Model order reduction for left ventricular mechanics via congruency training

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Quantitative systems pharmacology (QSP) modeling is applied to address essential questions in drug development, such as the mechanism of action of a therapeutic agent and the progression of disease. Meanwhile, machine learning (ML) approaches also contribute to answering these questions via the analysis of multi-layer ‘omics’ data such as gene expression, proteomics, metabolomics, and high-throughput imaging. Furthermore, ML approaches can also be applied to aspects of QSP modeling. Both approaches are powerful tools and there is considerable interest in integrating QSP modeling and ML. So far, a few successful implementations have been carried out from which we have learned about how each approach can overcome unique limitations of the other. The QSP + ML working group of the International Society of Pharmacometrics QSP Special Interest Group was convened in September, 2019 to identify and begin realizing new opportunities in QSP and ML integration. The working group, which comprises 21 members representing 18 academic and industry organizations, has identified four categories of current research activity which will be described herein together with case studies of applications to drug development decision making. The working group also concluded that the integration of QSP and ML is still in its early stages of moving from evaluating available technical tools to building case studies. This paper reports on this fast-moving field and serves as a foundation for future codification of best practices.

Section: Current Approachesmentioning

confidence: 99%

Two heads are better than one: current landscape of integrating QSP and machine learning

Zhang

Androulakis

Bonate

et al. 2022

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“…For information on other parameters in equations ( 4)-( 6) please refer to the Supplemental Material and our previous publication [1]. passive a .…”

Section: Model Of Sarcomerementioning

confidence: 99%

“…A wide variety of parameter inference problems for populations of models exist, for which different requirements from prior biophysical knowledge must be satisfied, but current MCMC-based methods are limited to tackling simpler problems, as formulated in (1). Therefore, other statistical and machine learning methods are needed to solve these complex parameter inference scenarios.…”

Section: Introductionmentioning

confidence: 99%

“…Several GANs have been proposed for variational inference and could replace MCMC methods for sampling from posterior distributions in general [17]. We have recently proposed a novel GAN [39] specifically for parameter inference problems as given in (1), which is readily extensible to incorporate estimation of q X c ðx c Þ and q X d ðx d Þ from experimental data q Y c ðy c Þ and q Y d ðy d Þ as in (2). This GAN architecture implements a constrained optimization formulation of the stochastic inverse problem and is designed to solve parameter inference problems with these difficult requirements using the complex structures it makes possible.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Generative adversarial networks for construction of virtual populations of mechanistic models: simulations to study Omecamtiv Mecarbil action

Parikh

Rumbell

Butova

et al. 2021

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Biophysical models are increasingly used to gain mechanistic insights by fitting and reproducing experimental and clinical data. The inherent variability in the recorded datasets, however, presents a key challenge. In this study, we present a novel approach, which integrates mechanistic modeling and machine learning to analyze in vitro cardiac mechanics data and solve the inverse problem of model parameter inference. We designed a novel generative adversarial network (GAN) and employed it to construct virtual populations of cardiac ventricular myocyte models in order to study the action of Omecamtiv Mecarbil (OM), a positive cardiac inotrope. Populations of models were calibrated from mechanically unloaded myocyte shortening recordings obtained in experiments on rat myocytes in the presence and absence of OM. The GAN was able to infer model parameters while incorporating prior information about which model parameters OM targets. The generated populations of models reproduced variations in myocyte contraction recorded during in vitro experiments and provided improved understanding of OM’s mechanism of action. Inverse mapping of the experimental data using our approach suggests a novel action of OM, whereby it modifies interactions between myosin and tropomyosin proteins. To validate our approach, the inferred model parameters were used to replicate other in vitro experimental protocols, such as skinned preparations demonstrating an increase in calcium sensitivity and a decrease in the Hill coefficient of the force–calcium (F–Ca) curve under OM action. Our approach thereby facilitated the identification of the mechanistic underpinnings of experimental observations and the exploration of different hypotheses regarding variability in this complex biological system.

“…PINNs have been applied to cardiac modeling [ 110 ]. Moreover, machine learning methods have been also applied to cardiac modeling for uncertainty quantification [ 111 ], model order reduction [ 112 ], surrogate generation and acceleration of large scale of simulations [ 113 ].…”

Section: Machine Learning-assisted Qsp For Heart Failurementioning

confidence: 99%

Review of applications and challenges of quantitative systems pharmacology modeling and machine learning for heart failure

Cheng

Qiu

Schmidt

et al. 2021

Quantitative systems pharmacology (QSP) is an important approach in pharmaceutical research and development that facilitates in silico generation of quantitative mechanistic hypotheses and enables in silico trials. As demonstrated by applications from numerous industry groups and interest from regulatory authorities, QSP is becoming an increasingly critical component in clinical drug development. With rapidly evolving computational tools and methods, QSP modeling has achieved important progress in pharmaceutical research and development, including for heart failure (HF). However, various challenges exist in the QSP modeling and clinical characterization of HF. Machine/deep learning (ML/DL) methods have had success in a wide variety of fields and disciplines. They provide data-driven approaches in HF diagnosis and modeling, and offer a novel strategy to inform QSP model development and calibration. The combination of ML/DL and QSP modeling becomes an emergent direction in the understanding of HF and clinical development new therapies. In this work, we review the current status and achievement in QSP and ML/DL for HF, and discuss remaining challenges and future perspectives in the field.