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Wu YE, Zheng YY, Li QY, Yao BF, Cao J, Liu HX, Hao GX, van den Anker J, Zheng Y, Zhao W. Model-informed drug development in pediatric, pregnancy and geriatric drug development: States of the art and future. Adv Drug Deliv Rev 2024; 211:115364. [PMID: 38936664 DOI: 10.1016/j.addr.2024.115364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 06/09/2024] [Accepted: 06/19/2024] [Indexed: 06/29/2024]
Abstract
The challenges of drug development in pediatric, pregnant and geriatric populations are a worldwide concern shared by regulatory authorities, pharmaceutical companies, and healthcare professionals. Model-informed drug development (MIDD) can integrate and quantify real-world data of physiology, pharmacology, and disease processes by using modeling and simulation techniques to facilitate decision-making in drug development. In this article, we reviewed current MIDD policy updates, reflected on the integrity of physiological data used for MIDD and the effects of physiological changes on the drug PK, as well as summarized current MIDD strategies and applications, so as to present the state of the art of MIDD in pediatric, pregnant and geriatric populations. Some considerations are put forth for the future improvements of MIDD including refining regulatory considerations, improving the integrity of physiological data, applying the emerging technologies, and exploring the application of MIDD in new therapies like gene therapies for special populations.
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Affiliation(s)
- Yue-E Wu
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Yuan-Yuan Zheng
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Qiu-Yue Li
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Bu-Fan Yao
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Jing Cao
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Hui-Xin Liu
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Guo-Xiang Hao
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - John van den Anker
- Division of Clinical Pharmacology, Children's National Medical Center, Washington, DC, USA; Departments of Pediatrics, Pharmacology & Physiology, George Washington University, School of Medicine and Health Sciences, Washington, DC, USA; Department of Paediatric Pharmacology and Pharmacometrics, University Children's Hospital Basel, Basel, Switzerland
| | - Yi Zheng
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Wei Zhao
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China.
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Bisaso KR, Mukonzo JK, Ette EI. A mechanistic assessment of the nature of pharmacodynamic drug-drug interaction in vivo and in vitro. In Silico Pharmacol 2023; 11:31. [PMID: 37899968 PMCID: PMC10611690 DOI: 10.1007/s40203-023-00168-y] [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: 05/29/2023] [Accepted: 10/04/2023] [Indexed: 10/31/2023] Open
Abstract
Combination pharmacotherapy is becoming increasingly necessary because most diseases are pathophysiologically controlled at the subcellular level by target proteins in a combinatorial manner. We demonstrate the application of the stimulus-response mechanistic model in characterising the drug and physiological properties of pharmacodynamic drug-drug interactions (PDDI) using previously published in vitro and in vivo drug combination experiments. The in vitro experiment tested the effect of a combination of SCH66336 and 4-HPR on the survival of in squamous cell carcinoma cell lines, while the in vivo experiment tested the effect of a combination of cetuximab and cisplatin on tumour growth inhibition in female xenograft mice. The model adequately described both experiments, quantified both system and drug properties and predicted the nature of the PDDI mechanism. Strong baseline signals of 7.35 and 610 units existed in the in vitro and in vivo experiments respectively. An overall synergistic relationship (interaction index = 1.03E-8) was detected in the in vitro experiment. In the in vivo model, the overall interaction index was 70,139.45 implying an antagonistic interaction between the cisplatin and the cetuximab signals.
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Affiliation(s)
| | - Jackson K. Mukonzo
- Deparment of Pharmacology, Makerere University College of Health Sciences, Kampala, Uganda
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Guzzetti S, Morentin Gutierrez P. An integrated modelling approach for targeted degradation: insights on optimization, data requirements and PKPD predictions from semi- or fully-mechanistic models and exact steady state solutions. J Pharmacokinet Pharmacodyn 2023; 50:327-349. [PMID: 37120680 PMCID: PMC10460745 DOI: 10.1007/s10928-023-09857-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 03/28/2023] [Indexed: 05/01/2023]
Abstract
The value of an integrated mathematical modelling approach for protein degraders which combines the benefits of traditional turnover models and fully mechanistic models is presented. Firstly, we show how exact solutions of the mechanistic models of monovalent and bivalent degraders can provide insight on the role of each system parameter in driving the pharmacological response. We show how on/off binding rates and degradation rates are related to potency and maximal effect of monovalent degraders, and how such relationship can be used to suggest a compound optimization strategy. Even convoluted exact steady state solutions for bivalent degraders provide insight on the type of observations required to ensure the predictive capacity of a mechanistic approach. Specifically for PROTACs, the structure of the exact steady state solution suggests that the total remaining target at steady state, which is easily accessible experimentally, is insufficient to reconstruct the state of the whole system at equilibrium and observations on different species (such as binary/ternary complexes) are necessary. Secondly, global sensitivity analysis of fully mechanistic models for PROTACs suggests that both target and ligase baselines (actually, their ratio) are the major sources of variability in the response of non-cooperative systems, which speaks to the importance of characterizing their distribution in the target patient population. Finally, we propose a pragmatic modelling approach which incorporates the insights generated with fully mechanistic models into simpler turnover models to improve their predictive ability, hence enabling acceleration of drug discovery programs and increased probability of success in the clinic.
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Affiliation(s)
- Sofia Guzzetti
- DMPK, Research and Early Development, Oncology R&D, AstraZeneca, Cambridge, UK
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Rollins J, Worthington T, Dransfield A, Whitney J, Stanford J, Hooke E, Hobson J, Wengler J, Hope S, Mizrachi D. Expression of Cell-Adhesion Molecules in E. coli: A High Throughput Screening to Identify Paracellular Modulators. Int J Mol Sci 2023; 24:9784. [PMID: 37372932 DOI: 10.3390/ijms24129784] [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: 05/17/2023] [Revised: 05/31/2023] [Accepted: 06/02/2023] [Indexed: 06/29/2023] Open
Abstract
Cell-adhesion molecules (CAMs) are responsible for cell-cell, cell-extracellular matrix, and cell-pathogen interactions. Claudins (CLDNs), occludin (OCLN), and junctional adhesion molecules (JAMs) are CAMs' components of the tight junction (TJ), the single protein structure tasked with safeguarding the paracellular space. The TJ is responsible for controlling paracellular permeability according to size and charge. Currently, there are no therapeutic solutions to modulate the TJ. Here, we describe the expression of CLDN proteins in the outer membrane of E. coli and report its consequences. When the expression is induced, the unicellular behavior of E. coli is replaced with multicellular aggregations that can be quantified using Flow Cytometry (FC). Our method, called iCLASP (inspection of cell-adhesion molecules aggregation through FC protocols), allows high-throughput screening (HTS) of small-molecules for interactions with CAMs. Here, we focused on using iCLASP to identify paracellular modulators for CLDN2. Furthermore, we validated those compounds in the mammalian cell line A549 as a proof-of-concept for the iCLASP method.
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Affiliation(s)
- Jay Rollins
- Department of Cell Biology and Physiology, College of Life Sciences, Brigham Young University, Provo, UT 84602, USA
| | - Tyler Worthington
- Department of Cell Biology and Physiology, College of Life Sciences, Brigham Young University, Provo, UT 84602, USA
| | - Allison Dransfield
- Department of Cell Biology and Physiology, College of Life Sciences, Brigham Young University, Provo, UT 84602, USA
| | - Jordan Whitney
- Department of Cell Biology and Physiology, College of Life Sciences, Brigham Young University, Provo, UT 84602, USA
| | - Jordan Stanford
- Department of Cell Biology and Physiology, College of Life Sciences, Brigham Young University, Provo, UT 84602, USA
| | - Emily Hooke
- Department of Cell Biology and Physiology, College of Life Sciences, Brigham Young University, Provo, UT 84602, USA
| | - Joseph Hobson
- Department of Cell Biology and Physiology, College of Life Sciences, Brigham Young University, Provo, UT 84602, USA
| | - Jacob Wengler
- Department of Cell Biology and Physiology, College of Life Sciences, Brigham Young University, Provo, UT 84602, USA
| | - Sandra Hope
- Department of Microbiology and Molecular Biology, College of Life Sciences, Brigham Young University, Provo, UT 84602, USA
| | - Dario Mizrachi
- Department of Cell Biology and Physiology, College of Life Sciences, Brigham Young University, Provo, UT 84602, USA
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Rao R, Musante CJ, Allen R. A quantitative systems pharmacology model of the pathophysiology and treatment of COVID-19 predicts optimal timing of pharmacological interventions. NPJ Syst Biol Appl 2023; 9:13. [PMID: 37059734 PMCID: PMC10102696 DOI: 10.1038/s41540-023-00269-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 02/09/2023] [Indexed: 04/16/2023] Open
Abstract
A quantitative systems pharmacology (QSP) model of the pathogenesis and treatment of SARS-CoV-2 infection can streamline and accelerate the development of novel medicines to treat COVID-19. Simulation of clinical trials allows in silico exploration of the uncertainties of clinical trial design and can rapidly inform their protocols. We previously published a preliminary model of the immune response to SARS-CoV-2 infection. To further our understanding of COVID-19 and treatment, we significantly updated the model by matching a curated dataset spanning viral load and immune responses in plasma and lung. We identified a population of parameter sets to generate heterogeneity in pathophysiology and treatment and tested this model against published reports from interventional SARS-CoV-2 targeting mAb and antiviral trials. Upon generation and selection of a virtual population, we match both the placebo and treated responses in viral load in these trials. We extended the model to predict the rate of hospitalization or death within a population. Via comparison of the in silico predictions with clinical data, we hypothesize that the immune response to virus is log-linear over a wide range of viral load. To validate this approach, we show the model matches a published subgroup analysis, sorted by baseline viral load, of patients treated with neutralizing Abs. By simulating intervention at different time points post infection, the model predicts efficacy is not sensitive to interventions within five days of symptom onset, but efficacy is dramatically reduced if more than five days pass post symptom onset prior to treatment.
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Affiliation(s)
- Rohit Rao
- Early Clinical Development, Pfizer Worldwide Research, Development and Medical, Cambridge, MA, USA.
| | - Cynthia J Musante
- Early Clinical Development, Pfizer Worldwide Research, Development and Medical, Cambridge, MA, USA
| | - Richard Allen
- Early Clinical Development, Pfizer Worldwide Research, Development and Medical, Cambridge, MA, USA
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Courcelles E, Boissel JP, Massol J, Klingmann I, Kahoul R, Hommel M, Pham E, Kulesza A. Solving the Evidence Interpretability Crisis in Health Technology Assessment: A Role for Mechanistic Models? FRONTIERS IN MEDICAL TECHNOLOGY 2022; 4:810315. [PMID: 35281671 PMCID: PMC8907708 DOI: 10.3389/fmedt.2022.810315] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Accepted: 01/17/2022] [Indexed: 01/11/2023] Open
Abstract
Health technology assessment (HTA) aims to be a systematic, transparent, unbiased synthesis of clinical efficacy, safety, and value of medical products (MPs) to help policymakers, payers, clinicians, and industry to make informed decisions. The evidence available for HTA has gaps-impeding timely prediction of the individual long-term effect in real clinical practice. Also, appraisal of an MP needs cross-stakeholder communication and engagement. Both aspects may benefit from extended use of modeling and simulation. Modeling is used in HTA for data-synthesis and health-economic projections. In parallel, regulatory consideration of model informed drug development (MIDD) has brought attention to mechanistic modeling techniques that could in fact be relevant for HTA. The ability to extrapolate and generate personalized predictions renders the mechanistic MIDD approaches suitable to support translation between clinical trial data into real-world evidence. In this perspective, we therefore discuss concrete examples of how mechanistic models could address HTA-related questions. We shed light on different stakeholder's contributions and needs in the appraisal phase and suggest how mechanistic modeling strategies and reporting can contribute to this effort. There are still barriers dissecting the HTA space and the clinical development space with regard to modeling: lack of an adapted model validation framework for decision-making process, inconsistent and unclear support by stakeholders, limited generalizable use cases, and absence of appropriate incentives. To address this challenge, we suggest to intensify the collaboration between competent authorities, drug developers and modelers with the aim to implement mechanistic models central in the evidence generation, synthesis, and appraisal of HTA so that the totality of mechanistic and clinical evidence can be leveraged by all relevant stakeholders.
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Affiliation(s)
| | | | - Jacques Massol
- Phisquare Institute, Transplantation Foundation, Paris, France
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Cheng L, Qiu Y, Schmidt BJ, Wei GW. Review of applications and challenges of quantitative systems pharmacology modeling and machine learning for heart failure. J Pharmacokinet Pharmacodyn 2022; 49:39-50. [PMID: 34637069 PMCID: PMC8837528 DOI: 10.1007/s10928-021-09785-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Accepted: 09/22/2021] [Indexed: 12/24/2022]
Abstract
Quantitative systems pharmacology (QSP) is an important approach in pharmaceutical research and development that facilitates in silico generation of quantitative mechanistic hypotheses and enables in silico trials. As demonstrated by applications from numerous industry groups and interest from regulatory authorities, QSP is becoming an increasingly critical component in clinical drug development. With rapidly evolving computational tools and methods, QSP modeling has achieved important progress in pharmaceutical research and development, including for heart failure (HF). However, various challenges exist in the QSP modeling and clinical characterization of HF. Machine/deep learning (ML/DL) methods have had success in a wide variety of fields and disciplines. They provide data-driven approaches in HF diagnosis and modeling, and offer a novel strategy to inform QSP model development and calibration. The combination of ML/DL and QSP modeling becomes an emergent direction in the understanding of HF and clinical development new therapies. In this work, we review the current status and achievement in QSP and ML/DL for HF, and discuss remaining challenges and future perspectives in the field.
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Affiliation(s)
- Limei Cheng
- Quantitative Systems Pharmacology and Physiologically Based Pharmacokinetics, Bristol Myers Squibb, Princeton, NJ, 08536, USA.
| | - Yuchi Qiu
- Department of Mathematics, Michigan State University, East Lansing, MI, 48824, USA
| | - Brian J Schmidt
- Quantitative Systems Pharmacology and Physiologically Based Pharmacokinetics, Bristol Myers Squibb, Princeton, NJ, 08536, USA
| | - Guo-Wei Wei
- Department of Mathematics, Michigan State University, East Lansing, MI, 48824, USA
- Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI, 48824, USA
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, 48824, USA
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8
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Ataeinia B, Heidari P. Artificial Intelligence and the Future of Diagnostic and Therapeutic Radiopharmaceutical Development:: In Silico Smart Molecular Design. PET Clin 2021; 16:513-523. [PMID: 34364818 PMCID: PMC8453048 DOI: 10.1016/j.cpet.2021.06.008] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Novel diagnostic and therapeutic radiopharmaceuticals are increasingly becoming a central part of personalized medicine. Continued innovation in the development of new radiopharmaceuticals is key to sustained growth and advancement of precision medicine. Artificial intelligence has been used in multiple fields of medicine to develop and validate better tools for patient diagnosis and therapy, including in radiopharmaceutical design. In this review, we first discuss common in silico approaches and focus on their usefulness and challenges in radiopharmaceutical development. Next, we discuss the practical applications of in silico modeling in design of radiopharmaceuticals in various diseases.
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Affiliation(s)
- Bahar Ataeinia
- Department of Radiology, Massachusetts General Hospital, 55 Fruit St, Wht 427, Boston, MA 02114, USA
| | - Pedram Heidari
- Department of Radiology, Massachusetts General Hospital, 55 Fruit St, Wht 427, Boston, MA 02114, USA.
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9
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Chang CJ, Taniguchi A. Establishment of a Nanopatterned Renal Disease Model by Mimicking the Physical and Chemical Cues of a Diseased Mesangial Cell Microenvironment. ACS APPLIED BIO MATERIALS 2021; 4:1573-1583. [PMID: 35014506 DOI: 10.1021/acsabm.0c01406] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Modulation of mesangial cell (MC) response by in vitro disease models offers therapeutic strategies for the treatment of several glomerular diseases. However, traditional cell culture models lack the nanostructured extracellular matrix (ECM), which has unique physical and chemical properties, so they poorly reflect the complexities of the native microenvironment. Therefore, a cell disease model with ECM nanostructures is required to better mimic the in vivo diseased nanoenvironment. To establish a renal disease model, we used a titanium dioxide-based disease-mimic nanopattern as the physical cues and transforming growth factor-beta 1 (TGF-β1) as a chemical cue. The effects of this renal disease model on proliferation and mesangial matrix (MM) component changes in the SV40MES13 (MES13) mouse mesangial cell line were evaluated. Our results showed that both the presence of the disease-mimic nanopattern and TGF-β1 intensified proliferation and resulted in increased type I collagen and fibronectin and decreased type IV collagen expressions in MES13 cells. These effects could be involved in increased TGF-β type I receptor expression in MES13 cells. The intracellular reactive oxygen species (ROS) level as a biomarker of this renal disease model indicated that the cells were in a diseased state. A small molecule A83-01 and known drug dexamethasone markedly attenuated the intracellular ROS production in MES13 that was induced by the disease-mimic nanopattern and TGF-β1. These results highlight the significant effects of physical and chemical cues in facilitating disease-like behavior in MES13 cells, providing an important theoretical basis for developing a drug screening platform for glomerular diseases.
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Affiliation(s)
- Chia-Jung Chang
- Research Center for Functional Materials, National Institute for Materials Science, 1-1 Namiki, Tsukuba, Ibaraki 305-0044, Japan.,Department of Nanoscience and Nanoengineering, Graduate School of Advanced Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan
| | - Akiyoshi Taniguchi
- Research Center for Functional Materials, National Institute for Materials Science, 1-1 Namiki, Tsukuba, Ibaraki 305-0044, Japan.,Department of Nanoscience and Nanoengineering, Graduate School of Advanced Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan
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Joshi A, Wang DH, Watterson S, McClean PL, Behera CK, Sharp T, Wong-Lin K. Opportunities for multiscale computational modelling of serotonergic drug effects in Alzheimer's disease. Neuropharmacology 2020; 174:108118. [PMID: 32380022 PMCID: PMC7322519 DOI: 10.1016/j.neuropharm.2020.108118] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2019] [Revised: 04/13/2020] [Accepted: 04/27/2020] [Indexed: 12/17/2022]
Abstract
Alzheimer's disease (AD) is an age-specific neurodegenerative disease that compromises cognitive functioning and impacts the quality of life of an individual. Pathologically, AD is characterised by abnormal accumulation of beta-amyloid (Aβ) and hyperphosphorylated tau protein. Despite research advances over the last few decades, there is currently still no cure for AD. Although, medications are available to control some behavioural symptoms and slow the disease's progression, most prescribed medications are based on cholinesterase inhibitors. Over the last decade, there has been increased attention towards novel drugs, targeting alternative neurotransmitter pathways, particularly those targeting serotonergic (5-HT) system. In this review, we focused on 5-HT receptor (5-HTR) mediated signalling and drugs that target these receptors. These pathways regulate key proteins and kinases such as GSK-3 that are associated with abnormal levels of Aβ and tau in AD. We then review computational studies related to 5-HT signalling pathways with the potential for providing deeper understanding of AD pathologies. In particular, we suggest that multiscale and multilevel modelling approaches could potentially provide new insights into AD mechanisms, and towards discovering novel 5-HTR based therapeutic targets.
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Affiliation(s)
- Alok Joshi
- Intelligent Systems Research Centre, Ulster University, Derry~Londonderry, Northern Ireland, UK.
| | - Da-Hui Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; School of System Science, Beijing Normal University, Beijing, China
| | - Steven Watterson
- Northern Ireland Centre for Stratified Medicine, Biomedical Sciences Research Institute, Ulster University, Derry~Londonderry, Northern Ireland, UK
| | - Paula L McClean
- Northern Ireland Centre for Stratified Medicine, Biomedical Sciences Research Institute, Ulster University, Derry~Londonderry, Northern Ireland, UK
| | - Chandan K Behera
- Intelligent Systems Research Centre, Ulster University, Derry~Londonderry, Northern Ireland, UK
| | - Trevor Sharp
- Department of Pharmacology, University of Oxford, Oxford, UK
| | - KongFatt Wong-Lin
- Intelligent Systems Research Centre, Ulster University, Derry~Londonderry, Northern Ireland, UK.
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11
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Richelle A, David B, Demaegd D, Dewerchin M, Kinet R, Morreale A, Portela R, Zune Q, von Stosch M. Towards a widespread adoption of metabolic modeling tools in biopharmaceutical industry: a process systems biology engineering perspective. NPJ Syst Biol Appl 2020; 6:6. [PMID: 32170148 PMCID: PMC7070029 DOI: 10.1038/s41540-020-0127-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2019] [Accepted: 02/12/2020] [Indexed: 01/09/2023] Open
Abstract
In biotechnology, the emergence of high-throughput technologies challenges the interpretation of large datasets. One way to identify meaningful outcomes impacting process and product attributes from large datasets is using systems biology tools such as metabolic models. However, these tools are still not fully exploited for this purpose in industrial context due to gaps in our knowledge and technical limitations. In this paper, key aspects restraining the routine implementation of these tools are highlighted in three research fields: monitoring, network science and hybrid modeling. Advances in these fields could expand the current state of systems biology applications in biopharmaceutical industry to address existing challenges in bioprocess development and improvement.
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12
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Pore D, Hoque KM, Chakrabarti MK. Animal models in advancement of research in enteric diseases. Anim Biotechnol 2020. [DOI: 10.1016/b978-0-12-811710-1.00032-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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13
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Regan-Fendt KE, Xu J, DiVincenzo M, Duggan MC, Shakya R, Na R, Carson WE, Payne PRO, Li F. Synergy from gene expression and network mining (SynGeNet) method predicts synergistic drug combinations for diverse melanoma genomic subtypes. NPJ Syst Biol Appl 2019; 5:6. [PMID: 30820351 PMCID: PMC6391384 DOI: 10.1038/s41540-019-0085-4] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2018] [Accepted: 01/23/2019] [Indexed: 12/31/2022] Open
Abstract
Systems biology perspectives are crucial for understanding the pathophysiology of complex diseases, and therefore hold great promise for the discovery of novel treatment strategies. Drug combinations have been shown to improve durability and reduce resistance to available first-line therapies in a variety of cancers; however, traditional drug discovery approaches are prohibitively cost and labor-intensive to evaluate large-scale matrices of potential drug combinations. Computational methods are needed to efficiently model complex interactions of drug target pathways and identify mechanisms underlying drug combination synergy. In this study, we employ a computational approach, SynGeNet (Synergy from Gene expression and Network mining), which integrates transcriptomics-based connectivity mapping and network centrality analysis to analyze disease networks and predict drug combinations. As an exemplar of a disease in which combination therapies demonstrate efficacy in genomic-specific contexts, we investigate malignant melanoma. We employed SynGeNet to generate drug combination predictions for each of the four major genomic subtypes of melanoma (BRAF, NRAS, NF1, and triple wild type) using publicly available gene expression and mutation data. We validated synergistic drug combinations predicted by our method across all genomic subtypes using results from a high-throughput drug screening study across. Finally, we prospectively validated the drug combination for BRAF-mutant melanoma that was top ranked by our approach, vemurafenib (BRAF inhibitor) + tretinoin (retinoic acid receptor agonist), using both in vitro and in vivo models of BRAF-mutant melanoma and RNA-sequencing analysis of drug-treated melanoma cells to validate the predicted mechanisms. Our approach is applicable to a wide range of disease domains, and, importantly, can model disease-relevant protein subnetworks in precision medicine contexts.
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Affiliation(s)
- Kelly E Regan-Fendt
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA
| | - Jielin Xu
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA
| | - Mallory DiVincenzo
- Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA
| | - Megan C Duggan
- Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA
| | - Reena Shakya
- Target Validation Shared Resource, The Ohio State University, Columbus, OH, USA
| | - Ryejung Na
- Target Validation Shared Resource, The Ohio State University, Columbus, OH, USA
| | - William E Carson
- Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA
| | - Philip R O Payne
- Institute for Informatics, Washington University in St. Louis, St. Louis, MO, USA
| | - Fuhai Li
- Institute for Informatics, Washington University in St. Louis, St. Louis, MO, USA.
- Department of Pediatrics, Washington University in St. Louis, St. Louis, MO, USA.
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14
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15
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Cheng Y, Thalhauser CJ, Smithline S, Pagidala J, Miladinov M, Vezina HE, Gupta M, Leil TA, Schmidt BJ. QSP Toolbox: Computational Implementation of Integrated Workflow Components for Deploying Multi-Scale Mechanistic Models. AAPS JOURNAL 2017; 19:1002-1016. [PMID: 28540623 DOI: 10.1208/s12248-017-0100-x] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2017] [Accepted: 05/08/2017] [Indexed: 01/09/2023]
Abstract
Quantitative systems pharmacology (QSP) modeling has become increasingly important in pharmaceutical research and development, and is a powerful tool to gain mechanistic insights into the complex dynamics of biological systems in response to drug treatment. However, even once a suitable mathematical framework to describe the pathophysiology and mechanisms of interest is established, final model calibration and the exploration of variability can be challenging and time consuming. QSP models are often formulated as multi-scale, multi-compartment nonlinear systems of ordinary differential equations. Commonly accepted modeling strategies, workflows, and tools have promise to greatly improve the efficiency of QSP methods and improve productivity. In this paper, we present the QSP Toolbox, a set of functions, structure array conventions, and class definitions that computationally implement critical elements of QSP workflows including data integration, model calibration, and variability exploration. We present the application of the toolbox to an ordinary differential equations-based model for antibody drug conjugates. As opposed to a single stepwise reference model calibration, the toolbox also facilitates simultaneous parameter optimization and variation across multiple in vitro, in vivo, and clinical assays to more comprehensively generate alternate mechanistic hypotheses that are in quantitative agreement with available data. The toolbox also includes scripts for developing and applying virtual populations to mechanistic exploration of biomarkers and efficacy. We anticipate that the QSP Toolbox will be a useful resource that will facilitate implementation, evaluation, and sharing of new methodologies in a common framework that will greatly benefit the community.
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Affiliation(s)
- Yougan Cheng
- Bristol-Myers Squibb, PO Box 4000, Princeton, New Jersey, 08543-4000, USA
| | - Craig J Thalhauser
- Bristol-Myers Squibb, PO Box 4000, Princeton, New Jersey, 08543-4000, USA
| | - Shepard Smithline
- Bristol-Myers Squibb, PO Box 4000, Princeton, New Jersey, 08543-4000, USA
| | - Jyotsna Pagidala
- Bristol-Myers Squibb, PO Box 4000, Princeton, New Jersey, 08543-4000, USA
| | - Marko Miladinov
- Bristol-Myers Squibb, PO Box 4000, Princeton, New Jersey, 08543-4000, USA
| | - Heather E Vezina
- Bristol-Myers Squibb, PO Box 4000, Princeton, New Jersey, 08543-4000, USA
| | - Manish Gupta
- Bristol-Myers Squibb, PO Box 4000, Princeton, New Jersey, 08543-4000, USA
| | - Tarek A Leil
- Bristol-Myers Squibb, PO Box 4000, Princeton, New Jersey, 08543-4000, USA
| | - Brian J Schmidt
- Bristol-Myers Squibb, PO Box 4000, Princeton, New Jersey, 08543-4000, USA.
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16
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Rieger TR, Musante CJ. Benefits and challenges of a QSP approach through case study: Evaluation of a hypothetical GLP-1/GIP dual agonist therapy. Eur J Pharm Sci 2016; 94:15-19. [DOI: 10.1016/j.ejps.2016.05.006] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2015] [Revised: 04/26/2016] [Accepted: 05/04/2016] [Indexed: 12/19/2022]
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17
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Pienaar E, Matern WM, Linderman JJ, Bader JS, Kirschner DE. Multiscale Model of Mycobacterium tuberculosis Infection Maps Metabolite and Gene Perturbations to Granuloma Sterilization Predictions. Infect Immun 2016; 84:1650-1669. [PMID: 26975995 PMCID: PMC4862722 DOI: 10.1128/iai.01438-15] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2015] [Accepted: 03/08/2016] [Indexed: 02/06/2023] Open
Abstract
Granulomas are a hallmark of tuberculosis. Inside granulomas, the pathogen Mycobacterium tuberculosis may enter a metabolically inactive state that is less susceptible to antibiotics. Understanding M. tuberculosis metabolism within granulomas could contribute to reducing the lengthy treatment required for tuberculosis and provide additional targets for new drugs. Two key adaptations of M. tuberculosis are a nonreplicating phenotype and accumulation of lipid inclusions in response to hypoxic conditions. To explore how these adaptations influence granuloma-scale outcomes in vivo, we present a multiscale in silico model of granuloma formation in tuberculosis. The model comprises host immunity, M. tuberculosis metabolism, M. tuberculosis growth adaptation to hypoxia, and nutrient diffusion. We calibrated our model to in vivo data from nonhuman primates and rabbits and apply the model to predict M. tuberculosis population dynamics and heterogeneity within granulomas. We found that bacterial populations are highly dynamic throughout infection in response to changing oxygen levels and host immunity pressures. Our results indicate that a nonreplicating phenotype, but not lipid inclusion formation, is important for long-term M. tuberculosis survival in granulomas. We used virtual M. tuberculosis knockouts to predict the impact of both metabolic enzyme inhibitors and metabolic pathways exploited to overcome inhibition. Results indicate that knockouts whose growth rates are below ∼66% of the wild-type growth rate in a culture medium featuring lipid as the only carbon source are unable to sustain infections in granulomas. By mapping metabolite- and gene-scale perturbations to granuloma-scale outcomes and predicting mechanisms of sterilization, our method provides a powerful tool for hypothesis testing and guiding experimental searches for novel antituberculosis interventions.
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Affiliation(s)
- Elsje Pienaar
- Department of Chemical Engineering, University of Michigan, Ann Arbor, Michigan, USA
- Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, Michigan, USA
| | - William M Matern
- Department of Biomedical Engineering and High-Throughput Biology Center, Johns Hopkins University, Baltimore, Maryland, USA
| | - Jennifer J Linderman
- Department of Chemical Engineering, University of Michigan, Ann Arbor, Michigan, USA
| | - Joel S Bader
- Department of Biomedical Engineering and High-Throughput Biology Center, Johns Hopkins University, Baltimore, Maryland, USA
| | - Denise E Kirschner
- Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, Michigan, USA
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18
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Hodos RA, Kidd BA, Khader S, Readhead BP, Dudley JT. In silico methods for drug repurposing and pharmacology. WILEY INTERDISCIPLINARY REVIEWS. SYSTEMS BIOLOGY AND MEDICINE 2016; 8:186-210. [PMID: 27080087 PMCID: PMC4845762 DOI: 10.1002/wsbm.1337] [Citation(s) in RCA: 179] [Impact Index Per Article: 22.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2015] [Revised: 02/08/2016] [Accepted: 02/11/2016] [Indexed: 12/18/2022]
Abstract
Data in the biological, chemical, and clinical domains are accumulating at ever-increasing rates and have the potential to accelerate and inform drug development in new ways. Challenges and opportunities now lie in developing analytic tools to transform these often complex and heterogeneous data into testable hypotheses and actionable insights. This is the aim of computational pharmacology, which uses in silico techniques to better understand and predict how drugs affect biological systems, which can in turn improve clinical use, avoid unwanted side effects, and guide selection and development of better treatments. One exciting application of computational pharmacology is drug repurposing-finding new uses for existing drugs. Already yielding many promising candidates, this strategy has the potential to improve the efficiency of the drug development process and reach patient populations with previously unmet needs such as those with rare diseases. While current techniques in computational pharmacology and drug repurposing often focus on just a single data modality such as gene expression or drug-target interactions, we argue that methods such as matrix factorization that can integrate data within and across diverse data types have the potential to improve predictive performance and provide a fuller picture of a drug's pharmacological action. WIREs Syst Biol Med 2016, 8:186-210. doi: 10.1002/wsbm.1337 For further resources related to this article, please visit the WIREs website.
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Affiliation(s)
- Rachel A Hodos
- New York University and Icahn School of Medicine at Mt. Sinai, New York, NY
| | - Brian A Kidd
- Icahn School of Medicine at Mt. Sinai, New York, NY
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19
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Rady Raz N, Akbarzadeh-T. MR, Tafaghodi M. Bioinspired Nanonetworks for Targeted Cancer Drug Delivery. IEEE Trans Nanobioscience 2015; 14:894-906. [DOI: 10.1109/tnb.2015.2489761] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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20
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Cosgrove J, Butler J, Alden K, Read M, Kumar V, Cucurull-Sanchez L, Timmis J, Coles M. Agent-Based Modeling in Systems Pharmacology. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2015; 4:615-29. [PMID: 26783498 PMCID: PMC4716580 DOI: 10.1002/psp4.12018] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2015] [Revised: 06/29/2015] [Accepted: 07/31/2015] [Indexed: 02/06/2023]
Abstract
Modeling and simulation (M&S) techniques provide a platform for knowledge integration and hypothesis testing to gain insights into biological systems that would not be possible a priori. Agent‐based modeling (ABM) is an M&S technique that focuses on describing individual components rather than homogenous populations. This tutorial introduces ABM to systems pharmacologists, using relevant case studies to highlight how ABM‐specific strengths have yielded success in the area of preclinical mechanistic modeling.
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Affiliation(s)
- J Cosgrove
- York Computational Immunology LabUniversity of YorkYorkUK; Centre for Immunology and InfectionUniversity of YorkYorkUK; Department of ElectronicsUniversity of YorkYorkUK
| | - J Butler
- York Computational Immunology LabUniversity of YorkYorkUK; Centre for Immunology and InfectionUniversity of YorkYorkUK; Department of ElectronicsUniversity of YorkYorkUK
| | - K Alden
- York Computational Immunology LabUniversity of YorkYorkUK; Centre for Immunology and InfectionUniversity of YorkYorkUK
| | - M Read
- Charles Perkins Centre University of Sydney Sydney Australia
| | - V Kumar
- University of California School of Medicine LA Jolla California USA
| | | | - J Timmis
- York Computational Immunology LabUniversity of YorkYorkUK; Department of ElectronicsUniversity of YorkYorkUK; SimOmicsYorkUK
| | - M Coles
- York Computational Immunology LabUniversity of YorkYorkUK; Centre for Immunology and InfectionUniversity of YorkYorkUK; SimOmicsYorkUK
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21
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Benson N. Network-based discovery through mechanistic systems biology. Implications for applications--SMEs and drug discovery: where the action is. DRUG DISCOVERY TODAY. TECHNOLOGIES 2015; 15:41-8. [PMID: 26464089 DOI: 10.1016/j.ddtec.2015.07.003] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2014] [Revised: 06/30/2015] [Accepted: 07/14/2015] [Indexed: 01/10/2023]
Abstract
Phase II attrition remains the most important challenge for drug discovery. Tackling the problem requires improved understanding of the complexity of disease biology. Systems biology approaches to this problem can, in principle, deliver this. This article reviews the reports of the application of mechanistic systems models to drug discovery questions and discusses the added value. Although we are on the journey to the virtual human, the length, path and rate of learning from this remain an open question. Success will be dependent on the will to invest and make the most of the insight generated along the way.
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Affiliation(s)
- Neil Benson
- Xenologiq Ltd., Unit 43, Canterbury Innovation Centre, University Road, Canterbury CT2 7FG, UK.
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22
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Klinke DJ. Enhancing the discovery and development of immunotherapies for cancer using quantitative and systems pharmacology: Interleukin-12 as a case study. J Immunother Cancer 2015; 3:27. [PMID: 26082838 PMCID: PMC4468964 DOI: 10.1186/s40425-015-0069-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2015] [Accepted: 04/28/2015] [Indexed: 12/22/2022] Open
Abstract
Recent clinical successes of immune checkpoint modulators have unleashed a wave of enthusiasm associated with cancer immunotherapy. However, this enthusiasm is dampened by persistent translational hurdles associated with cancer immunotherapy that mirror the broader pharmaceutical industry. Specifically, the challenges associated with drug discovery and development stem from an incomplete understanding of the biological mechanisms in humans that are targeted by a potential drug and the financial implications of clinical failures. Sustaining progress in expanding the clinical benefit provided by cancer immunotherapy requires reliably identifying new mechanisms of action. Along these lines, quantitative and systems pharmacology (QSP) has been proposed as a means to invigorate the drug discovery and development process. In this review, I discuss two central themes of QSP as applied in the context of cancer immunotherapy. The first theme focuses on a network-centric view of biology as a contrast to a "one-gene, one-receptor, one-mechanism" paradigm prevalent in contemporary drug discovery and development. This theme has been enabled by the advances in wet-lab capabilities to assay biological systems at increasing breadth and resolution. The second theme focuses on integrating mechanistic modeling and simulation with quantitative wet-lab studies. Drawing from recent QSP examples, large-scale mechanistic models that integrate phenotypic signaling-, cellular-, and tissue-level behaviors have the potential to lower many of the translational hurdles associated with cancer immunotherapy. These include prioritizing immunotherapies, developing mechanistic biomarkers that stratify patient populations and that reflect the underlying strength and dynamics of a protective host immune response, and facilitate explicit sharing of our understanding of the underlying biology using mechanistic models as vehicles for dialogue. However, creating such models require a modular approach that assumes that the biological networks remain similar in health and disease. As oncogenesis is associated with re-wiring of these biological networks, I also describe an approach that combines mechanistic modeling with quantitative wet-lab experiments to identify ways in which malignant cells alter these networks, using Interleukin-12 as an example. Collectively, QSP represents a new holistic approach that may have profound implications for how translational science is performed.
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Affiliation(s)
- David J Klinke
- Department of Chemical Engineering and Mary Babb Randolph Cancer Center, West Virginia University, Morgantown, WV 25606 USA
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23
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Linderman JJ, Cilfone NA, Pienaar E, Gong C, Kirschner DE. A multi-scale approach to designing therapeutics for tuberculosis. Integr Biol (Camb) 2015; 7:591-609. [PMID: 25924949 DOI: 10.1039/c4ib00295d] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Approximately one third of the world's population is infected with Mycobacterium tuberculosis. Limited information about how the immune system fights M. tuberculosis and what constitutes protection from the bacteria impact our ability to develop effective therapies for tuberculosis. We present an in vivo systems biology approach that integrates data from multiple model systems and over multiple length and time scales into a comprehensive multi-scale and multi-compartment view of the in vivo immune response to M. tuberculosis. We describe computational models that can be used to study (a) immunomodulation with the cytokines tumor necrosis factor and interleukin 10, (b) oral and inhaled antibiotics, and
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Affiliation(s)
- Jennifer J Linderman
- Department of Chemical Engineering, University of Michigan, Ann Arbor, Michigan 48109, USA.
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24
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Heinken A, Thiele I. Systems biology of host-microbe metabolomics. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2015; 7:195-219. [PMID: 25929487 PMCID: PMC5029777 DOI: 10.1002/wsbm.1301] [Citation(s) in RCA: 69] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/23/2015] [Revised: 03/25/2015] [Accepted: 04/01/2015] [Indexed: 12/15/2022]
Abstract
The human gut microbiota performs essential functions for host and well‐being, but has also been linked to a variety of disease states, e.g., obesity and type 2 diabetes. The mammalian body fluid and tissue metabolomes are greatly influenced by the microbiota, with many health‐relevant metabolites being considered ‘mammalian–microbial co‐metabolites’. To systematically investigate this complex host–microbial co‐metabolism, a systems biology approach integrating high‐throughput data and computational network models is required. Here, we review established top‐down and bottom‐up systems biology approaches that have successfully elucidated relationships between gut microbiota‐derived metabolites and host health and disease. We focus particularly on the constraint‐based modeling and analysis approach, which enables the prediction of mechanisms behind metabolic host–microbe interactions on the molecular level. We illustrate that constraint‐based models are a useful tool for the contextualization of metabolomic measurements and can further our insight into host–microbe interactions, yielding, e.g., in potential novel drugs and biomarkers. WIREs Syst Biol Med 2015, 7:195–219. doi: 10.1002/wsbm.1301 For further resources related to this article, please visit the WIREs website. Conflict of interest: The authors have declared no conflicts of interest for this article.
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Affiliation(s)
- Almut Heinken
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belval, Luxembourg
| | - Ines Thiele
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belval, Luxembourg
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25
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Boissel JP, Auffray C, Noble D, Hood L, Boissel FH. Bridging Systems Medicine and Patient Needs. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2015. [PMID: 26225243 PMCID: PMC4394618 DOI: 10.1002/psp4.26] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
While there is widespread consensus on the need both to change the prevailing research and development (R&D) paradigm and provide the community with an efficient way to personalize medicine, ecosystem stakeholders grapple with divergent conceptions about which quantitative approach should be preferred. The primary purpose of this position paper is to contrast these approaches. The second objective is to introduce a framework to bridge simulation outputs and patient outcomes, thus empowering the implementation of systems medicine.
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Affiliation(s)
| | - C Auffray
- European Institute for Systems Biology & Medicine, CNRS-UCBL-ENS, Université de Lyon France
| | - D Noble
- Department of Physiology, Anatomy & Genetics, University of Oxford Oxford, UK
| | - L Hood
- Institute for Systems Biology Seattle, Washington, USA
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26
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Ermakov S, Forster P, Pagidala J, Miladinov M, Wang A, Baillie R, Bartlett D, Reed M, Leil TA. Virtual Systems Pharmacology (ViSP) software for simulation from mechanistic systems-level models. Front Pharmacol 2014; 5:232. [PMID: 25374542 PMCID: PMC4205926 DOI: 10.3389/fphar.2014.00232] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2014] [Accepted: 09/30/2014] [Indexed: 12/27/2022] Open
Abstract
Multiple software programs are available for designing and running large scale system-level pharmacology models used in the drug development process. Depending on the problem, scientists may be forced to use several modeling tools that could increase model development time, IT costs and so on. Therefore, it is desirable to have a single platform that allows setting up and running large-scale simulations for the models that have been developed with different modeling tools. We developed a workflow and a software platform in which a model file is compiled into a self-contained executable that is no longer dependent on the software that was used to create the model. At the same time the full model specifics is preserved by presenting all model parameters as input parameters for the executable. This platform was implemented as a model agnostic, therapeutic area agnostic and web-based application with a database back-end that can be used to configure, manage and execute large-scale simulations for multiple models by multiple users. The user interface is designed to be easily configurable to reflect the specifics of the model and the user's particular needs and the back-end database has been implemented to store and manage all aspects of the systems, such as Models, Virtual Patients, User Interface Settings, and Results. The platform can be adapted and deployed on an existing cluster or cloud computing environment. Its use was demonstrated with a metabolic disease systems pharmacology model that simulates the effects of two antidiabetic drugs, metformin and fasiglifam, in type 2 diabetes mellitus patients.
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Affiliation(s)
- Sergey Ermakov
- Exploratory Clinical and Translational Research, Bristol-Myers Squibb Princeton, NJ, USA
| | | | - Jyotsna Pagidala
- Research IT and Automation, Bristol-Myers Squibb Princeton, NJ, USA
| | - Marko Miladinov
- Research IT and Automation, Bristol-Myers Squibb Princeton, NJ, USA
| | - Albert Wang
- Research IT and Automation, Bristol-Myers Squibb Princeton, NJ, USA
| | | | | | | | - Tarek A Leil
- Exploratory Clinical and Translational Research, Bristol-Myers Squibb Princeton, NJ, USA
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27
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Denayer T, Stöhr T, Roy MV. Animal models in translational medicine: Validation and prediction. ACTA ACUST UNITED AC 2014. [DOI: 10.1016/j.nhtm.2014.08.001] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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28
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29
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Nicolaou KC. Advancing the Drug Discovery and Development Process. Angew Chem Int Ed Engl 2014; 53:9128-40. [DOI: 10.1002/anie.201404761] [Citation(s) in RCA: 60] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2014] [Indexed: 11/05/2022]
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30
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Schmidt BJ. Systems biology for simulating patient physiology during the postgenomic era of medicine. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2014; 3:e106. [PMID: 24646725 PMCID: PMC4039391 DOI: 10.1038/psp.2014.2] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/25/2013] [Accepted: 01/02/2014] [Indexed: 01/31/2023]
Abstract
Systems pharmacology models capable of accurately recapitulating sophisticated patient phenotypes have enabled the investigation of mechanisms responsible for therapeutic efficacy. Although omics data sets are capable of characterizing the operation of subcellular networks, their utility in mechanistically predicting quantitative, clinically accessible outcome measures has been limited. Developing insights into clinical outcomes from omics data sets will benefit from modeling approaches that can integrate molecular networks mechanistically with simulations of patient pathophysiology across compartments and scales.
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Affiliation(s)
- B J Schmidt
- Infectious and Inflammatory Disease Center, Sanford-Burnham Medical Research Institute, La Jolla, California, USA
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31
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Kell DB, Goodacre R. Metabolomics and systems pharmacology: why and how to model the human metabolic network for drug discovery. Drug Discov Today 2014; 19:171-82. [PMID: 23892182 PMCID: PMC3989035 DOI: 10.1016/j.drudis.2013.07.014] [Citation(s) in RCA: 111] [Impact Index Per Article: 11.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2013] [Revised: 07/03/2013] [Accepted: 07/16/2013] [Indexed: 02/06/2023]
Abstract
Metabolism represents the 'sharp end' of systems biology, because changes in metabolite concentrations are necessarily amplified relative to changes in the transcriptome, proteome and enzyme activities, which can be modulated by drugs. To understand such behaviour, we therefore need (and increasingly have) reliable consensus (community) models of the human metabolic network that include the important transporters. Small molecule 'drug' transporters are in fact metabolite transporters, because drugs bear structural similarities to metabolites known from the network reconstructions and from measurements of the metabolome. Recon2 represents the present state-of-the-art human metabolic network reconstruction; it can predict inter alia: (i) the effects of inborn errors of metabolism; (ii) which metabolites are exometabolites, and (iii) how metabolism varies between tissues and cellular compartments. However, even these qualitative network models are not yet complete. As our understanding improves so do we recognise more clearly the need for a systems (poly)pharmacology.
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Affiliation(s)
- Douglas B Kell
- School of Chemistry and Manchester Institute of Biotechnology, The University of Manchester, 131 Princess Street, Manchester M1 7DN, UK.
| | - Royston Goodacre
- School of Chemistry and Manchester Institute of Biotechnology, The University of Manchester, 131 Princess Street, Manchester M1 7DN, UK
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32
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Leach AG. Predicting the activity and toxicity of new psychoactive substances: a pharmaceutical industry perspective. Drug Test Anal 2013; 6:739-45. [DOI: 10.1002/dta.1593] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2013] [Revised: 11/12/2013] [Accepted: 11/15/2013] [Indexed: 12/15/2022]
Affiliation(s)
- Andrew G. Leach
- Liverpool John Moores University; James Parsons Building, Byrom Street Liverpool L3 3AF UK
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33
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Singh R, Godfrey A, Gregertsen B, Muller F, Gernaey KV, Gani R, Woodley JM. Systematic substrate adoption methodology (SAM) for future flexible, generic pharmaceutical production processes. Comput Chem Eng 2013. [DOI: 10.1016/j.compchemeng.2013.07.010] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
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34
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Schmidt BJ, Ebrahim A, Metz TO, Adkins JN, Palsson BØ, Hyduke DR. GIM3E: condition-specific models of cellular metabolism developed from metabolomics and expression data. ACTA ACUST UNITED AC 2013; 29:2900-8. [PMID: 23975765 DOI: 10.1093/bioinformatics/btt493] [Citation(s) in RCA: 85] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
MOTIVATION Genome-scale metabolic models have been used extensively to investigate alterations in cellular metabolism. The accuracy of these models to represent cellular metabolism in specific conditions has been improved by constraining the model with omics data sources. However, few practical methods for integrating metabolomics data with other omics data sources into genome-scale models of metabolism have been developed. RESULTS GIM(3)E (Gene Inactivation Moderated by Metabolism, Metabolomics and Expression) is an algorithm that enables the development of condition-specific models based on an objective function, transcriptomics and cellular metabolomics data. GIM(3)E establishes metabolite use requirements with metabolomics data, uses model-paired transcriptomics data to find experimentally supported solutions and provides calculations of the turnover (production/consumption) flux of metabolites. GIM(3)E was used to investigate the effects of integrating additional omics datasets to create increasingly constrained solution spaces of Salmonella Typhimurium metabolism during growth in both rich and virulence media. This integration proved to be informative and resulted in a requirement of additional active reactions (12 in each case) or metabolites (26 or 29, respectively). The addition of constraints from transcriptomics also impacted the allowed solution space, and the cellular metabolites with turnover fluxes that were necessarily altered by the change in conditions increased from 118 to 271 of 1397. AVAILABILITY GIM(3)E has been implemented in Python and requires a COBRApy 0.2.x. The algorithm and sample data described here are freely available at: http://opencobra.sourceforge.net/ CONTACTS brianjamesschmidt@gmail.com
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Affiliation(s)
- Brian J Schmidt
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093-0412, USA and Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA
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35
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Schmidt BJ, Casey FP, Paterson T, Chan JR. Alternate virtual populations elucidate the type I interferon signature predictive of the response to rituximab in rheumatoid arthritis. BMC Bioinformatics 2013; 14:221. [PMID: 23841912 PMCID: PMC3717130 DOI: 10.1186/1471-2105-14-221] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2012] [Accepted: 07/05/2013] [Indexed: 02/08/2023] Open
Abstract
Background Mechanistic biosimulation can be used in drug development to form testable hypotheses, develop predictions of efficacy before clinical trial results are available, and elucidate clinical response to therapy. However, there is a lack of tools to simultaneously (1) calibrate the prevalence of mechanistically distinct, large sets of virtual patients so their simulated responses statistically match phenotypic variability reported in published clinical trial outcomes, and (2) explore alternate hypotheses of those prevalence weightings to reflect underlying uncertainty in population biology. Here, we report the development of an algorithm, MAPEL (Mechanistic Axes Population Ensemble Linkage), which utilizes a mechanistically-based weighting method to match clinical trial statistics. MAPEL is the first algorithm for developing weighted virtual populations based on biosimulation results that enables the rapid development of an ensemble of alternate virtual population hypotheses, each validated by a composite goodness-of-fit criterion. Results Virtual patient cohort mechanistic biosimulation results were successfully calibrated with an acceptable composite goodness-of-fit to clinical populations across multiple therapeutic interventions. The resulting virtual populations were employed to investigate the mechanistic underpinnings of variations in the response to rituximab. A comparison between virtual populations with a strong or weak American College of Rheumatology (ACR) score in response to rituximab suggested that interferon β (IFNβ) was an important mechanistic contributor to the disease state, a signature that has previously been identified though the underlying mechanisms remain unclear. Sensitivity analysis elucidated key anti-inflammatory properties of IFNβ that modulated the pathophysiologic state, consistent with the observed prognostic correlation of baseline type I interferon measurements with clinical response. Specifically, the effects of IFNβ on proliferation of fibroblast-like synoviocytes and interleukin-10 synthesis in macrophages each partially counteract reductions in synovial inflammation imparted by rituximab. A multianalyte biomarker panel predictive for virtual population therapeutic responses suggested population dependencies on B cell-dependent mediators as well as additional markers implicating fibroblast-like synoviocytes. Conclusions The results illustrate how the MAPEL algorithm can leverage knowledge of cellular and molecular function through biosimulation to propose clear mechanistic hypotheses for differences in clinical populations. Furthermore, MAPEL facilitates the development of multianalyte biomarkers prognostic of patient responses in silico.
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Affiliation(s)
- Brian J Schmidt
- Entelos Holding Corporation, 2121 South El Camino Real, Suite 600, San Mateo, CA 94403, USA
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Chaudhury S, Abdulhameed MDM, Singh N, Tawa GJ, D’haeseleer PM, Zemla AT, Navid A, Zhou CE, Franklin MC, Cheung J, Rudolph MJ, Love J, Graf JF, Rozak DA, Dankmeyer JL, Amemiya K, Daefler S, Wallqvist A. Rapid countermeasure discovery against Francisella tularensis based on a metabolic network reconstruction. PLoS One 2013; 8:e63369. [PMID: 23704901 PMCID: PMC3660459 DOI: 10.1371/journal.pone.0063369] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2013] [Accepted: 03/30/2013] [Indexed: 11/29/2022] Open
Abstract
In the future, we may be faced with the need to provide treatment for an emergent biological threat against which existing vaccines and drugs have limited efficacy or availability. To prepare for this eventuality, our objective was to use a metabolic network-based approach to rapidly identify potential drug targets and prospectively screen and validate novel small-molecule antimicrobials. Our target organism was the fully virulent Francisella tularensis subspecies tularensis Schu S4 strain, a highly infectious intracellular pathogen that is the causative agent of tularemia and is classified as a category A biological agent by the Centers for Disease Control and Prevention. We proceeded with a staggered computational and experimental workflow that used a strain-specific metabolic network model, homology modeling and X-ray crystallography of protein targets, and ligand- and structure-based drug design. Selected compounds were subsequently filtered based on physiological-based pharmacokinetic modeling, and we selected a final set of 40 compounds for experimental validation of antimicrobial activity. We began screening these compounds in whole bacterial cell-based assays in biosafety level 3 facilities in the 20th week of the study and completed the screens within 12 weeks. Six compounds showed significant growth inhibition of F. tularensis, and we determined their respective minimum inhibitory concentrations and mammalian cell cytotoxicities. The most promising compound had a low molecular weight, was non-toxic, and abolished bacterial growth at 13 µM, with putative activity against pantetheine-phosphate adenylyltransferase, an enzyme involved in the biosynthesis of coenzyme A, encoded by gene coaD. The novel antimicrobial compounds identified in this study serve as starting points for lead optimization, animal testing, and drug development against tularemia. Our integrated in silico/in vitro approach had an overall 15% success rate in terms of active versus tested compounds over an elapsed time period of 32 weeks, from pathogen strain identification to selection and validation of novel antimicrobial compounds.
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Affiliation(s)
- Sidhartha Chaudhury
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, Maryland, United States of America
| | - Mohamed Diwan M. Abdulhameed
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, Maryland, United States of America
| | - Narender Singh
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, Maryland, United States of America
| | - Gregory J. Tawa
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, Maryland, United States of America
| | - Patrik M. D’haeseleer
- Pathogen Bioinformatics, Lawrence Livermore National Laboratory, Livermore, California, United States of America
| | - Adam T. Zemla
- Pathogen Bioinformatics, Lawrence Livermore National Laboratory, Livermore, California, United States of America
| | - Ali Navid
- Pathogen Bioinformatics, Lawrence Livermore National Laboratory, Livermore, California, United States of America
| | - Carol E. Zhou
- Pathogen Bioinformatics, Lawrence Livermore National Laboratory, Livermore, California, United States of America
| | - Matthew C. Franklin
- New York Structural Biology Center, New York, New York, United States of America
| | - Jonah Cheung
- New York Structural Biology Center, New York, New York, United States of America
| | - Michael J. Rudolph
- New York Structural Biology Center, New York, New York, United States of America
| | - James Love
- New York Structural Biology Center, New York, New York, United States of America
| | - John F. Graf
- Computational Biology and Biostatistics Laboratory, Diagnostics and Biomedical Technologies, GE Global Research, General Electric Company, Niskayuna, New York, United States of America
| | - David A. Rozak
- Bacteriology Division, U.S. Army Medical Research Institute for Infectious Diseases, Fort Detrick, Maryland, United States of America
| | - Jennifer L. Dankmeyer
- Bacteriology Division, U.S. Army Medical Research Institute for Infectious Diseases, Fort Detrick, Maryland, United States of America
| | - Kei Amemiya
- Bacteriology Division, U.S. Army Medical Research Institute for Infectious Diseases, Fort Detrick, Maryland, United States of America
| | - Simon Daefler
- Mount Sinai School of Medicine, New York, New York, United States of America
| | - Anders Wallqvist
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, Maryland, United States of America
- * E-mail:
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Mardinoglu A, Gatto F, Nielsen J. Genome-scale modeling of human metabolism - a systems biology approach. Biotechnol J 2013; 8:985-96. [PMID: 23613448 DOI: 10.1002/biot.201200275] [Citation(s) in RCA: 82] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2012] [Revised: 01/10/2013] [Accepted: 02/14/2013] [Indexed: 12/21/2022]
Abstract
Altered metabolism is linked to the appearance of various human diseases and a better understanding of disease-associated metabolic changes may lead to the identification of novel prognostic biomarkers and the development of new therapies. Genome-scale metabolic models (GEMs) have been employed for studying human metabolism in a systematic manner, as well as for understanding complex human diseases. In the past decade, such metabolic models - one of the fundamental aspects of systems biology - have started contributing to the understanding of the mechanistic relationship between genotype and phenotype. In this review, we focus on the construction of the Human Metabolic Reaction database, the generation of healthy cell type- and cancer-specific GEMs using different procedures, and the potential applications of these developments in the study of human metabolism and in the identification of metabolic changes associated with various disorders. We further examine how in silico genome-scale reconstructions can be employed to simulate metabolic flux distributions and how high-throughput omics data can be analyzed in a context-dependent fashion. Insights yielded from this mechanistic modeling approach can be used for identifying new therapeutic agents and drug targets as well as for the discovery of novel biomarkers. Finally, recent advancements in genome-scale modeling and the future challenge of developing a model of whole-body metabolism are presented. The emergent contribution of GEMs to personalized and translational medicine is also discussed.
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Affiliation(s)
- Adil Mardinoglu
- Department of Chemical and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
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