Quantitative PK-PD model-based translational pharmacology of a novel kappa opioid receptor antagonist between rats and humans

Abstract

Pharmacokinetic-pharmacodynamic (PK-PD) modeling greatly enables quantitative implementation of the "learn and confirm" paradigm across different stages of drug discovery and development. This work describes the successful prospective application of this concept in the discovery and early development of a novel κ-opioid receptor (KOR) antagonist, PF-04455242, where PK-PD understanding from preclinical biomarker responses enabled successful prediction of the clinical response in a proof of mechanism study. Preclinical data obtained in rats included time course measures of the KOR antagonist (PF-04455242), a KOR agonist (spiradoline), and a KOR-mediated biomarker response (prolactin secretion) in plasma. Clinical data included time course measures of PF-04455242 and prolactin in 24 healthy volunteers following a spiradoline challenge and single oral doses of PF-04455242 (18 and 30 mg). In both species, PF-04455242 successfully reversed spiradoline-induced prolactin response. A competitive antagonism model was developed and implemented within NONMEM to describe the effect of PF-04455242 on spiradoline-induced prolactin elevation in rats and humans. The PK-PD model-based estimate of K(i) for PF-04455242 in rats was 414 ng/mL. Accounting for species differences in unbound fraction, in vitro K(i) and brain penetration provided a predicted human K(i) of 44.4 ng/mL. This prediction was in good agreement with that estimated via the application of the proposed PK-PD model to the clinical data (i.e., 39.2 ng/mL). These results illustrate the utility of the proposed PK-PD model in supporting the quantitative translation of preclinical studies into an accurate clinical expectation. As such, the proposed PK-PD model is useful for supporting the design, selection, and early development of novel KOR antagonists.

Fig. 1

Structure of PF-04455242

Fig. 2

PK–PD model structure. E _max is the maximal increase of prolactin under spiradoline stimulation; C _sp and C _pf represent plasma spiradoline and PF-04455242 concentrations, respectively; EC₅₀ represents the concentration of spiradoline that results in half the maximal prolactin increase in the absence of PF-04455242; K _i represents PF-04455242 in vivo potency toward rat KOR; and γ is the Hill coefficient describing steepness of the exposure–response relationship

Fig. 3

Comparison of model-simulated time courses of spiradoline concentration (a) and PF-04455242 concentration (b) to observations in rats. Open circles represent observed data, dashed red line represents the simulated median, and solid blue lines represent the mean 5th and 95th percentiles from the simulated trials. The blue band around the solid red line represents 90% prediction interval of the simulated median

Fig. 4

Comparison of model-simulated time courses of rat prolactin concentrations from spiradoline dose–response study (a) and PF-04455242 dose–response study (b) to observations. Open circles represent observed data, dashed red line represents the simulated median, and solid blue lines represent the mean 5th and 95th percentiles from the simulated trials. The blue band around the solid red line represents 90% prediction interval of the simulated median

Fig. 5

Predicted prolactin response in healthy volunteers based on preclinical PK–PD model (a) and observed prolactin response in healthy volunteers (b), where data shown are the mean ± SEM (n = 8 healthy volunteers per treatment group)

Fig. 6

Comparison of model-simulated time courses of PF-04455242 concentration in healthy volunteers to observations. Open circles represent observed data, dashed red line represents the simulated median, and solid blue lines represent the mean 5th and 95th percentiles from the simulated trials. The blue band around the solid red line represents 90% prediction interval of the simulated median

Fig. 7

Comparison of model-simulated time courses of prolactin concentrations in healthy volunteers to observed data in the clinical POM study. Open circles represent observed data, dashed red line represents the simulated median, and solid blue lines represent the mean 5th and 95th percentiles from the simulated trials. The blue band around the solid red line represents 90% prediction interval of the simulated median