Mathematical investigation of treatment interruptions and drug resistance in HIV treatment
Aim: To investigate the impact of antiretroviral treatment (ART) interrup-
tions on the outcomes of ART outcomes.
Methods: We extend existing mathematical model for the interactions between HIV and the immune system by introducing an explicit rate mutation toВ HIV drug resistant strain that depends on treatment interruptions often referredВ to as drug holidays.
Results: A relationship between the period of drug holidays and the emergence of drug resistance strains is derived in the case of cyclic treatment interruptions. When interruptions are not cyclic we perform model simulations
of various scenarios of interruptions such as weekend interruptions as well as
interruptions during festive seasons.
Adams B.M., Banks H.T., Davidian M. and Rosenberg E.S. (2007). Estimation and Prediction With HIV-Treatment Interruption Data. Bull. Math. Biol. 69 (2) 563-584.
Bongiovanni M., Casana M., Tincati C. and Monforte A.A. (2006). Treatment interruptions in HIV-infected subjects. Journal of Antimicrobial Chemotherapy 58, 502-505.
Bonhoeffer S., May R.M., Shaw G.M. and Nowak M.A. (1997). Virus dynamics and drug therapy. Proc. Natl. Acad. Sci. 94, 6971-6976.
Bonhoeffer S., Rembiszewski M., Ortiz G.M. and Nixon D.F. (2000). Risks and benefits of structured antiretroviral drug therapy interruptions in HIV-1 infection. AIDS 14, 2313-2322.
De Boer R.J. and Boucher C.A.B. (19960. Anti-CD4 therapy for AIDS suggested by mathematical models. Proc. R. Soc. Lond. B, 263, 899-905.
Frost S.D.W and McLean A.R. (1994). Quasispecies dynamics and the emergence of drug resistance during zidovudine therapy of HIV infection. AIDS 8, 323-332.
Kirschner D.E. and Webb G.F. (1997). Understanding drug resistance for montherapy treatment of HIV infection. Bull. Math. Biol. 59, 763-786.
Krakovska O and Wahl L.M. (2007). Optimal drug treatment regimens for HIV dependent on adherence. J. Theor. Biol. 246, 499-509.
Lori F. and Lisziewicz J. (2001). Structured treatment interruptions for the management of HIV infection. J. Amerin. Med. Assoc. 4286 (23), 2981-2987.
McLean A. R., Emery V.C. Webster A. and Griffiths P.D. (1991). Population dynamics of HIV within an individual after treatment with zidovudine. AIDS 5, 485-489.
McLean A.R. and Nowak M.A. (1992). Competition between zidovudine sensetive and resistant strains of HIV. AIDS 6, 71-79.WT., Goudsmit J., and May R.M. (1991). Antigenic diversity thresholds and the development of AIDS. Science 254, 963-969.
Nowak M.A., Anderson R.M., McLean A.R., Wolfs T., Goudsmit J. and May R.M. (1991). Antigenic diversity thresholds and the development of AIDS. Science 254, 963-969.
Nowak M.A. and May R.M. (2000). Virus dynamics: Mathematical Principles of Immuniology and Virology. Oxford University Press, Oxford.
Ouifki R, Welte A and Pretorius C. (2008). A model of HIV infection with two viral strains and cytotoxic T-Lymphocyte response under structured treatment interruptions. South African Journal of Science (Biological Modelling), 104, 216-220.
Perelson A.S and Nelson P.W. (1999). Mathematical analysis of HIV-1 dynamics in vivo, SIAM Review, 41 (1), 3-44.
Perelson A.S., Neumann A. U., Markowitz M., Leonard J.M., Ho D.D. (1996). HIV-1 dynamics in vivo: virion clearance rate, infected cell life-span, and viral generation time. Science 271, 1582-1586.
Stafford M.A., Corey L., Cao Y., Daar E., Ho D.D., Perelson A.S. (2000). Modeling plasma virus concentration during primary HIV infection. J. Theor. Biol. 203, 285-301.
van den Driessche P and Watmough J. (2002). Reproduction numbers and subthreshold endemic equilibria for compartmental models of desease transmission. Math. Biosci. 180, 29-48.
Wein L.M., Zenios S and Nowak M.A. (1997). Dynamic multidrug therapies for HIV: a control theoretic approach. J. Theor. Biol. 185, 15-29.