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Abstract
To date poor treatment options are available for patients with congenital pseudarthrosis of the tibia (CPT), a pediatric orphan disease. In this study we have performed an in silico clinical trial on 200 virtual subjects, generated from a previously established model of murine bone regeneration, to tackle the challenges associated with the small, pediatric patient population. Each virtual subject was simulated to receive no treatment and bone morphogenetic protein (BMP) treatment. We have shown that the degree of severity of CPT is significantly reduced with BMP treatment, although the effect is highly subject-specific. Using machine learning techniques we were also able to stratify the virtual subject population in adverse responders, non-responders, responders and asymptomatic. In summary, this study shows the potential of in silico medicine technologies as well as their implications for other orphan diseases.
Highlights
To date poor treatment options are available for patients with congenital pseudarthrosis of the tibia (CPT), a pediatric orphan disease
In this study we aim to combine data-driven and mechanistic modeling approaches to illustrate how in silico models can contribute to unraveling the mechanisms underlying orphan diseases, with a particular focus on congenital pseudarthrosis of the tibia (CPT) associated with mutations in Neurofibromatosis Type 1 (NF1)
The degree of severity of CPT was assessed by the complication index (CI), a linear combination of three phenotypic CPT symptoms
Summary
To date poor treatment options are available for patients with congenital pseudarthrosis of the tibia (CPT), a pediatric orphan disease. In this study we have performed an in silico clinical trial on 200 virtual subjects, generated from a previously established model of murine bone regeneration, to tackle the challenges associated with the small, pediatric patient population. Note that in this example, cartilage is produced at a rate Pmc, which is a numerical value that can be tuned to represent normal or pathological chondrocyte behavior. The challenges associated with (pediatric) randomized clinical trials are avoided by using the in silico model to generate a unique paired data set of both treated and non-treated virtual subjects
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