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Yildirim V, Sheraton VM, Brands R, Crielaard L, Quax R, van Riel NA, Stronks K, Nicolaou M, Sloot PM. A data-driven computational model for obesity-driven diabetes onset and remission through weight loss. iScience 2023; 26:108324. [PMID: 38026205 PMCID: PMC10665812 DOI: 10.1016/j.isci.2023.108324] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 08/22/2023] [Accepted: 10/20/2023] [Indexed: 12/01/2023] Open
Abstract
Obesity is a major risk factor for the development of type 2 diabetes (T2D), where a sustained weight loss may result in T2D remission in individuals with obesity. To design effective and feasible intervention strategies to prevent or reverse T2D, it is imperative to study the progression of T2D and remission together. Unfortunately, this is not possible through experimental and observational studies. To address this issue, we introduce a data-driven computational model and use human data to investigate the progression of T2D with obesity and remission through weight loss on the same timeline. We identify thresholds for the emergence of T2D and necessary conditions for remission. We explain why remission is only possible within a window of opportunity and the way that window depends on the progression history of T2D, individual's metabolic state, and calorie restrictions. These findings can help to optimize therapeutic intervention strategies for T2D prevention or treatment.
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Affiliation(s)
- Vehpi Yildirim
- Department of Public and Occupational Health, Amsterdam University Medical Centers, University of Amsterdam, 1081 BT Amsterdam, the Netherlands
- Institute for Advanced Study, University of Amsterdam, 1012 GC Amsterdam, the Netherlands
| | - Vivek M. Sheraton
- Institute for Advanced Study, University of Amsterdam, 1012 GC Amsterdam, the Netherlands
- Computational Science Lab, University of Amsterdam, 1098 XH Amsterdam, the Netherlands
- Center for Experimental and Molecular Medicine, Amsterdam University Medical Centers, 1100 DD Amsterdam, the Netherlands
| | - Ruud Brands
- AMRIF B.V., Agro Business Park, 6708 PW Wageningen, the Netherlands
- Institute for Risk Assessment Sciences, Utrecht University, 3584 CL Utrecht, the Netherlands
| | - Loes Crielaard
- Department of Public and Occupational Health, Amsterdam University Medical Centers, University of Amsterdam, 1081 BT Amsterdam, the Netherlands
- Institute for Advanced Study, University of Amsterdam, 1012 GC Amsterdam, the Netherlands
| | - Rick Quax
- Institute for Advanced Study, University of Amsterdam, 1012 GC Amsterdam, the Netherlands
- Computational Science Lab, University of Amsterdam, 1098 XH Amsterdam, the Netherlands
| | - Natal A.W. van Riel
- Department of Biomedical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, the Netherlands
- Department of Experimental and Vascular Medicine, Amsterdam University Medical Centers, 1100 DD Amsterdam, the Netherlands
| | - Karien Stronks
- Department of Public and Occupational Health, Amsterdam University Medical Centers, University of Amsterdam, 1081 BT Amsterdam, the Netherlands
- Institute for Advanced Study, University of Amsterdam, 1012 GC Amsterdam, the Netherlands
| | - Mary Nicolaou
- Department of Public and Occupational Health, Amsterdam University Medical Centers, University of Amsterdam, 1081 BT Amsterdam, the Netherlands
- Institute for Advanced Study, University of Amsterdam, 1012 GC Amsterdam, the Netherlands
| | - Peter M.A. Sloot
- Institute for Advanced Study, University of Amsterdam, 1012 GC Amsterdam, the Netherlands
- Computational Science Lab, University of Amsterdam, 1098 XH Amsterdam, the Netherlands
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2
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Bradshaw EL, Spilker ME, Zang R, Bansal L, He H, Jones RD, Le K, Penney M, Schuck E, Topp B, Tsai A, Xu C, Nijsen MJ, Chan JR. Applications of Quantitative Systems Pharmacology in Model-Informed Drug Discovery: Perspective on Impact and Opportunities. CPT Pharmacometrics Syst Pharmacol 2019; 8:777-791. [PMID: 31535440 PMCID: PMC6875708 DOI: 10.1002/psp4.12463] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Accepted: 07/19/2019] [Indexed: 12/15/2022] Open
Abstract
Quantitative systems pharmacology (QSP) approaches have been increasingly applied in the pharmaceutical since the landmark white paper published in 2011 by a National Institutes of Health working group brought attention to the discipline. In this perspective, we discuss QSP in the context of other modeling approaches and highlight the impact of QSP across various stages of drug development and therapeutic areas. We discuss challenges to the field as well as future opportunities.
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Affiliation(s)
| | - Mary E. Spilker
- Pfizer Worldwide Research and DevelopmentSan DiegoCaliforniaUSA
| | | | | | - Handan He
- Novartis Institutes for Biomedical ResearchEast HanoverNew JerseyUSA
| | | | - Kha Le
- AgiosCambridgeMassachusettsUSA
| | | | | | | | - Alice Tsai
- Vertex Pharmaceuticals IncorporatedBostonMassachusettsUSA
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3
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De Gaetano A, Hardy TA. A novel fast-slow model of diabetes progression: Insights into mechanisms of response to the interventions in the Diabetes Prevention Program. PLoS One 2019; 14:e0222833. [PMID: 31600232 PMCID: PMC6786566 DOI: 10.1371/journal.pone.0222833] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2018] [Accepted: 09/09/2019] [Indexed: 12/22/2022] Open
Abstract
Several models for the long-term development of T2DM already exist, focusing on the dynamics of the interaction between glycemia, insulinemia and β-cell mass. Current models consider representative (fasting or daily average) glycemia and insulinemia as characterizing the compensation state of the subject at some instant in slow time. This implies that only these representative levels can be followed through time and that the role of fast glycemic oscillations is neglected. An improved model (DPM15) for the long-term progression of T2DM is proposed, introducing separate peripheral and hepatic (liver and kidney) insulin actions. The DPM15 model no longer uses near-equilibrium approximation to separate fast and slow time scales, but rather describes, at each step in slow time, a complete day in the life of the virtual subject in fast time. The model can thus represent both fasting and postprandial glycemic levels and describe the effect of interventions acting on insulin-enhanced tissue glucose disposal or on insulin-inhibited hepatic glucose output, as well as on insulin secretion and β-cell replicating ability. The model can simulate long-term variations of commonly used clinical indices (HOMA-B, HOMA-IR, insulinogenic index) as well as of Oral Glucose Tolerance or Euglycemic Hyperinsulinemic Clamp test results. The model has been calibrated against observational data from the Diabetes Prevention Program study: it shows good adaptation to observations as a function of very plausible values of the parameters describing the effect of such interventions as Placebo, Intensive LifeStyle and Metformin administration.
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Affiliation(s)
- Andrea De Gaetano
- CNR-IASI BioMatLab (Italian National Research Council - Institute of Analysis, Systems and Computer Science - Biomathematics Laboratory), Rome, Italy
| | - Thomas Andrew Hardy
- Lilly Research Laboratories, Eli Lilly and Company, Lilly Corporate Center, Indianapolis, Indiana, United States of America
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4
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Helmlinger G, Sokolov V, Peskov K, Hallow KM, Kosinsky Y, Voronova V, Chu L, Yakovleva T, Azarov I, Kaschek D, Dolgun A, Schmidt H, Boulton DW, Penland RC. Quantitative Systems Pharmacology: An Exemplar Model-Building Workflow With Applications in Cardiovascular, Metabolic, and Oncology Drug Development. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2019; 8:380-395. [PMID: 31087533 PMCID: PMC6617832 DOI: 10.1002/psp4.12426] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Accepted: 05/03/2019] [Indexed: 12/13/2022]
Abstract
Quantitative systems pharmacology (QSP), a mechanistically oriented form of drug and disease modeling, seeks to address a diverse set of problems in the discovery and development of therapies. These problems bring a considerable amount of variability and uncertainty inherent in the nonclinical and clinical data. Likewise, the available modeling techniques and related software tools are manifold. Appropriately, the development, qualification, application, and impact of QSP models have been similarly varied. In this review, we describe the progressive maturation of a QSP modeling workflow: a necessary step for the efficient, reproducible development and qualification of QSP models, which themselves are highly iterative and evolutive. Furthermore, we describe three applications of QSP to impact drug development; one supporting new indications for an approved antidiabetic clinical asset through mechanistic hypothesis generation, one highlighting efficacy and safety differentiation within the sodium‐glucose cotransporter‐2 inhibitor drug class, and one enabling rational selection of immuno‐oncology drug combinations.
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Affiliation(s)
- Gabriel Helmlinger
- Quantitative Clinical Pharmacology, Early Clinical Development, IMED Biotech Unit, AstraZeneca Pharmaceuticals, Boston, Massachusetts, USA
| | | | - Kirill Peskov
- M&S Decisions LLC, Moscow, Russia.,Computational Oncology Group, I.M. Sechenov First Moscow State Medical University of the Russian Ministry of Health, Moscow, Russia
| | - Karen M Hallow
- School of Chemical, Materials, and Biomedical Engineering, University of Georgia, Athens, Georgia, USA.,Department of Epidemiology and Biostatistics, University of Georgia, Athens, Georgia, USA
| | | | | | - Lulu Chu
- Quantitative Clinical Pharmacology, Early Clinical Development, IMED Biotech Unit, AstraZeneca Pharmaceuticals, Boston, Massachusetts, USA
| | | | | | | | | | | | - David W Boulton
- Quantitative Clinical Pharmacology, Early Clinical Development, IMED Biotech Unit, AstraZeneca Pharmaceuticals, Gaithersburg, Maryland, USA
| | - Robert C Penland
- Quantitative Clinical Pharmacology, Early Clinical Development, IMED Biotech Unit, AstraZeneca Pharmaceuticals, Boston, Massachusetts, USA
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5
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Wang F, Shang Y, Zhang R, Gao X, Zeng Q. A SIRT1 agonist reduces cognitive decline in type 2 diabetic rats through antioxidative and anti‑inflammatory mechanisms. Mol Med Rep 2018; 19:1040-1048. [PMID: 30483738 PMCID: PMC6323206 DOI: 10.3892/mmr.2018.9699] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2017] [Accepted: 03/02/2018] [Indexed: 12/21/2022] Open
Abstract
Sirtuin 1 (SIRT1) is an NAD+‑dependent protein deacetylase that is involved in cell differentiation, aging, apoptosis, physiological rhythms, metabolic regulation, oxidative stress and numerous other important biological processes. In the present study, the ability of a sirtuin‑1 (SIRT1) agonist, SRT1720, to reduce cognitive decline in type 2 diabetes mellitus (T2DM) was investigated. Streptozotocin‑induced male Sprague‑Dawley rats were used to establish a T2DM model and the protective effect of SRT1720 and its underlying mechanisms were investigated. Body weight and fasting blood glucose (FBG) were recorded and cognitive function was measured with the Morris water maze. Levels of oxidative stress, inflammation, caspase‑3 activity and nuclear factor κB (NF‑κB) mRNA expression were detected with a series of commercial assay kits and reverse transcription‑quantitative polymerase chain reaction, respectively. Western blot analysis was performed to determine the protein expression of NF‑κB, endothelial nitric oxide synthase (eNOS), peroxisome proliferator‑activated receptor γ (PPARγ), AMP‑activated protein kinase (AMPK), heat shock 70 kDa protein (HSP70), SIRT1, nuclear factor erythroid 2‑related factor 2 (Nrf2) and heme oxygenase 1 (HO‑1). The results revealed that SRT1720 significantly increased body weight, decreased FBG, improved cognitive function and reduced the levels of proteins associated with oxidative stress and inflammation damage in T2DM rats. Additionally, SRT1720 significantly decreased NF‑κB p65 mRNA expression and increased eNOS and PPARγ expression. SRT1720 significantly reduced caspase‑3 activity and HSP70 protein expression, and increased p‑AMPK, SIRT1, Nrf2 and HO‑1 protein expression. Collectively, the results indicate that SRT1720 may reduce cognitive decline in T2DM rats through antioxidative and anti‑inflammatory action via NF‑κB and AMPK‑dependent mechanisms.
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Affiliation(s)
- Fei Wang
- Health Management Institute, Chinese PLA General Hospital, Beijing 100853, P.R. China
| | - Yanchang Shang
- Department of Geriatric Neurology, Chinese PLA General Hospital, Beijing 100853, P.R. China
| | - Rong Zhang
- Health Management Institute, Chinese PLA General Hospital, Beijing 100853, P.R. China
| | - Xiangyang Gao
- Health Management Institute, Chinese PLA General Hospital, Beijing 100853, P.R. China
| | - Qiang Zeng
- Health Management Institute, Chinese PLA General Hospital, Beijing 100853, P.R. China
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6
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Magnusson R, Mariotti GP, Köpsén M, Lövfors W, Gawel DR, Jörnsten R, Linde J, Nordling TEM, Nyman E, Schulze S, Nestor CE, Zhang H, Cedersund G, Benson M, Tjärnberg A, Gustafsson M. LASSIM-A network inference toolbox for genome-wide mechanistic modeling. PLoS Comput Biol 2017. [PMID: 28640810 PMCID: PMC5501685 DOI: 10.1371/journal.pcbi.1005608] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
Recent technological advancements have made time-resolved, quantitative, multi-omics data available for many model systems, which could be integrated for systems pharmacokinetic use. Here, we present large-scale simulation modeling (LASSIM), which is a novel mathematical tool for performing large-scale inference using mechanistically defined ordinary differential equations (ODE) for gene regulatory networks (GRNs). LASSIM integrates structural knowledge about regulatory interactions and non-linear equations with multiple steady state and dynamic response expression datasets. The rationale behind LASSIM is that biological GRNs can be simplified using a limited subset of core genes that are assumed to regulate all other gene transcription events in the network. The LASSIM method is implemented as a general-purpose toolbox using the PyGMO Python package to make the most of multicore computers and high performance clusters, and is available at https://gitlab.com/Gustafsson-lab/lassim. As a method, LASSIM works in two steps, where it first infers a non-linear ODE system of the pre-specified core gene expression. Second, LASSIM in parallel optimizes the parameters that model the regulation of peripheral genes by core system genes. We showed the usefulness of this method by applying LASSIM to infer a large-scale non-linear model of naïve Th2 cell differentiation, made possible by integrating Th2 specific bindings, time-series together with six public and six novel siRNA-mediated knock-down experiments. ChIP-seq showed significant overlap for all tested transcription factors. Next, we performed novel time-series measurements of total T-cells during differentiation towards Th2 and verified that our LASSIM model could monitor those data significantly better than comparable models that used the same Th2 bindings. In summary, the LASSIM toolbox opens the door to a new type of model-based data analysis that combines the strengths of reliable mechanistic models with truly systems-level data. We demonstrate the power of this approach by inferring a mechanistically motivated, genome-wide model of the Th2 transcription regulatory system, which plays an important role in several immune related diseases. There are excellent methods to mathematically model time-resolved biological data on a small scale using accurate mechanistic models. Despite the rapidly increasing availability of such data, mechanistic models have not been applied on a genome-wide level due to excessive runtimes and the non-identifiability of model parameters. However, genome-wide, mechanistic models could potentially answer key clinical questions, such as finding the best drug combinations to induce an expression change from a disease to a healthy state. We present LASSIM, which is a toolbox built to infer parameters within mechanistic models on a genomic scale. This is made possible due to a property shared across biological systems, namely the existence of a subset of master regulators, here denoted the core system. The introduction of a core system of genes simplifies the network inference into small solvable sub-problems, and implies that all main regulatory actions on peripheral genes come from a small set of regulator genes. This separation allows substantial parts of computations to be solved in parallel, i.e. permitting the use of a computer cluster, which substantially reduces computation time.
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Affiliation(s)
- Rasmus Magnusson
- Bioinformatics Unit, Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden
| | - Guido Pio Mariotti
- Bioinformatics Unit, Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden
| | - Mattias Köpsén
- Centre for Personalised Medicine, Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden
- Integrative Systems Biology, Department of Biomedical Engineering, Linköping University, Linköping, Sweden
| | - William Lövfors
- Centre for Personalised Medicine, Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden
- Integrative Systems Biology, Department of Biomedical Engineering, Linköping University, Linköping, Sweden
| | - Danuta R. Gawel
- Centre for Personalised Medicine, Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden
| | - Rebecka Jörnsten
- Mathematical Sciences, Chalmers University of Technology, University of Gothenburg, Gothenburg, Sweden
| | - Jörg Linde
- Leibniz-Institute for Natural Product Research and Infection Biology, Hans-Knoell-Institute, Research Group Systems Biology and Bioinformatics, Jena, Germany
- Research Group PiDOMICS, Leibniz Institute for Natural Product Research and Infection Biology -Hans Knöll Institute, Jena, Germany
| | - Torbjörn E. M. Nordling
- Department of Mechanical Engineering, National Cheng Kung University, Tainan, Taiwan
- Stockholm Bioinformatics Center, Science for Life Laboratory, Solna, Sweden
| | - Elin Nyman
- Integrative Systems Biology, Department of Biomedical Engineering, Linköping University, Linköping, Sweden
| | - Sylvie Schulze
- Leibniz-Institute for Natural Product Research and Infection Biology, Hans-Knoell-Institute, Research Group Systems Biology and Bioinformatics, Jena, Germany
| | - Colm E. Nestor
- Centre for Personalised Medicine, Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden
| | - Huan Zhang
- Centre for Personalised Medicine, Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden
| | - Gunnar Cedersund
- Integrative Systems Biology, Department of Biomedical Engineering, Linköping University, Linköping, Sweden
- Cell Biology, Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden
| | - Mikael Benson
- Centre for Personalised Medicine, Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden
| | - Andreas Tjärnberg
- Bioinformatics Unit, Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden
| | - Mika Gustafsson
- Bioinformatics Unit, Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden
- * E-mail:
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7
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Irurzun-Arana I, Pastor JM, Trocóniz IF, Gómez-Mantilla JD. Advanced Boolean modeling of biological networks applied to systems pharmacology. Bioinformatics 2017; 33:1040-1048. [PMID: 28073755 DOI: 10.1093/bioinformatics/btw747] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2016] [Accepted: 11/22/2016] [Indexed: 12/24/2022] Open
Abstract
Motivation Literature on complex diseases is abundant but not always quantitative. Many molecular pathways are qualitatively well described but this information cannot be used in traditional quantitative mathematical models employed in drug development. Tools for analysis of discrete networks are useful to capture the available information in the literature but have not been efficiently integrated by the pharmaceutical industry. We propose an expansion of the usual analysis of discrete networks that facilitates the identification/validation of therapeutic targets. Results In this article, we propose a methodology to perform Boolean modeling of Systems Biology/Pharmacology networks by using SPIDDOR (Systems Pharmacology for effIcient Drug Development On R) R package. The resulting models can be used to analyze the dynamics of signaling networks associated to diseases to predict the pathogenesis mechanisms and identify potential therapeutic targets. Availability and Implementation The source code is available at https://github.com/SPIDDOR/SPIDDOR . Contact itzirurzun@alumni.unav.es , itroconiz@unav.es. Supplementary information Supplementary data are available at Bioinformatics online.
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8
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Ruiz-Cerdá ML, Irurzun-Arana I, González-Garcia I, Hu C, Zhou H, Vermeulen A, Trocóniz IF, Gómez-Mantilla JD. Towards patient stratification and treatment in the autoimmune disease lupus erythematosus using a systems pharmacology approach. Eur J Pharm Sci 2016; 94:46-58. [DOI: 10.1016/j.ejps.2016.04.010] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2015] [Revised: 04/07/2016] [Accepted: 04/07/2016] [Indexed: 01/28/2023]
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9
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Knight-Schrijver V, Chelliah V, Cucurull-Sanchez L, Le Novère N. The promises of quantitative systems pharmacology modelling for drug development. Comput Struct Biotechnol J 2016; 14:363-370. [PMID: 27761201 PMCID: PMC5064996 DOI: 10.1016/j.csbj.2016.09.002] [Citation(s) in RCA: 64] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2016] [Revised: 09/08/2016] [Accepted: 09/19/2016] [Indexed: 01/01/2023] Open
Abstract
Recent growth in annual new therapeutic entity (NTE) approvals by the U.S. Food and Drug Administration (FDA) suggests a positive trend in current research and development (R&D) output. Prior to this, the cost of each NTE was considered to be rising exponentially, with compound failure occurring mainly in clinical phases. Quantitative systems pharmacology (QSP) modelling, as an additional tool in the drug discovery arsenal, aims to further reduce NTE costs and improve drug development success. Through in silico mathematical modelling, QSP can simulate drug activity as perturbations in biological systems and thus understand the fundamental interactions which drive disease pathology, compound pharmacology and patient response. Here we review QSP, pharmacometrics and systems biology models with respect to the diseases covered as well as their clinical relevance and applications. Overall, the majority of modelling focus was aligned with the priority of drug-discovery and clinical trials. However, a few clinically important disease categories, such as Immune System Diseases and Respiratory Tract Diseases, were poorly covered by computational models. This suggests a possible disconnect between clinical and modelling agendas. As a standard element of the drug discovery pipeline the uptake of QSP might help to increase the efficiency of drug development across all therapeutic indications.
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Affiliation(s)
| | - V. Chelliah
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | | | - N. Le Novère
- Babraham Institute, Babraham Research Campus, Cambridge CB22 3AT, UK
- Corresponding author.
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10
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Nyman E, Rozendaal YJW, Helmlinger G, Hamrén B, Kjellsson MC, Strålfors P, van Riel NAW, Gennemark P, Cedersund G. Requirements for multi-level systems pharmacology models to reach end-usage: the case of type 2 diabetes. Interface Focus 2016; 6:20150075. [PMID: 27051506 DOI: 10.1098/rsfs.2015.0075] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
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 decision-support systems combined with the engagement of other disciplines are needed to create user-friendly and cost-efficient software packages.
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Affiliation(s)
- Elin Nyman
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden; CVMD iMed DMPK AstraZeneca R&D, Gothenburg, Sweden
| | - Yvonne J W Rozendaal
- Department of Biomedical Engineering , Eindhoven University of Technology , Eindhoven , The Netherlands
| | - Gabriel Helmlinger
- Quantitative Clinical Pharmacology, AstraZeneca , Pharmaceuticals LP, Waltham, MA , USA
| | - Bengt Hamrén
- Quantitative Clinical Pharmacology , AstraZeneca , Gothenburg , Sweden
| | - Maria C Kjellsson
- Department of Pharmaceutical Biosciences , Uppsala University , Uppsala , Sweden
| | - Peter Strålfors
- Department of Clinical and Experimental Medicine , Linköping University , Linköping , Sweden
| | - Natal A W van Riel
- Department of Biomedical Engineering , Eindhoven University of Technology , Eindhoven , The Netherlands
| | | | - Gunnar Cedersund
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden; Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden
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11
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Bosley JR, Maurer TS, Musante CJ. Systems Pharmacology Modeling in Type 2 Diabetes Mellitus. SYSTEMS PHARMACOLOGY AND PHARMACODYNAMICS 2016. [DOI: 10.1007/978-3-319-44534-2_20] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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12
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Juty N, Ali R, Glont M, Keating S, Rodriguez N, Swat MJ, Wimalaratne SM, Hermjakob H, Le Novère N, Laibe C, Chelliah V. BioModels: Content, Features, Functionality, and Use. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2015. [PMID: 26225232 PMCID: PMC4360671 DOI: 10.1002/psp4.3] [Citation(s) in RCA: 49] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
BioModels is a reference repository hosting mathematical models that describe the dynamic interactions of biological components at various scales. The resource provides access to over 1,200 models described in literature and over 140,000 models automatically generated from pathway resources. Most model components are cross-linked with external resources to facilitate interoperability. A large proportion of models are manually curated to ensure reproducibility of simulation results. This tutorial presents BioModels' content, features, functionality, and usage.
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Affiliation(s)
- N Juty
- European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Trust Genome Campus Hinxton, Cambridge, UK
| | - R Ali
- European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Trust Genome Campus Hinxton, Cambridge, UK
| | - M Glont
- European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Trust Genome Campus Hinxton, Cambridge, UK
| | - S Keating
- European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Trust Genome Campus Hinxton, Cambridge, UK
| | - N Rodriguez
- Babraham Institute, Babraham Research Campus Cambridge, UK
| | - M J Swat
- European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Trust Genome Campus Hinxton, Cambridge, UK
| | - S M Wimalaratne
- European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Trust Genome Campus Hinxton, Cambridge, UK
| | - H Hermjakob
- European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Trust Genome Campus Hinxton, Cambridge, UK
| | - N Le Novère
- Babraham Institute, Babraham Research Campus Cambridge, UK
| | - C Laibe
- European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Trust Genome Campus Hinxton, Cambridge, UK
| | - V Chelliah
- European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Trust Genome Campus Hinxton, Cambridge, UK
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13
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Esser N, Paquot N, Scheen AJ. Anti-inflammatory agents to treat or prevent type 2 diabetes, metabolic syndrome and cardiovascular disease. Expert Opin Investig Drugs 2014; 24:283-307. [PMID: 25345753 DOI: 10.1517/13543784.2015.974804] [Citation(s) in RCA: 177] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
INTRODUCTION There is a growing body of evidence to suggest that chronic silent inflammation is a key feature in abdominal obesity, metabolic syndrome, type 2 diabetes (T2DM) and cardiovascular disease (CVD). These observations suggest that pharmacological strategies, which reduce inflammation, may be therapeutically useful in treating obesity, type 2 diabetes and associated CVD. AREA COVERED The article covers novel strategies, using either small molecules or monoclonal antibodies. These strategies include: approaches targeting IKK-b-NF-kB (salicylates, salsalate), TNF-α (etanercept, infliximab, adalimumab), IL-1β (anakinra, canakinumab) and IL-6 (tocilizumab), AMP-activated protein kinase activators, sirtuin-1 activators, mammalian target of rapamycin inhibitors and C-C motif chemokine receptor 2 antagonists. EXPERT OPINION The available data supports the concept that targeting inflammation improves insulin sensitivity and β-cell function; it also ameliorates glucose control in insulin-resistant patients with inflammatory rheumatoid diseases as well in patients with metabolic syndrome or T2DM. Although promising, the observed metabolic effects remain rather modest in most clinical trials. The potential use of combined anti-inflammatory agents targeting both insulin resistance and insulin secretion appears appealing but remains unexplored. Large-scale prospective clinical trials are underway to investigate the safety and efficacy of different anti-inflammatory drugs. Further evidence is needed to support the concept that targeting inflammation pathways may represent a valuable option to tackle the cardiometabolic complications of obesity.
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Affiliation(s)
- Nathalie Esser
- University of Liege and Division of Diabetes, Nutrition and Metabolic Disorders, Department of Medicine, Virology and Immunology Unit, GIGA-ST , CHU Liège, Liège , Belgium
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