Target mediated drug disposition with drug-drug interaction, Part II: competitive and uncompetitive cases

Population Pharmacokinetics of siRNA JNJ-73763989 in Healthy Participants and Patients With Chronic Hepatitis B

JNJ-73763989 is a combination product consisting of two N-acetylgalactosamine-conjugated short-interfering RNA triggers (JNJ-73763976 and JNJ-73763924) that are in development as a potential treatment for chronic hepatitis B virus infection. A population pharmacokinetic model for JNJ-73763989 was developed based on pooled data from seven clinical studies to characterize the plasma pharmacokinetics of the short-interfering RNAs following subcutaneous administration. Additionally, simulations of liver (target organ) exposure using the final population pharmacokinetic model in conjunction with preclinical information were performed. Disposition of JNJ-73763976 and JNJ-73763924 was governed by a linear two-compartment model describing the peripheral distribution of both short-interfering RNAs and a saturable component describing liver uptake via the asialoglycoprotein receptor. While the estimated first-order absorption rate constant was similar for both short-interfering RNAs, the corresponding absorption half-life values were 20- to 40-fold longer than the estimated plasma elimination half-life for both short-interfering RNAs, indicating absorption rate-limited or "flip-flop" kinetics. Plasma-to-liver transport of each short-interfering RNA was modeled by a saturable, receptor-mediated competitive process, and the affinity for the asialoglycoprotein receptor was 2.5-fold higher for JNJ-73763924 relative to JNJ-73763976. Predicted liver concentrations of both short-interfering RNA triggers approached steady state after 12 months of JNJ-73763989 treatment. The 2:1 dosing ratio of JNJ-73763976 to JNJ-73763924 was predicted to maintain an ~2:1 liver concentration ratio, irrespective of the identified plasma disposition differences between the triggers. Body weight, creatinine clearance, presence of chronic hepatitis B, and hepatic impairment were associated with plasma pharmacokinetic parameters and were included in the final population pharmacokinetic model.

Plasma and Liver Pharmacokinetics of the N-Acetylgalactosamine Short Interfering RNA JNJ-73763989 in Recombinant Adeno-Associated-Hepatitis B Virus-Infected Mice

JNJ-73763989 is an N-acetylgalactosamine conjugated short interfering RNA combination product consisting of two triggers in clinical development for chronic hepatitis B virus (HBV) infection treatment that induces a selective degradation of all HBV mRNA transcripts. Our aim is to characterize the plasma and liver pharmacokinetics (PK) of JNJ-73763989 after intravenous and subcutaneous administration in recombinant adeno-associated (rAAV) HBV infected mice. Forty-two male rAAV-HBV infected C57Bl/6 mice received JNJ-73763989 doses of 10 mg/kg i.v. or 1, 3 and 10 mg/kg s.c. Plasma and liver concentrations were analyzed simultaneously using nonlinear mixed-effects modeling with the NONMEM 7.4. A population PK model consisting of a two-compartment disposition model with transporter-mediated drug disposition, including internalization to the liver compartment, linear elimination from plasma and liver, and first-order absorption following subcutaneous administration, was suitable to describe both plasma and liver PK. After subcutaneous dosing, absolute bioavailability was complete and flip-flop kinetics were observed. JNJ-73763989 distributes from plasma to liver via transporter-mediated liver internalization in less than 24 hours, with sustained (>42 days) liver exposure. The saturation of transporter-mediated liver internalization was hypothesized to be due to asialoglycoprotein receptor saturation. Increasing the dose decreased the relative liver uptake efficiency in mice for intravenously and, to a lesser extent, subcutaneously administered JNJ-73763989. Lower dose levels administered subcutaneously in mice can maximize the proportion of the dose reaching the liver. SIGNIFICANCE STATEMENT: Pharmacokinetic modeling of JNJ-73763989 liver and plasma concentration-time data in mice indicated that the proportion of JNJ-73763989 reaching the liver may be increased by administering lower subcutaneous doses compared to higher intravenous doses. Model-based simulations can be applied to optimize the dose and regimen combination.

An Extended Model Including Target Turnover, Ligand-Target Complex Kinetics, and Binding Properties to Describe Drug-Receptor Interactions

Since the beginning of this century, target-mediated drug disposition has become a central concept in modeling drug action in drug development. It combines a range of processes, such as turnover, protein binding, internalization, and non-specific elimination, and often serves as a nucleus of more complex pharmacokinetic models. It is simple enough to comprehend but complex enough to be able to describe a wide range of phenomena and data sets. However, the complexity comes at a price: many parameters. In this chapter, we present an overview of the temporal development of the compounds involved after different types of drug doses and offer convenient handles for dissecting data sets in a sophisticated manner in order to estimate the values of these parameters, such as rate constants and pertinent concentrations.

Estimating drug potency in the competitive target mediated drug disposition (TMDD) system when the endogenous ligand is included

Predictions for target engagement are often used to guide drug development. In particular, when selecting the recommended phase 2 dose of a drug that is very safe, and where good biomarkers for response may not exist (e.g. in immuno-oncology), a receptor occupancy prediction could even be the main determinant in justifying the approved dose, as was the case for atezolizumab. The underlying assumption in these models is that when the drug binds its target, it disrupts the interaction between the target and its endogenous ligand, thereby disrupting downstream signaling. However, the interaction between the target and its endogenous binding partner is almost never included in the model. In this work, we take a deeper look at the in vivo system where a drug binds to its target and disrupts the target's interaction with an endogenous ligand. We derive two simple steady state inhibition metrics (SSIMs) for the system, which provides intuition for when the competition between drug and endogenous ligand should be taken into account for guiding drug development.

Population pharmacokinetics and covariate analysis of Sym004, an antibody mixture against the epidermal growth factor receptor, in subjects with metastatic colorectal cancer and other solid tumors

Sym004 is an equimolar mixture of two monoclonal antibodies, futuximab and modotuximab, which non-competitively block the epidermal growth factor receptor (EGFR). Sym004 has been clinically tested for treatment of solid tumors. The present work characterizes the non-linear pharmacokinetics (PK) of Sym004 and its constituent antibodies and investigates two types of covariate models for interpreting the interindividual variability of Sym004 exposure. Sym004 serum concentration data from 330 cancer patients participating in four Phase 1 and 2 trials (n = 247 metastatic colorectal cancer, n = 87 various types advanced solid tumors) were pooled for non-linear mixed effects modeling. Dose regimens of 0.4-18 mg/kg Sym004 dosed by i.v. infusion weekly or every 2nd week were explored. The PK profiles for futuximab and modotuximab were parallel, and the parameter values for their population PK models were similar. The PK of Sym004 using the sum of the serum concentrations of futuximab and modotuximab was well captured by a 2-compartment model with parallel linear and saturable, Michaelis-Menten-type elimination. The full covariate model including all plausible covariates included in a single step showed no impact on Sym004 exposure of age, Asian race, renal and hepatic function, tumor type and previous anti-EGFR treatments. The reduced covariate model contained statistically and potentially clinically significant influences of body weight, albumin, sex and baseline tumor size. Population PK modeling and covariate analysis of Sym004 were feasible using the sum of the serum concentrations of the two constituent antibodies. Full and reduced covariate models provided insights into which covariates may be clinically relevant for dose modifications and thus may need further exploration.

Target-Mediated Drug Disposition Model for Bispecific Antibodies: Properties, Approximation, and Optimal Dosing Strategy

Bispecific antibodies (BsAbs) bind to two different targets, and create two binary and one ternary complex (TC). These molecules have shown promise as immuno-oncology drugs, and the TC is considered the pharmacologically active species that drives their pharmacodynamic effect. Here, we have presented a general target-mediated drug disposition (TMDD) model for these BsAbs, which bind to two different targets on different cell membranes. The model includes four different binding events for BsAbs, turnover of the targets, and internalization of the complexes. In addition, a quasi-equilibrium (QE) approximation with decreased number of binding parameters and, if necessary, reduced internalization parameters is presented. The model is further used to investigate the kinetics of BsAb and TC concentrations. Our analysis shows that larger doses of BsAbs may delay the build-up of the TC. Consequently, a method to compute the optimal dosing strategy of BsAbs, which will immediately create and maintain maximal possible TC concentration, is presented.

Target-mediated drug disposition with drug-drug interaction, Part I: single drug case in alternative formulations

Target-mediated drug disposition (TMDD) describes drug binding with high affinity to a target such as a receptor. In application TMDD models are often over-parameterized and quasi-equilibrium (QE) or quasi-steady state (QSS) approximations are essential to reduce the number of parameters. However, implementation of such approximations becomes difficult for TMDD models with drug-drug interaction (DDI) mechanisms. Hence, alternative but equivalent formulations are necessary for QE or QSS approximations. To introduce and develop such formulations, the single drug case is reanalyzed. This work opens the route for straightforward implementation of QE or QSS approximations of DDI TMDD models. The manuscript is the first part to introduce DDI TMDD models with QE or QSS approximations.