Evaluation of drug-drug interactions for oncology therapies: in vitro-in vivo extrapolation model-based risk assessment

Abstract

Aims: Understanding drug-drug interactions (DDI) is a critical part of the drug development process as polypharmacy has become commonplace in many therapeutic areas including the cancer patient population. The objectives of this study were to investigate cytochrome P450 (CYP)-mediated DDI profiles available for therapies used in the oncology setting and evaluate how models based on in vitro-in vivo extrapolation performed in predicting CYP-mediated DDI risk.

Methods: A dataset of 125 oncology therapies was collated using drug label and approval history information, incorporating in vitro and clinical PK data. The predictive accuracy of the basic and net effect mechanistic static models was assessed using this oncology drug dataset, for both victim and perpetrator potential of CYP3A-mediated DDI.

Results: The incidence of CYP3A-mediated interaction potential was 47%, 22% and 11% for substrates, inhibitors and inducers, respectively. The basic models for precipitants gave conservative predictions with no false negatives, whilst the mechanistic static models provided reasonable quantitative predictions (2.3-3-fold error). Further analysis revealed that incorporating DDI at the level of the intestine was in most cases over-predicting interaction magnitude due to overestimates of the rate and extent of oral absorption of the precipitant. Quantifying victim DDI potential was also demonstrated using fmCYP3A estimates from ketoconazole clinical DDI studies to predict the magnitude of interaction on co-administration with the CYP3A inducer, rifampicin (1.6-3.3 fold error).

Conclusions: This work illustrates the utility and limitations of current DDI risk assessment approaches applied to a range of contemporary anti-cancer agents, and discusses the implications for therapeutic combination strategies.

Keywords: CYP induction; CYP inhibition; CYP3A; anti-cancer therapy; drug interactions; pharmacokinetics.

Figure 1

Observed vs. predicted AUCR for oncology drugs as perpetrators of CYP3A DDI using the mechanistic static model for liver and intestine (A) and liver only (B). AUCR threshold of 0.8 and 1.25 shown as dotted lines as well as line of unity and 2-fold error lines

Figure 2

(A) Relationship between f_mCYP3A and AUCR for ketoconazole-mediated inhibition of CYP3A. Combined effect of liver and intestine (solid line) and liver only (dashed line). AUCR thresholds of 1.25, 2 and 5 shown as horizontal dotted lines. (B) Comparative relationship between AUCR and f_mCYP3A for ketoconazole (solid line) and itraconazole (dashed line) using the liver and intestine model. (C) Observed AUCR on co-administration with strong or moderate CYP3A inhibitors. Diltiazem (dark grey), itraconazole (medium grey) and ketoconazole (light grey)

Figure 3

(A) Relationship between f_mCYP3A and AUCR for rifampicin-mediated induction of CYP3A. Combined effect of liver and intestine (solid line) and liver only (dashed line). (B) Observed AUCR on co-administration with rifampicin

Figure 4

Observed vs. predicted AUCR for oncology drugs as victims of CYP3A DDI using the mechanistic static model for liver and intestine (closed circles) and liver only (open circles). Line of unity and 2-fold error lines shown for clarity