Life cycle synchronization is a viral drug resistance mechanism

Viral infections are one of the major causes of death worldwide, with HIV infection alone resulting in over 1.2 million casualties per year. Antiviral drugs are now being administered for a variety of viral infections, including HIV, hepatitis B and C, and influenza. These therapies target a specific phase of the virus’s life cycle, yet their ultimate success depends on a variety of factors, such as adherence to a prescribed regimen and the emergence of viral drug resistance. The epidemiology and evolution of drug resistance have been extensively characterized, and it is generally assumed that drug resistance arises from mutations that alter the virus’s susceptibility to the direct action of the drug. In this paper, we consider the possibility that a virus population can evolve towards synchronizing its life cycle with the pattern of drug therapy. The periodicity of the drug treatment could then allow for a virus strain whose life cycle length is a multiple of the dosing interval to replicate only when the concentration of the drug is lowest. This process, referred to as “drug tolerance by synchronization”, could allow the virus population to maximize its overall fitness without having to alter drug binding or complete its life cycle in the drug’s presence. We use mathematical models and stochastic simulations to show that life cycle synchronization can indeed be a mechanism of viral drug tolerance. We show that this effect is more likely to occur when the variability in both viral life cycle and drug dose timing are low. More generally, we find that in the presence of periodic drug levels, time-averaged calculations of viral fitness do not accurately predict drug levels needed to eradicate infection, even if there is no synchronization. We derive an analytical expression for viral fitness that is sufficient to explain the drug-pattern-dependent survival of strains with any life cycle length. We discuss the implications of these findings for clinically relevant antiviral strategies

Modelling and controlling infectious diseases

The financial support by IDRC has made it much easier to put together network activities involving scientists in both countries, a special example is the large presence of the Chinese students in the 2012 Summer School on Mathematics for Public Health the Canadian group organized in Edmonton in May of 2012.Infectious disease control is a major challenge in China due to China’s fast growing economy, changing social networks and evolving health service infrastructures. The success of disease control in China has a profound impact beyond its borders. In support of better disease control, this five year research program was designed to enhance China’s national capacity for analyzing, modeling and predicting transmission dynamics of infectious diseases through joint research, training young scientists, and building collaborative relationships. This successful program was led by the National Center for AIDS/STD Control and Prevention (Chinese Centre for Disease Control and Prevention, China) and the Centre for Disease Modeling (York University, Canada), and involved a number of Canadian and Chinese universities in various areas of infectious disease modelling and control. The bilateral collaboration also trained numerous highly qualified personnel and built a network for sustaining collaboration. This capacity building was facilitated by joint projects and bilateral annual meetings in major cities in China and Canada. The research activities on modeling major public health threats of infectious diseases focused on major diseases in China and/or issues of global public health concern including HIV transmission and prevention among high risk population, HIV treatment and drug resistance, influenza, schistosomiasis, mutation and stemma of SIV and HIV, latent and active tuberculosis infection, HBV control and vaccination. The outputs of the project were reported through peer-reviewed publications and modelling– based and science-informed public policy recommendations

Dynamics of HIV treatment and social contagion

Modern-day management of infectious diseases is critically linked to the use of mathematical models to understand and predict dynamics at many levels, from the mechanisms of pathogenesis to the patterns of population-wide transmission and evolution. This thesis describes the development and application of mathematical techniques for HIV infection and dynamics on social networks. Treatment of HIV infection has improved dramatically in the past few decades but is still limited by the development of drug resistance and the inability of current therapies to completely eradicate the virus from an individual. We begin with a synthesis of the important evolutionary principles governing the HIV epidemic, emphasizing the role of modeling. We then describe a modeling framework to study the emergence of drug-resistant HIV within a patient. Our model integrates laboratory data and patient behavior, with the goal of predicting outcomes of clinical trials. Current results demonstrate how pharmacologic properties of antiretroviral drugs affect selection for drug resistance, and can explain drug-class-specific resistance risks. Thirdly, we describe models for a new class of drugs that aim to eliminate cells with latent viral infection. We provide estimates for the required efficacy of these drugs and describe the potential challenges of future clinical trials. Finally, models and mechanisms for understanding viral dynamics are increasingly finding applications outside traditional virology. They can be used to study the dynamics of behaviors, to help predict and intervene in their spread. We describe techniques for applying infectious disease models to social contagion, drawing on techniques for network epidemiology. We use this framework to interpret data on the interpersonal spread of health-related behaviors

A multiscale systems pharmacology framework to assess the prophylactic utility of antivirals against HIV-1

Pre-exposure prophylaxis (PrEP) has recently been identified as one of the five pillars by UNAIDS to achieve the goal of reducing the new infections to approximately 500,000 by 2020. Truvada is the only medication that is approved for PrEP. Although PrEP with Truvada is beneficial, there are a number of limitations. There are a number of novel compounds and treatment-approved antivirals that might overcome these limitations. The challenge is to screen for potential candidates and to design roll-out strategies. Pre-clinical experiments are very insightful but owning to a number of limitations they do not readily guide the candidate-screening and the designing deployment strategies. Although clinical trials provide these answers, they are ethically problematic and prohibitively costly. This highlights that tools are urgently needed that can determine the prophylactic utility of antivirals in order to prioritize candidates and to design the deployment strategies. To this end, we built a mathematical framework (pipeline) to serve as a tool to predict the prophylactic utility of antivirals. Building such a framework for PrEP is a challenging task, which requires solving modelling and simulation problems owing to the various complex processes occurring at different scales (multiscale). We presented the multiscale systems pharmacology framework that integrates processes occurring at various scales including; 1) microscale interactions of active moiety of NRTIs with viral DNA polymerization; 2) meso- and macroscale processes, such as the drug pharmacokinetics, viral replication dynamics; and 3) population scale processes, such as viral exposure and long-term infection probabilities after repeated virus exposures, similar to a clinical trial. The main algorithmic challenge considered in the work is the task of quantifying the infection probability after a viral challenge within a host under the influence of antiviral pharmacokinetics. Time-invariant reaction propensities are valid for constant target-site antiviral concentrations. We employed the theory of branching processes to derive extinction and infection probabilities for time-invariant reaction propensities. However, for time-varying antiviral concentrations at the target-site, the reaction propensities are time-variant. For time-variant reaction propensities, we introduced the reduced-state chemical master equation as an approximation. Furthermore, we adapted the recently developed rejection-based stochastic simulation algorithm. We tackled the challenge of the classification of stochastic trajectories as infection or extinction events which improves the run-time of the algorithm and at the same time guarantees that the misclassification error is below the user-defined threshold. In this work, we derived drug-class specific concentration-prophylactic efficacy (dose-response) curves. The framework allows for the translation of in vitro and ex vivo parameters into the measure of in vivo potency/efficacy. We analyzed all the treatment-approved antivirals for PrEP using the framework. We quantified the role of TDF and FTC and emphasized their complementary roles in the Truvada combination for PrEP. Furthermore, we suggested cost-effective alternatives, such as lamivudine, efavirenz, nevirapine etc. Using the pharmacokinetic model of DTG, we analyzed various roll-out scheme and found that it is non-inferior to truvada.Die HIV-Epidemie ist nach wie vor ein globales Problem. Während die Suche nach einer Heilung und einem Impfstoff weitergeht, hat sich das Hauptaugenmerk auf antiretrovirale Präventionsstrategien zur Eindämmung der Epidemie gelegt. Eine solche Strategie ist die sogenannte Präexpositionsprophylaxe (PrEP), die kürzlich von UNAIDS als eine der fünf Säulen zur Prävention identifiziert wurde. Dabei ist Truvada das einzige für PrEP zugelassen Medikament. Obwohl der Einsatz von Truvada Erfolge gezeigt hat, bestehen einige Einschränkungen. Eine Reihe neuartiger Wirkstoffe und behandlungserprobter antiviraler Mittel, die noch nicht zur PrEP Behandlung eingesetzt werden, könnten diese Einschränkungen bewältigen. Die große Aufgabe besteht darin, unter diesen Wirkstoffen potenzielle PrEP Kandidaten aus- findig zu machen und Einsatzstrategien zu entwickeln. Präklinische Experimente liefern hierbei nicht genügend Ergebnisse um ein Kandidaten-Screening vorzunehmen und klinische Studien sind ethisch problematisch und sehr kostspielig, da Tausende von Personen über mehrere Jahre hinweg beobachtet und untersucht werden müssen. Als Hilfestellung haben wir ein Systempharmakologie- Framework entwickelt, welches es ermöglicht, den prophylaktischen Nutzen von antiviralen Medikamenten zu bestimmen, Medikamenten-Kandidaten zu priorisieren und Einsatzstrategien zu entwerfen. Um ein solches Framework zu entwickeln müssen verschiedene Modellierungs- und die Simulationensprobleme gelöst werden, da bei der PrEP komplexe Prozesse verschiedene Größenordnungen (Multiskala) involviert sind. Das Framework integriert flexibel Prozesse: (1) molekularen Interaktionen zwischen dem Medikament und den viralen Enzymen auf der mikroskalen Ebene (2) antivirale Pharmakokinetik, Pharmakodynamik (viraler Replikationszyklus) auf den mesoskalen und makroskalen Ebenen und (3) populationsebene Prozesse wie virale Exposition und die Infektionswährscheinlichkeit nach vermehrter viraler Exposition; Prozesse, wie sie auch in klinischen Studien auftreten können. Eine der größten algorithmischen Herausforderungen, die in dieser Arbeit bewältigt wurde, ist die Quantifizierung der Infektionswährscheinlichkeit. Wir haben mit Hilfe der Theorie des Verzweigungsprozesses die Formeln für eine zeitkonstante Wirkstoffkonzentration am Zielort abgeleitet. Für die zeitvariable Wirkstoffkonzentration am Zielort haben wir eine chemische Master-Gleichung mit reduziertem Zustand eingeführt und einen stochastischen Algorithmus (EXTRANDE) adaptiert, die das Problem der Dimensionalität der chemische Master-Gleichung umgehen. Das Framework ermöglicht es präklinisches Wissen in Parameter klinischer Relevanz zu über- setzen. Dabei hilft es unnötige klinische Studien zu vermeiden, die nicht nur Geld und Zeit kosten, sondern auch das Risiko bergen, dass Menschen Schaden nehmen. Mithilfe dieses Frameworks haben wir alle bisherigen für die HIV-behandlung zugelassenen Medikamenten zum Präventionszweck überprüft. Wir haben die komplementäre Rolen von Tenofovir Disoproxil Fumarate and Emtricitabine für PrEP erklärt. Darüber hinaus haben wir einige kostengünstige Alternative (Lamivudine, Nevirapine, Efavirenz u.a.) zu Truvada für weiter Überprüfung vorgeschlagen. Außerdem hat unsere Analyse gezeigt, dass Dolutegravir Truvada nicht unterlegen ist

Contributions to the Mathematical Systems Medicine of Antimicrobial Therapy and Genotype-Phenotype Inference.

The following summary of my publications describes the main ideas in the corresponding research articles and clarfifies my contribution in multi-author publications. I decided to apply for habilitation according to x2.I.1.(c) of the Habilitationsordnung (this path is usually referred as Kumulative Habilitation"). I selected 13 first- or last author publications for this habilitation that concern contributions to the mathematical systems medicine of antiviral therapy [tMH10, tMS+11, FtK+11, tMMS12, DSt12, DWSt15, Dt16, DSt16, DDKt18, DSD+19, DDKt19], as well as inference of genotype-phenotype associations [SDH+15, SSJ+18]. The selected publications represent my major contributions in this research eld since submitting my doctoral thesis in September 2009

When to Initiate, When to Switch, and How to Sequence HIV Therapies: A Markov Decision Process Approach

HIV and AIDS are major health care problems throughout the world,with 40 million people living with HIV by the end of 2005. Inthat year alone, 5 million people acquired HIV, and 3 millionpeople died of AIDS. For many patients, advances in therapies overthe past ten years have changed HIV from a fatal disease to achronic, yet manageable condition. The purpose of thisdissertation is to address the challenge of effectively managingHIV therapies, with a goal of maximizing a patient's totalexpected lifetime or quality-adjusted lifetime.Perhaps the most important issue in HIV care is when a patientshould initiate therapy. Benefits of delaying therapy includeavoiding the negative side effects and toxicities associated withthe drugs, delaying selective pressures that induce thedevelopment of resistant strains of the virus, and preserving alimited number of treatment options. On the other hand, the risksof delayed therapy include the possibility of irreversible damageto the immune system, development of AIDS-related complications,and death. We develop a Markov decision process (MDP) model thatexamines this question, and we solve it using clinical data.Because of the development of resistance to administered therapiesover time, an extension to the initiation question arises: whenshould a patient switch therapies? Also, inherent in both theinitiation and switching questions is the question of whichtherapy to use each time. We develop MDP models that consider theswitching and sequencing problems, and we discuss the challengesinvolved in solving these models

Mathematical models of HIV epidemics in Australia and South East Asia

This thesis consists of a series of publications that address timely public health questions in the field of mathematical epidemiology of HIV/AIDS and sexually transmissible infections (STIs). Mathematical epidemiology requires the forecasting of epidemic trajectories coupled with degrees of uncertainty. This study commenced with the development of new software and utilisation of techniques from a variety of disciplines to assist the conduct of uncertainty and sensitivity analyses. A user-friendly software package was developed and also used in the subsequent projects of this study. The number of HIV diagnoses in Australia has been increasing over the past decade and it was timely for a detailed analysis to be conducted. This study investigated the differing trends observed in three States of Australia: New South Wales, Queensland, and Victoria. The model included epidemiological, clinical, behavioural, and biological data to analyse and identify the differences in each State. It found that the only way to fit the data was to incorporate changes in other STIs as interactive biological cofactors. This model was then extended to examine the impact of testing and early treatment of HIV as a means of preventing new HIV infections. It was found that increasing testing rates for HIV can have a significant impact on reducing further secondary infections. This has since become a topic of very large international interest. Since syphilis epidemics have resurged and could facilitate HIV transmission, possible public health intervention strategies were simulated using a detailed individual-based model. This formed the foundation for Australia’s National Gay Men’s Syphilis Action Plan (NGMSAP). This study then examined the potential benefits in HIV incidence that could be due to the implementation of the NGMSAP. Lastly, this study examined an important current issue for neighbouring countries in the region, namely, the impact of universal HIV treatment access in Southeast Asia on the development of drug resistance. This model-based investigation found that a high prevalence of drug resistance can potentially develop, however, increased treatment access will likely reduce the incidence of new HIV infections. It also found that regular viral load tests can mitigate the prevalence of drug resistance in the population

Predicting the outcomes of HIV treatment interruptions using computational modelling

In the past 30 years, HIV infection made a transition from fatal to chronic disease due to the emergence of potent treatment largely suppressing viral replication. However, this medication must be administered life-long on a regular basis to maintain viral suppression and is not always well tolerated. Any interruption of treatment causes residual virus to be reactivated and infection to progress, where the underlying processes occurring and consequences for the immune system are still poorly understood. Nonetheless, treatment interruptions are common due to adherence issues or limited access to antiretroviral drugs. Early clinical studies, aiming at application of treatment interruptions in a structured way, gave contradictory results concerning patient safety, discouraging further trials. In-silico models potentially add to knowledge but a review of the Literature indicates most current models used for studying treatment interruptions (equation-based), neglect recent clinical findings of collagen formation in lymphatic tissue due to HIV and its crucial role in immune system stability and efficacy. The aim of this research is the construction and application of so-called ‘Bottom-Up’ models to allow improved assessment of these processes in relation to HIV treatment interruptions. In this regard, a novel computational model based on 2D Cellular Automata for lymphatic tissue depletion and associated damage to the immune system was developed. Hence, (i) using this model, the influence of spatial distribution of collagen formation on HIV infection progression speed was evaluated while discussing aspects of computational performance. Further, (ii) direct Monte Carlo simulations were employed to explore the accumulation of tissue impairment due to repeated treatment interruptions and consequences for long-term prognosis. Finally, (iii) an inverse Monte Carlo approach was used to reconstruct yet unknown characteristics of patient groups. This is based on sparse data from past clinical studies on treatment interruptions with the aim of explaining their contradictory results

In-vivo dynamics of HIV-1 evolution

Ph. D, Faculty of Science, University of Witwatersrand, 2011The evolution of drug resistance in human immunodeficiency virus (HIV) infection has been a focus of research in many fields, as it continues to pose a problem to disease prevention and HIV patient management. In addition to techniques of molecular biology, studies in mathematical modelling have contributed to the knowledge here, but many questions remain unanswered. This thesis explores the application of a number of hybrid stochastic/deterministic models of viral replication to scenarios where viral evolution may be clinically or epidemiologically important. The choice of appropriate measures of viral evolution/diversity is non-trivial, and this impacts on the choice of mathematical techniques deployed. The use of probability generating functions to describe mutations occurring during early infection scenarios suggest that very early interventions such as pre-exposure prophylaxis (PrEP) or vaccines may substantially reduce viral diversity in cases of breakthrough infection. A modified survival analysis coupled to a deterministic model of viral replication during transient and chronic treatment helps identify clinically measurable indicators of the time it takes for deleterious rare mutations to appear. Lastly, persistence of problematic mutations is studied through the use of deterministic models with stochastic averaging over initial conditions

Effet des antirétroviraux sur la pathogénèse du VIH : une étude par modélisation mathématique intégrant la cinétique du virus, de l’immunité, du médicament, et le comportement d’adhésion avec leurs variabilités interindividuelles

Les traitements antirétroviraux actuels permettent à beaucoup de patients du VIH de maintenir leurs charges virales à de très faibles niveaux sur plusieurs décennies. Or, malgré ce succès scientifique, de nombreux problèmes persistent, et à ce jour, aucun traitement ne permet de venir à bout du virus. Une prise à vie d’antirétroviraux est donc nécessaire, impliquant ainsi des contraintes posologiques pour le patient, une potentielle atteinte à sa qualité de vie et un fardeau financier pour la société. À ces inconvénients s’ajoute le risque de développer de la résistance aux médicaments. Même si les traitements demeurent efficaces, la persistance du virus peut également causer des dommages aux différents tissus et organes de l’hôte. En se basant sur des connaissances de pointe dans le domaine du VIH, cette thèse aborde ces problématiques par une approche de pharmacologie quantitative des systèmes, appuyée par des données cliniques. L’objectif principal fut d’informer les mécanismes sous-jacents au développement de la résistance et à la persistance du virus in vivo. Nous avons tiré profit d’un maximum d’information sur l’ensemble des composantes impliquées dans la réponse virologique aux médicaments. Les modèles que nous avons développés joignent différentes échelles d’information, allant de l’échelle moléculaire, à virale, puis cellulaire, jusqu’au niveau clinique. Nous avons ensuite évalué la cohérence d’hypothèses de causalité aux phénomènes étudiés en testant la capacité de ces modèles à expliquer et reproduire des données empiriques. En premier lieu, nous avons développé un modèle visant à mieux comprendre l’ampleur du développement de la résistance chez les patients sous plusieurs traitements. Le modèle combine plusieurs composantes, dont la cinétique virale, l’immunité, la pharmacocinétique et pharmacodynamique, l’adhésion au médicament ainsi que leurs variabilités interindividuelles. Les prédictions du modèle in silico concordent avec les observations cliniques d’échec virologique pour les trois traitements considérés et qui font intervenir l’efavirenz, l’emtricitabine, le ténofovir, le darunavir et le ritonavir. Par cette approche intégrative, nous avons remédié à la lacune des modèles précédents qui sous-estimaient grandement le risque de résistance. Nos résultats soulignent le rôle important que joue la faible pénétration des médicaments au niveau des ganglions lymphatiques dans le développement de la résistance. Ce modèle se veut prometteur de son utilité dans la prédiction de réponse virologique en clinique. Nous nous sommes ensuite intéressés au phénomène du déclin en différentes phases, de plus en plus lentes, des charges virales des patients sous traitement antirétroviral. Les causes sous-jacentes à ce phénomène restent encore obscures. Une divergence d’opinions sur le rôle de la faible pénétration tissulaire des médicaments quant à l’existence de ces phases, divise actuellement les efforts de recherche. Afin de mettre la lumière sur cette implication, nous avons ajouté à notre modèle intégratif des taux différents de pénétration tissulaire. Nos résultats indiquent que l’implication seule de la pénétration des médicaments dans l’explication des phases de déclin serait synonyme d’un grand risque de développement de résistance. Ces prédictions contredisent quantitativement la réalité observée (peu de résistance), nous faisant conclure que cette hypothèse ne peut vraisemblablement pas expliquer le phénomène en question. La dernière partie de la thèse se penche sur la capacité de certains patients à maintenir de faibles charges virales après l’interruption d’un traitement prolongé. Nous avons revisité une corrélation rapportée entre la charge virale résiduelle et la durée de maintien post-interruption de charges faibles. L’interprétation de cette corrélation s’avère difficile, puisque la durée en question n’inclut pas seulement le temps de contrôle de la virémie, mais également le temps nécessaire à la charge virale d’atteindre un seuil de tolérance à partir de la charge virale résiduelle. En utilisant un modèle mécanistique et des techniques statistiques avancées, nous avons réussi à estimer la durée attendue de contrôle réel de la charge virale ainsi que la variabilité interindividuelle associée. Contrairement à l’interprétation directe de la corrélation rapportée dans la littérature, notre analyse révèle que la variabilité interindividuelle du temps de contrôle de la virémie n’est pas associée à la charge virale résiduelle. L’approche in silico adoptée dans cette thèse s’inscrit dans l’effort global de ces dernières années visant à minimiser le fardeau humain et le coût financier dans le développement du médicament. L’ensemble de nos résultats de modélisation suggèrent qu’une meilleure pénétration dans les ganglions lymphatiques diminuerait le nombre de cas de résistance chez les patients non-adhérents. Cependant, ils indiquent qu’une telle amélioration aurait peu d’influence sur la vitesse de déclin des charges virales. Aussi, quelle que soit l’influence de la pénétration lymphatique sur la virémie résiduelle, son amélioration n’aurait pas d’impact sur la capacité des patients à contrôler leurs charges virales après avoir cessé les antirétroviraux.Tremendous progress was made in treating people living with HIV. Nowadays, antiretroviral therapy usually allows patients to suppress viral loads for several years, if not decades. Despite this scientific achievement, chronic drug intake is usually necessary as no treatment can completely eradicate the virus. Patients under these conditions may experience constant side effects. Further, patients’ tissues and organs may become damaged over time, as chronic immune activation is observed in most patients despite adequate drug intake and undetectable viremia. The number of treatment options can also become seriously limited over time if patients’ viruses develop and accumulate drug resistance. These issues motivate current scientific efforts. Hopefully, results from these efforts may lead to improvements in patient’s quality of life and lower the financial burden HIV imposes on society. Using the most up-to-date knowledge in the field of HIV, this thesis addresses some of the above-mentioned issues through a quantitative systems pharmacology approach supported by clinical data. Our main objective was to inform the mechanisms underlying the development of resistance and the persistence of the virus in vivo. We used available information on the components involved in the virologic response to drugs. This allowed developing models that bridge multiple scales, going from molecular, to viral, then cellular and finally to the clinical level. By assessing the ensuing models’ ability to explain and reproduce empirical data, we studied the consistency of hypotheses regarding the causality of studied phenomena. First, we developed a model allowing to better understand the extent of drug resistance development in patients undergoing antiretrovial therapy. The model combines several components, including viral kinetics, immunity, pharmacokinetics and pharmacodynamics, drug adherence as well as their interindividual variability. Predictions originating from our in silico model are consistent with clinical observations of virologic failure for the three treatments that were considered consisting of efavirenz, emtricitabine, tenofovir, darunavir and ritonavir. Through this integrative approach, we have remedied previous models that largely underestimated the risk of resistance. Our results highlight the important role played by low lymph node drug penetration in the development of resistance. The model we developed is an added forward step toward the use of in silico methods in the prediction of virologic responses in HIV patients. We then investigated the causes underlying the increasingly slow phases of viral decline observed in patients initiating antiretroviral therapy. Opinions differ as to the role played by low drug penetration in tissues in the existence of these phases, leading to a fragmentation in research efforts. To shed light on this issue, we additionally considered, in our integrative model, different rates of tissue penetration. Our results indicate that, if low drug penetration were the only cause of the decline in phases of the viral load, then the ensuing low drug exposure would put patients at a very high risk of developing drug resistance. Prediction of high risk quantitatively contradicts the observed reality (little to no resistance), leading us to conclude that low penetration is unlikely a fair explanation to the phases of viral decline. The last part of the thesis examines the ability of some patients to maintain low viral loads after interrupting prolonged antiretroviral therapy. We revisited a reported correlation between viral load values at interruption (also called residual viremia) and the duration of time patients maintained low viral loads afterwards. The interpretation of this correlation proved to be challenging since this duration includes both a time of complete control of viremia and a time during which viremia grows to a low-level threshold once control is lost. Using a mechanistic model and advanced statistical techniques, we were able to estimate the expected duration of complete viremia control along with its interindividual variability. Contrary to a direct interpretation of the correlation reported in the literature, our analysis reveals that the time of viremia control is unlikely associated with patients’ residual viremia. In summary, the in silico approach adopted in this thesis is part of an overall effort aiming to minimize patient recruitment and the financial costs involved in drug development. Our models suggest that better drug penetration in lymph nodes would likely lead to a decreased risk of drug resistance in non-adherent patients. However, our results also suggest that such an improvement would have little influence on the rate of decline of viral loads. Further, and regardless of the influence lymphatic tissue drug penetration may have on residual viremia, this improvement would not favour viremia control after antiretroviral discontinuation