Inhibition-based metabolic drug-drug interactions: predictions from in vitro data

There has been a growing interest in predicting in vivo metabolic drug-drug interactions from in vitro systems. High-throughput screening methods aimed at assessing the potential of drug candidates for drug interactions are widely used in industry. However, at present, there is no consensus on methodologies that would yield reliable quantitative predictions, because a number of issues remain unsolved, such as estimations of inhibition constants in vitro and inhibitor concentration around the enzyme site in vivo. In the present review, different approaches to estimation of inhibitor concentration around the enzyme site are summarized; also, the problems associated with estimation of in vitro K(i) values due to incubation conditions and environment differences between in vitro and in vivo are presented. A new approach based on comparisons of in vitro and in vivo inhibition potencies by calculation of in vivo inhibition constants is discussed. Examples of predictions of in vivo drug interactions based on mechanism-based inactivation are described. Unresolved issues that would allow further refinement of existing prediction models are also evaluated.     

In vitro-to-in vivo predictions of drug-drug interactions involving multiple reversible inhibitors

INTRODUCTION: Predictions of drug-drug interactions (DDIs) are commonly performed for single inhibitors, but interactions involving multiple inhibitors also frequently occur. Predictions of such interactions involving stereoisomer pairs, parent/metabolite combinations and simultaneously administered multiple inhibitors are increasing in importance. This review provides the framework for predicting inhibitory DDIs of multiple inhibitors with any combination of reversible inhibition mechanism. AREAS COVERED: The review provides an overview of the reliability of the in vitro determined reversible inhibition mechanism. Furthermore, the article provides a method to predict DDIs for multiple reversible inhibitors that allows substituting the inhibition constant (K(i)) with an inhibitor affinity (IC(50)) value determined at S < K(M). EXPERT OPINION: A better understanding and the prediction methods of DDIs, resulting from multiple inhibitors, are important. The inhibition mechanism of a reversible inhibitor is often equivocal across studies and unreliable. Determination of the K(i) requires the assignment of reversible inhibition mechanism but in vitro-to-in vivo prediction of DDI risk can be achieved for multiple inhibitors from estimates of the inhibitor affinity (IC(50)) only, regardless of the inhibition mechanism.     

Importance of P-glycoprotein for drug-drug interactions

P-glycoprotein (ABCB1) is one of the most extensively studied transporters regarding drug resistance and drug-drug interactions. P-glycoprotein is expressed in multiple key organs in drug disposition such as small intestine, blood-brain barrier, kidney, and liver. Therefore, P-glycoprotein mediated drug-drug interactions can occur at various organs and tissues. This chapter will mainly focus on drug-drug interactions that are mediated by the intestinal P-glycoprotein.During the last decade, many in vitro and in vivo studies reported that the induction or inhibition of P-glycoprotein can lead to drug-drug interactions. For instance, induction of the intestinal P-glycoprotein activity can cause reduced bioavailability of orally administered drugs and decreased therapeutic efficacy. On the other hand, the inhibition of the intestinal P-glycoprotein activity can lead to increased bioavailability, thus leading to an increased risk of adverse side effects.     

Hepatic transporter drug-drug interactions: an evaluation of approaches and methodologies

Introduction: Drug-drug interactions (DDIs) continue to account for 5% of hospital admissions and therefore remain a major regulatory concern. Effective, quantitative prediction of DDIs will reduce unexpected clinical findings and encourage projects to frontload DDI investigations rather than concentrating on risk management (‘manage the baggage’) later in drug development. A key challenge in DDI prediction is the discrepancies between reported models.

Areas covered: The current synopsis focuses on four recent influential publications on hepatic drug transporter DDIs using static models that tackle interactions with individual transporters and in combination with other drug transporters and metabolising enzymes. These models vary in their assumptions (including input parameters), transparency, reproducibility and complexity. In this review, these facets are compared and contrasted with recommendations made as to their application.

Expert opinion: Over the past decade, static models have evolved from simple [I]/k_i models to incorporate victim and perpetrator disposition mechanisms including the absorption rate constant, the fraction of the drug metabolised/eliminated and/or clearance concepts. Nonetheless, models that comprise additional parameters and complexity do not necessarily out-perform simpler models with fewer inputs. Further, consideration of the property space to exploit some drug target classes has also highlighted the fine balance required between frontloading and back-loading studies to design out or ‘manage the baggage’.     

In vivo animal models for investigating potential CYP3A- and Pgp-mediated drug-drug interactions

With the advent of polytherapy it has become prudent to minimize, as much as possible, the potential for drug-drug interactions. Towards this end, the metabolic and transporter pathways involved in the disposition of a drug candidate (phenotyping) are evaluated in vitro employing available human tissue and specific reagents. Likewise, in vitro screening for inhibition and induction of drug-metabolizing enzymes and transporters is conducted also. Such in vitro human data can be made available prior to human dosing and enable in vitro to in vivo-based predictions of clinical outcomes. Despite some success, however, in vitro systems are not dynamic and sometimes fail to predict drug-drug interactions for a variety of reasons. In comparison, relatively less effort has been made to evaluate predictions based on data derived from in vivo animal models. This review will attempt to summarize different examples from the literature where animal models have been used to predict cytochrome P450 3A (CYP3A)- and P-glycoprotein (Pgp)-based drug-drug interactions. When employing data from animal models one needs to be aware of species differences in pharmacokinetics, clearance pathways and selectivity and affinity of probe substrates and inhibitors. Because of these differences, in vivo animal studies alone, cannot be predictive of human drug-drug interactions. Despite these caveats, the information obtained from validated in vivo animal models may prove useful when used in conjunction with in vitro-in vivo extrapolation methods. Such an integrated data set can be used to select drug candidates with a reduced drug interaction potential.     

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.     

Kinetics of drug-drug interactions

Since many drug-drug pharmacokinetic interactions are dependent on the concentrations of the interacting species, the degree of interaction should be a graded phenomenon varying with drug and/or metabolite concentration and thus drug administration and time. Hence one should be able to develop predictive kinetic models for such interactions. A change in drug plasma levels when a compound is administered as a single dose together with another drug can arise from a change drug clearance, displacement from binding sites, a change in elimination rates, or a combination of any or all of these possibilities. The interaction of phenobarbital and the sparingly soluble oral antifungal agent, griseofulvin, is one example. Analysis shows that there is no change in elimination half- life of griseofulvin but that phenobarbital reduces the extent of griseofulvin absorption rather than enhances its elimination. Sulfaphenazole inhibits the metabolism and markedly prolongs tolbutamide plasma levels. An anticipated sudden drop in the excretion rate of the tolbutamide metabolites at maximum sulfonamide plasma levels is associated with an almost complete block of tolbutamide oxidation. The inhibitor constant K₁ for this interaction has been calculated, allowing one to predict tolbutamide and metabolite levels when the inhibitor is administered. Drug- drug interaction resulting from protein displacement has been hypothesized by a number of authors. However, the potentiation of the anticoagulant warfarin in patients receiving phenylbutazone is more complicated than has been envisioned previously. While displacement occurs, data suggest that phenylbutazone primarily acts through selective inhibition to alter the isomeric composition and potency of the racemic warfarin administered. The warfarin- phenylbutazone interaction study stresses the importance of measuring metabolites as well as intact drug.Much of the work reported in this paper was supported by a grant from the National Institutes of Health, Bethesda, Maryland, NIGMS 16496.This paper was presented by Dr. Rowland at the Conference on Pharmacology and Pharma-cokinetics; Problems and Perspectives, October 30–November 1, 1972, at the Fogarty International Center, National Institutes of Health, Bethesda, Maryland. This paper, in a slightly different format, will be published in the Proceedings of the Conference by Plenum Press, New York.     

Monitoring of drug-drug and drug-food interactions

A program for detecting and preventing potentially serious drug-drug and drug-food interactions is described. Two clinical pharmacists developed drug interaction alert (DIA) cards for each potential interaction to be monitored. The cards contain information about the proposed mechanism and potential result of the interaction, as well as information about how to monitor or circumvent the interaction. Staff pharmacists check for the occurrence of potential interactions daily as they verify the filling of the patient-medication cassettes; a poster of all the interactions that are included in the program is posted in each satellite pharmacy to serve as a quick reference for the pharmacists. When a pharmacist detects a potential interaction, he or she completes a DIA card and places it in the medication cassette drawer (if the notice is directed to the nurse) or on the front of the patient's chart (if the notice is directed to the physician). The program was introduced to hospital personnel through inservice education programs and departmental newsletters. The results of a quality assurance review indicated that 95 of 279 (34%) cards dispensed to nurses and 40 of 49 (82%) cards dispensed to physicians resulted in some form of action. The program to detect and prevent potentially serious drug-drug and drug-food interactions has been successful.     

Assessment of algorithms for predicting drug-drug interactions via inhibition mechanisms: comparison of dynamic and static models

AIMS

Static and dynamic models (incorporating the time course of the inhibitor) were assessed for their ability to predict drug–drug interactions (DDIs) using a population-based ADME simulator (Simcyp®V8). The impact of active metabolites, dosing time and the ability to predict inter-individual variability in DDI magnitude were investigated using the dynamic model.

METHODS

Thirty-five in vivo DDIs involving azole inhibitors and benzodiazepines were predicted using the static and dynamic model; both models were employed within Simcyp for consistency in parameters. Simulations comprised of 10 trials with matching population demographics and dosage regimen to the in vivo studies. Predictive utility of the static and dynamic model was assessed relative to the inhibitor or victim drug investigated.

RESULTS

Use of the dynamic and static models resulted in comparable prediction success, with 71 and 77% of DDIs predicted within two-fold, respectively. Over 40% of strong DDIs (>five-fold AUC increase) were under-predicted by both models. Incorporation of the itraconazole metabolite into the dynamic model resulted in increased prediction accuracy of strong DDIs (80% within two-fold). Bias and imprecision in prediction of triazolam DDIs were higher in comparison with midazolam and alprazolam; >50% of triazolam DDIs were under-predicted regardless of the model used. Predicted inter-individual variability in the AUC ratio (coefficient of variation of 45%) was consistent with the observed variability (50%).

CONCLUSIONS

High prediction accuracy was observed using both the Simcyp dynamic and static models. The differences observed with the dose staggering and the incorporation of active metabolite highlight the importance of these variables in DDI prediction.     

Identification of serious drug-drug interactions: results of the partnership to prevent drug-drug interactions.

OBJECTIVE: To develop a list of clinically important drug-drug interactions (DDIs) likely to be encountered in community and ambulatory pharmacy settings and detected by a computerized pharmacy system. DESIGN: Cross-sectional, one-time evaluation. SETTING: United States in fall 2001. PARTICIPANTS: An expert panel comprising two physicians, two clinical pharmacists, and an expert on DDIs. INTERVENTIONS: Systematic review of drug interaction compendia and published literature, ratings (on a 1 to 10 scale) of various clinical aspects of DDIs (e.g., clinical importance, quality and quantity of evidence, causal relationship, risk of morbidity and mortality), and a modified Delphi consensus-building process. MAIN OUTCOME MEASURE: Panelists' opinions about clinical importance of DDIs. RESULTS: The expert panel considered 56 DDIs. Of these, 28 had a mean clinical importance score of 8.0 or more. The ratings for clinical importance ranged from 3.2 to 9.6, with a mean +/- SD of 7.5 +/- 1.5 across the combinations examined. The mean score for the quality of literature suggesting the interaction exists ranged from 1.0 to 9.6, with a mean +/- SD of 5.8 +/- 2.5. In terms of substantiation of the interactions evaluated, the mean +/- SD rating was 6.3 +/- 2.2, with a range from 1.4 to 9.2. Through the modified Delphi process, the panel determined that 25 interactions were clinically important. CONCLUSION: Using an expert panel and a standard evaluation tool, 25 clinically important drug interactions that are likely to occur in the community and ambulatory pharmacy settings were identified. Pharmacists should take steps to prevent patients from receiving these interacting medications, and computer software vendors should focus interaction alerts on these and similarly important DDIs.     

Angiotensin receptor blockers and drug-drug interactions

The pharmacokinetic characteristics of the angiotensin receptor blocker class are such that significant drug-drug interactions are unlikely. Moreover, this drug class is devoid of relevant class-specific side effects. These features provide some of the basis for the excellent tolerance of drugs in this class.     

Aspirin,stroke and drug-drug interactions

Low-dose aspirin, alone or in combination, is recommended for the secondary prevention of acute non-cardioembolic ischemic stroke and transient ischemic attack, starting soon after the acute event.Clinically-relevant drug-drug interactions (DDIs) are a major concern of regulatory agencies and practicing physicians. Drug's pharmacodynamics and/or pharmacokinetics account for clinically-relevant DDIs that modify efficacy and/or safety of one or more of the co-administered drugs.Some non-steroidal anti-inflammatory drugs interact with aspirin pharmacodynamics by competing on the drug target, i.e. the platelet's cyclooxygenase-1 protein. Although the molecular mechanism(s) of this DDI and its effect on the degree of platelet inhibition in vitro and ex vivo are well unraveled, nevertheless, the extent to which this DDI impacts on long-term antithrombotic efficacy of aspirin in secondary prevention remains unclear. Aspirin pharmacokinetics does not involve critical cytochrome P450 enzymes nor efflux transporters, therefore clinically-relevant DDIs competing on pharmacokinetic pathways seem unlikely. The co-administration of antiplatelet drugs with serotonin storage reuptake inhibitors can create a synergistic effect with antiplatelet agents on platelet inhibition.Low-dose aspirin, alone or in combination with other antiplatelet agents, remains a cornerstone in treating cerebrovascular disorders. The relatively straightforward pharmacokinetics of aspirin limits DDIs, giving it a unique advantage over most antiplatelet drugs.     

Metabolic drug-drug interactions of chlorphenethiazine and chlorpromazine

Owing to enzyme induction, the pretreatment of female Wistar rats with chlorphenethazine (Marophen, Elroquil) causes a shortening of the hexobarbital sleeping time and an increase in aminophenazone-N-, codeine phosphate-O- and p-nitroanisole-O-demethylation, a slight stimulation of p-nitrophenol-glucuronidation, but no change in aniline hydroxylation in the 9000-g-supernatant of liver homogenates. Comparative studies with chlorpromazine (Propaphenin) showed almost identical inductive activities which were in both cases inferior to that of phenobarbital. Consequently, the occurrence of drug-drug interactions of chlorphenethazine and chlorpromazine cannot be excluded in the framework of combined therapy.     

Ilexsaponin A1: In vitro metabolites identification and evaluation of inhibitory drug-drug interactions

As a triterpene saponin, ilexsaponin A1 is one of the most abundant, representative and active components in plants of Ilex pubescens, used in the treatment of cardiovascular diseases. This study aimed to identify the metabolites of ilexsaponin A1 and evaluate its in vitro inhibitory drug-drug interaction (DDI) potential by using human liver microsomes (HLM) and cytochrome P450 enzymes (CYPs)-specific probes, with all the qualitative and quantitative analysis performed by LC-MS/MS. As a result, two metabolites generated through the metabolic pathways of glucuronic acid conjugation and glucose conjugation were first time detected in the HLM. An inhibitory DDI evaluating system consisting of 7 major CYP enzymes involving 8 CYP-catalyzed reactions was established, validated and then used for the DDI evaluation. Our data suggested ilexsaponin A1 and its metabolite, ilexgenin A, are not direct or mechanism-based inhibitors of CYP1A2, 2B6, 2C8, 2C9, 2D6, 2E1 or 3A4/5 at 0.05–10 μM. A significant decreased remaining activity of CYP2B6 (from 77.89 % to 23.19 %) was observed in a dose-dependent manner when increased the concentration of ilexsaponin A1 from 50 to 500 μM. Collectively, our data demonstrate ilexsaponin A1 is unlikely to cause DDIs by inhibiting co-administered drugs metabolized by these CYP enzymes.