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Pediatric Clinical Pharmacology pp 125–148Cite as

Study Design and Simulation Approach

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Part of the book series: Handbook of Experimental Pharmacology ((HEP,volume 205))

Abstract

Modeling and simulation techniques are a mainstay of clinical drug development and are particularly useful to support clinical trials in children. If a pediatrician wants to use these tools most efficiently, a basic understanding of the principles and methods of classical and novel techniques of modeling and simulation is essential. Key elements comprise the definition and description of terms like deterministic simulation, Monte Carlo simulation, classical “top down” or novel “bottom up” approach, as well as the term “virtual world simulation.” The illustrated examples in this chapter from pediatric clinical trials will help to understand and demonstrate these key elements. The importance of the understanding of developmental physiology and pharmacokinetics will become visible when explaining novel “bottom up” approaches like physiologically based pharmacokinetic simulations which also bridge to current research tools from other areas such as systems biology using mathematical models to describe biological systems.

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Abbreviations

BSA:

Body surface area

CO:

Cardiac output

CYP:

Cytochrome P450

f u :

Unbound fraction of a drug

GFR:

Glomerular filtration rate

K a :

Acid constant

K m :

Michaelis–Menten constant

LogP:

Parameter describing lipophilicity of a drug

LOQ:

Limit of quantification

M&S:

Modeling and simulation

MM:

Michaelis–Menten

M wt :

Molecular weight

PBPK:

Physiology-based pharmacokinetics

PD:

Pharmacodynamic

PK:

Pharmacokinetic

Q H :

Total hepatic blood flow

QT interval:

Section of track of the electrocardiogram

QTc interval:

Section of track of the electrocardiogram corrected for heart rate

t 1/2 :

Terminal half life

UGT:

UDP-glucuronyltransferases

USA:

United States of America

V d :

Volume of distribution

V max :

The maximum initial velocity or rate of a reaction

References

  • Alcorn J, McNamara PJ (2003) Pharmacokinetics in the newborn. Adv Drug Deliv Rev 55:667–686

    Article  PubMed  CAS  Google Scholar 

  • Andrew MA, Hebert MF, Vicini P (2008) Physiologically based pharmacokinetic model of midazolam disposition during pregnancy. Conf Proc IEEE Eng Med Biol Soc 54:54–57

    Google Scholar 

  • Arant BS Jr (1978) Developmental patterns of renal functional maturation compared in the human neonate. J Pediatr 92:705–712

    Article  PubMed  CAS  Google Scholar 

  • Bartelink IH, Rademaker CM, Schobben AF et al (2006) Guidelines on paediatric dosing on the basis of developmental physiology and pharmacokinetic considerations. Clin Pharmacokinet 45:1077–1097

    Article  PubMed  CAS  Google Scholar 

  • Benjamin DK Jr, Smith PB, Jadhav P et al (2008) Pediatric antihypertensive trial failures: analysis of end points and dose range. Hypertension 51:834–840

    Article  PubMed  CAS  Google Scholar 

  • Björkman S (2004) Prediction of drug disposition in infants and children by means of physiologically based pharmacokinetic (PBPK) modelling: theophylline and midazolam as model drugs. Br J Clin Pharmacol 59:691–704

    Article  Google Scholar 

  • Bonate PL (2000) Clinical trial simulation in drug development. Pharm Res 17:252–256

    Article  PubMed  CAS  Google Scholar 

  • Bouwmeester NJ, Anderson BJ, Tibboel D et al (2004) Developmental pharmacokinetics of morphine and its metabolites in neonates, infants and young children. Br J Anaesth 92:208–217

    Article  PubMed  CAS  Google Scholar 

  • Eddy DM, Schlessinger L (2003) Validation of the Archimedes diabetes model. Diabetes Care 26:3102–3110

    Article  PubMed  Google Scholar 

  • Edginton A, Willmann S (2008) Physiology-based simulations of a pathological condition: prediction of pharmacokinetics in patients with liver cirrhosis. Clin Pharmacokinet 47:743–752

    Article  PubMed  Google Scholar 

  • Edginton A, Schmitt W, Willmann S (2006) Development and evaluation of a generic physiologically based pharmacokinetic model for children. Clin Pharmacokinet 45:1013–1034

    Article  PubMed  CAS  Google Scholar 

  • El-Tahtawy A, Kokki H, Reidenberg BE (2006) Population pharmacokinetics of oxycodone in children 6 months to 7 years old. J Clin Pharmacol 46:433–442

    Article  PubMed  CAS  Google Scholar 

  • Girard P (2005) Clinical trial simulation: a tool for understanding study failures and preventing them. Basic Clin Pharmacol Toxicol 96:228–234

    Article  PubMed  CAS  Google Scholar 

  • Gobburu JV (2010) How to double success rate of pediatric trials? U.S. Food and Drug Administration, Office of Clinical Pharmacology, Silver Spring, MD, http://www.slideshare.net/JogaGobburu/how-to-double-success-rate-of-pediatric-trials. Accessed 17 Jun 2010

    Google Scholar 

  • Hines RN (2008) The ontogeny of drug metabolism enzymes and implications for adverse drug events. Pharmacol Ther 118:250–267

    Article  PubMed  CAS  Google Scholar 

  • Hsien L (2010) Identifying paediatric needs in cardiology and the prediction of sildenafil exposure in children with pulmonary arterial hypertension. PhD thesis at the Faculty of Mathematics and Sciences, University of Düsseldorf, Germany

    Google Scholar 

  • Jadhav PR, Zhang J, Gobburu JV (2009) Leveraging prior quantitative knowledge in guiding pediatric drug development: a case study. Pharm Stat 8:216–224

    Article  PubMed  Google Scholar 

  • Johnson TN, Rostami-Hodjegan A, Tucker GT (2006) Prediction of the clearance of eleven drugs and associated variability in neonates, infants and children. Clin Pharmacokinet 45:931–956

    Article  PubMed  CAS  Google Scholar 

  • Khalil F, Laer S (2011) Physiologically based pharmacokinetic modeling: Methodology, applications, and limitations with a focus on its role in pediatric drug development. J Biomed Biotechnol. In press

    Google Scholar 

  • Laer S, Wauer I, Behn F et al (2001) Pharmacokinetics of sotalol in different age groups of children with tachycardia. J Pediatr Pharmacol Ther 6:50–59

    Google Scholar 

  • Laer S, Elshoff JP, Meibohm B et al (2005) Development of a safe and effective pediatric dosing regimen for sotalol based on population pharmacokinetics and pharmacodynamics in children with supraventricular tachycardia. J Am Coll Cardiol 46:1322–1330

    Article  PubMed  Google Scholar 

  • Laer S, Barrett JS, Meibohm B (2009) The in silico child: using simulation to guide pediatric drug development and manage pediatric pharmacotherapy. J Clin Pharmacol 49:889–904

    Article  PubMed  CAS  Google Scholar 

  • Li F, Nandy P, Chien S et al (2010) Pharmacometrics-based dose selection of levofloxacin as a treatment for postexposure inhalational anthrax in children. Antimicrob Agents Chemother 54:375–379

    Article  PubMed  CAS  Google Scholar 

  • Meibohm B, Laer S, Panetta JC et al (2005) Population pharmacokinetic studies in pediatrics: issues in design and analysis. AAPS J 7:E475–E487

    Article  PubMed  Google Scholar 

  • Morselli PL, Morselli-Franco R, Bossi L (1980) Clinical pharmacokinetics in newborns and infants. Clin Pharmacokinet 5:485–527

    Article  PubMed  CAS  Google Scholar 

  • Mouksassi MS, Marier JF, Cyran J et al (2009) Clinical trial simulations in pediatric patients using realistic covariates: application to teduglutide, a glucagon-like peptide-2 analog in neonates and infants with short-bowel syndrome. Clin Pharmacol Ther 86:667–671

    Article  PubMed  CAS  Google Scholar 

  • Schlessinger L, Eddy DM (2002) Archimedes: a new model for simulating health care systems – the mathematical formulation. J Biomed Inform 35:37–50

    Article  PubMed  Google Scholar 

  • Sheiner LB, Ludden TM (1992) Population pharmacokinetics/dynamics. Ann Rev Pharmacol Toxicol 32:185–209

    Article  CAS  Google Scholar 

  • van der Marel CD, Anderson BJ, van Lingen RA et al (2003) Paracetamol and metabolite pharmacokinetics in infants. Eur J Clin Pharmacol 59:243–251

    Article  PubMed  Google Scholar 

  • Willlmann S (2009) The in silico child. Can computer simulations replace clinical pharmacokinetic studies? Pharm Unserer Zeit 38:62–67

    Article  Google Scholar 

  • Willmann S, Lippert J, Sevestre M et al (2003) PK-Sim®: a physiologically based pharmacokinetic “whole-body” model. Biosilico 1:121–124

    Article  CAS  Google Scholar 

  • Yared A, Ichikara I (1994) Glomerular circulation and function in paediatric nephrology. Williams and Wilkins, Baltimore

    Google Scholar 

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Acknowledgement

The authors want to thank Feras Khalil from the Department of Clinical Pharmacy and Pharmacotherapy at the Heinrich-Heine University of Düsseldorf for some literature review and comments about the physiologically based pharmacokinetics (see also Khalil and Laer 2011).

Author information

Authors and Affiliations

  1. Department of Clinical Pharmacy and Pharmacotherapy, Heinrich-Heine-University of Düsseldorf, Universitätsstrasse 1, 40225, Düsseldorf, Germany

    Stephanie Läer

  2. College of Pharmacy, University of Tennessee Health Science Center, 874 Union Avenue, Rm. 5p, Memphis, TN, 38163, USA

    Bernd Meibohm

Authors
  1. Stephanie Läer
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  2. Bernd Meibohm
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Corresponding author

Correspondence to Stephanie Läer .

Editor information

Editors and Affiliations

  1. Klinik für Kinder- und Jugendmedizin, Univ.Klinikum Marburg, Baldingerstr. 12, Marburg, 35043, Germany

    Hannsjörg W. Seyberth

  2. Karolinska Institutet, Dept. Laboratory Medicine, Karolinska University Hostpital, Stockholm, 141 86, Sweden

    Anders Rane

  3. für Klinische Pharmakologie (IKP), Dr. Margarete Fischer-Bosch Institut, Auerbachstr. 112, Stuttgart, 70376, Germany

    Matthias Schwab

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Läer, S., Meibohm, B. (2011). Study Design and Simulation Approach. In: Seyberth, H., Rane, A., Schwab, M. (eds) Pediatric Clinical Pharmacology. Handbook of Experimental Pharmacology, vol 205. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20195-0_6

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