Modeling brain lentiviral infections during antiretroviral therapy in AIDS
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Understanding HIV-1 replication and latency in different reservoirs is an ongoing challenge in the care of patients with HIV/AIDS. A mathematical model was created to describe and predict the viral dynamics of HIV-1 and SIV infection within the brain during effective combination antiretroviral therapy (cART). The mathematical model was formulated based on the biology of lentiviral infection of brain macrophages and used to describe the dynamics of transmission and progression of lentiviral infection in brain. Based on previous reports quantifying total viral DNA levels in brain from HIV-1 and SIV infections, estimates of integrated proviral DNA burden were made, which were used to calibrate the mathematical model predicting viral accrual in brain macrophages from primary infection. The annual rate at which susceptible brain macrophages become HIV-1 infected was estimated to be 2.90×10−7–4.87×10−6 per year for cART-treated HIV/AIDS patients without comorbid neurological disorders. The transmission rate for SIV infection among untreated macaques was estimated to be 5.30×10−6–1.37×10−5 per year. An improvement in cART effectiveness (1.6–48%) would suppress HIV-1 infection in patients without neurological disorders. Among patients with advanced disease, a substantial improvement in cART effectiveness (70%) would eradicate HIV-1 provirus from the brain within 3–32 (interquartile range 3–9) years in patients without neurological disorders, whereas 4–51 (interquartile range 4–16) years of efficacious cART would be required for HIV/AIDS patients with comorbid neurological disorders. HIV-1 and SIV provirus burdens in the brain increase over time. A moderately efficacious antiretroviral therapy regimen could eradicate HIV-1 infection in the brain that was dependent on brain macrophage lifespan and the presence of neurological comorbidity.
KeywordsMathematical model Viral dynamics Brain macrophage HIV-1 SIV Combination antiretroviral therapy (cART)
The authors thank Mr. William Branton for the technical information, Ms. Suzanne E. Queen for the database assistance, and Dr. Arianna Bianchi for the useful suggestion about predictive interval. This work was supported by the NSERC, CIHR, CFI, and Alberta Innovates-Health Solutions. BG acknowledges support from NIH grants U24MH100930 and R01MH101017.
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Support for the work
ELA holds fellowship awards from the Canadian Institutes of Health Research and Alberta Innovates-Health Solutions. CP holds a Canada Research Chair in Neurological Infection and Immunity. Research for MYL is supported in part by grants from the Natural Science and Engineering Research Council of Canada (NSERC) and Canada Foundation for Innovation (CFI).
Conflict of interest
The authors declare that they have no conflict of interest.
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