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Validation of a Multi-Strain HIV Within-Host Model with AIDS Clinical Studies

Author

Listed:
  • Necibe Tuncer

    (Department of Mathematics and Statistics, Florida Atlantic University, Boca Raton, FL 33431, USA)

  • Kia Ghods

    (Department of Mathematics, Princeton University, Princeton, NJ 08544, USA
    These authors contributed equally to this work.)

  • Vivek Sreejithkumar

    (Department of Mathematics and Statistics, Florida Atlantic University, Boca Raton, FL 33431, USA
    These authors contributed equally to this work.)

  • Adin Garbowit

    (Department of Mathematics and Statistics, Florida Atlantic University, Boca Raton, FL 33431, USA
    These authors contributed equally to this work.)

  • Mark Zagha

    (Department of Mathematics and Statistics, Florida Atlantic University, Boca Raton, FL 33431, USA
    These authors contributed equally to this work.)

  • Maia Martcheva

    (Department of Mathematics, University of Florida, P.O. Box 118105, Gainesville, FL 32611, USA)

Abstract

We used a previously introduced HIV within-host model with sensitive and resistant strains and validated it with two data sets. The first data set is from a clinical study that investigated multi-drug treatments and measured the total CD4 + cell count and viral load. All nine patients in this data set experienced virologic failure. The second data set includes a unique patient who was treated with a unique drug and for whom both the sensitive and resistant strains were measured as well as the CD4 + cells. We studied the structural identifiability of the model with respect to each data set. With respect to the first data set, the model was structurally identifiable when the viral production rate of the sensitive strain was fixed and distinct from the viral production rate of the resistant strain. With respect to the second data set, the model was always structurally identifiable. We fit the model to the first data set using nonlinear mixed effect modeling in Monolix and estimated the population-level parameters. We inferred that the average time to emergence of a resistant strain is 844 days after treatment starts. We fit the model to the second data set and found out that the all the parameters except the mutation rate were practically identifiable.

Suggested Citation

  • Necibe Tuncer & Kia Ghods & Vivek Sreejithkumar & Adin Garbowit & Mark Zagha & Maia Martcheva, 2024. "Validation of a Multi-Strain HIV Within-Host Model with AIDS Clinical Studies," Mathematics, MDPI, vol. 12(16), pages 1-20, August.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:16:p:2583-:d:1460972
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    References listed on IDEAS

    as
    1. Walter, Eric & Lecourtier, Yves, 1982. "Global approaches to identifiability testing for linear and nonlinear state space models," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 24(6), pages 472-482.
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