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Meta-Analysis
. 2023 Apr 11;100(15):e1540-e1554.
doi: 10.1212/WNL.0000000000201626. Epub 2023 Feb 1.

DMD Genotypes and Motor Function in Duchenne Muscular Dystrophy: A Multi-institution Meta-analysis With Implications for Clinical Trials

Francesco Muntoni  1 , James Signorovitch  2 , Gautam Sajeev  1 , Henry Lane  1 , Madeline Jenkins  1 , Ibrahima Dieye  1 , Susan J Ward  1 , Craig McDonald  1 , Nathalie Goemans  1 , Erik H Niks  1 , Brenda Wong  1 , Laurent Servais  1 , Volker Straub  1 , Michela Guglieri  1 , Imelda J M de Groot  1 , Mary Chesshyre  1 , Cuixia Tian  1 , Adnan Y Manzur  1 , Eugenio Mercuri  1 , Annemieke Aartsma-Rus  1 ; Association Française Contre Les Myopathies; on behalf of Universitaire Ziekenhuizen Leuven Group, PRO-DMD-01, The UK NorthStar Clinical Network, CCHMC, and The DMD Italian Group
Collaborators, Affiliations
Meta-Analysis

DMD Genotypes and Motor Function in Duchenne Muscular Dystrophy: A Multi-institution Meta-analysis With Implications for Clinical Trials

Francesco Muntoni et al. Neurology. .

Abstract

Background and objectives: Clinical trials of genotype-targeted treatments in Duchenne muscular dystrophy (DMD) traditionally compare treated patients with untreated patients with the same DMD genotype class. This avoids confounding of drug efficacy by genotype effects but also shrinks the pool of eligible controls, increasing challenges for trial enrollment in this already rare disease. To evaluate the suitability of genotypically unmatched controls in DMD, we quantified effects of genotype class on 1-year changes in motor function endpoints used in clinical trials.

Methods: More than 1,600 patient-years of follow-up (>700 patients) were studied from 6 real-world/natural history data sources (UZ Leuven, PRO-DMD-01 shared by CureDuchenne, iMDEX, North Star UK, Cincinnati Children's Hospital Medical Center, and the DMD Italian Group), with genotypes classified as amenable to skipping exons 44, 45, 51, or 53, or other skippable, nonsense, and other mutations. Associations between genotype class and 1-year changes in North Star Ambulatory Assessment total score (ΔNSAA) and in 10-m walk/run velocity (Δ10MWR) were studied in each data source with and without adjustment for baseline prognostic factors.

Results: The studied genotype classes accounted for approximately 2% of variation in ΔNSAA outcomes after 12 months, whereas other prognostic factors explained >30% of variation in large data sources. Based on a meta-analysis across all data sources, pooled effect estimates for the studied skip-amenable mutation classes were all small in magnitude (<2 units in ΔNSAA total score in 1-year follow up), smaller than clinically important differences in NSAA, and were precisely estimated with standard errors <1 unit after adjusting for nongenotypic prognostic factors.

Discussion: These findings suggest the viability of trial designs incorporating genotypically mixed or unmatched controls for up to 12 months in duration for motor function outcomes, which would ease recruitment challenges and reduce numbers of patients assigned to placebos. Such trial designs, including multigenotype platform trials and hybrid designs, should ensure baseline balance between treatment and control groups for the most important prognostic factors, while accounting for small remaining genotype effects quantified in this study.

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Conflict of interest statement

F. Muntoni is a member of the Rare Disease Scientific Advisory Group for Pfizer and of Dyne Therapeutics SAB and has participated in SAB meetings for PTC, Sarepta, Pfizer, Roche, Santhera, and Wave Therapeutics. UCL and Great Ormond Street Hospital have received funding from Pfizer, Italfarmaco, Wave, Santhera, Roche, NF Pharma, ReveraGen, Genethon, and Sarepta regarding clinical trials. J. Signorovitch cofounded the collaborative Trajectory Analysis Project (cTAP) and is an employee of Analysis Group, Inc., a consulting firm that received funding from the membership of cTAP to conduct this study. G. Sajeev and H. Lane are current employees, and M. Jenkins and I. Dieye are former employees of Analysis Group, Inc., a consulting firm that received funding from the membership of cTAP to conduct this study. S.J. Ward cofounded and manages the collaborative Trajectory Analysis Project and has received funding from the membership of cTAP to facilitate this study. C. McDonald has served as a consultant for PTC Therapeutics, BioMarin Pharmaceutical, Sarepta Therapeutics, Eli Lilly, Pfizer Inc., Santhera Pharmaceuticals, Cardero Therapeutics, Inc., Catabasis Pharmaceuticals, Capricor Therapeutics, Astellas Pharma (Mitobridge), and FibroGen, Inc.; serves on external advisory boards related to DMD for PTC Therapeutics, Sarepta Therapeutics, Santhera Pharmaceuticals, and Capricor Therapeutics; and reports grants from the US Department of Education/National Institute on Disability and Rehabilitation Research, National Institute on Disability, Independent Living, and Rehabilitation Research, US NIH/National Institute of Arthritis and Musculoskeletal and Skin Diseases, NIH/National Institute of Neurologic Disorders and Stroke, US Department of Defense, and Parent Project Muscular Dystrophy US. N. Goemans has received compensation for consultancy services from Eli Lilly, Italfarmaco, PTC Therapeutics, BioMarin Pharmaceutical, Pfizer, Avidity, Daiichi Sankyo, Wave, and Santhera and has served as site investigator for GlaxoSmithKline, Prosensa, BioMarin Pharmaceutical, Italfarmaco, Eli Lilly, Wave, and Sarepta. E.H. Niks is a member of the European Reference Network for Rare Neuromuscular Diseases (ERN EURO‐NMD). He reported grants from Duchenne Parent Project, ZonMW, and AFM, consultancies for BioMarin and Summit, and worked as a local investigator of clinical trials of BioMarin, GSK, Lilly, Santhera, Givinostat, and Roche outside the submitted work. He reports ad hoc consultancies for WAVE, Santhera, Regenxbio, and PTC, and he worked as an investigator of clinical trials of Italfarmaco, NS Pharma, Reveragen, Roche, WAVE, and Sarepta outside the submitted work. B. Wong has participated in advisory board/committee meetings for Prosensa, Biomarin, Catabasis, and PepGen and has received compensation for consultancy services for Eli Lilly, Gilead Sciences, Pfizer, and GSK. L. Servais is member of the SAB or has performed consultancy for Sarepta, Dynacure, Santhera, Avexis, Biogen, Cytokinetics, Roche, Audentes Therapeutics, and Affinia Therapeutics. He has given lectures and has served as a consultant for Roche, Biogen, Avexis, and Cytokinetics. He is the project leader of the newborn screening in Southern Belgium funded by Avexis, Roche, and Biogen. V. Straub has participated in advisory boards for Audentes Therapeutics, Biogen, Vertex Pharmaceuticals, Italfarmaco S.p.A., Roche, Sanofi Genzyme, Sarepta Therapeutics, Summit Therapeutics, UCB, and Wave Therapeutics. He has research collaborations with Sarepta Therapeutics and Sanofi Genzyme. M. Guglieri reported serving as study chair for a ReveraGen study and the FOR DMD study (funded by the NIH). She has research collaborations with ReveraGen and Sarepta and is currently or previously acting as PI for clinical trials sponsored by Pfizer, Italfarmaco, Santhera, ReveraGen, Dynacure, Roche, PTC, and Summit. She has participated in advisory boards for Pfizer, NS Pharma, and Dyne (consultancies through Newcastle University) and performed consultancy work (speaker) for Sarepta. I.J.M. de Groot has no disclosures. M. Chesshyre has had the costs associated with attending a conference (travel, accommodation, conference fee, and food) funded by PTC Therapeutics. C. Tian has served as the site investigator for trials sponsored by PTC Therapeutics, Eli Lilly, GSK, Prosensa/Biomarin, Bristol Myers Squibb, Roche, Pfizer, Santhera, Sarepta, Fibrogen, Capricor, Pfizer, Avexis, and Catabasis. A.Y. Manzur has no disclosures. E. Mercuri has served on clinical steering committees and/or as a consultant for Eli Lilly, Italfarmaco, PTC Therapeutics, Sarepta, Santhera, and Pfizer; has served as PI for GlaxoSmithKline, Prosensa, BioMarin Pharmaceutical, Italfarmaco, Roche, PTC, Pfizer, Sarepta, Santhera, Wave, NS Pharma, and Eli Lilly. A. Aartsma-Rus discloses being employed by LUMC, which has patents on exon-skipping technology, some of which has been licensed to BioMarin and subsequently sublicensed to Sarepta. As a coinventor of some of these patents, A. Aartsma-Rus is entitled to a share of royalties. A. Aartsma-Rus further discloses being ad hoc consultant for PTC Therapeutics, Sarepta Therapeutics, CRISPR Therapeutics, Summit PLC, Alpha Anomeric, BioMarin Pharmaceuticals Inc., Eisai, Astra Zeneca, Santhera, Audentes, Global Guidepoint and GLG consultancy, Grunenthal, Wave, and BioClinica, having been a member of the Duchenne Network Steering Committee (BioMarin), and being a member of the scientific advisory boards of ProQR, Eisai, hybridize therapeutics, silence therapeutics, Sarepta therapeutics, and Philae Pharmaceuticals. Remuneration for these activities is paid to LUMC. LUMC also received speaker honoraria from PTC Therapeutics and BioMarin Pharmaceuticals and funding for contract research from Italfarmaco and Alpha Anomeric. Go to Neurology.org/N for full disclosures.

Figures

Figure 1
Figure 1. Genotype Associations With an Ambulatory Milestone (10MWR >10 Seconds) Measured as (A) Age at Milestone or as (B) Time to Milestone From First Recorded 10MWR Assessment
10MWR = 10-m walk/run; HR = hazard ratio.
Figure 2
Figure 2. Percentages of Variation in 1-Year ΔNSAA Explained by Genotype Class* and Other Sets of Prognostic Factors
*Classified as amenable to skipping of exons 44, 45, 51, 53, other skip-amenable, nonsense and all other genotypes. CCHMC = Cincinnati Children's Hospital Medical Center; NSAA = North Star Ambulatory Assessment; NSUK = North Star UK.
Figure 3
Figure 3. Meta-analysis of Adjusted Genotype Effects on 1-Year ΔNSAA vs Other Skip-Amenable Genotypes Across Data Sources
10MWR = 10-m walk/run; CCHMC = Cincinnati Children's Hospital Medical Center; DMD = Duchenne muscular dystrophy; NSAA = North Star Ambulatory Assessment; NSUK = North Star UK.
Figure 4
Figure 4. Examples of Trial Designs Incorporating Genotypically Mixed or Unmatched Controls
(A) Hypothetical randomized, parallel group, blinded platform trial of multiple genotype-specific investigational therapies. In this hypothetical platform trial, patients are enrolled from 4 genotype groups (A–D) that are each amenable to 1 of 4 trialed genotype-specific investigational therapies. Patients in each genotype group are blinded to treatment assignment and randomly assigned to 1 of the 4 genotype-specific therapies or to a mixed-genotype common placebo arm in a 4:1 ratio. Comparisons of each genotype-specific therapy vs placebo are based on the shared, genotype-mixed control arm, adjusting for the genotype mix as outlined in Figure 5. This trial design could include strictly concurrent genotype-specific treatment groups (e.g., if run by a single sponsor with a multigenotype pipeline) or could admit nonconcurrent genotype-specific treatment arms (e.g., including different mechanisms and drug developers over time). The use of a shared, genotype-mixed control arm enables blinding and may reduce the overall sample size needed and the number of patients from each genotype group that are required to be assigned to placebo. (B) Hypothetical hybrid trial of a genotype-specific investigational therapy using (1) randomized genotype-matched, (2) external genotype-unmatched or (3) external mixed genotype controls. A trial of a genotype-specific investigational therapy may include different control groups: (1) concurrent, randomized, and blinded genotype-matched controls (possibly with a 1:4 or other reduced ratio of those receiving control vs active therapy), (2) external, genotype-unmatched controls, or (3) external, mixed genotype controls. Hybrid control groups can be composed of type (1) in addition to type (2) and/or type (3). Comparisons of the genotype-specific investigational therapy vs these external or hybrid control groups will require adjustment for genotype differences between groups as outlined in Figure 5 and consideration of the risk of bias due to lack of randomization and lack of blinding. In the absence of randomization, comparisons should adjust for baseline prognostic factors to mitigate the risk of bias. Bias due to unmeasured confounding cannot be ruled out in these designs, but the impacts of different magnitudes of confounding on treatment effects may be explored. The risks of unblinded designs should be considered carefully, and in light of evidence showing that functional outcomes in DMD have not differed between blinded placebo arms, natural history, and real-world settings, and that adjustment for strong baseline prognostic factors can mitigate bias. Inclusion of at least some randomized and blinded controls is preferred to allow direct assessment of these risks of bias.
Figure 5
Figure 5. Schematic for Genotype Mix Adjustment in Trial Designs Employing Mixed or Unmatched Genotype Controls
Unadjusted comparisons of functional outcomes between a genotype-specific treatment arm (A) and a mixed or unmatched genotype control arm (B) will reflect differences arising due to both treatment and the mix of genotypes across groups. Adjustment for genotype effects is therefore needed to capture differences due to treatment alone. An “adjustment factor” (C) for a specific mix of genotypes in the treatment and control arms can be calculated based on estimates of genotype effects, such as those presented in this study, and used to obtain a genotype-adjusted estimate of outcomes in the control arm (D). The estimated, adjusted effect of the genotype-specific treatment can then be calculated by comparing the genotype-specific treatment arm with the genotype-adjusted control arm (A–D).
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References

    1. Emery A, Muntoni F, Quinlivan R. Duchenne Muscular Dystrophy, 4th ed. Oxford University Press, 2015.
    1. Mah JK, Korngut L, Fiest KM, et al. . A systematic review and meta-analysis on the epidemiology of the muscular dystrophies. Can J Neurol Sci. 2016;43(1):163-177. doi:10.1017/cjn.2015.311. - DOI - PubMed
    1. Aartsma-Rus A, Van Deutekom JCT, Fokkema IF, Van Ommen GJB, Den Dunnen JT. Entries in the Leiden Duchenne muscular dystrophy mutation database: an overview of mutation types and paradoxical cases that confirm the reading-frame rule. Muscle Nerve. 2006;34(2):135-144. doi:10.1002/mus.20586. - DOI - PubMed
    1. Bladen CL, Salgado D, Monges S, et al. . The TREAT-NMD DMD Global Database: analysis of more than 7,000 Duchenne muscular dystrophy mutations. Hum Mutat. 2015;36(4):395-402. doi:10.1002/humu.22758. - DOI - PMC - PubMed
    1. Duan D, Goemans N, Takeda S, Mercuri E, Aartsma-Rus A. Duchenne muscular dystrophy. Nat Rev Dis Primers. 2021;7(1):13. doi:10.1038/s41572-021-00248-3. - DOI - PMC - PubMed

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