The ‘Digital Twin’ to enable the vision of precision cardiology

Acero, Jorge Corral; Margara, Francesca; Marciniak, Maciej; Rodero, Cristóbal; Lončarić, Filip; Feng, Yingjing; Gilbert, Andrew; Fernandes, João Filipe; Bukhari, Hassaan; Wajdan, Ali; Martinez, Manuel Villegas; Santos, Mariana Sousa; Shamohammdi, Mehrdad; Luo, Hongxing; Westphal, Philip; Leeson, Paul; DiAchille, Paolo; Gurev, Viatcheslav; Mayr, Manuel; Geris, Liesbet; Pathmanathan, Pras; Morrison, Tina; Cornelussen, Richard; Prinzen, Frits W.; Delhaas, Tammo; Doltra, Adelina; Sitges, Marta; Vigmond, Edward J.; Zacur, Ernesto; Grau, Vicente; Rodriguez, Blanca; Remme, Espen W.; Niederer, Steven; Mortier, Peter; McLeod, Kristin; Potse, Mark; Pueyo, Esther; Bueno‐Orovio, Alfonso; Lamata, Pablo

doi:10.1093/eurheartj/ehaa159

Cited by 441 publications

(369 citation statements)

References 87 publications

Supporting

Mentioning

358

Contrasting

Unclassified

Order By: Relevance

“…Another area that may benefit from AI is "image-based computational cardiology, " which builds patient-specific digital models of the heart to simulate treatment response. While this area has been traditionally addressed using pure physiological and mechanistic models, researchers are now investigating the integration of machine learning to improve the accuracy and speed of the personalized simulated outputs (11). Furthermore, as larger datasets become available, it is expected that predictive models of disease progression will be developed and validated, including by integrating imaging with non-imaging predictors (e.g., socio-demographic, biomarker, lifestyle and genomic data).…”

Section: Future Perspectivesmentioning

confidence: 99%

Editorial: Current and Future Role of Artificial Intelligence in Cardiac Imaging

Lekadir

Leiner

Young

et al. 2020

Front. Cardiovasc. Med.

View full text Add to dashboard Cite

Section: Future Perspectivesmentioning

confidence: 99%

Editorial: Current and Future Role of Artificial Intelligence in Cardiac Imaging

Lekadir

Leiner

Young

et al. 2020

Front. Cardiovasc. Med.

View full text Add to dashboard Cite

“…Therefore, current practice involves a trialand-error approach where the dose of the oral morphine is gradually increased while carefully monitoring the patient's response until an individualized dosage and therapy are reached. With the advent of physics-based simulations of drug delivery processes, truly individualized therapies can be achieved [16].…”

Section: Introductionmentioning

confidence: 99%

Predicting transdermal fentanyl delivery using physics-based simulations for tailored therapy

Bahrami

Defraeye

2021

Preprint

View full text Add to dashboard Cite

Transdermal fentanyl patches are an effective alternative to the sustained-release of oral morphine for chronic pain treatment. Due to the narrow therapeutic range of fentanyl, the fentanyl concentration in the blood needs to be controlled carefully. Only then, effective pain relief can be reached while avoiding adverse effects such as respiratory depression. In this study, a physics-based digital twin of the patient was developed by implementing mechanistic models for transdermal drug uptake and the patient’s pharmacokinetic and pharmacodynamics response. A digital twin is a virtual representation of the patient and the transdermal drug delivery system, which is linked to the real-world patient by patient feedback, sensor data of specific biomarkers, or customizing the twin to a particular patient characteristic, for example, based on the age. This digital twin can predict the transdermal drug delivery processes in-silico. Our twin is used first to predict conventional therapy’s effect for using fentanyl patches on a virtual patient at different ages. The results show that by aging, the maximum transdermal fentanyl flux and maximum concentration of fentanyl in the blood decrease by 11.4% and 7.0%, respectively. Nonetheless, by aging, the pain relief increases by 45.2% despite the lower concentration of fentanyl in the blood for older patients. As a next step, the digital twin was used to propose a tailored therapy, based on the patient’s age, to deliver fentanyl based on the patient’s needs to alleviate pain. This predesigned therapy consisted of customizing the duration of applying and changing the commercialized fentanyl patches based on the calculated pain intensity. According to this therapy, a patient of 20 years old needs to change the patch 2.1 times more frequently compared to conventional therapy, which led to 30% more pain relief and 315% more time without pain. In addition, the digital twin was updated by the patient’s pain intensity feedback. Such therapy led to an increase in the patient’s breathing rate while having effective pain relief, therefore providing a safer and more comfortable treatment for the patient. We quantified the added value of a patient’s physics-based digital twin and sketched the future roadmap for implementing such twin-assisted treatment into the clinics.NomenclatureSymbolsci The concentration of fentanyl in layer i (in the drug uptake model) [ng ml-1]cp The concentration of fentanyl in the central compartment [ng ml-1]cr The concentration of fentanyl in the rapid equilibrated compartment [ng ml-1]cs The concentration of fentanyl in the slow equilibrated compartment [ng ml-1]cg The concentration of fentanyl in the gastrointestinal compartment [ng ml-1]cl The concentration of fentanyl in the hepatic compartment [ng ml-1]ce The concentration of fentanyl in the effect compartment [ng ml-1]Di Diffusion coefficient of fentanyl in layer i (in the mechanistic model) [m2 s-1]D0 Base diffusion coefficient of fentanyl [m2 s-1]DT Diffusion coefficient of fentanyl at temperature T [m2 s-1]D306 Diffusion coefficient of fentanyl at 306[K] [m2 s-1]dpt The thickness of the transdermal patch [µm]dsc The thickness of the stratum corneum [µm]dvep The thickness of the viable epidermis [µm]dEdm The thickness of the equivalent dermis [µm]Ei The intensity of effect i The baseline of effect i The maximum effect iEC50,i The concentration related to half-maximum effect i [ng ml-1]fu The fraction of unbound fentanyl in plasmaji Fentanyl flux in layer i (in the mechanistic model)Ki/j The partition coefficient of fentanyl between layer i to j (in the mechanistic model)Ki The drug capacity in layer i (in the mechanistic model)kcs Inter-compartmental first-order equilibrium rate constant (central to slow equilibrated) [min-1]kcr Inter-compartmental first-order equilibrium rate constant (central to rapid equilibrated) [min-1]kcg Inter-compartmental first-order equilibrium rate constant (central to gastrointestinal) [min-1]kch Inter-compartmental first-order equilibrium rate constant (central to hepatic) [min-1]ksc Inter-compartmental first-order equilibrium rate constant (slow equilibrated to central) [min-1]krc Inter-compartmental first-order equilibrium rate constant (rapid equilibrated to central) [min-1]khc Inter-compartmental first-order equilibrium rate constant (hepatic to central) [min-1]kgh Inter-compartmental first-order equilibrium rate constant (gastrointestinal to hepatic) [min-1]kmet Metabolization rate constant [min-1]kre Renal clearance rate constant [min-1]ke Inter-compartmental first-order equilibrium rate constant (for effect compartment) [min-1]SI Sensitivity indext Time [h]tD Time lag [h] Dependent variable related to xi for sensitivity analysisVc The apparent volume of the central compartment [L]Vs The apparent volume of the slow equilibrated compartment [L]Vr The apparent volume of the rapid equilibrated compartment [L]Vg The apparent volume of the gastrointestinal compartment [L]Vh The apparent volume of the hepatic compartment [L]xi The independent variable which sensitivity analysis is done based on itγ Hill coefficientψi Drug potential in domain i [ng ml-1]

show abstract

“…1 The concept is increasingly entering the healthcare industry with the aim of creating molecular and phenotypic copies of human beings that can allow for trialling of different therapies to elucidate the most efficacious treatment for the real-life patient. 2 Although the literature is increasingly discussing the potential for medical specialities such as cardiology and oncology, 3,4 there are few articles discussing their potential in surgical practice.…”

Section: Introductionmentioning

confidence: 99%