| 1. |
- Simonsson, Christian, et al.
(author)
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A unified framework for prediction of liver steatosis dynamics in response to different diet and drug interventions
- 2024
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In: Clinical Nutrition. - : CHURCHILL LIVINGSTONE. - 0261-5614 .- 1532-1983. ; 43:6, s. 1532-1543
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Journal article (peer-reviewed)abstract
- Background & aims: Non-alcoholic fatty liver disease (NAFLD) is a common metabolic disorder, characterized by the accumulation of excess fat in the liver, and is a driving factor for various severe liver diseases. These multi -factorial and multi-timescale changes are observed in different clinical studies, but these studies have not been integrated into a uni fied framework. In this study, we aim to present such a uni fied framework in the form of a dynamic mathematical model. Methods: For model training and validation, we collected data for dietary or drug -induced interventions aimed at reducing or increasing liver fat. The model was formulated using ordinary differential equations (ODEs) and the mathematical analysis, model simulation, model formulation and the model parameter estimation were all performed in MATLAB. Results: Our mathematical model describes accumulation of fat in the liver and predicts changes in lipid fluxes induced by both dietary and drug interventions. The model is validated using data from a wide range of drug and dietary intervention studies and can predict both short-term (days) and long-term (weeks) changes in liver fat. Importantly, the model computes the contribution of each individual lipid flux to the total liver fat dynamics. Furthermore, the model can be combined with an established bodyweight model, to simulate even longer scenarios (years), also including the effects of insulin resistance and body weight. To help prepare for corresponding eHealth applications, we also present a way to visualize the simulated changes, using dynamically changing lipid droplets, seen in images of liver biopsies. Conclusion: In conclusion, we believe that the minimal model presented herein might be a useful tool for future applications, and to further integrate and understand data regarding changes in dietary and drug induced changes in ectopic TAG in the liver. With further development and validation, the minimal model could be used as a disease progression model for steatosis. (c) 2024 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
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| 2. |
- Herrgårdh, Tilda, et al.
(author)
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A multi-scale digital twin for adiposity-driven insulin resistance in humans : diet and drug effects
- 2023
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In: Diabetology & Metabolic Syndrome. - : BioMed Central (BMC). - 1758-5996. ; 15:1
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Journal article (peer-reviewed)abstract
- BACKGROUND: The increased prevalence of insulin resistance is one of the major health risks in society today. Insulin resistance involves both short-term dynamics, such as altered meal responses, and long-term dynamics, such as the development of type 2 diabetes. Insulin resistance also occurs on different physiological levels, ranging from disease phenotypes to organ-organ communication and intracellular signaling. To better understand the progression of insulin resistance, an analysis method is needed that can combine different timescales and physiological levels. One such method is digital twins, consisting of combined mechanistic mathematical models. We have previously developed a model for short-term glucose homeostasis and intracellular insulin signaling, and there exist long-term weight regulation models. Herein, we combine these models into a first interconnected digital twin for the progression of insulin resistance in humans.METHODS: The model is based on ordinary differential equations representing biochemical and physiological processes, in which unknown parameters were fitted to data using a MATLAB toolbox. RESULTS: The interconnected twin correctly predicts independent data from a weight increase study, both for weight-changes, fasting plasma insulin and glucose levels, and intracellular insulin signaling. Similarly, the model can predict independent weight-change data in a weight loss study with the weight loss drug topiramate. The model can also predict non-measured variables.CONCLUSIONS: The model presented herein constitutes the basis for a new digital twin technology, which in the future could be used to aid medical pedagogy and increase motivation and compliance and thus aid in the prevention and treatment of insulin resistance.
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| 3. |
- Nyman, Elin, et al.
(author)
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Requirements for multi-level systems pharmacology models to reach end-usage : the case of type 2 diabetes
- 2016
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In: Interface Focus. - London, UK : The Royal Society. - 2042-8898 .- 2042-8901. ; 6:2
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Research review (peer-reviewed)abstract
- We are currently in the middle of a major shift in biomedical research: unprecedented and rapidly growing amounts of data may be obtained today, from in vitro, in vivo and clinical studies, at molecular, physiological and clinical levels. To make use of these large-scale, multi-level datasets, corresponding multi-level mathematical models are needed, i.e. models that simultaneously capture multiple layers of the biological, physiological and disease-level organization (also referred to as quantitative systems pharmacology-QSP-models). However, today's multi-level models are not yet embedded in end-usage applications, neither in drug research and development nor in the clinic. Given the expectations and claims made historically, this seemingly slow adoption may seem surprising. Therefore, we herein consider a specific example-type 2 diabetes-and critically review the current status and identify key remaining steps for these models to become mainstream in the future. This overview reveals how, today, we may use models to ask scientific questions concerning, e.g., the cellular origin of insulin resistance, and how this translates to the whole-body level and short-term meal responses. However, before these multi-level models can become truly useful, they need to be linked with the capabilities of other important existing models, in order to make them 'personalized' (e.g. specific to certain patient phenotypes) and capable of describing long-term disease progression. To be useful in drug development, it is also critical that the developed models and their underlying data and assumptions are easily accessible. For clinical end-usage, in addition, model links to decisionsupport systems combined with the engagement of other disciplines are needed to create user-friendly and cost-efficient software packages.
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| 4. |
- Silfvergren, Oscar, et al.
(author)
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Digital twin predicting diet response before and after long-term fasting
- 2022
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In: PloS Computational Biology. - : Public Library of Science. - 1553-734X .- 1553-7358. ; 18:9
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Journal article (peer-reviewed)abstract
- Today, there is great interest in diets proposing new combinations of macronutrient compositions and fasting schedules. Unfortunately, there is little consensus regarding the impact of these different diets, since available studies measure different sets of variables in different populations, thus only providing partial, non-connected insights. We lack an approach for integrating all such partial insights into a useful and interconnected big picture. Herein, we present such an integrating tool. The tool uses a novel mathematical model that describes mechanisms regulating diet response and fasting metabolic fluxes, both for organ-organ crosstalk, and inside the liver. The tool can mechanistically explain and integrate data from several clinical studies, and correctly predict new independent data, including data from a new study. Using this model, we can predict non-measured variables, e.g. hepatic glycogen and gluconeogenesis, in response to fasting and different diets. Furthermore, we exemplify how such metabolic responses can be successfully adapted to a specific individuals sex, weight, height, as well as to the individuals historical data on metabolite dynamics. This tool enables an offline digital twin technology.
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| 5. |
- Simonsson, Christian, 1992-
(author)
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Mathematical Modelling of MASLD ‐ Towards Digital Twins in Liver Disease
- 2024
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Doctoral thesis (other academic/artistic)abstract
- Unhealthy dieting and a sedentary lifestyle are causing an increased prevalence of obesity related complications. One such complication is metabolic dysfunction-associated steatotic liver disease (MASLD) the manifestation of metabolic dysregulation and insulin resistance in the liver. Today, MASLD effect a third of the world’s population. One of the main characteristics of MASLD is accumulation of ectopic lipids in the liver, also denoted steatosis. Steatosis is not inherently dangerous but is an indication of metabolic dysregulation, and long-term MASLD can progress into severe conditions such as chronic hepatic inflammation denoted metabolic dysfunction-associated steatohepatitis (MASH), liver scarring (cirrhosis), and primary liver cancer (hepatocellular carcinoma, HCC). Moreover, these conditions can be further aggravated by alcohol consumption. The increase in potential patients with MASLD will have an enormous burden on future healthcare. Thus, future healthcare has a need for innovative solutions to lessen this burden. Such solutions should be capable of personalized and preventive measures, cost-effective high throughput screening methods, and frameworks integrating all available patient data, for all stages of MASLD. Today, some of these methodologies already exist, however there is still a need for ways to integrate different liver biomarkers into a user-friendly framework, with strong personalization and predictive capabilities. For this purpose, data-driven mathematical modelling is of use. Data-driven mathematical models has proven useful for such integration in other disease areas such as stroke. In this thesis, I have created and explored several mathematical models aimed at exploring different aspects of MASLD, as well as developed several models using data from example: our own collected magnetic resonance imaging (MRI) data from patients suffering from chronic liver disease or HCC, and pre-clinical mouse data of insulin resistance progression. The studies presented in this thesis investigate diet-driven insulin resistance development, steatosis development and screening, as well as lifestyle interventions for alcohol and dietary habits, and liver function evaluation at late-stage liver disease. Thus, this thesis presents a possible fundament to create a so-called digital twin of MASLD – a highly personalized model capable of making predictions based on lifestyle.
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| 6. |
- Abadpour, S., et al.
(author)
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Inhibition of the prostaglandin D-2-GPR44/DP2 axis improves human islet survival and function
- 2020
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In: Diabetologia. - : Springer Science and Business Media LLC. - 0012-186X .- 1432-0428. ; 63, s. 1355-1367
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Journal article (peer-reviewed)abstract
- Aims/hypothesis Inflammatory signals and increased prostaglandin synthesis play a role during the development of diabetes. The prostaglandin D-2 (PGD(2)) receptor, GPR44/DP2, is highly expressed in human islets and activation of the pathway results in impaired insulin secretion. The role of GPR44 activation on islet function and survival rate during chronic hyperglycaemic conditions is not known. In this study, we investigate GPR44 inhibition by using a selective GPR44 antagonist (AZ8154) in human islets both in vitro and in vivo in diabetic mice transplanted with human islets. Methods Human islets were exposed to PGD(2) or proinflammatory cytokines in vitro to investigate the effect of GPR44 inhibition on islet survival rate. In addition, the molecular mechanisms of GPR44 inhibition were investigated in human islets exposed to high concentrations of glucose (HG) and to IL-1 beta. For the in vivo part of the study, human islets were transplanted under the kidney capsule of immunodeficient diabetic mice and treated with 6, 60 or 100 mg/kg per day of a GPR44 antagonist starting from the transplantation day until day 4 (short-term study) or day 17 (long-term study) post transplantation. IVGTT was performed on mice at day 10 and day 15 post transplantation. After termination of the study, metabolic variables, circulating human proinflammatory cytokines, and hepatocyte growth factor (HGF) were analysed in the grafted human islets. Results PGD(2) or proinflammatory cytokines induced apoptosis in human islets whereas GPR44 inhibition reversed this effect. GPR44 inhibition antagonised the reduction in glucose-stimulated insulin secretion induced by HG and IL-1 beta in human islets. This was accompanied by activation of the Akt-glycogen synthase kinase 3 beta signalling pathway together with phosphorylation and inactivation of forkhead box O-1and upregulation of pancreatic and duodenal homeobox-1 and HGF. Administration of the GPR44 antagonist for up to 17 days to diabetic mice transplanted with a marginal number of human islets resulted in reduced fasting blood glucose and lower glucose excursions during IVGTT. Improved glucose regulation was supported by increased human C-peptide levels compared with the vehicle group at day 4 and throughout the treatment period. GPR44 inhibition reduced plasma levels of TNF-alpha and growth-regulated oncogene-alpha/chemokine (C-X-C motif) ligand 1 and increased the levels of HGF in human islets. Conclusions/interpretation Inhibition of GPR44 in human islets has the potential to improve islet function and survival rate under inflammatory and hyperglycaemic stress. This may have implications for better survival rate of islets following transplantation.
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| 7. |
- Almquist, Joachim, et al.
(author)
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Model-Based Analysis Reveals a Sustained and Dose-Dependent Acceleration of Wound Healing by VEGF-A mRNA (AZD8601)
- 2020
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In: CPT. - : WILEY. - 2163-8306. ; 9:7, s. 384-394
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Journal article (peer-reviewed)abstract
- Intradermal delivery of AZD8601, an mRNA designed to produce vascular endothelial growth factor A (VEGF-A), has previously been shown to accelerate cutaneous wound healing in a murine diabetic model. Here, we develop population pharmacokinetic and pharmacodynamic models aiming to quantify the effect of AZD8601 injections on the dynamics of wound healing. A dataset of 584 open wound area measurements from 131 mice was integrated from 3 independent studies encompassing different doses, dosing timepoints, and number of doses. Evaluation of several candidate models showed that wound healing acceleration is not likely driven directly by time-dependent VEGF-A concentration. Instead, we found that administration of AZD8601 induced a sustained acceleration of wound healing depending on the accumulated dose, with a dose producing 50% of the maximal effect of 92 mu g. Simulations with this model showed that a single dose of 200 mu g AZD8601 can reduce the time to reach 50% wound healing by up to 5 days.
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| 8. |
- Almquist, Joachim, 1980, et al.
(author)
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Overexpressing cell systems are a competitive option to primary adipocytes when predicting in vivo potency of dual GPR81/GPR109A agonists
- 2018
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In: European Journal of Pharmaceutical Sciences. - : Elsevier BV. - 0928-0987 .- 1879-0720. ; 114, s. 155-165
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Journal article (peer-reviewed)abstract
- Mathematical models predicting in vivo pharmacodynamic effects from in vitro data can accelerate drug discovery, and reduce costs and animal use. However, data integration and modeling is non-trivial when more than one drug-target receptor is involved in the biological response. We modeled the inhibition of non-esterified fatty acid release by dual G-protein-coupled receptor 81/109A (GPR81/GPR109A) agonists in vivo in the rat, to estimate the in vivo EC50 values for 12 different compounds. We subsequently predicted those potency estimates using EC 50 values obtained from concentration-response data in isolated primary adipocytes and cell systems overexpressing GPR81 or GPR109A in vitro. A simple linear regression model based on data from primary adipocytes predicted the in vivo EC50 better than simple linear regression models based on in vitro data from either of the cell systems. Three models combining the data from the overexpressing cell systems were also evaluated: two piecewise linear models defining logical OR- and AND-circuits, and a multivariate linear regression model. All three models performed better than the simple linear regression model based on data from primary adipocytes. The OR-model was favored since it is likely that activation of either GPR81 or GPR109A is sufficient to deactivate the cAMP pathway, and thereby inhibit non-esterified fatty acid release. The OR-model was also able to predict the in vivo selectivity between the two receptors. Finally, the OR-model was used to predict the in vivo potency of 1651 new compounds. This work suggests that data from the overexpressing cell systems are sufficient to predict in vivo potency of GPR81/GPR109A agonists, an approach contributing to faster and leaner drug discovery.
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| 9. |
- Almquist, Joachim, 1980, et al.
(author)
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Unraveling the Pharmacokinetic Interaction of Ticagrelor and MEDI2452 (Ticagrelor Antidote) by Mathematical Modeling
- 2016
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In: CPT: Pharmacometrics and Systems Pharmacology. - : Wiley. - 2163-8306. ; 5:6, s. 313-323
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Journal article (peer-reviewed)abstract
- The investigational ticagrelor-neutralizing antibody fragment, MEDI2452, is developed to rapidly and specifically reverse the antiplatelet effects of ticagrelor. However, the dynamic interaction of ticagrelor, the ticagrelor active metabolite (TAM), and MEDI2452, makes pharmacokinetic (PK) analysis nontrivial and mathematical modeling becomes essential to unravel the complex behavior of this system. We propose a mechanistic PK model, including a special observation model for post-sampling equilibration, which is validated and refined using mouse in vivo data from four studies of combined ticagrelor-MEDI2452 treatment. Model predictions of free ticagrelor and TAM plasma concentrations are subsequently used to drive a pharmacodynamic (PD) model that successfully describes platelet aggregation data. Furthermore, the model indicates that MEDI2452-bound ticagrelor is primarily eliminated together with MEDI2452 in the kidneys, and not recycled to the plasma, thereby providing a possible scenario for the extrapolation to humans. We anticipate the modeling work to improve PK and PD understanding, experimental design, and translational confidence.
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| 10. |
- Aoki, Yasunori, 1982-, et al.
(author)
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PopED lite : an optimal design software for preclinical pharmacokinetic and pharmacodynamic studies
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Other publication (other academic/artistic)abstract
- Optimal experimental design approaches are seldom used in pre-clinical drug discovery. Main reasons for this lack of use are that available software tools require relatively high insight in optimal design theory, and that the design-execution cycle of in vivo experiments is short, making time-consuming optimizations infeasible. We present the publicly available software PopED lite in order to increase the use of optimal design in pre-clinical drug discovery. PopED lite is designed to be simple, fast and intuitive. Simple, to give many users access to basic optimal design calculations. Fast, to fit the short design-execution cycle and allow interactive experimental design (test one design, discuss proposed design, test another design, etc). Intuitive, so that the input to and output from the software can easily be understood by users without knowledge of the theory of optimal design. In this way, PopED lite is highly useful in practice and complements existing tools. Key functionality of PopED lite is demonstrated by three case studies from real drug discovery projects.
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