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Silverberg JI, Toth D, Bieber T, Alexis AF, Elewski BE, Pink AE, Hijnen D, Jensen TN, Bang B, Olsen CK, Kurbasic A, Weidinger S. Tralokinumab plus topical corticosteroids for the treatment of moderate-to-severe atopic dermatitis: results from the double-blind, randomized, multicentre, placebo-controlled phase III ECZTRA 3 trial. Br J Dermatol 2021; 184:450-463. [PMID: 33000503 PMCID: PMC7986183 DOI: 10.1111/bjd.19573] [Citation(s) in RCA: 137] [Impact Index Per Article: 45.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/25/2020] [Indexed: 12/13/2022]
Abstract
Background Tralokinumab is a fully human monoclonal antibody that specifically neutralizes interleukin‐13, a key driver of atopic dermatitis (AD). Objectives To evaluate the efficacy and safety of tralokinumab in combination with topical corticosteroids (TCS) in patients with moderate‐to‐severe AD who were candidates for systemic therapy. Methods This was a double‐blind, placebo plus TCS controlled phase III trial. Patients were randomized 2 : 1 to subcutaneous tralokinumab 300 mg or placebo every 2 weeks (Q2W) with TCS as needed over 16 weeks. Patients who achieved an Investigator’s Global Assessment (IGA) score of 0/1 and/or 75% improvement in Eczema Area and Severity Index (EASI 75) at week 16 with tralokinumab were rerandomized 1 : 1 to tralokinumab Q2W or every 4 weeks (Q4W), with TCS as needed, for another 16 weeks. Results At week 16, more patients treated with tralokinumab than with placebo achieved IGA 0/1: 38·9% vs. 26·2% [difference (95% confidence interval): 12·4% (2·9–21·9); P = 0·015] and EASI 75: 56·0% vs. 35·7% [20·2% (9·8–30·6); P < 0·001]. Of the patients who were tralokinumab responders at week 16, 89·6% and 92·5% of those treated with tralokinumab Q2W and 77·6% and 90·8% treated with tralokinumab Q4W maintained an IGA 0/1 and EASI 75 response at week 32, respectively. Among patients who did not achieve IGA 0/1 and EASI 75 with tralokinumab Q2W at 16 weeks, 30·5% and 55·8% achieved these endpoints, respectively, at week 32. The overall incidence of adverse events was similar across treatment groups. Conclusions Tralokinumab 300 mg in combination with TCS as needed was effective and well tolerated in patients with moderate‐to‐severe AD. What is already known about this topic?Atopic dermatitis (AD) is a chronic interleukin (IL)‐13‐mediated disease. In clinical practice, biologics are commonly initiated as add‐on therapy to topical corticosteroids (TCS). Tralokinumab is a fully human monoclonal antibody that binds specifically to the IL‐13 cytokine with high affinity, thereby preventing receptor interaction and subsequent downstream signalling. Tralokinumab combined with TCS showed early and sustained efficacy and safety in a 12‐week, phase IIb trial in moderate‐to‐severe AD.
What does this study add?This is the first phase III trial evaluating a targeted anti‐IL‐13 biologic in combination with TCS. These data demonstrate that tralokinumab plus TCS can achieve significant improvements in AD signs and symptoms and quality of life, as well as exert a steroid‐sparing effect. Response with tralokinumab in combination with TCS was maintained over 32 weeks. Tralokinumab may be considered a targeted biological treatment option for patients with moderate‐to‐severe AD.
Linked Comment: Morra and Drucker. Br J Dermatol 2021; 184:386–387. Plain language summary available online
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Affiliation(s)
- J I Silverberg
- Department of Dermatology, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA
| | - D Toth
- XLR8 Medical Research and Probity Medical Research, Windsor, ON, Canada
| | - T Bieber
- Department of Dermatology and Allergy, University Medical Center, Bonn, Germany
| | - A F Alexis
- Department of Dermatology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - B E Elewski
- Department of Dermatology, University of Alabama, Birmingham, AL, USA
| | - A E Pink
- St John's Institute of Dermatology, Guy's and St Thomas' Hospitals, London, UK
| | - D Hijnen
- Department of Dermatology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | | | - B Bang
- LEO Pharma A/S, Ballerup, Denmark
| | | | | | - S Weidinger
- Department of Dermatology and Allergy, University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany
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Obura M, Beulens JWJ, Slieker R, Koopman ADM, Hoekstra T, Nijpels G, Elders P, Dekker JM, Koivula RW, Kurbasic A, Laakso M, Hansen TH, Ridderstråle M, Hansen T, Pavo I, Forgie I, Jablonka B, Ruetten H, Mari A, McCarthy MI, Walker M, McDonald TJ, Perry MH, Pearson ER, Franks PW, 't Hart LM, Rutters F. Clinical profiles of post-load glucose subgroups and their association with glycaemic traits over time: An IMI-DIRECT study. Diabet Med 2021; 38:e14428. [PMID: 33067862 DOI: 10.1111/dme.14428] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Revised: 09/10/2020] [Accepted: 10/14/2020] [Indexed: 12/11/2022]
Abstract
AIM To examine the hypothesis that, based on their glucose curves during a seven-point oral glucose tolerance test, people at elevated type 2 diabetes risk can be divided into subgroups with different clinical profiles at baseline and different degrees of subsequent glycaemic deterioration. METHODS We included 2126 participants at elevated type 2 diabetes risk from the Diabetes Research on Patient Stratification (IMI-DIRECT) study. Latent class trajectory analysis was used to identify subgroups from a seven-point oral glucose tolerance test at baseline and follow-up. Linear models quantified the associations between the subgroups with glycaemic traits at baseline and 18 months. RESULTS At baseline, we identified four glucose curve subgroups, labelled in order of increasing peak levels as 1-4. Participants in Subgroups 2-4, were more likely to have higher insulin resistance (homeostatic model assessment) and a lower Matsuda index, than those in Subgroup 1. Overall, participants in Subgroups 3 and 4, had higher glycaemic trait values, with the exception of the Matsuda and insulinogenic indices. At 18 months, change in homeostatic model assessment of insulin resistance was higher in Subgroup 4 (β = 0.36, 95% CI 0.13-0.58), Subgroup 3 (β = 0.30; 95% CI 0.10-0.50) and Subgroup 2 (β = 0.18; 95% CI 0.04-0.32), compared to Subgroup 1. The same was observed for C-peptide and insulin. Five subgroups were identified at follow-up, and the majority of participants remained in the same subgroup or progressed to higher peak subgroups after 18 months. CONCLUSIONS Using data from a frequently sampled oral glucose tolerance test, glucose curve patterns associated with different clinical characteristics and different rates of subsequent glycaemic deterioration can be identified.
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Affiliation(s)
- M Obura
- Epidemiology and Data Science, Amsterdam Public Health research institute, VU University Medical Center, Amsterdam, The Netherlands
| | - J W J Beulens
- Epidemiology and Data Science, Amsterdam Public Health research institute, VU University Medical Center, Amsterdam, The Netherlands
- Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - R Slieker
- Epidemiology and Data Science, Amsterdam Public Health research institute, VU University Medical Center, Amsterdam, The Netherlands
- Department of Cell and Chemical Biology, Leiden University Medical Centre, Leiden, The Netherlands
| | - A D M Koopman
- Epidemiology and Data Science, Amsterdam Public Health research institute, VU University Medical Center, Amsterdam, The Netherlands
| | - T Hoekstra
- Epidemiology and Data Science, Amsterdam Public Health research institute, VU University Medical Center, Amsterdam, The Netherlands
- Department of Health Sciences, Faculty of Earth and Life Sciences, VU University, Amsterdam, The Netherlands
| | - G Nijpels
- Department of General Practice and Elderly Care Medicine, Amsterdam Public Health Research Institute, VU University Medical Centre, Amsterdam, The Netherlands
| | - P Elders
- Department of General Practice and Elderly Care Medicine, Amsterdam Public Health Research Institute, VU University Medical Centre, Amsterdam, The Netherlands
| | - J M Dekker
- Epidemiology and Data Science, Amsterdam Public Health research institute, VU University Medical Center, Amsterdam, The Netherlands
| | - R W Koivula
- Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University, Malmö, Sweden
- Oxford Centre for Diabetes, Endocrinology and Metabolism (OCDEM), University of Oxford, Oxford, UK
| | - A Kurbasic
- Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University, Malmö, Sweden
| | - M Laakso
- Department of Medicine, University of Eastern Finland and Kuopio University Hospital, Finland
| | - T H Hansen
- The Novo Nordisk Foundation Centre for Basic Metabolic Research, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Cardiology and Endocrinology, Slagelse Hospital, Slagelse, Denmark
| | - M Ridderstråle
- The Novo Nordisk Foundation Centre for Basic Metabolic Research, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark
| | - T Hansen
- The Novo Nordisk Foundation Centre for Basic Metabolic Research, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark
- Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark
| | - I Pavo
- Eli Lilly Regional Operations GmbH, Vienna, Austria
| | - I Forgie
- Division of Cardiovascular & Diabetes Medicine, Medical Research Institute, University of Dundee, Dundee, UK
| | - B Jablonka
- Sanofi-Aventis Deutschland GmbH, R&D, Frankfurt am Main, Germany
| | - H Ruetten
- Sanofi-Aventis Deutschland GmbH, R&D, Frankfurt am Main, Germany
| | - A Mari
- Institute of Biomedical Engineering, National Research Council, Padova, Italy
| | - M I McCarthy
- Oxford Centre for Diabetes, Endocrinology and Metabolism (OCDEM), University of Oxford, Oxford, UK
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | - M Walker
- Institute of Cellular Medicine (Diabetes), Newcastle University, Newcastle upon Tyne, UK
| | - T J McDonald
- NIHR Exeter Clinical Research Facility, University of Exeter Medical School and Royal Devon and Exeter NHS Foundation Trust, Exeter, UK
| | - M H Perry
- Department of Blood Sciences, Royal Devon and Exeter NHS Foundation Trust, Exeter, UK
| | - E R Pearson
- Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, UK
| | - P W Franks
- Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University, Malmö, Sweden
- Oxford Centre for Diabetes, Endocrinology and Metabolism (OCDEM), University of Oxford, Oxford, UK
- Department of Nutrition, Harvard School of Public Health, Boston, MA, USA
| | - L M 't Hart
- Epidemiology and Data Science, Amsterdam Public Health research institute, VU University Medical Center, Amsterdam, The Netherlands
- Department of Cell and Chemical Biology, Leiden University Medical Centre, Leiden, The Netherlands
- Department of Biomedical Data Sciences, Molecular Epidemiology Section, Leiden University Medical Centre, Leiden, The Netherlands
| | - F Rutters
- Epidemiology and Data Science, Amsterdam Public Health research institute, VU University Medical Center, Amsterdam, The Netherlands
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Keller M, Dalla-Riva J, Kurbasic A, Al-Majdoub M, Spegel P, de Marinis Y, Wierup N, Ling C, Renström E, Hansson O, Mulder H, Franks PW. Genome editing (CRISPR-Cas9) to identify and characterise functional variants determining metformin response. DIABETOL STOFFWECHS 2018. [DOI: 10.1055/s-0038-1657798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Affiliation(s)
- M Keller
- Universität Leipzig, Leipzig, Germany
- Lund University, Department of Clinical Science, Clinical Research Centre, Skåne University Hospital, Malmö, Sweden
| | - J Dalla-Riva
- Lund University, Department of Clinical Science, Clinical Research Centre, Skåne University Hospital, Malmö, Sweden
| | - A Kurbasic
- Lund University, Department of Clinical Science, Clinical Research Centre, Skåne University Hospital, Malmö, Sweden
| | - M Al-Majdoub
- Lund University, Department of Clinical Science, Clinical Research Centre, Skåne University Hospital, Malmö, Sweden
| | - P Spegel
- Lund University, Department of Clinical Science, Clinical Research Centre, Skåne University Hospital, Malmö, Sweden
| | - Y de Marinis
- Lund University, Department of Clinical Science, Clinical Research Centre, Skåne University Hospital, Malmö, Sweden
| | - N Wierup
- Lund University, Department of Clinical Science, Clinical Research Centre, Skåne University Hospital, Malmö, Sweden
| | - C Ling
- Lund University, Department of Clinical Science, Clinical Research Centre, Skåne University Hospital, Malmö, Sweden
| | - E Renström
- Lund University, Department of Clinical Science, Clinical Research Centre, Skåne University Hospital, Malmö, Sweden
| | - O Hansson
- Lund University, Department of Clinical Science, Clinical Research Centre, Skåne University Hospital, Malmö, Sweden
| | - H Mulder
- Lund University, Department of Clinical Science, Clinical Research Centre, Skåne University Hospital, Malmö, Sweden
| | - PW Franks
- Lund University, Department of Clinical Science, Clinical Research Centre, Skåne University Hospital, Malmö, Sweden
- Umeå University, Department of Plublic Health and Clinical Medicine, Section for Medicine, Umeå, Sweden
- Harvard T.H. Chan School of Public Health, Department of Nutrition, Boston, United States
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Kurbasic A, Hössjer O. Relative Risks and Effective Number of Meioses: A Unified Approach for General Genetic Models and Phenotypes. Ann Hum Genet 2006; 70:907-22. [PMID: 17044865 DOI: 10.1111/j.1469-1809.2006.00266.x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Many common diseases are known to have genetic components, but since they are non-Mendelian, i.e. a large number of genetic factors affect the phenotype, these components are difficult to localize. These traits are often called complex and analysis of siblings is a valuable tool for mapping them. It has been shown that the power of the affected relative pairs method to detect linkage of a disease susceptibility locus depends on the locus contribution to increased risk of relatives compared with population prevalence (Risch, 1990a,b). In this paper we generalize calculation of relative risk to arbitrary phenotypes and genetic models, but also show that the relative risk can be split into the relative risk at the main locus and the relative risk due to interaction between the main locus and loci at other chromosomes. We demonstrate how the main locus contribution to the relative risk is related to probabilities of allele sharing identical by descent at the main locus, as well as power to detect linkage. To this end we use the effective number of meioses, introduced by Hössjer (2005a) as a convenient tool. Relative risks and effective number of meioses are computed for several genetic models with binary or quantitative phenotypes, with or without polygenic effects.
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Affiliation(s)
- A Kurbasic
- Mathematical Statistics, Centre for Mathematical Sciences, Lund University, Box 118, SE-221 00 Lund, Sweden.
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