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/1.73 m2 were included in a population PK analysis. PK simulations were conducted using a population PK model, and the area under concentration-time curve (AUC) was extrapolated with power regression analysis to lower eGFR. The influence of eGFR, weight, and age on apparent clearance and of dose on relative bioavailability were quantified by population PK analysis. Simulations and extrapolation revealed that the recommended dosing regimen based on the AUC was 500 mg twice daily (b.i.d.) for patients with eGFR 15-45 ml/min/1.73 m2 , and 500 mg with a longer dosing interval was suggested for those with eGFR less than 15. Simulations revealed that differences in plasma AUCs between Japanese and Western patients at the same dose were mainly driven by a difference in the eGFR and that the plasma AUC after 1000 and 1500 mg b.i.d. in Japanese and Western patients, respectively, was comparable in the phase IIb studies. These results indicate suitable dosages of imeglimin in the clinical setting of T2DM with renal impairment.

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. Template PBPK models were developed for interactions between fluvoxamine, S-mephenytoin, moclobemide, omeprazole, mexiletine, tizanidine, and ethinylestradiol as the perpetrators or victims. Predicted concentration-time profiles accurately described a validation dataset, including data from patients with genetic polymorphisms, demonstrating that the models characterized the CYP2C19 and CYP1A2 network over the whole range of DDI studies investigated. The models are provided on GitHub (GitHub Inc., San Francisco, CA, USA), expanding the library of publicly available qualified whole-body PBPK models for DDI predictions, and they are thereby available to support potential recommendations for dose adaptations, support labeling, inform the design of clinical DDI trials, and potentially waive those.

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. METHODS: Patients were randomized to control or active treatment with otelixizumab (1:4), administered via infusion over 6 days, in a dose-ascending study consisted of three cohorts (n = 10 per cohort) at doses of 9, 18 or 27 mg respectively. The study allowed quantification of otelixizumab PK, CD3ɛ target engagement and its pharmacodynamic effect (CD3ε/TCR modulation on circulating T lymphocytes). RESULTS: Otelixizumab concentrations increased and averaged to 364.09 (54.3), 1625.55 (72.5) and 2781.35 (28.0) ng ml-1 (Geom.mean, %CV) at the 9, 18 and 27 mg dose respectively. CD3ɛ target engagement was found to be rapid (within the first 30 min), leading to a receptor occupancy of ~60% within 6 h of dosing in all three doses. A dose-response relationship was observed with the two highest doses achieving a ~90% target engagement and consequential CD3ɛ/TCR downmodulation by Day 6. CONCLUSIONS: Data from this study revealed maximum target engagement and CD3ɛ/TCR modulation is achieved at doses of 18, 27 mg of otelixizumab. These findings can be useful in guiding dose selection in clinical trials with anti-CD3ɛ mAbs.

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. Approximation (a) did not affect model-derived concentrations, while with the inclusion of approximation (b) or (c), target concentration profiles alone, or both drug and target concentration profiles respectively deviated from the reference model profiles. A local sensitivity analysis was performed, highlighting the potential importance of sampling in the terminal pharmacokinetic phase and of collecting target concentration data. The a priori and a posteriori identifiability of the mPBPK-TMDD models were investigated under different experimental scenarios and designs. The reference model and QSS approximation (a) on antibody-target complex were both found to be a priori identifiable in all scenarios, while under the further inclusion of QSS approximation (b) target concentration data were needed for a priori identifiability to be preserved. The property could not be assessed for the model including all three QSS approximations. A posteriori identifiability issues were detected for all models, although improvement was observed when appropriate sampling and dose range were selected. In conclusion, this work provides a theoretical framework for the assessment of key properties of mathematical models before their experimental application. Attention should be paid when applying integrated mPBPK-TMDD models, as identifiability issues do exist, especially when rich study designs are not feasible.

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. At each time point, free, bound, and total CD3/TCR expression on both CD4+ and CD8+ T cells and the amount of free otelixizumab antibody in the supernatant were measured. The pharmacokinetics of free otelixizumab in the culture supernatants was saturable, with a shorter apparent half-life at low concentration. Correspondingly, a rapid, otelixizumab concentration-, and time-dependent reduction in CD3/TCR expression was observed. These combined observations were consistent with the phenomenon known as target-mediated drug disposition (TMDD). A mechanistic, mathematical pharmacokinetic/pharmacodynamic (PK/PD) model was then used to characterize the free otelixizumab-CD3 expression-time relationship. CD3/TCR modulation induced by otelixizumab was found to be relatively fast compared with the re-expression rate of CD3/TCR complexes following otelixizumab removal from supernatants. In summary, the CD3/TCR receptor has been shown to have a major role in determining otelixizumab disposition. A mechanistic PK/PD model successfully captured the PK and PD in vitro data, confirming TMDD by 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. Internal validation was performed through simplified posterior predictive check (sPPC), visual predictive check (VPC) for categorical data, and new visual methods based on stochastic simulation and estimation and called visual estimation check (VEC). External validation mainly relied on the evaluation of the objective function value and sPPC. Covariate effects were identified through stepwise covariate modeling within NONMEM VI. New model features were introduced in the model, providing significant sPPC improvements. Outcomes from VPC, VEC, and external validation were generally very good. Age, gender, and BMI were found to be statistically significant covariates, but their inclusion did not improve substantially the model's predictive performance. In summary, an improved model for sleep internal architecture has been developed and suitably validated in insomniac patients treated with placebo. Thereafter, covariate effects have been included into the final model.