O'Kane, M. | bioRxiv Science

bioRxiv2018

Computational modelling of atherosclerosis: developing a community resource

RationaleAtherosclerosis is a dynamical process that emerges from the interplay between lipid metabolism, inflammation and innate immunity. The arterial location of atherosclerosis makes it logistically and ethically difficult to study in vivo. To improve our understanding of the disease, we must find alternative ways to investigate its progression. There is currently no computational model of atherosclerosis openly available to the research community for use in future studies and for refinement and development.\n\nObjectiveHere we develop the first predictive computational model to be made openly available and demonstrate its use for therapeutic hypothesis generation.\n\nMethods and ResultsWe compiled a dataset of relevant interactions from the literature along with available parameters. These were used to build a network model describing atherosclerotic plaque development. A visual map of the network model was produced using the Systems Biology Graphical Notation (SBGN) and a dynamic mathematical description of the network model that enables us to simulate plaque growth was developed and is made available using the Systems Biology Markup Language (SBML). We used this model to investigate whether multi-drug therapeutic interventions could be identified that stimulate plaque regression. The model produced comprised 20 cell types and 41 proteins with 89 species in total. The visual map is available for reuse and refinement using the SBGN Markup Language standard format and the mathematical model is available using the SBML standard format. We used a genetic algorithm to identify a multi-drug intervention hypothesis comprising five drugs that comprehensively reverse plaque growth within the model.\n\nConclusionsWe have produced the first predictive mathematical and computational model of atherosclerosis that can be reused and refined by the cardiovascular research community. We demonstrated its potential as a tool for future studies of cardiovascular disease by using it to identify multi-drug intervention hypotheses.\n\nSubject CodesAtherosclerosis, Computational Biology, Lipids and Cholesterol, Cell Signaling/Signal Transduction, Cardiovascular Disease

physiology↗

bioRxiv2018

A laboratory demand optimisation project in primary care

BackgroundThere is evidence of increasing use of laboratory tests with substantial variation between clinical teams which is difficult to justify on clinical grounds. The aim of this project was to assess the effect of a demand optimisation intervention on laboratory test requesting in primary care.\n\nMethodsThe intervention comprised educational initiatives, feedback to 55 individual practices on test request rates with ranking relative to other practices, and a small financial incentive for practices to engage and reflect on their test requesting activity. Data on test request numbers were collected from the laboratory databases for consecutive 12 month periods; pre-intervention 2011-12, intervention 2012-13, 2013-14, 2014-15, and post-intervention 2015-16.\n\nResultsThe intervention was associated with a 3.6% reduction in the mean number of profile test requests between baseline and 2015-16, although this was seen only in rural practices. In both rural and urban practices, there was a significant reduction in-between practice variability in request rates. The mean number of HbA1c requests increased from 1.9 to 3.0 per practice patient with diabetes. Variability in HbA1c request rates increased from 23.8% to 36.6%. At all considered time points, test request rates and variability were higher in rural than in urban areas.\n\nConclusionsThe intervention was associated with a reduction in both the volume and between practice variability of profile test requests, with differences noted between rural and urban practices. The increase in HbA1c requests may reflect a more appropriate rate of diabetes monitoring and also the adoption of HbA1c as a diagnostic test.\n\nStrengths & limitations of the studyO_LIWe assessed the effect of a laboratory demand optimisation intervention both on the value and between GP practice variability in laboratory test requesting.\nC_LIO_LIThe changes in laboratory test requesting were separately evaluated for rural and urban GP practices.\nC_LIO_LIOther factors (GP practice organisation, characteristics of general practitioners) potentially affecting between practice differences in laboratory test ordering were not taken into account due to data unavailability.\nC_LIO_LIThe demand management initiative was not accompanied by the cost-effectiveness analysis.\nC_LIO_LIThe demand optimisation intervention was conducted in a Northern Ireland (NI) Western Health and Social Care Trust and the findings have not been independently replicated in any other NI trusts.\nC_LI

pathology↗