Building SuperModels: emerging patient avatars for use in precision and systems medicine

Brown, Sherry‐Ann

doi:10.3389/fphys.2015.00318

Cited by 14 publications

(15 citation statements)

References 38 publications

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“…3,144,145 Genetic or systems medicine counselling should also be incorporated into pretreatment consultations with patients to help inform t reatment choices. 136 In the future, such counsel ing will likely include addressing a potential role of pharmaco genomics and use of other systems medicine data in predicting responses to protective drugs, such as ACE inhibitors and β-blockers, which are often used in patients with cardio-oncology related-health problems ( Figure 5). Before such information can be incorporated into routine clinical practice, however, answers to a variety of questions would need to be provided.…”

Section: The P*3 Pathway To Systems Medicinementioning

confidence: 99%

“…Integration of data from a systems medicine approach into patients' electronic medical or health records will likely provide a foundation for implementation into clinical practice. 109,114,136,143 With pre-emptive information readily available electronically to the practitioner, 114 real-time decisions can be made to enable modification of the approach to therapy, thus avoiding cardiotoxicity. Therapy modification in P*3 ( Figure 5) for cardiotoxicity prevention would include initiation of medications such as dexrazoxane, angiotensin-converting-enzyme (ACE) inhibitors, β-blockers, angiotensin receptor blockers, statins and/or institution of an exercise programme (with positive effects potentially mediated by NRG1 91 ).…”

Section: The P*3 Pathway To Systems Medicinementioning

confidence: 99%

“…Customized computational avatars could, therefore, be created for each individual patient. 136 Bolus injections, continuous drug infusions of various doses and durations and liposomal and thermoliposomal delivery methods could be simulated, as well as dose adjustments with provision of personalized recommendations. As such, dynamic systems and signalling networks that are already established can be individualized, modelled and perturbed to pre-empt, predict and prevent cardiotoxicity.…”

Section: Development Of Computational Avatarsmentioning

confidence: 99%

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Systems biology approaches to adverse drug effects: the example of cardio-oncology

2015

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Increased awareness of the cardiovascular toxic effects of chemotherapy has led to the emergence of cardio-oncology (or onco-cardiology), which focuses on screening, monitoring and treatment of patients with cardiovascular dysfunctions resulting from chemotherapy. Anthracyclines, such as doxorubicin, and HER2 inhibitors, such as trastuzumab, both have cardiotoxic effects. The biological rationale, mechanisms of action and cardiotoxicity profiles of these two classes of drugs, however, are completely different, suggesting that cardiotoxic effects can occur in a range of different ways. Advances in genomics and proteomics have implicated several genomic variants and biological pathways that can influence the susceptibility to cardiotoxicity from these, and other drugs. Established pathways include multidrug resistance proteins, energy utilization pathways, oxidative stress, cytoskeletal regulation and apoptosis. Gene-expression profiles that have revealed perturbed pathways have vastly increased our knowledge of the complex processes involved in crosstalk between tumours and cardiac function. Utilization of mathematical and computational modelling can complement pharmacogenomics and improve individual patient outcomes. Such endeavours should enable identification of variations in cardiotoxicity, particularly in those patients who are at risk of not recovering, even with the institution of cardioprotective therapy. The application of systems biology holds substantial potential to advance our understanding of chemotherapy-induced cardiotoxicity.

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Section: The P*3 Pathway To Systems Medicinementioning

confidence: 99%

Section: The P*3 Pathway To Systems Medicinementioning

confidence: 99%

Section: Development Of Computational Avatarsmentioning

confidence: 99%

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Systems biology approaches to adverse drug effects: the example of cardio-oncology

2015

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“…Computational modelling is a rapidly developing area in biomedical sciences, and is becoming mature for human physiology (e.g. the VPH, [6]) with applications in understanding disease (Systems Medicine) and development of drugs (Systems Pharmacology) [7]. Mechanistic computer simulation models are applied for hypothesis generation and testing, to support analysis of complex data, to discover biological mechanisms, and modelling contributes to the development of medical therapies.…”

Section: Virtual Patients In Need Of a Digital Mousementioning

confidence: 99%

The Digital Mouse: why computational modelling of mouse models of disease can improve translation

Riel

Müller

Dall’Ara

2020

Preprint

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Highlights 19• Computational modelling of human (patho)physiology is advancing rapidly, often using and 20 extrapolating experimental findings from preclinical disease models. 21• The lack of in silico models to support in vivo modelling in mice is a missing link in current 22approaches to study complex, chronic diseases. 23• The development of mechanistic computational models to simulate disease in mice can boost 24 the discovery of novel therapeutic interventions. 25• The 'Digital Mouse' is proposed as a framework to implement this ambition. The 26 development of a Digital Mouse Frailty Index (DM:FI) to study aging and age-related 27 diseases is provided as an example. 28 29Abstract 30 Computational models can be used to study the mechanistic phenomena of disease. Current 31 mechanistic computer simulation models mainly focus on (patho)physiology in humans. However, 32often data and experimental findings from preclinical studies are used as input to develop such 33 models. Biological processes underlying age-related chronic diseases are studied in animal models. 34The translation of these observations to clinical applications is not trivial. As part of a group of 35 international scientists working in the COST Action network MouseAGE, we argue that in order to 36 boost the translation of pre-clinical research we need to develop accurate in silico counterparts of the 37 in vivo animal models. The Digital Mouse is proposed as framework to support the development of 38 evidence-based medicine, for example to develop geroprotectors, which are drugs that target 39 fundamental mechanisms of ageing. 40 41 Keywords: 42 regulatory decision-making, computational modelling, in silico pre-clinical trial, mouse models, aging, 43 digital twin, geroprotectors, frailty 44 45 2 46

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“…In step 3, this information combines with data from the query or index patient's EHR to form the data input for the patient similarity algorithms. Each “omic” or systems medicine data type or tool (Brown, 2015b) functions as a predictor variable vector, all of which are incorporated into the multidimensional feature space for the patient. In step 4, the entire available EHR patient populous is interrogated with a patient similarity network analysis tool; efficient data mining is completed using patient similarity algorithms.…”

Section: Introductionmentioning

confidence: 99%

Patient Similarity: Emerging Concepts in Systems and Precision Medicine

Brown

2016

Front. Physiol.

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Building SuperModels: emerging patient avatars for use in precision and systems medicine

Cited by 14 publications

References 38 publications

Systems biology approaches to adverse drug effects: the example of cardio-oncology

Systems biology approaches to adverse drug effects: the example of cardio-oncology

The Digital Mouse: why computational modelling of mouse models of disease can improve translation

Patient Similarity: Emerging Concepts in Systems and Precision Medicine

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