A Comprehensive CYP2D6 Drug-Drug-Gene Interaction Network for Application in Precision Dosing and Drug Development

Abstract

Conducting clinical studies on drug-drug-gene interactions (DDGIs) and extrapolating the findings into clinical dose recommendations is challenging due to the high complexity of these interactions. Here, physiologically-based pharmacokinetic (PBPK) modeling networks present a new avenue for exploring such complex scenarios, potentially informing clinical guidelines and handling patient-specific DDGIs at the bedside. Moreover, they provide an established framework for drug-drug interaction (DDI) submissions to regulatory agencies. The cytochrome P450 (CYP) 2D6 enzyme is particularly prone to DDGIs due to the high prevalence of genetic variation and common use of CYP2D6 inhibiting drugs. In this study, we present a comprehensive PBPK network covering CYP2D6 drug-gene interactions (DGIs), DDIs, and DDGIs. The network covers sensitive and moderate sensitive substrates, and strong and weak inhibitors of CYP2D6 according to the United States Food and Drug Administration (FDA) guidance. For the analyzed CYP2D6 substrates and inhibitors, DD(G)Is mediated by CYP3A4 and P-glycoprotein were included. Overall, the network comprises 23 compounds and was developed based on 30 DGI, 45 DDI, and seven DDGI studies, covering 32 unique drug combinations. Good predictive performance was demonstrated for all interaction types, as reflected in mean geometric mean fold errors of 1.40, 1.38, and 1.56 for the DD(G)I area under the curve ratios as well as 1.29, 1.43, and 1.60 for DD(G)I maximum plasma concentration ratios. Finally, the presented network was utilized to calculate dose adaptations for CYP2D6 substrates atomoxetine (sensitive) and metoprolol (moderate sensitive) for clinically untested DDGI scenarios, showcasing a potential clinical application of DDGI model networks in the field of model-informed precision dosing.

Figure 1

(a) Physiologically‐based pharmacokinetic drug–drug–gene interaction network. Schematic illustration of the modeled interactions of CYP2D6 perpetrator and victim drugs. Black arrows indicate metabolism or transport, green arrows indicate induction, red solid lines indicate competitive inhibition, red dashed lines down‐regulation (bupropion), noncompetitive inhibition (verapamil P‐gp inhibition), or mechanism‐based inactivation (others). (b, c) Drug–drug‐(gene) interaction matrix for modeled interactions mediated by (a) CYP2D6 and (b) CYP3A4 and P‐gp. Colors indicate categories according to the FDA's Examples of Drugs that Interact with CYP Enzymes and Transporter Systems. Height of the gray ribbons indicates the number of clinical studies for the respective interaction covered by the network, numbers in brackets indicate the number of clinical interaction studies for the corresponding compound. CYP, cytochrome P450; P‐gp, P‐glycoprotein.

Figure 2

Predicted vs. observed DGI (a) AUC_last and (b) C _max ratios as well as DDI (c, e) AUC_last and (d, f) C _max ratios, stratified by (c, d) CYP2D6‐mediated DDIs and (e, f) non‐CYP2D6‐mediated DDIs, of CYP2D6 substrates included in the network. Colored symbols represent the victim drugs; their shape corresponds to the respective perpetrator and their colored borders correspond to the respective phenotype or genotype as well as the reference genotype or phenotype. The solid straight black line marks the line of identity. Curved black lines show prediction success limits according to Guest et al., including 1.25‐fold variability. Black dotted lines show the 1.25‐fold range, dashed black lines indicate the twofold range. ALP, alprazolam; AS, CYP2D6 activity score; ATO, atomoxetine; AUC_last, area under the plasma concentration–time curve from the time of the first measurement to the time of the last measurement; BUP, bupropion; CBZ, carbamazepine; CIM, cimetidine; CLA, clarithromycin; CLO, (E)‐clomiphene; C _max, maximum plasma concentration; DDI, drug–drug interaction; DES, desipramine; DEX, dextromethorphan; DGI, drug–gene interaction; DIG, digoxin; DTT, total dextrorphan; DXT, dextrorphan; ERY, erythromycin; FLV, fluvoxamine; HDC, (E)‐4‐hydroxy‐N‐desethylclomiphene; IM, intermediate metabolizer; ITR, itraconazole; KET, ketoconazole; MET, metoprolol racemate; MEX, mexiletine; MID, midazolam; NDC, (E)‐N‐desethylclomiphene; NM, normal metabolizer; OHC, (E)‐4‐hydroxyclomiphene; OHD, 2‐hydroxydesipramine; OHM, α‐hydroxymetoprolol; OHQ, 3‐hydroxyquinidine; OHR, 9‐hydroxyrisperidone; OME, omeprazole; PAR, paroxetine; PM, poor metabolizer; QUI, quinidine; RIF, rifampicin; RIS, risperidone; RME, (R)‐metoprolol; SME, (S)‐metoprolol; VER, verapamil.

Figure 3

Predicted vs. observed DDGI (a) AUC_last and (b) C _max ratios of CYP2D6 substrates included in the network. Colored symbols represent the victim drugs; their shape corresponds to the respective perpetrator; their colored borders correspond to the respective phenotype or genotype as well as the reference genotype or phenotype. The solid straight black line marks the line of identity. Curved black lines show prediction success limits according to Guest et al., including 1.25‐fold variability. Black dotted lines show the 1.25‐fold range, dashed black lines indicate the twofold range. (c–f) Selection of modeled DDGIs. Predicted compared with observed plasma concentration–time profiles of the respective victim drug alone and after pretreatment with and/or concomitant administration of a perpetrator drug: (c, d) atomoxetine with and without bupropion pretreatment in (c) CYP2D6 normal metabolizers and (d) poor metabolizers and (e, f) desipramine with and without paroxetine pretreatment in (e) fast CYP2D6 normal metabolizers and (f) poor metabolizers. Predicted population geometric means are shown as lines (solid: victim drug alone, dashed: victim drug during DDGI), predicted geometric standard deviations are shown as shaded areas and observed data are shown as dots (parent compound) and triangles (metabolite, if available) (± standard deviation, if reported)., AS, CYP2D6 activity score; ATO, atomoxetine; AUC_last, area under the plasma concentration–time curve from the time of the first measurement to the time of the last measurement; b.i.d., twice daily; BUP, bupropion; CLA, clarithromycin; CLO, (E)‐clomiphene; C _max, maximum plasma concentration; DDGI, drug–drug–gene interaction; DES, desipramine; DEX, dextromethorphan; DXT, dextrorphan; HDC, (E)‐4‐hydroxy‐N‐desethylclomiphene; IM, intermediate metabolizer; MET, metoprolol racemate; MEX, mexiletine; MID, midazolam; n, number of study participants; NDC, (E)‐N‐desethylclomiphene; NM, normal metabolizer; OHC, (E)‐4‐hydroxyclomiphene; OHD, 2‐hydroxydesipramine; OHQ, 3‐hydroxyquinidine; PAR, paroxetine; PM, poor metabolizer; po, oral; q.d., once daily; QUI, quinidine; s.d., single dose.

Figure 4

Fold‐change in AUC_ss relative to the reference AUC_ss (activity score 2, no DDI) across different D(D)GI scenarios before dose adaptations for (a) atomoxetine and (b) metoprolol. Colors indicate the extent and direction of the deviation from the reference AUC_ss. AUC_ss, area under the concentration–time curve during steady state; b.i.d., twice a day; D(D)GI, drug(−drug)–gene interaction; q.d., once daily; q.i.d., four times a day.

Figure 5

Overview of model‐based dose adaptations for (a) atomoxetine and (b) metoprolol within single and multiple D(D)GI scenarios based on the exposure matching principle, where points and squares show the percentage of the original dose that match the PBPK simulated monotherapy AUC_ss for activity score 2. Colored symbols depict dose reductions for the different activity scores. AUC_ss, area under the concentration–time curve during steady state; b.i.d., twice a day; D(D)GI, drug(−drug)–gene interaction; PBPK, physiologically‐based pharmacokinetic; q.d., once daily; q.i.d., four times a day.