Latent process model of the 6-minute walk test in Duchenne muscular dystrophy : A Bayesian approach to quantifying rare disease progression

Population longitudinal analysis of Gait Profile Score and North Star Ambulatory Assessment in children with Duchenne muscular dystrophy

Duchenne muscular dystrophy (DMD) is a rare X-linked recessive disorder characterized by loss-of-function mutations in the gene encoding dystrophin. These mutations lead to progressive functional deterioration including muscle weakness, respiratory insufficiency, and musculoskeletal deformities. Three-dimensional gait analysis (3DGA) has been used as a tool to analyze gait pathology through the quantification of altered joint kinematics, kinetics, and muscle activity patterns. Among 3DGA indices, the Gait Profile Score (GPS), has been used as a sensitive overall measure to detect clinically relevant changes in gait patterns in children with DMD. To enhance our understanding of the clinical translation of 3DGA, we report here the development of a population nonlinear mixed-effect model that jointly describes the disease progression of the 3DGA index, GPS, and the functional endpoint, North Star Ambulatory Assessment (NSAA). The final model consists of a quadratic structure for GPS progression and a linear structure for GPS-NSAA correlation. Our model was able to capture the improvement in function in GPS and NSAA in younger subjects, as well as the decline of function in older subjects. Furthermore, the model predicted NSAA (CFB) at 1 year reasonably well for DMD subjects ≤7 years old at baseline. The model tended to slightly underpredict the decline in NSAA after 1 year for those >7 years old at baseline, but the prediction summary statistics were well maintained within the standard deviation of observed data. Quantitative models such as this may help answer clinically relevant questions to facilitate the development of novel therapies in DMD.

Disease progression modeling of the North Star Ambulatory Assessment for Duchenne Muscular Dystrophy

Duchenne muscular dystrophy (DMD) is a rare genetic disorder caused by decreased or absent dystrophin gene leading to progressive muscle degeneration and weakness in young boys. Disease progression models for the North Star Ambulatory Assessment (NSAA), a functional measurement widely used to assess outcomes in clinical trials, were developed using a longitudinal population modeling approach. The relationship between NSAA total score over time, loss of ambulation, and potential covariates that may influence disease progression were evaluated. Data included individual participant observations from an internal placebo-controlled phase II clinical trial and from the external natural history database for male patients with DMD obtained through the Cooperative International Neuromuscular Research Group (CINRG). A modified indirect response model for NSAA joined to a loss of ambulation (LOA) time-to-event model described the data well. Age was used as the independent variable because ambulatory function is known to vary with age. The NSAA and LOA models were linked using the dissipation rate constant parameter from the NSAA model by including the parameter as a covariate on the hazard equation for LOA. No covariates were identified. The model was then used as a simulation tool to explore various clinical trial design scenarios. This model contributes to the quantitative understanding of disease progression in DMD and may guide model-informed drug development decisions for ongoing and future DMD clinical trials.

Application of Bayesian methods to accelerate rare disease drug development: scopes and hurdles

Background

Design and analysis of clinical trials for rare and ultra-rare disease pose unique challenges to the practitioners. Meeting conventional power requirements is infeasible for diseases where sample sizes are inherently very small. Moreover, rare disease populations are generally heterogeneous and widely dispersed, which complicates study enrollment and design. Leveraging all available information in rare and ultra-rare disease trials can improve both drug development and informed decision-making processes.

Main text

Bayesian statistics provides a formal framework for combining all relevant information at all stages of the clinical trial, including trial design, execution, and analysis. This manuscript provides an overview of different Bayesian methods applicable to clinical trials in rare disease. We present real or hypothetical case studies that address the key needs of rare disease drug development highlighting several specific Bayesian examples of clinical trials. Advantages and hurdles of these approaches are discussed in detail. In addition, we emphasize the practical and regulatory aspects in the context of real-life applications.

Conclusion

The use of innovative trial designs such as master protocols and complex adaptive designs in conjunction with a Bayesian approach may help to reduce sample size, select the correct treatment and population, and accurately and reliably assess the treatment effect in the rare disease setting.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13023-022-02342-5.

Bayesian modeling and simulation to inform rare disease drug development early decision-making: Application to Duchenne muscular dystrophy

Rare disease clinical trials are constrained to small sample sizes and may lack placebo-control, leading to challenges in drug development. This paper proposes a Bayesian model-based framework for early go/no-go decision making in rare disease drug development, using Duchenne muscular dystrophy (DMD) as an example. Early go/no-go decisions were based on projections of long-term functional outcomes from a Bayesian model-based analysis of short-term trial data informed by prior knowledge based on 6MWT natural history literature data in DMD patients. Frequentist hypothesis tests were also applied as a reference analysis method. A number of combinations of hypothetical trial designs, drug effects and cohort comparison methods were assessed. The proposed Bayesian model-based framework was superior to the frequentist method for making go/no-go decisions across all trial designs and cohort comparison methods in DMD. The average decision accuracy rates across all trial designs for the Bayesian and frequentist analysis methods were 45.8 and 8.98%, respectively. A decision accuracy rate of at least 50% was achieved for 42 and 7% of the trial designs under the Bayesian and frequentist analysis methods, respectively. The frequentist method was limited to the short-term trial data only, while the Bayesian methods were informed with both the short-term data and prior information. The specific results of the DMD case study were limited due to incomplete specification of individual-specific covariates in the natural history literature data and should be reevaluated using a full natural history dataset. These limitations aside, the framework presented provides a proof of concept for the utility of Bayesian model-based methods for decision making in rare disease trials.

Exposure-Response Analysis of Vamorolone (VBP15) in Boys With Duchenne Muscular Dystrophy

Exposure-response relationships of vamorolone, a novel dissociative steroidal anti-inflammatory drug, were investigated in clinical trials in boys with Duchenne muscular dystrophy. Variables were clinical outcome measures, Fridericia-corrected QT (QTcF) duration, and pharmacodynamic (PD) biomarkers. Exposure metrics were area under the plasma concentration time curve (AUC) and maximum plasma concentration (Cmax ), with a sigmoid Emax model applied. Significant improvement in clinical efficacy outcomes was observed after 24 weeks of daily dosing. The primary outcome, time to stand from supine velocity, exhibited the highest sensitivity to vamorolone, with the lowest AUC value providing 50% of maximum effect (E50  = 186 ng·h/mL), followed by time to climb 4 stairs (E50  = 478 ng·h/mL), time to run/walk 10 m (E50  = 1220 ng·h/mL), and 6-minute walk test (E50  = 1770 ng·h/mL). Week 2 changes of proinflammatory PD biomarkers showed exposure-dependent decreases. The E50 was 260 ng·h/mL for insulin-like growth factor-binding protein 2, 1200 ng·h/mL for matrix metalloproteinase 12, 1260 ng·h/mL for lymphotoxin α1/β2, 1340 ng·h/mL for CD23, 1420 ng·h/mL for interleukin-22-binding protein, and 1600 ng·h/mL for macrophage-derived chemokine/C-C motif chemokine 22. No relationship was found between QTcF interval changes from baseline and Cmax in week 2 or 24. This analysis showed that improvements in clinical efficacy end points in week 24 and PD biomarkers in week 2 were achieved at typical vamorolone exposure of 2 mg/kg daily dose with a median AUC dose of 6 mg/kg (3651 ng·h/mL), corresponding to approximately 95% of maximum effects for most response variables.