This article is a commentary on:
Efficacy and safety of the innovative monoclonal antibodies in adults with generalized myasthenia gravis: a Bayesian network analysis
Ina Zhang
Jeroen P. Jansen1
Adrian Kielhorn- 1Evidence Synthesis and Decision Modeling, PRECISIONheor, Oakland, CA, United States
- 2Alexion, AstraZeneca Rare Disease, Boston, MA, United States
A Commentary on
Efficacy and safety of the innovative monoclonal antibodies in adults with generalized myasthenia gravis: a Bayesian network analysis
By Chen H, Qiu Y, Yin Z, Wang Z, Tang Y, Ni H, Lu J, Chen Z, Kong Y and Wang Z (2023). Front. Immunol. 14:1280226. doi: 10.3389/fimmu.2023.1280226
Introduction
We read with interest the recent article by Chen et al., 2023 reporting results of their network meta-analysis (NMA) comparing interventions for generalized Myasthenia Gravis (gMG) (1). NMA is an established technique to estimate relative treatment effects among competing interventions based on the available randomized controlled trials (RCTs). NMA findings are relevant for healthcare decision-making when the compared interventions are appropriate for the same target population of interest (2, 3). In essence, we consider the comparisons made in an NMA as relevant if it would make sense to compare all the NMA interventions simultaneously in a new head-to-head RCT (assuming this would be ethically defendable). The credibility of an NMA hinges upon whether studies included in the analysis are representative of the target population of interest and do not differ systematically in their distributions of treatment effect-modifiers. Treatment effect-modifiers are study design characteristics, patient characteristics, or contextual factors that impact the observed treatment effect for a particular study intervention (4). With this in mind, we would like to highlight key aspects of the NMA by Chen et al. that limits their findings to inform decision making and difficult to apply in routine clinical care.
Discussion
Chen et al. did not explicitly define the target population of interest for their NMA. This is crucial from a relevance perspective because it defines which interventions are relevant to compare and to whom the results are applicable. The NMA aims to compare the efficacy of monoclonal antibodies across multiple treatment classes including FcRn inhibitors, complement inhibitors, and B-cell targeting therapies for gMG. Unfortunately, Chen et al. did not specify whether the target population of interest for their NMA are anti-acetylcholine receptor antibody-seropositive (AChR+) patients, muscle-specific kinase antibody-seropositive (MuSK+) patients, or a mixed population. According to the Food and Drug Administration, eculizumab, ravulizumab, zilucoplan, efgartigimod are indicated for patients with gMG who are AChR+, whereas rozanolixizumab is indicated for patients who are AChR+ or MuSK+ (5–9). Complement inhibitors (eculizumab, ravulizumab, zilucoplan) are not considered suitable for MUSK+ patients (10). Comparing eculizumab, ravulizumab, zilucoplan, efgartigimod, and rozanolixizumab in a single NMA, as done by Chen et al., implies that the actual target population of their interest is the AChR+ gMG population. Without an explicit statement of the target population in the study objective, readers may be confused as to which population the findings are applicable thereby undermining clinical relevance. Worse, readers may mistakenly conclude that comparisons and findings of the NMA are reflective of any gMG population.
A clearly defined target population is also important to judge the credibility of the NMA: Are the trials included appropriate and provide relevant evidence for the target population of interest? The trials for eculizumab (REGAIN), ravulizumab (CHAMPION-MG), and zilucoplan (RAISE) did not include MuSK+ patients, which ensures that the observed trial-specific results are representative of the presumed AChR+ target population of interest and can be included as such in the NMA (11–13). Positive serology for MuSK was not an exclusion criterion in the MycarinG trial evaluating rozanolixizumab or the ADAPT trial evaluating efgartigimod (14, 15). Although the proportion of MuSK+ patients in MycarinG or ADAPT is relatively small, this raises the question whether the trial specific treatment effects of MycarinG or ADAPT as used in the NMA is reflective of an AChR+ target population. This same question applies to other trials for which the study population was not restricted to AChR+ patients and for which there is no access to AChR+ subgroup data (16–19). If this is not the case, the NMA results are “externally biased” relative to the AChR+ target population of interest.
Regarding the risk of a biased NMA due to differences in effect-modifiers between studies, the authors mentioned differences in patient characteristics at baseline of the trials but did not discuss which factors are likely to impact study-specific relative treatment effects. In our opinion, potential treatment-effect modifiers worth exploring relate to disease severity and include baseline MG-ADL, QMG, and MG-QoL scores. The REGAIN, CHAMPION-MG, and RAISE trials enrolled patients with MG-ADL score ≥6, whereas ADAPT and MycarinG enrolled patients with lower severity (MG-ADL score ≥5 and ≥3, respectively). Ignoring these differences in baseline MG-ADL among-studies implies that the relative treatment effects of all monoclonal antibodies are the same for a population with higher MG-ADL as for a population with lower MG-ADL scores at the start of therapy. At present, it is unclear whether this is a reasonable model assumption. If one would have access to individual patient-level data for some or all included trials, one could attempt to adjust for this potential bias in the NMA.
Another limitation not discussed by Chen et al. concerns the differences in timing of outcome assessments across the included RCTs in which the endpoints varied between 43 days (MycarinG) to 52 weeks (BeatMG). Treatment effects observed in studies with a short duration may represent a biased estimate for long-term benefits if those effects do not persist over time. Similarly, treatment effects observed at longer follow-up may not be representative of early treatment effects. Two indirect treatment comparison studies comparing ADAPT with REGAIN and ADAPT with CHAMPION-MG have illustrated how treatment effects at different timepoints can vary (20, 21). As such, timing of outcome assessment may be an effect-modifier, and comparing treatment effects from different time points among trials can compromise the credibility of the NMA as well (22).
Conclusion
NMA is a powerful evidence synthesis method. However, it is essential to clearly define the target population of interest such that compared interventions in the NMA are considered appropriate for this target population to provide valuable information on how competing interventions “stack up” against one another and help inform clinical decision-making. In addition, detailed considerations of potential differences in treatment effect modifiers among trials included in the NMA should be provided to ensure the interested reader can judge the credibility of NMA findings. The importance of addressing relevance and credibility concerns in an NMA to ensure clinical meaningfulness is evident from the contrasting conclusions in other NMAs evaluating newer therapies for gMG (23, 24).
Author contributions
IZ: Writing – original draft, Writing – review & editing. JJ: Writing – original draft, Writing – review & editing. BY: Writing – review & editing. AK: Writing – review & editing. KY: Writing – review & editing.
Funding
The author(s) declare financial support was received for the research, authorship, and/or publication of this article. The authors disclosed financial support for the authorship and/or publication of this article. Publication fees were provided by Alexion Pharmaceuticals, AstraZeneca Rare Disease.
Conflict of interest
IZ and JJ are employees of PRECISIONheor, who received funding from Alexion Pharmaceuticals to support this work. BY, AK, and KY are employees and stockholders of Alexion Pharmaceuticals, AstraZeneca Rare Disease.
The authors declare that this study received funding from Alexion Pharmaceuticals. The funder had the following involvement in the study: decision to submit it for publication and provision of publication fees.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
References
1. Chen H, Qiu Y, Yin Z, Wang Z, Tang Y, Ni H, et al. Efficacy and safety of the innovative monoclonal antibodies in adults with generalized myasthenia gravis: a Bayesian network analysis. Front Immunol. (2023) 14. doi: 10.3389/fimmu.2023.1280226
2. Jansen JP, Trikalinos T, Cappelleri JC, Daw J, Andes S, Eldessouki R, et al. Indirect treatment comparison/network meta-analysis study questionnaire to assess relevance and credibility to inform health care decision making: an ISPOR-AMCP-NPC Good Practice Task Force report. Value Health. (2014) 17:157–73. doi: 10.1016/j.jval.2014.01.004
3. Dias S, Ades AE, Welton NJ, Jansen JP, Sutton AJ. Network meta-analysis for decision-making. In: Chapter 12, Validity of Network Meta-Analysis. Hoboken, NJ: John Wiley & Sons (2018).
4. Jansen JP, Naci H. Is network meta-analysis as valid as standard pairwise meta-analysis? It all depends on the distribution of effect modifiers. BMC Med. (2013) 11:159. doi: 10.1186/1741-7015-11-159
5. SOLIRIS (eculizumab) [package insert]. U.S. Food and Drug Administration. Available online at: https://www.accessdata.fda.gov/drugsatfda_docs/label/2019/125166s431lbl.pdf (Accessed January 1, 2024).
6. ULTOMIRIS (ravulizumab-cwvz) [package insert] . U.S. Food and Drug Administration. Available online at: https://www.accessdata.fda.gov/drugsatfda_docs/label/2022/761108s023lbl.pdf (Accessed January 1, 2024).
7. ZILBRYSQ (zilucoplan) [package insert] . U.S. Food and Drug Administration. Available online at: https://www.accessdata.fda.gov/drugsatfda_docs/label/2023/216834s000lbl.pdf (Accessed January 1, 2024).
8. VYVGART (efgartigimod alfa-fcab) [package insert] . U.S. Food and Drug Administration. Available online at: https://www.accessdata.fda.gov/drugsatfda_docs/label/2021/761195s000lbl.pdf (Accessed January 1, 2024).
9. RYSTIGGO(rozanolixizumab-noli) [package insert] . U.S. Food and Drug Administration. Available online at: https://www.accessdata.fda.gov/drugsatfda_docs/label/2023/761286s000lbl.pdf (Accessed January 1, 2024).
10. Vakrakou AG, Karachaliou E, Chroni E, Zouvelou V, Tzanetakos D, Salakou S, et al. Immunotherapies in MuSK-positive Myasthenia Gravis; an IgG4 antibody-mediated disease. Front Immunol. (2023) 14. doi: 10.3389/fimmu.2023.1212757
11. Howard JF, Bresch S, Genge A, Hewamadduma C, Hinton J, Hussain Y, et al. Safety and efficacy of zilucoplan in patients with generalised myasthenia gravis (RAISE): a randomised, double-blind, placebo-controlled, phase 3 study. Lancet Neurol. (2023) 22:395–406. doi: 10.1016/S1474-4422(23)00080-7
12. Howard JF, Utsugisawa K, Benatar M, Murai H, Barohn RJ, Illa I, et al. Safety and efficacy of eculizumab in anti-acetylcholine receptor antibody-positive refractory generalised myasthenia gravis (REGAIN): a phase 3, randomised, double-blind, placebo-controlled, multicentre study. Lancet Neurol. (2017) 16(12):976–86. doi: 10.1016/S1474-4422(17)30369-1
13. Vu T, Meisel A, Mantegazza R, Annane D, Katsuno M, Aguzzi R, et al. Terminal complement inhibitor ravulizumab in generalized myasthenia gravis. NEJM Evidence. (2022) 1(5):EVIDoa2100066. doi: 10.1056/EVIDoa2100066
14. Bril V, Drużdż A, Grosskreutz J, Habib AA, Mantegazza R, Sacconi S, et al. Safety and efficacy of rozanolixizumab in patients with generalised myasthenia gravis (MycarinG): a randomised, double-blind, placebo-controlled, adaptive phase 3 study. Lancet Neurol. (2023) 22(5):383–94. doi: 10.1016/S1474-4422(23)00077-7
15. Howard JF, Bril V, Vu T, Karam C, Peric S, Margania T, et al. Safety, efficacy, and tolerability of efgartigimod in patients with generalised myasthenia gravis (ADAPT): a multicentre, randomised, placebo-controlled, phase 3 trial. Lancet Neurol. (2021) 20(7):526–36. doi: 10.1212/WNL.96.15_supplement.4520
16. Yan C, Duan R-S, Yang H, Li H-F, Zou Z, Zhang H, et al. Therapeutic effects of batoclimab in Chinese patients with generalized myasthenia gravis: a double-blinded, randomized, placebo-controlled phase II study. Neurol Ther. (2022) 11(2):815–34. doi: 10.1007/s40120-022-00345-9
17. Hewett K, Sanders DB, Grove RA, Broderick CL, Rudo TJ, Bassiri A, et al. Randomized study of adjunctive belimumab in participants with generalized myasthenia gravis. Neurology. (2018) 90(16):e1425–e34. doi: 10.1212/WNL.0000000000005323
18. Nowak RJ, Coffey CS, Goldstein JM, Dimachkie MM, Benatar M, Kissel JT, et al. Phase 2 trial of rituximab in acetylcholine receptor antibody-positive generalized myasthenia gravis: the BeatMG study. Neurology. (2022) 98(4):e376–e89. doi: 10.1212/WNL.0000000000013121
19. Piehl F, Eriksson-Dufva A, Budzianowska A, Feresiadou A, Hansson W, Hietala MA, et al. Efficacy and safety of rituximab for new-onset generalized myasthenia gravis: the RINOMAX randomized clinical trial. JAMA Neurol. (2022) 79(11):1105–12. doi: 10.1001/jamaneurol.2022.2887
20. Meuth S, Hagenacker T, Scheiner C, Masuda M, Kielhorn A, Werneburg B, et al. EPO-225 Matching-adjusted indirect comparison of ravulizumab/efgartigimod in generalised myasthenia gravis: Timepoint challenges. Budapest, Hungary: European Academy of Neurology (2023).
21. Celico L, Spaepen E, Francesco MD, Chiroli S, Iannazzo S, Ruck T, et al. A matching-adjusted indirect comparison of efgartigimod versus ravulizumab for generalized myasthenia gravis. Vienna, Austria: ISPOR Europe (2022). doi: 10.1016/j.jval.2022.09.096
22. Phillippo DM, Dias S, Ades AE, Belger M, Brnabic A, Schacht A, et al. Multilevel network meta-regression for population-adjusted treatment comparisons. J R Stat Soc. (2020) 183(3):1189–210. doi: 10.1111/rssa.12579
23. Saccà F, Pane C, Espinosa PE, Sormani MP, Signori A. Efficacy of innovative therapies in myasthenia gravis: systematic review, meta-analysis and network meta-analysis. Eur J Neurol. (2023) 30(12):3854–67. doi: 10.1111/ene.15872
Keywords: generalized myasthenia gravis, monoclonal antibody, FcRn inhibitor, complement inhibitor, B-cell targeting therapy, network meta-analysis
Citation: Zhang I, Jansen JP, Yungher BJ, Kielhorn A and Yee KS (2024) Commentary: Efficacy and safety of the innovative monoclonal antibodies in adults with generalized myasthenia gravis: a Bayesian network analysis. Front. Immunol. 15:1403802. doi: 10.3389/fimmu.2024.1403802
Received: 19 March 2024; Accepted: 12 April 2024;
Published: 17 May 2024.
Edited by:
Steven O’Reilly, STipe Therapeutics, DenmarkReviewed by:
Luisa Brussino, University of Turin, ItalyCopyright © 2024 Zhang, Jansen, Yungher, Kielhorn and Yee. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Karen S. Yee, a2FyZW4ueWVlQGFsZXhpb24uY29t