Quantitative PK–PD Model-Based Translational Pharmacology of a Novel Kappa Opioid Receptor Antagonist Between Rats and Humans

Abstract

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.

Behavioral Characterization of κ Opioid Receptor Agonist Spiradoline and Cannabinoid Receptor Agonist CP55940 Mixtures in Rats

“…CP55940 and spiradoline have similar onsets and durations of action when administered i.p., with onset occurring within 30 minutes and offset occurring within 3 hours (Briggs et al, 1998;Tseng and Craft, 2001;Barrett et al, 2002;Hamamoto et al, 2007;Chang et al, 2011); this similarity in time course was the basis for using cumulative dosing for drugs alone and in mixtures. Mixtures comprising smaller doses of CP55940 and spiradoline had effects that were equivalent to or greater than the effects observed with larger doses of either drug administered alone.…”

The macrocyclic tetrapeptide [D‐Trp]CJ‐15,208 produces short‐acting κ opioid receptor antagonism in the CNS after oral administration

“…The recent termination of the phase 1 clinical trials of JDTic due to undisclosed adverse effects (ClinicalTrials.gov, NCT01431586) has further spurred the search for new, safe and shorter acting KOR-selective antagonists. As discussed above, while several new non-peptide KOR antagonists have recently been reported, including some with finite durations of KOR antagonist activity (Runyon et al, 2010;Peters et al, 2011) or with short residence time in the brain (Grimwood et al, 2011;Mitch et al, 2011) after peripheral administration, reports of orally active KOR antagonists have been limited to only four KOR-selective antagonists: JDTic (Beardsley et al, 2005), an analogue of JDTic (Beardsley et al, 2010), an aminobenzyloxyarylamide (Mitch et al, 2011), and a biphenylsulfonamide (Chang et al, 2011). To date, p.o.…”

“…However, activity after p.o. administration has been reported for only four KOR selective antagonists (Beardsley et al, 2005(Beardsley et al, , 2010Chang et al, 2011;Mitch et al, 2011), but not for any of the shorter acting antagonists, prompting a continued search for KOR antagonists.…”

Translational Modeling to Guide Study Design and Dose Choice in Obesity Exemplified by AZD1979, a Melanin‐concentrating Hormone Receptor 1 Antagonist

“…Typically, the preclinical modelers integrate in vitro cellular, animal, and potential human data, as well as literature data for relevant in‐house or competitor compounds into a mathematical model that predicts temporal profiles of key biomarkers and endpoints. Important means to advance this research field of translational modeling are to present general strategies1, 2, 3, 4 and to share specific examples 5, 6, 7, 8. This contribution belongs to the second category.…”