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

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 se...

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Bibliographic Details
Published in:Mathematics (Basel) 2024-08, Vol.12 (16), p.2583
Main Authors: Tuncer, Necibe, Ghods, Kia, Sreejithkumar, Vivek, Adin Garbowit, Zagha, Mark, Martcheva, Maia
Format: Article
Language:English
Subjects:
Acquired immune deficiency syndrome
AIDS
Algebra
Datasets
Drug resistance
HIV
HIV within-host model
Human immunodeficiency virus
identifiability analysis
Lymphocytes
Parameter estimation
Parameter identification
Parameter sensitivity
Simulation
two-strain model
Viruses
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Summary: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.
ISSN:2227-7390
DOI:10.3390/math12162583