Database Harmonization for Creation and Validation of Outcome Assessments and Prediction Tools for Metachromatic Leukodystrophy (MLD)

Abstract:
Metachromatic leukodystrophy (MLD) is a rare, fatal, progressive neurologic disease. Therapeutic and diagnostic
innovations are changing the landscape of this rare disease. However, identifying which patients should receive
preventative treatments is currently unknown and is a critical area of need. The foundation of understanding of
the natural history of disease is derived from case series and single institutional reports. This siloed research
approach in a rare disease and lack of data harmonization and sharing has slowed scientific progress. With MLD
newborn screening pilot studies underway at multiple international sites, there is a clinical urgency for early
disease stratification and characterization, when children are still minimally symptomatic and eligible for life-
saving interventions. We hypothesize that risk prediction models and sensitive tools capable of measuring
function across the lifespan can be used to accurately determine who is at high risk for early onset MLD and
assist in determining appropriate interventions. To address the needs, in the R61 phase, we propose to
harmonize and curate the MLD-CORE Project to create a rigorous clinical-trial ready natural history database
using pooled data from 10 major leukodystrophy centers. This harmonization project represents the Rare
Diseases Clinical Research Network (RDCRN) Global Leukodystrophy Initiative Clinical Trials Network (GLIA-
CTN; U54TR002823), an NIH-funded research consortium for leukodystrophy network of 8 large US-based
academic institutions, University of Minnesota, and University of Pittsburgh Medical Center Children’s Center for
NeuroGenomics [formerly known as the Neurodevelopment in Rare Disorders (NDRD)]. From each
subject, longitudinal medical records from birth, diagnostic information, and research clinical outcome
assessments will be collected, and entered into a rigorous regulatory-ready database with source
documentation. We anticipate that this rigorous Natural History platform (MLD-CORE) will inform future clinical
(CCNG)
trials and be capable for use as non-concurrent control arm. Next, we will work with stakeholders to create a
publicly accessible web-based platform for research transparency (MLD-LINK). We anticipate that this aim will
facilitate novel collaborations and grow the network of MLD researchers. In the R33 phase, we will use MLD-
CORE to
create and validate a risk stratification tools and develop a novel
clinical outcome assessment (COA)
fit for presymptomatic monitoring. In pursuit of these aims, we address the NINDS’ mission and respond to the
need for a harmonized rare disease database. The MLD-CORE platform will support collaboration and will be
used to validate clinical outcome assessments in novel populations and to develop data-driven statistical
methodology for validated clinical outcome assessments and constructing prediction models. Furthermore,
because the fundamental methodology is not disease-specific, our approach could be used to enable similar
exploration for other rare diseases.

Grant Number: 1R61NS135584-01A1
NIH Institute/Center: NIH
Principal Investigator: Laura Adang