Online Program

Modeling within Host Dynamics of HIV Infection and HAART Interruptions

Monday, November 2, 2015 : 3:30 p.m. - 3:50 p.m.

Nargesalsadat Dorratoltaj, MS, MPH, Department of Population Health Sciences, Virginia Tech, Blacksburg, VA
Stephen Eubank, PhD, Network Dynamics and Simulation Science Laboratory, Virginia Bioinformatics Institute at Virginia Tech, Blacksburg, VA
Hazhir Rahmandad, PhD, Department of Industrial and Systems Engineering, Virginia Tech, Falls Church, VA
Josep Bassaganya-Riera, DVM, PhD, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA
Margaret O'Dell, MD, MFA, New River Health District, Virginia Department of Health, Christiansburg, VA
Stanca Ciupe, PhD, Department of Mathematics, VIrginia Tech, Blacksburg, VA
Kaja Abbas, PhD, Department of Population Health Sciences, Virginia Tech, Blacksburg, VA
Objective: The objective of this study is to predict HIV dynamics during both structured and unstructured HIV treatment interruptions and to design treatment strategies in order to increase treatment adherence. 

Background: Due to the adverse effects of antiretroviral therapy HIV-positive patients, at times, may experience treatment interruptions. However, unstructured interruption may cause irreversible damage to the patient's immune system. The ability to predict the changes in HIV dynamics within-host during interruption and design interruption periods can help patients benefit from periodic treatment interruptions.

Methods: We used ordinary differential equations to build within-host immune-viral dynamics of HIV. We added combinational antiretroviral therapy and studied treatment effects on CD4+ T cells and HIV dynamics. To study the impacts of treatment interruption, we added time- and threshold-based periodic interruptions. Then we calibrated model with available experimental data and predicted interruption impacts on disease prognosis.

Results: For time-based treatment interruption method, we simulated the effects of 200, 100, and 30 day treatment interruption periods. The results showed variations in dynamics of HIV and CD4 cells. For threshold-based treatment interruption, we chose lower and upper thresholds of 500 and 700 CD4 cell counts per microliter for interruption. The results showed an average of 67 (62,70) days for on-treatment and 66 (65,69) days  for off-treatment periods.

Conclusion: The results of this study can be used to find a scientifically valid explanation for HIV dynamics within-host during treatment interruption. Comparing the threshold-based and time-based interruption methods, threshold-based strategy takes into account patients' immune response in interruption period.

Learning Areas:

Public health biology
Systems thinking models (conceptual and theoretical models), applications related to public health

Learning Objectives:
Explain HIV dynamics within-host during structured or unstructured treatment interruption periods.

Keyword(s): HIV/AIDS, Treatment Adherence

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: I am a PhD candidate and I am working on dynamics of HIV treatment interruptios within- and between-hosts. I use mathematical modeling to design and study the impact of treatment interruptions.
Any relevant financial relationships? No

I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.