#2898 Exposure response model describing hematologic changes with chronic treatment of CKD anemia with vadadustat

Abstract

Abstract Background and Aims Vadadustat (VADA) is an oral hypoxia-inducible factor (HIF) prolyl hydroxylase inhibitor, a class of drugs that stabilize HIF and stimulate endogenous erythropoietin production, increasing iron mobilization and red blood cell production. VADA is approved in 36 countries globally and is indicated in the European Union for the treatment of symptomatic anaemia associated with chronic kidney disease (CKD) in adults on chronic maintenance dialysis and is an alternative to treatment with erythropoiesis-stimulating agents (ESAs). The aim of this work was to develop a predictive, semi-mechanistic model to understand the effects of VADA and erythropoiesis-stimulating agents (ESAs) on hemoglobin response and to evaluate the impact of intrinsic and extrinsic patient characteristics on that response for non-dialysis dependent (NDD) and dialysis-dependent (DD) patient populations. Method A predictive semi-mechanistic pharmacokinetic/pharmacodynamic (PK/PD) model for VADA and ESA treatment was developed using nonlinear mixed effects methods. The model describes changes in reticulocyte count (RETICs), mature erythrocytes (RBCs) and hemoglobin (Hb) as a function of drug exposure and subject-specific characteristics. The dataset was based on data from 7 Phase 1 to Phase 3 studies (Table 1; N = 894 for VADA; N = 560 for ESA modeling) in healthy volunteers and subjects with NDD- or DD-CKD who were either ESA-naïve or ESA experienced but not receiving treatment at screening. Prior population PK modeling of VADA provided estimated drug exposures for VADA-treated subjects, while dose normalized to standard units (IU/kg/wk) was used as the exposure metric for subjects on ESA. Each of these exposure metrics could vary with time, as the dose was adjusted according to observed Hb response. This work evaluated alternative model structures, as well as covariate effects affecting Hb individual response. Results A predictive semi-mechanistic pharmacokinetic/pharmacodynamic (PK/PD) model for VADA and ESA treatment was developed using nonlinear mixed effects methods. The model describes changes in reticulocyte count (RETICs), mature erythrocytes (RBCs) and hemoglobin (Hb) as a function of drug exposure and subject-specific characteristics. The dataset was based on data from 7 Phase 1 to Phase 3 studies (Table 1; N = 894 for VADA; N = 560 for ESA modeling) in healthy volunteers and subjects with NDD- or DD-CKD who were either ESA-naïve or ESA experienced but not receiving treatment at screening. Prior population PK modeling of VADA provided estimated drug exposures for VADA-treated subjects, while dose normalized to standard units (IU/kg/wk) was used as the exposure metric for subjects on ESA. Each of these exposure metrics could vary with time, as the dose was adjusted according to observed Hb response. This work evaluated alternative model structures, as well as covariate effects affecting Hb individual response. A similar model structure was found to describe early changes in RETICs and longer-term changes in Hb for both VADA and ESAs. In each case, the magnitude of response increases with increased drug exposure up to a maximum value (Emax), and the Emax values were similar between VADA and ESAs. Subject intrinsic predictors of response included eGFR, subject age, BMI, and serum albumin, while extrinsic factors affecting response included geographic region, limits on acceptable Hb values (which also differ by geographic region) and the presence of previous ESA treatment. Interestingly, Hb responses were also correlated with changes in serum hepcidin, a key regulator of iron homeostasis, following both VADA and ESA treatment. Differences between the response to VADA and ESA were also described, including differences in temporal changes in RETICs and Hb. Conclusion This model showed an exposure-response relationship between VADA or ESA treatment and changes in RETICs and Hb for ESA naïve or ESA-experienced CKD subjects. The model will be used to inform dosage recommendations for VADA based on patient characteristics.