Abstract:Recent discoveries of direct acting antivirals against Hepatitis C virus (HCV) have raised hopes of effective treatment via combination therapies. Yet rapid evolution and high diversity of HCV populations, combined with the reality of suboptimal treatment adherence, make drug resistance a clinical and public health concern. We develop a general model incorporating viral dynamics and pharmacokinetics/ pharmacodynamics to assess how suboptimal adherence affects resistance development and clinical outcomes. We de… Show more
“…Figure 1.Time to first detection of human pathogens resistant to vaccines [1–6] and antimicrobial drugs [7]. Similar patterns exist for antiviral drugs, although antiviral resistance evolution can often be slowed by the use of combination antiviral therapy [8,9]. Viral vaccines are labelled in purple, bacterial vaccines are labelled in green.…”
Section: Introductionmentioning
confidence: 99%
“…But that cannot be a general explanation: viruses rapidly evolve resistance to antiviral drugs. For example, resistance to influenza [33,34] and herpesvirus drugs [35,36] emerged within a few years of FDA approval, and resistance to antivirals rapidly arises within human immunodeficiency virus (HIV) and hepatitis C virus (HCV)-infected patients unless they strictly adhere to certain treatment protocols [8,9] (a point to which we return below). Moreover, vaccine resistance has yet to emerge in several bacteria species (figure 1) even though drug resistance readily does.…”
Why is drug resistance common and vaccine resistance rare? Drugs and vaccines both impose substantial pressure on pathogen populations to evolve resistance and indeed, drug resistance typically emerges soon after the introduction of a drug. But vaccine resistance has only rarely emerged. Using well-established principles of population genetics and evolutionary ecology, we argue that two key differences between vaccines and drugs explain why vaccines have so far proved more robust against evolution than drugs. First, vaccines tend to work prophylactically while drugs tend to work therapeutically. Second, vaccines tend to induce immune responses against multiple targets on a pathogen while drugs tend to target very few. Consequently, pathogen populations generate less variation for vaccine resistance than they do for drug resistance, and selection has fewer opportunities to act on that variation. When vaccine resistance has evolved, these generalities have been violated. With careful forethought, it may be possible to identify vaccines at risk of failure even before they are introduced.
“…Figure 1.Time to first detection of human pathogens resistant to vaccines [1–6] and antimicrobial drugs [7]. Similar patterns exist for antiviral drugs, although antiviral resistance evolution can often be slowed by the use of combination antiviral therapy [8,9]. Viral vaccines are labelled in purple, bacterial vaccines are labelled in green.…”
Section: Introductionmentioning
confidence: 99%
“…But that cannot be a general explanation: viruses rapidly evolve resistance to antiviral drugs. For example, resistance to influenza [33,34] and herpesvirus drugs [35,36] emerged within a few years of FDA approval, and resistance to antivirals rapidly arises within human immunodeficiency virus (HIV) and hepatitis C virus (HCV)-infected patients unless they strictly adhere to certain treatment protocols [8,9] (a point to which we return below). Moreover, vaccine resistance has yet to emerge in several bacteria species (figure 1) even though drug resistance readily does.…”
Why is drug resistance common and vaccine resistance rare? Drugs and vaccines both impose substantial pressure on pathogen populations to evolve resistance and indeed, drug resistance typically emerges soon after the introduction of a drug. But vaccine resistance has only rarely emerged. Using well-established principles of population genetics and evolutionary ecology, we argue that two key differences between vaccines and drugs explain why vaccines have so far proved more robust against evolution than drugs. First, vaccines tend to work prophylactically while drugs tend to work therapeutically. Second, vaccines tend to induce immune responses against multiple targets on a pathogen while drugs tend to target very few. Consequently, pathogen populations generate less variation for vaccine resistance than they do for drug resistance, and selection has fewer opportunities to act on that variation. When vaccine resistance has evolved, these generalities have been violated. With careful forethought, it may be possible to identify vaccines at risk of failure even before they are introduced.
“…393 In addition, established microbial populations are structured, even within a single 394 patient [56], and competition is local, which decreases their effective size, thus making 395 stochasticity relevant. While a few previous studies did take stochasticity into account, 396 some did not include logistic growth or compensation of the cost of resistance [36], while 397 others made specific assumptions on treatments or epidemiology [57,58], focused on 398 numerical results with few analytical predictions [59], or assumed a constant population 399 size [33]. The present model has the advantage of being quite general while fully 400 accounting for stochasticity and finite-population effects.…”
AbstractThe evolution of antimicrobial resistance can be strongly affected by variations of antimicrobial concentration. Here, we study the impact of periodic alternations of absence and presence of antimicrobial on resistance evolution in a microbial population, using a stochastic model that includes variations of both population composition and size, and fully incorporates stochastic population extinctions. We show that fast alternations of presence and absence of antimicrobial are inefficient to eradicate the microbial population and strongly favor the establishment of resistance, unless the antimicrobial increases enough the death rate. We further demonstrate that if the period of alternations is longer than a threshold value, the microbial population goes extinct upon the first addition of antimicrobial, if it is not rescued by resistance. We express the probability that the population is eradicated upon the first addition of antimicrobial, assuming rare mutations. Rescue by resistance can happen either if resistant mutants preexist, or if they appear after antimicrobial is added to the environment. Importantly, the latter case is fully prevented by perfect biostatic antimicrobials that completely stop division of sensitive microorganisms. By contrast, we show that the parameter regime where treatment is efficient is larger for biocidal drugs than for biostatic drugs. This sheds light on the respective merits of different antimicrobial modes of action.Author summaryAntimicrobials select for resistance, which threatens to make antimicrobials useless. Understanding the evolution of antimicrobial resistance is therefore of crucial importance. Under what circumstances are microbial populations eradicated by antimicrobials? Conversely, when are they rescued by resistance? We address these questions employing a stochastic model that incorporates variations of both population composition and size. We consider periodic alternations of absence and presence of antimicrobial, which may model a treatment. We find a threshold period above which the first phase with antimicrobial fully determines the fate of the population. Faster alternations strongly select for resistance, and are inefficient to eradicate the microbial population, unless the death rate induced by the treatment is large enough. For longer alternation periods, we calculate the probability that the microbial population gets eradicated. We further demonstrate the different merits of biostatic antimicrobials, which prevent sensitive microbes from dividing, and of biocidal ones, which kill sensitive microbes.
“…When an ODE‐based mechanistic model is available, the modeled treatment can be adapted using this model. This has been described by Rosenberg et al for the supervised treatment interruption strategies, or in the pharmacokinetic‐pharmacodynamic field . The optimal control theory can be applied for globally optimizing the treatment regime, which has been proposed by Castiglione and Piccoli, and applied to optimizing the treatment of HIV infected patients .…”
Section: Introductionmentioning
confidence: 99%
“…This has been described by Rosenberg et al 30 for the supervised treatment interruption strategies, or in the pharmacokinetic-pharmacodynamic field. 17,31 The optimal control theory can be applied for globally optimizing the treatment regime, which has been proposed by Castiglione and Piccoli, 32 and applied to optimizing the treatment of HIV infected patients. 33,34 However, as noted by Chakraborty and Murphy 35 these works do not sufficiently take into account the statistical issues of the problem, ie, model parameters have to be estimated and for efficient estimation and random effects have to be introduced in the statistical model.…”
In human immunodeficiency virus–infected patients, antiretroviral therapy suppresses the viral replication, which is followed in most patients by a restoration of CD4+ T cells pool. For patients who fail to do so, repeated injections of exogenous interleukin 7 (IL7) are experimented. The IL7 is a cytokine that is involved in the T cell homeostasis and the INSPIRE study has shown that injections of IL7 induced a proliferation of CD4+ T cells. Phase I/II INSPIRE 2 and 3 studies have evaluated a protocol in which a first cycle of three IL7 injections is followed by a new cycle at each visit when the patient has less than 550 CD4 cells/μL. Restoration of the CD4 concentration has been demonstrated, but the long‐term best adaptive protocol is yet to be determined. A mechanistic model of the evolution of CD4 after IL7 injections has been developed, which is based on a system of ordinary differential equations and includes random effects. Based on the estimation of this model, we use a Bayesian approach to forecast the dynamics of CD4 in new patients. We propose four prediction‐based adaptive protocols of injections to minimize the time spent under 500 CD4 cells/μL for each patient, without increasing the number of injections received too much. We show that our protocols significantly reduce the time spent under 500 CD4 over a period of two years, without increasing the number of injections. These protocols have the potential to increase the efficiency of this therapy.
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