Imeglimin population pharmacokinetics and dose adjustment predictions for renal impairment in Japanese and Western patients with type 2 diabetes.

Imeglimin is an orally administered first-in-class drug to treat type 2 diabetes mellitus (T2DM) and is mainly excreted unchanged by the kidneys. The present study aimed to define the pharmacokinetic (PK) characteristics of imeglimin using population PK analysis and to determine the optimal dosing regimen for Japanese patients with T2DM and chronic kidney disease (CKD). Imeglimin plasma concentrations in Japanese and Western healthy volunteers, and patients with T2DM, including patients with mild to severe CKD with an estimated glomerular filtration rate (eGFR) greater than 14 ml/min/1.

A Physiologically-Based Pharmacokinetic (PBPK) Model Network for the Prediction of CYP1A2 and CYP2C19 Drug-Drug-Gene Interactions with Fluvoxamine, Omeprazole, S-mephenytoin, Moclobemide, Tizanidine, Mexiletine, Ethinylestradiol, and Caffeine.

Physiologically-based pharmacokinetic (PBPK) modeling is a well-recognized method for quantitatively predicting the effect of intrinsic/extrinsic factors on drug exposure. However, there are only few verified, freely accessible, modifiable, and comprehensive drug-drug interaction (DDI) PBPK models. We developed a qualified whole-body PBPK DDI network for cytochrome P450 (CYP) CYP2C19 and CYP1A2 interactions.

Target engagement and cellular fate of otelixizumab: a repeat dose escalation study of an anti-CD3ε mAb in new-onset type 1 diabetes mellitus patients.

Aims: This paper describes the pharmacological findings from a study where otelixizumab, an anti-CD3ɛ mAb, was dosed in new onset Type 1 diabetes mellitus (NOT1DM) patients. This is the first time that the full dose-response of an anti-CD3ɛ mAb has been investigated in the clinic. The data have been validated using a previously developed pharmacokinetic/pharmacodynamic (PK/PD) model of otelixizumab to simulate the interplay between drug administration, CD3ɛ target engagement and downmodulation.

MPBPK-TMDD models for mAbs: alternative models, comparison, and identifiability issues.

The aim of the present study was to evaluate model identifiability when minimal physiologically-based pharmacokinetic (mPBPK) models are integrated with target mediated drug disposition (TMDD) models in the tissue compartment. Three quasi-steady-state (QSS) approximations of TMDD dynamics were explored: on (a) antibody-target complex, (b) free target, and (c) free antibody concentrations in tissue. The effects of the QSS approximations were assessed via simulations, taking as reference the mPBPK-TMDD model with no simplifications.

Temporal pharmacokinetic/pharmacodynamic interaction between human CD3ε antigen-targeted monoclonal antibody otelixizumab and CD3ε binding and expression in human peripheral blood mononuclear cell static culture.

Otelixizumab is a monoclonal antibody (mAb) directed to human CD3ε, a protein forming part of the CD3/T-cell receptor (TCR) complex on T lymphocytes. This study investigated the temporal interaction between varying concentrations of otelixizumab, binding to human CD3 antigen, and expression of CD3/TCR complexes on lymphocytes in vitro, free from the confounding influence of changing lymphocyte frequencies observed in vivo. A static in vitro culture system was established in which primary human peripheral blood mononuclear cells (PBMCs) were incubated over an extended time course with titrated concentrations of otelixizumab.

Multinomial logistic functions in markov chain models of sleep architecture: internal and external validation and covariate analysis.

Mixed-effect Markov chain models have been recently proposed to characterize the time course of transition probabilities between sleep stages in insomniac patients. The most recent one, based on multinomial logistic functions, was used as a base to develop a final model combining the strengths of the existing ones. This final model was validated on placebo data applying also new diagnostic methods and then used for the inclusion of potential age, gender, and BMI effects.