Abstract
We have previously shown how screening experiments for neuropathic pain can be optimised taking into account parameter and model uncertainty. Here we demonstrate how optimised protocols can be used to screen and rank candidate molecules. The concept is illustrated by pregabalin as a new chemical entity and gabapentin as a reference compound. ED-optimality was applied to a logistic regression model describing the relationship between drug exposure and response to evoked pain in the complete Freund’s adjuvant (CFA) model in rats. Design variables for optimisation of the experimental protocol included dose levels and sampling times. Prior information from the reference compound was used in conjunction with relative in vitro potency as priors. Results from simulated scenarios were then combined with fitting of experimental data to estimate precision and bias of model parameters for the empirical and optimised designs. The pharmacokinetics of pregabalin was described by a two-compartment model. The expected value of EC50 of pregabalin was 637.5 ng ml−1. Model-based analysis of the data yielded median (range) of EC50 values of 1,125 (898–2412) ng ml−1 for the empirical protocol and 755 (189–756) ng ml−1 for the optimised design. In contrast to current practice, optimal design entails different sampling schedule across dose levels. ED-optimised designs should become standard practice in the screening of candidate molecules. It ensures lower bias when estimating the drug potency, facilitating accurate ranking and selection of compounds for further development.
Similar content being viewed by others
References
Agresti A (1999) On logit confidence intervals for the odds ratio with small samples. Biometrics 55:597–602
Belliotti TR, Capiris T, Ekhato IV, Kinsora JJ, Field MJ et al (2005) Structure–activity relationships of pregabalin and analogues that target the alpha(2)-delta protein. J Med Chem 48:2294–2307
Bender G, Gosset J, Florian J, Tan K, Field M et al (2009) Population pharmacokinetic model of the pregabalin–sildenafil interaction in rats: application of simulation to preclinical PK-PD study design. Pharm Res 26:2259–2269
Bergstrand M (2009) VPCs for censored and categorical data. St Petersburg, Russia, p 18
Campbell EA, Gentry C, Patel S, Kidd B, Cruwys S et al (2000) Oral anti-hyperalgesic and anti-inflammatory activity of NK(1) receptor antagonists in models of inflammatory hyperalgesia of the guinea-pig. Pain 87:253–263
Danhof M, Lange EC, Ploeger BA, Voskuyl RA, Della Pasqua O (2008) Mechanism-based pharmacokinetic-pharmacodynamic (PK-PD) modelling in translational drug research. Trends Pharmacol Sci 29:186–191
Del Bene F, Germani M, De Nicolao G, Magni P, Re CE et al (2009) A model-based approach to the in vitro evaluation of anticancer activity. Cancer Chemother Pharmacol 63:827–836
Ette EI, Williams PJ, Kim YH, Lane JR, Liu MJ et al (2003) Model appropriateness and population pharmacokinetic modeling. J Clin Pharmacol 43:610–623
Fiedler-Kelly J (2007) PKPD analysis of binary (logistic) outcome data. In: EIW PJ (ed) Pharmacometrics: the science of quantitative pharmacology. Wiley, New Jersey, pp 633–654
Foracchia M, Hooker A, Vicini P, Ruggeri A (2004) POPED, a software for optimal experiment design in population kinetics. Comput Methods Programs Biomed 74:29–46
Frei CR, Burgess DS (2008) Pharmacokinetic/pharmacodynamic modeling to predict in vivo effectiveness of various dosing regimens of piperacillin/tazobactam and piperacillin monotherapy against gram-negative pulmonary isolates from patients managed in intensive care units in 2002. Clin Ther 30:2335–2341
Gabrielsson J, Green AR (2009) Quantitative pharmacology or pharmacokinetic pharmacodynamic integration should be a vital component in integrative pharmacology. J Pharmacol Exp Ther 331:767–774
Gabrielsson J, Dolgos H, Gillberg PG, Bredberg U, Benthem B et al (2009) Early integration of pharmacokinetic and dynamic reasoning is essential for optimal development of lead compounds: strategic considerations. Drug Discov Today 14:358–372
Gierse J, Nickols M, Leahy K, Warner J, Zhang Y et al (2008) Evaluation of COX-1/COX-2 selectivity and potency of a new class of COX-2 inhibitors. Eur J Pharmacol 588:93–98
Hooker A, Vicini P (2005) Simultaneous population optimal design for pharmacokinetic-pharmacodynamic experiments. AAPS J 7:E759–E785
Nyberg J, Strömberg E, Karlsson MO, Hooker AC (2011) Poped manual series 2011. In: Dept of Pharmaceutical Biosciences, UU (ed) Poped Manual series 2011, release version 2.11 edn. Dept of Pharmaceutical Biosciences, Uppsala University, Uppsala
Kjellsson MC, Jonsson S, Karlsson MO (2004) The back-step method–method for obtaining unbiased population parameter estimates for ordered categorical data. AAAPS J 6:e19
Li C, Sekiyama H, Hayashida M, Takeda K, Sumida T et al (2007) Effects of topical application of clonidine cream on pain behaviors and spinal Fos protein expression in rat models of neuropathic pain, postoperative pain, and inflammatory pain. Anesthesiology 107:486–494
Lindbom L, Pihlgren P, Jonsson EN (2005) PsN-Toolkit: a collection of computer intensive statistical methods for non-linear mixed effect modeling using NONMEM. Comput Methods Programs Biomed 79:241–257
Lunn DJ, Wakefield J, Racine-Poon A (2001) Cumulative logit models for ordinal data: a case study involving allergic rhinitis severity scores. Stat Med 20:2261–2285
Nyberg J, Karlsson MO, Hooker AC (2009) Simultaneous optimal experimental design on dose and sample times. J Pharmacokinet Pharmacodyn 36:125–145
Pai SM, Girgis S, Batra VK, Girgis IG (2009) Population pharmacodynamic parameter estimation from sparse sampling: effect of sigmoidicity on parameter estimates. AAPS J 11:535–540
Quartino A, Karlsson MO, Freijs A, Jonsson N, Nygren P et al (2007) Modeling of in vitro drug activity and prediction of clinical outcome in acute myeloid leukemia. J Clin Pharmacol 47:1014–1021
Rodriguez MJ, Diaz S, Vera-Llonch M, Dukes E, Rejas J (2007) Cost-effectiveness analysis of pregabalin versus gabapentin in the management of neuropathic pain due to diabetic polyneuropathy or post-herpetic neuralgia. Curr Med Res Opin 23:2585–2596
Schoemaker RC, van Gerven JM, Cohen AF (1998) Estimating potency for the Emax-model without attaining maximal effects. J Pharmacokinet Biopharm 26:581–593
Sebaugh JL, Wilson JD, Tucker MW, Adams WJ (1991) A study of the shape of dose-response curves for acute lethality at low response: a “megadaphnia study”. Risk Anal 11:633–640
Severiano A, Carrico JA, Robinson DA, Ramirez M, Pinto FR (2011) Evaluation of jackknife and bootstrap for defining confidence intervals for pairwise agreement measures. PLoS ONE 6:e19539
Sheiner LB, Beal SL (1981) Some suggestions for measuring predictive performance. J Pharmacokinet Biopharm 9:503–512
SSE user guide. 2012-01-18 PsN 3.5.3 (2011) Introduction. SSE-Stochastic simulation and estimation, Department of Pharmaceutical Biosciences, University of Uppsala, Sweden (ed). Accessed from http://psn.sourceforge.net/pdfdocs/sse_userguide.pdf
Taneja A, Di Iorio VL, Danhof M, Della Pasqua O (2012) Translation of drug effects from experimental models of neuropathic pain and analgesia to humans. Drug Discov Today. doi:10.1016/j.drudis.2012.02.01
Taneja A, Nyberg J, de Lange ECM, Danhof M, Della Pasqua O (2012) Application of ED-optimality to screening experiments for analgesic compounds in an experimental model of neuropathic pain. J Pharmacokinet Pharmacodyn. doi:10.1007/s10928-012-9278-9
Whiteside GT, Adedoyin A, Leventhal L (2008) Predictive validity of animal pain models? A comparison of the pharmacokinetic-pharmacodynamic relationship for pain drugs in rats and humans. Neuropharmacology 54:767–775
Woodcock J, Witter J, Dionne RA (2007) Stimulating the development of mechanism-based, individualized pain therapies. Nat Rev Drug Discov 6:703–710
Author information
Authors and Affiliations
Corresponding author
Additional information
This study was conducted on behalf of the Pain Project members of the TI Pharma mechanism-based PKPD modelling platform. The members of this study group are Benson N, Marshall S (Modelling & Simulation, Pfizer, Sandwich, UK); Machin I (Pain Research Unit, Sandwich, UK); DeAlwis D (Global PK/PD/TS Europe, Eli Lilly, Erl Wood, UK).
Rights and permissions
About this article
Cite this article
Taneja, A., Nyberg, J., Danhof, M. et al. Optimised protocol design for the screening of analgesic compounds in neuropathic pain. J Pharmacokinet Pharmacodyn 39, 661–671 (2012). https://doi.org/10.1007/s10928-012-9277-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10928-012-9277-x