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A time-to-event analysis of the exposure-response relationship for bezlotoxumab concentrations and CDI recurrence.

Ka Lai YeeHuub Jan KleijnStefan ZajicMary Beth DorrRebecca E Wrishko
Published in: Journal of pharmacokinetics and pharmacodynamics (2020)
Bezlotoxumab is a monoclonal antibody approved for the prevention of recurrent Clostridium difficile infection (rCDI). In a previous exposure-response (E-R) analysis of bezlotoxumab exposure and rCDI, based on data from two phase 3 trials in participants who received placebo or bezlotoxumab 10 mg/kg, rCDI was treated as a binary endpoint and discontinued subjects were imputed as not having rCDI, resulting in an apparent positive E-R trend between rCDI rates and bezlotoxumab exposure. Therefore, a time-to-event (TTE) analysis was applied to investigate the E-R relationship, accounting for the time to rCDI occurrence and participant discontinuation. A TTE model, applying a time-dependent hazard function and right-censoring of data based on rCDI, discontinuation, or study end was developed. Exposure effects and covariates effects were evaluated as predictors affecting the hazard. The TTE model consisted of a Gompertz function with age, endogenous immunoglobulin G to C. difficile toxin B (IgG-B), history of CDI, hospitalization, sex, Charlson Comorbidity Index, and concomitant use of systemic antibiotics affecting the hazard. Exposure effects were characterized with a maximum effect (Emax) E-R relationship on the baseline parameter, and bezlotoxumab exposures achieved at the 10 mg/kg dose were found to be on the plateau of the E-R curve. Endogenous IgG-B significantly impacted the Emax, indicating that low-titer participants derive a greater benefit from bezlotoxumab treatment compared with high-titer participants. The results support the conclusions of the previous E-R analysis, where exposures achieved at the 10 mg/kg dose are on the plateau of the E-R curve.
Keyphrases
  • monoclonal antibody
  • risk assessment
  • escherichia coli
  • clinical trial
  • electronic health record
  • magnetic resonance
  • big data
  • newly diagnosed
  • deep learning
  • artificial intelligence
  • open label
  • contrast enhanced