title: Modeling and design of challenge tests: Inflammatory and metabolic biomarker study examples.
abstract: Given the complexity of pharmacological challenge experiments, it is perhaps not surprising that design and analysis, and in turn interpretation and communication of results from a quantitative point of view, is often suboptimal. Here we report an inventory of common designs sampled from anti-inflammatory, respiratory and metabolic disease drug discovery studies, all of which are based on animal models of disease involving pharmacological and/or patho/physiological interaction challenges. The corresponding data are modeled and analyzed quantitatively, the merits of the respective approach discussed and inferences made with respect to future design improvements. Although our analysis is limited to these disease model examples, the challenge approach is generally applicable to the vast majority of pharmacological intervention studies. In the present five Case Studies results from pharmacodynamic effect models from different therapeutic areas were explored and analyzed according to five typical designs. Plasma exposures of test compounds were assayed by either liquid chromatography/mass spectrometry or ligand binding assays. To describe how drug intervention can regulate diverse processes, turnover models of test compound-challenger interaction, transduction processes, and biophase time courses were applied for biomarker response in eosinophil count, IL6 response, paw-swelling, TNFα response and glucose turnover in vivo. Case Study 1 shows results from intratracheal administration of Sephadex, which is a glucocorticoid-sensitive model of airway inflammation in rats. Eosinophils in bronchoalveolar fluid were obtained at different time points via destructive sampling and then regressed by the mixed-effects modeling. A biophase function of the Sephadex time course was inferred from the modeled eosinophil time courses. In Case Study 2, a mouse model showed that the time course of cytokine-induced IL1β challenge was altered with or without drug intervention. Anakinra reversed the IL1β induced cytokine IL6 response in a dose-dependent manner. This Case Study contained time courses of test compound (drug), challenger (IL1β) and cytokine response (IL6), which resulted in high parameter precision. Case Study 3 illustrates collagen-induced arthritis progression in the rat. Swelling scores (based on severity of hind paw swelling) were used to describe arthritis progression after the challenge and the inhibitory effect of two doses of an orally administered test compound. In Case Study 4, a cynomolgus monkey model for lipopolysaccharide LPS-induced TNFα synthesis and/or release was investigated. This model provides integrated information on pharmacokinetics and in vivo potency of the test compounds. Case Study 5 contains data from an oral glucose tolerance test in rats, where the challenger is the same as the pharmacodynamic response biomarker (glucose). It is therefore convenient to model the extra input of glucose simultaneously with baseline data and during intervention of a glucose-lowering compound at different dose levels. Typically time-series analyses of challenger- and biomarker-time data are necessary if an accurate and precise estimate of the pharmacodynamic properties of a test compound is sought. Erosion of data, resulting in the single-point assessment of drug action after a challenge test, should generally be avoided. This is particularly relevant for situations where one expects time-curve shifts, tolerance/rebound, impact of disease, or hormetic concentration-response relationships to occur.
authors.affiliation: Department of Biomedical Sciences and Veterinary Public Health, Division of Pharmacology and Toxicology, Swedish University of Agricultural Sciences, Box 7028, SE-750 07 Uppsala, Sweden. Electronic address: Johan.Gabrielsson@slu.se.
authors.forename: Johan
authors.initials: J
authors.lastname: Gabrielsson
authors.affiliation: CVMD iMed Bioscience, AstraZeneca R&D Mölndal, R&D, Innovative Medicines, S-431 83 Mölndal, Sweden.
authors.forename: Stephan
authors.initials: S
authors.lastname: Hjorth
authors.affiliation: Novartis Institutes for Biomedical Research, DMPK/Nonclinical PK/PD, Fabrikstrasse 28, CH-4056 Basel, Switzerland.
authors.forename: Barbara
authors.initials: B
authors.lastname: Vogg
authors.affiliation: Novartis Institutes for BioMedical Research, Metabolism and Pharmacokinetics, CH-4002 Basel, Switzerland.
authors.forename: Stephanie
authors.initials: S
authors.lastname: Harlfinger
authors.affiliation: AstraZeneca R&D Alderley Park, Oncology iMED, DMPK, 35F173, Macclesfield SK10 4TG, UK.
authors.forename: Pablo Morentin
authors.initials: PM
authors.lastname: Gutierrez
authors.affiliation: Mathematical Institute, Leiden University, PB 9512, 2300 RA Leiden, The Netherlands.
authors.forename: Lambertus
authors.initials: L
authors.lastname: Peletier
authors.affiliation: RIRA iMed DMPK, AstraZeneca R&D Mölndal, R&D, Innovative Medicines, S-431 83 Mölndal, Sweden.
authors.forename: Rikard
authors.initials: R
authors.lastname: Pehrson
authors.affiliation: CVMD iMed Translational Science, AstraZeneca R&D Mölndal, R&D, Innovative Medicines, S-431 83 Mölndal, Sweden.
authors.forename: Pia
authors.initials: P
authors.lastname: Davidsson
mesh_terms: D000818:Animals; D000893:Anti-Inflammatory Agents / Q000493:pharmacokinetics / Q000494:pharmacology; D001169:Arthritis, Experimental / Q000097:blood / Q000473:pathology; D015415:Biomarkers / Q000378:metabolism*; D001786:Blood Glucose / Q000032:analysis; D003911:Dextrans / Q000493:pharmacokinetics / Q000494:pharmacology; D055808:Drug Discovery; D004802:Eosinophilia / Q000139:chemically induced; D005260:Female; D005947:Glucose / Q000493:pharmacokinetics / Q000494:pharmacology; D007249:Inflammation / Q000378:metabolism; D053590:Interleukin 1 Receptor Antagonist Protein / Q000493:pharmacokinetics / Q000494:pharmacology; D053583:Interleukin-1beta / Q000494:pharmacology; D015850:Interleukin-6 / Q000378:metabolism; D008070:Lipopolysaccharides / Q000494:pharmacology; D008252:Macaca fascicularis; D008297:Male; D008807:Mice, Inbred BALB C; D008954:Models, Biological; D011014:Pneumonia / Q000139:chemically induced / Q000276:immunology / Q000378:metabolism; D011917:Rats, Inbred Lew; D017207:Rats, Sprague-Dawley; D012107:Research Design; D014409:Tumor Necrosis Factor-alpha / Q000378:metabolism
chemical_list: D000893:Anti-Inflammatory Agents; D015415:Biomarkers; D001786:Blood Glucose; D003911:Dextrans; D053590:Interleukin 1 Receptor Antagonist Protein; D053583:Interleukin-1beta; D015850:Interleukin-6; D008070:Lipopolysaccharides; D014409:Tumor Necrosis Factor-alpha; C025614:sephadex; D005947:Glucose
keywords: Hormetic concentration–response relationship; Inhibitory drug action; Mixture dynamics; Oral glucose tolerance test; Quantitative pharmacology; Turnover models