Register now After registration you will be able to apply for this opportunity online.
This opportunity is not published. No applications will be accepted.
A Personalized Bone Organoid Diagnostic Framework for Predicting Drug Response in Children with Rare Bone Diseases
Rare genetic disorders are defined by a prevalence of fewer than 1/2000 people, are chronic and affect patients throughout their lifespan. Osteogenesis imperfecta (OI) is a heterogeneous group of rare genetic bone disorders. OI is a debilitating condition that involves impaired mobility, high fracture incidence and subsequent limb deformities. No treatment exists today that targets the underlying abnormal collagen structure and organization. The mainstay in pediatric care of OI remains antiresorptive therapy with bisphosphonates, despite concerns of long-term effects on depressed bone turnover. While antiresorptive monoclonal antibody treatments are currently undergoing clinical trials in children and young adults, anabolic treatments that directly increase bone formation are currently approved for adults only and decrease in efficacy over a relatively short time span. The experience with these drugs in OI therapy is limited, as clinical studies are still ongoing.
Keywords: bone organoid, diagnostics, bone diseases, 3D bioprinting, personalized medicine
This project aims to develop the experimental bone organoid technology available at the Laboratory for Bone Biomechanics into a personalized bone organoid in vitro diagnostic framework, including a machine learning model for predicting drug response in osteogenesis imperfecta patient
This project aims to develop the experimental bone organoid technology available at the Laboratory for Bone Biomechanics into a personalized bone organoid in vitro diagnostic framework, including a machine learning model for predicting drug response in osteogenesis imperfecta patient
We plan to combine several orthogonal methods: Time-lapsed micro-CT imaging of mineralized 3D bioprinted bone organoids, mechanical property evaluations, clinical biomarker assays and next generation sequencing (NGS). The readouts from these measurements will be used to fit a machine learning model to a dataset of drug sensitivity and dosing generated within the proposed project. Finally, the model will be validated by an independent dataset of drug response in personalized OI organoids.
Several projects are available and the specific goals will be tailored to the student's expertise and experience. Students at all levels (BSc, MSc) are welcome with strong interest in experimental research, computational work, modeling, 3D printing, image processing, etc.
We plan to combine several orthogonal methods: Time-lapsed micro-CT imaging of mineralized 3D bioprinted bone organoids, mechanical property evaluations, clinical biomarker assays and next generation sequencing (NGS). The readouts from these measurements will be used to fit a machine learning model to a dataset of drug sensitivity and dosing generated within the proposed project. Finally, the model will be validated by an independent dataset of drug response in personalized OI organoids.
Several projects are available and the specific goals will be tailored to the student's expertise and experience. Students at all levels (BSc, MSc) are welcome with strong interest in experimental research, computational work, modeling, 3D printing, image processing, etc.
Gian Nutal Schädli, PhD, Project leader and iPostdoc PHRT Fellow, GLC H20.2, giannutal.schaedli@hest.ethz.ch
Gian Nutal Schädli, PhD, Project leader and iPostdoc PHRT Fellow, GLC H20.2, giannutal.schaedli@hest.ethz.ch
