Quantitative retrospective natural history modeling for orphan drug development

Abstract

The natural history of most rare diseases is incompletely understood and usually relies on studies with low level of evidence. Consistent with the goals for future research of rare disease research set by the International Rare Diseases Research Consortium in 2017, the purpose of this paper is to review the recently developed method of quantitative retrospective natural history modeling (QUARNAM) and to illustrate its usefulness through didactically selected analyses examples in an overall population of 849 patients worldwide with seven (ultra‐) rare neurogenetic disorders. A quantitative understanding of the natural history of the disease is fundamental for the development of specific interventions and counseling afflicted families. QUARNAM has a similar relationship to a published case study as a meta‐analysis has to an individual published study. QUARNAM relies on sophisticated statistical analyses of published case reports focusing on four research questions: How long does it take to make the diagnosis? How long do patients live? Which factors predict disease severity (eg, genotypes, signs/symptoms, biomarkers)? Where can patients be recruited for studies? Useful statistical techniques include Kaplan‐Meier estimates, cluster analysis, regression techniques, binary decisions trees, word clouds, and geographic mapping. In comparison to other natural history study methods (prospective studies or retrospective studies such as chart reviews), QUARNAM can provide fast information on hard clinical endpoints (ie, survival, diagnostic delay) with a lower effort. The choice of method for a particular drug development program may be driven by the research question and may encompass combinatory approaches.

Cross-sectional quantitative analysis of the natural history of TUBA1A and TUBB2B tubulinopathies

“…Termination of pregnancy (TOP) was performed in 24 cases (16.7%) around GW 28 (range: GW 21-38) due to severe brain abnormalities on prenatal ultrasound (US) and/ or magnetic resonance imaging (MRI). From 47 cases with TUBB2B tubulinopathy, 40 individuals (85.1%) were delivered at term and TOP was induced in 7 cases (14.9%) on average in GW 28 (range: GW [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33]. Sexual distribution was balanced in both study cohorts.…”

“…Raising disease awareness by quantitative natural history data has the potential to shorten diagnostic delay and ensure optimal supportive patient care. 22 In this study, we therefore focused on quantification of relevant clinical endpoints such as survival, disease onset, and diagnostic delay. In addition, we describe cardinal features at disease onset and characterize associated epilepsies to improve the understanding of the clinical course and to complement therapeutic concepts for affected individuals.…”

Rare diseases and space health: optimizing synergies from scientific questions to care

“…As few as 70 articles include data collected during space flight addressing psychological, behavioral challenges and performance of astronauts arising from space exposure 38 . Improving natural history studies has been proposed to address small n sample sizes and replication concerns, thus helping refine questions and fill knowledge gaps in space health and rare disease research 53 . A natural history study collects health information over time to understand how the medical condition or disease develops and to give insight into how it might be treated 53 .…”

Quantitative longitudinal natural history of eight gangliosidoses – conceptual framework and baseline data of the German 8-in-1 disease registry. A cross-sectional analysis

“…A quantitative understanding will inform the development of therapeutic interventions with regard to endpoints and the degree of their variability. 7 A precise knowledge of the natural history is helpful for counselling afflicted families and planning future therapeutic clinical trials. Therefore, research on the natural history of rare diseases was defined as a crucial element to advance therapeutic developments by the International Rare Diseases Research Consortium.…”