Clinical and Translational Science (Apr 2024)

Advancing clinical development for neuronopathic Hunter syndrome through a quantitatively‐driven reverse translation framework

  • Robert D. Latzman,
  • Olivia Campagne,
  • Meera E. Modi,
  • Marta Karas,
  • C. J. Malanga,
  • David A. H. Whiteman

DOI
https://doi.org/10.1111/cts.13776
Journal volume & issue
Vol. 17, no. 4
pp. n/a – n/a

Abstract

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Abstract A quantitatively‐driven evaluation of existing clinical data and associated knowledge to accelerate drug discovery and development is a highly valuable approach across therapeutic areas, but remains underutilized. This is especially the case for rare diseases for which development is particularly challenging. The current work outlines an organizational framework to support a quantitatively‐based reverse translation approach to clinical development. This approach was applied to characterize predictors of the trajectory of cognition in Hunter syndrome (Mucopolysaccharidosis Type II; MPS‐II), a rare X‐linked lysosomal storage disorder, highly heterogeneous in its course. Specifically, we considered ways to refine target populations based on age, cognitive status, and biomarkers, that is, cerebrospinal fluid glycosaminoglycans (GAG), at trial entry. Data from a total of 138 subjects (age range 2.5 to 10.1 years) from Takeda‐sponsored internal studies and external natural history studies in MPS‐II were included. Quantitative analyses using mixed‐effects models were performed to characterize the relationships between neurocognitive outcomes and potential indicators of disease progression. Results revealed a specific trajectory in cognitive development across age with an initial progressive phase, followed by a plateau between 4 and 8 years and then a variable declining phase. Additionally, results suggest a faster decline in cognition among subjects with lower cognitive scores or with higher cerebrospinal fluid GAG at enrollment. These results support differences in the neurocognitive course of MPS‐II between distinct groups of patients based on age, cognitive function, and biomarker status at enrollment. These differences should be considered when designing future clinical trials.