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Budak M, Via LE, Weiner DM, Barry CE, Nanda P, Michael G, Mdluli K, Kirschner D. A systematic efficacy analysis of tuberculosis treatment with BPaL-containing regimens using a multiscale modeling approach. CPT Pharmacometrics Syst Pharmacol 2024; 13:673-685. [PMID: 38404200 PMCID: PMC11015080 DOI: 10.1002/psp4.13117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 12/22/2023] [Accepted: 02/07/2024] [Indexed: 02/27/2024] Open
Abstract
Tuberculosis (TB) is a life-threatening infectious disease. The standard treatment is up to 90% effective; however, it requires the administration of four antibiotics (isoniazid, rifampicin, pyrazinamide, and ethambutol [HRZE]) over long time periods. This harsh treatment process causes adherence issues for patients because of the long treatment times and a myriad of adverse effects. Therefore, the World Health Organization has focused goals of shortening standard treatment regimens for TB in their End TB Strategy efforts, which aim to reduce TB-related deaths by 95% by 2035. For this purpose, many novel and promising combination antibiotics are being explored that have recently been discovered, such as the bedaquiline, pretomanid, and linezolid (BPaL) regimen. As a result, testing the number of possible combinations with all possible novel regimens is beyond the limit of experimental resources. In this study, we present a unique framework that uses a primate granuloma modeling approach to screen many combination regimens that are currently under clinical and experimental exploration and assesses their efficacies to inform future studies. We tested well-studied regimens such as HRZE and BPaL to evaluate the validity and accuracy of our framework. We also simulated additional promising combination regimens that have not been sufficiently studied clinically or experimentally, and we provide a pipeline for regimen ranking based on their efficacies in granulomas. Furthermore, we showed a correlation between simulation rankings and new marmoset data rankings, providing evidence for the credibility of our framework. This framework can be adapted to any TB regimen and can rank any number of single or combination regimens.
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Affiliation(s)
- Maral Budak
- Department of Microbiology and ImmunologyUniversity of Michigan Medical SchoolAnn ArborMichiganUSA
| | - Laura E. Via
- Tuberculosis Research Section, Laboratory of Clinical Immunology and MicrobiologyNational Institute of Allergy and Infectious Diseases (NIAID)BethesdaMarylandUSA
- Tuberculosis Imaging Program, Division of Intramural ResearchNIAIDBethesdaMarylandUSA
| | - Danielle M. Weiner
- Tuberculosis Research Section, Laboratory of Clinical Immunology and MicrobiologyNational Institute of Allergy and Infectious Diseases (NIAID)BethesdaMarylandUSA
- Tuberculosis Imaging Program, Division of Intramural ResearchNIAIDBethesdaMarylandUSA
| | - Clifton E. Barry
- Tuberculosis Research Section, Laboratory of Clinical Immunology and MicrobiologyNational Institute of Allergy and Infectious Diseases (NIAID)BethesdaMarylandUSA
- Centre for Infectious Diseases Research in AfricaInstitute of Infectious Disease and Molecular MedicineObservatoryRepublic of South Africa
- Department of MedicineUniversity of Cape TownObservatoryRepublic of South Africa
| | - Pariksheet Nanda
- Department of Microbiology and ImmunologyUniversity of Michigan Medical SchoolAnn ArborMichiganUSA
| | - Gabrielle Michael
- Molecular, Cellular and Developmental BiologyUniversity of MichiganAnn ArborMichiganUSA
| | - Khisimuzi Mdluli
- Bill & Melinda Gates Medical Research InstituteCambridgeMassachusettsUSA
| | - Denise Kirschner
- Department of Microbiology and ImmunologyUniversity of Michigan Medical SchoolAnn ArborMichiganUSA
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2
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Sambarey A, Smith K, Chung C, Arora HS, Yang Z, Agarwal PP, Chandrasekaran S. Integrative analysis of multimodal patient data identifies personalized predictors of tuberculosis treatment prognosis. iScience 2024; 27:109025. [PMID: 38357663 PMCID: PMC10865408 DOI: 10.1016/j.isci.2024.109025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 12/08/2023] [Accepted: 01/22/2024] [Indexed: 02/16/2024] Open
Abstract
Tuberculosis (TB) afflicted 10.6 million people in 2021, and its global burden is increasing due to multidrug-resistant TB (MDR-TB) and extensively resistant TB (XDR-TB). Here, we analyze multi-domain information from 5,060 TB patients spanning 10 countries with high burden of MDR-TB from the NIAID TB Portals database to determine predictors of TB treatment outcome. Our analysis revealed significant associations between radiological, microbiological, therapeutic, and demographic data modalities. Our machine learning model, built with 203 features across modalities outperforms models built using each modality alone in predicting treatment outcomes, with an accuracy of 83% and area under the curve of 0.84. Notably, our analysis revealed that the drug regimens Bedaquiline-Clofazimine-Cycloserine-Levofloxacin-Linezolid and Bedaquiline-Clofazimine-Linezolid-Moxifloxacin were associated with treatment success and failure, respectively, for MDR non-XDR-TB. Drug combinations predicted to be synergistic by the INDIGO algorithm performed better than antagonistic combinations. Our prioritized set of features predictive of treatment outcomes can ultimately guide the personalized clinical management of TB.
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Affiliation(s)
- Awanti Sambarey
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
| | - Kirk Smith
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
| | - Carolina Chung
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
| | - Harkirat Singh Arora
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
| | - Zhenhua Yang
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
| | - Prachi P. Agarwal
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Sriram Chandrasekaran
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
- Program in Chemical Biology, University of Michigan, Ann Arbor, MI 48109, USA
- Center for Bioinformatics and Computational Medicine, Ann Arbor, MI 48109, USA
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Nanda P, Budak M, Michael CT, Krupinsky K, Kirschner DE. Development and Analysis of Multiscale Models for Tuberculosis: From Molecules to Populations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.13.566861. [PMID: 38014103 PMCID: PMC10680629 DOI: 10.1101/2023.11.13.566861] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Although infectious disease dynamics are often analyzed at the macro-scale, increasing numbers of drug-resistant infections highlight the importance of within-host modeling that simultaneously solves across multiple scales to effectively respond to epidemics. We review multiscale modeling approaches for complex, interconnected biological systems and discuss critical steps involved in building, analyzing, and applying such models within the discipline of model credibility. We also present our two tools: CaliPro, for calibrating multiscale models (MSMs) to datasets, and tunable resolution, for fine- and coarse-graining sub-models while retaining insights. We include as an example our work simulating infection with Mycobacterium tuberculosis to demonstrate modeling choices and how predictions are made to generate new insights and test interventions. We discuss some of the current challenges of incorporating novel datasets, rigorously training computational biologists, and increasing the reach of MSMs. We also offer several promising future research directions of incorporating within-host dynamics into applications ranging from combinatorial treatment to epidemic response.
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Budak M, Cicchese JM, Maiello P, Borish HJ, White AG, Chishti HB, Tomko J, Frye LJ, Fillmore D, Kracinovsky K, Sakal J, Scanga CA, Lin PL, Dartois V, Linderman JJ, Flynn JL, Kirschner DE. Optimizing tuberculosis treatment efficacy: Comparing the standard regimen with Moxifloxacin-containing regimens. PLoS Comput Biol 2023; 19:e1010823. [PMID: 37319311 PMCID: PMC10306236 DOI: 10.1371/journal.pcbi.1010823] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 06/28/2023] [Accepted: 05/17/2023] [Indexed: 06/17/2023] Open
Abstract
Tuberculosis (TB) continues to be one of the deadliest infectious diseases in the world, causing ~1.5 million deaths every year. The World Health Organization initiated an End TB Strategy that aims to reduce TB-related deaths in 2035 by 95%. Recent research goals have focused on discovering more effective and more patient-friendly antibiotic drug regimens to increase patient compliance and decrease emergence of resistant TB. Moxifloxacin is one promising antibiotic that may improve the current standard regimen by shortening treatment time. Clinical trials and in vivo mouse studies suggest that regimens containing moxifloxacin have better bactericidal activity. However, testing every possible combination regimen with moxifloxacin either in vivo or clinically is not feasible due to experimental and clinical limitations. To identify better regimens more systematically, we simulated pharmacokinetics/pharmacodynamics of various regimens (with and without moxifloxacin) to evaluate efficacies, and then compared our predictions to both clinical trials and nonhuman primate studies performed herein. We used GranSim, our well-established hybrid agent-based model that simulates granuloma formation and antibiotic treatment, for this task. In addition, we established a multiple-objective optimization pipeline using GranSim to discover optimized regimens based on treatment objectives of interest, i.e., minimizing total drug dosage and lowering time needed to sterilize granulomas. Our approach can efficiently test many regimens and successfully identify optimal regimens to inform pre-clinical studies or clinical trials and ultimately accelerate the TB regimen discovery process.
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Affiliation(s)
- Maral Budak
- Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, Michigan, United States of America
| | - Joseph M. Cicchese
- Department of Chemical Engineering, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Pauline Maiello
- Department of Microbiology and Molecular Genetics and Center for Vaccine Research, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States of America
| | - H. Jacob Borish
- Department of Microbiology and Molecular Genetics and Center for Vaccine Research, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States of America
| | - Alexander G. White
- Department of Microbiology and Molecular Genetics and Center for Vaccine Research, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States of America
| | - Harris B. Chishti
- Department of Microbiology and Molecular Genetics and Center for Vaccine Research, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States of America
| | - Jaime Tomko
- Department of Microbiology and Molecular Genetics and Center for Vaccine Research, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States of America
| | - L. James Frye
- Department of Microbiology and Molecular Genetics and Center for Vaccine Research, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States of America
| | - Daniel Fillmore
- Department of Microbiology and Molecular Genetics and Center for Vaccine Research, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States of America
| | - Kara Kracinovsky
- Department of Microbiology and Molecular Genetics and Center for Vaccine Research, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States of America
| | - Jennifer Sakal
- Department of Microbiology and Molecular Genetics and Center for Vaccine Research, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States of America
| | - Charles A. Scanga
- Department of Microbiology and Molecular Genetics and Center for Vaccine Research, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States of America
| | - Philana Ling Lin
- Department of Microbiology and Molecular Genetics and Center for Vaccine Research, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States of America
| | - Véronique Dartois
- Center for Discovery and Innovation, Hackensack Meridian Health, Nutley, New Jersey, United States of America
- Department of Medical Sciences, Hackensack Meridian School of Medicine, Nutley, New Jersey, United States of America
| | - Jennifer J. Linderman
- Department of Chemical Engineering, University of Michigan, Ann Arbor, Michigan, United States of America
| | - JoAnne L. Flynn
- Department of Microbiology and Molecular Genetics and Center for Vaccine Research, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States of America
| | - Denise E. Kirschner
- Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, Michigan, United States of America
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5
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Greenstein T, Aldridge BB. Tools to develop antibiotic combinations that target drug tolerance in Mycobacterium tuberculosis. Front Cell Infect Microbiol 2023; 12:1085946. [PMID: 36733851 PMCID: PMC9888313 DOI: 10.3389/fcimb.2022.1085946] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 12/20/2022] [Indexed: 01/08/2023] Open
Abstract
Combination therapy is necessary to treat tuberculosis to decrease the rate of disease relapse and prevent the acquisition of drug resistance, and shorter regimens are urgently needed. The adaptation of Mycobacterium tuberculosis to various lesion microenvironments in infection induces various states of slow replication and non-replication and subsequent antibiotic tolerance. This non-heritable tolerance to treatment necessitates lengthy combination therapy. Therefore, it is critical to develop combination therapies that specifically target the different types of drug-tolerant cells in infection. As new tools to study drug combinations earlier in the drug development pipeline are being actively developed, we must consider how to best model the drug-tolerant cells to use these tools to design the best antibiotic combinations that target those cells and shorten tuberculosis therapy. In this review, we discuss the factors underlying types of drug tolerance, how combination therapy targets these populations of bacteria, and how drug tolerance is currently modeled for the development of tuberculosis multidrug therapy. We highlight areas for future studies to develop new tools that better model drug tolerance in tuberculosis infection specifically for combination therapy testing to bring the best drug regimens forward to the clinic.
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Affiliation(s)
- Talia Greenstein
- Department of Molecular Biology and Microbiology, Tufts University School of Medicine, Boston, MA, United States
- Graduate School of Biomedical Sciences, Tufts University School of Medicine, Boston, MA, United States
| | - Bree B Aldridge
- Department of Molecular Biology and Microbiology, Tufts University School of Medicine, Boston, MA, United States
- Graduate School of Biomedical Sciences, Tufts University School of Medicine, Boston, MA, United States
- Stuart B. Levy Center for Integrated Management of Antimicrobial Resistance, Boston, MA, United States
- Department of Biomedical Engineering, Tufts University School of Engineering, Medford, MA, United States
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Larkins-Ford J, Aldridge BB. Advances in the design of combination therapies for the treatment of tuberculosis. Expert Opin Drug Discov 2023; 18:83-97. [PMID: 36538813 PMCID: PMC9892364 DOI: 10.1080/17460441.2023.2157811] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 12/08/2022] [Indexed: 12/24/2022]
Abstract
INTRODUCTION Tuberculosis requires lengthy multi-drug therapy. Mycobacterium tuberculosis occupies different tissue compartments during infection, making drug access and susceptibility patterns variable. Antibiotic combinations are needed to ensure each compartment of infection is reached with effective drug treatment. Despite drug combinations' role in treating tuberculosis, the design of such combinations has been tackled relatively late in the drug development process, limiting the number of drug combinations tested. In recent years, there has been significant progress using in vitro, in vivo, and computational methodologies to interrogate combination drug effects. AREAS COVERED This review discusses the advances in these methodologies and how they may be used in conjunction with new successful clinical trials of novel drug combinations to design optimized combination therapies for tuberculosis. Literature searches for approaches and experimental models used to evaluate drug combination effects were undertaken. EXPERT OPINION We are entering an era richer in combination drug effect and pharmacokinetic/pharmacodynamic data, genetic tools, and outcome measurement types. Application of computational modeling approaches that integrate these data and produce predictive models of clinical outcomes may enable the field to generate novel, effective multidrug therapies using existing and new drug combination backbones.
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Affiliation(s)
- Jonah Larkins-Ford
- Department of Molecular Biology and Microbiology and Tufts University School of Graduate Biomedical Sciences, Tufts University School of Medicine, Boston, MA, USA
- Stuart B. Levy Center for Integrated Management of Antimicrobial Resistance (CIMAR), Tufts University, Boston, MA, USA
- Current address: MarvelBiome Inc, Woburn, MA, USA
| | - Bree B. Aldridge
- Department of Molecular Biology and Microbiology and Tufts University School of Graduate Biomedical Sciences, Tufts University School of Medicine, Boston, MA, USA
- Stuart B. Levy Center for Integrated Management of Antimicrobial Resistance (CIMAR), Tufts University, Boston, MA, USA
- Department of Biomedical Engineering, Tufts University School of Engineering, Medford, MA, USA
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7
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Curreli C, Di Salvatore V, Russo G, Pappalardo F, Viceconti M. A Credibility Assessment Plan for an In Silico Model that Predicts the Dose-Response Relationship of New Tuberculosis Treatments. Ann Biomed Eng 2023; 51:200-210. [PMID: 36115895 PMCID: PMC9483464 DOI: 10.1007/s10439-022-03078-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Accepted: 09/06/2022] [Indexed: 01/13/2023]
Abstract
Tuberculosis is one of the leading causes of death in several developing countries and a public health emergency of international concern. In Silico Trials can be used to support innovation in the context of drug development reducing the duration and the cost of the clinical experimentations, a particularly desirable goal for diseases such as tuberculosis. The agent-based Universal Immune System Simulator was used to develop an In Silico Trials environment that can predict the dose-response of new therapeutic vaccines against pulmonary tuberculosis, supporting the optimal design of clinical trials. But before such in silico methodology can be used in the evaluation of new treatments, it is mandatory to assess the credibility of this predictive model. This study presents a risk-informed credibility assessment plan inspired by the ASME V&V 40-2018 technical standard. Based on the selected context of use and regulatory impact of the technology, a detailed risk analysis is described together with the definition of all the verification and validation activities and related acceptability criteria. The work provides an example of the first steps required for the regulatory evaluation of an agent-based model used in the context of drug development.
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Affiliation(s)
- Cristina Curreli
- Department of Industrial Engineering, Alma Mater Studiorum - University of Bologna, Bologna, Italy.
- Medical Technology Lab, IRCCS Istituto Ortopedico Rizzoli, Via di Barbiano 1/10, 40136, Bologna, Italy.
| | | | - Giulia Russo
- Department of Drug and Health Sciences, University of Catania, Catania, Italy
- Mimesis srl, Catania, Italy
| | | | - Marco Viceconti
- Department of Industrial Engineering, Alma Mater Studiorum - University of Bologna, Bologna, Italy
- Medical Technology Lab, IRCCS Istituto Ortopedico Rizzoli, Via di Barbiano 1/10, 40136, Bologna, Italy
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Sambarey A, Smith K, Chung C, Arora HS, Yang Z, Agarwal P, Chandrasekaran S. Integrative analysis of clinical health records, imaging and pathogen genomics identifies personalized predictors of disease prognosis in tuberculosis. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2022:2022.07.20.22277862. [PMID: 35898335 PMCID: PMC9327630 DOI: 10.1101/2022.07.20.22277862] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Tuberculosis (TB) afflicts over 10 million people every year and its global burden is projected to increase dramatically due to multidrug-resistant TB (MDR-TB). The Covid-19 pandemic has resulted in reduced access to TB diagnosis and treatment, reversing decades of progress in disease management globally. It is thus crucial to analyze real-world multi-domain information from patient health records to determine personalized predictors of TB treatment outcome and drug resistance. We conduct a retrospective analysis on electronic health records of 5060 TB patients spanning 10 countries with high burden of MDR-TB including Ukraine, Moldova, Belarus and India available on the NIAID-TB portals database. We analyze over 200 features across multiple host and pathogen modalities representing patient social demographics, disease presentations as seen in cChest X rays and CT scans, and genomic records with drug susceptibility features of the pathogen strain from each patient. Our machine learning model, built with diverse data modalities outperforms models built using each modality alone in predicting treatment outcomes, with an accuracy of 81% and AUC of 0.768. We determine robust predictors across countries that are associated with unsuccessful treatmentclinical outcomes, and validate our predictions on new patient data from TB Portals. Our analysis of drug regimens and drug interactions suggests that synergistic drug combinations and those containing the drugs Bedaquiline, Levofloxacin, Clofazimine and Amoxicillin see more success in treating MDR and XDR TB. Features identified via chest imaging such as percentage of abnormal volume, size of lung cavitation and bronchial obstruction are associated significantly with pathogen genomic attributes of drug resistance. Increased disease severity was also observed in patients with lower BMI and with comorbidities. Our integrated multi-modal analysis thus revealed significant associations between radiological, microbiological, therapeutic, and demographic data modalities, providing a deeper understanding of personalized responses to aid in the clinical management of TB.
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Affiliation(s)
| | - Kirk Smith
- Chemical BIology, University of Michigan
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9
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Chung CH, Chandrasekaran S. A flux-based machine learning model to simulate the impact of pathogen metabolic heterogeneity on drug interactions. PNAS NEXUS 2022; 1:pgac132. [PMID: 36016709 PMCID: PMC9396445 DOI: 10.1093/pnasnexus/pgac132] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Accepted: 07/19/2022] [Indexed: 02/06/2023]
Abstract
Drug combinations are a promising strategy to counter antibiotic resistance. However, current experimental and computational approaches do not account for the entire complexity involved in combination therapy design, such as the effect of pathogen metabolic heterogeneity, changes in the growth environment, drug treatment order, and time interval. To address these limitations, we present a comprehensive approach that uses genome-scale metabolic modeling and machine learning to guide combination therapy design. Our mechanistic approach (a) accommodates diverse data types, (b) accounts for time- and order-specific interactions, and (c) accurately predicts drug interactions in various growth conditions and their robustness to pathogen metabolic heterogeneity. Our approach achieved high accuracy (area under the receiver operating curve (AUROC) = 0.83 for synergy, AUROC = 0.98 for antagonism) in predicting drug interactions for Escherichia coli cultured in 57 metabolic conditions based on experimental validation. The entropy in bacterial metabolic response was predictive of combination therapy outcomes across time scales and growth conditions. Simulation of metabolic heterogeneity using population FBA identified two subpopulations of E. coli cells defined by the levels of three proteins (eno, fadB, and fabD) in glycolysis and lipid metabolism that influence cell tolerance to a broad range of antibiotic combinations. Analysis of the vast landscape of condition-specific drug interactions revealed a set of 24 robustly synergistic drug combinations with potential for clinical use.
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Affiliation(s)
- Carolina H Chung
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
| | - Sriram Chandrasekaran
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
- Program in Chemical Biology, University of Michigan, Ann Arbor, MI 48109, USA
- Center for Bioinformatics and Computational Medicine, Ann Arbor, MI 48109, USA
- Rogel Cancer Center, University of Michigan Medical School, Ann Arbor, MI 48109, USA
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10
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Cantrell JM, Chung CH, Chandrasekaran S. Machine learning to design antimicrobial combination therapies: promises and pitfalls. Drug Discov Today 2022; 27:1639-1651. [DOI: 10.1016/j.drudis.2022.04.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 01/20/2022] [Accepted: 04/04/2022] [Indexed: 01/13/2023]
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Advancing therapies for viral infections using mechanistic computational models of the dynamic interplay between the virus and host immune response. Curr Opin Virol 2021; 50:103-109. [PMID: 34450519 PMCID: PMC8384423 DOI: 10.1016/j.coviro.2021.07.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 07/20/2021] [Accepted: 07/22/2021] [Indexed: 12/17/2022]
Abstract
The COVID-19 pandemic has highlighted a need for improved frameworks for drug discovery, repurposing, clinical trial design and therapy optimization and personalization. Mechanistic computational models can play an important role in developing these frameworks. We discuss how mechanistic models, which consider viral entry, replication in target cells, viral spread in the body, immune response, and the complex factors involved in tissue and organ damage and recovery, can clarify the mechanisms of humoral and cellular immune responses to the virus, viral distribution and replication in tissues, the origins of pathogenesis and patient-to-patient heterogeneity in responses. These models are already improving our understanding of the mechanisms of action of antivirals and immune modulators. We discuss how closer collaboration between the experimentalists, clinicians and modelers could result in more predictive models which may guide therapies for viral infections, improving survival and leading to faster and more complete recovery.
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12
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Chandrasekaran S, Danos N, George UZ, Han JP, Quon G, Müller R, Tsang Y, Wolgemuth C. The Axes of Life: A roadmap for understanding dynamic multiscale systems. Integr Comp Biol 2021; 61:2011-2019. [PMID: 34048574 DOI: 10.1093/icb/icab114] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
The biological challenges facing humanity are complex, multi-factorial, and are intimately tied to the future of our health, welfare, and stewardship of the Earth. Tackling problems in diverse areas, such as agriculture, ecology, and health care require linking vast data sets that encompass numerous components and spatio-temporal scales. Here, we provide a new framework and a road map for using experiments and computation to understand dynamic biological systems that span multiple scales. We discuss theories that can help understand complex biological systems and highlight the limitations of existing methodologies and recommend data generation practices. The advent of new technologies such as big data analytics and artificial intelligence can help bridge different scales and data types. We recommend ways to make such models transparent, compatible with existing theories of biological function, and to make biological data sets readable by advanced machine learning algorithms. Overall, the barriers for tackling pressing biological challenges are not only technological, but also sociological. Hence, we also provide recommendations for promoting interdisciplinary interactions between scientists.
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Affiliation(s)
| | - Nicole Danos
- Department of Biology, University of San Diego, San Diego, CA, USA
| | - Uduak Z George
- Department of Mathematics & Statistics, San Diego State University, San Diego, CA, USA
| | - Jin-Ping Han
- IBM TJ Watson Research Center, Ossining, NY, USA
| | - Gerald Quon
- Department of Molecular and Cellular Biology, University of California-Davis, Davis, CA,USA
| | - Rolf Müller
- Department of Mechanical Engineering, Virginia Tech, Blacksburg, VI, USA
| | - Yinphan Tsang
- Department of Natural Resources and Environmental Management, University of Hawai'i at Mānoa, Honolulu, HI, USA
| | - Charles Wolgemuth
- Departments of Physics and Molecular and Cellular Biology, University of Arizona, Tucson, AZ, USA
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