Quantitative PK–PD Model-Based Translational Pharmacology of a Novel Kappa Opioid Receptor Antagonist Between Rats and Humans
Journal Title: The AAPS Journal - Year 2011, Vol 13, Issue 4
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 Ki for PF-04455242 in rats was 414 ng/mL. Accounting for species differences in unbound fraction, in vitro Ki and brain penetration provided a predicted human Ki 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.
Authors and Affiliations
Cheng Chang, Wonkyung Byon, Yifeng Lu, Leslie K. Jacobsen, Lori L. Badura, Aarti Sawant-Basak, Emily Miller, Jing Liu, Sarah Grimwood, Ellen Q. Wang, Tristan S. Maurer
Keywords
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