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Antiretroviral dynamics determines HIV evolution and predicts therapy outcome

Abstract

Despite the high inhibition of viral replication achieved by current anti-HIV drugs, many patients fail treatment, often with emergence of drug-resistant virus. Clinical observations show that the relationship between adherence and likelihood of resistance differs dramatically among drug classes. We developed a mathematical model that explains these observations and predicts treatment outcomes. Our model incorporates drug properties, fitness differences between susceptible and resistant strains, mutations and adherence. We show that antiviral activity falls quickly for drugs with sharp dose-response curves and short half-lives, such as boosted protease inhibitors, limiting the time during which resistance can be selected for. We find that poor adherence to such drugs causes treatment failure via growth of susceptible virus, explaining puzzling clinical observations. Furthermore, our model predicts that certain single-pill combination therapies can prevent resistance regardless of patient adherence. Our approach represents a first step for simulating clinical trials of untested anti-HIV regimens and may help in the selection of new drug regimens for investigation.

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Figure 1: Drug concentrations determine the relative fitness of the wild-type virus and a resistant mutant.
Figure 2: Selection windows can be calculated for particular drug-mutation pairs.
Figure 3: Schematic of algorithm for simulating viral dynamics in a patient undergoing treatment.
Figure 4: Outcomes for simulated patients in a clinical trial.
Figure 5: Our calculated adherence-resistance relations are in agreement with those observed in clinical trials.
Figure 6: Outcomes of DRV/r plus RAL dual suppression therapy simulations, considering resistant mutants for both drugs.

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Acknowledgements

We thank T. Antal, I. Božić, F. Fu, M. Sampah and L. Shen for discussion during the conception of this work, and we thank J. Gallant, J.-B. Michel and P. Pennings for their comments on the manuscript. We thank D. Bangsberg of Massachusetts General Hospital for supplying adherence data from the REACH study (supported by US National Institutes of Health grant R01 MH054907). Simulations were run on the Odyssey cluster supported by the Research Computing Group of Harvard University. We are grateful for support from the US National Institutes of Health (R01 AI081600 (R.F.S., S.A.R.), R01 GM078986 (M.A.N., A.L.H.)), the Bill & Melinda Gates Foundation (M.A.N., A.L.H.), a Cancer Research Institute Fellowship (S.A.R.), a US National Science Foundation Graduate Research Fellowship (D.I.S.R.), the Howard Hughes Medical Institute (R.F.S., S.A.R.), a Canadian Natural Sciences and Engineering Research Council Post-Graduate Scholarship (A.L.H.), the John Templeton Foundation (M.A.N.) and J. Epstein (M.A.N.).

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D.I.S.R., A.L.H. and S.A.R. designed the models and conducted the simulations. D.I.S.R., A.L.H., S.A.R., R.F.S. and M.A.N. conceived of the study and wrote the manuscript.

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Correspondence to Robert F Siliciano or Martin A Nowak.

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The authors declare no competing financial interests.

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Supplementary Figures 1–13, Supplementary Tables 1–7 and Supplementary Methods (PDF 9956 kb)

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Rosenbloom, D., Hill, A., Rabi, S. et al. Antiretroviral dynamics determines HIV evolution and predicts therapy outcome. Nat Med 18, 1378–1385 (2012). https://doi.org/10.1038/nm.2892

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