Abstract
Assumptions inherent to pharmacometric model development and use are not routinely acknowledged, described, or evaluated. The aim of this work is to present a framework for systematic evaluation of assumptions. To aid identification of assumptions, we categorise assumptions into two types: implicit and explicit assumptions. Implicit assumptions are inherent in a method or model and underpin its derivation and use. Explicit assumptions arise from heuristic principles and are typically defined by the user to enable the application of a method or model. A flowchart was developed for systematic evaluation of assumptions. For each assumption, the impact of assumption violation (‘significant’, ‘insignificant’, ‘unknown’) and the probability of assumption violation (‘likely’, ‘unlikely’, ‘unknown’) will be evaluated based on prior knowledge or the result of an additional bespoke study to arrive at a decision (‘go’, ‘no-go’) for both model building and model use. A table of assumptions with standardised headings has been proposed to facilitate the documentation of assumptions and evaluation of results. The utility of the proposed framework was illustrated using four assumptions underpinning a top-down model describing the warfarin-coagulation proteins’ relationship. The next step of this work is to apply the framework to a series of other settings to fully assess its practicality and its value in identifying and making inferences from assumptions.
Similar content being viewed by others
References
Marshall SF, Burghaus R, Cosson V, Cheung SY, Chenel M, DellaPasqua O, et al. Good practices in model-informed drug discovery and development: practice, application, and documentation. CPT Pharmacometrics Syst Pharmacol. 2016;5(3):93–122.
Food and Drug Administration (FDA). Guidance for industry: population pharmacokinetics 1999 [Available from: https://www.fda.gov/downloads/drugs/guidances/UCM072137.pdf. Accessed 15 April 2019.
European Medicines Agency (EMA). Guideline on reporting the results of population pharmacokinetic analyses 2007 [Available from: http://www.ema.europa.eu/docs/en_GB/document_library/Scientific_guideline/2009/09/WC500003067.pdf. Accessed 15 April 2019.
Manolis E, Brogren J, Cole S, Hay JL, Nordmark A, Karlsson KE, et al. Commentary on the MID3 good practices paper. CPT Pharmacometrics Syst Pharmacol. 2017;6(7):416–7.
Byon W, Smith MK, Chan P, Tortorici MA, Riley S, Dai H, et al. Establishing best practices and guidance in population modeling: an experience with an internal population pharmacokinetic analysis guidance. CPT Pharmacometrics Syst Pharmacol. 2013;2:e51.
Dykstra K, Mehrotra N, Tornøe CW, Kastrissios H, Patel B, Al-Huniti N, et al. Reporting guidelines for population pharmacokinetic analyses. J Pharmacokinet Pharmacodyn. 2015;42(3):301–14.
Karlsson MO, Jonsson EN, Wiltse CG, Wade JR. Assumption testing in population pharmacokinetic models: illustrated with an analysis of moxonidine data from congestive heart failure patients. J Pharmacokinet Biopharm. 1998;26(2):207–46.
Wade JR, Edholm M, Salmonson T. A guide for reporting the results of population pharmacokinetic analyses: a Swedish perspective. AAPS J. 2005;7(2):45.
Bonate PL, Strougo A, Desai A, Roy M, Yassen A, van der Walt JS, et al. Guidelines for the quality control of population pharmacokinetic-pharmacodynamic analyses: an industry perspective. AAPS J. 2012;14(4):749–58.
Jamsen KM, McLeay SC, Barras MA, Green B. Reporting a population pharmacokinetic-pharmacodynamic study: a journal’s perspective. Clin Pharmacokinet. 2014;53(2):111–22.
Timmis J, Alden K, Andrews P, Clark E, Nellis A, Naylor B, et al. Building confidence in quantitative systems pharmacology models: an engineer’s guide to exploring the rationale in model design and development. CPT Pharmacometrics Syst Pharmacol. 2017;6(3):156–67.
Jones H, Rowland-Yeo K. Basic concepts in physiologically based pharmacokinetic modeling in drug discovery and development. CPT Pharmacometrics Syst Pharmacol. 2013;2:e63.
Jones HM, Chen Y, Gibson C, Heimbach T, Parrott N, Peters SA, et al. Physiologically based pharmacokinetic modeling in drug discovery and development: a pharmaceutical industry perspective. Clin Pharmacol Ther. 2015;97(3):247–62.
Rowland M, Peck C, Tucker G. Physiologically-based pharmacokinetics in drug development and regulatory science. Annu Rev Pharmacol Toxicol. 2011;51:45–73.
Zhao P, Rowland M, Huang SM. Best practice in the use of physiologically based pharmacokinetic modeling and simulation to address clinical pharmacology regulatory questions. Clin Pharmacol Ther. 2012;92(1):17–20.
(EMA) EMA. EFPIA-EMA modelling and simulation workshop (2011) report. London, United Kingdom; 2012.
Cockcroft DW, Gault MH. Prediction of creatinine clearance from serum creatinine. Nephron. 1976;16(1):31–41.
Ooi QX, Wright DFB, Tait RC, Isbister GK, Duffull SB. A joint model for vitamin K-dependent clotting factors and anticoagulation proteins. Clin Pharmacokinet. 2017;56(12):1555–66.
Karlsson MO, Beal SL, Sheiner LB. Three new residual error models for population PK/PD analyses. J Pharmacokinet Biopharm. 1995;23(6):651–72.
Hubbard DW. The failure of risk management: why it’s broken and how to fix it: John Wiley & Sons; 2009.
Woodruff JM. Consequence and likelihood in risk estimation: a matter of balance in UK health and safety risk assessment practice. Saf Sci. 2005;43(5):345–53.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
ESM 1
(PDF 429 kb)
Appendix
Appendix
Assumptions underpinning a joint model for six vitamin K-dependent coagulation proteins (18) were evaluated. The joint model is described by the following system of ordinary differential equations:
Pharmacokinetic model:
Pharmacodynamic model:
Here, LAG, general lag parameter; CL , clearance of warfarin; D, warfarin dose; II, factor II; IA50,P , warfarin amount in the body that gives half the maximum inhibitory effect; Imax,P, maximum inhibitory effect; IX, factor IX; ka, first-order absorption rate constant of warfarin; kout,P, first-order coagulation protein degradation rate constant; PC, protein C; PS, protein S; Rin,P, zero-order functional coagulation protein production rate; t, time; V, volume of distribution of warfarin; VII, factor VII; X, factor X; Pt = 0, coagulation protein concentration at baseline.
Rights and permissions
About this article
Cite this article
Ooi, QX., Wright, D.F.B., Isbister, G.K. et al. Evaluation of Assumptions Underpinning Pharmacometric Models. AAPS J 21, 97 (2019). https://doi.org/10.1208/s12248-019-0366-2
Received:
Accepted:
Published:
DOI: https://doi.org/10.1208/s12248-019-0366-2