A physiome interoperability roadmap for personalized drug development

Thomas, Shl; Wolstencroft, Katy; Bono, Bernard de; Hunter, Peter

doi:10.1098/rsfs.2015.0094

Cited by 8 publications

(4 citation statements)

References 66 publications

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“…Thus, models incorporating several to multi-million degrees of freedom can offer great insight into metabolic, pulmonary, cardiovascular, and, indeed, whole body systems (e.g. [ 15 – 17 , 19 , 20 , 27 , 29 – 33 , 38 , 49 , 56 , 65 , 67 , 68 ]). However, especially given limited available bedside data, their parameter sets cannot necessarily be identified in real-time for use at the bedside in personalised care or virtual patients (to guide care) at this point.…”

Section: Model Types and Requirementsmentioning

confidence: 99%

“…There is thus a role for both levels of modeling to integrate and inform each other. Hence, while this review focuses predominantly on the emerging use of bedside, highly patient-specific models and virtual patients, the potential links to physiome models, well reviewed elsewhere [ 33 , 48 , 57 , 58 , 63 , 65 – 68 ], are also presented.…”

Section: Introductionmentioning

confidence: 99%

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Next-generation, personalised, model-based critical care medicine: a state-of-the art review of in silico virtual patient models, methods, and cohorts, and how to validation them

et al. 2018

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Critical care, like many healthcare areas, is under a dual assault from significantly increasing demographic and economic pressures. Intensive care unit (ICU) patients are highly variable in response to treatment, and increasingly aging populations mean ICUs are under increasing demand and their cohorts are increasingly ill. Equally, patient expectations are growing, while the economic ability to deliver care to all is declining. Better, more productive care is thus the big challenge. One means to that end is personalised care designed to manage the significant inter- and intra-patient variability that makes the ICU patient difficult. Thus, moving from current “one size fits all” protocolised care to adaptive, model-based “one method fits all” personalised care could deliver the required step change in the quality, and simultaneously the productivity and cost, of care. Computer models of human physiology are a unique tool to personalise care, as they can couple clinical data with mathematical methods to create subject-specific models and virtual patients to design new, personalised and more optimal protocols, as well as to guide care in real-time. They rely on identifying time varying patient-specific parameters in the model that capture inter- and intra-patient variability, the difference between patients and the evolution of patient condition. Properly validated, virtual patients represent the real patients, and can be used in silico to test different protocols or interventions, or in real-time to guide care. Hence, the underlying models and methods create the foundation for next generation care, as well as a tool for safely and rapidly developing personalised treatment protocols over large virtual cohorts using virtual trials. This review examines the models and methods used to create virtual patients. Specifically, it presents the models types and structures used and the data required. It then covers how to validate the resulting virtual patients and trials, and how these virtual trials can help design and optimise clinical trial. Links between these models and higher order, more complex physiome models are also discussed. In each section, it explores the progress reported up to date, especially on core ICU therapies in glycemic, circulatory and mechanical ventilation management, where high cost and frequency of occurrence provide a significant opportunity for model-based methods to have measurable clinical and economic impact. The outcomes are readily generalised to other areas of medical care.

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Section: Model Types and Requirementsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Next-generation, personalised, model-based critical care medicine: a state-of-the art review of in silico virtual patient models, methods, and cohorts, and how to validation them

et al. 2018

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“…The FTU was initially determined as a cylindrical tissue parcelation centerd around a blood capillary in which any two points are within diffusion distance. Organizing FTU knowledge over treemaps ( de Bono et al, 2012 ; Kokash et al, 2012 ), which are directly generated from partonomy and subsumption networks drawn from anatomy ontologies, was a first approach to model in ApiNATOMY whole-body flow-routes inspired by well-established physiology-based pharmacokinetic (PB-PK) methods (e.g., Sager et al, 2015 ; Jamei, 2016 ; Thomas et al, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

Representing Normal and Abnormal Physiology as Routes of Flow in ApiNATOMY

et al. 2022

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We present (i) the ApiNATOMY workflow to build knowledge models of biological connectivity, as well as (ii) the ApiNATOMY TOO map, a topological scaffold to organize and visually inspect these connectivity models in the context of a canonical architecture of body compartments. In this work, we outline the implementation of ApiNATOMY’s knowledge representation in the context of a large-scale effort, SPARC, to map the autonomic nervous system. Within SPARC, the ApiNATOMY modeling effort has generated the SCKAN knowledge graph that combines connectivity models and TOO map. This knowledge graph models flow routes for a number of normal and disease scenarios in physiology. Calculations over SCKAN to infer routes are being leveraged to classify, navigate and search for semantically-linked metadata of multimodal experimental datasets for a number of cross-scale, cross-disciplinary projects.

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“…Issues associated with the development and use of Human Physiome models of the heart and the musculo-skeletal system in the clinic and for industry are discussed by Chabiniok et al [15] and Fernandez et al [16], respectively. Finally, advancements needed for wide practical use of models in the drug development pipeline design and for in silico trials are discussed by Thomas et al [17].…”

mentioning

confidence: 99%