INSERM U436, Mathematical and Statistical Modelling in Biology and Medicine, CHU Pitié-Salpêtrière, Paris, France
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Abstract |
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Keywords: resistance tests, modelling, therapy outcome
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Introduction |
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CD4 and CD8 levels as predictors of therapy outcome |
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Resistance characteristics of viral strains as predictors of therapy outcome |
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Several review papers1214 summarize some of these retrospective and prospective studies. In a recent work, Torre & Tambini15 performed a meta-analysis of six published randomized controlled trials to estimate the impact of resistance-guided antiretroviral therapy on virological outcome by comparison with patients based on standard of care. Their results supported the use of genotypic testing followed by expert interpretation. Two retrospective studies quoted by Haubrich & Demeter,14 including patients receiving ritonavir/saquinavir therapy, have shown that response to antiretroviral therapy can be predicted based on genotypic patterns, baseline genotypic resistance being correlated with virological failure. In other retrospective analyses it has emerged clearly that phenotypic drug susceptibility predicts sustained viral load suppression, particularly when virus remains susceptible to two or three drugs at the initiation of therapy. All these reanalysed studies underline the importance of genotype and phenotype testing as predictors of virological failure. However, they do not evaluate the utility of resistance assays in clinical care, the appropriate method to estimate this impact being a prospective evaluation. Hence, prospective studies have been performed. VIRADAPT, a prospective, non-blinded, randomized, controlled study of patients with a first antiretroviral therapy failure, has shown greater reduction in viral load over 6 months when the treatment was guided by genotype resistance testing. The improvement in viral suppression in groups treated based on genotypic testing compared with empirical treatment assignment has also been stressed by the GART trial, another randomized study including patients after a first triple-drug therapy failure. Phenotypic testing was explored in an open-label randomized comparison with standard of care management in patients whose first drug regimen failed. After 16 weeks, change in viral load was significantly greater in the phenotype group. The suggestion inspired by this study was in agreement with those of analysis using genotypic testing: significant improvement in short-term virological outcomes may be obtained when the selection of the new regimen is guided by phenotypic testing in patients whose previous therapy failed. However, phenotypic testing is clearly more time-consuming and, in many cases, more difficult to interpret than genotypic testing.
Retrospective and prospective studies re-analysed by DeGruttola et al.12 using a standardized data analysis plan pointed out the fact that cumulative susceptibility scores (such as GSS, genotypic susceptibility score, and PSS, phenotypic susceptibility score) containing summarized complex information could be used in clinical management of patients. However, the authors stress the importance of improving the accuracy of these scores and the need to use standard analytical methods when analysing data. Indeed, all the studies quoted above claim the interest of using resistance tests, but further investigations are necessary in order to eliminate their intervariability due to the quality of interpretation, the number of prior regimens and the quality of standard of care decisions.
Several questions persist (currently submitted to expert interpretation) regarding the clinical use of resistance testing to monitor antiretroviral therapy. Do strains present at undetectable levels at the initiation of the therapy contribute to therapy outcome? How can the use of resistance test results be optimized when making a therapeutic decision?
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A model-based approach for the prediction of therapy outcome |
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First, we simulated a cohort of HIV-infected patients using a dynamic model describing the interactions illustrated in Figure 1. Its deterministic part includes equations defining the time course of T cell subpopulations and virus strains under a combination therapy including one or more reverse transcriptase inhibitors (RTIs, nucleoside or non-nucleoside) and one protease inhibitor (PI). Lack of efficacy of treatment, interpreted as resistance levels, are symbolized by b (a composite parameter, expressed as the product of treatment-specific resistance levels and acting on interaction between target cells and virus, K1VT term, according to Figure 1) and h [an infected T cell producing Nh infectious virus particles and N(1 h) non-infectious ones, as represented in Figure 1], respectively. These parameters (b and h) only characterize global susceptibility of a strain to drugs, without explicit distinction between resistance conferred by one major mutation and resistance generated by several minor mutations, their values varying between 0 (complete susceptibility) and 1 (total resistance). The state variables are: target cells, latently infected cells, productively infected cells, infectious virus, non-infectious virus and immune responses (cytotoxic T lymphocytes) specific to two epitopes. This model also comprises a stochastic component that specifies the emergence of viral strains by mutation on one of the two epitopes chosen randomly, and associates characteristics (virulence, immunogenicity and specific drug efficacy) to each viral mutant, drawn from predefined distributions. HAART was initiated at different times in disease evolution, chosen randomly. All parameters of the model were chosen consistent with biological plausibility within ranges allowing realistic dynamics.
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Finally, we have obtained a regressive model predicting the variation in CD4 count and in log10 of viral load from baseline:
CD4 = f [baseline viral load, baseline CD8 count, number of detectable strains, sum of (resistance levels to RTIs), sum of (resistance levels to PI), max of (resistance levels to PI), min of (resistance levels to PI)] (1)
log10(viral load) = f [baseline viral load, baseline CD4 count, baseline CD8 count, sum of (resistance levels to PI), max of (resistance levels to PI), min of (resistance levels to PI), max (resistance levels to RTIs)] (2)
In expressions above, sum, max and min of resistance levels (parameters b and h) were calculated on detectable strains present at commencement of HAART.
These findings, consistent with the experimental data recalled in previous sections, may be considered as a validation of the underlying dynamic model and of the regressive model (that could be used further to deal with the experimentally out of reach question and to predict the therapy outcome). For example, the best predictive model of the viral load decrease includes both baseline CD8 and CD4 cell counts, while the model with the best predictive power explaining the increase in CD4 cell count contains baseline CD8 cell count. Our predictive regression model underlines a continuous effect (without any threshold) of baseline CD4 count on disease progression.
The first point highlighted by our modelling approach is related to viral diversity and its role in treatment outcome. Our results indicate that ignoring strains present at undetectable levels does not influence the quality of the prediction; moreover, they degrade it, the best predictive model does not rely on the characteristics of less frequent strains, nor even on their existence. This could be explained by the fact that, even if highly resistant, a strain present at low level at therapy initiation will not grow enough to become preponderant in a short time. Hence, our findings suggest that resistance assays could provide worthwhile complete information concerning the choice of the best-tailored drug combination. Since long-term results are the goal of the treatment, resistance tests have to be regularly updated.
The next aspect that results from our study is the use of the regressive model defined by Equations 1 and 2 when choosing adapted treatment regimens in HIV-infected patients. Indeed, all aforementioned studies stress the importance of resistance testing, especially when previous therapy has failed. We propose an efficient tool that incorporates knowledge provided by these tests in addition to classic predictive factors (such as baseline viral load, and CD4 and CD8 counts). However, in our approach, these classic markers are not likely to govern the choice of the best therapy combination, since the regressive model does not include interaction terms between them and treatments. Therefore, the choice will be based on resistance levels to drugs.
For example, consider a case where a combination of new drugs must be chosen for an HIV-infected person after treatment has failed. Suppose that the resistance characteristics of viral strains from this person are also known, the efficacy of each potential new therapy on patient quasispecies having been quantified by genotypic or phenotypic assays. In the context of our modelling approach this means that values for parameters b and h are given for each tested combination of drugs and for each viral strain identified in patients blood. In fact, it could be sufficient to identify parameters corresponding to the most susceptible and the most resistant strains (min and max of bs and hs in our regressive model) and global resistance levels to tested treatments (sum of bs and sum of hs here), since the predictive model (Equations 1 and 2) only includes this information. Therefore, candidate therapies can be classified according to the expected variations in viral load and CD4 count they induce, based on the regressive model. The interest of using a predictive model is enhanced when the viral quasispecies of the patient is not completely susceptible to any of the treatments available to select; in such a case the optimal therapy is not obvious. For instance, consider a simple configuration: the plasma sample of our patient contains three viral strains and we have to choose between two combinations of RTIs (treatments 1 and 2, T1 + T2, versus treatments 1 and 3, T1 + T3) to include in the best-adapted multidrugs regimen. Strains 1 and 2 are susceptible to T1 and T3 (treatment-specific resistance levels all equal to 0.1) and moderately resistant to T2 (treatment-specific resistance levels equal to 0.3), while strain 3 is completely susceptible to T1, susceptible to T2 and totally resistant to T3 (treatment-specific resistance levels equal to 0, 0.1 and 1, respectively). Specifically for these values, the intuitive choice (based on the minimum sum of treatment-specific resistance levels for each combination of drugs) would be T1 + T2, whereas the optimal choice based on our regressive model (more precisely on the minimum sum of composite resistance levels bs, participating in the prediction in Equation 1) is T1 + T3. The explanation is that total resistance of strain 3 to T3 is annihilated by its complete susceptibility to T1 (as detailed in dynamic model description, for each viral strain, parameter b is expressed as a product of treatment-specific resistance levels).
The regressive model described by Equations 1 and 2 could be replaced by logistic models if the response to predict is binary (treatment failure or not, viral load below or above a given threshold, etc.), or by survival regression models if the progression to a new AIDS-defining event or death is analysed. The dynamic model that generated the set of infected patients on which the regressive model was built could also be used as a predictive model (all the more because it provides long-term results) if specific information (infected CD4 count, CD8 count and viral load for each strain) were available.
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Conclusions |
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Acknowledgements |
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Footnotes |
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References |
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