Drug Interaction PBPK Modeling: Review of the Literature Exposes the Need for Increased Verification of Model Inputs and Outputs as Part of Credibility Assessment

Abstract

In vitro data are routinely used to support both static and physiologically based pharmacokinetic (PBPK) model-based drug-drug interaction (DDI) predictions. Such efforts are possible after years of rapid progress, enabled by the greater availability of in vitro reagents, kinetic models, and ready access to PBPK software packages with increased computing power supported by drug-specific compound files. While acknowledging the progress, however, various investigators have documented the challenges and pitfalls associated with PBPK modeling and have called for improved model verification, credibility assessment, and greater confidence building. As summarized in the current narrative, a review of the DDI literature does expose the need for PBPK model parameter (input and output) verification. Representative examples of PBPK-based modeling involving induction (cytochrome P450 (CYP) 3A4 and organic anion transporting polypeptide 1B1 and 1B3), pregnancy-associated upregulation (CYP2D6), and inhibition (CYP1A2-mediated metabolism and creatinine renal clearance) are described. The narrative also includes the clinical application of biomarkers (e.g., CYP3A4 and CYP2D6) and tissue biopsy expression profiling as a means of providing additional mechanistic information and DDI data that are independent and complementary to PBPK models. With the advent of in vitro microphysiological systems, biomarkers, burgeoning plasma-based (liquid) biopsy protocols, and the possibility of machine learning-enabled literature searches integrated with modeling software, it is envisioned that such tools could be used jointly to further enhance PBPK model verification efforts within a predefined credibility assessment framework. Ultimately, the goal is to deploy PBPK modeling with greater confidence in lieu of time and resource-intensive clinical DDI studies.

Keywords: PBPK; biopsy; drug interaction; induction; inhibition; modeling.

FIGURE 1

High‐level summary of model‐based predictions of victim and perpetrator DDI. V _max, maximal rate of enzyme reaction; K _m, concentration of substrate at half‐maximal rate of enzyme reaction or transport; J _max, maximal rate of transporter‐mediated uptake or efflux; f _m, fraction metabolized by the inhibited enzyme; f _t, fraction transported by the inhibited transporter; IC₅₀, concentration of inhibitor rendering a 50% reduction in activity; K _i, inhibition constant; k _inact, maximal rate of enzyme inactivation; K _I, inhibitor concentration at half of k _inact; E _max, maximal induction; EC₅₀, half‐maximal induction; P_app, apparent permeability; f _u,plasma, fraction unbound in plasma; f _u,inc, fraction unbound in the in vitro incubation; PBPK, physiologically based pharmacokinetics; AUC, area under the plasma concentration versus time curve; AUC ratio, AUC_{(with perpetrator)}/AUC_{(control, reference, or placebo)}; C_max, maximal plasma concentration; C _max ratio, C _{max(with perpetrator)}/C _{max(control, reference, or placebo)}; t1/2, plasma half‐life; t1/2 ratio, t1/2_{(with perpetrator)}/t1/2_{(control, reference, or placebo)}; CL_renal, renal clearance; CL_renal ratio, CL_{renal(with perpetrator)}/CL_{renal(control, reference, or placebo)}; AFE, average fold error; GMFE, geometric mean fold error; RMSE, root mean square error; % prediction error = [(predicted value—observed value)/observed value] × 100.

FIGURE 2

Clinical study of perpetrator DDI using biomarkers and tissue biopsy. sEV, small extracellular vesicles; f _g, fraction surviving gut first pass; f _m, fraction metabolized by target enzyme; X, fraction activity remaining in enterocytes; A, fraction activity remaining in liver; Y, expression fold‐increase in enterocytes; B, expression fold‐increase in the liver; AUC, area under the plasma concentration versus time curve (AUC) ratio (AUC_+perpetrator/AUC_baseline); 6βHC, 6β‐hydroxycortisol; 4βHC, 4β‐hydroxycholesterol; 1βHDCA, 1β‐hydroxydeoxycholic acid; OATP, organic anion transporting polypeptide; CYP3A4, cytochrome P450 3A4; PK, pharmacokinetics; PBPK, physiologically based pharmacokinetics. See Figure S1 for more details.

FIGURE 3

Evaluation and prediction of CYP3A4 induction based on tissue and liquid biopsy profiling and oral midazolam plasma AUC ratio. (A) Relating CYP3A4 expression fold‐increase in gut (Y, filled triangles) and liver (B, filled circles) to the predicted oral midazolam AUC ratio (bars), assuming f _m = 0.9; f _g = 0.51 [26, 27]. The equation shown has been simplified and only considers induction (based on equation in Figure 2). For modafinil (Y > B), rifampicin (B = Y) and carbamazepine (B = Y), the shaded area represents the reported range of CYP3A4 increases in biopsy samples and the reported oral midazolam AUC ratio (references in Tables S1 and S2). Only model data for efavirenz are shown in the figure (assuming B > Y). (B) Fold‐increase in CYP3A4 expression (or activity) in tissue and liquid (plasma‐derived sEV) biopsy samples following induction. References for source data are described in Table S1. Modafinil liquid biopsy data were obtained following once daily 400 mg × 14 days. (C) Reported oral midazolam plasma AUC ratios following different inducers (references for source data described in Table S2). AUC ratio for midazolam following once daily modafinil 200 mg × 7 days is shown. AUC ratio (AUCR), area under the plasma concentration versus time curve (AUC) ratio (AUC_inducer/AUC_reference); sEV, small extracellular vesicles; CYP3A4, cytochrome P450 3A4; f _g, fraction surviving gut first pass; f _m, fraction metabolized by impacted enzyme (CYP3A4); MOD, modafinil; RIF, rifampicin; CBZ, carbamazepine; (M), mRNA; (P), protein; (EVA), ex vivo activity; mRNA, messenger RNA.

FIGURE 4

Relating CYP3A4 fold‐increase in gut and liver to fold‐increase in plasma 4β‐hydroxycholesterol and 1β‐hydroxydeoxycholic acid. Fold‐increases in gut (shaded bars) and liver (unshaded bars) CYP3A4 were used as model input to predict the fold‐increase plasma 4β‐hydroxycholesterol (4βHC) and 1β‐hydroxydeoxycholic acid (1βHC) following carbamazepine assuming induction in gut ~ liver (A), rifampicin assuming induction in gut ~ liver (B), modafinil induction in gut > liver (C), and efavirenz induction in liver > gut (D). A model for 4β‐hydroxycholesterol has been published and was adapted for 1β‐hydroxydeoxycholic (Table S3) [27]. For each inducer, the shaded area represents the reported range of CYP3A4 fold‐increases in gut and liver biopsy (tissue and plasma‐based sEV) samples and the published fold‐increase in plasma 4β‐hydroxycholesterol and 1β‐hydroxydeoxycholic acid (see references in Tables S1 and S2). sEV, small extracellular vesicles.

FIGURE 5

Inhibition of CYP1A2 and its impact on victim drug AUC ratio. (A) Reported plasma AUC ratios for oral caffeine and theophylline when co‐administered with 17α‐ethinyl estradiol‐containing oral contraceptives [93]. (B) Relating the AUC ratio to the fraction of liver CYP1A2 activity remaining. The described model tests two values for CYP1A2 f _m (0.85 and 0.95) and was adapted from the model described in Figure 2 (intestine is ignored and induction not considered for 17α‐ethinyl estradiol‐containing oral contraceptives). The shaded area represents the reported AUC ratio range and the corresponding fraction activity remaining [93]. CYP1A2, cytochrome P450 1A2; f _m, fraction metabolized by CYP1A2; B, fraction activity remaining in the liver (fraction inhibited = 1 − B, Figure 2); AUC, area under the plasma concentration versus time curve; AUC ratio, area under the plasma concentration versus time curve (AUC) ratio (AUCR = AUC_{oral contraceptive}/AUC_reference).