A stochastic finite element modelling framework to predict effect sizes on bone mechanics in preclinical studies

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Presently, in the process of developing a new drug, its effect is quantified experimentally first on groups of animals and then on groups of humans. This process is time consuming and expensive. It also does not explain how a candidate drug can be tuned for optimal effect. As such, it takes 15 years and costs £2 billion on average to bring a new drug to market. This situation is particularly untenable for people with osteoporosis, where currently available drugs have uncertain long-term benefit and pose risks of side-effects such as atypical fractures, necrosis, cancer and stroke. It is expected that over the next 10 years, the number of people in the UK with osteoporosis (currently, 3 million) and the annual costs to NHS for treating osteoporotic fractures (currently, £4 billion) will increase by up to 30%.Innovative approaches, such as computational modelling to predict effect sizes of candidate drugs, could dramatically reduce the cost and time involved in drug development. Effect size is a commonly used statistical quantifier of a drug's effectiveness. The effect size of an osteoporosis drug depends on the variation in bone mechanics within and between groups of individuals receiving the drug and a placebo. The proposed research aims to develop a computational framework that can predict effect sizes by quantifying such variations.Past research has shown that the bone mechanical response of an individual mouse (75% of all animals used in UK research) can be accurately predicted from its bone geometry using finite-element (FE) analysis. The novel and innovative contribution of the proposed research is to apply a stochastic FE (sFE) approach to compute the above-mentioned variations in bone mechanics within and between groups of mice given a candidate drug and placebo and thereby to predict the drug's effect size.An sFE solver will be developed by adapting a highly efficient deterministic FE solver that is already used widely in bone research. In itself, this adaptation will substantially advance current sFE capability in analysing problems with very large model size (typical in bone research). This advance is relevant to multiple engineering fields at once. The novel predictive capabilities of the proposed framework will have a major impact on studies using mice. For existing drugs, it will predict the induced variation in mouse bone mechanics more completely than is possible in experiments because of finite (and typically small) sample of animals used in the latter. It will also predict the effect-size on bone mechanics for any change in bone geometry (including those not yet observed) that falls within the range of bone geometry changes recorded in past studies. This can help identify regions of optimal effect within the space of existing drugs. By coupling with computational models informed by in vitro studies (e.g. cell culturing, tissue engineering) the framework can be adapted to predict effect sizes for entirely new molecules.A key focus of the proposed research is to assess in detail the credibility of the modelling framework to be developed. This will be achieved by using mouse bone geometry data already collected in past experiments for a range of interventions. This rigorous credibility assessment will support the future adaptation of the framework in the clinical context, where the effect size of candidate drugs on humans can be predicted, thus further bringing down costs and time involved in drug development.