ACoP13

ACoP 2022: General Pharmacometrics e.g. popPK, PKPD, E-R, trial simulation, C-QT
Kelly Mahar

Population dose-Hgb modelling of daprodustat across five phase 3 studies in chronic kidney disease (CKD) patients with anemia

Objectives: To update the existing daprodustat population dose-hemoglobin (Hgb) model with the aim to support its dosing algorithm in CKD patients with anemia.

Methods: A dose-Hgb model based on global phase 2 data supported the starting doses and titration algorithms in phase 3 studies through simulation. This model was updated in 2019 with the inclusion of three phase 3 studies in Japanese patients [1] and was the starting point for this analysis. It consisted of a precursor cell compartment and 12 transit compartments to describe the red blood cell (RBC) lifespan [1, 2]. Treatment was modelled as the stimulation of the precursor cell production rate by a power of allometrically scaled dose [1, 3]. The analysis updated the model using five global phase 3 studies with QD and TIW titrated dosing. It consisted of four major steps:

  • Prediction of 2/3 of the current data set by the 2019 model
  • Restructuring/optimization of this model
  • Covariate analysis, including backward deletion of pre-existing covariates
  • Prediction of the remaining 1/3 of the data


Titration-based Visual Predictive Checks [4] (VPC) were used to assess model adequacy in addition to the usual goodness-of-fit diagnostics.

Results:

Model development used 53,535 Hgb values from 2770 patients. The final dose-Hgb model adequately described this data and predicted the remaining 1/3 of the data (26,317 values, 1384 patients).

The model had the following features:

  • Separate Hgb baselines were estimated for different patient groups (depending on dialysis status and ESA use)
  • Prior ESA dose was a covariate on true Hgb baseline (ie, baseline after washout of prior ESA treatment; −14% at 95th PI)
  • Disease progression was included as exponential decline of the Hgb production rate over time
    • Faster progression in patients starting dialysis (+96.8%) vs non-dialysis (ND) patients
    • Slower progression in ESA users (−31.8%) vs ESA non-users, and chronic hemodialysis (HD)/peritoneal dialysis (PD) patients (−57.3%) vs ND patients
  • The daprodustat treatment effect in ESA hyporesponders did not differ from other patients, in line with statistical analyses
  • RBC life span was longer in HD patients (+13.8%) and patients just starting dialysis (+30.9%) as compared to ND or PD patients
  • Other covariates (effects <13%) were CKD stage, hepcidin, body weight, ESA hyporesponder, race on (true) Hgb baseline; clopidogrel on dose; body weight and prior ESA use on RBC life span; and race on drug effect slope

Conclusions: The dose-Hgb model captured the daprodustat effect on Hgb in ND, HD, PD, ESA non-user, ESA user, and ESA hyporesponder patients, supporting the daprodustat dosing algorithm. Disease progression varied with dialysis status, reflecting the variable decline in Hgb over the disease course. The analysis showed ESA hyporesponders and normoresponders to benefit equally well from daprodustat treatment. TIW dosing response was comparable to equivalent QD dosing.

Citations:

[1] van den Berg P et al. PI-033. ASCPT; 2021.

[2] Lledó-García M et al. J Pharmacokinet Pharmacodyn 2012;39:453–62.

[3] Companion Presentation 1, Goulooze B et al. Daprodustat PopPK. ACoP; 2022.

[4] Companion Presentation 2, Goulooze B et al. Titration Based VPCs. ACoP; 2022.





Author(s)
  • Paul van den Berg, Leiden Experts on Advanced Pharmacokinetics and Pharmacodynamics (LAP&P), The Netherlands (Presenting Author)
  • Martijn van Noort, Leiden Experts on Advanced Pharmacokinetics and Pharmacodynamics (LAP&P), The Netherlands (CoAuthor)
  • Teun M. Post, Leiden Experts on Advanced Pharmacokinetics and Pharmacodynamics (LAP&P), The Netherlands (CoAuthor)
  • Misba Beerahee, Clinical Pharmacology Modelling and Simulation, GlaxoSmithKline, London, UK (CoAuthor)
  • Kelly M. Mahar, Clinical Pharmacology Modeling & Simulation, GlaxoSmithKline, Collegeville, Pennsylvania, USA (CoAuthor)
  • Shuying Yang, Clinical Pharmacology Modelling and Simulation, GlaxoSmithKline, London, UK (CoAuthor)



Reference: ACoP13 (2022) PMX-443 [www.go-acop.org/?abstract=443]
General Pharmacometrics e.g. popPK, PKPD, E-R, trial simulation, C-QT
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