Integrated Use of In Vitro and In Vivo Information for Comprehensive Prediction of Drug Interactions Due to Inhibition of Multiple CYP Isoenzymes

BACKGROUND

Mechanistic static pharmacokinetic (MSPK) models are simple, have fewer data requirements, and have broader applicability; however, they cannot use in vitro information and cannot distinguish the contributions of multiple cytochrome P450 (CYP) isoenzymes and the hepatic and intestinal first-pass effects appropriately. We aimed to establish a new MSPK analysis framework for the comprehensive prediction of drug interactions (DIs) to overcome these disadvantages.

METHODS

Drug interactions that occurred by inhibiting CYP1A2, CYP2C9, CYP2C19, CYP2D6, and CYP3A in the liver and CYP3A in the intestine were simultaneously analyzed for 59 substrates and 35 inhibitors. As in vivo information, the observed changes in the area under the concentration-time curve (AUC) and elimination half-life (t1/2), hepatic availability, and urinary excretion ratio were used. As in vitro information, the fraction metabolized (fm) and the inhibition constant (Ki) were used. The contribution ratio (CR) and inhibition ratio (IR) for multiple clearance pathways and hypothetical volume (VHyp) were inferred using the Markov Chain Monte Carlo (MCMC) method.

RESULT

Using in vivo information from 239 combinations and in vitro 172 fm and 344 Ki values, changes in AUC, and t1/2 were estimated for all 2065 combinations, wherein the AUC was estimated to be more than doubled for 602 combinations. Intake-dependent selective intestinal CYP3A inhibition by grapefruit juice has been suggested. By separating the intestinal contributions, DIs after intravenous dosing were also appropriately inferred.

CONCLUSION

This framework would be a powerful tool for the reasonable management of various DIs based on all available in vitro and in vivo information.

Evaluation of Renal Impairment Influence on Metabolic Drug Clearance using a Modelling Approach

BACKGROUND AND OBJECTIVE

Chronic kidney disease (CKD) may alter drug renal elimination but is also known for interacting with hepatic metabolism via multiple uremic components. However, few global models, considering the five major cytochromes, have been published, and none specifically address the decrease in cytochrome P450 (CYP450) activity. The aim of our study was to estimate the possibility of quantifying residual cytochrome activity as a function of filtration rate, according to the data available in the literature.

METHODS

For each drug in the DDI-predictor database, we collected available pharmacokinetic data comparing drug exposition in the healthy patient and in various stages of CKD, before building a model capable of predicting the variation of exposure according to the degree of renal damage. We followed an In vivo Mechanistic Static Model (IMSM) approach, previously validated for predicting change in liver clearance. We estimated the remaining fraction parameters at glomerular filtration rate (GFR) = 0 and the alpha value of GFR to 50% impairment for the 5 major cytochromes using a non-linear constrained regression using Matlab software.

RESULTS

Thirty-one compounds had usable pharmacokinetic data, with 51 AUC ratios between healthy and renal impaired patients. The remaining CYP3A4 activity was estimated to be 0.4 when CYP2D6, 2C9, 2C19 and 1A2 activity was estimated to be 0.43; 1; 0.73 and 0.7, respectively. The alpha value was estimated to be at 6.62; 25; 9.8; 1.38 and 11.04 for each cytochrome. In comparison with published data, all estimates but one were correctly predicted in the range of 0.5-2.

CONCLUSION

Our approach was able to describe the impact of CKD on metabolic elimination. Modelling this process makes it possible to anticipate changes in clearance and drug exposure in CKD patients, with the advantage of greater simplicity than approaches based on physiologically-based pharmacokinetic modelling. However, a precise estimation of the impact of renal failure is not possible with an IMSM approach due to the large variability of the published data, and thus should rely on specific pharmacokinetic modelling for narrow therapeutic margin drugs.

Safety implications of concomitant administration of antidepressants and opioid analgesics in surgical patients

BACKGROUND

Commonly prescribed antidepressants (paroxetine, fluoxetine, duloxetine, bupropion) inhibit bioconversion of several prodrug opioid medications to their active metabolite, potentially decreasing analgesic effect. There is a paucity of studies assessing the risk-benefit of concomitant administration of antidepressants and opioids.

RESEARCH DESIGN AND METHODS

Observational study of adult patients taking antidepressants prior to scheduled surgery using 2017-2019 electronic medical record data to assess perioperative use of opioids and to determine the incidence and risk factors for developing postoperative delirium. We conducted a generalized linear regression with the Gamma log-link to assess the association between use of antidepressants and opioids and a logistic regression to assess the association between antidepressants use and the likelihood of developing postoperative delirium.

RESULTS

After controlling for patient demographic and clinical characteristics, and postoperative pain, use of inhibiting antidepressants was associated with 1.67 times greater use of opioids per hospitalization day (p = 0.00154), a two-fold increase in the risk for developing postoperative delirium (p = 0.0224), and an estimated average of four additional days of hospitalization (p < 0.00001) compared to use of non-inhibiting antidepressants.

CONCLUSIONS

Careful consideration to drug-drug interactions and risk of related adverse events remains critical in the safe and optimal management of postoperative pain in patients taking concomitantly antidepressants.

Quantitative Prediction of Drug Interactions Caused by Cytochrome P450 2B6 Inhibition or Induction

BACKGROUND

Numerous drugs have the potential to be affected by cytochrome P450 (CYP) 2B6-mediated drug-drug interactions (DDIs).

OBJECTIVES

In this work, we extend a static approach to the prediction of the extent of pharmacokinetics DDIs between substrates and inhibitors or inducers of CYP2B6.

METHODS

This approach is based on the calculation of two parameters (the contribution ratio [CR], representing the fraction of dose of the substrate metabolized via this pathway and the inhibitory or inducing potency of the perpetrator [IR or IC, respectively]) calculated from the area under the concentration-time curve (AUC) ratios obtained in in-vivo DDI studies.

RESULTS

Forty-eight studies involving 5 substrates, 11 inhibitors and 18 inducers of CYP2B6 (overall 15 inhibition and 33 induction studies) were divided into test and validation sets and considered for estimation of the parameters. The proposed approach demonstrated a fair accuracy for predicting the extent of DDI related to CYP2B6 inhibition and induction, all predictions related to the validation test (N = 18) being 50-200% of the observed ratios.

CONCLUSIONS

This methodology can be used for proposing initial dose adaptations to be adopted, for example in clinical use or for designing DDI studies involving this enzyme.

Quantitative Prediction of Adverse Event Probability Due to Pharmacokinetic Interactions

INTRODUCTION

Iatrogeny due to drug-drug interactions is insufficiently documented, due to the high number of possible combinations.

OBJECTIVE

This study aimed to design a simple but general method to predict the variation of adverse events (AE) frequency due to a pharmacokinetic or pharmacodynamic interaction.

METHODS

Three prediction models were designed using a logistic probability density function. Each prediction model was based on three components: the AE odds ratio of each drug in the combination, and the area under the curve ratio (Rauc) of the pharmacokinetic interaction, if any. Pharmacodynamic interaction was assumed to be additive on logit scale. Rauc was predicted using a well-validated mechanistic static model, freely available online. No combination study is required. The method was evaluated against a wide range of AEs (28 High Level Terms) and 211 drug combinations (involving 43 victim drugs and 55 perpetrators), by comparing the observed and predicted frequencies. The observed odds ratios were estimated with a disproportionality analysis from the FDA Adverse Event Reporting System, using an approach that minimizes biases.

RESULTS

With the best model, the rate of prediction considered as correct (within 50-200% of the observed value) was 72%, and the bias was negligible (-5%). The AE odds ratio due to pharmacokinetic and pharmacodynamic interactions was equally well predicted.

CONCLUSIONS

A simple workflow to implement the method in practice is proposed. This method may help to foresee and to anticipate the harmful consequences associated with drug-drug interactions, at virtually no experimental cost, when the odds ratio of an AE is known for each drug alone and the AUC ratio is known or predicted by a suitable model.

Drug-drug Interactions of Artemisinin-based Combination Therapies in Malaria Treatment: A narrative review of the literature

Artemisinin is an antimalarial compound derived from the plant Artemisia annua L., also known as sweet wormwood. According to the World Health Organization, artemisinin-based combination therapy (ACT) is an essential treatment for malaria, specifically Plasmodium falciparum, which accounts for most of malaria related mortality. ACT used to treat uncomplicated malaria include artemether-lumefantrine, artesunate-amodiaquine, artesunate-mefloquine, artesunate-sulphadoxine-pyrimethamine, and dihydroartemisinin-piperaquine. Although the mechanism of action and clinical capabilities of artemisinin in malaria treatment are widely known, more information on the potential for drug interactions needs to be further investigated. Some studies show pharmacokinetic and pharmacodynamic drug interactions with HIV-antiviral treatment but few studies have been conducted on most other drug classes. Based on known genotypes of cytochrome P450 (CYP) enzymes, CYP2B6 and CYP3A are primarily involved in the metabolism of artemisinin and its derivatives. Reduced functions in these enzymes can lead to subtherapeutic concentrations of the active metabolite dihydroartemisinin that may cause treatment failure which has been shown in some studies with cardiovascular, antibiotic, and antiparasitic drugs. Although the clinical importance remains unclear to date, clinicians should be aware of potential drug-drug interactions and monitor patients on ACT closely. This article is protected by copyright. All rights reserved.

Inhibitory effects of fluoxetine and duloxetine on the pharmacokinetics of metoprolol in vivo and in vitro

Depression is common among people with cardiovascular diseases. Therefore, the combined use of antidepressants and cardiovascular drugs is very common, which increases the possibility of drug interaction. Simultaneously compare the effects of duloxetine and fluoxetine on metoprolol metabolism, and provide evidence-based guidance for medication safety. Sprague-Dawley rats were randomly divided into three groups: group A (10.3 mg/kg metoprolol alone), group B (10.3 mg/kg metoprolol + 6.2 mg/kg fluoxetine), and group C (10.3 mg/kg metoprolol + 6.2 mg/kg duloxetine). Tail vein blood was collected and subjected to the ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) detection. Moreover, in vitro inhibition of fluoxetine and duloxetine were assessed by incubating liver microsomes and CYP2D6.1 with metoprolol. In in vivo study, the administration of fluoxetine or duloxetine significantly increased the AUC_(0-𝑡) and AUC_(0-∞) of metoprolol (P < 0.05). Differences between fluoxetine and duloxetine in plasma concentration were also investigated, and their pharmacokinetic parameters such as AUC_(0-𝑡) and AUC_(0-∞) were significantly distinct (P < 0.05). In vitro, fluoxetine and duloxetine inhibited the metabolism of metoprolol via mixed competitive mechanism of cytochrome P450. IC₅₀ values of fluoxetine and duloxetine were 12.86 and 2.51 μM, respectively. Moreover, the metabolism rate of metoprolol was inhibited to 19.62% and 17.14% in recombinant human CYP2D6.1 by fluoxetine and duloxetine, respectively. Duloxetine showed a more significant inhibitory potential compared to fluoxetine in vitro, but the main pharmacokinetic parameters of fluoxetine and duloxetine revealed differences in inhibiting metoprolol metabolism in vivo.

A Quantitative Approach to the Prediction of Drug-Drug Interactions Mediated by Cytochrome P450 2C8 Inhibition

BACKGROUND

Ohno and Colleagues proposed an approach for predicting drug-drug interactions (DDIs) mediated by cytochrome P450 (CYP) 3A4 based on the use of the ratio of the inhibited to non-inhibited area under the plasma concentration time curve (AUC) of substrates to estimate the fraction of the dose metabolized via CYP3A4 (contribution ratio, CR) and the in vivo inhibitory potency of a perpetrator (inhibition ratio, IR). This study evaluated the performance of this approach on DDIs mediated by CYP2C8 inhibitors.

RESEARCH DESIGN AND METHODS

Initial estimates of CR and IR of CYP2C8 substrates and inhibitors were calculated for 33 DDI in vivo studies. The approach was externally validated with 17 additional studies. Bayesian orthogonal regression was used to refine the estimates of the parameters. Assessment of prediction success was conducted by plotting observed versus predicted AUC ratios.

RESULTS

Final estimates of CRs and IRs were obtained for 19 CYP2C8 substrates and 23 inhibitors, respectively. The method demonstrated good predictive capacity, with only two values outside of the prespecified limits.

CONCLUSIONS

The approach may help to adapt dose regimens for CYP2C8 substrates when given in combination with CYP2C8 inhibitors and to map the potential DDIs of new molecular entities.

Model-based comparative analysis of rifampicin and rifabutin drug-drug interaction profile

Rifamycins are widely used for treating mycobacterial and staphylococcal infections. Drug-drug interactions (DDI) caused by rifampicin (RIF) is a major issue. We used a model-based approach to predict the magnitude of DDI with RIF and rifabutin (RBT) for 217 cytochrome P450 (CYP) substrates. On average, DDI caused by low-dose RIF were twice more potent than those caused by RBT. Contrary to RIF, RBT appears unlikely to cause severe DDI, even with sensitive CYP substrates.

Influence of Cation Transporters (OCTs and MATEs) on the Renal and Hepatobiliary Disposition of [11C]Metoclopramide in Mice

PURPOSE

To investigate the role of cation transporters (OCTs, MATEs) in the renal and hepatic disposition of the radiolabeled antiemetic drug [11C]metoclopramide in mice with PET.

METHODS

PET was performed in wild-type mice after administration of an intravenous microdose (<1 μg) of [11C]metoclopramide without and with co-administration of either unlabeled metoclopramide (5 or 10 mg/kg) or the prototypical cation transporter inhibitors cimetidine (150 mg/kg) or sulpiride (25 mg/kg). [11C]Metoclopramide PET was also performed in wild-type and Slc22a1/2(-/-) mice. Radiolabeled metabolites were measured at 15 min after radiotracer injection and PET data were corrected for radiolabeled metabolites.

RESULTS

[11C]Metoclopramide was highly metabolized and [11C]metoclopramide-derived radioactivity was excreted into the urine. The different investigated treatments decreased (~2.5-fold) the uptake of [11C]metoclopramide from plasma into the kidney and liver, inhibited metabolism and decreased (up to 3.8-fold) urinary excretion, which resulted in increased plasma concentrations of [11C]metoclopramide. Kidney and liver uptake were moderately (~1.3-fold) reduced in Slc22a1/2(-/-) mice.

CONCLUSIONS

Our results suggest a contribution of OCT1/2 to the kidney and liver uptake and of MATEs to the urinary excretion of [11C]metoclopramide in mice. Cation transporters may contribute, next to variability in the activity of metabolizing enzymes, to variability in metoclopramide pharmacokinetics and side effects.

A Scoping Review of the Evidence Behind Cytochrome P450 2D6 Isoenzyme Inhibitor Classifications

The US Food and Drug Administration (FDA) lists 22 medications as clinical inhibitors of cytochrome P450 2D6 isoenzyme, with classifications of strong, moderate, and weak. It is accepted that strong inhibitors result in nearly null enzymatic activity, but reduction caused by moderate and weak inhibitors is less well characterized. The objective was to identify if the classification of currently listed FDA moderate and weak inhibitors is supported by publicly available primary literature. We conducted a literature search and reviewed product labels for area under the plasma concentration-time curve (AUC) fold-changes caused by inhibitors in humans and identified 89 inhibitor-substrate pairs. Observed AUC fold-change of the substrate was used to create an observed inhibitor classification per FDA-defined AUC fold-change thresholds. We then compared the observed inhibitor classification with the classification listed in the FDA Table of Inhibitors. We found 62% of the inhibitors within the pairs matched the listed FDA classification. We explored reasons for discordance and suggest modifications to the FDA table of clinical inhibitors for cimetidine, desvenlafaxine, and fluvoxamine.

Identification of Cytochrome P450-Mediated Drug-Drug Interactions at Risk in Cases of Gene Polymorphisms by Using a Quantitative Prediction Model

BACKGROUND AND OBJECTIVE

The magnitude of drug-drug interactions mediated by cytochrome P450 (CYP) may depend on the genotype of polymorphic cytochromes. The objective of this study was to identify drug-drug interactions with greater magnitude in CYP variant groups than in extensive metabolizers.

METHODS

The in-vivo mechanistic static model was used to predict the area under the curve ratio of drug-drug interactions. Five cytochromes (CYP3A4/5, 2D6, 2C9, 2C19, 1A2) and five groups of genotypes for each polymorphic cytochrome (CYP2D6, 2C9, 2C19) were considered. The area under the curve ratios were calculated for all combinations and all genotypes for 196 substrates and 96 inhibitors. Among the strongest interactions (area under the curve ratio greater than 5), two levels of gene sensitivity of drug-drug interactions were defined: the intermediate sensitivity, with a three- to five-fold stronger interaction in genotype groups other than in extensive metabolizers, and the high sensitivity, with a more than five-fold stronger interaction than in genotype groups other than extensive metabolizers.

RESULTS

A red list of 104 interactions with a sensitivity greater than 3, involving 13 substrates and 24 interactors was obtained. There were 59 and 45 cases of high and intermediate sensitivity, respectively. The genotypes associated with a high sensitivity were CYP2D6 *3-8 *3-8 (sensitivity up to 24.3) and CYP2C19 *2-3*2-3 (sensitivity up to 37.8).

CONCLUSIONS

A cytochrome polymorphism may lead to major drug-drug interactions in poor metabolizers, while these interactions may not be significant in extensive metabolizers. Among the 104 cases studied, the interaction could be of ca. 30-fold larger magnitude in the worst case. Genotyping of the patient and/or therapeutic drug monitoring of the substrate should be carried out when an association mentioned in the red list is prescribed. The concept of gene sensitivity of drug-drug interactions appears promising for the development of precision medicine.

Efficacy of Tramadol for Pain Management in Patients Receiving Strong Cytochrome P450 2D6 Inhibitors

STUDY OBJECTIVE

Tramadol is metabolized by cytochrome P450 (CYP) 2D6 to form an active metabolite that exhibits its analgesic effect. Medications that inhibit this enzyme are used often in practice, yet the clinical impact of this interaction on the analgesic effects of tramadol has yet to be fully described. The objective was to determine whether a clinically relevant decrease in pain control is observed in patients taking scheduled tramadol concomitantly with a strong CYP2D6 inhibitor.

DESIGN

Retrospective cohort study.

SETTING

Large health care system.

PATIENTS

One hundred fifty-two adult inpatients who received scheduled tramadol for at least 24 hours with (76 patients) or without (76 patients) a strong CYP2D6 inhibitor between January 1, 2012, and February 28, 2017, were included in the analysis. Patients hospitalized for opioid use disorder or those receiving substandard dosing of tramadol were excluded.

MEASUREMENTS AND MAIN RESULTS

The primary outcome was mean breakthrough opiate consumption in the presence and absence of CYP2D6 inhibitors. Secondary outcomes included mean pain scores, length of hospital stay, tramadol discontinuation rates, and prespecified subgroup analyses based on patient sex, race, and specific CYP2D6 inhibitor administered. Patients receiving concurrent CYP2D6 inhibitors required significantly more breakthrough morphine milligram equivalents per day compared with patients receiving scheduled tramadol without CYP2D6 inhibitors (geometric mean ± SD 18.2 ± 6.3 vs 5.7 ± 6.7 mg morphine milligram equivalents, p<0.001). No significant differences existed between cohorts for mean pain score, length of hospital stay, or tramadol discontinuation rate.

CONCLUSION

This study demonstrated a clinically relevant decrease in the efficacy of tramadol when used for pain control in patients receiving a strong CYP2D6 inhibitor. These results should encourage clinicians to review medication lists for this interaction and adjust regimens accordingly to ensure adequate pain control.

Analysis of Clinical Drug-Drug Interaction Data To Predict Magnitudes of Uncharacterized Interactions between Antiretroviral Drugs and Comedications

Despite their high potential for drug-drug interactions (DDI), clinical DDI studies of antiretroviral drugs (ARVs) are often lacking, because the full range of potential interactions cannot feasibly or pragmatically be studied, with some high-risk DDI studies also being ethically difficult to undertake. Thus, a robust method to screen and to predict the likelihood of DDIs is required. We developed a method to predict DDIs based on two parameters: the degree of metabolism by specific enzymes, such as CYP3A, and the strength of an inhibitor or inducer. These parameters were derived from existing studies utilizing paradigm substrates, inducers, and inhibitors of CYP3A to assess the predictive performance of this method by verifying predicted magnitudes of changes in drug exposure against clinical DDI studies involving ARVs. The derived parameters were consistent with the FDA classification of sensitive CYP3A substrates and the strength of CYP3A inhibitors and inducers. Characterized DDI magnitudes (n = 68) between ARVs and comedications were successfully quantified, meaning 53%, 85%, and 98% of the predictions were within 1.25-fold (0.80 to 1.25), 1.5-fold (0.66 to 1.48), and 2-fold (0.66 to 1.94) of the observed clinical data. In addition, the method identifies CYP3A substrates likely to be highly or, conversely, minimally impacted by CYP3A inhibitors or inducers, thus categorizing the magnitude of DDIs. The developed effective and robust method has the potential to support a more rational identification of dose adjustment to overcome DDIs, being particularly relevant in an HIV setting, given the treatment's complexity, high DDI risk, and limited guidance on the management of DDIs.

[Quantitative Prediction of Drug-Drug Interaction Caused by CYP Inhibition and Induction from In Vivo Data and Its Application in Daily Clinical Practices-Proposal for the Pharmacokinetic Interaction Significance Classification System (PISCS)]

　Drug-drug interactions (DDIs) can affect the clearance of various drugs from the body; however, these effects are difficult to sufficiently evaluate in clinical studies. This article outlines our approach to improving methods for evaluating and providing drug information relative to the effects of DDIs. In a previous study, total exposure changes to many substrate drugs of CYP caused by the co-administration of inhibitor or inducer drugs were successfully predicted using in vivo data. There are two parameters for the prediction: the contribution ratio of the enzyme to oral clearance for substrates (CR), and either the inhibition ratio for inhibitors (IR) or the increase in clearance of substrates produced by induction (IC). To apply these predictions in daily pharmacotherapy, the clinical significance of any pharmacokinetic changes must be carefully evaluated. We constructed a pharmacokinetic interaction significance classification system (PISCS) in which the clinical significance of DDIs was considered in a systematic manner, according to pharmacokinetic changes. The PISCS suggests that many current 'alert' classifications are potentially inappropriate, especially for drug combinations in which pharmacokinetics have not yet been evaluated. It is expected that PISCS would contribute to constructing a reliable system to alert pharmacists, physicians and consumers of a broad range of pharmacokinetic DDIs in order to more safely manage daily clinical practices.

Analysis of the Mechanism of Prolonged Persistence of Drug Interaction between Terbinafine and Amitriptyline or Nortriptyline

The purpose of the study was to quantitatively estimate and predict drug interactions between terbinafine and tricyclic antidepressants (TCAs), amitriptyline or nortriptyline, based on in vitro studies. Inhibition of TCA-metabolizing activity by terbinafine was investigated using human liver microsomes. Based on the unbound K_i values obtained in vitro and reported pharmacokinetic parameters, a pharmacokinetic model of drug interaction was fitted to the reported plasma concentration profiles of TCAs administered concomitantly with terbinafine to obtain the drug-drug interaction parameters. Then, the model was used to predict nortriptyline plasma concentration with concomitant administration of terbinafine and changes of area under the curve (AUC) of nortriptyline after cessation of terbinafine. The CYP2D6 inhibitory potency of terbinafine was unaffected by preincubation, so the inhibition seems to be reversible. Terbinafine competitively inhibited amitriptyline or nortriptyline E-10-hydroxylation, with unbound K_i values of 13.7 and 12.4 nM, respectively. Observed plasma concentrations of TCAs administered concomitantly with terbinafine were successfully simulated with the drug interaction model using the in vitro parameters. Model-predicted nortriptyline plasma concentration after concomitant nortriptylene/terbinafine administration for two weeks exceeded the toxic level, and drug interaction was predicted to be prolonged; the AUC of nortriptyline was predicted to be increased by 2.5- or 2.0- and 1.5-fold at 0, 3 and 6 months after cessation of terbinafine, respectively. The developed model enables us to quantitatively predict the prolonged drug interaction between terbinafine and TCAs. The model should be helpful for clinical management of terbinafine-CYP2D6 substrate drug interactions, which are difficult to predict due to their time-dependency.

Potential drug-drug interactions with abiraterone in metastatic castration-resistant prostate cancer patients: a prevalence study in France

PURPOSE

Abiraterone acetate combined with prednisone improves survival in metastatic castration-resistant prostate cancer (mCRPC) patients. This oral anticancer agent may result in drug-drug interactions (DDI). We aimed to evaluate the prevalence of DDI with abiraterone and the possible determinants for the occurrence of these DDI.

METHODS

We performed a single centre retrospective review from electronic medical records of mCRPC patients treated with abiraterone from 2011 to 2015. Potential DDI with abiraterone were identified using Micromedex and were categorized by a 4-point scale severity.

RESULTS

Seventy-two out of ninety-five mCRPC pts (median age: 77 years [68-82]) had comorbidities. The median number of drugs used per patient was 7 [5-9]. 66 potential DDI with abiraterone were detected in 49 patients (52%): 39 and 61% were classified as major and moderate DDI, respectively. In the univariate analysis, pain (p < 0.0001), hypo-albuminemia (p = 0.032), and higher ECOG performance status (PS) (p = 0.013) were significantly associated with a higher risk of DDI with abiraterone. Pain (p < 0.0001) and PS (p = 0.018) remained significant in the multivariate analysis.

CONCLUSIONS

Polypharmacy is an issue among mCRPC patients. In our study, half of the patients have potential DDI with abiraterone. Patients with pain and poor PS are at higher risk of DDI with abiraterone. A medication review by a pharmacist is of crucial importance to prevent DDI with abiraterone.

A Model for Predicting the Interindividual Variability of Drug-Drug Interactions

Pharmacokinetic drug-drug interactions are frequently characterized and quantified by an AUC ratio (Rauc). The typical value of the AUC ratio in case of cytochrome-mediated interactions may be predicted by several approaches, based on in vitro or in vivo data. Prediction of the interindividual variability of Rauc would help to anticipate more completely the consequences of a drug-drug interaction. We propose and evaluate a simple approach for predicting the standard deviation (sd) of Ln(Rauc), a metric close to the interindividual coefficient of variation of Rauc. First, a model was derived to link sd(Ln Rauc) with the substrate fraction metabolized by each cytochrome and the potency of the interactors, in case of induction or inhibition. Second, the parameters involved in these equations were estimated by a Bayesian hierarchical model, using the data from 56 interaction studies retrieved from the literature. Third, the model was evaluated by several metrics based on the fold prediction error (PE) of sd(Ln Rauc). The median PE was 0.998 (the ideal value is 1) and the interquartile range was 0.96-1.03. The PE was in the acceptable interval (0.5 to 2) in 52 cases out of 56. Fourth, a surface plot of sd(Ln Rauc) as a function of the characteristics of the substrate and the interactor has been built. The minimal value of sd(Ln Rauc) was about 0.08 (obtained for Rauc = 1) while the maximal value, 0.7, was obtained for interactions involving highly metabolized substrates with strong interactors.

Comparison of the static in vivo approach to a physiologically based pharmacokinetic approach for metabolic drug–drug interactions prediction

Background: The in vivo mechanistic static model (IMSM) and the physiologically based pharmacokinetic (PBPK) model are two approaches used to predict the magnitude of drug–drug interactions (DDIs). The aim of this study was to evaluate the performance of IMSM and to compare IMSM with the PBPK approach implemented in Simcyp. Methods: The predictive performances of IMSM were evaluated on a panel of 628 DDIs. Subsequently, the IMSM and PBPK approaches were compared on a set of 104 DDIs. Results: The IMSM yielded 85% of predictions within 1.5-fold of the observed value on the 628 DDIs panel. The predictive performances of IMSM were better than those of the PBPK approach (median fold error 1 vs 0.86 on 104 studies; p = 0.02). Conclusion: The IMSM approach is an alternative tool for metabolic DDIs prediction.

A Prediction Model of Drug Exposure in Cirrhotic Patients According to Child-Pugh Classification

BACKGROUND AND OBJECTIVE

Prediction of drug clearance in liver cirrhosis patients is currently based on in vitro-in vivo extrapolation and physiologically-based pharmacokinetic models. No static model for this purpose has been described. The objectives of this study were to (1) derive a static model for predicting drug exposure in cirrhotic patients, and (2) to evaluate the model on a large set of published data.

METHODS

The impact of cirrhosis was characterized by the ratio of the total and unbound drug area under the concentration-time curve (AUC) in cirrhotic patients to the AUC measured in healthy subjects These ratios were predicted for Child-Pugh classes A, B, and C. The AUC ratios observed in published data were compared with AUC ratios predicted by the model.

RESULTS

Among 171 drugs examined, 83 published AUC ratios for 45 drugs in cirrhotic patients were available for analysis. The mean ± standard deviation relative prediction error for the total and unbound AUC ratios was 0.22 ± 0.58 and 0.24 ± 0.56, respectively. There were four outliers among the 83 predicted values. Simulations showed that the prediction error was negligible provided that the hepatic extraction coefficient was less than 0.8.

CONCLUSIONS

For mild and moderate cirrhosis (classes A and B), the predicted unbound AUC ratio is typically approximately 2 and 3.5, respectively, for most drugs. In the absence of data in cirrhotic patients, the drug dose might be empirically reduced by these factors. In severe cirrhosis (class C), our model may help clinicians to adjust their prescriptions.

Pre-clinical evaluation of CYP 2D6 dependent drug-drug interactions between primaquine and SSRI/SNRI antidepressants

Background

The liver-stage anti-malarial activity of primaquine and other 8-aminoquinoline molecules has been linked to bio-activation through CYP 2D6 metabolism. Factors such as CYP 2D6 poor metabolizer status and/or co-administration of drugs that inhibit/interact with CYP 2D6 could alter the pharmacological properties of primaquine.

Methods

In the present study, the inhibitory potential of the selective serotonin reuptake inhibitor (SSRI) and serotonin norepinephrine reuptake inhibitor (SNRI) classes of antidepressants for CYP 2D6-mediated primaquine metabolism was assessed using in vitro drug metabolism and in vivo pharmacological assays.

Results

The SSRI/SNRI classes of drug displayed a range of inhibitory activities on CYP 2D6-mediated metabolism of primaquine in vitro (IC₅₀ 1–94 μM). Fluoxetine and paroxetine were the most potent inhibitors (IC₅₀ ~1 µM) of CYP 2D6-mediated primaquine metabolism, while desvenlafaxine was the least potent (IC₅₀ ~94 µM). The most potent CYP 2D6 inhibitor, fluoxetine, was chosen to investigate the potential pharmacological consequences of co-administration with primaquine in vivo. The pharmacokinetics of a CYP 2D6-dependent primaquine metabolite were altered upon co-administration with fluoxetine. Additionally, in a mouse malaria model, co-administration of fluoxetine with primaquine reduced primaquine anti-malarial efficacy.

Conclusions

These results are the first from controlled pre-clinical experiments that indicate that primaquine pharmacological properties can be modulated upon co-incubation/administration with drugs that are known to interact with CYP 2D6. These results highlight the potential for CYP 2D6-mediated drug–drug interactions with primaquine and indicate that the SSRI/SNRI antidepressants could be used as probe molecules to address the primaquine-CYP 2D6 DDI link in clinical studies. Additionally, CYP 2D6-mediated drug–drug interactions can be considered when examining the possible causes of human primaquine therapy failures.

Electronic supplementary material

The online version of this article (doi:10.1186/s12936-016-1329-z) contains supplementary material, which is available to authorized users.

How Informative Are Drug-Drug Interactions of Gene-Drug Interactions?

FDA recommendations to manage polymorphic CYP-mediated drug-drug interactions (DDIs) and gene-drug interactions (GDIs) are typically similar. However, DDIs may not always reliably predict GDIs because the victim drug may have multiple metabolic pathways and the perpetrator drug may affect multiple enzymes or transporters. Consequently, it is of great interest to both the pharmaceutical industry and regulatory agencies to determine if DDI studies can be leveraged to inform GDIs or vice versa for dose adjustment and labeling. The objective of this study was to investigate under what circumstances DDIs can be used to predict GDIs for prototypical CYP2C9, CYP2C19, and CYP2D6 substrates. We investigated model substrates for CYP2D6 (metoprolol, dextromethorphan, atomoxetine, and vortioxetine), CYP2C9 (warfarin, flurbiprofen, and celecoxib), and CYP2C19 (omeprazole and clopidogrel). Data on drug exposure for poor metabolizers (GDI) and for DDIs mediated by strong/moderate inhibitors in extensive metabolizers were collected. The impact of DDIs and GDIs on drug exposure was compared using: (1) a descriptive and (2) a physiologically based pharmacokinetic convergence analysis. Results from both approaches indicate that information on DDIs can be used to reliably predict GDIs for CYP2D6 substrates. The situation is more complex for CYP2C9 and CYP2C19 substrates because dose of the inhibitor (CYP2C9) and potency of the inhibitor (CYP2C19) impact the extent to which perpetrator drugs phenotypically convert extensive metabolizers to poor(er) metabolizers.

Reliability and extension of quantitative prediction of CYP3A4-mediated drug interactions based on clinical data

An approach was proposed in 2007 for quantitative predictions of cytochrome P450 (CYP)3A4-mediated drug-drug interactions. It is based on two characteristic parameters: the contribution ratio (CR; i.e., the fraction of victim drug clearance by CYP) and the inhibition ratio (IR) of the inhibitor. Knowledge of these parameters allows forecasting of the ratio between the area under the plasma concentration-time curve (AUC) of the victim drug when given with the inhibitor and the AUC of the victim drug when it is given alone. So far, these parameters were established for 21 substrates and 17 inhibitors. The goals of our study were to test the assumption of substrate independence of the potency of inhibitors in vivo and to estimate the CR and IR for an extended list of substrates and inhibitors of CYP3A4. The assumption of independence of IRs from the substrate was evaluated on a set of eight victim drugs and eight inhibitors. Forty-four AUC ratios were available. This assumption was rejected in four cases, but it did not result in more than a twofold error in AUC ratio predictions. The extended list of substrates and inhibitors was defined by a thorough literature search. Fifty-nine AUC ratios were available for the global analysis. Final estimates of CRs and IRs were obtained for 37 substrates and 25 inhibitors, respectively. The mean prediction error of the ratios was 0.02, while the mean absolute prediction error was 0.58. Predictive distributions for 917 possible interactions were obtained, giving detailed information on some drugs or inhibitors that have been poorly studied so far.

Pharmacokinetic interaction between pyronaridine-artesunate and metoprolol

The objectives of this study were to characterize any drug-drug interaction between the antimalarial Pyramax (pyronaridine-artesunate [PA]) and the CYP2D6 probe substrate metoprolol and to assess the safety of 60-day or 90-day PA redosing, particularly with regard to liver biochemistry parameters. Healthy adult subjects were randomized to arm A (n = 26) or arm B (n = 30), with the arm A subjects administered 100 mg metoprolol tartrate in the first period, 100 mg metoprolol tartrate with the third of three daily doses of PA in the second period, and three daily doses of PA alone in the 90-day redosing period. The arm B subjects received the three-day PA regimen in the first period, with redosing of the regimen after 60 days in the second period. The noncompartmental pharmacokinetic parameters were computed for metoprolol, its metabolite alpha-hydroxymetoprolol, and pyronaridine. The coadministration of metoprolol and PA was associated with an average 47.93% (90% confidence interval [CI], 30.52, 67.66) increase in the maximum concentration of metoprolol and a 25.60% (90% CI, 15.78, 36.25) increase in the metoprolol area under the concentration-time curve from time zero to the last quantifiable concentration obtained (AUC0-t); these increases most likely resulted from pyronaridine-mediated CYP2D6 inhibition. No interaction effect of metoprolol with pyronaridine was apparent. Following dosing with PA, some subjects experienced rises in liver function tests above the upper limit of normal during the first few days following PA administration. All such elevations resolved typically within 10 days, and up to 30 days at most. In subjects who were redosed, the incidences of alanine aminotransferase (ALT) or aspartate transaminase (AST) level elevations were similar on the first and second administrations, with no marked difference between the 60-day and 90-day redosing.

Impact of genetic polymorphism on drug-drug interactions mediated by cytochromes: a general approach

Currently, quantitative prediction of the impact of genetic polymorphism and drug-drug interactions mediated by cytochromes, based on in vivo data, is made by two separate methods and restricted to a single cytochrome. We propose a unified approach for describing the combined impact of drug-drug interactions and genetic polymorphism on drug exposure. It relies on in vivo data and uses the following three characteristic parameters: one for the victim drug, one for the interacting drug, and another for the genotype. These parameters are known for a wide range of drugs and genotypes. The metrics of interest are the ratio of victim drug area under the curve (AUC) in patients with genetic variants taking both drugs, to the AUC in patients with either variant or wild-type genotype taking the victim drug alone. The approach was evaluated by external validation, comparing predicted and observed AUC ratios found in the literature. Data were found for 22 substrates, 30 interacting drugs, and 38 substrate-interacting drug couples. The mean prediction error of AUC ratios was 0.02, and the mean prediction absolute error was 0.38 and 1.34, respectively. The model may be used to predict the variations in exposure resulting from a number of drug-drug-genotype combinations. The proposed approach will help (1) to identify comedications and population at risk, (2) to adapt dosing regimens, and (3) to prioritize the clinical pharmacokinetic studies to be done.

In vivo quantitative prediction of the effect of gene polymorphisms and drug interactions on drug exposure for CYP2C19 substrates

We present a unified quantitative approach to predict the in vivo alteration in drug exposure caused by either cytochrome P450 (CYP) gene polymorphisms or CYP-mediated drug-drug interactions (DDI). An application to drugs metabolized by CYP2C19 is presented. The metrics used is the ratio of altered drug area under the curve (AUC) to the AUC in extensive metabolizers with no mutation or no interaction. Data from 42 pharmacokinetic studies performed in CYP2C19 genetic subgroups and 18 DDI studies were used to estimate model parameters and predicted AUC ratios by using Bayesian approach. Pharmacogenetic information was used to estimate a parameter of the model which was then used to predict DDI. The method adequately predicted the AUC ratios published in the literature, with mean errors of -0.15 and -0.62 and mean absolute errors of 0.62 and 1.05 for genotype and DDI data, respectively. The approach provides quantitative prediction of the effect of five genotype variants and 10 inhibitors on the exposure to 25 CYP2C19 substrates, including a number of unobserved cases. A quantitative approach for predicting the effect of gene polymorphisms and drug interactions on drug exposure has been successfully applied for CYP2C19 substrates. This study shows that pharmacogenetic information can be used to predict DDI. This may have important implications for the development of personalized medicine and drug development.

In vivo information-guided prediction approach for assessing the risks of drug-drug interactions associated with circulating inhibitory metabolites

The in vivo drug-drug interaction (DDI) risks associated with cytochrome P450 inhibitors that have circulating inhibitory metabolites cannot be accurately predicted by conventional in vitro-based methods. A novel approach, in vivo information-guided prediction (IVIP), was recently introduced for CYP3A- and CYP2D6-mediated DDIs. This technique should be applicable to the prediction of DDIs involving other important cytochrome P450 metabolic pathways. Therefore, the aims of this study were to extend the IVIP approach to CYP2C9-mediated DDIs and evaluate the IVIP approach for predicting DDIs associated with inhibitory metabolites. The analysis was based on data from reported DDIs in the literature. The IVIP approach was modified and extended to CYP2C9-mediated DDIs. Thereafter, the IVIP approach was evaluated for predicting the DDI risks of various inhibitors with inhibitory metabolites. Although the data on CYP2C9-mediated DDIs were limited compared with those for CYP3A- and CYP2D6-mediated DDIs, the modified IVIP approach successfully predicted CYP2C9-mediated DDIs. For the external validation set, the prediction accuracy for area under the plasma concentration-time curve (AUC) ratios ranged from 70 to 125%. The accuracy (75-128%) of the IVIP approach in predicting DDI risks of inhibitors with circulating inhibitory metabolites was more accurate than in vitro-based methods (28-805%). The IVIP model accommodates important confounding factors in the prediction of DDIs, which are difficult to handle using in vitro-based methods. In conclusion, the IVIP approach could be used to predict CYP2C9-mediated DDIs and is easily modified to incorporate the additive effect of circulating inhibitory metabolites.