponsorship from Guerbet and Hass Avocado Board. B.W.J.M. reports consultancy fees from Guerbet, iGenomix, Merck, Merck KGaA and ObsEva. C.N. reports being the Co Editor-in-Chief of Fertility and Sterility and Section Editor of the Journal of Urology, research sponsorship from Ferring, and retains a financial interest in NexHand. A.S. reports consultancy fees from Guerbet. E.H.Y.N. reports research sponsorship from Merck. N.L.V. reports consultancy and conference fees from Ferring, Merck and Merck Sharp and Dohme. The remaining authors declare no competing interests in relation to the work presented. All authors have completed the disclosure form

NIST Interlaboratory Study on Glycosylation Analysis of Monoclonal Antibodies: Comparison of Results from Diverse Analytical Methods

Glycosylation is a topic of intense current interest in the development of biopharmaceuticals because it is related to drug safety and efficacy. This work describes results of an interlaboratory study on the glycosylation of the Primary Sample (PS) of NISTmAb, a monoclonal antibody reference material. Seventy-six laboratories from industry, university, research, government, and hospital sectors in Europe, North America, Asia, and Australia submit- Avenue, Silver Spring, Maryland 20993; 22Glycoscience Research Laboratory, Genos, Borongajska cesta 83h, 10 000 Zagreb, Croatia; 23Faculty of Pharmacy and Biochemistry, University of Zagreb, A. Kovacˇ ic´ a 1, 10 000 Zagreb, Croatia; 24Department of Chemistry, Georgia State University, 100 Piedmont Avenue, Atlanta, Georgia 30303; 25glyXera GmbH, Brenneckestrasse 20 * ZENIT / 39120 Magdeburg, Germany; 26Health Products and Foods Branch, Health Canada, AL 2201E, 251 Sir Frederick Banting Driveway, Ottawa, Ontario, K1A 0K9 Canada; 27Graduate School of Advanced Sciences of Matter, Hiroshima University, 1-3-1 Kagamiyama Higashi-Hiroshima 739–8530 Japan; 28ImmunoGen, 830 Winter Street, Waltham, Massachusetts 02451; 29Department of Medical Physiology, Jagiellonian University Medical College, ul. Michalowskiego 12, 31–126 Krakow, Poland; 30Department of Pathology, Johns Hopkins University, 400 N. Broadway Street Baltimore, Maryland 21287; 31Mass Spec Core Facility, KBI Biopharma, 1101 Hamlin Road Durham, North Carolina 27704; 32Division of Mass Spectrometry, Korea Basic Science Institute, 162 YeonGuDanji-Ro, Ochang-eup, Cheongwon-gu, Cheongju Chungbuk, 363–883 Korea (South); 33Advanced Therapy Products Research Division, Korea National Institute of Food and Drug Safety, 187 Osongsaengmyeong 2-ro Osong-eup, Heungdeok-gu, Cheongju-si, Chungcheongbuk-do, 363–700, Korea (South); 34Center for Proteomics and Metabolomics, Leiden University Medical Center, P.O. Box 9600, 2300 RC Leiden, The Netherlands; 35Ludger Limited, Culham Science Centre, Abingdon, Oxfordshire, OX14 3EB, United Kingdom; 36Biomolecular Discovery and Design Research Centre and ARC Centre of Excellence for Nanoscale BioPhotonics (CNBP), Macquarie University, North Ryde, Australia; 37Proteomics, Central European Institute for Technology, Masaryk University, Kamenice 5, A26, 625 00 BRNO, Czech Republic; 38Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstrasse 1, 39106 Magdeburg, Germany; 39Department of Biomolecular Sciences, Max Planck Institute of Colloids and Interfaces, 14424 Potsdam, Germany; 40AstraZeneca, Granta Park, Cambridgeshire, CB21 6GH United Kingdom; 41Merck, 2015 Galloping Hill Rd, Kenilworth, New Jersey 07033; 42Analytical R&D, MilliporeSigma, 2909 Laclede Ave. St. Louis, Missouri 63103; 43MS Bioworks, LLC, 3950 Varsity Drive Ann Arbor, Michigan 48108; 44MSD, Molenstraat 110, 5342 CC Oss, The Netherlands; 45Exploratory Research Center on Life and Living Systems (ExCELLS), National Institutes of Natural Sciences, 5–1 Higashiyama, Myodaiji, Okazaki 444–8787 Japan; 46Graduate School of Pharmaceutical Sciences, Nagoya City University, 3–1 Tanabe-dori, Mizuhoku, Nagoya 467–8603 Japan; 47Medical & Biological Laboratories Co., Ltd, 2-22-8 Chikusa, Chikusa-ku, Nagoya 464–0858 Japan; 48National Institute for Biological Standards and Control, Blanche Lane, South Mimms, Potters Bar, Hertfordshire EN6 3QG United Kingdom; 49Division of Biological Chemistry & Biologicals, National Institute of Health Sciences, 1-18-1 Kamiyoga, Setagaya-ku, Tokyo 158–8501 Japan; 50New England Biolabs, Inc., 240 County Road, Ipswich, Massachusetts 01938; 51New York University, 100 Washington Square East New York City, New York 10003; 52Target Discovery Institute, Nuffield Department of Medicine, University of Oxford, Roosevelt Drive, Oxford, OX3 7FZ, United Kingdom; 53GlycoScience Group, The National Institute for Bioprocessing Research and Training, Fosters Avenue, Mount Merrion, Blackrock, Co. Dublin, Ireland; 54Department of Chemistry, North Carolina State University, 2620 Yarborough Drive Raleigh, North Carolina 27695; 55Pantheon, 201 College Road East Princeton, New Jersey 08540; 56Pfizer Inc., 1 Burtt Road Andover, Massachusetts 01810; 57Proteodynamics, ZI La Varenne 20–22 rue Henri et Gilberte Goudier 63200 RIOM, France; 58ProZyme, Inc., 3832 Bay Center Place Hayward, California 94545; 59Koichi Tanaka Mass Spectrometry Research Laboratory, Shimadzu Corporation, 1 Nishinokyo Kuwabara-cho Nakagyo-ku, Kyoto, 604 8511 Japan; 60Children’s GMP LLC, St. Jude Children’s Research Hospital, 262 Danny Thomas Place Memphis, Tennessee 38105; 61Sumitomo Bakelite Co., Ltd., 1–5 Muromati 1-Chome, Nishiku, Kobe, 651–2241 Japan; 62Synthon Biopharmaceuticals, Microweg 22 P.O. Box 7071, 6503 GN Nijmegen, The Netherlands; 63Takeda Pharmaceuticals International Co., 40 Landsdowne Street Cambridge, Massachusetts 02139; 64Department of Chemistry and Biochemistry, Texas Tech University, 2500 Broadway, Lubbock, Texas 79409; 65Thermo Fisher Scientific, 1214 Oakmead Parkway Sunnyvale, California 94085; 66United States Pharmacopeia India Pvt. Ltd. IKP Knowledge Park, Genome Valley, Shamirpet, Turkapally Village, Medchal District, Hyderabad 500 101 Telangana, India; 67Alberta Glycomics Centre, University of Alberta, Edmonton, Alberta T6G 2G2 Canada; 68Department of Chemistry, University of Alberta, Edmonton, Alberta T6G 2G2 Canada; 69Department of Chemistry, University of California, One Shields Ave, Davis, California 95616; 70Horva´ th Csaba Memorial Laboratory for Bioseparation Sciences, Research Center for Molecular Medicine, Doctoral School of Molecular Medicine, Faculty of Medicine, University of Debrecen, Debrecen, Egyetem ter 1, Hungary; 71Translational Glycomics Research Group, Research Institute of Biomolecular and Chemical Engineering, University of Pannonia, Veszprem, Egyetem ut 10, Hungary; 72Delaware Biotechnology Institute, University of Delaware, 15 Innovation Way Newark, Delaware 19711; 73Proteomics Core Facility, University of Gothenburg, Medicinaregatan 1G SE 41390 Gothenburg, Sweden; 74Department of Medical Biochemistry and Cell Biology, University of Gothenburg, Institute of Biomedicine, Sahlgrenska Academy, Medicinaregatan 9A, Box 440, 405 30, Gothenburg, Sweden; 75Department of Clinical Chemistry and Transfusion Medicine, Sahlgrenska Academy at the University of Gothenburg, Bruna Straket 16, 41345 Gothenburg, Sweden; 76Department of Chemistry, University of Hamburg, Martin Luther King Pl. 6 20146 Hamburg, Germany; 77Department of Chemistry, University of Manitoba, 144 Dysart Road, Winnipeg, Manitoba, Canada R3T 2N2; 78Laboratory of Mass Spectrometry of Interactions and Systems, University of Strasbourg, UMR Unistra-CNRS 7140, France; 79Natural and Medical Sciences Institute, University of Tu¨ bingen, Markwiesenstrae 55, 72770 Reutlingen, Germany; 80Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands; 81Division of Bioanalytical Chemistry, Amsterdam Institute for Molecules, Medicines and Systems, Vrije Universiteit Amsterdam, de Boelelaan 1085, 1081 HV Amsterdam, The Netherlands; 82Department of Chemistry, Waters Corporation, 34 Maple Street Milford, Massachusetts 01757; 83Zoetis, 333 Portage St. Kalamazoo, Michigan 49007 Author’s Choice—Final version open access under the terms of the Creative Commons CC-BY license. Received July 24, 2019, and in revised form, August 26, 2019 Published, MCP Papers in Press, October 7, 2019, DOI 10.1074/mcp.RA119.001677 ER: NISTmAb Glycosylation Interlaboratory Study 12 Molecular & Cellular Proteomics 19.1 Downloaded from https://www.mcponline.org by guest on January 20, 2020 ted a total of 103 reports on glycan distributions. The principal objective of this study was to report and compare results for the full range of analytical methods presently used in the glycosylation analysis of mAbs. Therefore, participation was unrestricted, with laboratories choosing their own measurement techniques. Protein glycosylation was determined in various ways, including at the level of intact mAb, protein fragments, glycopeptides, or released glycans, using a wide variety of methods for derivatization, separation, identification, and quantification. Consequently, the diversity of results was enormous, with the number of glycan compositions identified by each laboratory ranging from 4 to 48. In total, one hundred sixteen glycan compositions were reported, of which 57 compositions could be assigned consensus abundance values. These consensus medians provide communityderived values for NISTmAb PS. Agreement with the consensus medians did not depend on the specific method or laboratory type. The study provides a view of the current state-of-the-art for biologic glycosylation measurement and suggests a clear need for harmonization of glycosylation analysis methods. Molecular & Cellular Proteomics 19: 11–30, 2020. DOI: 10.1074/mcp.RA119.001677.L

Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570