A comprehensive in silico approach to measure spatio-temporal changes of bone tissue in mouse models of osteoporosis and osteoarthritis

This project aims to improve the current preclinical assessment of interventions for bone diseases (i.e. osteoporosis, OP, and osteoarthritis, OA), by developing more accurate longitudinal measurements in mice and evaluating endpoints, which are more relevant to predict the outcomes of the treatment in patients, and therefore facilitate the clinical translation.

We will develop and validate in vivo microCT based computational models for prediction of the mouse tibia strength, which is more related to risk of fracture than standard bone density measurements.

We will also develop a semi-automated web-service for allowing other researchers worldwide to perform non-invasive estimations of bone strength and spatio-temporal
measurements of bone properties, which are more accurate than those performed with standard cross-sectional experimental designs, and lead therefore to a 63% reduction of the number of mice to be used. In this project we will convert the manual procedure we can use only in our laboratory into a semi-automatic approach, share the developed scripts with expert bone modellers, and offer a web-service to all researchers worldwide, who are willing to use this approach but do not have the expertise.
Finally we will extend the previously developed protocols for accurate measurements of early changes of knee subchondral bone in OA in vivo. We will evaluate the longitudinal changes induced by destabilization of the medial meniscus, the most common OA mouse model, and we will validate the outcomes of the longitudinal approach against ex vivo measurements of cartilage and bone from a standard cross-sectional experiment. We are confident that this will extend the reduction of required number of mice for projects, which aim to understand OA pathogenesis, to create new less invasive OA animal models and to develop new interventions for this disease.

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Project grant

Status:

Not yet active

Principal investigator

Dr Enrico Dall'Ara

Institution

University of Sheffield

Co-Investigator

Professor Ilaria Bellantuono
Professor Marco Viceconti

Grant reference number

NC/R001073/1

Award date:

Sep 2017 - Feb 2020

Grant amount

£352,327