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Timely HAART initiation may pave the way for a better viral control
- Paola Paci†1Email author,
- Federico Martini2,
- Massimo Bernaschi3,
- Gianpiero D'Offizi2 and
- Filippo Castiglione†3Email author
© Paci et al; licensee BioMed Central Ltd. 2011
Received: 25 October 2010
Accepted: 1 March 2011
Published: 1 March 2011
When to initiate antiretroviral therapy in HIV infected patients is a diffcult clinical decision. Actually, it is still a matter of discussion whether early highly active antiretroviral therapy (HAART) during primary HIV infection may influence the dynamics of the viral rebound, in case of therapy interruption, and overall the main disease course.
In this article we use a computational model and clinical data to identify the role of HAART timing on the residual capability to control HIV rebound after treatment suspension. Analyses of clinical data from three groups of patients initiating HAART respectively before seroconversion (very early), during the acute phase (early) and in the chronic phase (late), evidence differences arising from the very early events of the viral infection.
The computational model allows a fine grain assessment of the impact of HAART timing on the disease outcome, from acute to chronic HIV-1 infection. Both patients' data and computer simulations reveal that HAART timing may indeed affect the HIV control capability after treatment discontinuation. In particular, we find a median time to viral rebound that is significantly longer in very early than in late patients.
A timing threshold is identified, corresponding to approximately three weeks post-infection, after which the capability to control HIV replication is lost. Conversely, HAART initiation occurring within three weeks from the infection could allow to preserve a significant control capability. This time could be related to the global triggering of uncontrolled immune activation, affecting residual immune competence preservation and HIV reservoir establishment.
The question of when antiretroviral therapy has to be initiated remains a challenging issue. Recent studies show that the early immune response to HIV-1 infection is likely to be an important factor in determining the clinical course of disease . The first weeks following HIV-1 transmission are extremely dynamic. They are associated with rapid damage to generative immune cell micro-environments and with immune responses that partially control the virus. Following HIV-1 infection, the virus first replicates locally in the mucosa and then is transported to draining lymph nodes where further amplification occurs. This initial phase of infection, until the systemic viral dissemination begins, constitutes the eclipse phase . In general, there is an exponential increase in plasma viremia with a peak 21-28 days after infection. By this time, significant depletion of mucosal CD4+T cells has already occurred. Around the time of peak viremia, patients may become symptomatic and reservoirs of latent virus are established [1, 2].
The "window of opportunity" between the infection and peaking of viremia, prior to massive CD4+ T cell destruction and the establishment of viral reservoirs, seems to be a narrow but crucial period in which an antiretroviral therapy can control viral replication, prevent an extensive CD4+ T cell depletion from occurring and curb generalized immune activation. Thus, thwarting HIV replication by introducing HAART in the early phases of infection could have a substantial impact on the whole disease course. In particular, suggested factors that may contribute to the observed better viral control after treatment interruption in very early treated patients are : i) early arrest of viral escape, leaving the virus vulnerable; ii) preservation or even enhancement of the immune response resulting from the early clearing of antigen; iii) prevention of the establishment of a pool of HIV-specific memory CD4 T cells thus leaving fewer target cells available for viral infection.
An ideal clinical model aimed at addressing such issue should compare a number of patients treated starting on different times: from very early to very late. Besides the ethical issues, it is rather difficult to collect enough patients to significantly represent the whole spectrum of possible HAART initiation timings. As a matter of fact, a practical clinical model would compare very early to late-treated patients. While informative on the overall role of HAART timing on disease course, this approach would not allow to verify if there are events in the early infection influenced by the starting time of HAART that affect directly and decisively the course of the disease.
We have already shown in [4–6] that an agent-based model of HIV-1 infection could be a valuable tool for the study of the AIDS disease progression and treatment. The computerized simulation allows us to track the effect of HAART timing on the progression of the disease.
The aim of the present work is to verify the effect of HAART timing on subsequent events. Indeed, both a clinical model and a computational simulation show that a late initiation of treatment affects HIV-1 replication control. Interestingly, the in silico model identifies a significant three-week time threshold as the "ultimate" time point beyond which the decisive HIV-induced damages already occurred, affecting the whole disease course.
In a previous work , we analyzed clinical data of patients initiating HAART within six months from infection (i.e., we called that early phase) and performed computer simulations to predict the differences in viral rebound at therapy interruption between those patients and subjects initiating therapy during the chronic phase (i.e., we called that late phase, corresponding to initiating six or more months after infection). Our conclusion was that early initiation of therapy does not prolong the disease-free period when compared to a treatment started during the late phase. However, other studies suggest that an earlier initiation is preferable [7–9]. This motivated us to better identify the meaning of early initiation. In the present article, we extend the analysis in  to get a more complete picture. We analyze clinical data of very early patients (i.e., treated before seroconvertion) against late-treated patients.
We analyzed the results of clinical studies performed at the Clinical Department of the National Institute for Infectious Disease "L. Spallanzani" in Rome.
Very early subjects with an immediate treatment before seroconversion.
Elapsed days ‡ therapy
4 weeks after**
8 weeks after**
Early subjects with an immediate treatment of acute HIV-1 infection.
Elapsed days ‡
Days on therapy
Days out therapy
At first interruption
Late subjects with deferred treatment of acute HIV-1 infection.
Elapsed days ‡
Days on therapy
Days out therapy
At first interruption
D4T, 3TC, SQV
AZT, ddC, Rit
ddl, D4T, NVP
Plasma HIV-1 determination
Plasma HIV-1 RNA levels were determined by a second-generation assay based on nucleic acid sequence based amplification (NASBA), for samples collected until 2001 and by the branched-chain DNA assay (Versant HIV RNA test, Version 3.0, lower limit of quantification 50 copies/ml; Bayer Diagnostics, Milan, Italy) from 2001 until 2008.
The current version of the model we employ derives from an early simulator that has been quite extensively described in previous publications [11, 12]. Recently it has been specialized to simulate the HIV-1 infection  and the effects of antiretroviral therapy .
Briefly, it resorts to bit strings to represent "binding sites" of cells and molecules, as for example lymphocyte receptors, MHC, antigen peptides and epitopes, immunocomplexes, etc. . The model includes the major classes of cells of the lymphoid lineage (T helper lymphocytes, cytotoxic T lymphocytes, B lymphocytes and antibody-producer plasma cells) and some of the myeloid lineage (macrophages and dendritic cells). These entities are individually represented. In contrast to cells, cytokines like interleukin-2 are represented in terms of concentrations and their dynamics described by a parabolic partial differential equation plus a degradation term accounting for the finite half-life [5, 14]. Modeling features of the HIV infection include HIV replication inside infected lymphocytes, T production impairment; specific response against HIV strains and HIV mutation.
The simulated life cycle of the virus is represented by the following stages: 1) the virus infects CD4+ T cells, macrophages, dendritic cells; 2) reverse transcriptase copies the viral single stranded RNA genome into a double-stranded viral DNA. The viral DNA is then integrated into the host chromosomal DNA; 3) the virus remains at rest until an event activates the transcription; 4) the replicating virus buds from the cell membrane. Fully assembled virions are then able to infect other cells to restart the life cycle. HAAR effects are modeled as follows: Reverse transcriptase inhibitors block reverse transcriptase enzymatic functions and avoid completion of synthesis of the double-stranded viral DNA thus preventing HIV-1 from replicating (i.e., it prevents the virus in stage 1 from reaching stage 2); Protease inhibitors prevent viral replication by inhibiting the activity of HIV-1 protease, an enzyme used by the virus to cleave nascent proteins for final assembly of new virions (i.e., it prevents virus assembly in stage 4). Further details and parameter settings of the simulations can be found in the Additional file 1.
For what concerns the setting of the parameters related to the therapy, we performed computer simulations in which we fixed the immunological parameters at the time of therapy initiation on the basis of the average values measured in patients in vivo: 5.8 ± 0.2 RNA copies/ml (in logarithmic scale), 870 ± 50 CD4 cells/μl and 430 ± 50 CD8 cells/μl. For all simulations we applied a one-year course of HAART. Further details on the tuning of the simulation parameters can be found in the Additional file 2.
We analyze virological data from HIV patients treated during the very early, early and late phase of infection and compare them with computer simulations.
In the present work we extend the simulations of  to include the new clinical settings corresponding to a very early initiation of therapy. In particular, the very early simulation settings correspond to a beginning of the therapy within the first week whereas the late settings correspond to initiating therapy between week five and six from infection.
where the parameter k is the carrying capacity or the upper asymptote, a is the lower asymptote, d is the growth rate, and is the time of maximum growth. By moving the time of the measurements beyond one week after therapy interruption, the resulting data still fit the same V (t s ) but with a greater a, a smaller d and a greater . In particular the limit for d going to zero, of V (t s ) is (a + k)/2 may lead to the deceiving conclusion that there is no window of opportunity because the viral rebound is independent from t s .
Figure 4 shows with clarity that very early initiation of the treatment can down-regulate the immune activation, hence limiting viral replication and spread. Interestingly, this view is supported by the observation that HIV triggers the immune activation directly (e.g., HIV gene products can induce the activation of lymphocytes and macrophages as well as the production of pro-inflammatory cytokines and chemokines ) or indirectly (e.g., sustained antigen-mediated immune activation occurs in HIV-1-infected patients due also to other viruses like the cytomegalovirus or the Epstein-Barr virus ). In both case, the result is a high level of pro-inflammatory cytokines, such as tumor necrosis factor alpha, interleukin 6 and interleukin 1 beta, right from the early stages of HIV-1 infection .
Recent analysis (performed by Fiebig et al. ) of samples from individuals that have been infected by HIV-1 has revealed that patients can be categorized into six stages on the basis of a sequential gain in positive HIV-1 clinical diagnostic assays (viral RNA measured by PCR, p24 and p31 viral antigens measured by enzyme-linked immunosorbent assay (ELISA), HIV-1-specific antibody detected by ELISA and HIV-1-specific antibodies detected by western blot, ). Patients progress from acute to early chronic infection at the end of stage V, approximately 100 days following infection, as the plasma viral load begins to stabilize.
With respect to the study conducted by clinical data analysis and computer simulation described so far, we identify three regimens, as highlighted in Figure 3. These can be paralleled to Fiebig et al. stages . In particular, we observe that patients treated with HAART in very early stages of the infection (stage I-III) are likely to better control the viremia after treatment interruption . If therapy starts in the acute phase (stage V-VI) then the action of the drug foils the immune response and, as a consequence, at the end of the therapeutic period, the virus rebounds undisturbed.
A number of studies indicate that interfering with HIV replication by starting the therapy in the early phases of the infection could have a deep impact on the whole disease course. However, HAART is costly, it is onerous for both patient and health care provider, and often brings adverse effects. Its clinical benefit must therefore be weighed against its burden.
In the present study, we resorted to a computer model to study the dynamics of the plasma viral load after prolonged treatment interruption in two groups of in silico patients: those who initiate HAART very early and those who start it lately. We evaluated the model comparing the results to clinical data. We found that an opportunity time-window exists for the initiation of HAART (roughly within three weeks before the establishment of viral reservoirs), in which the therapy can control viral replication, preventing generalized immune activation and extensive CD4+ T cell depletion.
Availability and requirements
Operating system(s): Linux, Unix Mac OS X, Windows
Programming language: C
Licence: C-ImmSim is available under a LICENSE AGREEMENT that needs to be signed: http://www.iac.rm.cnr.it/~filippo/how-to-get-cimmsim_files/LicenseAgreement.pdf
We acknowledge Rossella Carello and Chrysoula Vlassi for assistance in data extraction. We wish to acknowledge support of the European Community through the contract FP6-2004-IST-4, No. 028069 (ImmunoGrid). We thank the "Consorzio interuniversitario per le Applicazioni di Supercalcolo Per Universitá e Ricerca" (CASPUR) for computing resources and support (Standard HPC grant 2009 and 2010).
- McMichael AJ, Borrow P, Tomara GD, Goonetilleke N, Haynes BF: The immune response during acute HIV-1 infection: clues for vaccine development. Nat Rev Immunol. 2010, 10: 11-23. 10.1038/nri2674.View ArticlePubMedGoogle Scholar
- Appay V, Sauce D: Immune activation and inflammation in HIV-1 infection: causes and consequences. J Pathol. 2008, 214: 231-241. 10.1002/path.2276.View ArticlePubMedGoogle Scholar
- Steingrover R, Pogány K, Garcia EF, Jurriaans S, Brinkman K, et al: HIV-1 viral rebound dynamics after a single treatment interruption depends on time of initiation of highly active antiretroviral therapy. AIDS. 2008, 22: 1583-1588. 10.1097/QAD.0b013e328305bd77.View ArticlePubMedGoogle Scholar
- Paci P, Carello R, Bernaschi M, D'Offizi G, Castiglione F: Immune control of HIV-1 infection after therapy interruption: immediate versus deferred antiretroviral therapy. BMC Infect Dis. 2009, 9: 172-10.1186/1471-2334-9-172.View ArticlePubMedPubMed CentralGoogle Scholar
- Paci P, Castiglione F, Bernaschi m, Baldazzi V: A discrete/continuous model of anti-HIV response and therapy. IEEE Computer Society, Digital library Proceedins UKSIM. 2008, 481-486.Google Scholar
- Castiglione F, Poccia F, D'Offizi G, Bernaschi M: Mutation, Fitness, Viral Diversity, and Predictive Markers of Disease Progression in a Camputational Model of HIV Type 1 infection. AIDS Res Hum Retrovirus. 2004, 20 (12): 1314-1323. 10.1089/aid.2004.20.1314.View ArticleGoogle Scholar
- Ahdien-Grant L, Yamashita TE, Phair JP, Detels R, Wolinsky SM, et al: When to Initiate Highly Active Antiretroviral Therapy: A Cohort Approach. Am J Epidemiol. 2003, 157: 738-746. 10.1093/aje/kwg036.View ArticleGoogle Scholar
- Autran B, Carcelain G, Li TS, Blanc C, Mathez D, et al: Positive Effects of Combined Antiretroviral Therapy on CD4+ T Cell Homeostasis and Function in Advanced HIV Disease. Science. 1997, 277: 112-116. 10.1126/science.277.5322.112.View ArticlePubMedGoogle Scholar
- Yamashita TE, Phair JP, Muñoz A, Margolick JB, Detels R, et al: Immunologic and virologic response to highly active antiretroviral therapy in the Multicenter AIDS Cohort Study. AIDS. 2001, 15: 735-745. 10.1097/00002030-200104130-00009.View ArticlePubMedGoogle Scholar
- DHHS: Guidelines for the use of Antiretroviral Agents in HIV-1 Infected Adults and Adolescent. 2008, date last accessed, [http://aidsinfo.nih.gov]Google Scholar
- Celada F, Seiden PE: A computer model of cellular interaction in the immune system. Immunology Today. 1992, 13 (2): 56-62. 10.1016/0167-5699(92)90135-T.View ArticlePubMedGoogle Scholar
- Bernaschi M, Castiglione F: Design and implementation of an immune system simulator. Computers in Biology and Medicine. 2001, 31 (5): 303-331. 10.1016/S0010-4825(01)00011-7.View ArticlePubMedGoogle Scholar
- Farmer JD, Packard NH, Perelson AS: The immune system, adaptation and machine learning. Physica D. 1986, 22: 187-204. 10.1016/0167-2789(86)90240-X.View ArticleGoogle Scholar
- Baldazzi V, Paci P, Bernaschi M, Castiglione F: Modeling lymphocytes homing and encounters in lymph nodes. BMC Bioinformatics. 2009, 10: 387-10.1186/1471-2105-10-387.View ArticlePubMedPubMed CentralGoogle Scholar
- Richards FJ: A flexible growth function for empirical use. J Exp Bot. 1959, 10: 290-300. 10.1093/jxb/10.2.290.View ArticleGoogle Scholar
- Fiebig EW, Wright DJ, Rawal BD, Garrett PE, Schumacher RT, Peddada L, Heldebrant C, Smith R, Conrad A, Kleinman SH, Busch MP: Dynamics of HIV viremia and antibody seroconversion in plasma donors: implications for diagnosis and staging of primary HIV infection. AIDS. 2003, 17: 1871-1879. 10.1097/00002030-200309050-00005.View ArticlePubMedGoogle Scholar
- The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2334/11/56/prepub
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