Utilizing Virtual Clinical Trials to Inform Target Coverage That Drives RSV Antiviral Efficacy

Figures

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  FIGURE 1. A quantitative systems pharmacology modeling (QSP) approach supports strategy and trial design for RSV antiviral therapeutics. Clinical and preclinical data inputs inform the curation of QSP model virtual populations. Simulated virtual populations are calibrated to match virological efficacy in healthy adult and pediatric trials. Projected virological efficacy in challenge and patient studies impact treatment window expectations.

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  FIGURE 2. In vitro RSV potencies and clinical target coverage. (A) In vitro EC90 values from plaque assay data for compounds with a group of RSV A and B viral strains. Black line reflects the median RSV strain for each compound. (B) Pharmacokinetic time‐courses relative to in vitro EC90 value for the M37b RSV A strain (5.52 nM free for sisunatovir, 36.45 nM free for zelicapavir).

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  FIGURE 3. Studies suggest different viral load measures differ between healthy adult volunteers and pediatric patients; similar metrics captured by model. (A) Peak (or baseline if highest) viral load and (B) post‐peak viral load slopes were measured across challenge studies and pediatric studies (either RCTs or observational, Supporting Information). The same metrics were also measured for simulated individuals for the healthy adult and pediatric patient virtual populations. Mean with one standard deviation is shown. For studies, copies/mL and PFUe/mL were assumed to be equivalent. Using a Wilcoxon rank‐sum test, * denotes statistical significance (p < 0.005). (C) Placebo simulations for healthy adult and pediatric patient virtual populations, assuming symptom onset occurs 1 day prior to peak viral load in each individual trajectory. Mean with 95% prediction interval (PI) is shown.

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  FIGURE 4. Virtual populations calibrated to capture key virological metrics in challenge studies (A‐C) and pediatric RCTs (D‐F). (A) Schematic of viral challenge studies, including early treatment time (within 0.5 days of testing positive). (B, C) viral load AUC matching to create a VPop based on the sisunatovir challenge study (B) and the zelicapavir challenge study for both part 1 and part 2 (C). Mean with SE is shown (note that simulations assumed the same N as the data when calculating SE). (D) Schematic of RCTs, including treatment time (likely after viral load peak). (E, F) Change from baseline (CFBL) of viral load matching for the mITT3 cohort (E) and difference between trial arms (mean) matching for the Part 2 cohort (F) to create a VPop based on the zelicapavir Ph2 pediatric study. Mean with StDev is shown (E). Mean difference between arms (data and simulated) shown (F). Simulated error (F) calculated as the difference between the mean + StDev between the placebo and treatment arms. mITT3 is the “mean intent‐to‐treat 3” cohort, which included individuals treated by day 3 post‐symptom onset (PSO) and was compared to simulated treatment at 3 days PSO. The Part 2 cohort included…

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  FIGURE 5. Predicted dose response of AUC viral load reduction (%) with varying target coverage of Cmin at Day 1. Mean predicted dose response and 95% prediction interval (PI) generated by varying the model EC50 between 0.1 and 30‐fold‐over in vitro EC90. Published compound target coverage and viral load AUC reduction ranges shown for sisunatovir (blue), zelicapavir (red), and EDP‐323 (orange).

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  FIGURE 6. Projections suggest pediatric patients have a wider treatment window for reducing viral load compared to healthy adults with RSV infection. Treatment of zelicapavir (600 mg 5 × QD) was simulated for the healthy adult and pediatric patient VPops. Intervention (at day 1) was simulated starting at either 3, 4, 5, or 7‐days PSO using the assumption that symptom onset was 1 day prior to peak viral load in all individuals. Viral load reductions were measured between the simulated treatment and placebo groups on (A) day 3 and (B) day 5. Mean with 95% prediction integral (PI) is shown.