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New Tools Support Developing Better TB Drugs

Tuberculosis (TB)—caused by infection with the mycobacterium Mycobacterium tuberculosis—is one of the top 10 leading causes of death worldwide with a total of 1.8 million people dying from the disease in 2015. TB is also the leading cause of death in HIV-infected individuals. TB usually attacks the lungs but can infect any part of the body. A hallmark of pulmonary TB is the formation of mycobacteria containing granulomas—heterogeneous lesions composed of a macrophage- and neutrophil-rich cellular rim surrounding a necrotic core. To effectively treat TB infections, drugs have to move from the site of administration (usually the intestine following oral administration) into the blood stream and from there they need to effectively distribute into the lung tissue and attain sufficient concentrations within the granuloma to kill the mycobacteria. The lack of correlation between the administered dose and the drug concentration in the plasma, lung, and granulomas is thought to contribute to the need for long treatment durations and also to the failure of novel drug regimens.

Most TB drugs are more than 40 years old, have significant side effects and drug interactions, and require long treatment periods (treatment courses usually last for at least 6 months). In addition, strains of mycobacterium tuberculosis resistant to the standard of care drugs have begun to emerge. These challenges with current anti-TB therapy have led to attempts to improve TB treatment regimens and to develop novel anti-TB drugs. In this blog post, I’ll discuss our work with the Critical Path to TB Drug Regimens (CPTR) Initiative to develop new modeling and simulation tools to help drug developers combat TB.

The CPTR Initiative is a cross-sector initiative to develop novel approaches to expedite new, safe, and effective TB treatment regimens with shorter therapy durations. As part of this mission, the Regulatory Science Consortium for the Critical Path to TB Drug Regimens (CPTR) Initiative, led by the Critical Path Institute, coordinates collaborations to develop quantitative platforms to revamp drug development.1

Building a more physiologically-relevant lung model

In an effort to allow drug developers to gain a better understanding of new and existing anti-TB drugs in the lung and granuloma lesions, CPTR and C-Path partnered with colleagues in the Certara Strategic Consulting and Simcyp groups to develop a multiple-compartment, permeability-limited model of the human lung.2 The final model structure (representing the lung and airways as 7 compartments) balances a realistic representation of lung physiology with reasonable computational speed. Built to work in conjunction with our Simcyp Population-based Simulator, this model can predict the disposition of drugs within the plasma, lung, and epithelial lining fluid (ELF) and the potential impact of disease-progression on drug kinetics at different stages of TB infection. The model also allows regional physiological differences in gas exchange, blood perfusion,3,4 and transporter expression in the lung5 to be considered as these differences may affect local drug concentrations and efficacy.

Extending the multiple-compartment, permeability limited lung model

The first iteration of the multiple-compartment, permeability-limited lung model assumed only passive movement of drugs within the lung compartments.2 However, some drugs such as moxifloxacin, an antibiotic being tested in anti-TB regimens, are transported by drug transporters such as P-glycoprotein (P-gp).6

To account for the action of P-gp in the lung, a full body physiologically-based pharmacokinetic (PBPK) model was constructed for moxifloxacin in the Simcyp Simulator7 with disposition in the lung being represented by the multiple-compartment, permeability-limited model.2 The in vitro intrinsic clearance of moxifloxacin by P-gp was estimated using the Simcyp in vitro analysis (SIVA) toolkit and was extrapolated to the in vivo situation by accounting for differences in surface area and assumed differences in transporter expression between the in vitro system and the lung in vivo.6 Including P-gp transport in the PBPK model of moxifloxacin improved the accuracy of the prediction of ELF:plasma ratio for this drug.

Finally, the multiple-compartment, permeability-limited lung model was extended to describe drug disposition within a tuberculosis granuloma. The mechanistic, multi-compartment granuloma model includes compartments representing macrophages, interstitial fluid, caseum, and blood.8 Four drugs, with different dosing regimens, can be studied concurrently with this model. This is especially important as the most common dosing regimen for TB uses four drugs.

A new tool in the war against TB

This newly developed PBPK model can help drug developers leverage in vitro and in silico data to better understand drug disposition and penetration in plasma, lung tissue, ELF, and TB granulomas. In addition, these tools will allow researchers to simulate a range of variables—drug dose, disease state, and concomitant medications—and thus support designing more effective drug regimens. Likewise, these modeling and simulation tools could potentially support personalized dosing for TB patients. Using model-informed drug development approaches is a critical weapon in winning the war against the global TB scourge.


[1] Hanna D, Romero K, Schito M. Advancing tuberculosis drug regimen development through innovative quantitative translational pharmacology methods and approaches. Int J Infect Dis. 2016 Oct 24.[Epub ahead of print]

[2] Gaohua L, Wedagedera J, Small BG, et al. Development of a multicompartment permeability-limited lung PBPK model and its application in predicting pulmonary pharmacokinetics of antituberculosis drugs. CPT Pharmacometrics Syst Pharmacol. 2015;4(10):605-613.

[3] West JB. Regional differences in gas exchange in the lung of erect man. J Appl Physiol. 1962;17:893-898.

[4] Kobashi S, Kuramoto K, and Hata Y (2011). Functional Assessment of Individual Lung Lobes with MDCT Images, Theory and Applications of CT Imaging and Analysis, Prof. Noriyasu Homma (Ed.), InTech, DOI: 10.5772/15627. Available from: http://www.intechopen.com/books/theory-and-applications-of-ct-imaging-and-analysis/functional-assessment-of-individual-lung-lobes-with-mdct-images

[5] Sakamoto A, Matsumaru T, Yamamura N, et al. Quantitative expression of human drug transporter proteins in lung tissues: Analysis of regional, gender, and interindividual differences by liquid chromatography-tandem mass spectrometry. J Pharm Sci. 2013;102(9):3395-3406.

[6] Endter S, Becker U, Daum N, et al. P-glycoprotein (MDR1) functional activity in human alveolar epithelial cell monolayers. Cell Tissue Res. 2007;328(1):77-84.

[7] Hatley O, Patel N, Burt HJ, et al. Application of a Multi-Compartment Permeability-Limited Lung Model to Predict Lung Concentrations of Moxifloxacin in Virtual Human Subjects. Presented at the 20th North American ISSX Meeting. October 18-22, 2015, Orlando, Florida, USA.

[8] Rose RH, Gaohua L, Wedagedera J, et al. Development of a Novel Multi-Compartment Granuloma Model to Predict Local Drug Distribution and its Impact on Pharmacodynamics and Disease Progression in Tuberculosis. Presented at the Population Approach Group in Europe (PAGE) Annual Meeting, June 7-10, 2016, Lisbon, Portugal.

Getting the most from your in vitro data to inform PBPK model building

To learn more about how to use software like the SIVA Toolkit to analyze complex data from in vitro drug assays, please watch this webinar by our colleagues Nikunj Patel and Howard Burt. Let me know what you think in the comments section!

About the author

By: I. Gardner & O. Hatley