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Pathway-level analysis of genome-wide circadian dynamics in diverse tissues in rat and mouse. J Pharmacokinet Pharmacodyn 2021; 48:361-374. [PMID: 33768484 DOI: 10.1007/s10928-021-09750-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Accepted: 03/17/2021] [Indexed: 10/21/2022]
Abstract
A computational framework is developed to enable the characterization of genome-wide, multi-tissue circadian dynamics at the level of "functional groupings of genes" defined in the context of signaling, cellular/genetic processing and metabolic pathways in rat and mouse. Our aim is to identify how individual genes come together to generate orchestrated rhythmic patterns and how these may vary within and across tissues. We focus our analysis on four tissues (adipose, liver, lung, and muscle). A genome-wide pathway-centric analysis enables us to develop a comprehensive picture on how the observed circadian variation at the individual gene level, orchestrates functional responses at the pathway level. Such pathway-based "meta-data" analysis enables the rational integration and comparison across platforms and/or experimental designs evaluating emergent dynamics, as opposed to comparisons of individual elements. One of our key findings is that when considering the dynamics at the pathway level, a complex behavior emerges. Our work proposes that tissues tend to coordinate gene's circadian expression in a way that optimizes tissue-specific pathway activity, depending of each tissue's broader role in homeostasis.
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2
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Ayyar VS, Jusko WJ. Transitioning from Basic toward Systems Pharmacodynamic Models: Lessons from Corticosteroids. Pharmacol Rev 2020; 72:414-438. [PMID: 32123034 DOI: 10.1124/pr.119.018101] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Technology in bioanalysis, -omics, and computation have evolved over the past half century to allow for comprehensive assessments of the molecular to whole body pharmacology of diverse corticosteroids. Such studies have advanced pharmacokinetic and pharmacodynamic (PK/PD) concepts and models that often generalize across various classes of drugs. These models encompass the "pillars" of pharmacology, namely PK and target drug exposure, the mass-law interactions of drugs with receptors/targets, and the consequent turnover and homeostatic control of genes, biomarkers, physiologic responses, and disease symptoms. Pharmacokinetic methodology utilizes noncompartmental, compartmental, reversible, physiologic [full physiologically based pharmacokinetic (PBPK) and minimal PBPK], and target-mediated drug disposition models using a growing array of pharmacometric considerations and software. Basic PK/PD models have emerged (simple direct, biophase, slow receptor binding, indirect response, irreversible, turnover with inactivation, and transduction models) that place emphasis on parsimony, are mechanistic in nature, and serve as highly useful "top-down" methods of quantitating the actions of diverse drugs. These are often components of more complex quantitative systems pharmacology (QSP) models that explain the array of responses to various drugs, including corticosteroids. Progressively deeper mechanistic appreciation of PBPK, drug-target interactions, and systems physiology from the molecular (genomic, proteomic, metabolomic) to cellular to whole body levels provides the foundation for enhanced PK/PD to comprehensive QSP models. Our research based on cell, animal, clinical, and theoretical studies with corticosteroids have provided ideas and quantitative methods that have broadly advanced the fields of PK/PD and QSP modeling and illustrates the transition toward a global, systems understanding of actions of diverse drugs. SIGNIFICANCE STATEMENT: Over the past half century, pharmacokinetics (PK) and pharmacokinetics/pharmacodynamics (PK/PD) have evolved to provide an array of mechanism-based models that help quantitate the disposition and actions of most drugs. We describe how many basic PK and PK/PD model components were identified and often applied to the diverse properties of corticosteroids (CS). The CS have complications in disposition and a wide array of simple receptor-to complex gene-mediated actions in multiple organs. Continued assessments of such complexities have offered opportunities to develop models ranging from simple PK to enhanced PK/PD to quantitative systems pharmacology (QSP) that help explain therapeutic and adverse CS effects. Concurrent development of state-of-the-art PK, PK/PD, and QSP models are described alongside experimental studies that revealed diverse CS actions.
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Affiliation(s)
- Vivaswath S Ayyar
- Department of Pharmaceutical Sciences University at Buffalo, School of Pharmacy and Pharmaceutical Sciences, Buffalo, New York
| | - William J Jusko
- Department of Pharmaceutical Sciences University at Buffalo, School of Pharmacy and Pharmaceutical Sciences, Buffalo, New York
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3
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Acevedo A, DuBois D, Almon RR, Jusko WJ, Androulakis IP. Modeling Pathway Dynamics of the Skeletal Muscle Response to Intravenous Methylprednisolone (MPL) Administration in Rats: Dosing and Tissue Effects. Front Bioeng Biotechnol 2020; 8:759. [PMID: 32760706 PMCID: PMC7371857 DOI: 10.3389/fbioe.2020.00759] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2019] [Accepted: 06/15/2020] [Indexed: 12/27/2022] Open
Abstract
A model-based approach for the assessment of pathway dynamics is explored to characterize metabolic and signaling pathway activity changes characteristic of the dosing-dependent differences in response to methylprednisolone in muscle. To consistently compare dosing-induced changes we extend the principles of pharmacokinetics and pharmacodynamics and introduce a novel representation of pathway-level dynamic models of activity regulation. We hypothesize the emergence of dosing-dependent regulatory interactions is critical to understanding the mechanistic implications of MPL dosing in muscle. Our results indicate that key pathways, including amino acid and lipid metabolism, signal transduction, endocrine regulation, regulation of cellular functions including growth, death, motility, transport, protein degradation, and catabolism are dependent on dosing, exhibiting diverse dynamics depending on whether the drug is administered acutely of continuously. Therefore, the dynamics of drug presentation offer the possibility for the emergence of dosing-dependent models of regulation. Finally, we compared acute and chronic MPL response in muscle with liver. The comparison revealed systematic response differences between the two tissues, notably that muscle appears more prone to adapt to MPL.
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Affiliation(s)
- Alison Acevedo
- Department of Biomedical Engineering, Rutgers University, Piscataway, NJ, United States
| | - Debra DuBois
- Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, State University of New York at Buffalo, Buffalo, NY, United States.,Department of Biological Sciences, State University of New York at Buffalo, Buffalo, NY, United States
| | - Richard R Almon
- Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, State University of New York at Buffalo, Buffalo, NY, United States.,Department of Biological Sciences, State University of New York at Buffalo, Buffalo, NY, United States
| | - William J Jusko
- Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, State University of New York at Buffalo, Buffalo, NY, United States.,Department of Biological Sciences, State University of New York at Buffalo, Buffalo, NY, United States
| | - Ioannis P Androulakis
- Department of Biomedical Engineering, Rutgers University, Piscataway, NJ, United States.,Department of Chemical and Biochemical Engineering, Rutgers University, Piscataway, NJ, United States.,Department of Surgery, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, United States
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Acevedo A, Berthel A, DuBois D, Almon RR, Jusko WJ, Androulakis IP. Pathway-Based Analysis of the Liver Response to Intravenous Methylprednisolone Administration in Rats: Acute Versus Chronic Dosing. GENE REGULATION AND SYSTEMS BIOLOGY 2019; 13:1177625019840282. [PMID: 31019365 PMCID: PMC6466473 DOI: 10.1177/1177625019840282] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Accepted: 03/05/2019] [Indexed: 12/25/2022]
Abstract
Pharmacological time-series data, from comparative dosing studies, are critical to characterizing drug effects. Reconciling the data from multiple studies is inevitably difficult; multiple in vivo high-throughput -omics studies are necessary to capture the global and temporal effects of the drug, but these experiments, though analogous, differ in (microarray or other) platforms, time-scales, and dosing regimens and thus cannot be directly combined or compared. This investigation addresses this reconciliation issue with a meta-analysis technique aimed at assessing the intrinsic activity at the pathway level. The purpose of this is to characterize the dosing effects of methylprednisolone (MPL), a widely used anti-inflammatory and immunosuppressive corticosteroid (CS), within the liver. A multivariate decomposition approach is applied to analyze acute and chronic MPL dosing in male adrenalectomized rats and characterize the dosing-dependent differences in the dynamic response of MPL-responsive signaling and metabolic pathways. We demonstrate how to deconstruct signaling and metabolic pathways into their constituent pathway activities, activities which are scored for intrinsic pathway activity. Dosing-induced changes in the dynamics of pathway activities are compared using a model-based assessment of pathway dynamics, extending the principles of pharmacokinetics/pharmacodynamics (PKPD) to describe pathway activities. The model-based approach enabled us to hypothesize on the likely emergence (or disappearance) of indirect dosing-dependent regulatory interactions, pointing to likely mechanistic implications of dosing of MPL transcriptional regulation. Both acute and chronic MPL administration induced a strong core of activity within pathway families including the following: lipid metabolism, amino acid metabolism, carbohydrate metabolism, metabolism of cofactors and vitamins, regulation of essential organelles, and xenobiotic metabolism pathway families. Pathway activities alter between acute and chronic dosing, indicating that MPL response is dosing dependent. Furthermore, because multiple pathway activities are dominant within a single pathway, we observe that pathways cannot be defined by a single response. Instead, pathways are defined by multiple, complex, and temporally related activities corresponding to different subgroups of genes within each pathway.
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Affiliation(s)
- Alison Acevedo
- Department of Biomedical Engineering,
Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey,
Piscataway, NJ, USA
| | - Ana Berthel
- Department of Biochemistry, Mount
Holyoke College, South Hadley, MA, USA
| | - Debra DuBois
- Department of Pharmaceutical Sciences,
School of Pharmacy and Pharmaceutical Sciences, The State University of New York at
Buffalo, Buffalo, NY, USA
- Department of Biological Sciences, The
State University of New York at Buffalo, Buffalo, NY, USA
| | - Richard R Almon
- Department of Pharmaceutical Sciences,
School of Pharmacy and Pharmaceutical Sciences, The State University of New York at
Buffalo, Buffalo, NY, USA
- Department of Biological Sciences, The
State University of New York at Buffalo, Buffalo, NY, USA
| | - William J Jusko
- Department of Pharmaceutical Sciences,
School of Pharmacy and Pharmaceutical Sciences, The State University of New York at
Buffalo, Buffalo, NY, USA
- Department of Biological Sciences, The
State University of New York at Buffalo, Buffalo, NY, USA
| | - Ioannis P Androulakis
- Department of Biomedical Engineering,
Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey,
Piscataway, NJ, USA
- Department of Chemical and Biochemical
Engineering, Robert Wood Johnson Medical School, Rutgers, The State University of
New Jersey, Piscataway, NJ, USA
- Department of Surgery, Robert Wood
Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, NJ,
USA
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Kamisoglu K, Acevedo A, Almon RR, Coyle S, Corbett S, Dubois DC, Nguyen TT, Jusko WJ, Androulakis IP. Understanding Physiology in the Continuum: Integration of Information from Multiple - Omics Levels. Front Pharmacol 2017; 8:91. [PMID: 28289389 PMCID: PMC5327699 DOI: 10.3389/fphar.2017.00091] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2016] [Accepted: 02/13/2017] [Indexed: 01/18/2023] Open
Abstract
In this paper, we discuss approaches for integrating biological information reflecting diverse physiologic levels. In particular, we explore statistical and model-based methods for integrating transcriptomic, proteomic and metabolomics data. Our case studies reflect responses to a systemic inflammatory stimulus and in response to an anti-inflammatory treatment. Our paper serves partly as a review of existing methods and partly as a means to demonstrate, using case studies related to human endotoxemia and response to methylprednisolone (MPL) treatment, how specific questions may require specific methods, thus emphasizing the non-uniqueness of the approaches. Finally, we explore novel ways for integrating -omics information with PKPD models, toward the development of more integrated pharmacology models.
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Affiliation(s)
- Kubra Kamisoglu
- Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, University at Buffalo, Buffalo NY, USA
| | - Alison Acevedo
- Department of Biomedical Engineering, Rutgers University, Piscataway NJ, USA
| | - Richard R Almon
- Department of Biological Sciences, University at Buffalo, Buffalo NY, USA
| | - Susette Coyle
- Department of Surgery, Rutgers Robert Wood Johnson Medical School, New Brunswick NJ, USA
| | - Siobhan Corbett
- Department of Surgery, Rutgers Robert Wood Johnson Medical School, New Brunswick NJ, USA
| | - Debra C Dubois
- Department of Biological Sciences, University at Buffalo, Buffalo NY, USA
| | - Tung T Nguyen
- BioMaPS Institute for Quantitative Biology, Rutgers University, Piscataway NJ, USA
| | - William J Jusko
- Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, University at Buffalo, Buffalo NY, USA
| | - Ioannis P Androulakis
- Department of Biomedical Engineering, Rutgers University, PiscatawayNJ, USA; Department of Chemical Engineering, Rutgers University, PiscatawayNJ, USA
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Jusko WJ. Moving from basic toward systems pharmacodynamic models. J Pharm Sci 2013; 102:2930-40. [PMID: 23681608 DOI: 10.1002/jps.23590] [Citation(s) in RCA: 60] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2013] [Revised: 04/17/2013] [Accepted: 04/18/2013] [Indexed: 11/11/2022]
Abstract
Building upon many classical foundations of pharmacology, a diverse array of mechanistic pharmacokinetic-pharmacodynamic (PK/PD) models have emerged based on mechanisms of drug action and primary rate-limiting or turnover processes in physiology. An array of basic models can be extended to handle various complexities including tolerance and can readily be employed as building blocks in assembling enhanced PK/PD or small systems models. Our corticosteroid models demonstrate these concepts as well as elements of horizontal and vertical integration of molecular to whole-body processes. The potential advantages and challenges in moving PK/PD toward systems models are described.
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Affiliation(s)
- William J Jusko
- Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, State University of New York at Buffalo, Buffalo, New York 14214, USA.
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Scheff JD, Almon RR, DuBois DC, Jusko WJ, Androulakis IP. A new symbolic representation for the identification of informative genes in replicated microarray experiments. OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY 2010; 14:239-48. [PMID: 20455749 DOI: 10.1089/omi.2010.0005] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Microarray experiments generate massive amounts of data, necessitating innovative algorithms to distinguish biologically relevant information from noise. Because the variability of gene expression data is an important factor in determining which genes are differentially expressed, analysis techniques that take into account repeated measurements are critically important. Additionally, the selection of informative genes is typically done by searching for the individual genes that vary the most across conditions. Yet because genes tend to act in groups rather than individually, it may be possible to glean more information from the data by searching specifically for concerted behavior in a set of genes. Applying a symbolic transformation to the gene expression data allows the detection overrepresented patterns in the data, in contrast to looking only for genes that exhibit maximal differential expression. These challenges are approached by introducing an algorithm based on a new symbolic representation that searches for concerted gene expression patterns; furthermore, the symbolic representation takes into account the variance in multiple replicates and can be applied to long time series data. The proposed algorithm's ability to discover biologically relevant signals in gene expression data is exhibited by applying it to three datasets that measure gene expression in the rat liver.
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Affiliation(s)
- Jeremy D Scheff
- Biomedical Engineering Department, Rutgers University, Piscataway, New Jersey 08854, USA
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de Graaf AA, Freidig AP, De Roos B, Jamshidi N, Heinemann M, Rullmann JAC, Hall KD, Adiels M, van Ommen B. Nutritional systems biology modeling: from molecular mechanisms to physiology. PLoS Comput Biol 2009; 5:e1000554. [PMID: 19956660 PMCID: PMC2777333 DOI: 10.1371/journal.pcbi.1000554] [Citation(s) in RCA: 62] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
The use of computational modeling and simulation has increased in many biological fields, but despite their potential these techniques are only marginally applied in nutritional sciences. Nevertheless, recent applications of modeling have been instrumental in answering important nutritional questions from the cellular up to the physiological levels. Capturing the complexity of today's important nutritional research questions poses a challenge for modeling to become truly integrative in the consideration and interpretation of experimental data at widely differing scales of space and time. In this review, we discuss a selection of available modeling approaches and applications relevant for nutrition. We then put these models into perspective by categorizing them according to their space and time domain. Through this categorization process, we identified a dearth of models that consider processes occurring between the microscopic and macroscopic scale. We propose a "middle-out" strategy to develop the required full-scale, multilevel computational models. Exhaustive and accurate phenotyping, the use of the virtual patient concept, and the development of biomarkers from "-omics" signatures are identified as key elements of a successful systems biology modeling approach in nutrition research--one that integrates physiological mechanisms and data at multiple space and time scales.
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