Physiologically based pharmacokinetic modelling in pregnancy: Model reproducibility and external validation

Abstract

Aims: Physiologically based pharmacokinetic (PBPK) models have been previously developed for betamethasone and buprenorphine for pregnant women. The goal of this work was to replicate and reassess these models using data from recently completed studies.

Methods: Betamethasone and buprenorphine PBPK models were developed in Simcyp V19 based on prior publications using V17 and V15. Ability to replicate models was verified by comparing predictions in V19 to those previously published. Once replication was verified, models were reassessed by comparing predictions to observed data from additional studies in pregnant women. Model performance was based upon visual inspection of concentration vs. time profiles, and comparison of pharmacokinetic parameters. Models were deemed reproducible if parameter estimates were within 10% of previously reported values. External validations were considered acceptable if the predicted area under the concentration-time curve (AUC) and peak plasma concentration fell within 2-fold of the observed.

Results: The betamethasone model was successfully replicated using Simcyp V19, with ratios of reported (V17) to reproduced (V19) peak plasma concentration of 0.98-1.04 and AUC of 0.95-1.07. The model-predicted AUC ratios ranged from 0.98-1.79 compared to external data. The previously published buprenorphine PBPK model was not reproducible, as we predicted intravenous clearance of 70% that reported previously (both in Simcyp V15).

Conclusion: While high interstudy variability was observed in the newly available clinical data, the PBPK model sufficiently predicted changes in betamethasone exposure across gestation. Model reproducibility and reassessment with external data are important for the advancement of the discipline. PBPK modelling publications should contain sufficient detail and clarity to enable reproducibility.

Keywords: obstetrics; pharmacometrics; physiologically based pharmacokinetic modelling; research ethics.

Figure 1.

Best practices approach for pregnancy PBPK model development includes use of physciochemical, in vitro, and clinical data to inform drug parameters to predict concentrations in a healthy adult population using a learn and confirm paradigm (indicated by dashed arrows), if needed. Verification should include external validation of the model using data not implemented during model development. Once drug parameters are verified in a healthy adult population, system (population) parameters are adjusted to a special population of interest (e.g. pregnant women). Model-predicted plasma concentrations in pregnant population are compared to available literature data also in pregnant population. If necessary, adjustments in drug parameters (e.g. accounting for placental transport/metabolism) are made based on a learn-confirm paradigm (denoted by dashed arrow). After verification of model performance based on available literature data (if possible), the model can be used to simulate the new study and predictions can be compared to observed data.

Figure 2.

Comparison of predicted betamethasone concentation-time curves using Simcyp V17 and V19. (A) 8 mg IV in healthy volunteers [18], (B) 2 mg PO in healthy volunteers [19], (C) 6 mg IM in healthy volunteers [20], (D) 12 mg IM in healthy volunteers [21], (E) 8 mg IV in pregnant women [22]. In all figures, the dashed red lines represent the mean predicted concentration data using Simcyp V17 and the solid black line is the mean predicted concentration data using Simcyp V19.

Figure 3.

Mean predicted (solid black line) versus observed (open symbols) betamethasone concentrations in pregnant women administered two intramuscular doses of 12 mg betamethasone phosphate:acetate (in-house data). Predicted concentrations are based on (A) estimated gestational age (EGA) 26 weeks, (B) EGA 30 weeks, and (C) EGA 34 weeks. Grey dashed lines represent the 90% prediction interval.