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Applying Optimization Algorithms to Tuberculosis Antibiotic Treatment Regimens

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Abstract

Introduction

Tuberculosis (TB), one of the most common infectious diseases, requires treatment with multiple antibiotics taken over at least 6 months. This long treatment often results in poor patient-adherence, which can lead to the emergence of multi-drug resistant TB. New antibiotic treatment strategies are sorely needed. New antibiotics are being developed or repurposed to treat TB, but as there are numerous potential antibiotics, dosing sizes and potential schedules, the regimen design space for new treatments is too large to search exhaustively. Here we propose a method that combines an agent-based multi-scale model capturing TB granuloma formation with algorithms for mathematical optimization to identify optimal TB treatment regimens.

Methods

We define two different single-antibiotic treatments to compare the efficiency and accuracy in predicting optimal treatment regimens of two optimization algorithms: genetic algorithms (GA) and surrogate-assisted optimization through radial basis function (RBF) networks. We also illustrate the use of RBF networks to optimize double-antibiotic treatments.

Results

We found that while GAs can locate optimal treatment regimens more accurately, RBF networks provide a more practical strategy to TB treatment optimization with fewer simulations, and successfully estimated optimal double-antibiotic treatment regimens.

Conclusions

Our results indicate surrogate-assisted optimization can locate optimal TB treatment regimens from a larger set of antibiotics, doses and schedules, and could be applied to solve optimization problems in other areas of research using systems biology approaches. Our findings have important implications for the treatment of diseases like TB that have lengthy protocols or for any disease that requires multiple drugs.

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Abbreviations

TB:

Tuberculosis

INH:

Isoniazid

RIF:

Rifampin

GA:

Genetic algorithm

RBF:

Radial basis function

PK/PD:

Pharmacokinetics/pharmacodynamics

ODE:

Ordinary differential equation

PDE:

Partial differential equation

LHS:

Latin hypercube sampling

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Acknowledgements

This research was supported by the following grants from the National Institutes of Health: U01HL131072, R01AI123093 and R01HL110811. This research also used resources of the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231 and the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation Grant Number MCB140228. We thank Paul Wolberg for computational assistance and Chang Gong for initial efforts on the GA and Fig. 1 calculations.

Conflict of interest

Joseph M. Cicchese, Elsje Pienaar, Denise E. Kirschner, and Jennifer J. Linderman declare that they have no conflicts of interest.

Ethical Standards

No human or animal studies were performed by the authors for this article.

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Authors and Affiliations

  1. Department of Chemical Engineering, University of Michigan, 2800 Plymouth Rd, NCRC B28, Ann Arbor, MI, 48109-2800, USA

    Joseph M. Cicchese, Elsje Pienaar & Jennifer J. Linderman

  2. Department of Microbiology and Immunology, University of Michigan Medical School, 1150 W Medical Center Drive, 5641 Medical Science II, Ann Arbor, MI, 48109-2136, USA

    Elsje Pienaar & Denise E. Kirschner

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  1. Joseph M. Cicchese
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Correspondence to Denise E. Kirschner or Jennifer J. Linderman.

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Associate Editor Michael R. King oversaw the review of this article.

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Cicchese, J.M., Pienaar, E., Kirschner, D.E. et al. Applying Optimization Algorithms to Tuberculosis Antibiotic Treatment Regimens. Cel. Mol. Bioeng. 10, 523–535 (2017). https://doi.org/10.1007/s12195-017-0507-6

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