Pharmacokinetic-Pharmacodynamic modelling of intracellular Mycobacterium tuberculosis growth and kill rates is predictive of clinical treatment duration

Aljayyoussi, Ghaith; Jenkins, Victoria A; Sharma, Raman; Ardrey, Alison; Donnellan, Samantha; Ward, Stephen A.; Biagini, Giancarlo A.

doi:10.1038/s41598-017-00529-6

Cited by 37 publications

(76 citation statements)

References 64 publications

(74 reference statements)

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“…For lung simulations, a correction for protein binding was not necessary as the epithelial lung fluid (ELF) albumin level is approximately 0.5mg/dL (35), which is similar to the albumin level in the in-vitro assays containing 10% Foetal Bovine Serum (FBS). PK-PD simulations relating total lung concentration to activity were previously successful in predicting the clinical activity of various anti-tubercular drugs without the need to correct for protein binding within the lung as the protein content was assumed to be similar here (36)(37)(38).…”

Section: Discussionmentioning

confidence: 99%

Modelling of Systemic versus Pulmonary Chloroquine Exposure in Man for COVID-19 Dose Selection

Aljayyoussi

Rajoli

Pertinez

et al. 2020

Preprint

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Chloroquine has attracted intense attention as a potential clinical candidate for prevention and treatment of COVID-19 based on reports of in-vitro efficacy against SARS-CoV-2. While the pharmacokinetic-pharmacodynamic (PK-PD) relationship of chloroquine is well established for malaria, there is sparse information regarding its dose-effect relationship in the context of COVID-19.Here, we explore the PK-PD relationship of chloroquine for COVID-19 by modelling both achievable systemic and pulmonary drug concentrations. Our data indicate that the standard anti-malarial treatment dose of 25mg/kg over three days does not deliver sufficient systemic drug exposures for the inhibition of viral replication. In contrast, PK predictions of chloroquine in the lungs using in-vivo data or human physiologicallybased PK models, suggest that doses as low as 3mg/kg/day for 3 days could deliver exposures that are significantly higher than reported antiviral-EC90s for up to a week. Moreover, if pulmonary exposure is a driver for prevention, simulations show that chronic daily dosing of chloroquine may be unnecessary for prophylaxis purposes. Instead, once weekly doses of 5mg/kg would be sufficient to achieve a continuous cover of therapeutically active pulmonary exposures.

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Section: Discussionmentioning

confidence: 99%

Modelling of Systemic versus Pulmonary Chloroquine Exposure in Man for COVID-19 Dose Selection

Aljayyoussi

Rajoli

Pertinez

et al. 2020

Preprint

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“…The decay rate of these drugs are calculated using the half life values of the drugs obtained from the literature and are also outlined in Table 2. The threshold drug concentrations, DrugKill f , DrugKill s and DrugKill Mi , below which the drug has no effect on the TB bacteria have been chosen to be the average density of total drugs delivered through the vessels (total drug delivered/total number of grid points) and the total drug given is kept the same for all drugs types. Values for DrugKill f , DrugKill s and DrugKill Mi are based on data arising from in vitro experiments and are reported in (Hammond et al, 2015;Aljayyoussi et al, 2017). They are given in Table 3.…”

Section: Parameter Estimationmentioning

confidence: 99%

“…Activated T cells are immune effector cells that can kill chronically infected macrophages. If a T cell encounters an infected or chronically infected macrophage, it kills the Drug needed to kill intracellular bacteria 20 (Aljayyoussi et al, 2017) macrophage (and all intracellular bacteria) with probability T kill and that grid cell becomes caseum. T cells also activate resting macrophages when they are in their Moore neighbourhood, with probability MrMa multiplied by the number of T cells in the neighbourhood.…”

Section: Rules For the T Cellsmentioning

confidence: 99%

Modelling the effects of bacterial cell state and spatial location on tuberculosis treatment: Insights from a hybrid multiscale cellular automaton model

Bowness

Chaplain

Powathil

et al. 2016

Preprint

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If improvements are to be made in tuberculosis (TB) treatment, an increased understanding of disease in the lung is needed. Studies have shown that bacteria in a less metabolically active state, associated with the presence of lipid bodies, are less susceptible to antibiotics, and recent results have highlighted the disparity in concentration of different compounds into lesions. Treatment success therefore depends critically on the responses of the individual bacteria that constitute the infection.We propose a hybrid, individual-based approach that analyses spatio-temporal dynamics at the cellular level, linking the behaviour of individual bacteria and host cells with the macroscopic behaviour of the microenvironment. The individual elements (bacteria, macrophages and T cells) are modelled using cellular automaton (CA) rules, and the evolution of oxygen, drugs and chemokine dynamics are incorporated in order to study the effects of the microenvironment in the pathological lesion. We allow bacteria to switch states depending on oxygen concentration, which affects how they respond to treatment. This is the first multiscale model of its type to consider both oxygen-driven phenotypic switching of the Mycobacterium tuberculosis and antibiotic treatment. Using this model, we investigate the role of bacterial cell state and of initial bacterial location on treatment outcome. We demonstrate that when bacteria are located further away from blood vessels, less favourable outcomes are more likely, i.e. longer time before infection is contained/cleared, treatment failure or later relapse. We also show that in cases where bacteria remain at the end of simulations, the organisms tend to be slower-growing and are often located within granulomas, surrounded by caseous material.

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“…A recent meta-analysis combining microarray and RNAseq data from studies aimed at identifying active TB transcriptional signatures, confirmed the findings about a specific set of peripheral blood transcripts that are biomarkers of active TB disease, relative to healthy individuals or those with latent TB infection (LTBI) 19 . Antibiotic treatment for active TB involves combination therapy with narrow spectrum and prodrug agents with mostly Mycobacterial-specific targets, (HRZE) is given for two months and is then followed by an HR-only administration for an additional four months, in order to achieve over 95% likelihood of Mtb clearance 20 . The disruptive effect of HRZE therapy on the intestinal microbiome was demonstrated in a longitudinal study in mice 21 and cross-sectional study in humans 22 , which indicated that the major phyla perturbed are from the class Clostridia, a group of obligate anaerobes in the gut with well described immunomodulatory effects on the host 2,3,23,24 .…”

Section: Introductionmentioning

confidence: 99%

Intestinal microbiome composition influences the peripheral inflammatory state during treatment of human tuberculosis

Wipperman¹,

Bhattarai²,

Vorkas³

et al. 2020

Preprint

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Although the composition of the intestinal microbiota influences systemic immune responses, the contribution of this relationship to infectious disease pathogenesis and to the resolution of infectious diseases by antibiotic therapy is poorly understood. This question is rarely examined in humans due to the difficulty in dissociating the immunologic effects of antibiotic-induced pathogen clearance and salutary microbiome alteration. To address these questions in the context of Tuberculosis, we analyzed three independent human datasets from Haiti (two longitudinal treatment and one cross-sectional control), a prototypical infection remarkable for chronic inflammation, in which we had measured sputum TB bacterial load, gut microbiota composition, and peripheral blood transcriptomics. Using data from the longitudinal datasets combined with inflammatory pathway enrichment analysis, we determined that antibiotic treatment of TB, despite significantly perturbing the gut microbiota, dampens the proinflammatory signature characteristic of active TB. Contrarily, an investigational TB treatment that failed to clear TB, but that caused similar microbiota perturbations, exacerbated peripheral inflammation. To decouple the effects of antibiotic induced changes in the microbiota from Mtb sterilization as predictors of normalization of TB associated inflammation, we applied random forest regression to the microbiome-transcriptome-sputum data from the two longitudinal datasets. We found inflammatory renormalization is positively affected by both pathogen sterilization and by the abundance of health-associated Cluster IV and Cluster XIa Clostridia. Oppositely, increases in the abundance of commonly known pathobionts such as Bacilli and Proteobacteria clusters predict inflammatory exacerbation. We independently investigated and validated these microbiota-peripheral inflammatory signature associations by applying machine learning to the peripheral gene expression and microbiota profiling in an independent human cohort of 52 healthy control individuals. Together, our findings indicate that antibiotic-induced reduction in pathogen burden and changes in the microbiome are independently associated with treatment-induced changes of the inflammatory response of active TB, and more broadly indicate that response to antibiotic therapy may be a combined effect of pathogen killing and microbiome driven immunomodulation. Our results provide support to the hypothesis that there exists clear links between microbiome composition and host peripheral gene expression in humans that can be biologically elucidated using common and well validated molecular pathway analyses.

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Pharmacokinetic-Pharmacodynamic modelling of intracellular Mycobacterium tuberculosis growth and kill rates is predictive of clinical treatment duration

Cited by 37 publications

References 64 publications

Modelling of Systemic versus Pulmonary Chloroquine Exposure in Man for COVID-19 Dose Selection

Modelling of Systemic versus Pulmonary Chloroquine Exposure in Man for COVID-19 Dose Selection

Modelling the effects of bacterial cell state and spatial location on tuberculosis treatment: Insights from a hybrid multiscale cellular automaton model

Intestinal microbiome composition influences the peripheral inflammatory state during treatment of human tuberculosis

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