Key Points• NK cells decline as daratumumab exposure increases in a maximum effect type doseresponse relationship, with no efficacy and safety impact.• Remaining PBMC cell fractions maintained the ability to carry out daratumumab-mediated ex vivo ADCC.

The population pharmacokinetics (PK) of rivaroxaban have been evaluated in several population‐specific models. We developed an integrated population PK model using pooled data from 4,918 patients in 7 clinical trials across all approved indications. Effects of gender, age, and weight on apparent clearance (CL/F) and apparent volume of distribution (V/F), renal function, and comedication on CL/F, and relative bioavailability as a function of dose (F) were analyzed. Virtual subpopulations for exposure simulations were defined by age, creatinine clearance (CrCL) and body mass index (BMI). Rivaroxaban PK were adequately described by a one‐compartment disposition model with a first‐order absorption rate constant. Significant effects of CrCL, use of comedications, and study population on CL/F, age, weight, and gender on V/F, and dose on F were identified. CrCL had a modest influence on exposure, whereas age and BMI had a minor influence. The model was suitable to predict rivaroxaban exposure in patient subgroups of special interest.

A hydrogel scaffold for direct tissue-engineering application in water-irrigated, arthroscopic cartilage repair, is badly needed. However, such hydrogels must cure quickly under water, bind strongly and permanently to the surrounding tissue, and maintain sufficient mechanical strength to withstand the hydraulic pressure of arthroscopic irrigation (~10 kilopascal). To address these challenges, we report a versatile hybrid photocrosslinkable (HPC) hydrogel fabricated though a combination of photoinitiated radical polymerization and photoinduced imine cross-linking. The ultrafast gelation, high mechanical strength, and strong adhesion to native tissue enable the direct use of these hydrogels in irrigated arthroscopic treatments. We demonstrate, through in vivo articular cartilage defect repair in the weight-bearing regions of swine models, that the HPC hydrogel can serve as an arthroscopic autologous chondrocyte implantation scaffold for long-term cartilage regeneration, integration, and reconstruction of articular function.

Target-mediated drug disposition (TMDD) models have been applied to describe the pharmacokinetics of drugs whose distribution and/or clearance are affected by its target due to high binding affinity and limited capacity. The Michaelis–Menten (M–M) model has also been frequently used to describe the pharmacokinetics of such drugs. The purpose of this study is to investigate conditions for equivalence between M–M and TMDD pharmacokinetic models and provide guidelines for selection between these two approaches. Theoretical derivations were used to determine conditions under which M–M and TMDD pharmacokinetic models are equivalent. Computer simulations and model fitting were conducted to demonstrate these conditions. Typical M–M and TMDD profiles were simulated based on literature data for an anti-CD4 monoclonal antibody (TRX1) and phenytoin administered intravenously. Both models were fitted to data and goodness of fit criteria were evaluated for model selection. A case study of recombinant human erythropoietin was conducted to qualify results. A rapid binding TMDD model is equivalent to the M–M model if total target density Rtot is constant, and RtotKD/(KD + C)2 ≪ 1 where KD represents the dissociation constant and C is the free drug concentration. Under these conditions, M–M parameters are defined as: Vmax = kintRtotVc and Km = KD where kint represents an internalization rate constant, and Vc is the volume of the central compartment. Rtot is constant if and only if kint = kdeg, where kdeg is a degradation rate constant. If the TMDD model predictions are not sensitive to kint or kdeg parameters, the condition of RtotKD/(KD + C)2 ≪ 1 alone can preserve the equivalence between rapid binding TMDD and M–M models. The model selection process for drugs that exhibit TMDD should involve a full mechanistic model as well as reduced models. The best model should adequately describe the data and have a minimal set of parameters estimated with acceptable precision.