In silico evaluation and exploration of antibiotic tuberculosis treatment regimens

Pienaar, Elsje; Dartois, VÃ©ronique; Linderman, Jennifer J.; Kirschner, Denise E.

doi:10.1186/s12918-015-0221-8

Cited by 45 publications

(68 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Genes (and combinations of genes) that we identify to be important for in vivo survival can now be tested as potential new drug targets using existing drugs or KO strains in animal models. Combining GranSim-CBM with our existing model of antibiotic distribution and activity in the granuloma (40,41) could also help elucidate the contributions of bacterial heterogeneity, asymmetric division and growth, and lipid inclusion levels to treatment outcomes (5,111). Such insight can in turn inform new regimens and strategic drug design.…”

Section: Discussionmentioning

confidence: 99%

“…At the molecular level, the model accounts for secretion, diffusion, binding, and degradation of cytokines and chemokines. The model has been extensively calibrated to NHP data and successfully predicts granuloma outcomes for tumor necrosis factor alpha (TNF-␣), interleukin-10 (IL-10), and IFN-␥ knockouts (34)(35)(36)(37)(38)(39)(40)(41)61).…”

Section: Methodsmentioning

confidence: 99%

“…We have established computational models of granuloma formation (34)(35)(36)(37) to map host immune mechanisms from the molecular scale to the granuloma scale (34,35,38) and to explore new antibiotic treatment strategies (39)(40)(41). Other models have predicted hypoxic granuloma regions based on granuloma size (42) or have used oxygen and nitric oxide concentrations to determine M. tuberculosis growth and to predict M. tuberculosis dynamics (43).…”

mentioning

confidence: 99%

See 2 more Smart Citations

Multiscale Model of Mycobacterium tuberculosis Infection Maps Metabolite and Gene Perturbations to Granuloma Sterilization Predictions

et al. 2016

Self Cite

View full text Add to dashboard Cite

cGranulomas are a hallmark of tuberculosis. Inside granulomas, the pathogen Mycobacterium tuberculosis may enter a metabolically inactive state that is less susceptible to antibiotics. Understanding M. tuberculosis metabolism within granulomas could contribute to reducing the lengthy treatment required for tuberculosis and provide additional targets for new drugs. Two key adaptations of M. tuberculosis are a nonreplicating phenotype and accumulation of lipid inclusions in response to hypoxic conditions. To explore how these adaptations influence granuloma-scale outcomes in vivo, we present a multiscale in silico model of granuloma formation in tuberculosis. The model comprises host immunity, M. tuberculosis metabolism, M. tuberculosis growth adaptation to hypoxia, and nutrient diffusion. We calibrated our model to in vivo data from nonhuman primates and rabbits and apply the model to predict M. tuberculosis population dynamics and heterogeneity within granulomas. We found that bacterial populations are highly dynamic throughout infection in response to changing oxygen levels and host immunity pressures. Our results indicate that a nonreplicating phenotype, but not lipid inclusion formation, is important for long-term M. tuberculosis survival in granulomas. We used virtual M. tuberculosis knockouts to predict the impact of both metabolic enzyme inhibitors and metabolic pathways exploited to overcome inhibition. Results indicate that knockouts whose growth rates are below ϳ66% of the wild-type growth rate in a culture medium featuring lipid as the only carbon source are unable to sustain infections in granulomas. By mapping metabolite-and gene-scale perturbations to granuloma-scale outcomes and predicting mechanisms of sterilization, our method provides a powerful tool for hypothesis testing and guiding experimental searches for novel antituberculosis interventions.T uberculosis (TB), caused by inhalation of the pathogen Mycobacterium tuberculosis, is a leading cause of death worldwide (1, 2). The main sites of infection during TB are lung granulomas, dense collections of immune cells and bacteria that develop following infection (3). Understanding M. tuberculosis dynamics within granulomas could aid in development of new antibiotic therapies, since M. tuberculosis growth rates, heterogeneity, and dynamics can affect antibiotic efficacy (3-7).Two in vitro-observed adaptations of M. tuberculosis, suspected to be important in the ability of M. tuberculosis to persist in the host lung, are (i) the adoption of a nonreplicating phenotype and (ii) the accumulation of lipid inclusions, aggregates of neutral lipids inside bacteria (8-13). Hypoxia is known to be a trigger for the transition to a nonreplicating phenotype and the formation of lipid inclusions (9,12,14,15).The in vivo role of the nonreplicating phenotype remains controversial. It has been suggested that M. tuberculosis acquires the nonreplicating phenotype in response to stresses present in the host (such as hypoxia) and that this phenotype allows M. tubercul...

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Multiscale Model of Mycobacterium tuberculosis Infection Maps Metabolite and Gene Perturbations to Granuloma Sterilization Predictions

et al. 2016

Self Cite

View full text Add to dashboard Cite

show abstract

“…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

View full text Add to dashboard Cite

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...

show abstract

“…Computational modelling and simulation has emerged as a tool for investigating a wide range of biological systems, spanning immunology [1,2], drug and intervention design [3,4], developmental biology [5] and ecology [6]. Biological simulation is particularly insightful when used to complement traditional methods, such as wet-lab in vivo and in vitro work; laboratory work generates experimental data and suggests hypotheses that can be evaluated by way of their integration with simulation, which in turn can suggest further experiments or highlight areas of lacking knowledge [7,8].…”

Section: Introductionmentioning

confidence: 99%

Automated multi-objective calibration of biological agent-based simulations

Read

Alden

Rose

et al. 2016

J. R. Soc. Interface.

View full text Add to dashboard Cite

Computational agent-based simulation (ABS) is increasingly used to complement laboratory techniques in advancing our understanding of biological systems. Calibration, the identification of parameter values that align simulation with biological behaviours, becomes challenging as increasingly complex biological domains are simulated. Complex domains cannot be characterized by single metrics alone, rendering simulation calibration a fundamentally multi-metric optimization problem that typical calibration techniques cannot handle. Yet calibration is an essential activity in simulation-based science; the baseline calibration forms a control for subsequent experimentation and hence is fundamental in the interpretation of results. Here, we develop and showcase a method, built around multi-objective optimization, for calibrating ABSs against complex target behaviours requiring several metrics (termed objectives) to characterize. Multi-objective calibration (MOC) delivers those sets of parameter values representing optimal trade-offs in simulation performance against each metric, in the form of a Pareto front. We use MOC to calibrate a well-understood immunological simulation against both established a priori and previously unestablished target behaviours. Furthermore, we show that simulation-borne conclusions are broadly, but not entirely, robust to adopting baseline parameter values from different extremes of the Pareto front, highlighting the importance of MOC's identification of numerous calibration solutions. We devise a method for detecting overfitting in a multi-objective context, not previously possible, used to save computational effort by terminating MOC when no improved solutions will be found. MOC can significantly impact biological simulation, adding rigour to and speeding up an otherwise time-consuming calibration process and highlighting inappropriate biological capture by simulations that cannot be well calibrated. As such, it produces more accurate simulations that generate more informative biological predictions.

show abstract

In silico evaluation and exploration of antibiotic tuberculosis treatment regimens

Cited by 45 publications

References 55 publications

Multiscale Model of Mycobacterium tuberculosis Infection Maps Metabolite and Gene Perturbations to Granuloma Sterilization Predictions

Multiscale Model of Mycobacterium tuberculosis Infection Maps Metabolite and Gene Perturbations to Granuloma Sterilization Predictions

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

Automated multi-objective calibration of biological agent-based simulations

Contact Info

Product

Resources

About