Applying Optimization Algorithms to Tuberculosis Antibiotic Treatment Regimens

Journal Article · 30 August 2017 · Cellular and Molecular Bioengineering

DOI: https://doi.org/10.1007/s12195-017-0507-6 · OSTI ID:1461962

Cicchese, Joseph M. ^[1]; Pienaar, Elsje ^[2]; Kirschner, Denise E. ^[2]; Linderman, Jennifer J. ^[1]

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1. Univ. of Michigan, Ann Arbor, MI (United States). Dept. of Chemical Engineering
2. Univ. of Michigan, Ann Arbor, MI (United States). Dept. of Chemical Engineering and Dept. of Microbiology and Immunology

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. Furthermore 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.

• Research Organization: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)
• Sponsoring Organization: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
• Grant/Contract Number: AC02-05CH11231
• OSTI ID: 1461962
• Journal Information: Cellular and Molecular Bioengineering, Vol. 10, Issue 6; ISSN 1865-5025
• Publisher: SpringerCopyright Statement
• Country of Publication: United States
• Language: English