Four compartment mammillary model applied to the pharmacokinetics of a spiroarsorane administered orally to rabbits

Biasi, J. De; Rekik, L.

doi:10.1016/0141-5425(91)90028-6

Cited by 2 publications

(2 citation statements)

References 2 publications

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“…Five first-order homogenous ordinary differential equations (ODEs) were used as a model representation of the physiological features of the system. The system of equations was based on previously published pharmacokinetic models[ 18 , 46 ]. Each autonomous ODE represented the instantaneous rate of change of bacteria concentration in the respective compartment, where L , S , H , E , and B were the bacterial concentrations (CFU mL -1 ), in the lungs, spleen, liver, extracorporeal bacterial separation device, and blood, respectively.…”

Section: Methodsmentioning

confidence: 99%

Dynamic Computational Model of Symptomatic Bacteremia to Inform Bacterial Separation Treatment Requirements

et al. 2016

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The rise of multi-drug resistance has decreased the effectiveness of antibiotics, which has led to increased mortality rates associated with symptomatic bacteremia, or bacterial sepsis. To combat decreasing antibiotic effectiveness, extracorporeal bacterial separation approaches have been proposed to capture and separate bacteria from blood. However, bacteremia is dynamic and involves host-pathogen interactions across various anatomical sites. We developed a mathematical model that quantitatively describes the kinetics of pathogenesis and progression of symptomatic bacteremia under various conditions, including bacterial separation therapy, to better understand disease mechanisms and quantitatively assess the biological impact of bacterial separation therapy. Model validity was tested against experimental data from published studies. This is the first multi-compartment model of symptomatic bacteremia in mammals that includes extracorporeal bacterial separation and antibiotic treatment, separately and in combination. The addition of an extracorporeal bacterial separation circuit reduced the predicted time of total bacteria clearance from the blood of an immunocompromised rodent by 49%, compared to antibiotic treatment alone. Implementation of bacterial separation therapy resulted in predicted multi-drug resistant bacterial clearance from the blood of a human in 97% less time than antibiotic treatment alone. The model also proposes a quantitative correlation between time-dependent bacterial load among tissues and bacteremia severity, analogous to the well-known ‘area under the curve’ for characterization of drug efficacy. The engineering-based mathematical model developed may be useful for informing the design of extracorporeal bacterial separation devices. This work enables the quantitative identification of the characteristics required of an extracorporeal bacteria separation device to provide biological benefit. These devices will potentially decrease the bacterial load in blood. Additionally, the devices may achieve bacterial separation rates that allow consequent acceleration of bacterial clearance in other tissues, inhibiting the progression of symptomatic bacteremia, including multi-drug resistant variations.

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Section: Methodsmentioning

confidence: 99%

Dynamic Computational Model of Symptomatic Bacteremia to Inform Bacterial Separation Treatment Requirements

et al. 2016

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“…The most used PK model is the compartmentally based, which represents a very simple and useful tool in PK. In this model type, tissues are grouped into compartments, depending on their blood flow and drug binding (tissues with similar blood flow and drug tissue binding are grouped in the same compartment) [ 3 , 4 , 5 , 7 , 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

New In Vitro-In Silico Approach for the Prediction of In Vivo Performance of Drug Combinations

et al. 2021

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Pharmacokinetic (PK) studies improve the design of dosing regimens in preclinical and clinical settings. In complex diseases like cancer, single-agent approaches are often insufficient for an effective treatment, and drug combination therapies can be implemented. In this work, in silico PK models were developed based on in vitro assays results, with the goal of predicting the in vivo performance of drug combinations in the context of cancer therapy. Combinations of reference drugs for cancer treatment, gemcitabine and 5-fluorouracil (5-FU), and repurposed drugs itraconazole, verapamil or tacrine, were evaluated in vitro. Then, two-compartment PK models were developed based on the previous in vitro studies and on the PK profile reported in the literature for human patients. Considering the quantification parameter area under the dose-response-time curve (AUCeffect) for the combinations effect, itraconazole was the most effective in combination with either reference anticancer drugs. In addition, cell growth inhibition was itraconazole-dose dependent and an increase in effect was predicted if itraconazole administration was continued (24-h dosing interval). This work demonstrates that in silico methods and AUCeffect are powerful tools to study relationships between tissue drug concentration and the percentage of cell growth inhibition over time.

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Four compartment mammillary model applied to the pharmacokinetics of a spiroarsorane administered orally to rabbits

Cited by 2 publications

References 2 publications

Dynamic Computational Model of Symptomatic Bacteremia to Inform Bacterial Separation Treatment Requirements

Dynamic Computational Model of Symptomatic Bacteremia to Inform Bacterial Separation Treatment Requirements

New In Vitro-In Silico Approach for the Prediction of In Vivo Performance of Drug Combinations

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