Antibody 10-1074 suppresses viremia in HIV-1-infected individuals

Journal name:
Nature Medicine
Year published:
Published online


Monoclonal antibody 10-1074 targets the V3 glycan supersite on the HIV-1 envelope (Env) protein. It is among the most potent anti-HIV-1 neutralizing antibodies isolated so far. Here we report on its safety and activity in 33 individuals who received a single intravenous infusion of the antibody. 10-1074 was well tolerated and had a half-life of 24.0 d in participants without HIV-1 infection and 12.8 d in individuals with HIV-1 infection. Thirteen individuals with viremia received the highest dose of 30 mg/kg 10-1074. Eleven of these participants were 10-1074-sensitive and showed a rapid decline in viremia by a mean of 1.52 log10 copies/ml. Virologic analysis revealed the emergence of multiple independent 10-1074-resistant viruses in the first weeks after infusion. Emerging escape variants were generally resistant to the related V3-specific antibody PGT121, but remained sensitive to antibodies targeting nonoverlapping epitopes, such as the anti-CD4-binding-site antibodies 3BNC117 and VRC01. The results demonstrate the safety and activity of 10-1074 in humans and support the idea that antibodies targeting the V3 glycan supersite might be useful for the treatment and prevention of HIV-1 infection.

At a glance


  1. Study design and pharmacokinetics of 10-1074 in HIV-1-negative participants and individuals with HIV-1 infection.
    Figure 1: Study design and pharmacokinetics of 10-1074 in HIV-1-negative participants and individuals with HIV-1 infection.

    (a) Schematic representation of the study design. (b) Serum levels of 10-1074 as determined by assay. Mean values for each dose group ± s.e.m. for HIV-1-negative participants (left) and individuals with HIV-1 infection (right). Dotted horizontal lines at the bottom indicate lower limit of detection of the assays when performed using HIV-1 strains Du422.1 (top, 1.59 μg/ml), 3103.v3.c10 (middle, 0.43 μg/ml) and 3103.v3.c10 in an antiretroviral therapy-resistant backbone (bottom, 0.26 μg/ml). Each sample was measured in duplicate. Serum half-life (t1/2) of 10-1074 between individuals with and without HIV-1 was significantly different (P < 0.0001). Samples demonstrating nonspecific activity were excluded from the analysis (Supplementary Table 3). Scr, screening visit; pre, pre-infusion visit.

  2. Viral load dynamics after 10-1074 infusion in participants with HIV-1.
    Figure 2: Viral load dynamics after 10-1074 infusion in participants with HIV-1.

    (a) 10-1074 dose of either 10 or 30 mg/kg is indicated on the left. Left graphs show absolute viral loads in HIV-1 RNA copies/ml (y axis) versus time in days after infusion (x axis). Middle graphs demonstrate log10 changes in HIV-1 RNA levels from day 0. Right graphs show average log10 change in viremia after 10-1074 (red line) or 3BNC117 infusion34 (dotted black; curves were fitted by robust lowess regression with 40% of the data using MATLAB_R2016a). At the 30-mg/kg dose level, viremia was significantly suppressed from about day 3 to day 27 post-infusion as compared to viral load at day 0. The window of significant viral suppression was assessed by computing simultaneous confidence bands and determined when these excluded zero (Supplementary Fig. 5). Stars indicate the initiation of antiretroviral therapy. (b) 10-1074 serum levels at the time of rebound for individuals receiving 10 or 30 mg/kg of 10-1074 (black or red circles, respectively). Dotted lines indicate mean time to rebound after 10-1074 infusion (black, 10 mg/kg, and red, 30 mg/kg). (c) Maximum log10 decline in viremia as measured by RNA copies/ml versus 10-1074 IC80 (Spearman coefficient rho = −0.05; P = 0.88) of primary culture virus from samples obtained 557 d to 61 d before infusion as determined by assay. No sensitivity data were obtained from 1HC2, 1HD2 and 1HD10K before enrollment. Colors as in b. (d) Maximum log10 decline in viremia in 10-1074-sensitive individuals versus initial viral load as measured by RNA copies/ml (Spearman coefficient rho = −0.19; P = 0.52). Colors as in b.

  3. Viral evolution after 10-1074 infusion in participants with HIV-1 infection.
    Figure 3: Viral evolution after 10-1074 infusion in participants with HIV-1 infection.

    (a) Maximum-likelihood phylogenetic tree of single-genome-derived env gene sequences obtained from plasma before (day 0, gray) and 4 weeks after 10-1074 infusion (week 4, red). Asterisks indicate nodes with bootstrap support of 100% (100 replicates). (b) Frequency of resistance mutations found in circulating viruses by SGS before infusion for each individual. Gray indicates the absence of potential resistance mutation at positions 325, 332 and 334. Colors correspond to mutations indicated in c. For all pie charts, the number of analyzed sequences is shown in the center. (c) Amino acid frequencies at three recurrently mutated 10-1074 contact sites for all pooled circulating virus sequences obtained by SGS 4 weeks after infusion. Outer rings indicate position of mutation (orange, 325; blue, 332; green, 334; gray, not mutated). (d) As in c, but for each individual. Colors indicate the type of mutation. For 1HD6K and 1HD10K, both week 4 and week 8 were included in c and d. (e) Sensitivity to the indicated anti-HIV-1 antibodies of 114 different viral isolates obtained from 11 individuals before (gray, 55 isolates) and 4 weeks after 10-1074 infusion (red, 59 isolates) with IC80 values (μg/ml) on the y axis (log10 scale). Each dot represents one viral isolate. Lines indicate geometric mean. Samples were run in duplicate.

  4. Temporal evolution of escape from 10-1074 over time in individuals 1HB1, 1HB3, 1HC1, 1HD1, 1HD6K and 1HD10K.
    Figure 4: Temporal evolution of escape from 10-1074 over time in individuals 1HB1, 1HB3, 1HC1, 1HD1, 1HD6K and 1HD10K.

    Plots display relative frequencies of escape mutations observed in SGS data at envelope positions 325, 332 and 334 as shaded areas over time. Sequencing was performed on day 0 (all subjects) and at week 1 (1HB3, 1HD1), week 2 (1HB3), week 4 (all subjects), week 8 (1HD6K, 1HD10K), week 12 (1HD6K, 1HD10K), week 16 (1HB3), week 20 (1HD6K) and week 24 (1HB1, 1HC1, 1HD1) (see Supplementary Table 5 for absolute numbers). White line indicates serum concentration of 10-1074 as determined by assay (Supplementary Table 3). White circles without border depict 10-1074 serum levels below the limit of detection.

  5. SMRT sequencing analysis.
    Figure 5: SMRT sequencing analysis.

    (a) Maximum-likelihood phylogenetic tree of full-length plasma envelope sequences obtained on day 0 and week 4 after 10-1074 infusion from individual 1HD1. Branches show high-quality consensus sequences (HQCSs) with their respective copy number visualized as the size of the colored circle (day 0, gray; week 4, red). (b) Insets highlight two day-0 minority variants that carry 10-1074 escape mutations 324GKIR327 (blue, mutation in yellow) and 332NIR334 (green, mutation in yellow). (c) Number of filtered reads for the indicated sequence variants at positions 324–334 and the relative frequency of each variant. Residues 325 and 332–334 are shaded in gray. Deviations from the day-0 majority variant are in bold.

Accession codes

Primary accessions

NCBI Reference Sequence


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Author information

  1. These authors contributed equally to this work.

    • Marina Caskey,
    • Till Schoofs,
    • Henning Gruell,
    • Michel C Nussenzweig &
    • Florian Klein


  1. Laboratory of Molecular Immunology, The Rockefeller University, New York, New York, USA.

    • Marina Caskey,
    • Till Schoofs,
    • Allison Settler,
    • Theodora Karagounis,
    • Lilian Nogueira,
    • Thiago Y Oliveira,
    • Yehuda Z Cohen,
    • Irina Shimeliovich,
    • Cecilia Unson-O'Brien,
    • Rebeka Levin,
    • Maggi Witmer-Pack,
    • Sarah J Schlesinger &
    • Michel C Nussenzweig
  2. Laboratory of Experimental Immunology, Center for Molecular Medicine Cologne (CMMC), University of Cologne, Cologne, Germany.

    • Henning Gruell,
    • Daniela Weiland &
    • Florian Klein
  3. Department I of Internal Medicine, University Hospital Cologne, Cologne, Germany.

    • Henning Gruell,
    • Clara Lehmann,
    • Daniel Gillor,
    • Daniela Weiland,
    • Tim Kümmerle,
    • Christoph Wyen,
    • Gerd Fätkenheuer &
    • Florian Klein
  4. German Center for Infection Research, partner site Bonn–Cologne, Cologne, Germany.

    • Henning Gruell,
    • Clara Lehmann,
    • Daniela Weiland,
    • Gerd Fätkenheuer &
    • Florian Klein
  5. Departments of Medicine and Microbiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.

    • Edward F Kreider,
    • Gerald H Learn &
    • Beatrice H Hahn
  6. Department of Medicine, University of California, San Diego, San Diego, California, USA.

    • Ben Murrell &
    • Caroline Ignacio
  7. Department of Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Saarbrücken, Germany.

    • Nico Pfeifer
  8. Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA.

    • Alexander Robles &
    • Michael S Seaman
  9. Department of Biomedical Informatics, University of California, San Diego, San Diego, California, USA.

    • Kemal Eren
  10. Bioinformatics and System Biology, University of California, San Diego, San Diego, California, USA.

    • Kemal Eren
  11. Department of Ophthalmology, Weill Cornell Medical College of Cornell University, New York, New York, USA.

    • Szilard Kiss
  12. Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, USA.

    • Anthony P West Jr &
    • Pamela J Bjorkman
  13. Laboratory of Humoral Response to Pathogens, Department of Immunology, Institut Pasteur, Paris, France.

    • Hugo Mouquet
  14. Division of Infectious Diseases, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, New York, USA.

    • Barry S Zingman
  15. Einstein/Rockefeller/CUNY Center for AIDS Research, Bronx, New York, USA.

    • Barry S Zingman
  16. Division of Infectious Diseases, Weill Cornell Medicine, New York, New York, USA.

    • Roy M Gulick
  17. Celldex Therapeutics, Hampton, New Jersey, USA.

    • Tibor Keler
  18. Howard Hughes Medical Institute, The Rockefeller University, New York, New York, USA.

    • Michel C Nussenzweig


M.C. (principal investigator, US), M.C.N. and F.K. (principal investigator, Germany) designed the trial; M.C., T.S., H.G., M.C.N. and F.K. analyzed the data and wrote the manuscript; R.M.G., G.F. and S.J.S. contributed to study design and implementation. M.C., H.G., A.S., Y.Z.C., R.L., M.W.-P. and F.K. implemented the study. C.L., D.G., T. Kümmerle., C.W., S.K., B.S.Z. and G.F. contributed to participant recruitment and clinical assessments. I.S., C.U.-O. and D.W. coordinated sample processing. T.S., T. Karagounis and L.N., performed viral culture, SGS and Primer-ID sequencing work. T.Y.O. performed Primer-ID analyses and bioinformatics processing of SGS data. A.R. and M.S.S. performed neutralization assays. B.M., K.E. and C.I. carried out SMRT sequencing and analysis. E.F.K., G.H.L. and B.H.H. analyzed SGS data. N.P. performed statistical analysis. H.M., A.P.W. and P.J.B. contributed to data analysis. T. Keler was responsible for 10-1074 manufacture and provided regulatory guidance. All authors read and contributed to the writing of the manuscript.

Competing financial interests

T. Keler is employed by Celldex Therapeutics. 10-1074 was manufactured by Celldex Therapeutics.

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