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HIV-1 antibody 3BNC117 suppresses viral rebound in humans during treatment interruption

Nature volume 535, pages 556–560 (2016)Cite this article

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Abstract

Interruption of combination antiretroviral therapy (ART) in HIV-1-infected individuals leads to rapid viral rebound. Here we report the results of a phase IIa open label clinical trial evaluating 3BNC117, a broad and potent neutralizing antibody (bNAb) against the CD4 binding site of HIV-1 Env1, in the setting of analytical treatment interruption (ATI) in 13 HIV-1-infected individuals. Participants with 3BNC117-sensitive virus outgrowth cultures were enrolled. Two or four 30 mg/kg infusions of 3BNC117, separated by 3 or 2 weeks, respectively, were generally well tolerated. The infusions were associated with a delay in viral rebound for 5-9 weeks after 2 infusions, and up to 19 weeks after 4 infusions, or an average of 6.7 and 9.9 weeks respectively, compared with 2.6 weeks for historical controls (p=<1e-5). Rebound viruses arose predominantly from a single provirus. In most individuals, emerging viruses showed increased resistance indicating escape. However, 30% of participants remained suppressed until antibody concentrations waned below 20 μg/ml, and the viruses emerging in all but one of these individuals showed no apparent resistance to 3BCN117, suggesting failure to escape over a period of 9-19 weeks. We conclude that administration of 3BNC117 exerts strong selective pressure on HIV-1 emerging from latent reservoirs during ATI in humans.

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Figure 1: 3BNC117 neutralization coverage, trial design and pharmacokinetics of 3BNC117 in HIV-1-infected individuals during Analytical Treatment Interruption.
Figure 2: Delay in viral rebound in the presence of 3BNC117.
Figure 3: Viral rebound during ATI and 3BNC117 treatment.
Figure 4: 3BNC117 resistance in rebound viruses.

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Acknowledgements

We would like to thank the trial participants for their invaluable support; We thank the Rockefeller University Hospital Clinical Research Support Office and nursing staff for help with recruitment and study implementation, especially Noreen Buckley, Arlene Hurley, Sivan Ben Avraham Shulman and Lauren Corregano. All members of the Nussenzweig lab, especially Till Schoofs, Ari Halper-Stromberg, Mila and Zoran Jankovic. Cecille Unson-O'Brien, Juan Dizon, Renise Baptiste and Rebeka Levin for sample processing and study coordination; Audrey Louie for regulatory support; Pat Fast and Harriet Park for clinical monitoring. Elena Giorgi and William Fischer from Los Alamos National Laboratory. Rajesh Tim Gandhi, Jonathan Li and The AIDS Clinical Trials Group (grant UM1 AI068636) and its Statistical and Data Management Center (grant UM1 AI068634). This study was supported by the following grants: Collaboration for AIDS Vaccine Discovery grant OPP1033115 (M.C.N.) and OPP1032144 (M.S.S.). Grant 8 UL1 TR000043 from the National Center for Advancing Translational Sciences (NCATS); NIH Clinical and Translational Science Award (CTSA) program; NIH Center for HIV/AIDS Vaccine Immunology and Immunogen Discovery (CHAVI-ID) 1UM1 AI100663-01 (M.C.N) and 5UM1 AI100645-03 (B.H.H.); Bill and Melinda Gates Foundation grants OPP1092074 and OPP1124068 (M.C.N); NIH HIVRAD P01 AI100148 (PJB and MCN); the Robertson Foundation to M.C.N. M.C.N. is a Howard Hughes Medical Institute Investigator. Ruth L. Kirschstein National Research Service Award. F30 AI112426 (E.F.K); F31 AI118555 (J.A.H.); The NIH Center for HIV/AIDS Vaccine Immunology and Immunogen Discovery (CHAVI-ID) 1UM1 AI00645 (B.H.H.); The University of Pennsylvania Center for AIDS Research (CFAR) Single Genome Amplification Service Center P30 AI045008 (B.H.H.); The NIH Scripps Center for HIV/AIDS Vaccine Immunology and Immunogen Discovery (CHAVI-ID and 1UM1-AI100663) (B.D.W).

Author information

Author notes

  1. Johannes F. Scheid and Joshua A. Horwitz: These authors contributed equally to this work.

Authors and Affiliations

  1. Laboratory of Molecular Immunology, and 14 Howard Hughes Medical Institute, The Rockefeller University, New York, 10065, NY

    Johannes F. Scheid, Joshua A. Horwitz, Yotam Bar-On, Ching-Lan Lu, Julio C. C. Lorenzi, Malte Braunschweig, Lilian Nogueira, Thiago Oliveira, Irina Shimeliovich, Roshni Patel, Yehuda Z. Cohen, Sonya Hadrigan, Allison Settler, Maggi Witmer-Pack, Florian Klein, Sarah J. Schlesinger, Michel C. Nussenzweig & Marina Caskey

  2. Massachusetts General Hospital and Harvard Medical School, Boston, 02114, MA

    Johannes F. Scheid

  3. Departments of Medicine and Microbiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, 19104, Pennsylvania

    Edward F. Kreider, Gerald H. Learn & Beatrice H. Hahn

  4. Department of Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Campus E1 4, Saarbrücken, 66123, Germany

    Anna Feldmann & Nico Pfeifer

  5. Division of Infectious Diseases, Weill Medical College of Cornell University, New York, 10065, NY, USA

    Leah Burke & Roy M. Gulick

  6. Division of Biology, California Institute of Technology, Pasadena, 91125, California

    Anthony P. West, Jr. & Pamela J. Bjorkman

  7. Ragon Institute of MGH, MIT and Harvard, Cambridge, 02139, MA, USA

    Boris Juelg & Bruce D. Walker

  8. Celldex Therapeutics, Inc., Hampton, 08827, NJ, USA

    Tibor Keler & Thomas Hawthorne

  9. Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, 10467, NY

    Barry Zingman

  10. Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Boston, 02215, Massachusetts

    Michael S. Seaman

  11. Laboratory of Experimental Immunology, Center for Molecular Medicine Cologne (CMMC), University of Cologne, Cologne, 50931, Germany

    Florian Klein

  12. Department I of Internal Medicine, Center of Integrated Oncology Cologne-Bonn, University Hospital Cologne, 50937, Cologne, Germany

    Florian Klein

  13. Howard Hughes Medical Institute, Massachusetts General Hospital, Boston, 02114, MA

    Bruce D. Walker & Michel C. Nussenzweig

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  1. Johannes F. Scheid
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Contributions

M.C.N, J.F.S, J.A.H and M.C wrote the manuscript; J.F.S, M.C and M.C.N designed the trial; J.F.S, J.A.H, Y.B, J.C.C.L, L.N, Y.Z.C, C-L.L and M.B performed tissue culture experiments and SGS amplifications; M.S.S performed TZM-bl assays; J.F.S, J.A.H, Y.B, E.F.K, T.O, A.P.W, G.H.L, P.J.B, F.K, S.J.S, B.H.H, M.C.N and M.C analyzed the data; E.F.K, G.H.L and B.H.H performed SGA analysis; I.S, R.P and J.F.S processed patient samples; L.B, S.H, A.S, M.W-P, B.Z, R.M.G, S.J.S and M.C performed patient recruitment; A.F and N.P performed statistical analyses; B.J and B.D.W performed antigen specific T cell experiments; T.K and T.H produced 3BNC117 and provided PK data.

Corresponding authors

Correspondence to Michel C. Nussenzweig or Marina Caskey.

Ethics declarations

Competing interests

There are pending patent applications on the 3BNC117 and 10-1074 antibodies by Rockefeller University on which M.C.N. and J.F.S. are inventors. The patents are not licensed by any companies. Neither M.C.N. nor J.F.S. have any competing financial interests.

Additional information

There are pending patent applications on the 3BNC117 and 10-1074 antibodies by Rockefeller University on which M.C.N and J.F.S are inventors. The patents are not licensed by any companies.

Reviewer Information Nature thanks S. Deeks, D. Richman and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Extended data figures and tables

Extended Data Figure 1 Study Participant Selection and Neutralization of Pre-Infusion Cultures by 3BNC117.

a, Flow diagram showing selection of study participants. b, Bar diagrams showing IC50 values (μg/ml) in TZM-bl assays for 3BNC117 against bulk virus outgrowth culture supernatants from the indicated time point pre-infusion for each participant (Supplementary Table 3). In some participants both screen and day 0 cultures were obtained and showed a less than three-fold variation in IC50 values. The red dotted line indicates an IC50 of 2 μg/ml which was used as a threshold for inclusion in the study.

Extended Data Figure 2 CD4+ and CD8+ T cells during Study Period in Participants.

Absolute T cell counts (a, c) and percent CD4+ and CD8+ T cells among CD3+T cells (b, d) for Group A and B respectively (Supplementary Table 4). 3BNC117 infusions are indicated with red arrows. e, Comparison of absolute CD4+ T cell counts and percent CD4+ T cells among CD3+T cells at screen, day 0, rebound and after re-suppression. Shown is the data from participants 701, 702, 703, 704, 707, 708, 709, 711 and 713 for whom re-suppression CD4 counts were available (Supplementary Table 4). The last available time point was used as re-suppression time point. Red lines indicate the mean value and error bars indicate standard deviation. p-values were obtained using a paired t-test comparing the indicated time points. f, Plasma viral loads and CD4 counts in all study participants. 3BNC117 infusions are indicated with red arrows. The left y-axis shows plasma viral loads in RNA copies/ml (black curves), and right y-axis shows absolute CD4 counts in cells/μl (red curves). Gray areas indicate ART therapy.

Extended Data Figure 3 HIV-specific T-cell responses.

Total breadth (open squares) and magnitude (bars) of T-cell responses against HIV-1 overlapping peptides at the designated time points following administration of 3BNC117 (yellow arrows indicate infusions of 3BNC117 at 30 mg/kg). For all study participants, antiretroviral therapy was discontinued on day 2 after the first 3BNC117 administration. Blue arrows indicate the time of viral rebound. For study participants 710, 712 and 715 rebound occurred at week 19, 16 and 11, respectively. Baseline samples for study participant 710 and week 12 samples for study participant 714 were not available for ELISpot analysis. Overall, breadth, magnitude and protein specificity were heterogeneous among the study participants.

Extended Data Figure 4 Viral Rebound in ACTG Control Subjects and Trial Participants.

a, Kaplan-Meier plot summarizing viral rebound in 52 ACTG trial participants who underwent ATI without antibody treatment (black, Supplementary Table 6) and trial participants (Fig. 2 a, b, Supplementary Table 4). Six Group A participants are shown in red, seven Group B participants in blue and the combination in green as indicated. y-axis indicates % participants with viral levels below 200 RNA copies/ml, x-axis indicates weeks after ATI initiation. The survival curves of all considered partitions of the trial participants (Group A, Group B and Group A + B) differed significantly at significance level α = 0.05 from the survival curve of the ACTG trial participants. For the comparison of Group A (Group A + B) with the ACTG trial participants, we performed a weighted log-rank test adjusting for the clinical variables ‘years on ART’ and ‘age’ to correct for possible confounding factors (Supplementary Table 7, p-value < 0.00001). We identified those potential confounders by univariate parametric survival regression using a likelihood ratio test (Statistical Methods). Since we did not discover any confounders with the same analysis among all available clinical variables for the comparison between Group B participants and the ACTG trial participants, we performed a standard log-rank test in that setting (p-value < 0.0001). b-d, In order to perform a survival regression, the distribution of the rebound times has to be determined. Therefore, we compared the empirical cumulative distribution function (CDF) of the rebound times (black, solid line) with the CDF of the rebound times to a fitted distribution (Weibull, exponential, normal, logistic, log-normal, and gamma) for each comparison group (combined trial participants, group A or group B with ACTG control patients). Since the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC) were smallest for the log-normal distribution (green), we have chosen to model the rebound times with the log-normal distribution. e, Dot plot indicating the relationship between cell associated HIV DNA in pre-infusion PBMCs (y-axis) and the week of rebound (x-axis). Group A and group B participants are colored red and blue respectively. The p-value was derived from calculating the pearson correlation coefficient.

Extended Data Figure 5 In vitro neutralization of pre-infusion and rebound virus outgrowth cultures by 3BNC117 or 10-1074.

TZM-bl assay neutralization by 3BNC117 (a) and 10-1074 (b) are shown for individual virus outgrowth cultures derived from pre-infusion (black lines/symbols) or rebound (red lines/symbols) time points for each participant. In some cases, multiple independent cultures were grown from a single time point and assayed for neutralization (Supplementary Table 3). “Screen” refers to cultures of PBMC samples taken weeks before infusion during screening, while “Day 0” refers to cultures of PBMC collected immediately before the first 3BNC117 infusion. Rebound culture time points are denoted by the week (“W”) at which the samples were collected. Symbols reflect the means of two technical replicates; error bars denote standard deviation.

Extended Data Figure 6 Phylogenetic tree of env nucleotide sequences from trial participants.

A maximum likelihood phylogenetic tree was constructed from single genome derived viral env sequences from outgrowth culture supernatants as well as plasma from participants 701 (olive), 702 (black), 703 (pink), 704 (yellow), 707 (light blue), 708 (green), 709 (dark blue) and 711 (brown). Hypervariable (as defined in http://www.hiv.lanl.gov/content/sequence/VAR_REG_CHAR/) and other poorly aligned regions were excluded from the analysis. The tree was constructed using PhyML with a GTR+I+G substitution model and midpoint rooted. Asterisks indicate 100% bootstrap support (only values for major nodes are shown). The scale bar indicates 0.01 substitutions per site.

Extended Data Figure 7 Rebound virus clonality and neutralization sensitivity to 3BNC117.

Maximum likelihood phylogenetic trees of plasma and culture-derived env sequences are shown for participants 701, 702, 703, 704. Sequences obtained at screening, on Day 0, and consecutive rebound time points (plasma and cultures) are color coded as indicated. The trees were rooted based on the branch insertion identified in the between-subject tree (Extended Data Fig. 6). Bootstrap values ≥90% are shown. Names of env sequences used to generate pseudoviruses for 3BNC117 neutralization analysis are indicated along with the respective IC80 titers in μg/ml. Representative rebound viruses selected in Fig. 4b are marked with red stars (Fig. 4b, Supplementary Table 9). Zero branch length viruses in multi rebounders 702 and 703 are marked with black stars.

Extended Data Figure 8 Rebound virus clonality and neutralization sensitivity to 3BNC117.

Maximum likelihood phylogenetic trees of plasma and culture-derived env sequences are shown for participants 707, 708, 709 and 711. Sequences obtained at screening, on Day 0, and consecutive rebound time points (plasma and cultures) are color coded as indicated. The trees were rooted based on the branch insertion identified in the between-subject tree (Extended Data Fig. 6). Bootstrap values ≥90% are shown. Names of env sequences used to generate pseudoviruses for 3BNC117 neutralization analysis are indicated along with the respective IC80 titers in μg/ml. Representative rebound viruses selected in Fig. 4b are marked with red stars (Fig. 4b, Supplementary Table 9). Zero branch length viruses in multi rebounder 709 are marked with black stars.

Supplementary information

Supplementary Information

This file contains supplementary Figures 1-2, Supplementary Tables 1-9 and Supplementary References. This file was updated on 27 July 2016 to correct the reference list. (PDF 11137 kb)

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Scheid, J., Horwitz, J., Bar-On, Y. et al. HIV-1 antibody 3BNC117 suppresses viral rebound in humans during treatment interruption. Nature 535, 556–560 (2016). https://doi.org/10.1038/nature18929

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