Planning of patient-specific drug-specific optimal HIV treatment strategies

In this study, we present a mathematical modelling and optimal control approach to formulate patient-specific drug-specific treatment strategies for HIV-infected patients. Central to this is the fact that no two individuals respond to infection and treatment in quite the same way, and that different drugs are associated with varied efficacies in the body as well as with different side-effects. We hereby present a methodology which allows optimal planning on a case-by-case basis, unlike previous work in the field which formulated treatment protocols for general use by considering drug efficacies only. Investigation of optimal strategies by using models which consider the pharmacokinetic behaviour of drugs as well as their side-effects by using a well-documented chart may be considered a novel contribution and allows for the optimum administration of all drugs depending on their degree of toxicity as well as their effectiveness in the body. The formulated model is able to replicate clinical data from different progressors to AIDS by estimation of immune system parameters only. The latter have been suggested to be key in determining the degree of progression and the ability of the model to reproduce this phenomenon further validates the formulation. Optimal treatment strategies are produced for different patients and we can conclude that a general treatment protocol cannot be proposed and therapy has to be designed on an individual basis.

[1]  J. Dolezal,et al.  A mathematical model and CD4+ lymphocyte dynamics in HIV infection. , 1996, Emerging infectious diseases.

[2]  Alain Mallet,et al.  A modeling approach to the impact of HIV mutations on the immune system , 2005, Comput. Biol. Medicine.

[3]  R. Pomerantz.  Reservoirs of human immunodeficiency virus type 1: the main obstacles to viral eradication. , 2002, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[4]  Antonios Armaou,et al.  Sensitivity analysis of HIV infection response to treatment via stochastic modeling , 2008 .

[5]  D. Kirschner,et al.  Predicting differential responses to structured treatment interruptions during HAART , 2004, Bulletin of mathematical biology.

[6]  Gary Collins,et al.  Structured treatment interruption in patients with multidrug-resistant human immunodeficiency virus. , 2003, The New England journal of medicine.

[7]  K. Lange,et al.  Mutation Takes No Vacation: Can Structured Treatment Interruptions Increase the Risk of Drug‐Resistant HIV‐1? , 2000, Journal of acquired immune deficiency syndromes.

[8]  L. Wahl,et al.  Drug-Sparing Regimens for HIV Combination Therapy: Benefits Predicted for “Drug Coasting” , 2007, Bulletin of mathematical biology.

[9]  Raúl Conejeros,et al.  Long-term HIV dynamics subject to continuous therapy and structured treatment interruptions , 2009 .

[10]  Antonios Armaou,et al.  An extracellular stochastic model of early HIV infection and the formulation of optimal treatment policy , 2008 .

[11]  C. Sabin,et al.  Course of viral load throughout HIV-1 infection. , 2000 .

[12]  Marios M. Hadjiandreou,et al.  Towards a long-term model construction for the dynamic simulation of HIV infection. , 2007, Mathematical biosciences and engineering : MBE.

[13]  Denise E. Kirschner,et al.  Remarks on Modeling Host-Viral Dynamics and Treatment , 2001 .

[14]  C. Mahé,et al.  HIV-1 infection in rural Africa: is there a difference in median time to AIDS and survival compared with that in industrialized countries? , 2002, AIDS.

[15]  R. Paredes,et al.  Role of structured treatment interruption before a 5-drug salvage antiretroviral regimen: the Retrogene Study. , 2003, The Journal of infectious diseases.

[16]  Carlos Castillo-Chavez,et al.  Mathematical approaches for emerging and reemerging infectious diseases , 2002 .

[17]  Jose M. Pinto,et al.  Role of mathematical modeling on the optimal control of HIV-1 pathogenesis , 2006 .

[18]  C. A. Macken,et al.  Persistence of HIV-1 transcription in peripheral-blood mononuclear cells in patients receiving potent antiretroviral therapy. , 1999, The New England journal of medicine.

[19]  E Comets,et al.  An in vivo Pharmacokinetic/Pharmacodynamic Model for Antiretroviral Combination , 2003, HIV clinical trials.

[20]  J. Margolick,et al.  T Lymphocyte Homeostasis After HIV Seroconversion , 1994, Journal of acquired immune deficiency syndromes.

[21]  L M Wahl,et al.  Adherence and drug resistance: predictions for therapy outcome , 2000, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[22]  M A Nowak,et al.  How HIV defeats the immune system. , 1995, Scientific American.

[23]  N Bischofberger,et al.  Macrophage are the principal reservoir and sustain high virus loads in rhesus macaques after the depletion of CD4+ T cells by a highly pathogenic simian immunodeficiency virus/HIV type 1 chimera (SHIV): Implications for HIV-1 infections of humans. , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[24]  E Pennisi,et al.  Antiviral Therapy: Eradicating HIV From a Patient: Not Just a Dream? , 1996, Science.

[25]  M A Nowak,et al.  Anti-viral drug treatment: dynamics of resistance in free virus and infected cell populations. , 1997, Journal of theoretical biology.

[26]  S. Twu,et al.  Decreased HIV transmission after a policy of providing free access to highly active antiretroviral therapy in Taiwan. , 2004, The Journal of infectious diseases.

[27]  F. D. Souza.  Modeling the dynamics of HIV-1 and CD4 and CD8 lymphocytes , 1999 .

[28]  Robert F Stengel,et al.  Mutation and Control of the Human Immunodeficiency Virus , 2022 .

[29]  Ruben A. Filter,et al.  Dynamic HIV/AIDS parameter estimation with application to a vaccine readiness study in Southern Africa , 2005, IEEE Transactions on Biomedical Engineering.

[30]  A. Perelson,et al.  A Model for the Immune System Response to HIV: AZT Treatment Studies , 1993 .

[31]  D. Weissman,et al.  Immunopathogenic Mechanisms of HIV Infection , 1996, Annals of Internal Medicine.

[32]  Thomas M. Ludden,et al.  Pharmacokinetics of Quinidine in Male Patients , 1992, Clinical pharmacokinetics.

[33]  Mark S Roberts,et al.  Estimating the proportion of patients infected with HIV who will die of comorbid diseases. , 2005, The American journal of medicine.

[34]  B. Adams,et al.  Dynamic multidrug therapies for hiv: optimal and sti control approaches. , 2004, Mathematical biosciences and engineering : MBE.

[35]  Milton C Weinstein,et al.  Expanded screening for HIV in the United States--an analysis of cost-effectiveness. , 2005, The New England journal of medicine.

[36]  M. Nowak,et al.  Virus dynamics: Mathematical principles of immunology and virology , 2001 .

[37]  J. J. Henning,et al.  Guidelines for the Use of Antiretroviral Agents in HIV-Infected Adults and Adolescents, January 28, 2000 , 1998, HIV clinical trials.

[38]  R. Chung,et al.  Pharmacokinetic evaluation of the effects of ribavirin on zidovudine triphosphate formation: ACTG 5092s Study Team , 2007, HIV medicine.

[39]  Hulin Wu,et al.  Modeling HIV dynamics and antiviral response with consideration of time-varying drug exposures, adherence and phenotypic sensitivity. , 2003, Mathematical biosciences.

[40]  D. Kirschner,et al.  A Mathematical Model of Combined Drug Therapy of HIV Infection , 1997 .

[41]  J. Beijnen,et al.  Development and validation of a population pharmacokinetic model for ritonavir used as a booster or as an antiviral agent in HIV-1-infected patients. , 2005, British journal of clinical pharmacology.