A Model-Informed Drug Development (MIDD) Approach for a Low Dose of Empagliflozin in Patients with Type 1 Diabetes.

In clinical trials, sodium-glucose co-transporter (SGLT) inhibitor use as adjunct to insulin therapy in type 1 diabetes (T1D) provides glucometabolic benefits while diabetic ketoacidosis risk is increased. The SGLT2 inhibitor empagliflozin was evaluated in two phase III trials: EASE-2 and EASE-3. A low, 2.5-mg dose was included in EASE-3 only. As the efficacy of higher empagliflozin doses (i.e., 10 and 25 mg) in T1D has been established in EASE-2 and EASE-3, a modeling and simulation approach was used to generate additional supportive evidence on efficacy for the 2.5-mg dose. We present the methodology behind the development and validation of two modeling and simulation frameworks: M-EASE-1, a semi-mechanistic model integrating information on insulin, glucose, and glycated hemoglobin; and M-EASE-2, a descriptive model informed by prior information. Both models were developed independently of data from EASE-3. Simulations based on these models assessed efficacy in untested clinical trial scenarios. In this manner, the models provide supportive evidence for efficacy of low-dose empagliflozin 2.5 mg in patients with T1D, illustrating how pharmacometric analyses can support efficacy assessments in the context of limited data.

Population pharmacokinetics of adalimumab biosimilar adalimumab-adbm and reference product in healthy subjects and patients with rheumatoid arthritis to assess pharmacokinetic similarity.

AIMS: Adalimumab-adbm is a monoclonal antibody developed as a biosimilar to adalimumab (Humira, AbbVie Inc.). The key objectives of this study were using a population pharmacokinetic (PPK) approach to assess pharmacokinetic (PK) similarity between adalimumab-adbm and Humira in patients with active rheumatoid arthritis (RA), to quantify the effects of potential covariates on adalimumab PK and to assess the impact of switching treatment from Humira to adalimumab-adbm on PK. METHODS: A PPK model was firstly developed using intensive PK data from the phase-1 study in healthy subjects (NCT02045979). PPK models were developed separately for phase-3 base study (NCT02137226) and its extension study (NCT02640612) in patients with active RA. RESULTS: PPK models were developed for adalimumab from adalimumab-adbm and Humira treatment in healthy subjects and RA patients. Weight and anti-drug antibodies were found to be important predictors of adalimumab clearance. Adalimumab PK was similar between adalimumab-adbm and Humira. The estimated effect of Humira on clearance, relative to the adalimumab-adbm, was 1.02 (i.e., Humira has 0.02 greater clearance). Similarly, the effect of treatment arms (switching) on clearance was estimated to be 1.00 and 0.997 for Humira:Humira:BI and Humira:BI:BI arms, respectively, relative to the BI:BI:BI arm (BI refers to adalimumab-adbm) in the phase-3 extension study. CONCLUSION: PK similarity between adalimumab-adbm and Humira in patients with active RA was demonstrated using PPK approach. Adalimumab PK was also similar when switching treatment from Humira to adalimumab-adbm at either week 24 or 48.

Low-dose empagliflozin as adjunct-to-insulin therapy in type 1 diabetes: A valid modelling and simulation analysis to confirm efficacy.

AIM: To confirm the observed reduction in HbA1c for the 2.5 mg dose in EASE-3 by modelling and simulation analyses. MATERIALS AND METHODS: Independent of data from EASE-3 that tested 2.5 mg, we simulated the effect of a 2.5 mg dose through patient-level, exposure-response modelling in the EASE-2 clinical study. A primary semi-mechanistic model evaluated efficacy considering clinical insulin dose adjustments made after treatment initiation that potentially limited HbA1c reductions. The model was informed by pharmacokinetic, insulin dose, mean daily glucose and HbA1c data, and was verified by comparing the simulations with the observed HbA1c change in EASE-3. One of two empagliflozin phase 3 trials in type 1 diabetes (EASE-3 but not EASE-2) included a lower 2.5 mg dose. A placebo-corrected HbA1c reduction of 0.28% was demonstrated without the increased risk of diabetic ketoacidosis observed at higher doses (10 mg and 25 mg). Since only one trial included the lower dose, we aimed to confirm the observed reduction in HbA1c for the 2.5 mg dose by modelling and simulation analyses. RESULTS: The simulated 26-week mean HbA1c change was -0.41% without insulin dose adjustment and -0.29% at 26 weeks with insulin dose adjustment. A simplified (descriptive) model excluding insulin dose and mean daily glucose confirmed the -0.29% HbA1c change that would have been observed had the EASE-2 population received a 2.5 mg dose for 26/52 weeks. CONCLUSIONS: The HbA1c benefit of low-dose empagliflozin directly observed in the EASE-3 trial was confirmed by two modelling and simulation approaches.

A model of fracture risk used to examine the link between bone mineral density and the impact of different therapeutic mechanisms on fracture outcomes in patients with osteoporosis.

A hazard model of fracture was developed using individual patient data (IPD) from the NHANES (2005-2008) database and summary-level data from an aggregate dataset (AD). The AD was built by performing a comprehensive and systematic literature search of clinical studies published from 1995 to 2015, recording fracture rate and bone mineral density (BMD) for both treatment and placebo arms. The search resulted in a metadata set comprised of 21 studies investigating the effects of various bisphosphonates, teriparatide, denosumab, and raloxifene in 65,254 patients over a cumulative 56.75 years of study. The IPD was used to augment an AD in a model-based meta-analysis (MBMA) hierarchical modeling approach. The resulting model predicts the probability of fracture events in patients with osteoporosis. The object of model building using this approach was to promote understanding of the impact of therapeutic drug effects on the probability of fracture together with, or independent of their effects on BMD. Candidate models were evaluated by deviance information criteria and posterior predictive check. The model with covariates for lumbar spine BMD with interaction with a drug effect on BMD, and patient body mass index, years post-menopause, fracture measure method (clinical or radiological) and an additional drug effect outperformed those models without interaction and without additional drug effects. The model quantitatively supports the widely held notion that changes in bone microarchitecture, which cannot be measured by areal BMD elicited by therapy contribute in a significant way to a reduction in fracture. Furthermore, this model can be used to simulate fracture risk in a clinical cohort similar to those contained in the MBMA.