Computational pharmacokinetics/pharmacodynamics of rifampin in a mouse tuberculosis infection model

Lyons, Michael A.; Lenaerts, Anne J.

doi:10.1007/s10928-015-9419-z

Cited by 12 publications

(9 citation statements)

References 57 publications

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“…Most often the efficacy of antibacterials is estimated based on the data from in vitro time kill curve experiments and/or in vivo studies using neutropenic mice, thus ignoring the role of neutrophils and other immune cells. Recently, Lyons et al described a physiologically based PKPD model of rifampin therapy in a mouse tuberculosis infection model also accounting for dynamics for host immune response to Mycobacterium tuberculosis infection [49]. In this work we wanted to also illustrate how this type of framework can be used to explore the relative response of the immune system and drug treatment.…”

Section: Discussionmentioning

confidence: 99%

A whole-body physiologically based pharmacokinetic (WB-PBPK) model of ciprofloxacin: a step towards predicting bacterial killing at sites of infection

Sadiq

Nielsen

Khachman

et al. 2016

J Pharmacokinet Pharmacodyn

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The purpose of this study was to develop a whole-body physiologically based pharmacokinetic (WB-PBPK) model for ciprofloxacin for ICU patients, based on only plasma concentration data. In a next step, tissue and organ concentration time profiles in patients were predicted using the developed model. The WB-PBPK model was built using a non-linear mixed effects approach based on data from 102 adult intensive care unit patients. Tissue to plasma distribution coefficients (Kp) were available from the literature and used as informative priors. The developed WB-PBPK model successfully characterized both the typical trends and variability of the available ciprofloxacin plasma concentration data. The WB-PBPK model was thereafter combined with a pharmacokinetic–pharmacodynamic (PKPD) model, developed based on in vitro time-kill data of ciprofloxacin and Escherichia coli to illustrate the potential of this type of approach to predict the time-course of bacterial killing at different sites of infection. The predicted unbound concentration–time profile in extracellular tissue was driving the bacterial killing in the PKPD model and the rate and extent of take-over of mutant bacteria in different tissues were explored. The bacterial killing was predicted to be most efficient in lung and kidney, which correspond well to ciprofloxacin’s indications pneumonia and urinary tract infections. Furthermore, a function based on available information on bacterial killing by the immune system in vivo was incorporated. This work demonstrates the development and application of a WB-PBPK–PD model to compare killing of bacteria with different antibiotic susceptibility, of value for drug development and the optimal use of antibiotics .Electronic supplementary materialThe online version of this article (doi:10.1007/s10928-016-9486-9) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

A whole-body physiologically based pharmacokinetic (WB-PBPK) model of ciprofloxacin: a step towards predicting bacterial killing at sites of infection

Sadiq

Nielsen

Khachman

et al. 2016

J Pharmacokinet Pharmacodyn

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“…To the encouragement of the QSP community, QSP modeling aided in studying the dosing regimens of a new biologic, NATPARA, in the regulatory domain [ 31 ]. Particularly, QSP modeling has been useful in aiding the treatment of infectious diseases, such as tuberculosis, where it has been used for dose optimization of anti-Tuberculosis drugs [ 28 – 30 ]. Moreover, QSP models have shown great promise as powerful quantitative tools to study the dosing regimen for novel pharmaceutical compounds [ 31 ].…”

Section: Discussionmentioning

confidence: 99%

“…By combining traditional PK and PD analysis with systems biology modeling, QSP can summarize available information into a convenient framework, which can then be used to rigorously test different hypotheses, and scan through treatment regimens in an efficient and cost-effective manner [ 26 , 27 ]. QSP modeling has been useful in the treatment of infectious diseases, such as Tuberculosis, where it has been used for dose optimization of anti-Tuberculosis drugs [ 28 – 30 ]. In addition, QSP models have shown great promise as powerful quantitative tools to study the dosing regimens for novel compounds [ 31 ].…”

Section: Introductionmentioning

confidence: 99%

A quantitative systems pharmacology (QSP) model for Pneumocystis treatment in mice

et al. 2018

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BackgroundThe yeast-like fungi Pneumocystis, resides in lung alveoli and can cause a lethal infection known as Pneumocystis pneumonia (PCP) in hosts with impaired immune systems. Current therapies for PCP, such as trimethoprim-sulfamethoxazole (TMP-SMX), suffer from significant treatment failures and a multitude of serious side effects. Novel therapeutic approaches (i.e. newly developed drugs or novel combinations of available drugs) are needed to treat this potentially lethal opportunistic infection. Quantitative Systems Pharmacological (QSP) models promise to aid in the development of novel therapies by integrating available pharmacokinetic (PK) and pharmacodynamic (PD) knowledge to predict the effects of new treatment regimens.ResultsIn this work, we constructed and independently validated PK modules of a number of drugs with available pharmacokinetic data. Characterized by simple structures and well constrained parameters, these PK modules could serve as a convenient tool to summarize and predict pharmacokinetic profiles. With the currently accepted hypotheses on the life stages of Pneumocystis, we also constructed a PD module to describe the proliferation, transformation, and death of Pneumocystis. By integrating the PK module and the PD module, the QSP model was constrained with observed levels of asci and trophic forms following treatments with multiple drugs. Furthermore, the temporal dynamics of the QSP model were validated with corresponding data.ConclusionsWe developed and validated a QSP model that integrates available data and promises to facilitate the design of future therapies against PCP.

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“…Using the present model as a foundation, efforts are under way to add additional anti-TB agents (e.g., isoniazid or bedaquiline) to simulate combination therapies and quantify pharmacokinetic drug-drug interactions. Other enhancements include integration of pharmacodynamic descriptions that include M. tuberculosis growth and drug-induced killing kinetics (43,44) and descriptions of RPT-induced hepatotoxicity (5,42).…”

Section: Methodsmentioning

confidence: 99%

Physiologically Based Pharmacokinetic Model of Rifapentine and 25-Desacetyl Rifapentine Disposition in Humans

Zurlinden

Eppers

Reisfeld

2016

Antimicrob Agents Chemother

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b Rifapentine (RPT) is a rifamycin antimycobacterial and, as part of a combination therapy, is indicated for the treatment of pulmonary tuberculosis (TB) caused by Mycobacterium tuberculosis. Although the results from a number of studies indicate that rifapentine has the potential to shorten treatment duration and enhance completion rates compared to other rifamycin agents utilized in antituberculosis drug regimens (i.e., regimens 1 to 4), its optimal dose and exposure in humans are unknown. To help inform such an optimization, a physiologically based pharmacokinetic (PBPK) model was developed to predict time course, tissue-specific concentrations of RPT and its active metabolite, 25-desacetyl rifapentine (dRPT), in humans after specified administration schedules for RPT. Starting with the development and verification of a PBPK model for rats, the model was extrapolated and then tested using human pharmacokinetic data. Testing and verification of the models included comparisons of predictions to experimental data in several rat tissues and time course RPT and dRPT plasma concentrations in humans from several singleand repeated-dosing studies. Finally, the model was used to predict RPT concentrations in the lung during the intensive and continuation phases of a current recommended TB treatment regimen. Based on these results, it is anticipated that the PBPK model developed in this study will be useful in evaluating dosing regimens for RPT and for characterizing tissue-level doses that could be predictors of problems related to efficacy or safety. R ifapentine (RPT) is a rifamycin-class antibiotic indicated for the treatment of pulmonary tuberculosis (TB) caused by Mycobacterium tuberculosis and in the treatment of latent TB infection in patients at high risk of progression to TB disease. RPT has a longer half-life, increased affinity to serum protein binding (1), and a lower MIC against M. tuberculosis than rifampin, which is currently used as part of several first-line TB treatment regimens (2, 3). Moreover, the primary metabolite for RPT, 25-desacetyl rifapentine (dRPT), has also been found to be active against M. tuberculosis, although at markedly lower MICs (1, 3, 4). Because of these characteristics, RPT has been the subject of a number of clinical pharmacology studies aimed at evaluating pharmacokinetics and developing effective therapies (5-15). Although data from these investigations are valuable in their own right, mathematical modeling offers a way to complement these studies, synthesize their disparate data, and provide the clinician an additional tool to characterize and predict the absorption, distribution, metabolism, and excretion (ADME) of RPT under dosing conditions of interest.One of the very few such mathematical models was developed by Savic et al. (16), who used a classical compartmental modeling approach to assess human population pharmacokinetics of both RPT and dRPT. This model described the absorption, metabolism, and clearance of these two species and accurately predicted their time cour...

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Computational pharmacokinetics/pharmacodynamics of rifampin in a mouse tuberculosis infection model

Cited by 12 publications

References 57 publications

A whole-body physiologically based pharmacokinetic (WB-PBPK) model of ciprofloxacin: a step towards predicting bacterial killing at sites of infection

A whole-body physiologically based pharmacokinetic (WB-PBPK) model of ciprofloxacin: a step towards predicting bacterial killing at sites of infection

A quantitative systems pharmacology (QSP) model for Pneumocystis treatment in mice

Physiologically Based Pharmacokinetic Model of Rifapentine and 25-Desacetyl Rifapentine Disposition in Humans

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