Association of genetic mutations and loss of ambulation in childhood‐onset dystrophinopathy

Abstract

Background Quantifying associations between genetic mutations and loss of ambulation (LoA) among males diagnosed with childhood‐onset dystrophinopathy is important for understanding variation in disease progression and may be useful in clinical trial design. Methods Genetic and clinical data from the Muscular Dystrophy Surveillance, Tracking, and Research Network for 358 males born and diagnosed from 1982 to 2011 were analyzed. LoA was defined as the age at which independent ambulation ceased. Genetic mutations were defined by overall type (deletion/duplication/point mutation) and among deletions, those amenable to exon‐skipping therapy (exons 8, 20, 44–46, 51–53) and another group. Cox proportional hazards regression modeling was used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). Results Mutation type did not predict time to LoA. Controlling for corticosteroids, Exons 8 (HR = 0.22; 95% CI = 0.08, 0.63) and 44 (HR = 0.30; 95% CI = 0.12, 0.78) were associated with delayed LoA compared to other exon deletions. Conclusions Delayed LoA in males with mutations amenable to exon‐skipping therapy is consistent with previous studies. These findings suggest that clinical trials including exon 8 and 44 skippable males should consider mutation information prior to randomization.

Prognostic indicators of disease progression in Duchenne muscular dystrophy: A literature review and evidence synthesis

“…In total, nine prognostic indicators were identified: age at diagnosis, age at onset of symptoms, ataluren treatment, DMD genetic modifiers, DMD mutation type, glucocorticoid exposure, eteplirsen treatment, height, and weight ( Table 2 ). Prolonged independent ambulation was found in patients with later onset of symptoms [Level 2]; [ 83 , 84 ] patients treated with glucocorticoids, including age at treatment initiation, duration of exposure, and pharmacological agent [Level 2]; [ 11 , 18 – 20 , 38 , 46 , 51 , 61 – 64 , 66 , 70 – 82 , 88 , 199 ]; ataluren treatment [Level 2] [ 87 , 109 , 110 , 192 , 193 ]; eteplirsen treatment [Level 2] [ 126 – 130 ]; LTBP4 genotype [Level 2]; [ 65 ] lower limb surgery [Level 2] [ 89 , 90 ] and mutations in exons 44 [Level 2] [ 11 , 67 , 73 , 86 , 88 ] and exons 3–7 [Level 2]; [ 11 , 88 ] exon 8 [Level 4] [ 86 , 88 ]; exon 45 [Level 4] [ 88 , 199 ]; exon 53 [Level 4] [ 91 ];and the minor allele at rs1883832 [Level 4] [ 85 ]. Earlier loss of ambulation was found in patients with TG/GG genotype at the rs28357094 secreted phosphoprotein 1 (SPP1) promoter [Level 2]; [ 63 – 66 ] exon 51 skipping and exon 49–50 deletions [Level 4] [ 88 ]; and deletions in the dystrophin gene [Level 4] [ …”

Natural History and Real‐World Data in Rare Diseases: Applications, Limitations, and Future Perspectives

“…Haber et al 49 quantified the associations between genetic mutations and loss of ambulation (LoA) among male patients diagnosed with childhood-onset…”

Model‐Informed Approach Supporting Drug Development and Regulatory Evaluation for Rare Diseases

“…Disease progression models can be used to optimize clinical trial design as well as increase the understanding of influential baseline factors and important biomarkers for disease progression that could potentially be used as surrogate end points 33 . For example, in the case of childhood‐onset dystrophinopathy, associations between genetic variants and loss of ambulation (LoA) were quantified in male patients to understand how variation in disease progression may be useful for clinical trial design 34 . In this research, Haber et al 34 analyzed genetic and clinical data from the Muscular Dystrophy Surveillance, Tracking, and Research Network for 358 males born and diagnosed from 1982 to 2011.…”

“…Other quantitative approaches, for example, mechanistic modeling, machine learning, and modeling of real‐world data, are potential emerging tools that can tackle the challenges in rare disease drug development. Liu et al 34 reviewed the application of artificial intelligence and machine learning (ML) in regulatory submissions for drug development based on the FDA's experience from 2016 to 2021, providing a comprehensive illustration on a variety of tasks for which artificial intelligence/ML was used, including clinical trial design, dose optimization, biomarker assessment, and so on 43 . In the rare disease setting, we are expecting more submissions using ML across different aspects of the drug development cycle.…”

“…33 For example, in the case of childhood-onset dystrophinopathy, associations between genetic variants and loss of ambulation (LoA) were quantified in male patients to understand how variation in disease progression may be useful for clinical trial design. 34 In this research, Haber et al 34 analyzed genetic and clinical data from the Muscular Dystrophy Surveillance, Tracking, and Research Network for 358 males born and diagnosed from 1982 to 2011. Genetic variants were defined by overall type (deletion/duplication/point mutation) and, among deletions, those amenable to exon-skipping therapy (exons 8, 20, 44-46, 51-53) and another group.…”