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Optimal Combinations of Broadly Neutralizing Antibodies for Prevention and Treatment of HIV-1 Clade C Infection.

Wagh K, Bhattacharya T, Williamson C, Robles A, Bayne M, Garrity J, Rist M, Rademeyer C, Yoon H, Lapedes A, Gao H, Greene K, Louder MK, Kong R, Karim SA, Burton DR, Barouch DH, Nussenzweig MC, Mascola JR, Morris L, Montefiori DC, Korber B, Seaman MS - PLoS Pathog. (2016)

Bottom Line: While combinations of bnAbs targeting distinct epitopes on the viral envelope (Env) will likely be required to overcome the extraordinary diversity of HIV-1, a key outstanding question is which bnAbs, and how many, will be needed to achieve optimal clinical benefit.Using this model, we performed a comprehensive and systematic comparison of the predicted neutralizing activity of over 1,600 possible double, triple, and quadruple bnAb combinations.By this set of criteria, triple and quadruple combinations of bnAbs were identified that were significantly more effective than the best double combinations, and further improved the probability of having multiple bnAbs simultaneously active against a given virus, a requirement that may be critical for countering escape in vivo.

View Article: PubMed Central - PubMed

Affiliation: Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America.

ABSTRACT
The identification of a new generation of potent broadly neutralizing HIV-1 antibodies (bnAbs) has generated substantial interest in their potential use for the prevention and/or treatment of HIV-1 infection. While combinations of bnAbs targeting distinct epitopes on the viral envelope (Env) will likely be required to overcome the extraordinary diversity of HIV-1, a key outstanding question is which bnAbs, and how many, will be needed to achieve optimal clinical benefit. We assessed the neutralizing activity of 15 bnAbs targeting four distinct epitopes of Env, including the CD4-binding site (CD4bs), the V1/V2-glycan region, the V3-glycan region, and the gp41 membrane proximal external region (MPER), against a panel of 200 acute/early clade C HIV-1 Env pseudoviruses. A mathematical model was developed that predicted neutralization by a subset of experimentally evaluated bnAb combinations with high accuracy. Using this model, we performed a comprehensive and systematic comparison of the predicted neutralizing activity of over 1,600 possible double, triple, and quadruple bnAb combinations. The most promising bnAb combinations were identified based not only on breadth and potency of neutralization, but also other relevant measures, such as the extent of complete neutralization and instantaneous inhibitory potential (IIP). By this set of criteria, triple and quadruple combinations of bnAbs were identified that were significantly more effective than the best double combinations, and further improved the probability of having multiple bnAbs simultaneously active against a given virus, a requirement that may be critical for countering escape in vivo. These results provide a rationale for advancing bnAb combinations with the best in vitro predictors of success into clinical trials for both the prevention and treatment of HIV-1 infection.

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Comparison of Additive and Bliss-Hill models for predicting bnAb combination neutralization scores.Additive and Bliss-Hill models were used to analyze bnAb combination IC80 scores for the Clade C Panel. In (A), BH model predictions are plotted against observed IC80 values for 20 viruses, with different bnAb combinations (n = 10) shown by different colors and/or symbols. (B) For each bnAb combination tested, the absolute difference between the predicted and the observed Log10 IC80 values for each virus was calculated using both BH and additive models (Fig D in S1 Text). Median Log10 differences using BH model are shown as blue bars and using additive model are shown as green bars, with vertical grey bars representing half the interquartile range. Wilcoxon paired rank test was used to determine whether the Bliss Hill model provides a statistically significantly smaller prediction error for this panel of viruses. Fig D in S1 Text illustrates each of the paired model predictions for the Envs and antibody combinations tested. The additive model often slightly underestimates the observed combination potency, while BH model estimates are closer to the observed. Combinations of bnAbs for which the p-value was smaller than the threshold established by a false discovery rate of q<0.1 are indicated. See Figs C and E in S1 Text for equivalent analysis using the Kong et al. dataset [60].
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ppat.1005520.g002: Comparison of Additive and Bliss-Hill models for predicting bnAb combination neutralization scores.Additive and Bliss-Hill models were used to analyze bnAb combination IC80 scores for the Clade C Panel. In (A), BH model predictions are plotted against observed IC80 values for 20 viruses, with different bnAb combinations (n = 10) shown by different colors and/or symbols. (B) For each bnAb combination tested, the absolute difference between the predicted and the observed Log10 IC80 values for each virus was calculated using both BH and additive models (Fig D in S1 Text). Median Log10 differences using BH model are shown as blue bars and using additive model are shown as green bars, with vertical grey bars representing half the interquartile range. Wilcoxon paired rank test was used to determine whether the Bliss Hill model provides a statistically significantly smaller prediction error for this panel of viruses. Fig D in S1 Text illustrates each of the paired model predictions for the Envs and antibody combinations tested. The additive model often slightly underestimates the observed combination potency, while BH model estimates are closer to the observed. Combinations of bnAbs for which the p-value was smaller than the threshold established by a false discovery rate of q<0.1 are indicated. See Figs C and E in S1 Text for equivalent analysis using the Kong et al. dataset [60].

Mentions: To overcome these limitations of the additive model, we developed a new model, the “Bliss-Hill model” (BH model). This model combines single bnAb Hill curves (with arbitrary slopes) within the framework of the Bliss independence model for the binding of multiple species of ligands to a substrate [72, 73], and incorporates a correction for multiple ligands independently attaching to the substrate (S1 Text). We tested the BH model by using experimental data from combination bnAb neutralization assays. The assays comprised 10 combinations of 2, 3 and 4 bnAbs (including 2-bnAb combinations with both antibodies targeting similar epitopes, Fig B in S1 Text) assayed against a smaller panel of 20 viruses. The 20 viruses were chosen because they are sensitive to almost all bnAbs tested and comprise a maximized range of IC80 titers for the bnAb combinations. The BH model proved highly accurate in explaining the clade C panel bnAb combination data (Fig 2A, R2 = 0.9154, Pearson r = 0.9584). Moreover, the BH predictions were closer to the observed data than the additive model for 9 of the 10 combinations tested (Fig 2B, p = 0.021 using Binomial Test), with the only exception being the combination VRC07-523 + 10-1074V. Thus the BH model offered a significant, though modest in magnitude, improvement in prediction accuracy over the additive model. We confirmed this by reanalyzing a larger dataset from Kong et al., and again found the BH model predictions to be highly accurate (R2 = 0.9655, Pearson r = 0.9862, Fig C in S1 Text). The BH model performed slightly better than the additive model in all cases, and the difference reached high levels of statistical significance for most of the 2, 3, and 4 bnAb combinations tested. This improvement was due to the systematic trend of BH predictions being more potent than the additive model predictions (Figs D and E in S1 Text), and thus closer to the observed titers since additive model predictions were found to be less potent than the observed titers for most combinations [60].


Optimal Combinations of Broadly Neutralizing Antibodies for Prevention and Treatment of HIV-1 Clade C Infection.

Wagh K, Bhattacharya T, Williamson C, Robles A, Bayne M, Garrity J, Rist M, Rademeyer C, Yoon H, Lapedes A, Gao H, Greene K, Louder MK, Kong R, Karim SA, Burton DR, Barouch DH, Nussenzweig MC, Mascola JR, Morris L, Montefiori DC, Korber B, Seaman MS - PLoS Pathog. (2016)

Comparison of Additive and Bliss-Hill models for predicting bnAb combination neutralization scores.Additive and Bliss-Hill models were used to analyze bnAb combination IC80 scores for the Clade C Panel. In (A), BH model predictions are plotted against observed IC80 values for 20 viruses, with different bnAb combinations (n = 10) shown by different colors and/or symbols. (B) For each bnAb combination tested, the absolute difference between the predicted and the observed Log10 IC80 values for each virus was calculated using both BH and additive models (Fig D in S1 Text). Median Log10 differences using BH model are shown as blue bars and using additive model are shown as green bars, with vertical grey bars representing half the interquartile range. Wilcoxon paired rank test was used to determine whether the Bliss Hill model provides a statistically significantly smaller prediction error for this panel of viruses. Fig D in S1 Text illustrates each of the paired model predictions for the Envs and antibody combinations tested. The additive model often slightly underestimates the observed combination potency, while BH model estimates are closer to the observed. Combinations of bnAbs for which the p-value was smaller than the threshold established by a false discovery rate of q<0.1 are indicated. See Figs C and E in S1 Text for equivalent analysis using the Kong et al. dataset [60].
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4814126&req=5

ppat.1005520.g002: Comparison of Additive and Bliss-Hill models for predicting bnAb combination neutralization scores.Additive and Bliss-Hill models were used to analyze bnAb combination IC80 scores for the Clade C Panel. In (A), BH model predictions are plotted against observed IC80 values for 20 viruses, with different bnAb combinations (n = 10) shown by different colors and/or symbols. (B) For each bnAb combination tested, the absolute difference between the predicted and the observed Log10 IC80 values for each virus was calculated using both BH and additive models (Fig D in S1 Text). Median Log10 differences using BH model are shown as blue bars and using additive model are shown as green bars, with vertical grey bars representing half the interquartile range. Wilcoxon paired rank test was used to determine whether the Bliss Hill model provides a statistically significantly smaller prediction error for this panel of viruses. Fig D in S1 Text illustrates each of the paired model predictions for the Envs and antibody combinations tested. The additive model often slightly underestimates the observed combination potency, while BH model estimates are closer to the observed. Combinations of bnAbs for which the p-value was smaller than the threshold established by a false discovery rate of q<0.1 are indicated. See Figs C and E in S1 Text for equivalent analysis using the Kong et al. dataset [60].
Mentions: To overcome these limitations of the additive model, we developed a new model, the “Bliss-Hill model” (BH model). This model combines single bnAb Hill curves (with arbitrary slopes) within the framework of the Bliss independence model for the binding of multiple species of ligands to a substrate [72, 73], and incorporates a correction for multiple ligands independently attaching to the substrate (S1 Text). We tested the BH model by using experimental data from combination bnAb neutralization assays. The assays comprised 10 combinations of 2, 3 and 4 bnAbs (including 2-bnAb combinations with both antibodies targeting similar epitopes, Fig B in S1 Text) assayed against a smaller panel of 20 viruses. The 20 viruses were chosen because they are sensitive to almost all bnAbs tested and comprise a maximized range of IC80 titers for the bnAb combinations. The BH model proved highly accurate in explaining the clade C panel bnAb combination data (Fig 2A, R2 = 0.9154, Pearson r = 0.9584). Moreover, the BH predictions were closer to the observed data than the additive model for 9 of the 10 combinations tested (Fig 2B, p = 0.021 using Binomial Test), with the only exception being the combination VRC07-523 + 10-1074V. Thus the BH model offered a significant, though modest in magnitude, improvement in prediction accuracy over the additive model. We confirmed this by reanalyzing a larger dataset from Kong et al., and again found the BH model predictions to be highly accurate (R2 = 0.9655, Pearson r = 0.9862, Fig C in S1 Text). The BH model performed slightly better than the additive model in all cases, and the difference reached high levels of statistical significance for most of the 2, 3, and 4 bnAb combinations tested. This improvement was due to the systematic trend of BH predictions being more potent than the additive model predictions (Figs D and E in S1 Text), and thus closer to the observed titers since additive model predictions were found to be less potent than the observed titers for most combinations [60].

Bottom Line: While combinations of bnAbs targeting distinct epitopes on the viral envelope (Env) will likely be required to overcome the extraordinary diversity of HIV-1, a key outstanding question is which bnAbs, and how many, will be needed to achieve optimal clinical benefit.Using this model, we performed a comprehensive and systematic comparison of the predicted neutralizing activity of over 1,600 possible double, triple, and quadruple bnAb combinations.By this set of criteria, triple and quadruple combinations of bnAbs were identified that were significantly more effective than the best double combinations, and further improved the probability of having multiple bnAbs simultaneously active against a given virus, a requirement that may be critical for countering escape in vivo.

View Article: PubMed Central - PubMed

Affiliation: Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America.

ABSTRACT
The identification of a new generation of potent broadly neutralizing HIV-1 antibodies (bnAbs) has generated substantial interest in their potential use for the prevention and/or treatment of HIV-1 infection. While combinations of bnAbs targeting distinct epitopes on the viral envelope (Env) will likely be required to overcome the extraordinary diversity of HIV-1, a key outstanding question is which bnAbs, and how many, will be needed to achieve optimal clinical benefit. We assessed the neutralizing activity of 15 bnAbs targeting four distinct epitopes of Env, including the CD4-binding site (CD4bs), the V1/V2-glycan region, the V3-glycan region, and the gp41 membrane proximal external region (MPER), against a panel of 200 acute/early clade C HIV-1 Env pseudoviruses. A mathematical model was developed that predicted neutralization by a subset of experimentally evaluated bnAb combinations with high accuracy. Using this model, we performed a comprehensive and systematic comparison of the predicted neutralizing activity of over 1,600 possible double, triple, and quadruple bnAb combinations. The most promising bnAb combinations were identified based not only on breadth and potency of neutralization, but also other relevant measures, such as the extent of complete neutralization and instantaneous inhibitory potential (IIP). By this set of criteria, triple and quadruple combinations of bnAbs were identified that were significantly more effective than the best double combinations, and further improved the probability of having multiple bnAbs simultaneously active against a given virus, a requirement that may be critical for countering escape in vivo. These results provide a rationale for advancing bnAb combinations with the best in vitro predictors of success into clinical trials for both the prevention and treatment of HIV-1 infection.

Show MeSH
Related in: MedlinePlus