Background <p>Anemia is a common and debilitating complication in patients with chronic kidney disease (CKD), often managed with erythropoiesis-stimulating agents. While PEGylation extends drug half-life, it may alter pharmacodynamics, requiring careful dose optimization. This study applies a middle-out translational pharmacokinetic/pharmacodynamic modeling approach, aligned with Model-Informed Drug Development principles, to evaluate two pegylated recombinant human erythropoietin candidates (PEG-EPO 32&#xa0;kDa and PEG-EPO 40&#xa0;kDa) and guide dose selection for CKD patients.</p> Methods <p>A semi-mechanistic pharmacokinetic/pharmacodynamic model developed in rabbits was extrapolated to humans using allometric scaling for pharmacokinetics and physiological adaptation for pharmacodynamics. The model was verified using intravenous data from Mircera®. Simulations were conducted in virtual CKD stage 4 and 5 populations to predict hemoglobin (Hb) trajectories over 90&#xa0;days of dosing. Clinical thresholds were applied to assess efficacy and safety.</p> Results <p>Simulations with 0.6&#xa0;µg/kg Q2W reproduced Mircera® profiles but showed higher proportions of patients exceeding Hb safety thresholds (&gt; 11&#xa0;g/dL in stage 4, &gt; 9&#xa0;g/dL in stage 5) for both PEG-EPOs. Dose reduction to 0.4&#xa0;µg/kg Q2W aligned Hb responses with Mircera®, reducing the risk of excessive Hb elevation.</p> Conclusions <p>Middle-out modeling successfully predicted clinical performance of PEG-EPO candidates and identified 0.4&#xa0;µg/kg Q2W as optimal starting dose for clinical trials. PEG-EPO 32&#xa0;kDa and 40&#xa0;kDa emerges as a promising candidate for further development. This study exemplifies the value of MIDD in optimizing dose selection, enhancing translational relevance, and de-risking early clinical evaluation of long-acting erythropoiesis-stimulating agents in CKD.</p>

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Forecasting the Biological Effect of PEGylated-rHuEPO Candidates in Chronic Kidney Disease Patients using a Middle-out Translation Approach

  • Gledys Reynaldo-Fernandez,
  • Leyanis Rodriguez-Vera,
  • Daniel Amaro,
  • Joaquin Solozábal,
  • Jorge Duconge,
  • Victor Mangas-Sanjuan,
  • Iñaki F. Troconiz,
  • Francine Johansson Azeredo,
  • Valvanera Vozmediano

摘要

Background

Anemia is a common and debilitating complication in patients with chronic kidney disease (CKD), often managed with erythropoiesis-stimulating agents. While PEGylation extends drug half-life, it may alter pharmacodynamics, requiring careful dose optimization. This study applies a middle-out translational pharmacokinetic/pharmacodynamic modeling approach, aligned with Model-Informed Drug Development principles, to evaluate two pegylated recombinant human erythropoietin candidates (PEG-EPO 32 kDa and PEG-EPO 40 kDa) and guide dose selection for CKD patients.

Methods

A semi-mechanistic pharmacokinetic/pharmacodynamic model developed in rabbits was extrapolated to humans using allometric scaling for pharmacokinetics and physiological adaptation for pharmacodynamics. The model was verified using intravenous data from Mircera®. Simulations were conducted in virtual CKD stage 4 and 5 populations to predict hemoglobin (Hb) trajectories over 90 days of dosing. Clinical thresholds were applied to assess efficacy and safety.

Results

Simulations with 0.6 µg/kg Q2W reproduced Mircera® profiles but showed higher proportions of patients exceeding Hb safety thresholds (> 11 g/dL in stage 4, > 9 g/dL in stage 5) for both PEG-EPOs. Dose reduction to 0.4 µg/kg Q2W aligned Hb responses with Mircera®, reducing the risk of excessive Hb elevation.

Conclusions

Middle-out modeling successfully predicted clinical performance of PEG-EPO candidates and identified 0.4 µg/kg Q2W as optimal starting dose for clinical trials. PEG-EPO 32 kDa and 40 kDa emerges as a promising candidate for further development. This study exemplifies the value of MIDD in optimizing dose selection, enhancing translational relevance, and de-risking early clinical evaluation of long-acting erythropoiesis-stimulating agents in CKD.