Insight into treatment of<scp>HIV</scp>infection from viral dynamics models

Hill, Alison L.; Rosenbloom, Daniel I. S.; Nowak, Martin A.; Siliciano, Robert F.

doi:10.1111/imr.12698

Cited by 66 publications

(65 citation statements)

References 134 publications

(241 reference statements)

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“…However, in 20% of cases evolution toward more severe symptoms can take place leading to pneumonia or acute respiratory distress syndrome 4 . In other acute or chronic viral diseases (HIV, HCV, in uenza), the characterization of viral load kinetics has played an important role to understand the pathogenesis of the virus and design better antiviral drugs [5][6][7] . In the case of SARS-CoV-2, viral kinetics remain poorly characterized and its association with disease evolution is controversial.…”

Section: Introductionmentioning

confidence: 99%

Viral dynamic modeling of SARS-CoV-2 in non-human primates

Gonçalves¹,

Maisonnasse²,

Donati³

et al. 2020

Preprint

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Non-human primates infected with SARS-CoV-2 exhibit mild clinical signs. Here we used a mathematical model to characterize in detail the viral dynamics in 31 cynomolgus macaques infected with 106 pfu of SARS-CoV-2 for which nasopharyngeal and tracheal viral load were frequently assessed. We identified that infected cells had a large daily viral production (>104 virus) and a within-host reproductive basic number of 6 and 4 in nasopharyngeal and tracheal compartment, respectively. After peak viral load, infected cells were rapidly cleared with a half-life of 9 hours, with no significant association between cytokine elevation and clearance. Translating our model to the context of human-to-human infection, human mild infection may be characterized by a peak occurring 4 days after infection, a viral shedding of ~11 days and a generation time of 4 days. These results improve the understanding of SARS-CoV-2 viral replication and better understand the infection to SARS-CoV-2 in humans.

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Section: Introductionmentioning

confidence: 99%

Viral dynamic modeling of SARS-CoV-2 in non-human primates

Gonçalves¹,

Maisonnasse²,

Donati³

et al. 2020

Preprint

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“…4 Viral modeling in HIV and influenza. ( a ) Hill et al [ 73 ] described how an augmented viral dynamics model can be used to simulate antiretroviral therapy and the evolution of drug resistance in HIV. Uninfected cells ( U ) become infected ( I ) from infection by virus ( V ).…”

Section: Virusesmentioning

confidence: 99%

Leveraging Computational Modeling to Understand Infectious Diseases

et al. 2020

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Purpose of Review Computational and mathematical modeling have become a critical part of understanding in-host infectious disease dynamics and predicting effective treatments. In this review, we discuss recent findings pertaining to the biological mechanisms underlying infectious diseases, including etiology, pathogenesis, and the cellular interactions with infectious agents. We present advances in modeling techniques that have led to fundamental disease discoveries and impacted clinical translation. Recent Findings Combining mechanistic models and machine learning algorithms has led to improvements in the treatment of Shigella and tuberculosis through the development of novel compounds. Modeling of the epidemic dynamics of malaria at the within-host and between-host level has afforded the development of more effective vaccination and antimalarial therapies. Similarly, in-host and host-host models have supported the development of new HIV treatment modalities and an improved understanding of the immune involvement in influenza. In addition, large-scale transmission models of SARS-CoV-2 have furthered the understanding of coronavirus disease and allowed for rapid policy implementations on travel restrictions and contract tracing apps. Summary Computational modeling is now more than ever at the forefront of infectious disease research due to the COVID-19 pandemic. This review highlights how infectious diseases can be better understood by connecting scientists from medicine and molecular biology with those in computer science and applied mathematics.

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“…2.2, we expect that a small number of wild-type DNA gyrases should be enough for a bacterial cell to be sensitive, suggesting that phenotypic delay via gyrase dilution may be likely. Boe et al [47] report an unsatisfactory fit of their mutant number distribution data to the theoretical predictions of two different variants of the Luria-Delbrück model (the Lea-Coulson and Haldane models); in comparison to these models, Boe et al observed too many experiments yielding either no mutants or a high number of mutants (greater than 16), and a dearth of experiments resulting in intermediate mutant counts (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16). Qualitatively, this seems to be consistent with our expectations for the dilution model ( figure 4).…”

Section: Mutant Number Distributions May Support the Existence Of Phementioning

confidence: 99%

“…The emergence of resistance to drugs is a significant problem in the treatment of diseases such as cancer [1], and viral [2] and bacterial infections [3]. In infections with high pathogen load, the occurrence of de novo genetic mutations leading to resistance is a significant problem [4]; examples include endocarditis infections caused by Staphylococcus aureus [5,6], Pseudomonas aeruginosa infections of cystic fibrosis patients [7,8], as well as Burkholderia dolosa [9,4] infections.…”

Section: Introductionmentioning

confidence: 99%

Phenotypic delay in the evolution of bacterial antibiotic resistance: mechanistic models and their implications

Carballo-Pacheco

Nicholson

Lilja

et al. 2019

Preprint

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Phenotypic delay -the time delay between genetic mutation and expression of the corresponding phenotype -is generally neglected in evolutionary models, yet recent work suggests that it may be more common than previously assumed. Here, we use computer simulations and theory to investigate the significance of phenotypic delay for the evolution of bacterial resistance to antibiotics. We consider three mechanisms which could potentially cause phenotypic delay: effective polyploidy, dilution of antibiotic-sensitive molecules and accumulation of resistance-enhancing molecules. We find that the accumulation of resistant molecules is relevant only within a narrow parameter range, but both the dilution of sensitive molecules and effective polyploidy can cause phenotypic delay over a wide range of parameters. We further investigate whether these mechanisms could affect population survival under drug treatment and thereby explain observed discrepancies in mutation rates estimated by Luria-Delbrück fluctuation tests. While the effective polyploidy mechanism does not affect population survival, the dilution of sensitive molecules leads both to decreased probability of survival under drug treatment and underestimation of mutation rates in fluctuation tests. The dilution mechanism also changes the shape of the Luria-Delbrück distribution of mutant numbers, and we show that this modified distribution provides an improved fit to previously published experimental data. Author SummaryUnderstanding precisely how some bacteria survive exposure to antibiotics is a major research focus. Specific mutations in the bacterial genome are known to provide protection. However, it remains unclear how much time passes between a bacterium acquiring the genetic change and being able to tolerate antibiotics -termed the phenotypic delay -and what controls this delay. Here, using computer simulations and mathematical arguments we discuss three biologically plausible mechanisms of phenotypic delay. We investigate how each mechanism would affect the outcome of laboratory experiments often used to study the evolution of antibiotic resistance, and we highlight ¶These authors contributed equally to this work. *Corresponding authors: rosalind.allen@ed.ac.uk, bwaclaw@staffmail.ed.ac.uk 1 how the delay might be detected in such experiments. We also show that the existence of the delay could explain an observed discrepancy in the measurement of mutation rates, and demonstrate that one of our models provides a superior fit to experimental data. Our work exposes how molecular details at the intracellular level can have a direct effect on evolution at the population level.

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Insight into treatment ofHIVinfection from viral dynamics models

Cited by 66 publications

References 134 publications

Viral dynamic modeling of SARS-CoV-2 in non-human primates

Viral dynamic modeling of SARS-CoV-2 in non-human primates

Leveraging Computational Modeling to Understand Infectious Diseases

Phenotypic delay in the evolution of bacterial antibiotic resistance: mechanistic models and their implications

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