Understanding patterns of HIV multi-drug resistance through models of temporal and spatial drug heterogeneity
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Evaluation Summary:
This study considers how HIV evolutionary dynamics in a multiple drug-treated individual can give rise to the clinical patterns of the accrual of drug resistance mutations, including with understandings of the pharmacokinetics of the drugs in the body to help explain some of the patterns. The subject is of importance both clinically – for the optimal treatment choice for people living with HIV – and scientifically, due to the potential to predict and interpret evolutionary trajectories.
(This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1 and Reviewer #3 agreed to share their names with the authors.)
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- Evolutionary Biology (eLife)
Abstract
Triple-drug therapies have transformed HIV from a fatal condition to a chronic one. These therapies should prevent HIV drug resistance evolution, because one or more drugs suppress any partially resistant viruses. In practice, such therapies drastically reduced, but did not eliminate, resistance evolution. In this article, we reanalyze published data from an evolutionary perspective and demonstrate several intriguing patterns about HIV resistance evolution - resistance evolves (1) even after years on successful therapy, (2) sequentially, often via one mutation at a time and (3) in a partially predictable order. We describe how these observations might emerge under two models of HIV drugs varying in space or time. Despite decades of work in this area, much opportunity remains to create models with realistic parameters for three drugs, and to match model outcomes to resistance rates and genetic patterns from individuals on triple-drug therapy. Further, lessons from HIV may inform other systems.
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Author Response:
Reviewer #1 (Public Review):
My main concern with this work is the absence of formal statistical analyses to support the authors' interpretation. These assertions seem to be based on a visual analysis of the data. In my opinion, formal statistical analyses should be performed. Also, I am not certain the evidence for the predictable ordering of mutation is sufficient.
In particular, the statements regarding the rate of drug resistance evolution, the proportion of patients with 0-3 drug mutations, and the ordering of mutations do not receive formal statistical analysis, but are important to the interpretation. Indeed, formal statistical analysis does not appear in this manuscript.
Without these analysis, it does not seem possible at present to assess whether the authors have achieved their aims.
In response to Reviewer …
Author Response:
Reviewer #1 (Public Review):
My main concern with this work is the absence of formal statistical analyses to support the authors' interpretation. These assertions seem to be based on a visual analysis of the data. In my opinion, formal statistical analyses should be performed. Also, I am not certain the evidence for the predictable ordering of mutation is sufficient.
In particular, the statements regarding the rate of drug resistance evolution, the proportion of patients with 0-3 drug mutations, and the ordering of mutations do not receive formal statistical analysis, but are important to the interpretation. Indeed, formal statistical analysis does not appear in this manuscript.
Without these analysis, it does not seem possible at present to assess whether the authors have achieved their aims.
In response to Reviewer #1’s useful suggestion that we quantify formally the findings reported in Figure 2, we had added several new analyses.
Reviewer #2 (Public Review):
I found myself a little disappointed that the authors had stopped short of doing any modelling, especially given their remarks in the introduction about the need to match models to data, and the lack of a framework for understanding how best to recapitulate clinical data.
We share the desire to add quantitative modeling matched to observations from clinical data.
We focused on a combination of drugs with well-characterized mutation rates, mutant-selection windows, drug penetrances across multiple compartments, half-life and detailed clinical data (i.e., what is plotted in Fig 1A and Fig 2B and C) - 3TC+D4T+NFV. We extended two existing models of of spatial (Moreno-Gamez et al 2015) or temporal (Rosenbloom et al 2012) heterogeneity (via incomplete drug penetrance or adherence, respectively) to account for three drugs and simulated 1500 patients where we examined clinical features (resistance timing, number of mutations and order of mutations) similar to our analysis on real viral data. In doing so, we discovered that, for example, while the model of temporal heterogeneity can create sequential and predictable evolution of resistance, under such a model, very little resistance evolution emerges after initial virologic suppression, even in patients with moderate or low adherence. This model outcome is inconsistent with the ongoing resistance evolution observed so frequently in individuals with HIV. This finding validates our argument in the initial submission that quantitative models paired with clinical data are necessary to understand the evolution of multi-drug resistance, and motivates new questions about which types of adherence behaviors can allow ongoing resistance to emerge .
While we still very much believe that a future study should compare patterns across many different types of triple drug therapies, starting with one well-characterized therapy already helps us understand which clinical patterns emerge straightforwardly from simple models and which ones do not, and motivates future thinking about how these models must be extended.
Finally, I found it hard to pick out the new points being made by the authors from the previous literature, as well as the implications of these new ideas. For example, spatial and temporal differences in drug concentration have been used to explain viral rebounds etc. (which the authors discuss), however, is the central point in this paper that these two models of viral dynamics could also explain the three-fold pattern (as described in the Overview)? Perhaps the motivation could be clarified. I'm sure this will be a case of shortening and clarifying the introduction. (This confusion was compounded somewhat by the lack of quantitative analysis as the point above.)
While previous studies have certainly explored the role of spatial and temporal heterogeneity in drug levels (brought on by imperfect penetrance or adherence) in permitting the evolution of drug resistance, there are no current studies that we know about that examine multiple carefully parameterized triple-drug drug therapies that vary in time and space and are compared to multiple facets of clinical data (resistance timing and rates, mutation presence/absence, and mutational ordering). To help make this point clearer, we’ve added a table (Supplemental Table 1) that discusses the pre-existing literature of models, what types of therapies are examined (one, two or three drugs), whether or not they’re compared to patient data, and what type. In addition, we have attempted to clarify this point in the introduction.
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Evaluation Summary:
This study considers how HIV evolutionary dynamics in a multiple drug-treated individual can give rise to the clinical patterns of the accrual of drug resistance mutations, including with understandings of the pharmacokinetics of the drugs in the body to help explain some of the patterns. The subject is of importance both clinically – for the optimal treatment choice for people living with HIV – and scientifically, due to the potential to predict and interpret evolutionary trajectories.
(This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1 and Reviewer #3 agreed to share their names with the authors.)
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Reviewer #1 (Public Review):
The authors reanalyse previously published data to identify evolutionary patterns in HIV evolution during three drug treatment. Specifically they assert that the rate of resistant evolution is constant over time, that mutations occur sequentially (rather than multiple occurring at once) and that the order in which mutations occurs is largely consistent. They then use an understanding of the pharmacokinetics of the drugs in the body to explain some of the patterns. In my opinion, these are important observations that may have an important impact on HIV treatment (though this is not my area of expertise, which is in antibiotic combination therapy against bacteria).
My main concern with this work is the absence of formal statistical analyses to support the authors' interpretation. These assertions seem to be …
Reviewer #1 (Public Review):
The authors reanalyse previously published data to identify evolutionary patterns in HIV evolution during three drug treatment. Specifically they assert that the rate of resistant evolution is constant over time, that mutations occur sequentially (rather than multiple occurring at once) and that the order in which mutations occurs is largely consistent. They then use an understanding of the pharmacokinetics of the drugs in the body to explain some of the patterns. In my opinion, these are important observations that may have an important impact on HIV treatment (though this is not my area of expertise, which is in antibiotic combination therapy against bacteria).
My main concern with this work is the absence of formal statistical analyses to support the authors' interpretation. These assertions seem to be based on a visual analysis of the data. In my opinion, formal statistical analyses should be performed. Also, I am not certain the evidence for the predictable ordering of mutation is sufficient.
In particular, the statements regarding the rate of drug resistance evolution, the proportion of patients with 0-3 drug mutations, and the ordering of mutations do not receive formal statistical analysis, but are important to the interpretation. Indeed, formal statistical analysis does not appear in this manuscript.
Without these analysis, it does not seem possible at present to assess whether the authors have achieved their aims.
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Reviewer #2 (Public Review):
Overview:
This perspective piece considers two models of within-host HIV dynamics in the presence of triple drug therapy. The authors explain how the two models of spatial heterogeneity of drug concentration within the body and temporal fluctuations of drug concentration could give rise to three patterns that they characterise in drug resistance data. Namely, that DRMs evolve consistently through time (after a transient phase), that mutants evolve one mutation at a time, and that these multi-drug resistant mutants occur in a particular order.
General comments:
I found the paper engagingly written and mostly very clear. I particularly liked the clear figures.
I must admit that the paper was rather unusual in its presentation. The paper discusses the points in great detail and - while very clear - I wondered …
Reviewer #2 (Public Review):
Overview:
This perspective piece considers two models of within-host HIV dynamics in the presence of triple drug therapy. The authors explain how the two models of spatial heterogeneity of drug concentration within the body and temporal fluctuations of drug concentration could give rise to three patterns that they characterise in drug resistance data. Namely, that DRMs evolve consistently through time (after a transient phase), that mutants evolve one mutation at a time, and that these multi-drug resistant mutants occur in a particular order.
General comments:
I found the paper engagingly written and mostly very clear. I particularly liked the clear figures.
I must admit that the paper was rather unusual in its presentation. The paper discusses the points in great detail and - while very clear - I wondered whether there is ample opportunity to shorten and focus the piece.
I found myself a little disappointed that the authors had stopped short of doing any modelling, especially given their remarks in the introduction about the need to match models to data, and the lack of a framework for understanding how best to recapitulate clinical data.
Finally, I found it hard to pick out the new points being made by the authors from the previous literature, as well as the implications of these new ideas. For example, spatial and temporal differences in drug concentration have been used to explain viral rebounds etc. (which the authors discuss), however, is the central point in this paper that these two models of viral dynamics could also explain the three-fold pattern (as described in the Overview)? Perhaps the motivation could be clarified. I'm sure this will be a case of shortening and clarifying the introduction. (This confusion was compounded somewhat by the lack of quantitative analysis as the point above.)
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