A Multi-model Approach to Predict Efficacious Clinical Dose for an Anti-TGF-β Antibody (GC2008) in the Treatment of Osteogenesis Imperfecta
Overview
Authors
Affiliations
Osteogenesis imperfecta (OI) is a heterogeneous group of inherited bone dysplasias characterized by reduced skeletal mass and bone fragility. Although the primary manifestation of the disease involves the skeleton, OI is a generalized connective tissue disorder that requires a multidisciplinary treatment approach. Recent studies indicate that application of a transforming growth factor beta (TGF-β) neutralizing antibody increased bone volume fraction (BVF) and strength in an OI mouse model and improved bone mineral density (BMD) in a small cohort of patients with OI. In this work, we have developed a multitiered quantitative pharmacology approach to predict human efficacious dose of a new anti-TGF-β antibody drug candidate (GC2008). This method aims to translate GC2008 pharmacokinetic/pharmacodynamic (PK/PD) relationship in patients, using a number of appropriate mathematical models and available preclinical and clinical data. Compartmental PK linked with an indirect PD effect model was used to characterize both pre-clinical and clinical PK/PD data and predict a GC2008 dose that would significantly increase BMD or BVF in patients with OI. Furthermore, a physiologically-based pharmacokinetic model incorporating GC2008 and the body's physiological properties was developed and used to predict a GC2008 dose that would decrease the TGF-β level in bone to that of healthy individuals. By using multiple models, we aim to reveal information for different aspects of OI disease that will ultimately lead to a more informed dose projection of GC2008 in humans. The different modeling efforts predicted a similar range of pharmacologically relevant doses in patients with OI providing an informed approach for an early clinical dose setting.
Pillai N, Abos A, Teutonico D, Mavroudis P Clin Transl Sci. 2024; 17(5):e13824.
PMID: 38752574 PMC: 11097621. DOI: 10.1111/cts.13824.
Mavroudis P, Pillai N, Wang Q, Pouzin C, Greene B, Fretland J CPT Pharmacometrics Syst Pharmacol. 2022; 11(11):1485-1496.
PMID: 36004727 PMC: 9662198. DOI: 10.1002/psp4.12857.