Dose of midazolam should be reduced during diltiazem and verapamil treatments

Mechanism-Based Inactivation of Cytochrome P450 3A by Evodol

1. Evodol is one of the furanoids isolated from the fruits of Evodia rutaecarpa that has been widely prescribed for the treatment of gastrointestinal diseases in China. The aim of this study was to investigate the inhibitory effect of evodol on CYP3A.2. A 30-min preincubation of evodol with human liver microsomes raised an obvious left IC₅₀ shift, 3.9-fold for midazolam 1'-hydroxylation and 3.2-fold for testosterone 6β-hydroxylation. Evodol inactivated CYP3A in a time-, concentration- and NADPH-dependent manner, with K_I and k_inact of 5.1 μM and 0.028 min^-1 for midazolam 1'-hydroxylation and 3.0 μM and 0.022 min^-1 for testosterone 6β-hydroxylation.3. Co-incubation of ketoconazole attenuated the inactivation while inclusion of glutathione (GSH) and catalase/superoxide dismutase displayed no such protection.4. cis-Butene-1, 4-dial (BDA) intermediate derived from evodol were trapped by glutathione and N-acetyl-lysine in microsomes and characterized by HR-MS spectra. The BDA intermediate was believed to play a key role in CYP3A inactivation. CYP3A4 and 2C9 were the primary enzymes contributing to the bioactivation of evodol.5. To sum up, for the first time evodol was characterized as a mechanism-based inactivator of CYP3A.

Considerations for Satisfactory Sedation during Dental Implant Surgery

Implant surgery is a lengthy dental procedure, and sedation is often used to reduce discomfort. The effectiveness of sedation has traditionally been evaluated in terms of patient and surgeon satisfaction, but the most important goal is not to induce a deep sleep in the patient, but rather to ensure that the surgery is performed safely and as planned. Additionally, adequate pain control is a necessary requirement for patient and surgeon satisfaction. Most patients undergoing implant surgery are middle-aged or older, and a relatively large number of them have cardiovascular disease. Infiltration anesthesia using articaine or lidocaine in combination with adrenaline is widely used, but its use in patients with cardiovascular disease is limited because of adrenaline’s effects on the cardiovascular system. The use of long-acting local anesthetics and the potential efficacy of ultrasound-guided jaw nerve block have been investigated to enhance analgesia without resorting to adrenaline. Midazolam and propofol are usually used for sedation, but dexmedetomidine, which causes less respiratory depression, and the ultrashort-acting benzodiazepine remimazolam are emerging as potential alternatives. Monitoring of anesthetic depth using electroencephalography is effective in maintaining a constant level of sedation. In addition, sedation promotes the stabilization of heart rate and blood pressure, reducing the risks associated with adrenaline and allowing for safer management.

Physiologically based pharmacokinetic modelling to predict the clinical effect of CYP3A inhibitors/inducers on esaxerenone pharmacokinetics in healthy subjects and subjects with hepatic impairment

Purpose

Esaxerenone is a novel, oral, nonsteroidal treatment for hypertension. Physiologically based pharmacokinetic (PBPK) modelling was performed to predict the drug–drug interaction (DDI) effect of cytochrome P450 (CYP)3A modulators on esaxerenone pharmacokinetics in healthy subjects and subjects with hepatic impairment.

Methods

In our PBPK model, the fraction of esaxerenone metabolised by CYP3A was estimated from mass-balance data and verified and optimised by clinical DDI study results with strong CYP3A modulators. The model was also verified by the observed pharmacokinetics after multiple oral dosing and by the effect of hepatic impairment on esaxerenone pharmacokinetics. The model was applied to predict the DDI effects on esaxerenone pharmacokinetics with untested CYP3A modulators in healthy subjects and with strong CYP3A modulators in subjects with hepatic impairment.

Results

The PBPK model well described esaxerenone pharmacokinetics after multiple oral dosing. The predicted fold changes in esaxerenone plasma exposure after coadministration with strong CYP3A modulators were comparable with the observed data (1.53-fold with itraconazole and 0.31-fold with rifampicin). Predicted DDIs with untested moderate CYP3A modulators were less than the observed DDI with strong CYP3A modulators. The PBPK model also described the effect of hepatic impairment on esaxerenone plasma exposure. The predicted DDI results with strong CYP3A modulators in subjects with hepatic impairment indicate that, for concomitant use of CYP3A modulators, caution is advised for subjects with hepatic impairment, as is for healthy subjects.

Conclusion

The PBPK model developed predicted esaxerenone pharmacokinetics and DDIs and informed concurrent use of esaxerenone with CYP3A modulators.

Supplementary information

The online version contains supplementary material available at 10.1007/s00228-021-03194-x.

Cytochrome P450 3A Time-Dependent Inhibition Assays Are Too Sensitive for Identification of Drugs Causing Clinically Significant Drug-Drug Interactions: A Comparison of Human Liver Microsomes and Hepatocytes and Definition of Boundaries for Inactivation Rate Constants

Time-dependent inhibition (TDI) of CYP3A is an important mechanism underlying numerous drug-drug interactions (DDIs), and assays to measure this are done to support early drug research efforts. However, measuring TDI of CYP3A in human liver microsomes (HLMs) frequently yields overestimations of clinical DDIs and thus can lead to the erroneous elimination of many viable drug candidates from further development. In this investigation, 50 drugs were evaluated for TDI in HLMs and suspended human hepatocytes (HHEPs) to define appropriate boundary lines for the TDI parameter rate constant for inhibition (k_obs) at a concentration of 30 µM. In HLMs, a k_obs value of 0.002 minute^-1 was statistically distinguishable from control; however, many drugs show k_obs greater than this but do not cause DDI. A boundary line defined by the drug with the lowest k_obs that causes a DDI (diltiazem) was established at 0.01 minute^-1 Even with this boundary, of the 33 drugs above this value, only 61% cause a DDI (true positive rate). A corresponding analysis was done using HHEPs; k_obs of 0.0015 minute^-1 was statistically distinguishable from control, and the boundary was established at 0.006 minute^-1 Values of k_obs in HHEPs were almost always lower than those in HLMs. These findings offer a practical guide to the use of TDI data for CYP3A in early drug-discovery research. SIGNIFICANCE STATEMENT: Time-dependent inhibition of CYP3A is responsible for many drug interactions. In vitro assays are employed in early drug research to identify and remove CYP3A time-dependent inhibitors from further consideration. This analysis demonstrates suitable boundaries for inactivation rates to better delineate drug candidates for their potential to cause clinically significant drug interactions.

PBPK modeling of CYP3A and P-gp substrates to predict drug-drug interactions in patients undergoing Roux-en-Y gastric bypass surgery

Roux-en-Y gastric bypass surgery (RYGBS) is an effective surgical intervention to reduce mortality in morbidly obese patients. Following RYGBS, the disposition of drugs may be affected by anatomical alterations and changes in intestinal and hepatic drug metabolizing enzyme activity. The aim of this study was to better understand the drug-drug interaction (DDI) potential of CYP3A and P-gp inhibitors. The impacts of RYGBS on the absorption and metabolism of midazolam, acetaminophen, digoxin, and their major metabolites were simulated using physiologically-based pharmacokinetic (PBPK) modeling. PBPK models for verapamil and posaconazole were built to evaluate CYP3A- and P-gp-mediated DDIs pre- and post-RYGBS. The simulations suggest that for highly soluble drugs, such as verapamil, the predicted bioavailability was comparable pre- and post-RYGBS. For verapamil inhibition, RYGBS did not affect the fold-change of the predicted inhibited-to-control plasma AUC ratio or predicted inhibited-to-control peak plasma concentration ratio for either midazolam or digoxin. In contrast, the predicted bioavailability of posaconazole, a poorly soluble drug, decreased from 12% pre-RYGBS to 5% post-RYGBS. Compared to control, the predicted posaconazole-inhibited midazolam plasma AUC increased by 2.0-fold pre-RYGBS, but only increased by 1.6-fold post-RYGBS. A similar trend was predicted for pre- and post-RYGBS inhibited-to-control midazolam peak plasma concentration ratios (2.0- and 1.6-fold, respectively) following posaconazole inhibition. Absorption of highly soluble drugs was more rapid post-RYGBS, resulting in higher predicted midazolam peak plasma concentrations, which was further increased following inhibition by verapamil or posaconazole. To reduce the risk of a drug-drug interaction in patients post-RYGBS, the dose or frequency of object drugs may need to be decreased when administered with highly soluble inhibitor drugs, especially if toxicities are associated with plasma peak concentrations.

A Mechanistic, Enantioselective, Physiologically Based Pharmacokinetic Model of Verapamil and Norverapamil, Built and Evaluated for Drug-Drug Interaction Studies

The calcium channel blocker and antiarrhythmic agent verapamil is recommended by the FDA for drug–drug interaction (DDI) studies as a moderate clinical CYP3A4 index inhibitor and as a clinical Pgp inhibitor. The purpose of the presented work was to develop a mechanistic whole-body physiologically based pharmacokinetic (PBPK) model to investigate and predict DDIs with verapamil. The model was established in PK-Sim^®, using 45 clinical studies (dosing range 0.1–250 mg), including literature as well as unpublished Boehringer Ingelheim data. The verapamil R- and S-enantiomers and their main metabolites R- and S-norverapamil are represented in the model. The processes implemented to describe the pharmacokinetics of verapamil and norverapamil include enantioselective plasma protein binding, enantioselective metabolism by CYP3A4, non-stereospecific Pgp transport, and passive glomerular filtration. To describe the auto-inhibitory and DDI potential, mechanism-based inactivation of CYP3A4 and non-competitive inhibition of Pgp by the verapamil and norverapamil enantiomers were incorporated based on in vitro literature. The resulting DDI performance was demonstrated by prediction of DDIs with midazolam, digoxin, rifampicin, and cimetidine, with 21/22 predicted DDI AUC ratios or C_trough ratios within 1.5-fold of the observed values. The thoroughly built and qualified model will be freely available in the Open Systems Pharmacology model repository to support model-informed drug discovery and development.

A physiologically based pharmacokinetic - pharmacodynamic modelling approach to predict incidence of neutropenia as a result of drug-drug interactions of paclitaxel in cancer patients

Paclitaxel is the backbone of standard chemotherapeutic regimens used in a number of malignancies and is frequently given with concomitant medications. Newly developed oncolytic agents, including tyrosine kinase inhibitors are often shown to be CYP3A4 and P-gp inhibitors. The aim of this study was to develop a PBPK model for intravenously administered paclitaxel in order to predict the incidence of neutropenia and to estimate the DDI potential as a victim drug. The dose-dependent effects on paclitaxel plasma protein binding, volume of distribution and drug clearance were considered for dose levels of 80 mg/m², 135 mg/m² and 175 mg/m². A pharmacodynamics model that incorporate the impact of paclitaxel on the neutrophil was developed. The relative metabolic clearance via CYP3A4 and CYP2C8, the renal clearance as well as P-gp mediated biliary clearance were incorporated in the model in order to assess the neutropenia in the presence of DDI. The developed PBPK-PD model was able to recover the drop in neutrophils observed after the administration of 175mg/m2 of paclitaxel over a 3-h duration. The mean nadir observed was 1.9 × 10⁹ neutrophils/L and was attained after 10 days of treatment, and a fraction of 47% of the population was predicted to have at some point a neutropenia including 12% with severe neutropenia. In the case of concomitant administration of ketoconazole, 39% of the population was predicted to suffer from severe neutropenia. In summary, PBPK-PD modeling allows a priori prediction of DDIs and safety events involving complex combination therapies which are often utilized in an oncology setting.

Effects of the dietary inclusion of a probiotic or prebiotic on florfenicol pharmacokinetic profile in broiler chicken

We evaluated the effect of prebiotic or probiotic as feed additives on florfenicol kinetic in broilers feed. Unsexed two hundred, thirty-five-day-old broiler chickens, were put in four equal groups (n = 50). The first group was administrated florfenicol intravenous at 30 mg/kg body weight (BW) only once dosage without pre- or probiotic administration to determine the bioavailability. While, the second group was administrated florfenicol (intracrop routes; a dosage of 30 mg/kg BW for five progressive days) without pre- or probiotic co-administration. The third and the fourth groups were administrated the same dose of florfenicol (intracrop route) for five successive days, followed by 10 days of prebiotic or probiotic treatment respectively. The plasma florfenicol % was identified by high-pressure liquid chromatography (HPLC) after the first florfenicol administration (intravenous or intracrop routes) in all groups. Then, the residual levels of florfenicol were determined in liver, kidney and muscle tissues from the second, third and fourth groups which were exposed to florfenicol orally. Our results demonstrated that broilers pre-treated with prebiotic or probiotic significantly increased C_max , AUC_{0- t} , AUC_0-inf as well as AUMC values, while significant drop was recorded in V/F and CL/F. Prebiotic or probiotic influenced the cumulative effect of florfenicol in liver and kidney tissues of treated birds.

Time-dependent enzyme inactivation: Numerical analyses of in vitro data and prediction of drug-drug interactions

Cytochrome P450 (CYP) enzyme kinetics often do not conform to Michaelis-Menten assumptions, and time-dependent inactivation (TDI) of CYPs displays complexities such as multiple substrate binding, partial inactivation, quasi-irreversible inactivation, and sequential metabolism. Additionally, in vitro experimental issues such as lipid partitioning, enzyme concentrations, and inactivator depletion can further complicate the parameterization of in vitro TDI. The traditional replot method used to analyze in vitro TDI datasets is unable to handle complexities in CYP kinetics, and numerical approaches using ordinary differential equations of the kinetic schemes offer several advantages. Improvement in the parameterization of CYP in vitro kinetics has the potential to improve prediction of clinical drug-drug interactions (DDIs). This manuscript discusses various complexities in TDI kinetics of CYPs, and numerical approaches to model these complexities. The extrapolation of CYP in vitro TDI parameters to predict in vivo DDIs with static and dynamic modeling is discussed, along with a discussion on current gaps in knowledge and future directions to improve the prediction of DDI with in vitro data for CYP catalyzed drug metabolism.

Tofacitinib Is a Mechanism-Based Inactivator of Cytochrome P450 3A4

Tofacitinib (TFT) is an oral JAK inhibitor which has been approved for the treatment of moderately and severely active rheumatoid arthritis. TFT was found to show concentration-, time-, and NADPH-dependent inhibition of CYP3A4, and irreversibility of the inactivation was also observed. Incubation (40 min, 37 °C) of recombinant CYP3A4 with TFT at 200 μM resulted in >70% loss of CYP3A4 activity. Estimated k_inact and K_I were 0.037 min^-1 and 93.2 μM, respectively. GSH and superoxide dismutase/catalase revealed minor or little protection against the CYP3A4 inactivation. Furthermore, ketoconazole attenuated TFT-mediated CYP3A4 inactivation. Epoxide and α-keto-aldehyde intermediates of TFT were trapped and characterized in microsomal incubations, respectively. The aldehyde intermediate is believed to be the key for the enzyme inactivation. Multiple P450 enzymes, including CYPs2C19, 3A4, 2D6, and 1A2, participated in the metabolism of TFT to the epoxide, while the formation of the aldehyde was mainly catalyzed by CYP3A4. In conclusion, TFT was proven to be a mechanism-based inactivator of CYP3A4.

In Vitro Assessment of Potential for CYP-Inhibition-Based Drug-Drug Interaction Between Vonoprazan and Clopidogrel

BACKGROUND AND OBJECTIVES

It was recently proposed that CYP-mediated drug-drug interactions (DDIs) of vonoprazan with clopidogrel and prasugrel can attenuate the antiplatelet actions of the latter two drugs. Clopidogrel is metabolized to the pharmacologically active metabolite H4 and its isomers by multiple CYPs, including CYP2C19 and CYP3A4. Therefore, to investigate the possibility of CYP-based DDIs, in vitro metabolic inhibition studies using CYP probe substrates or radiolabeled clopidogrel and human liver microsomes (HLMs) were conducted in this work.

METHODS

Reversible inhibition studies focusing on the effects of vonoprazan on CYP marker activities and the formation of the [¹⁴C]clopidogrel metabolite H4 were conducted with and without pre-incubation using HLMs. Time-dependent inhibition (TDI) kinetics were also measured.

RESULTS

It was found that vonoprazan is not a significant direct inhibitor of any CYP isoforms (IC₅₀ ≥ 16 μM), but shows the potential for TDI of CYP2B6, CYP2C19, and CYP3A4/5. This TDI was weaker than the inhibition induced by the corresponding reference inhibitors ticlopidine, esomeprazole, and verapamil, based on the measured potencies (k_inact/K_I ratio and the R2 value). In a more direct in vitro experiment, vonoprazan levels of up to 10 µM (a 100-fold higher concentration than the plasma C_max of 75.9 nM after taking 20 mg once daily for 7 days) did not suppress the formation of the active metabolite H4 or other oxidative metabolites of [¹⁴C]clopidogrel in a reversible or time-dependent manner. Additionally, an assessment of clinical trials and post-marketing data suggested no evidence of a DDI between vonoprazan and clopidogrel.

CONCLUSIONS

The body of evidence shows that the pharmacodynamic DDI reported between vonoprazan and clopidogrel is unlikely to be caused by the inhibition of CYP2B6, CYP2C19, or CYP3A4/5 by vonoprazan.

Progress in the Consideration of Possible Sex Differences in Drug Interaction Studies

Background:

Anecdotal evidence suggests that there may be sex differences in Drug-drug Interactions (DDI) involving specific drugs. Regulators have provided general guidance for the inclusion of females in clinical studies. Some clinical studies have reported sex differences in the Pharmacokinetics (PK) of CYP3A4 substrates, suggesting that DDI involving CYP3A4 substrates could potentially show sex differences.

Objective:

The aim of this review was to investigate whether recent prospective DDI studies have included both sexes and whether there was evidence for the presence or absence of sex differences with the DDIs.

Methods:

The relevant details from 156 drug interaction studies within 124 papers were extracted and evaluated.

Results:

Only eight studies (five papers) compared the outcome of the DDI between males and females. The majority of the studies had only male volunteers. Five studies had females only while 60 had males only, with 7.7% of the studies having an equal proportion of both sexes. Surprisingly, four studies did not specify the sex of the subjects.

:

Based on the limited number of studies comparing males and females, no specific trends or conclusions were evident. Sex differences in the interaction were reported between ketoconazole and midazolam as well as clarithromycin and midazolam. However, no sex difference was observed with the interaction between clarithromycin and triazolam or erythromycin and triazolam. No sex-related PK differences were observed with the interaction between ketoconazole and domperidone, although sex-related differences in QT prolongation were observed.

Conclusion:

This review has shown that only limited progress had been made with the inclusion of both sexes in DDI studies.

Midazolam: Safety of use in palliative care: A systematic critical review

PURPOSE

The undesired effects of midazolam can be life-threatening. This paper delineates the findings related to the pharmacokinetics, adverse effects and drug-drug interactions as well as associated therapeutic implications for safe midazolam use.

METHODS

A systematic review of literature was conducted.

RESULTS

The pharmacokinetics of midazolam depends on hepatic and renal functions, fat tissue mass, route and duration of administration, as well as potential drug-drug interactions. Palliative care patients constitute a high-risk group prone to side effects of drugs, due to polytherapy and multi-organ failure.

CONCLUSION

Midazolam is one of three most frequently administered drugs in palliative care. The indications for its use include anxiety, dyspnea, seizures, vomiting refractory to treatment, agitation, myoclonus, status epilepticus, restlessness, delirium, pruritus, hiccups, insomnia, analgosedation, palliative sedation and preventing or counteracting undesired effects of ketamine.

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.

Improved Predictions of Drug-Drug Interactions Mediated by Time-Dependent Inhibition of CYP3A

Time-dependent inactivation (TDI) of cytochrome P450s (CYPs) is a leading cause of clinical drug-drug interactions (DDIs). Current methods tend to overpredict DDIs. In this study, a numerical approach was used to model complex CYP3A TDI in human-liver microsomes. The inhibitors evaluated included troleandomycin (TAO), erythromycin (ERY), verapamil (VER), and diltiazem (DTZ) along with the primary metabolites N-demethyl erythromycin (NDE), norverapamil (NV), and N-desmethyl diltiazem (NDD). The complexities incorporated into the models included multiple-binding kinetics, quasi-irreversible inactivation, sequential metabolism, inhibitor depletion, and membrane partitioning. The resulting inactivation parameters were incorporated into static in vitro-in vivo correlation (IVIVC) models to predict clinical DDIs. For 77 clinically observed DDIs, with a hepatic-CYP3A-synthesis-rate constant of 0.000 146 min-1, the average fold difference between the observed and predicted DDIs was 3.17 for the standard replot method and 1.45 for the numerical method. Similar results were obtained using a synthesis-rate constant of 0.000 32 min-1. These results suggest that numerical methods can successfully model complex in vitro TDI kinetics and that the resulting DDI predictions are more accurate than those obtained with the standard replot approach.

Drug-drug interactions in the treatment for alcohol use disorders: A comprehensive review

Drug interactions are one of the most common causes of side effects in polypharmacy. Alcoholics are a category of patients at high risk of pharmacological interactions, due to the presence of comorbidities, the concomitant intake of several medications and the pharmacokinetic and pharmacodynamic interferences of ethanol. However, the data available on this issue are limited. These reasons often frighten clinicians when prescribing appropriate pharmacological therapies for alcohol use disorder (AUD), where less than 15% of patients receive an appropriate treatment in the most severe forms. The data available in literature regarding the relevant drug-drug interactions of the medications currently approved in United States and in some European countries for the treatment of AUD (benzodiazepines, acamprosate, baclofen, disulfiram, nalmefene, naltrexone and sodium oxybate) are reviewed here. The class of benzodiazepines and disulfiram are involved in numerous pharmacological interactions, while they are not conspicuous for acamprosate. The other drugs are relatively safe for pharmacological interactions, excluding the opioid withdrawal syndrome caused by the combination of nalmefene or naltrexone with an opiate medication. The information obtained is designed to help clinicians in understanding and managing the pharmacological interactions in AUDs, especially in patients under multi-drug treatment, in order to reduce the risk of a negative interaction and to improve the treatment outcomes.

A quantitative systems pharmacology approach, incorporating a novel liver model, for predicting pharmacokinetic drug-drug interactions

All pharmaceutical companies are required to assess pharmacokinetic drug-drug interactions (DDIs) of new chemical entities (NCEs) and mathematical prediction helps to select the best NCE candidate with regard to adverse effects resulting from a DDI before any costly clinical studies. Most current models assume that the liver is a homogeneous organ where the majority of the metabolism occurs. However, the circulatory system of the liver has a complex hierarchical geometry which distributes xenobiotics throughout the organ. Nevertheless, the lobule (liver unit), located at the end of each branch, is composed of many sinusoids where the blood flow can vary and therefore creates heterogeneity (e.g. drug concentration, enzyme level). A liver model was constructed by describing the geometry of a lobule, where the blood velocity increases toward the central vein, and by modeling the exchange mechanisms between the blood and hepatocytes. Moreover, the three major DDI mechanisms of metabolic enzymes; competitive inhibition, mechanism based inhibition and induction, were accounted for with an undefined number of drugs and/or enzymes. The liver model was incorporated into a physiological-based pharmacokinetic (PBPK) model and simulations produced, that in turn were compared to ten clinical results. The liver model generated a hierarchy of 5 sinusoidal levels and estimated a blood volume of 283 mL and a cell density of 193 × 106 cells/g in the liver. The overall PBPK model predicted the pharmacokinetics of midazolam and the magnitude of the clinical DDI with perpetrator drug(s) including spatial and temporal enzyme levels changes. The model presented herein may reduce costs and the use of laboratory animals and give the opportunity to explore different clinical scenarios, which reduce the risk of adverse events, prior to costly human clinical studies.

A semi-physiologically-based pharmacokinetic model characterizing mechanism-based auto-inhibition to predict stereoselective pharmacokinetics of verapamil and its metabolite norverapamil in human

Verapamil and its major metabolite norverapamil were identified to be both mechanism-based inhibitors and substrates of CYP3A and reported to have non-linear pharmacokinetics in clinic. Metabolic clearances of verapamil and norverapmil as well as their effects on CYP3A activity were firstly measured in pooled human liver microsomes. The results showed that S-isomers were more preferential to be metabolized than R-isomers for both verapamil and norverapamil, and their inhibitory effects on CYP3A activity were also stereoselective with S-isomers more potent than R-isomers. A semi-physiologically based pharmacokinetic model (semi-PBPK) characterizing mechanism-based auto-inhibition was developed to predict the stereoselective pharmacokinetic profiles of verapamil and norverapamil following single or multiple oral doses. Good simulation was obtained, which indicated that the developed semi-PBPK model can simultaneously predict pharmacokinetic profiles of S-verapamil, R-verapamil, S-norverapamil and R-norverapamil. Contributions of auto-inhibition to verapamil and norverapamil accumulation were also investigated following the 38th oral dose of verapamil sustained-release tablet (240mg once daily). The predicted accumulation ratio was about 1.3-1.5 fold, which was close to the observed data of 1.4-2.1-fold. Finally, the developed semi-PBPK model was further applied to predict drug-drug interactions (DDI) between verapamil and other three CYP3A substrates including midazolam, simvastatin, and cyclosporine A. Successful prediction was also obtained, which indicated that the developed semi-PBPK model incorporating auto-inhibition also showed great advantage on DDI prediction with CYP3A substrates.

Drug-drug interactions in cardiovascular catheterizations and interventions

Patients presenting for invasive cardiovascular procedures are frequently taking a variety of medications aimed to treat risk factors related to heart and vascular disease. During the procedure, antithrombotic, sedative, and analgesic medications are commonly needed, and after interventional procedures, new medications are often added for primary and secondary prevention of ischemic events. In addition to these prescribed medications, the use of over-the-counter drugs and supplements continues to rise. Most elderly patients, for example, are taking 5 or more prescribed medications and 1 or more supplements, and they often have some degree of renal insufficiency. This polypharmacy might result in drug-drug interactions that affect the balance of thrombotic and bleeding events during the procedure and during long-term treatment. Mixing of anticoagulants, for instance, might lead to periprocedural bleeding, and this is associated with an increase in long-term adverse events. Furthermore, the range of possible interactions with thienopyridine antiplatelets is of concern, because these drugs are essential to immediate and extended interventional success. The practical challenges in the field are great-some drug-drug interactions are likely present yet not well understood due to limited assays, whereas other interactions have well-described biological effects but seem to be more theoretical, because there is little to no clinical impact. Interventional providers need to be attentive to the potential for drug-drug interaction, the associated harm, and the appropriate action, if any, to minimize the potential for medication-related adverse events. This review will focus on drug-drug interactions that have the potential to affect procedural success, either through increases in immediate complications or compromising longer-term outcome.

Application of permeability-limited physiologically-based pharmacokinetic models: part II - prediction of P-glycoprotein mediated drug-drug interactions with digoxin

Digoxin is the recommended substrate for assessment of P-glycoprotein (P-gp)-mediated drug-drug interactions (DDIs) in vivo. The overall aim of our study was to investigate the inhibitory potential of both verapamil and norverapamil on the P-gp-mediated efflux of digoxin in both gut and liver. Therefore, a physiologically-based pharmacokinetic (PBPK) model for verapamil and its primary metabolite was developed and validated through the recovery of observed clinical plasma concentration data for both moieties and the reported interaction with midazolam, albeit a cytochrome P450 3A4-mediated DDI. The validated inhibitor model was then used in conjunction with the model developed previously for digoxin. The range of values obtained for the 10 trials indicated that increases in area under the plasma concentration-time curve (AUC) profiles and maximum plasma concentration observed (Cmax ) values of digoxin following administration of verapamil were more comparable with in vivo observations, when P-gp inhibition by the metabolite, norverapamil, was considered as well. The predicted decrease in AUC and Cmax values of digoxin following administration of rifampicin because of P-gp induction was 1.57- (range: 1.42-1.77) and 1.62-fold (range: 1.53-1.70), which were reasonably consistent with observed values of 1.4- and 2.2-fold, respectively. This study demonstrates the application of permeability-limited models of absorption and distribution within a PBPK framework together with relevant in vitro data on transporters to assess the clinical impact of modulated P-gp-mediated efflux by drugs in development.

Inhibition of P-glycoprotein enhances transport of imipramine across the blood-brain barrier: microdialysis studies in conscious freely moving rats

BACKGROUND AND PURPOSE

Recent studies indicate that efflux of antidepressants by the multidrug resistance transporter P-glycoprotein (P-gp) at the blood-brain barrier (BBB) may contribute to treatment-resistant depression (TRD) by limiting intracerebral antidepressant concentrations. In addition, clinical experience shows that adjunctive treatment with the P-gp inhibitor verapamil may improve the clinical outcome in TRD. Therefore, the present study aimed to investigate the effect of P-gp inhibition on the transport of the tricyclic antidepressant imipramine and its active metabolite desipramine across the BBB.

EXPERIMENTAL APPROACH

Intracerebral microdialysis in rats was used to monitor brain levels of imipramine and desipramine following i.v. imipramine administration, with or without pretreatment with one of the P-gp inhibitors verapamil or cyclosporin A (CsA). Plasma drug levels were also determined at regular intervals.

KEY RESULTS

Pretreatment with either verapamil or CsA resulted in significant increases in imipramine concentrations in the microdialysis samples, without altering imipramine plasma pharmacokinetics. Furthermore, pretreatment with verapamil, but not CsA, led to a significant elevation in plasma and brain levels of desipramine.

CONCLUSIONS AND IMPLICATIONS

The present study demonstrated that P-gp inhibition enhanced the intracerebral concentration of imipramine , thus supporting the hypothesis that P-gp activity restricts brain levels of certain antidepressants, including imipramine. These findings may help to explain reports of a beneficial response to adjunctive therapy with verapamil in TRD.

Risk assessment of mechanism-based inactivation in drug-drug interactions

Drug-drug interactions (DDIs) that occur via mechanism-based inactivation of cytochrome P450 are of serious concern. Although several predictive models have been published, early risk assessment of MBIs is still challenging. For reversible inhibitors, the DDI risk categorization using [I]/K(i) ([I], the inhibitor concentration; K(i), the inhibition constant) is widely used in drug discovery and development. Although a simple and reliable methodology such as [I]/K(i) categorization for reversible inhibitors would be useful for mechanism-based inhibitors (MBIs), comprehensive analysis of an analogous measure reflecting in vitro potency for inactivation has not been reported. The aim of this study was to evaluate whether the term λ/k(deg) (λ, first-order inactivation rate at a given MBI concentration; k(deg), enzyme degradation rate constant) would be useful in the prediction of the in vivo DDI risk of MBIs. Twenty-one MBIs with both in vivo area under the curve (AUC) change of marker substrates and in vitro inactivation parameters were identified in the literature and analyzed. The results of this analysis show that in vivo DDIs with >2-fold change of object drug AUC can be identified with the cutoff value of λ/k(deg) = 1, where unbound steady-state C(max) is used for inhibitor concentration. However, the use of total C(max) led to great overprediction of DDI risk. The risk assessment using λ/k(deg) coupled with unbound C(max) can be useful for the DDI risk evaluation of MBIs in drug discovery and 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.

Time-dependent inhibition and estimation of CYP3A clinical pharmacokinetic drug-drug interactions using plated human cell systems

The current studies assessed the utility of freshly plated hepatocytes, cryopreserved plated hepatocytes, and cryopreserved plated HepaRG cells for the estimation of inactivation parameters k(inact) and K(I) for CYP3A. This was achieved using a subset of CYP3A time-dependent inhibitors (fluoxetine, verapamil, clarithromycin, troleandomycin, and mibefradil) representing a range of potencies. The estimated k(inact) and K(I) values for each time-dependent inhibitor were compared with those obtained using human liver microsomes and used to estimate the magnitude of clinical pharmacokinetic drug-drug interaction (DDI). The inactivation kinetic parameter, k(inact), was most consistent across systems tested for clarithromycin, verapamil, and troleandomycin, with a high k(inact) of 0.91 min(-1) observed for mibefradil in HepaRG cells. The apparent K(I) estimates derived from the various systems displayed a range of variability from 3-fold for clarithromycin (5.4-17.7 μM) to 6-fold for verapamil (1.9-12.6 μM). In general, the inactivation kinetic parameters derived from the cell systems tested fairly replicated what was observed in time-dependent inhibition studies using human liver microsomes. Despite some of the observed differences in inactivation kinetic parameters, the estimated DDIs derived from each of the tested systems generally agreed with the clinically reported DDI within approximately 2-fold. In addition, a plated cell approach offered the ability to conduct longer primary incubations (greater than 30 min), which afforded improved ability to identify the weak time-dependent inhibitor fluoxetine. Overall, results from these studies suggest that in vitro inactivation parameters generated from plated cell systems may be a practical approach for identifying time-dependent inhibitors and for estimating the magnitude of clinical DDIs.

Clinical outcomes and management of mechanism-based inhibition of cytochrome P450 3A4

Mechanism-based inhibition of cytochrome P450 (CYP) 3A4 is characterized by NADPH-, time-, and concentration-dependent enzyme inactivation, occurring when some drugs are converted by CYPs to reactive metabolites. Such inhibition of CYP3A4 can be due to the chemical modification of the heme, the protein, or both as a result of covalent binding of modified heme to the protein. The inactivation of CYP3A4 by drugs has important clinical significance as it metabolizes approximately 60% of therapeutic drugs, and its inhibition frequently causes unfavorable drug–drug interactions and toxicity. The clinical outcomes due to CYP3A4 inactivation depend on many factors associated with the enzyme, drugs, and patients. Clinical professionals should adopt proper approaches when using drugs that are mechanism-based CYP3A4 inhibitors. These include early identification of drugs behaving as CYP3A4 inactivators, rational use of such drugs (eg, safe drug combination regimen, dose adjustment, or discontinuation of therapy when toxic drug interactions occur), therapeutic drug monitoring, and predicting the risks for potential drug–drug interactions. A good understanding of CYP3A4 inactivation and proper clinical management are needed by clinical professionals when these drugs are used.

Enhancement of GABAergic activity: neuropharmacological effects of benzodiazepines and therapeutic use in anesthesiology

GABA is the major inhibitory neurotransmitter in the central nervous system (CNS). The type A GABA receptor (GABA(A)R) system is the primary pharmacological target for many drugs used in clinical anesthesia. The α1, β2, and γ2 subunit-containing GABA(A)Rs located in the various parts of CNS are thought to be involved in versatile effects caused by inhaled anesthetics and classic benzodiazepines (BZD), both of which are widely used in clinical anesthesiology. During the past decade, the emergence of tonic inhibitory conductance in extrasynaptic GABA(A)Rs has coincided with evidence showing that these receptors are highly sensitive to the sedatives and hypnotics used in anesthesia. Anesthetic enhancement of tonic GABAergic inhibition seems to be preferentially increased in regions shown to be important in controlling memory, awareness, and sleep. This review focuses on the physiology of the GABA(A)Rs and the pharmacological properties of clinically used BZDs. Although classic BZDs are widely used in anesthesiological practice, there is a constant need for new drugs with more favorable pharmacokinetic and pharmacodynamic effects and fewer side effects. New hypnotics are currently developed, and promising results for one of these, the GABA(A)R agonist remimazolam, have recently been published.

Effect of different durations and formulations of diltiazem on the single-dose pharmacokinetics of midazolam: how long do we go?

Understanding how inhibition of cytochrome P4503A (CYP3A) affects the metabolism of a new drug is critical in determining if a clinically relevant drug interaction will occur. Diltiazem interaction studies assess a given compound's sensitivity to moderate CYP3A inhibition. The present study compared the effect different durations and formulations of diltiazem (extended release [XR] and conventional release [CR]) had on the single-dose pharmacokinetics of midazolam. The geometric mean ratio (GMR; midazolam + diltiazem(XR × 5 days)/midazolam + diltiazem(XR × 2 days)) for midazolam AUC(0-∞) was 0.98 (90% confidence interval [CI], 0.87, 1.10). The GMR (midazolam + diltiazem(XR × 2 days)/midazolam + diltiazem(CR × 2 days)) for midazolam AUC(0-∞) was 0.82 (90% CI, 0.73, 0.92). Simcyp simulations accurately predicted the observed clinical results only when a hepatic CYP3A degradation rate (k(deg)) different from that provided by the software was used. The data suggest that dosing diltiazem XR for 2 days predicts the change in midazolam AUC as reliably as 5 days of XR dosing and 2 days of CR dosing. In addition, the authors believe that a hepatic CYP3A kdeg of 0.03 h(-1) should be considered for future Simcyp studies.

Consideration of reliable concentrations for prediction of change in enzyme activity by mechanism-based inactivation using physiologically-based pharmacokinetic model simulations

Using physiologically-based pharmacokinetic model simulations with the assumption that elimination of inactivator is not altered by mechanism-based inactivation (MBI) of the target enzyme, we examined at what concentrations the influence of MBI could be accurately and simply predicted. The method utilizing maximum unbound systemic concentration as the inactivator concentration (method 1) tended to overestimate this influence, and accuracy expressed as the ratio of estimated and exact fold decrease in enzyme activity ranged from 0.80 to 8.41. In addition, when the volume of distribution was large or the absorption rate constant was small, method 1 provided relatively precise estimation, with the ratio of nearly 1. We propose use of two concentrations, the steady-state average unbound liver concentration and maximum limit of steady-state average unbound liver concentration, to predict the effects of MBI. The accuracy of prediction of MBI using these two concentrations ranged from 0.90 to 1.04 and 0.92 to 2.96, respectively, and was higher than that with method 1. These two concentrations can be obtained early in the drug development process, and estimated results can be expected to contribute to determination of the effects of MBI.

Azole antimycotics and drug interactions in the perioperative period

PURPOSE OF REVIEW

A beneficial effect of antifungal prophylaxis on the prevention of invasive fungal infections has increased the use of azole antimycotics in intensive care and during the perioperative period. At the same time more severe illnesses are treated and multiple drug therapies are employed. Thus, the potential for severe drug-drug interactions has increased. Previous studies have shown that azole antimycotics increase the risk of many clinically significant drug interactions with potentially hazardous consequences.

RECENT FINDINGS

A recent pharmacoepidemiological study has found a more than five-fold incidence ratio in the adjusted rate of sudden death from cardiac diseases among those patients who were given simultaneously inhibitors of cytochrome P450 (CYP) enzymes and their substrates. Although new triazole antifungals are well tolerated, they still cause significant inhibition of CYP enzymes.

SUMMARY

This review focuses on azole antimycotics and anesthetic drugs being used during the perioperative period and discusses the possible clinically significant drug-drug interactions. Azole antimycotics are amongst the strongest inhibitors of CYP-mediated drug metabolism. Anesthesiologists must be aware of the interaction potential of azole antimycotics to be able to adjust their perioperative strategies according to the patient's condition and concomitant medication.