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Silfvergren, O.

Publications and source records attributed to Silfvergren, O..

3 recordsLinked to original sources

A Mathematical Model of Dietary Lipid Absorption and Postprandial Chylomicron Dynamics

Obesity and related conditions such as dyslipidemia impose an increasing burden on healthcare systems worldwide. These conditions are associated with altered postprandial chylomicron (CM) metabolism, the elusive and critical first step in lipid metabolism. This step remains elusive because it is governed by large interindividual variations and a complex set of intestinal processes. In particular, the second meal effect (SME) implies that enterocytes release previously stored fat during subsequent meals. To deal with this complexity, CM and lipid metabolism have previously been explored using mathematical modeling. However, existing models primarily describe TAG dynamics following a single meal or are too complex for practical personalization across datasets. Herein, we address these limitations by presenting a small-scale mathematical model of CM dynamics that incorporates the SME. The presented model successfully describes data from six clinical studies of both single and repeated meal interventions. Model performance was further evaluated by predicting independent datasets using a BMI-dependent calibration. Finally, to demonstrate model applicability, we simulated full-day responses consisting of three sequential meals in individuals with varying BMI values, with qualitative agreement to clinical observations. This work supports our understanding of the SME, person-specific CM postprandial responses, and mechanisms underlying obesity.

Systems Biology↗

An Integrated Analysis of GLP-1R Agonist Mechanisms: Addressing Study Variations in Heterogeneous Cell Systems

Experimental cell systems support the development of pharmacological therapies such as glucagon-like peptide-1 receptor agonists (GLP-1RAs). However, their utility in drug discovery is limited due to study variability, which complicates formation of unified conclusions based on all available data. To address this, we conducted a comprehensive analysis of the GLP-1RA exenatide, incorporating 16 new and five pre-existing mono- or co-culture studies of human liver and pancreatic models. We employed a new pragmatic model-based approach designed to handle the common situation of heterogeneous in vitro datasets with few replicates per condition. All studies are jointly explained (disagreement<{chi}{superscript 2}-limit; 542<732), thereby providing a unified conclusion based on all studies. This work links in vitro biology to clinically relevant mechanisms, such as exenatides effect on glucose-insulin interplay, and predicts previously undescribed inter-study variabilities. Independent validation confirms predictive performance (64<83). Our new integrative approach enhances the utility of experimental cell systems in preclinical drug discovery.

systems biology↗

M4 drug discovery: human drug predictions from integrated pre-clinical insights exemplified with a GLP1-R agonist

A major recent breakthrough in the treatment of type 2 diabetes has been the development of glucagon-like peptide-1 receptor agonists (GLP-1RAs). However, current translational frameworks struggle to predict the clinical outcomes of these drugs from preclinical data. There are several reasons for this struggle, which are generic for many drugs: GLP-1RAs act through multi-timescale mechanisms in which short-term effects propagate into long-term changes; no single preclinical system can capture all their effects in humans; and mechanistic extrapolation requires modelling numerous whole-body biological processes. To address this gap, we present a new extrapolation approach, M4 drug discovery, and retrospectively apply it to the GLP-1RA exenatide in a manner that is generalisable to other drugs. The method integrates: Multi-level data (cellular to whole-body), Multi-timescale data (minutes to months), Multi-species data (e.g., rodents to humans), and Mechanistic knowledge. In this study, we integrate human cell and animal data with drug-free human studies to successfully predict human pharmacokinetics (cost < {chi}2, p=0.05; 64 < 97) and the outcomes of a 30-week clinical trial (36 < 45). We found that integrating information across the four M4 axes improved predictive performance and physiological relevance: multi-species data inform pharmacokinetics, human cell data provide human population- and donor-specific potency estimates, animal data reveal additional drug effects not observable in cell cultures, and the multi-timescale mathematical modelling enables short-term effects of exenatide and meals to inform long-term changes in insulin sensitivity. This work provides new tools for drug extrapolations, supporting the community towards safer and more informed preclinical-to-clinical drug extrapolations.

systems biology↗