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Podéus H, Simonsson C, Nasr P, Ekstedt M, Kechagias S, Lundberg P, Lövfors W, Cedersund G. A physiologically-based digital twin for alcohol consumption-predicting real-life drinking responses and long-term plasma PEth. NPJ Digit Med 2024; 7:112. [PMID: 38702474 PMCID: PMC11068902 DOI: 10.1038/s41746-024-01089-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 03/29/2024] [Indexed: 05/06/2024] Open
Abstract
Alcohol consumption is associated with a wide variety of preventable health complications and is a major risk factor for all-cause mortality in the age group 15-47 years. To reduce dangerous drinking behavior, eHealth applications have shown promise. A particularly interesting potential lies in the combination of eHealth apps with mathematical models. However, existing mathematical models do not consider real-life situations, such as combined intake of meals and beverages, and do not connect drinking to clinical markers, such as phosphatidylethanol (PEth). Herein, we present such a model which can simulate real-life situations and connect drinking to long-term markers. The new model can accurately describe both estimation data according to a χ2 -test (187.0 < Tχ2 = 226.4) and independent validation data (70.8 < Tχ2 = 93.5). The model can also be personalized using anthropometric data from a specific individual and can thus be used as a physiologically-based digital twin. This twin is also able to connect short-term consumption of alcohol to the long-term dynamics of PEth levels in the blood, a clinical biomarker of alcohol consumption. Here we illustrate how connecting short-term consumption to long-term markers allows for a new way to determine patient alcohol consumption from measured PEth levels. An additional use case of the twin could include the combined evaluation of patient-reported AUDIT forms and measured PEth levels. Finally, we integrated the new model into an eHealth application, which could help guide individual users or clinicians to help reduce dangerous drinking.
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Affiliation(s)
- Henrik Podéus
- Department of Biomedical Engineering (IMT), Linköping University, Linköping, Sweden
| | - Christian Simonsson
- Department of Biomedical Engineering (IMT), Linköping University, Linköping, Sweden
- Center for Medicine Imaging and Visualization Science (CMIV), Linköping University, Linköping, Sweden
| | - Patrik Nasr
- Department of Health, Medicine, and Caring Sciences, Linköping University, Linköping, Sweden
- Wallenberg Center for Molecular Medicine, Linköping University, Linköping, Sweden
| | - Mattias Ekstedt
- Center for Medicine Imaging and Visualization Science (CMIV), Linköping University, Linköping, Sweden
- Department of Health, Medicine, and Caring Sciences, Linköping University, Linköping, Sweden
| | - Stergios Kechagias
- Department of Health, Medicine, and Caring Sciences, Linköping University, Linköping, Sweden
| | - Peter Lundberg
- Center for Medicine Imaging and Visualization Science (CMIV), Linköping University, Linköping, Sweden
- Department of Radiation Physics, and Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| | - William Lövfors
- Department of Biomedical Engineering (IMT), Linköping University, Linköping, Sweden
- School of Medical Sciences and Inflammatory Response and Infection Susceptibility Centre (iRiSC), Faculty of Medicine and Health, Örebro University, Örebro, Sweden
| | - Gunnar Cedersund
- Department of Biomedical Engineering (IMT), Linköping University, Linköping, Sweden.
- Center for Medicine Imaging and Visualization Science (CMIV), Linköping University, Linköping, Sweden.
- School of Medical Sciences and Inflammatory Response and Infection Susceptibility Centre (iRiSC), Faculty of Medicine and Health, Örebro University, Örebro, Sweden.
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Chalhoub ER, Belovich JM. Quantitative analysis of the interaction of ethanol metabolism with gluconeogenesis and fatty acid oxidation in the perfused liver of fasted rats. Arch Biochem Biophys 2022; 718:109148. [PMID: 35143783 DOI: 10.1016/j.abb.2022.109148] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 02/04/2022] [Accepted: 02/06/2022] [Indexed: 12/12/2022]
Abstract
Ethanol is known to significantly affect gluconeogenesis and lipid metabolism in the liver, primarily by altering the redox ratio in both cytosol and mitochondria. The effect of ethanol was analyzed using a comprehensive, dynamic model of liver metabolism that takes into account sub-cellular compartmentation, detailed kinetics for the citric acid cycle, ethanol and acetaldehyde oxidation, and gluconeogenesis, and inter-compartmental transport of metabolites, including the malate-aspartate shuttle. The kinetic expression for alcohol dehydrogenase takes into account inhibition by ethanol and NADH. Simulations of perfusions of the rat liver were performed with various combinations of substrates (lactate, pyruvate, and fatty acids), with subsequent addition of ethanol to the perfusate. The model successfully predicts NADH/NAD+, in both cytosol and mitochondria, the expected directional flux of reducing equivalents between the two compartments during perfusion with different gluconeogenic precursors, and the effect of ethanol on glucose and ketone body production. This model can serve as a platform for in silico experiments investigating the effects of ethanol on the many dehydrogenases, and thus the major carbohydrate and lipid metabolic pathways in the liver, as well as potential effects of various drugs that may interact with ethanol.
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Affiliation(s)
- Elie R Chalhoub
- Department of Chemical Engineering, University of Balamand, Faculty of Engineering, P.O.Box 100, Tripoli, Lebanon.
| | - Joanne M Belovich
- Department of Chemical and Biomedical Engineering, Cleveland State University, Cleveland, OH, 44115, USA
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Zhu L, Pei W, DiCiano P, Brands B, Wickens CM, Foll BL, Kwong B, Parashar M, Sivananthan A, Mahadevan R. Physiologically-based pharmacokinetic model for predicting blood and tissue tetrahydrocannabinol concentrations. Comput Chem Eng 2021. [DOI: 10.1016/j.compchemeng.2021.107461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Sahu A, Blätke MA, Szymański JJ, Töpfer N. Advances in flux balance analysis by integrating machine learning and mechanism-based models. Comput Struct Biotechnol J 2021; 19:4626-4640. [PMID: 34471504 PMCID: PMC8382995 DOI: 10.1016/j.csbj.2021.08.004] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2021] [Revised: 08/03/2021] [Accepted: 08/03/2021] [Indexed: 02/08/2023] Open
Abstract
The availability of multi-omics data sets and genome-scale metabolic models for various organisms provide a platform for modeling and analyzing genotype-to-phenotype relationships. Flux balance analysis is the main tool for predicting flux distributions in genome-scale metabolic models and various data-integrative approaches enable modeling context-specific network behavior. Due to its linear nature, this optimization framework is readily scalable to multi-tissue or -organ and even multi-organism models. However, both data and model size can hamper a straightforward biological interpretation of the estimated fluxes. Moreover, flux balance analysis simulates metabolism at steady-state and thus, in its most basic form, does not consider kinetics or regulatory events. The integration of flux balance analysis with complementary data analysis and modeling techniques offers the potential to overcome these challenges. In particular machine learning approaches have emerged as the tool of choice for data reduction and selection of most important variables in big data sets. Kinetic models and formal languages can be used to simulate dynamic behavior. This review article provides an overview of integrative studies that combine flux balance analysis with machine learning approaches, kinetic models, such as physiology-based pharmacokinetic models, and formal graphical modeling languages, such as Petri nets. We discuss the mathematical aspects and biological applications of these integrated approaches and outline challenges and future perspectives.
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Affiliation(s)
- Ankur Sahu
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstraße 3, 06466 Gatersleben, Germany
| | - Mary-Ann Blätke
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstraße 3, 06466 Gatersleben, Germany
| | - Jędrzej Jakub Szymański
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstraße 3, 06466 Gatersleben, Germany
| | - Nadine Töpfer
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstraße 3, 06466 Gatersleben, Germany
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Zhu L, Pei W, Thiele I, Mahadevan R. Integration of a physiologically-based pharmacokinetic model with a whole-body, organ-resolved genome-scale model for characterization of ethanol and acetaldehyde metabolism. PLoS Comput Biol 2021; 17:e1009110. [PMID: 34351898 PMCID: PMC8370625 DOI: 10.1371/journal.pcbi.1009110] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 08/17/2021] [Accepted: 05/24/2021] [Indexed: 11/25/2022] Open
Abstract
Ethanol is one of the most widely used recreational substances in the world and due to its ubiquitous use, ethanol abuse has been the cause of over 3.3 million deaths each year. In addition to its effects, ethanol's primary metabolite, acetaldehyde, is a carcinogen that can cause symptoms of facial flushing, headaches, and nausea. How strongly ethanol or acetaldehyde affects an individual depends highly on the genetic polymorphisms of certain genes. In particular, the genetic polymorphisms of mitochondrial aldehyde dehydrogenase, ALDH2, play a large role in the metabolism of acetaldehyde. Thus, it is important to characterize how genetic variations can lead to different exposures and responses to ethanol and acetaldehyde. While the pharmacokinetics of ethanol metabolism through alcohol dehydrogenase have been thoroughly explored in previous studies, in this paper, we combined a base physiologically-based pharmacokinetic (PBPK) model with a whole-body genome-scale model (WBM) to gain further insight into the effect of other less explored processes and genetic variations on ethanol metabolism. This combined model was fit to clinical data and used to show the effect of alcohol concentrations, organ damage, ALDH2 enzyme polymorphisms, and ALDH2-inhibiting drug disulfiram on ethanol and acetaldehyde exposure. Through estimating the reaction rates of auxiliary processes with dynamic Flux Balance Analysis, The PBPK-WBM was able to navigate around a lack of kinetic constants traditionally associated with PK modelling and demonstrate the compensatory effects of the body in response to decreased liver enzyme expression. Additionally, the model demonstrated that acetaldehyde exposure increased with higher dosages of disulfiram and decreased ALDH2 efficiency, and that moderate consumption rates of ethanol could lead to unexpected accumulations in acetaldehyde. This modelling framework combines the comprehensive steady-state analyses from genome-scale models with the dynamics of traditional PK models to create a highly personalized form of PBPK modelling that can push the boundaries of precision medicine.
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Affiliation(s)
- Leo Zhu
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Ontario, Canada
| | - William Pei
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Ontario, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada
| | - Ines Thiele
- School of Medicine, National University of Ireland at Galway, Galway, Ireland
- Discipline of Microbiology, National University of Ireland at Galway, Galway, Ireland
- APC Microbiome Ireland, Cork, Ireland
| | - Radhakrishnan Mahadevan
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Ontario, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada
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Toroghi MK, Cluett WR, Mahadevan R. A Personalized Multiscale Modeling Framework for Dose Selection in Precision Medicine. Ind Eng Chem Res 2020. [DOI: 10.1021/acs.iecr.0c01070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Masood Khaksar Toroghi
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Ontario, Canada, M5S 3E5
| | - William R. Cluett
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Ontario, Canada, M5S 3E5
| | - Radhakrishnan Mahadevan
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Ontario, Canada, M5S 3E5
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada, M5S 3E5
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