Biological therapeutics, especially antibodies and recombinant proteins, have transformed modern medicine by providing targeted treatment and minimizing off-target effects. However, their development often faces challenges due to inherent structural c...
Biological therapeutics, especially antibodies and recombinant proteins, have transformed modern medicine by providing targeted treatment and minimizing off-target effects. However, their development often faces challenges due to inherent structural complexities and unique pharmacokinetic (PK) issues. These include susceptibility to degradation by proteolytic enzymes, target-mediated drug disposition (TMDD), and the recycling facilitated by the neonatal Fc receptor (FcRn). To design optimal dosing regimens and predict clinical outcomes effectively, it is crucial to understand these elimination pathways mechanistically. This thesis addresses these significant challenges in drug development through three integrated approaches: mechanistic modeling of Fc-fusion kinetics, structural engineering to evade autoantibody responses, and refined modeling of TMDD.
First, to evaluate the kinetic effects of Fc-fusion strategies used for extending the half-life of proteins, we analyzed the plasma pharmacokinetic (PK) profiles of a recombinant fragment of ADAMTS13 (MDTCS) and its Fc-fusion counterpart (MDTCS-Fc) using mechanistic modeling. The study demonstrated that Fc-fusion altered cellular processing, increasing the recycling fraction (FR) by approximately 2.8-fold (0.85 for MDTCS-Fc compared to 0.31 for MDTCS) while predicting a slower rate of endosomal uptake. Additionally, a local sensitivity analysis revealed that the FcRn association rate constant (kon) is a crucial factor influencing the in vivo half-life of the proteins. These findings provide a quantitative framework for understanding how Fc-fusion affects recycling efficiency and cellular uptake kinetics, offering valuable insights for the development of long-acting protein therapeutics.
Second, addressing the immunological hurdle of drug neutralization in immune-mediated thrombotic thrombocytopenic purpura (iTTP), we characterized GC1126A, a novel ADAMTS13 mutein. In iTTP, patient-derived autoantibodies neutralize the ADAMTS13 enzyme, leading to life-threatening thrombosis. GC1126A was engineered to evade these autoantibodies while maintaining intrinsic enzymatic activity. In iTTP-mimic mouse models, GC1126A demonstrated superior therapeutic profiles compared to both recombinant wild-type ADAMTS13 and the standard-of-care, caplacizumab. Specifically, it significantly improved key biomarkers, including platelet counts and lactate dehydrogenase levels, and exhibited a prolonged half-life. Moreover, ex vivo assays using plasma from patients with high autoantibody titers confirmed that GC1126A maintained higher residual activity than the wild-type ADAMTS13, suggesting its potential as a promising next-generation therapeutic option for patients with iTTP.
Finally, a refined TMDD model was developed for MG1113, an anti-tissue factor pathway inhibitor (TFPI) antibody designed for hemophilia treatment. The refined model incorporated dual binding targets, soluble TFPI-α (sTFPI-α) and membrane-bound TFPI (mTFPI), and included a transit compartment to address the delayed absorption observed following subcutaneous administration. By fitting the model to PK/pharmacodynamic (PD) data from cynomolgus monkeys and applying allometric scaling, the study successfully predicted MG1113 profiles in rabbits. When MG1113 was administered subcutaneously once a week at a dosage of 3.3 mg/kg to patients with hemophilia, it was predicted that sTFPI-α concentrations could be suppressed by more than 75%. The study also confirmed that each patient’s basal sTFPI-α concentration influenced the efficacy of MG1113. These findings provide crucial evidence for selecting a customized dose for each patient.
Collectively, this thesis integrates mechanistic PK modeling with molecular engineering to address core challenges in biologics development. By providing quantitative insights into FcRn-mediated recycling, demonstrating a proof-of-concept for autoantibody evasion, and establishing a reliable dose-prediction framework via TMDD modeling, these findings are expected to accelerate the development of safer and more effective biological therapeutics.