Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-848d4c4894-tn8tq Total loading time: 0 Render date: 2024-07-05T06:16:35.446Z Has data issue: false hasContentIssue false

2 - Pharmacokinetic and Pharmacodynamic Modelling in Anaesthesia

from Section 1 - Basic Principles

Published online by Cambridge University Press:  03 December 2019

By
María J. Garrido  and
Iñaki F. Trocóniz
Edited by
Pedro L. Gambús  and
Jan F. A. Hendrickx
Pedro L. Gambús
Affiliation:
Hospital Clinic de Barcelona, Spain
Jan F. A. Hendrickx
Affiliation:
Aalst General Hospital, Belgium
Get access

Summary

Pharmacokinetic/pharmacodynamic (PK/PD) modelling is a discipline currently under the umbrella of pharmacometrics, and aims to describe, understand and predict the time course of in vivo drug action.

In general PK/PD comprises three major elements: (1) pharmacokinetics (PK), (2) pharmacodynamics (PD), and (3) disease progression. However, given the fact that anaesthesia procedures take place in short periods of time, where the general state of the patient remains unaltered, in this chapter we will limit the focus to the interrelationship between PK and PD.

Type
Chapter
Information
Personalized Anaesthesia
Targeting Physiological Systems for Optimal Effect
 , pp. 14 - 28
Publisher: Cambridge University Press
Print publication year: 2020

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Holford, N: Clinical pharmacology = disease progression + drug action. Br.J.Clin.Pharmacol. 2015; 79: 18–27.Google Scholar
Verotta, D, Sheiner, LB: A general conceptual model for non-steady state pharmacokinetic/pharmacodynamic data. J.Pharmacokinet.Biopharm. 1995; 23: 1–4.CrossRefGoogle ScholarPubMed
Tozer, TN, Rowland, M: Introduction to Pharmacokinetics and Pharmacodynamics: The Quantitative Basis of Drug Therapy. Philadelphia, PA: Lippincott Williams & Wilkins, 2006.Google Scholar
Holford, NH, Sheiner, LB: Understanding the dose-effect relationship: clinical application of pharmacokinetic-pharmacodynamic models. Clin.Pharmacokinet. 1981; 6: 429–53.CrossRefGoogle ScholarPubMed
Schnider, TW, Minto, CF, Gambus, PL, Andresen, C, Goodale, DB, Shafer, SL. Youngs, EJ: The influence of method of administration and covariates on the pharmacokinetics of propofol in adult volunteers. Anesthesiology. 1998; 88: 1170–82.Google Scholar
Hannivoort, LN, Eleveld, DJ, Proost, JH, Reyntjens, KM, Absalom, AR, Vereecke, HE, Struys, MM: Development of an optimized pharmacokinetic model of dexmedetomidine using target-controlled infusion in healthy volunteers. Anesthesiology. 2015; 123: 357–67.CrossRefGoogle ScholarPubMed
Fernández-Candil, J, Gambús, PL, Trocóniz, IF, Valero, R, Carrero, E, Bueno, L, Fábregas, N: Pharmacokinetic-pharmacodynamic modeling of the influence of chronic phenytoin therapy on the rocuronium bromide response in patients undergoing brain surgery. Eur.J.Clin.Pharmacol. 2008; 64: 795–806.CrossRefGoogle Scholar
Trocóniz, IF, Armenteros, S, Planelles, MV, Benítez, J, Calvo, R, Domínguez, R: Pharmacokinetic-pharmacodynamic modelling of the antipyretic effect of two oral formulations of ibuprofen. Clin.Pharmacokinet. 2000; 38: 505–18.Google Scholar
Anderson, BJ, Holford, NH: Understanding dosing: children are small adults, neonates are immature children. Arch.Dis.Child 2013; 98: 737–44.Google Scholar
Holford, N, Heo, YA, Anderson, B: A pharmacokinetic standard for babies and adults. J.Pharm.Sci. 2013; 102: 2941–52.Google Scholar
Allegaert, K, Holford, N, Anderson, BJ, Holford, S, Stuber, F, Rochette, A, Trocóniz, IF, Beier, H, de Hoon, JN, Pedersen, RS, Stamer, U: Tramadol and o-desmethyl tramadol clearance maturation and disposition in humans: a pooled pharmacokinetic study. Clin.Pharmacokinet. 2015; 54: 167–78.Google Scholar
Anderson, BJ, Larsson, P: A maturation model for midazolam clearance. Paediatr.Anaesth. 2011; 21: 302–8.CrossRefGoogle ScholarPubMed
Björkman, S, Wada, DR, Stanski, DR, Ebling, WF: Comparative physiological pharmacokinetics of fentanyl and alfentanil in rats and humans based on parametric single-tissue models. J.Pharmacokinet.Biopharm. 1994; 22: 381–410.Google Scholar
Masui, K, Upton, RN, Doufas, AG, Coetzee, JF, Kazama, T, Mortier, EP, Struys, MM: The performance of compartmental and physiologically based recirculatory pharmacokinetic models for propofol: a comparison using bolus, continuous, and target-controlled infusion data. Anesth.Analg. 2010; 111: 368–79.Google Scholar
Levitt, DG, Schnider, TW: Human physiologically based pharmacokinetic model for propofol. BMC Anesthesiol. 2005; 5: 4.CrossRefGoogle ScholarPubMed
Holford, NH, Sheiner, LB: Kinetics of pharmacologic response. Pharmacol.Ther. 1982; 16: 143–66.CrossRefGoogle ScholarPubMed
Yassen, A, Olofsen, E, Dahan, A. Danhof, M: Pharmacokinetic-pharmacodynamic modeling of the antinociceptive effect of buprenorphine and fentanyl in rats: role of receptor equilibration kinetics. J.Pharmacol.Exp.Ther 2005; 313: 1136–49.Google Scholar
Yassen, A, Olofsen, E, Romberg, R, Sarton, E, Teppema, L, Danhof, M. Dahan, A: Mechanism-based PK/PD modeling of the respiratory depressant effect of buprenorphine and fentanyl in healthy volunteers. Clin.Pharmacol.Ther. 2007; 81: 50–8.Google Scholar
Borrat, X, Trocóniz, IF, Valencia, JF, Rivadulla, S, Sendino, O, Llach, J, Muñoz, J, Castellví-Bel, S, Jospin, M, Jensen, EW, Castells, A, Gambús, PL: Modeling the influence of the A118G polymorphism in the OPRM1 gene and of noxious stimulation on the synergistic relation between propofol and remifentanil: sedation and analgesia in endoscopic procedures. Anesthesiology. 2013; 118: 1395–407.Google Scholar
Jonker, DM, Voskuyl, RA, Danhof, M. Pharmacodynamic analysis of the anticonvulsant effects of tiagabine and lamotrigine in combination in the rat. Epilepsia. 2004; 45: 424–35.CrossRefGoogle ScholarPubMed
Minto, CF, Schnider, TW, Short, TG, Gregg, KM, Gentilini, A, Shafer, SL: Response surface model for anesthetic drug interactions. Anesthesiology. 2000; 92: 1603–16.Google Scholar
Verotta, D, Kitts, J, Rodriguez, R, Coldwell, J, Miller, RD, Sheiner, LB: Reversal of neuromuscular blockade in humans by neostigmine and edrophonium: a mathematical model. J.Pharmacokinet.Biopharm. 1991; 19: 713–29.CrossRefGoogle ScholarPubMed
Wicha, SG, Chen, C, Clewe, O, Simonsson, USH. A general pharmacodynamic interaction model identifies perpetrators and victims in drug interactions. Nat.Commun. 2017; 8: 2129.CrossRefGoogle ScholarPubMed
Segre, G. Kinetics of interaction between drugs and biological systems. Farmaco Sci. 1968; 23: 907–18.Google Scholar
Sheiner, LB, Stanski, DR, Vozeh, S, Miller, RD, Ham, J: Simultaneous modeling of pharmacokinetics and pharmacodynamics: application to d-tubocurarine. Clin.Pharmacol.Ther. 1979; 25: 358–71.CrossRefGoogle ScholarPubMed
Struys, MM, Coppens, MJ, De Neve, N, Mortier, EP, Doufas, AG, Van Bocxlaer, JF, Shafer, SL: Influence of administration rate on propofol plasma-effect site equilibration. Anesthesiology. 2007; 107: 386–96.CrossRefGoogle ScholarPubMed
Wright, PM, McCarthy, G, Szenohradszky, J, Sharma, ML, Caldwell, JE: Influence of chronic phenytoin administration on the pharmacokinetics and pharmacodynamics of vecuronium. Anesthesiology. 2004; 100: 626–33.CrossRefGoogle ScholarPubMed
Olofsen, E, Burm, AG, Simon, MJ, Veering, BT, van Kleef, JW, Dahan, A: Population pharmacokinetic-pharmacodynamic modeling of epidural anesthesia. Anesthesiology. 2008; 109: 664–74.CrossRefGoogle ScholarPubMed
Dayneka, NL, Garg, V. Jusko, WJ: Comparison of four basic models of indirect pharmacodynamic responses. J.Pharmacokinet.Biopharm. 1993; 21: 457–78.CrossRefGoogle ScholarPubMed
Hannam, JA, Borrat, X, Trocóniz, IF, Valencia, JF, Jensen, EW, Pedroso, A, Muñoz, J, Castellví-Bel, S, Castells, A, Gambús, PL: Modeling respiratory depression induced by remifentanil and propofol during sedation and analgesia using a continuous non-invasive measurement of pCO2. J.Pharmacol.Exp.Ther. 2016; 356: 563–73.Google Scholar
Trocóniz, IF, Wolters, JM, Tillmann, C, Schaefer, HG, Roth, W: Modelling the anti-migraine effects of BIBN 4096 BS: a new calcitonin gene-related peptide receptor antagonist. Clin.Pharmacokinet. 2006; 45: 715–28.Google Scholar
Mandema, JW, Stanski, DR: Population pharmacodynamic model for ketorolac analgesia. Clin.Pharmacol.Ther. 1996; 60: 619–35.CrossRefGoogle ScholarPubMed
Fábregas, N, Rapado, J, Gambús, PL, Valero, R, Carrero, E, Salvador, L, Nalda-Felipe, MA, Trocóniz, IF: Modeling of the sedative and airway obstruction effects of propofol in patients with Parkinson’s disease undergoing stereotactic surgery. Anesthesiology. 2002; 97: 1378–86.Google Scholar
Juul, RV, Rasmussen, S, Kreilgaard, M, Christrup, LL, Simonsson, US, Lund, TM: Repeated time-to-event analysis of consecutive analgesic events in postoperative pain. Anesthesiology. 2015; 123: 1411–19.Google Scholar
Bonate, PL: Pharmacokinetic-pharmacodynamic Modeling and Simulation. New York: Springer, 2005.Google Scholar
Beal, S, Sheiner, LB, Boeckmann, A, Bauer, RJ: NONMEM User’s Guides (1989–2015). Ellicott City: Icon Development Solutions, 2015.Google Scholar
Monolix version 192018R1. Antony, France: Lixoft SAS, 2018. http://lixoft.com/products/monolix/ [last accessed 6 June 2019].Google Scholar
Petersson, KJ, Hanze, E, Savic, RM, Karlsson, MO: Semiparametric distributions with estimated shape parameters. Pharm.Res. 2009 Sep; 26(9): 2174–85.Google Scholar
Carlsson, KC, Savić, RM, Hooker, AC, Karlsson, MO: Modeling subpopulations with the $MIXTURE subroutine in NONMEM: finding the individual probability of belonging to a subpopulation for the use in model analysis and improved decision making. AAPS J. 2009 Mar; 11(1): 148–54.CrossRefGoogle ScholarPubMed
Ludden, TM, Beal, SL, Sheiner, LB: Comparison of the Akaike information criterion, the Schwarz criterion and the F test as guides to model selection. J.Pharmacokinet.Biopharm. 1994; 22: 431–45.Google Scholar
Jonsson, E, Karlsson, MO: Automated covariate model building within NONMEM. Pharm.Res. 1998; 15: 1463–8.CrossRefGoogle ScholarPubMed
Lindbom, L, Pihlgren, P, Jonsson, EN: PsN toolkit: a collection of computer intensive statistical methods for nonlinear mixed effect modelling using NONMEM. Comput. Methods. Programs. Biomed. 2005; 79: 241–57.CrossRefGoogle ScholarPubMed
Minto, CF, Schnider, TW, Egan, TD, Youngs, E, Lemmens, HJ, Gambus, PL, Billard, V, Hoke, JF, Moore, KH, Hermann, DJ, Muir, KT, Mandema, JW, Shafer, SL: Influence of age and gender on the pharmacokinetics and pharmacodynamics of remifentanil. I. Model development. Anesthesiology. 1997; 86: 10–23.Google Scholar
Schnider, TW, Minto, CF, Shafer, SL, Gambus, PL, Andresen, C, Goodale, DB, Youngs, EJ: The influence of age on propofol pharmacodynamics. Anesthesiology. 1999; 90: 1502–16.CrossRefGoogle ScholarPubMed
Cortínez, LI, Trocóniz, IF, Fuentes, R, Gambús, P, Hsu, YW, Altermatt, F, Muñoz, HR: The influence of age on the dynamic relationship between end-tidal sevoflurane concentrations and bispectral index. Anesth.Analg. 2008; 107: 1566–72.Google Scholar
Sarton, E, Olofsen, E, Romberg, R, den Hartigh, J, Kest, B, Nieuwenhuijs, D, Burm, A, Teppema, L, Dahan, A: Sex differences in morphine analgesia: an experimental study in healthy volunteers. Anesthesiology. 2000; 93: 1245–54.Google Scholar
Romberg, RR, Olofsen, E, Bijl, H, Taschner, PE, Teppema, LJ, Sarton, EY, van Kleef, JW, Dahan, A: Polymorphism of mu-opioid receptor gene (OPRM1:c.118A>G) does not protect against opioid-induced respiratory depression despite reduced analgesic response. Anesthesiology. 2005; 102: 522–30.Google Scholar
Bergstrand, M, Hooker, AC, Wallin, JE, Karlsson, MO: Prediction-corrected visual predictive checks for diagnosing nonlinear mixed-effects models. AAPS J. 2011; 13 (2): 143–51.CrossRefGoogle ScholarPubMed

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×