The drug–drug interaction of sorafenib mediated by P-glycoprotein and CYP3A4

1.?The aim of this study was to investigate the potential drug–drug interaction of sorafenib mediated by P-glycoprotein (P-gp) and cytochrome P450 3A4 (CYP3A4).

2.?In this research, a sensitive and reliable LC-MS/MS method was developed and applied for the determination of sorafenib in rat plasma. The pharmacokinetic profiles of orally administered sorafenib from rats with and without verapamil pretreatment were investigated.

3.?The results indicated that when the rats were pretreated with verapamil, the C_max of sorafenib increased from 55.73?ng/ml to 87.72?ng/ml (57.40%), and the AUC_(0?t) increased by approximately 58.2% when sorafenib was co-administered with verapamil. Additionally, the effects of verapamil on the absorption of sorafenib were investigated using the Caco-2 cell transwell model, and the effects of verapamil on the metabolic stability of sorafenib were also studied using rat liver microsomes incubation systems. A markedly higher transport of sorafenib across the Caco-2 cells was observed in the basolateral-to-apical direction and was abrogated in the presence of the P-gp inhibitor, verapamil. The results indicated that P-gp was involved in the transport of sorafenib, and verapamil could increase its absorption in the Caco-2 cell model, and the metabolic stability of sorafenib was prolonged by the pretreatment with verapamil.

4.?In conclusion, the drug–drug interaction of sorafenib might happen when sorafenib was co-administered with P-gp or CYP3A4 inhibitors.     

Does inhibition of P-glycoprotein lead to drug–drug interactions?

Permeability-glycoprotein (Pgp) is a drug transporter responsible for the efflux of xenobiotics out of cells that influence the pharmacokinetics of numerous drugs. However, the role of this transporter in drug-drug interactions is still poorly studied even though a lot of P-glycoprotein substrates and P-glycoprotein inhibitors are identified among drugs of standard usage. On one hand, Pgp is distributed within a lot of organs and tissues implicated in the absorption or excretion of xenobiotics, and drug-drug interactions with this protein may increase the bioavailability of simultaneously administered active drugs. On the other hand, Pgp is linked to the integrity of blood-tissue barriers, such as the blood-brain barrier or placenta, and a partial blockage of Pgp could be responsible for a new drug distribution in the organism with possible increase of drug rates in organs behind these barriers. Therefore, concomitant administration of substrates and Pgp inhibitors would modify drug pharmacokinetics by increasing bioavailability and organ uptake, leading to more adverse drug reactions and toxicities. Consequently, the identification and comprehension of these drug-drug interactions remain important keys to risk assessment.     

A strategy for early-risk predictions of clinical drug–drug interactions involving the GastroPlusTM DDI module for time-dependent CYP inhibitors

1.?A set of reference compounds for time-dependent inhibition (TDI) of cytochrome P450 with available literature data for k_inact and K_I was used to predict clinical implications using the GastroPlus^TM software. Comparisons were made to in vivo literature interaction data.

2.?The predicted AUC ratios (AUC_+inhibitor/AUC_control) could be compared with the observed ratios from literature for all compounds with detailed information about in vivo administration, pharmacokinetics and in vivo interactions (N?=?21). For this dataset, the difference between predicted and observed AUC ratios for interactions with midazolam was within twofold for all compounds except one (telaprevir, for which non-CYP-mediated metabolism likely plays a role after multiple dosing).

3.?The sensitivity, specificity and accuracy of the GastroPlus^TM predictions using a binary classification as no-to-weak interaction versus moderate-to-strong interaction for all compounds with available in vivo interaction data, were 80%, 82% and 81%, respectively (N?=?31).

4.?As a result of our evaluations of the DDI module in GastroPlus^TM, we have implemented an early TDI risk assessment decision tree for our drug discovery projects involving in vitro screening and early GastroPlus^TM predictions. Shifted IC₅₀ values are determined and k_inact/K_I estimated (by using a regression line established with in house-shifted IC₅₀ values and literature k_inact/K_I ratios), followed by GastroPlus^TM predictions.     

Cytochrome P450 3A and P-glycoprotein drug–drug interactions with voclosporin

Aims

Voclosporin is a novel calcineurin inhibitor intended for prevention of organ graft rejection and treatment of lupus nephritis. Pharmacokinetic drug interactions between voclosporin and a CYP3A inhibitor, inducer and substrate and a P-glycoprotein inhibitor and substrate were evaluated.

Methods

Voclosporin 0.4 mg kg⁻¹ was administered to 24 subjects in each of five studies, as follows: every 12 h (Q12H) alone and concomitantly with ketoconazole 400 mg once daily (QD); single dose before and single dose after rifampin 600 mg QD; Q12H where midazolam 7.5 mg was administered as a single dose alone before voclosporin and with last the dose of voclosporin; Q12H alone and concomitantly with verapamil 80 mg every 8 h; and Q12H with digoxin 0.25 mg QD. The noncompartmental pharmacokinetic parameters maximal concentration (C_max) and area under the concentration–time curve (AUC) were obtained, and geometric least squares mean ratios and 90% confidence intervals were evaluated.

Results

Ketoconazole increased voclosporin C_max (6.4-fold) and AUC (18-fold); rifampin reduced voclosporin AUC (0.9-fold); voclosporin did not change exposure of midazolam or α-hydroxy-midazolam; verapamil increased voclosporin C_max (2.1-fold) and AUC (2.7-fold); and voclosporin increased digoxin C_max (0.5-fold), AUC (0.25-fold) and urinary excretion (0.2-fold).

Conclusions

Administration of voclosporin concomitantly with strong inhibitors and inducers of CYP3A resulted in increased and decreased exposures, respectively, and should be considered contraindicated. Drug–drug interactions involving voclosporin and CYP3A substrates are not expected. Administration of voclosporin concomitantly with inhibitors and substrates of P-glycoprotein resulted in increased voclosporin and substrate exposures, respectively. Appropriate concentration and safety monitoring is recommended with co-administration of voclosporin and P-glycoprotein substrates and inhibitors.     

P-glycoprotein inhibition by the agricultural pesticide propiconazole and its hydroxylated metabolites: Implications for pesticide–drug interactions

The human efflux transporter P-glycoprotein (P-gp, MDR1) functions as an important cellular defense system against a variety of xenobiotics; however, little information exists on whether environmental chemicals interact with P-gp. Conazoles provide a unique challenge to exposure assessment because of their use as both pesticides and drugs. Propiconazole is an agricultural pesticide undergoing evaluation by the U.S. Environmental Protection Agency’s Endocrine Disruptor Screening Program. In this study, the P-gp interaction of propiconazole and its hydroxylated metabolites were evaluated using MDR1-expressing membrane vesicles and NIH-3T3/MDR1 cells. Membrane vesicle assays demonstrated propiconazole (IC_50,122.9 μM) and its metabolites (IC₅₀s, 350.8 μM, 366.4 μM, and 456.3 μM) inhibited P-gp efflux of a probe substrate, with propiconazole demonstrating the strongest interaction. P-gp mediated transport of propiconazole in MDR1-expressed vesicles was not detected indicating propiconazole interacts with P-gp as an inhibitor rather than a substrate. In NIH-3T3/MDR1 cells, propiconazole (1 and 10 μM) led to decreased cellular resistance (chemosensitization) to paclitaxel, a chemotherapeutic drug and known MDR1 substrate. Collectively, these results have pharmacokinetic and risk assessment implications as P-gp interaction may influence pesticide toxicity and the potential for pesticide–drug interactions.     

Model for the drug–drug interaction responsible for CYP3A enzyme inhibition. II: establishment and evaluation of dexamethasone-pretreated female rats

1.?Cytochrome P450 (CYP) 3A catalysis of testosterone 6β-hydroxylation in female rat liver microsomes was significantly induced, then reached a plateau level after pretreatment with 80?mg?kg^?1?day^?1 dexamethasone (DEX) for 3 days.

2.?Midazolam was mainly metabolized by CYP3A in DEX-treated female rat liver microsomes from an immuno-inhibition study, and the apparent K_m was 1.8?μM, similar to that in human microsomes.

3.?Ketoconazole and erythromycin, typical CYP3A inhibitors, demonstrated extensive inhibition of midazolam metabolism in DEX-treated female rat liver microsomes, and the apparent K_i values were 0.088 and 91.2?μM, respectively. The values were similar to those in humans, suggesting that DEX-treated female rat liver microsomes have properties similar to those of humans.

4.?After oral administration of midazolam, the plasma midazolam concentration in DEX-treated female rats significantly decreased compared with control female rats. The area under the plasma concentration curve (AUC) and elimination half-life were one-11th and one-20th of those of control female rats, respectively.

5.?Using DEX-treated female rats, the effect of CYP3A inhibitors on midazolam pharmacokinetics was evaluated. The AUC and maximum concentration in plasma (C_max) increased when ketoconazole was co-administered with midazolam.

6.?It was shown that the drug–drug interaction that occurs in vitro is also observed in vivo after oral administration of midazolam. In conclusion, the DEX-treated female rat could be a useful model for evaluating drug–drug interactions based on CYP3A enzyme inhibition.     

Enzymatic characteristics of CYP3A5 and CYP3A4: A comparison of in vitro kinetic and drug–drug interaction patterns

CYP3A4 and CYP3A5 exhibit significant overlap in substrate specificity, but can differ in catalytic activity and regioselectivity. To investigate their characteristics further, the enzymatic reactions of the two CYP3A enzymes were compared using midazolam, nifedipine, testosterone and terfenadine as substrates. Both CYP3A5 and CYP3A4 showed sigmoid and substrate inhibition patterns for testosterone 6β-hydroxylation and terfenadine t-butylhydroxylation (TFDOH), respectively. In the other reactions, the kinetic model for CYP3A5 was not similar to that for CYP3A4. An inhibition study demonstrated that the interactions between α-naphthoflavone (αNF) and CYP3A substrates were different for the two CYP3A enzymes. αNF stimulated nifedipine oxidation catalysed by CYP3A5, but did not stimulate that catalysed by CYP3A4. αNF at less than 32?µM inhibited TFDOH catalysed by CYP3A5, but did not inhibit that catalysed by CYP3A4. These results indicate that CYP3A5 has different enzymatic characteristics from CYP3A4 in some CYP3A catalysed reactions.     

Model for the drug–drug interaction responsible for CYP3A enzyme inhibition. I: evaluation of cynomolgus monkeys as surrogates for humans

1.?Anti-human cytochrome P450 (CYP) 3A4 antiserum completely inhibited midazolam metabolism in monkey liver microsomes, suggesting that midazolam was mainly metabolized by CYP3A enzyme(s) in monkey liver microsomes.

2.?Midazolam metabolism was also inhibited in vitro by typical chemical inhibitors of CYP3A, such as ketoconazole, erythromycin and diltiazem, and the apparent K_i values for ketoconazole, erythromycin and diltiazem were 0.127, 94.2 and 29.6?μM, respectively.

3.?CYP3A inhibitors increased plasma midazolam concentrations when midazolam and CYP3A inhibitors were co-administered orally. However, the pharmacokinetic parameters of midazolam were not changed by treatment with CYP3A inhibitors when midazolam was given intravenously. This suggests that CYP3A inhibitors modified the first-pass metabolism in the liver and/or intestine, but not systemic metabolism.

4.?The drug–drug interaction responsible for CYP3A enzyme(s) inhibition was observed when midazolam and inhibitors were co-administrated orally. Therefore, it was concluded that monkeys given midazolam orally could be useful models for predicting drug–drug interactions in man based on CYP3A enzyme inhibition.     

A critical evaluation of clinical decision support for the detection of drug–drug interactions

Introduction: Incorporation of clinical decision support systems (CDSSs) into computerized physician order entry assists prescribers with medication dosing, identification of duplicate therapies, drug-allergy alerts and drug–drug interactions (DDIs). The generation of DDI alerts is one aspect of CDSS that may improve patient safety and reduce adverse drug events.

Areas covered: Currents issues with the generation of DDI alerts, such as alert fatigue, unclear clinical significance and database inconsistencies are a few of the problems that have been identified with DDI alerting. Research has shown that DDI alerting may be improved through the tiering of alerts, generation of patient-specific alert and directing some alerts to clinicians other than physicians. More research in this area, such as how to decrease the variability of database rating systems, improve the identification of clinically significant alerts and increase the patient specificity of the generated DDI alerts, should be conducted.

Expert opinion: DDI knowledgebases need to take into account more patient-specific information. Strategies to avoid alert fatigue, such as DDI tiering and reducing signal:noise ratios, are important areas for future study. End-user participation and clinician feedback should be incorporated in the development of DDI knowledgebases to increase alert compliance.     

Patient- and physician-related risk factors for hyperkalaemia in potassium-increasing drug–drug interactions

Purpose

Hyperkalaemia due to potassium-increasing drug–drug interactions (DDIs) is a clinically important adverse drug event. The purpose of this study was to identify patient- and physician-related risk factors for the development of hyperkalaemia.

Methods

The risk for adult patients hospitalised in the University Hospital Zurich between 1 December 2009 and 31 December 2011 of developing hyperkalaemia was correlated with patient characteristics, number, type and duration of potassium-increasing DDIs and frequency of serum potassium monitoring.

Results

The 76,467 patients included in this study were prescribed 8,413 potentially severe potassium-increasing DDIs. Patient-related characteristics associated with the development of hyperkalaemia were pulmonary allograft [relative risk (RR) 5.1; p?<?0.0001), impaired renal function (RR 2.7; p?<?0.0001), diabetes mellitus (RR 1.6; p?=?0.002) and female gender (RR 1.5; p?=?0.007). Risk factors associated with medication were number of concurrently administered potassium-increasing drugs (RR 3.3 per additional drug; p?<?0.0001) and longer duration of the DDI (RR 4.9 for duration ≥6 days; p?<?0.0001). Physician-related factors associated with the development of hyperkalaemia were undetermined or elevated serum potassium level before treatment initiation (RR 2.2; p?<?0.001) and infrequent monitoring of serum potassium during a DDI (interval >48 h: RR 1.6; p?<?0.01).

Conclusion

Strategies for reducing the risk of hyperkalaemia during potassium-increasing DDIs should consider both patient- and physician-related risk factors.     

In vitro inhibition of UGT1A3, UGT1A4 by ursolic acid and oleanolic acid and drug–drug interaction risk prediction

1.?Ursolic acid (UA) and oleanolic acid (OA) may have important activity relevant to health and disease prevention. Thus, we studied the activity of UA and OA on UDP-glucuronosyltransferases (UGTs) and used trifluoperazine as a probe substrate to test UGT1A4 activity. Recombinant UGT-catalyzed 4-methylumbelliferone (4-MU) glucuronidation was used as a probe reaction for other UGT isoforms.

2.?UA and OA inhibited UGT1A3 and UGT1A4 activity but did not inhibit other tested UGT isoforms.

3.?UA-mediated inhibition of UGT1A3 catalyzed 4-MU-β-d-glucuronidation was via competitive inhibition (IC₅₀ 0.391?±?0.013?μM; K_i 0.185?±?0.015?μM). UA also competitively inhibited UGT1A4-mediated trifluoperazine-N-glucuronidation (IC₅₀ 2.651?±?0.201?μM; K_i 1.334?±?0.146?μM).

4.?OA offered mixed inhibition of UGT1A3-mediated 4-MU-β-d-glucuronidation (IC₅₀ 0.336?±?0.013?μM; K_i 0.176?±?0.007?μM) and competitively inhibited UGT1A4-mediated trifluoperazine-N-glucuronidation (IC₅₀ 5.468?±?0.697?μM; K_i 6.298?±?0.891?μM).

5.?Co-administering OA or UA with drugs or products that are substrates of UGT1A3 or UGT1A4 may produce drug-mediated side effects.     

Development of an ADME and drug–drug interactions knowledge database for the acceleration of drug discovery and development

It is widely recognised that predicting or determining the absorption, distribution, metabolism and excretion (ADME) properties of a compound as early as possible in the drug discovery process helps to prevent costly late-stage failures. Although in recent years high-throughput in vitro absorption distribution metabolism excretion toxicity (ADMET) screens have been implemented, more efficient in silico filters are still highly needed to predict and model the most relevant metabolic and pharmacokinetic end points, and thereby accelerate drug discovery and development. The usefulness of the data generated and published for the chemist, biologist or project manager who ultimately wants to understand and optimise the ADME properties of lead compounds cannot be argued with. Collecting and comparing data is an overwhelming task for the time-pressed scientist. Aureus Pharma provides a uniquely specialised solution for knowledge generation in drug discovery. AurSCOPE^® ADME/DDI (drug–drug interaction) is a fully annotated, structured knowledge database containing all the pertinent biological and chemical information on the metabolic properties of drugs. This Aureus knowledge database has proven to be highly useful in designing predictive models and identifying potential drug–drug interactions.     

Renal drug metabolism in humans: the potential for drug–endobiotic interactions involving cytochrome P450 (CYP) and UDP-glucuronosyltransferase (UGT)

Although knowledge of human renal cytochrome P450 (CYP) and UDP-glucuronosyltransferase (UGT) enzymes and their role in xenobiotic and endobiotic metabolism is limited compared with hepatic drug and chemical metabolism, accumulating evidence indicates that human kidney has significant metabolic capacity. Of the drug metabolizing P450s in families 1 to 3, there is definitive evidence for only CYP 2B6 and 3A5 expression in human kidney. CYP 1A1, 1A2, 1B1, 2A6, 2C19, 2D6 and 2E1 are not expressed in human kidney, while data for CYP 2C8, 2C9 and 3A4 expression are equivocal. It is further known that several P450 enzymes involved in the metabolism of arachidonic acid and eicosanoids are expressed in human kidney, CYP 4A11, 4F2, 4F8, 4F11 and 4F12. With the current limited evidence of drug substrates for human renal P450s drug–endobiotic interactions arising from inhibition of renal P450s, particularly effects on arachidonic acid metabolism, appear unlikely. With respect to the UGTs, 1A5, 1A6, 1A7, 1A9, 2B4, 2B7 and 2B17 are expressed in human kidney, whereas UGT 1A1, 1A3, 1A4, 1A8, 1A10, 2B10, 2B11 and 2B15 are not. The most abundantly expressed renal UGTs are 1A9 and 2B7, which play a significant role in the glucuronidation of drugs, arachidonic acid, prostaglandins, leukotrienes and P450 derived arachidonic acid metabolites. Modulation by drug substrates (e.g. NSAIDs) of the intrarenal activity of UGT1A9 and UGT2B7 has the potential to perturb the metabolism of renal mediators including aldosterone, prostaglandins and 20-hydroxyeicosatetraenoic acid, thus disrupting renal homeostasis.     

An update on the potential role of intestinal first-pass metabolism for the prediction of drug–drug interactions: the role of PBPK modeling

Introduction: The intestinal absorption process is a combination of several events that are governed by various factors. Several transport mechanisms are involved in drug absorption through enterocytes via active and/or passive processes. The transported molecules then undergo intestinal metabolism, which together with intestinal transport may affect the systemic availability of drugs. Many studies have provided clear evidence on the significant role of intestinal first-pass metabolism on drug bioavailability and degree of drug–drug interactions (DDIs).

Areas covered: This review provides an update on the role of intestinal first-pass metabolism in the oral bioavailability of drugs and prediction of DDIs. It also provides a comprehensive overview and summary of the latest update in the role of physiologically based pharmacokinetic models modeling in prediction of intestinal metabolism and DDIs in humans.

Expert opinion: The contribution of intestinal first-pass metabolism in the oral bioavailability of drugs and prediction of DDIs has become more evident over the last few years. Several in vitro, in situ, and in vivo models have been developed to evaluate the role of first-pass metabolism and to predict DDIs. Currently, physiologically based pharmacokinetic modeling is considered the most valuable tool for the prediction of intestinal first-pass metabolism and DDIs.     

Improvement in the handling of drug–drug interactions

Approximately one in 200 hospitalised patients has a serious adverse drug effect caused by drug–drug interactions (DDIs). Such adverse effects should be avoidable, but current information provided on DDIs is often incomplete and difficult or even impossible to translate into true risk and appropriate tangible action. Clinicians need to know the mean and maximal expected effect of a DDI on clinical endpoints, any dose adjustments required, and how to monitor tolerability and efficacy in patients subject to a DDI. To this end, improved study designs should take the objective of improving treatment explicitly into account, and any existing DDI data should be publicly accessible. Modelling needs to be used more extensively in order to quantitatively predict the effects of DDIs on clinical endpoints in patients and to relate clinical endpoint effects considered as acceptable to respective changes in experimental and clinical studies. Computer-based expert systems will be required to convert such DDI data into recommendations applicable to the individual patient. Therefore, the incorporation of DDIs in a more general procedure for personalisation of drug therapy is desirable.     

In vitro drug–drug interactions of budesonide: inhibition and induction of transporters and cytochrome P450 enzymes

1.?Budesonide is a glucocorticoid used in the treatment of several respiratory and gastrointestinal inflammatory diseases. Glucocorticoids have been demonstrated to induce cytochrome P450 (CYP) 3A and the efflux transporter P-glycoprotein (P-gp). This study aimed to evaluate the potential of budesonide to act as a perpetrator or a victim of transporter- or CYP-mediated drug–drug interactions (DDIs).

2.?In vitro studies were conducted for P-gp, breast cancer resistance protein and organic anion and cation transporters (OATP1B1, OATP1B3, OAT1, OAT3, OCT2) in transporter-transfected cells. Changes in mRNA expression in human hepatocytes and enzyme activity in human liver microsomes by budesonide were determined for CYP1A2, CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6 and CYP3A.

3.?The data indicated that budesonide is a substrate of P-gp but is not a substrate or an inhibitor of the other transporters investigated. Budesonide is neither an inducer nor an inhibitor of major CYP enzymes. The effect of P-gp on budesonide disposition is anticipated to be low owing to CYP3A-mediated clearance.

4.?Collectively, our data indicate there is a low risk of budesonide perpetrating clinical DDIs mediated by the transporters or CYPs studied.     

In silico methods for predicting drug–drug interactions with cytochrome P-450s,transporters and beyond

Drug–drug interactions (DDIs) are associated with severe adverse effects that may lead to the patient requiring alternative therapeutics and could ultimately lead to drug withdrawal from the market if they are severe. To prevent the occurrence of DDI in the clinic, experimental systems to evaluate drug interaction have been integrated into the various stages of the drug discovery and development process. A large body of knowledge about DDI has also accumulated through these studies and pharmacovigillence systems. Much of this work to date has focused on the drug metabolizing enzymes such as cytochrome P-450s as well as drug transporters, ion channels and occasionally other proteins. This combined knowledge provides a foundation for a hypothesis-driven in silico approach, using either cheminformatics or physiologically based pharmacokinetics (PK) modeling methods to assess DDI potential. Here we review recent advances in these approaches with emphasis on hypothesis-driven mechanistic models for important protein targets involved in PK-based DDI. Recent efforts with other informatics approaches to detect DDI are highlighted. Besides DDI, we also briefly introduce drug interactions with other substances, such as Traditional Chinese Medicines to illustrate how in silico modeling can be useful in this domain. We also summarize valuable data sources and web-based tools that are available for DDI prediction. We finally explore the challenges we see faced by in silico approaches for predicting DDI and propose future directions to make these computational models more reliable, accurate, and publically accessible.     

Comparison of two endogenous biomarkers of CYP3A4 activity in a drug–drug interaction study between midostaurin and rifampicin

Purpose

Midostaurin, a multitargeted tyrosine kinase inhibitor, is primarily metabolized by CYP3A4. This midostaurin drug–drug interaction study assessed the dynamic response and clinical usefulness of urinary 6β-hydroxycortisol to cortisol ratio (6βCR) and plasma 4β-hydroxycholesterol (4βHC) for monitoring CYP3A4 activity in the presence or absence of rifampicin, a strong CYP3A4 inducer.

Methods

Forty healthy adults were randomized into groups for either placebo or treatment with rifampicin 600 mg QD for 14 days. All participants received midostaurin 50 mg on day 9. Midostaurin plasma pharmacokinetic parameters were assessed. Urinary 6βCR and plasma 4βHC levels were measured on days 1, 9, 11, and 15.

Results

Both markers remained stable over time in the control group and increased significantly in the rifampicin group. In the rifampicin group, the median increases (vs day 1) on days 9, 11, and 15 were 4.1-, 5.2-, and 4.7-fold, respectively, for 6βCR and 3.4-, 4.1-, and 4.7-fold, respectively, for 4βHC. Inter- and intrasubject variabilities in the control group were 45.6 % and 30.5 %, respectively, for 6βCR, and 33.8 % and 7.5 %, respectively, for 4βHC. Baseline midostaurin area under the concentration–time curve (AUC) correlated with 4βHC levels (ρ?=??0.72; P?=?.003), but not with 6βCR (ρ?=?0.0925; P?=?.6981).

Conclusions

Both 6βCR and 4βHC levels showed a good dynamic response range upon strong CYP3A4 induction with rifampicin. Because of lower inter- and intrasubject variability, 4βHC appeared more reliable and better predictive of CYP3A4 activity compared with 6βCR. The data from our study further support the clinical utility of these biomarkers.     

Assessment of non-linear combination effect terms for drug–drug interactions

Drugs interact with their targets in different ways. A diversity of modeling approaches exists to describe the combination effects of two drugs. We investigate several combination effect terms (CET) regarding their underlying mechanism based on drug-receptor binding kinetics, empirical and statistical summation principles and indirect response models. A list with properties is provided and the interrelationship of the CETs is analyzed. A method is presented to calculate the optimal drug concentration pair to produce the half-maximal combination effect. This work provides a comprehensive overview of typically applied CETs and should shed light into the question as to which CET is appropriate for application in pharmacokinetic/pharmacodynamic models to describe a specific drug–drug interaction mechanism.     

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

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 f_mCYP3A 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.