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Combinatorial Optimization in Rapidly Mutating Drug-Resistant Viruses

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  • Richard H. Lathrop

    (University of California)

  • Michael J. Pazzani

    (University of California)

Abstract

Resistance to chemicals is a common current problem in many pests and pathogens that formerly were controlled by chemicals. An extreme case occurs in rapidly mutating viruses such as Human Immunodeficiency Virus (HIV), where the emergence of selective drug resistance within an individual patient may become an important factor in treatment choice. The HIV patient subpopulation that already has experienced at least one treatment failure due to drug resistance is considered more challenging to treat because the treatment options have been reduced. A triply nested combinatorial optimization problem occurs in computational attempts to optimize HIV patient treatment protocol (drug regimen) with respect to drug resistance, given a set of HIV genetic sequences from the patient. In this paper the optimization problem is characterized, and the objects involved are represented computationally. An implemented branch-and-bound algorithm that computes a solution to the problem is described and proved correct. Data shown includes empirical timing results on representative patient data, example clinical output, and summary statistics from an initial small-scale human clinical trial.

Suggested Citation

  • Richard H. Lathrop & Michael J. Pazzani, 1999. "Combinatorial Optimization in Rapidly Mutating Drug-Resistant Viruses," Journal of Combinatorial Optimization, Springer, vol. 3(2), pages 301-320, July.
    • Handle: RePEc:spr:jcomop:v:3:y:1999:i:2:d:10.1023_a:1009846028730
    DOI: 10.1023/A:1009846028730
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    References listed on IDEAS

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    1. Frank A. Sonnenberg & C. Greg Hagerty & Casimir A. Kulikowski, 1994. "An Architecture for Knowledge-based Construction of Decision Models," Medical Decision Making, , vol. 14(1), pages 27-39, February.
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    Cited by:

    1. André Altmann & Michal Rosen-Zvi & Mattia Prosperi & Ehud Aharoni & Hani Neuvirth & Eugen Schülter & Joachim Büch & Daniel Struck & Yardena Peres & Francesca Incardona & Anders Sönnerborg & Rolf Kaise, 2008. "Comparison of Classifier Fusion Methods for Predicting Response to Anti HIV-1 Therapy," PLOS ONE, Public Library of Science, vol. 3(10), pages 1-9, October.
    2. Bogojeska Jasmina & Lengauer Thomas, 2012. "Hierarchical Bayes Model for Predicting Effectiveness of HIV Combination Therapies," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(3), pages 1-21, April.

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