Creation and application of virtual patient cohorts of heart models

Niederer, Steven A.; Aboelkassem, Yasser; Cantwell, Chris D.; Corrado, Cesare; Coveney, Sam; Cherry, Elizabeth M.; Delhaas, Tammo; Fenton, Flavio H.; Panfilov, Alexander V.; Pathmanathan, Pras; Plank, Gernot; Riabiz, Marina; Roney, Caroline; Santos, Rodrigo Weber dos; Wang, L.

doi:10.1098/rsta.2019.0558

Cited by 70 publications

(62 citation statements)

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“…Notably, there are a number of methods for the virtual patient generation with different algorithms to address potential biases. [21][22][23][24][25] Here, we use the methods that are similar to the recently published studies. 53 54 The optimal techniques for virtual patient generation based on the availability of clinical data is an active area of research that is undergoing rapid development.…”

Section: Open Accessmentioning

confidence: 99%

“…While there exist various methodologies in the virtual patient generation, the optimization of these algorithms is under active investigation. [21][22][23][24][25] In this study, we aim to determine the relationship between our QSP platform and the virtual cohort with the patient cohort and results of the clinical trial. We discuss the limitations related to our choice of methodology, which need to be taken into account while interpreting the present numerical results and comparisons.…”

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confidence: 99%

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Quantitative systems pharmacology model predictions for efficacy of atezolizumab and nab-paclitaxel in triple-negative breast cancer

Wang¹,

Ma²,

Sové³

et al. 2021

J Immunother Cancer

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BackgroundImmune checkpoint blockade therapy has clearly shown clinical activity in patients with triple-negative breast cancer, but less than half of the patients benefit from the treatments. While a number of ongoing clinical trials are investigating different combinations of checkpoint inhibitors and chemotherapeutic agents, predictive biomarkers that identify patients most likely to benefit remains one of the major challenges. Here we present a modular quantitative systems pharmacology (QSP) platform for immuno-oncology that incorporates detailed mechanisms of immune–cancer cell interactions to make efficacy predictions and identify predictive biomarkers for treatments using atezolizumab and nab-paclitaxel.MethodsA QSP model was developed based on published data of triple-negative breast cancer. With the model, we generated a virtual patient cohort to conduct in silico virtual clinical trials and make retrospective analyses of the pivotal IMpassion130 trial that led to the accelerated approval of atezolizumab and nab-paclitaxel for patients with programmed death-ligand 1 (PD-L1) positive triple-negative breast cancer. Available data from clinical trials were used for model calibration and validation.ResultsWith the calibrated virtual patient cohort based on clinical data from the placebo comparator arm of the IMpassion130 trial, we made efficacy predictions and identified potential predictive biomarkers for the experimental arm of the trial using the proposed QSP model. The model predictions are consistent with clinically reported efficacy endpoints and correlated immune biomarkers. We further performed a series of virtual clinical trials to compare different doses and schedules of the two drugs for simulated therapeutic optimization.ConclusionsThis study provides a QSP platform, which can be used to generate virtual patient cohorts and conduct virtual clinical trials. Our findings demonstrate its potential for making efficacy predictions for immunotherapies and chemotherapies, identifying predictive biomarkers, and guiding future clinical trial designs.

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Section: Open Accessmentioning

confidence: 99%

mentioning

confidence: 99%

Quantitative systems pharmacology model predictions for efficacy of atezolizumab and nab-paclitaxel in triple-negative breast cancer

Wang¹,

Ma²,

Sové³

et al. 2021

J Immunother Cancer

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“…The snapshot matrix is built by solving problem (1), completed with the applied currents (18) and (19), by means of a semi-implicit scheme, over N t = 400 time instances. Moreover, we consider N train = 13 training-parameter instances uniformly distributed in the parameter space and N test = 12 testing-parameter instances, each of them corresponding to the midpoint of two consecutive training-parameter instances.…”

Section: Test 2: Two-dimensional Slab With Figure Of Eight Re-entrymentioning

confidence: 99%

“…Multi-query analysis is relevant in a variety of situations: when analyzing multiple scenarios, when dealing with sensitivity analysis and uncertainty quantification (UQ) problems in order to account for inter-subject variability [ 10 – 13 ], for parameter estimation or data assimilation, in which some unknown (or unaccessible) quantities characterizing the mathematical model must be inferred from a set of measurements [ 14 – 18 ]. In all these cases, to achieve computational efficiency, multi-query analysis in cardiac electrophysiology must rely on suitable surrogate models see, e.g., [ 19 ] for a recent review on the topic. Among surrogate models, several options are available, such as (i) emulators, obtained, e.g., via Polynomial Chaos Expansions or Gaussian process regression [ 20 – 22 ], aiming at the approximation of the input-output mapping by fitting a set of training data; (ii) lower-fidelity models, introducing suitable modeling simplifications—such as, for instance, the Eikonal model in this context [ 23 ]; and (iii) reduced order models (ROMs) obtained through a projection process on the equations governing the FOM to reduce the state-space dimensionality.…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based reduced order models in cardiac electrophysiology

et al. 2020

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Predicting the electrical behavior of the heart, from the cellular scale to the tissue level, relies on the numerical approximation of coupled nonlinear dynamical systems. These systems describe the cardiac action potential, that is the polarization/depolarization cycle occurring at every heart beat that models the time evolution of the electrical potential across the cell membrane, as well as a set of ionic variables. Multiple solutions of these systems, corresponding to different model inputs, are required to evaluate outputs of clinical interest, such as activation maps and action potential duration. More importantly, these models feature coherent structures that propagate over time, such as wavefronts. These systems can hardly be reduced to lower dimensional problems by conventional reduced order models (ROMs) such as, e.g., the reduced basis method. This is primarily due to the low regularity of the solution manifold (with respect to the problem parameters), as well as to the nonlinear nature of the input-output maps that we intend to reconstruct numerically. To overcome this difficulty, in this paper we propose a new, nonlinear approach relying on deep learning (DL) algorithms-such as deep feedforward neural networks and convolutional autoencodersto obtain accurate and efficient ROMs, whose dimensionality matches the number of system parameters. We show that the proposed DL-ROM framework can efficiently provide solutions to parametrized electrophysiology problems, thus enabling multi-scenario analysis in pathological cases. We investigate four challenging test cases in cardiac electrophysiology, thus demonstrating that DL-ROM outperforms classical projection-based ROMs.

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“…Moreover, the possibility of creating fully‐automatic pipelines for a specific application can also be exploited for clinical studies on huge datasets or to create virtual cohorts of heart models 73 …”

Section: Introductionmentioning

confidence: 99%

Polygonal surface processing and mesh generation tools for the numerical simulation of the cardiac function

Fedele

Quarteroni

2021

Numer Methods Biomed Eng

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In order to simulate the cardiac function for a patient‐specific geometry, the generation of the computational mesh is crucially important. In practice, the input is typically a set of unprocessed polygonal surfaces coming either from a template geometry or from medical images. These surfaces need ad‐hoc processing to be suitable for a volumetric mesh generation. In this work we propose a set of new algorithms and tools aiming to facilitate the mesh generation process. In particular, we focus on different aspects of a cardiac mesh generation pipeline: (1) specific polygonal surface processing for cardiac geometries, like connection of different heart chambers or segmentation outputs; (2) generation of accurate boundary tags; (3) definition of mesh‐size functions dependent on relevant geometric quantities; (4) processing and connecting together several volumetric meshes. The new algorithms—implemented in the open‐source software vmtk—can be combined with each other allowing the creation of personalized pipelines, that can be optimized for each cardiac geometry or for each aspect of the cardiac function to be modeled. Thanks to these features, the proposed tools can significantly speed‐up the mesh generation process for a large range of cardiac applications, from single‐chamber single‐physics simulations to multi‐chambers multi‐physics simulations. We detail all the proposed algorithms motivating them in the cardiac context and we highlight their flexibility by showing different examples of cardiac mesh generation pipelines.

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Creation and application of virtual patient cohorts of heart models

Cited by 70 publications

References 100 publications

Quantitative systems pharmacology model predictions for efficacy of atezolizumab and nab-paclitaxel in triple-negative breast cancer

Quantitative systems pharmacology model predictions for efficacy of atezolizumab and nab-paclitaxel in triple-negative breast cancer

Deep learning-based reduced order models in cardiac electrophysiology

Polygonal surface processing and mesh generation tools for the numerical simulation of the cardiac function

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