A model-based approach using GSK3772847, an anti-interleukin-33 receptor monoclonal antibody, as a showcase to predict SC administration PK and free target dynamics based on PK and total target measurements after IV administration.

Integrated modeling of the pharmacokinetic (PK) and target binding, by means of a TMDD model, can provide valuable insights into the expected pharmacodynamic (PD) effects of monoclonal antibodies (mAbs). Optimal characterization of the human PK and target binding for mAbs requires data obtained after intravenous (IV) administration which can be combined with subcutaneous (SC) data to further this characterization. Integration of free and/or total target measurements in a population TMDD model will allow quantification of target engagement which is the first step in the cascade leading to efficacy.

Two new user-friendly methods to assess pharmacometric parameter identifiability on categorical and continuous scales.

Parameter identifiability methods assess whether the parameters of a model are uniquely determined by the observations. While the success of a model fit can provide some information on this, it can be valuable to determine identifiability before any fit has been attempted, or to separate identifiability from other issues. Two concepts that lean themselves well for identifiability analysis and have been underutilized are the sensitivity matrix (SM) and the Fisher information matrix (FIM).

Navigating the landscape of parameter identifiability methods: A workflow recommendation for model development.

In pharmacometric modeling, it is often important to know whether the data is sufficiently rich to identify the parameters of a proposed model. While it may be possible to assess this based on the results of a model fit, it is often difficult to disentangle identifiability issues from other model fitting and numerical problems. Furthermore, it can be of value to ascertain identifiability beforehand.

Pharmacokinetics and pharmacodynamics of finerenone in patients with chronic kidney disease and type 2 diabetes: Insights based on FIGARO-DKD and FIDELIO-DKD.

Aims: To perform dose-exposure-response analyses to determine the effects of finerenone doses.

Materials And Methods: Two randomized, double-blind, placebo-controlled phase 3 trials enrolling 13 026 randomized participants with type 2 diabetes (T2D) from global sites, each with an estimated glomerular filtration rate (eGFR) of 25 to 90 mL/min/1.73 m , a urine albumin-creatinine ratio (UACR) of 30 to 5000 mg/g, and serum potassium ≤ 4.

Longitudinal efficacy and safety modeling and simulation framework to aid dose selection of belantamab mafodotin for patients with multiple myeloma.

Belantamab mafodotin, a monomethyl auristatin F (MMAF)-containing monoclonal antibody-drug conjugate (ADC), demonstrated deep and durable responses in the DRiving Excellence in Approaches to Multiple Myeloma (DREAMM)-1 and pivotal DREAMM-2 studies in patients with relapsed/refractory multiple myeloma. As with other MMAF-containing ADCs, ocular adverse events were observed. To predict the effects of belantamab mafodotin dosing regimens and dose-modification strategies on efficacy and ocular safety end points, DREAMM-1 and DREAMM-2 data across a range of doses were used to develop an integrated simulation framework incorporating two separate longitudinal models and the published population pharmacokinetic model.

Dose-Exposure-Response Analysis of the Nonsteroidal Mineralocorticoid Receptor Antagonist Finerenone on UACR and eGFR: An Analysis from FIDELIO-DKD.

Background And Objective: Finerenone reduces the risk of kidney failure in patients with chronic kidney disease and type 2 diabetes. Changes in the urine albumin-to-creatinine ratio (UACR) and estimated glomerular filtration rate (eGFR) are surrogates for kidney failure. We performed dose-exposure-response analyses to determine the effects of finerenone on these surrogates in the presence and absence of sodium glucose co-transporter-2 inhibitors (SGLT2is) using individual patient data from the FIDELIO-DKD study.

Mechanism-based modeling of the effect of a novel inhibitor of vascular adhesion protein-1 on albuminuria and renal function markers in patients with diabetic kidney disease.

The vascular adhesion protein-1 (VAP-1) inhibitor ASP8232 reduces albuminuria in patients with type 2 diabetes and chronic kidney disease. A mechanism-based model was developed to quantify the effects of ASP8232 on renal markers from a placebo-controlled Phase 2 study in diabetic kidney disease with 12 weeks of ASP8232 treatment. The model incorporated the available pharmacokinetic, pharmacodynamic (plasma VAP-1 concentration and activity), serum and urine creatinine, serum cystatin C, albumin excretion rate, urinary albumin-to-creatinine ratio, and urine volume information in an integrated manner.

Can cyclist safety be improved with intelligent transport systems?

In recent years, Intelligent Transport Systems (ITS) have assisted in the decrease of road traffic fatalities, particularly amongst passenger car occupants. Vulnerable Road Users (VRUs) such as pedestrians, cyclists, moped riders and motorcyclists, however, have not been that much in focus when developing ITS. Therefore, there is a clear need for ITS which specifically address VRUs as an integrated element of the traffic system.