Pharmacokinetics and Concentration‐Effect Analysis of Intravenous RGD891, a Platelet GPIIb/IIIa Antagonist, Using Mixed‐Effects Modeling (NONMEM)

RGD891 is a platelet GPIIb/IIIa receptor antagonist and potent inhibitor of platelet aggregation. This compound is biotransformed in vivo to RGD039, which also exhibits high affinity for the GPIIb/IIIa receptor. Pharmacokinetic/pharmacodynamic modeling was employed to describe the concentration‐effect relationship of both compounds following the intravenous administration of RGD891 to healthy volunteers. The overall objectives of this work were to support the dose selection process for future intravenous RGD891 safety and efficacy studies. Various intravenous regimens of RGD891 were administered to healthy volunteers enrolled in three Phase I studies. Frequent plasma samples were collected at regular intervals for later measurement of RGD891 and RGD039 concentrations (validated LC/MS/MS methods). The pharmacokinetics of RGD891 and RGD039 were simultaneously analyzed by nonlinear mixed‐effect modeling (NONMEM). Pharmacodynamic activity was assessed in all three studies by the degree to which ADP (20 μM)‐induced platelet aggregation was inhibited. Population parameters describing the concentration‐effect relationship of RGD891 and RGD039 were then generated using a modified competitive Emax‐based model. Results: Parent compound is by far the predominant active compound circulating in the plasma following intravenous administration of RGD891. The plasma RGD891 concentration‐time data were best fit by a two‐compartment structural model. The fit of the basic model was improved when total body weight was introduced as a covariate for RGD891 distribution. Between‐subject variability in the RGD891 pharmacokinetic parameters—V1, K10, and K21—was less than 17% (coefficient of variation). Formation of the active metabolite (RGD039; Km) and its elimination (Kem) were assumed to be first‐order processes (i.e., one‐compartment model). The population pharmacokinetic model could only provide a rough estimate of the plasma concentration‐time profile for RGD039 after administration of a given intravenous dosage regimen of RGD891 since metabolite concentrations were relatively low and highly variable. The first‐order rate constant describing the formation of RGD039 from RGD891 (Km) was also associated with a substantial degree of between‐subject variability (44.9%). The potency of RGD891 toward the inhibition of ADP‐induced platelet aggregation was described by the population IC50 value (plasma concentration yielding 50% of maximal inhibition), which ranged from 58.0 to 95.4 ng/mL, depending on the pharmacokinetic‐pharmacodynamic (PK‐PD) model and the data set used. The relatively low concentrations of the active metabolite achieved following intravenous administration of RGD891 did not permit independent estimation of a population IC50 value for RGD039. Therefore, its potency was fixed at 2.2‐fold greater than that of the parent compound (based on previous PK‐PD analyses). Intersubject variability in the IC50 values was 30%. Conclusions: Antagonism of the platelet IIb/IIIa receptors by intravenously administered RGD891 was effective in inhibiting ADP‐induced platelet aggregation in a reversible and dose‐dependent manner. Pharmacodynamic activity was largely attributed to the parent compound and less to the active metabolite based on the relative potencies of both compounds and the plasma concentrations of each achieved following intravenous administration. Intravenous bolus plus maintenance infusion regimens resulted in rapid attainment of steady‐state plasma RGD891 concentrations. This combination regimen also provided for a marked and sustained inhibition of platelet aggregation that reached 90% or greater (relative to baseline values) in the higher dose groups. The modified Emax model adequately described inhibition of platelet aggregation following a particular intravenous dosage regimen of RGD891 (within the range of doses administered in the present studies). Interestingly, the between‐subject variability in the pharmacokinetic‐pharmacodynamic parameters IC50p and IC50m was comparative to the variability in the pharmacokinetic parameters. Although this work should prove to be useful in designing the optimal dosing regimen(s) for larger clinical trials, it should serve as only a first step. Continued evaluation of the pharmacokinetic‐pharmacodynamic models presented is necessary. In particular, their predictive performance has yet to be addressed in a prospective manner.