Pharmacokinetic-Pharmacodynamic Relationship of Erenumab (AMG 334) and Capsaicin-Induced Dermal Blood Flow in Healthy and Migraine Subjects.

PURPOSE: Capsaicin-induced dermal blood flow (CIDBF) is a validated biomarker used to evaluate the target engagement of potential calcitonin gene-related peptide-blocking therapeutics for migraine. To characterize the pharmacokinetics (PK) and quantify the inhibitory effects of erenumab (AMG 334) on CIDBF, CIDBF data were pooled from a single- and a multiple-dose study in healthy and migraine subjects. METHODS: Repeated capsaicin challenges and DBF measurements were performed and serum erenumab concentrations determined. A population analysis was conducted using a nonlinear mixed-effects modeling approach. Effects of body weight, gender, and age on model parameters were evaluated. RESULTS: Two-compartment target-mediated drug disposition (TMDD) model assuming binding of erenumab in the central compartment best described the nonlinear PK of erenumab. Subcutaneous absorption half-life was 1.6 days and bioavailability was 74%. Erenumab produced a maximum inhibition of 89% (95% confidence interval: 87-91%). Erenumab concentrations required for 50% and 99% of maximum inhibition were 255 ng/mL and 1134 ng/mL, respectively. Increased body weight was associated with increased erenumab clearance but had no effect on the inhibitory effect on CIDBF. CONCLUSIONS: Our results show that erenumab pharmacokinetics was best characterized by a TMDD model and resulted in potent inhibition of CIDBF.

Using early biomarker data to predict long-term bone mineral density: application of semi-mechanistic bone cycle model on denosumab data.

Osteoporosis is a chronic skeletal disease characterized by low bone strength resulting in increased fracture risk. New treatments for osteoporosis are still an unmet medical need because current available treatments have various limitations. Bone mineral density (BMD) is an important endpoint for evaluating new osteoporosis treatments; however, the BMD response is often slower and less profound than that of bone turnover markers (BTMs). If the relationship between BTMs and BMD can be quantified, the BMD response can be predicted by the changes in BTM after a single dose; therefore, a decision based on BMD changes can be informed early. We have applied a bone cycle model to a phase 2 denosumab dose-ranging study in osteopenic women to quantitatively link serum denosumab pharmacokinetics, BTMs, and lumbar spine (LS) BMD. The data from two phase 3 denosumab studies in patients with low bone mass, FREEDOM and DEFEND, were used for external validation. Both internal and external visual predictive checks demonstrated that the model was capable of predicting LS BMD at the denosumab regimen of 60 mg every 6 months. It has been demonstrated that the model, in combination with the changes in BTMs observed from a single-dose study in men, is capable of predicting long-term BMD outcomes (e.g., LS BMD response in men after 1 year of treatment) in different populations. We propose that this model can be used to inform drug development decisions for osteoporosis treatment early via evaluating LS BMD response when BTM data become available in early trials.