General Framework for the Quantitative Prediction of CYP3A4-Mediated Oral Drug Interactions Based on the AUC Increase by Coadministration of??Standard Drugs

A genetic algorithm-based approach for the prediction of metabolic drug-drug interactions involving CYP2C8 or CYP2B6

BACKGROUND AND OBJECTIVES

A genetic algorithm (GA) approach was developed to predict drug-drug interactions (DDIs) caused by cytochrome P450 2C8 (CYP2C8) inhibition or cytochrome P450 2B6 (CYP2B6) inhibition or induction. Nighty-eight DDIs, obtained from published in vivo studies in healthy volunteers, have been considered using the area under the plasma drug concentration-time curve (AUC) ratios (i.e., ratios of AUC of the drug substrate administered in combination with a DDI perpetrator to AUC of the drug substrate administered alone) to describe the extent of DDI.

METHODS

The following parameters were estimated in this approach: the contribution ratios (CRCYP2B6 and CRCYP2C8, i.e., the fraction of the dose metabolized via CYP2B6 or CYP2C8, respectively) and the inhibitory or inducing potency of the perpetrator drug (IRCYP2B6, IRCYP2C8 and ICCYP2B6, for inhibition of CYP2B6 and CYP2C8, and induction of CYP2B6, respectively). The workflow consisted of three main phases. First, the initial estimates of the parameters were estimated through GA. Then, the model was validated using an external validation. Finally, the parameter values were refined via a Bayesian orthogonal regression using all data.

RESULTS

The AUC ratios of 5 substrates, 11 inhibitors and 19 inducers of CYP2B6, and the AUC ratios of 19 substrates and 23 inhibitors of CYP2C8 were successfully predicted by the developed methodology within 50-200% of observed values.

CONCLUSIONS

The approach proposed in this work may represent a useful tool for evaluating the suitable doses of a CYP2C8 or CYP2B6 substrates co-administered with perpetrators.

Impact of P-Glycoprotein-Mediated Drug-Endogenous Substrate Interactions on Androgen and Blood-Brain Barrier Permeability

This report focuses on pharmacokinetic drug-endogenous substrate interactions (DEIs). We hypothesized that P-glycoprotein (P-gp)-mediated DEI might affect androgen kinetics, especially its blood-brain barrier (BBB) permeability. The intracellular accumulation of the endogenous substrates of P-gp, testosterone (TES) and androstenedione (ADO) was increased by several tested drugs in uptake studies using P-gp overexpressing cells, indicating that these drugs inhibit P-gp-mediated efflux of TES of ADO from the cells. In a transport study using rat BBB kit, we found that the BBB limited the penetration of TES and ADO into the central nervous system. In addition, tested drugs that cause DEI were found to increase BBB permeability of TES and ADO via P-gp inhibition. In short, this study provides new findings regarding the possibility that DEI may affect the kinetics of endogenous substrates of P-gp.

Macrolide and Ketolide Antibiotics Inhibit the Cytotoxic Effect of Trastuzumab Emtansine in HER2-Positive Breast Cancer Cells: Implication of a Potential Drug-ADC Interaction in Cancer Chemotherapy

Macrolides are widely used for the long-term treatment of infections and chronic inflammatory diseases. The pharmacokinetic features of macrolides include extensive tissue distribution because of favorable membrane permeability and accumulation within lysosomes. Trastuzumab emtansine (T-DM1), a HER2-targeting antibody-drug conjugate (ADC), is catabolized in the lysosomes, where Lys-SMCC-DM1, a potent cytotoxic agent, is processed by proteinase degradation and subsequently released from the lysosomes to the cytoplasm through the lysosomal membrane transporter SLC46A3, resulting in an antitumor effect. We recently demonstrated that erythromycin and clarithromycin inhibit SLC46A3 and attenuate the cytotoxicity of T-DM1; however, the effect of other macrolides and ketolides has not been determined. In this study, we evaluated the effect of macrolide and ketolide antibiotics on T-DM1 cytotoxicity in a human breast cancer cell line, KPL-4. Macrolides used in the clinic, such as roxithromycin, azithromycin, and josamycin, as well as solithromycin, a ketolide under clinical development, significantly attenuated T-DM1 cytotoxicity in addition to erythromycin and clarithromycin. Of these, azithromycin was the most potent inhibitor of T-DM1 efficacy. These antibiotics significantly inhibited the transport function of SLC46A3 in a concentration-dependent manner. Moreover, these compounds extensively accumulated in the lysosomes at the levels estimated to be 0.41-13.6 mM when cells were incubated with them at a 2 μM concentration. The immunofluorescence staining of trastuzumab revealed that azithromycin and solithromycin inhibit the degradation of T-DM1 in the lysosomes. These results suggest that the attenuation of T-DM1 cytotoxicity by macrolide and ketolide antibiotics involves their lysosomal accumulation and results in their greater lysosomal concentrations to inhibit the SLC46A3 function and T-DM1 degradation. This suggests a potential drug-ADC interaction during cancer chemotherapy.

The Pharmacokinetics of the Nonsteroidal Mineralocorticoid Receptor Antagonist Finerenone

Finerenone, a selective and nonsteroidal antagonist of the mineralocorticoid receptor, has received regulatory approval with the indication of cardiorenal protection in patients with chronic kidney disease associated with type 2 diabetes. It is rapidly and completely absorbed and undergoes first-pass metabolism in the gut wall and liver resulting in a bioavailability of 43.5%. Finerenone can be taken with or without food. The pharmacokinetics of finerenone are linear and its half-life is 2 to 3 h in the dose range of up to 20 mg. Cytochrome P450 (CYP) 3A4 (90%) and CYP2C8 (10%) are involved in the extensive biotransformation of finerenone to pharmacologically inactive metabolites, which are excreted via both renal (80%) and biliary (20%) routes. Moderate or severe renal impairment, or moderate hepatic impairment result in area-under-the-curve increases of finerenone (< 40%), which do not require a dose adjustment per se, as the starting dose is based on estimated glomerular filtration rate (eGFR) and titrated according to serum potassium levels and eGFR decline. No relevant effects of age, sex, body size or ethnicity on systemic finerenone exposure were identified. Modulators of CYP3A4 activity were found to affect finerenone exposure, consistent with its classification as a sensitive CYP3A4 substrate. Serum potassium should be monitored during drug initiation or dosage adjustment of either a moderate or weak CYP3A4 inhibitor or finerenone, and the dose of finerenone should be adjusted as appropriate. Its use with strong inhibitors is contraindicated and strong or moderate inducers of CYP3A4 should be avoided. Finerenone has no potential to affect relevant CYP enzymes and drug transporters.

Model-based meta-analysis of ethnic differences and their variabilities in clearance of oral drugs classified by clearance mechanism

In this study, the ethnic ratios (ERs) of oral clearance between Japanese and Western populations were subjected to model-based meta-analysis (MBMA) for 81 drugs evaluated in 673 clinical studies. The drugs were classified into eight groups according to the clearance mechanism, and the ER for each group was inferred together with interindividual variability (IIV), interstudy variability (ISV), and inter-drug variability within a group (IDV) using the Markov chain Monte Carlo (MCMC) method. The ER, IIV, ISV, and IDV were dependent on the clearance mechanism, and, except for particular groups such as drugs metabolized by polymorphic enzymes or their clearance mechanism is not confirmative, the ethnic difference was found to be generally small. The IIV was well-matched across ethnicities, and the ISV was approximately half of the IIV as the coefficient of variation. To adequately assess ethnic differences in oral clearance without false detections, phase I studies should be designed with full consideration of the mechanism of clearance. This study suggests that the methodology of classifying drugs based on the mechanism that causes ethnic differences and performing MBMA with statistical techniques such as MCMC analysis is helpful for a rational understanding of ethnic differences and for strategic drug development.

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 the Drug-Drug Interaction Potential of Ensitrelvir Fumaric Acid with Cytochrome P450 3A Substrates in Healthy Japanese Adults

BACKGROUND

Management of drug-drug interactions (DDIs) for ensitrelvir, a novel 3-chymotrypsin-like protease inhibitor of SARS-CoV-2 infection is crucial. A previous clinical DDI study of ensitrelvir with midazolam, a clinical index cytochrome P450 (CYP) 3A substrate, demonstrated that ensitrelvir given for 5 days orally with a loading/maintenance dose of 750/250 mg acted as a strong CYP3A inhibitor.

OBJECTIVES

The objectives of this study were to investigate the effect of ensitrelvir on the pharmacokinetics of CYP3A substrates, dexamethasone, prednisolone and midazolam, and to assess the pharmacokinetics, safety, and tolerability of ensitrelvir following multiple-dose administration of ensitrelvir.

METHODS

This was a Phase 1, multicenter, single-arm, open-label study in healthy Japanese adult participants. The effects of multiple doses of ensitrelvir in the fasted state on the pharmacokinetics of dexamethasone, prednisolone, and midazolam were investigated. Ensitrelvir was administered from Day 1 through Day 5, with a loading/maintenance dose of 750/250 mg for the dexamethasone and prednisolone cohorts whereas 375/125 mg for the midazolam cohort. Either dexamethasone, prednisolone, or midazolam was administered alone (Day - 2) or in combination with ensitrelvir (Day 5) in each of the cohorts. Additionally, dexamethasone or prednisolone was administered on Days 9 and 14. The pharmacokinetic parameters of ensitrelvir, dexamethasone, prednisolone, and midazolam were calculated based on their plasma concentration data with non-compartmental analysis. In safety assessments, the nature, frequency, and severity of treatment-emergent adverse events were evaluated and recorded.

RESULTS

The area under the concentration-time curve (AUC) ratio of dexamethasone on Day 5 was 3.47-fold compared with the corresponding values for dexamethasone alone on Day - 2 and the effect diminished over time after the last dose of ensitrelvir. No clinically meaningful effect was observed for prednisolone. The AUC ratio of midazolam was 6.77-fold with ensitrelvir 375/125 mg suggesting ensitrelvir at 375/125 mg strongly inhibits CYP3A similar to that at 750/250 mg. No new safety signals with ensitrelvir were reported during the study.

CONCLUSION

The inhibitory effect for CYP3A was confirmed after the last dose of ensitrelvir, and the effect diminished over time. In addition, ensitrelvir at 375/125 mg showed CYP3A inhibitory potential similar to that at 750/250 mg. These findings can be used as a clinical recommendation for prescribing ensitrelvir with regard to concomitant medications.

CLINICAL TRIAL REGISTRATION

Japan Registry of Clinical Trials identifier: jRCT2031210202.

Metabolomics-based discovery of XHP as a CYP3A4 inhibitor against pancreatic cancer

Background: Xihuang Wan (XHW), a purgative and detoxifying agent, is commonly utilized in modern medicine as a treatment and adjuvant therapy for various malignancies, including breast cancer, liver cancer, and lung cancer. A clinical study demonstrated the potential usefulness of the combination of XHW and gemcitabine as a therapy for pancreatic cancer (PC), indicating that XHW’s broad-spectrum antitumor herbal combination could be beneficial in the treatment of PC. However, the precise therapeutic efficacy of XHW in treating pancreatic cancer remains uncertain.

Aim: This study assessed the biological activity of XHW by optimizing the therapeutic concentration of XHW (Xihuang pills, XHP). We performed cell culture and developed an animal test model to determine whether XHP can inhibit pancreatic cancer (PC). We also applied the well-known widely targeted metabolomics analysis and conducted specific experiments to assess the feasibility of our method in PC therapy.

Materials and Methods: We used UPLC/Q-TOF-MS to test XHP values to set up therapeutic concentrations for the in vivo test model. SW1990 pancreatic cancer cells were cultured to check the effect the anti-cancer effects of XHP by general in vitro cell analyses including CCK-8, Hoechst 33258, and flow cytometry. To develop the animal model, a solid tumor was subcutaneously formed on a mouse model of PC and assessed by immunohistochemistry and TUNEL apoptosis assay. We also applied the widely targeted metabolomics method following Western blot and RT-PCR to evaluate multiple metabolites to check the therapeutic effect of XHP in our cancer test model.

Results: Quantified analysis from UPLC/Q-TOF-MS showed the presence of the following components of XHP: 11-carbonyl-β-acetyl-boswellic acid (AKBA), 11-carbonyl-β-boswellic acid (KBA), 4-methylene-2,8,8-trimethyl-2-vinyl-bicyclo [5.2.0]nonane, and (1S-endo)-2-methyl-3-methylene-2-(4-methyl-3-3-pentenyl)-bicyclo [2.2.1heptane]. The results of the cell culture experiments demonstrated that XHP suppressed the growth of SW1990 PC cells by enhancing apoptosis. The results of the animal model tests also indicated the suppression effect of XHP on tumor growth. Furthermore, the result of the widely targeted metabolomics analysis showed that the steroid hormone biosynthesis metabolic pathway was a critical factor in the anti-PC effect of XHP in the animal model. Moreover, Western blot and RT-PCR analyses revealed XHP downregulated CYP3A4 expression as an applicable targeted therapeutic approach.

Conclusion: The results of this study demonstrated the potential of XHP in therapeutic applications in PC. Moreover, the widely targeted metabolomics method revealed CYP3A4 is a potential therapeutic target of XHP in PC control. These findings provide a high level of confidence that XHP significantly acts as a CYP3A4 inhibitor in anti-cancer therapeutic applications.

A case report of a prolonged decrease in tacrolimus clearance due to co-administration of nirmatrelvir/ritonavir in a lung transplant recipient receiving itraconazole prophylaxis

BACKGROUND

Drug-drug interaction management is complex. Nirmatrelvir/ritonavir is a potent cytochrome P450 (CYP) 3A inhibitor and influences pharmacokinetics of co-administered drugs. Although there are several reports about drug-drug interactions of nirmatrelvir/ritonavir, an influence of a concomitant use of nirmatrelvir/ritonavir and another potent CYP3A inhibitor on tacrolimus remains unclear. Here, we experienced a lung transplant patient with the novel coronavirus disease 2019 (COVID-19). In this patient, nirmatrelvir/ritonavir was administered, and the inhibitory effect of itraconazole on CYP3A was prolonged.

CASE PRESENTATION

We present a case in forties who had undergone lung transplantation. He was administered itraconazole and tacrolimus 1.0 mg/d, with a trough value of 8-12 ng/mL. The patient contracted the COVID-19, and a nirmatrelvir/ritonavir treatment was initiated. During the antiviral treatment, tacrolimus administration was discontinued for 5 d. Tacrolimus was resumed at 1.0 mg/d after completion of the nirmatrelvir/ritonavir treatment, but the trough value after 7 d was high at 31.6 ng/mL. Subsequently, the patient was placed on another 36-h tacrolimus discontinuation, but the trough value decreased to only 16.0 ng/mL.

CONCLUSIONS

Co-administration of ritonavir caused a prolonged decrease in tacrolimus clearance through its inhibitory effects on CYP3A in a patient taking itraconazole. Management of drug-drug interaction by pharmacists can be important for patients with multiple medications.

Prediction of the Contribution Ratio of a Target Metabolic Enzyme to Clearance from Chemical Structure Information

The contribution ratio of metabolic enzymes such as cytochrome P450 to in vivo clearance (fraction metabolized: fm) is a pharmacokinetic index that is particularly important for the quantitative evaluation of drug-drug interactions. Since obtaining experimental in vivo fm values is challenging, those derived from in vitro experiments have often been used alternatively. This study aimed to explore the possibility of constructing machine learning models for predicting in vivo fm using chemical structure information alone. We collected in vivo fm values and chemical structures of 319 compounds from a public database with careful manual curation and constructed predictive models using several machine learning methods. The results showed that in vivo fm values can be obtained from structural information alone with a performance comparable to that based on in vitro experimental values and that the prediction accuracy for the compounds involved in CYP induction or inhibition is significantly higher than that by using in vitro values. Our new approach to predicting in vivo fm values in the early stages of drug discovery should help improve the efficiency of the drug optimization process.

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.

Quantitative prediction of OATP1B-mediated drug-drug interactions using endogenous biomarker coproporphyrin I

1. Evaluation of the organic anion transporting polypeptide (OATP) 1B-mediated drug-drug interaction (DDI) potential is important for drug development. The focus of this study was coproporphyrin I (CP-I), an endogenous OATP1B biomarker.2. We investigated a new approach to OATP1B-mediated DDI prediction based on the mechanistic static pharmacokinetics (MSPK) model.3. The ratio of the area under the plasma concentration-time curve (AUCR) with and without co-administration of rifampicin (a typical OATP1B inhibitor) was found for CP-I and OATP1B substrate, respectively, and was then used to derive the correlation curve equation. The AUCR with and without co-administration of another OATP1B inhibitor than rifampicin was then predicted for the OATP1B substrates by substituting the AUCR of CP-I in the correlation curve equation to verify the predictability of the AUCR of the OATP1B substrates.4. The derived correlation curve equation between CP-I and the OATP1B substrates of the AUCRs with and without co-administration of rifampicin matched the observed AUCRs well. Regarding pitavastatin, rosuvastatin and pravastatin, 92.9% of the predicted AUCR values were within a two-fold range of the observed values, indicating that this approach may be a good way to quantitatively predict DDI potential.

Physiologically-based pharmacokinetic modeling to predict CYP3A4-mediated drug-drug interactions of finerenone

Finerenone is a nonsteroidal, selective mineralocorticoid receptor antagonist that recently demonstrated its efficacy to delay chronic kidney disease (CKD) progression and reduce cardiovascular events in patients with CKD and type 2 diabetes. Here, we report the development of a physiologically‐based pharmacokinetic (PBPK) model for finerenone and its application as a victim drug of cytochrome P450 3A4 (CYP3A4)‐mediated drug‐drug interactions (DDIs) using the open‐source PBPK platform PK‐Sim, which has recently been qualified for this application purpose. First, the PBPK model for finerenone was developed using physicochemical, in vitro, and clinical (including mass balance) data. Subsequently, the finerenone model was validated regarding the contribution of CYP3A4 metabolism to total clearance by comparing to observed data from dedicated clinical interaction studies with erythromycin (simulated geometric mean ratios of the area under the plasma concentration‐time curve [AUCR] of 3.46 and geometric mean peak plasma concentration ratios [C_maxRs] of 2.00 vs. observed of 3.48 and 1.88, respectively) and verapamil (simulated AUCR of 2.91 and C_maxR of 1.86 vs. observed of 2.70 and 2.22, respectively). Finally, the finerenone model was applied to predict clinically untested DDI studies with various CYP3A4 modulators. An AUCR of 6.31 and a C_maxR of 2.37 was predicted with itraconazole, of 5.28 and 2.25 with clarithromycin, 1.59 and 1.40 with cimetidine, 1.57 and 1.38 with fluvoxamine, 0.19 and 0.32 with efavirenz, and 0.07 and 0.14 with rifampicin. This PBPK analysis provides a quantitative basis to guide the label and clinical use of finerenone with concomitant CYP3A4 modulators.

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.

Effects of CYP3A inhibitors ketoconazole, voriconazole, and itraconazole on the pharmacokinetics of sunitinib and its main metabolite in rats

Sunitinib is a small molecule inhibitor of multiple receptor tyrosine kinases such as platelet derived growth factor receptor, vascular endothelial growth factor receptor, kit receptor and other receptors. The US Food and Drug Administration (FDA) has approved sunitinib for the treatment of advanced renal cell carcinoma and gastrointestinal stromal tumors. It has been reported that sunitinib was mainly metabolized by CYP3A but its pharmacokinetic interactions have not been revealed. In this study, we investigated whether CYP3A inhibitors (ketoconazole, voriconazole, and itraconazole) could influence the pharmacokinetics of sunitinib and its equipotent metabolite N-desethyl sunitinib in a drug-drug interaction study in Sprague Dawley (SD) rats. The results showed that ketoconazole and voriconazole significantly increased the exposure of sunitinib, decreased the exposure of N-desethyl sunitinib, and inhibited the metabolism of sunitinib in rats. However, itraconazole showed only a weak effect on pharmacokinetics and metabolism. Coadministration of sunitinib with ketoconazole and voriconazole should be avoided if possible or if not, there should be therapeutic drug monitoring of the levels of sunitinib and N-desethyl sunitinib. Therefore, drug-drug interaction should be considered when sunitinib is administered in conjunction with CYP3A inhibitors, which might lead to toxicity.

Mechanistic Considerations About an Unexpected Ramipril Drug-Drug Interaction in the Development of a Triple Fixed-Dose Combination Product Containing Ramipril, Amlodipine, and Atorvastatin

This open-label, repeat-dose, fixed-sequence study in healthy subjects examined pharmacokinetic drug-drug interactions between the components of a novel fixed-dose combination product containing ramipril, amlodipine, and atorvastatin. Sequential 5-day monotreatments (MTs) of ramipril (5 mg/d) and atorvastatin (40 mg/d) were followed by a 9-day amlodipine MT (5 mg/d), separated by 96 hours washout intervals. After amlodipine MT, all 3 single-entity drugs were coadministered for 5 days. Blood samples were taken over the dosing intervals and drug concentrations quantified by high-performance liquid chromatography-mass spectrometry. Pharmacokinetic parameters were assessed and compared between the MTs and combination treatments by analysis of variance. Eighteen healthy subjects were enrolled and completed the study. No significant difference in maximum concentration (C_max ) and area under the plasma concentration-time curve over the dosing interval (AUC_0-τ ) for amlodipine and AUC_0-τ of atorvastatin was observed upon combination treatments versus MTs. C_max of atorvastatin was slightly decreased (C_max ratio, 89.3%) when given in combination. Increased exposure of ramipril and less pronounced exposure of ramiprilat were observed in the presence of amlodipine and atorvastatin, with C_max ratios for ramipril and ramiprilat of 182.6% and 155.9%, and corresponding AUC_0-τ ratios of 150.0% and 112.1%, respectively. These ramiprilat increases are unlikely of clinical relevance, because complete angiotensin-converting enzyme occupation is achieved with ≥5-mg ramipril doses, and free ramiprilat is rapidly eliminated. As ramipril is known to be subject to a site-dependent absorption in the upper small intestine, it is hypothesized that slowing of intestinal motility by atorvastatin or amlodipine or a combined effect of both, increased the residence time of ramipril in its "absorption window," thereby enhancing its bioavailability.

Physiologically-Based Pharmacokinetic Modeling to Support the Clinical Management of Drug-Drug Interactions With Bictegravir

Bictegravir is equally metabolized by cytochrome P450 (CYP)3A and uridine diphosphate‐glucuronosyltransferase (UGT)1A1. Drug–drug interaction (DDI) studies were only conducted for strong inhibitors and inducers, leading to some uncertainty whether moderate perpetrators or multiple drug associations can be safely coadministered with bictegravir. We used physiologically‐based pharmacokinetic (PBPK) modeling to simulate DDI magnitudes of various scenarios to guide the clinical DDI management of bictegravir. Clinically observed DDI data for bictegravir coadministered with voriconazole, darunavir/cobicistat, atazanavir/cobicistat, and rifampicin were predicted within the 95% confidence interval of the PBPK model simulations. The area under the curve (AUC) ratio of the DDI divided by the control scenario was always predicted within 1.25‐fold of the clinically observed data, demonstrating the predictive capability of the used modeling approach. After the successful verification, various DDI scenarios with drug pairs and multiple concomitant drugs were simulated to analyze their effect on bictegravir exposure. Generally, our simulation results suggest that bictegravir should not be coadministered with strong CYP3A and UGT1A1 inhibitors and inducers (e.g., atazanavir, nilotinib, and rifampicin), but based on the present modeling results, bictegravir could be administered with moderate dual perpetrators (e.g., efavirenz). Importantly, the inducing effect of rifampicin on bictegravir was predicted to be reversed with the concomitant administration of a strong inhibitor such as ritonavir, resulting in a DDI magnitude within the efficacy and safety margin for bictegravir (0.5–2.4‐fold). In conclusion, the PBPK modeling strategy can effectively be used to guide the clinical management of DDIs for novel drugs with limited clinical experience, such as bictegravir.

COVID-19 associated with pulmonary aspergillosis: A literature review

Bacterial or virus co-infections with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been reported in many studies, however, the knowledge on Aspergillus co-infection among patients with coronavirus disease 2019 (COVID-19) was limited. This literature review aims to explore and describe the updated information about COVID-19 associated with pulmonary aspergillosis. We found that Aspergillus spp. can cause co-infections in patients with COVID-19, especially in severe/critical illness. The incidence of IPA in COVID-19 ranged from 19.6% to 33.3%. Acute respiratory distress syndrome requiring mechanical ventilation was the common complications, and the overall mortality was high, which could be up to 64.7% (n = 22) in the pooled analysis of 34 reported cases. The conventional risk factors of invasive aspergillosis were not common among these specific populations. Fungus culture and galactomannan test, especially from respiratory specimens could help early diagnosis. Aspergillus fumigatus was the most common species causing co-infection in COVID-19 patients, followed by Aspergillus flavus. Although voriconazole is the recommended anti-Aspergillus agent and also the most commonly used antifungal agent, aspergillosis caused by azole-resistant Aspergillus is also possible. Additionally, voriconazole should be used carefully in the concern of complicated drug-drug interaction and enhancing cardiovascular toxicity on anti-SARS-CoV-2 agents. Finally, this review suggests that clinicians should keep alerting the possible occurrence of pulmonary aspergillosis in severe/critical COVID-19 patients, and aggressively microbiologic study in addition to SARS-CoV-2 via respiratory specimens should be indicated.

Use of Alfentanil in Palliative Care

Alfentanil is used for chronic pain relief in palliative care. However, there is a dearth of data on its use. For this reason, a decision was made to review the use of alfentanil in palliative care. Retrospective study was carried out in a palliative care service. The files of patients who received alfentanil as an intravenous or subcutaneous continuous infusion for pain relief, between January 2018 and April 2019. In total, 111 patients received alfentanil out of 113 admissions. Of them, 56 were male, and the median age was 70 years. The median number of days on alfentanil was 6 (range 1 to 129). The most frequent primary reasons for switching to alfentanil was uncontrolled pain in 52 (46%) patients and renal impairment in 24 (21%) patients. The median 24-h initial dose of alfentanil was 4 mg (1–20), and the median final 24-h dose of alfentanil was 5 mg (1–60), (p < 0.001). The initial 24-h median number of rescue doses was 2 (0–8), and the final median number of rescue doses was 1 (0 to 8), (p = 0.025). In 56 patients who were on alfentanil for at least 7 days, the dose decreased in 3 (5%), remained stable in 10 (18%) and increased in 43 (77%). The patient on alfentanil for 129 days maintained the same dose throughout that period. Alfentanil can be a useful second-line opioid. The induction of tolerance does not seem to be particularly rapid with alfentanil.

Statin-induced rhabdomyolysis from azithromycin interaction in a patient with heterozygous SLCO1B1 polymorphism

WHAT IS KNOWN AND OBJECTIVE

Unlike other macrolide antibiotics, azithromycin is considered safe to co-prescribe with simvastatin. We aim to elucidate the mechanism of a rare azithromycin-simvastatin interaction.

CASE DESCRIPTION

We report a case of simvastatin-induced rhabdomyolysis caused by an azithromycin drug interaction in a patient with heterozygous SLCO1B1 loss-of-function polymorphism. We propose a dual-hit mechanism for this drug-drug-genome interaction. Azithromycin mildly inhibits simvastatin's CYP 3A4 hepatic metabolism, and the SLCO1B1 polymorphism reduces simvastatin hepatic uptake. The combination increases simvastatin serum concentrations significantly, inducing rhabdomyolysis.

WHAT IS NEW AND CONCLUSION

Patients with statin-induced myopathy associated with non-classic CYP inhibitors should be considered for genetic testing and alternative statins with less risk of future interactions.

Clinical Data Combined With Modeling and Simulation Indicate Unchanged Drug-Drug Interaction Magnitudes in the Elderly

Age-related comorbidities and consequently polypharmacy are highly prevalent in the elderly, resulting in an increased risk for drug-drug interactions (DDIs). The effect of aging on DDI magnitudes is mostly uncertain, leading to missing guidance regarding the clinical DDI management in the elderly. Clinical data obtained in aging people living with HIV ≥ 55 years, who participated in the Swiss HIV Cohort Study, demonstrated unchanged DDI magnitudes with advanced aging for four studied DDI scenarios. These data plus published data for midazolam in the presence of clarithromycin and rifampicin in elderly individuals assessed the predictive potential of the used physiologically-based pharmacokinetic (PBPK) model to simulate DDIs in the elderly. All clinically observed data were generally predicted within the 95% confidence interval of the PBPK simulations. The verified model predicted subsequently the magnitude of 50 DDIs across adulthood (20-99 years) with 42 scenarios being only verified in adults aged 20-50 years in the absence of clinically observed data in the elderly. DDI magnitudes were not impacted by aging regardless of the involved drugs, DDI mechanism, mediators of DDIs, or the sex of the investigated individuals. The prediction of unchanged DDI magnitudes with advanced aging were proofed by 17 published, independent DDIs that were investigated in young and elderly subjects. In conclusion, this study demonstrated by combining clinically observed data with modeling and simulation that aging does not impact DDI magnitudes and thus, clinical management of DDIs can a priori be similar in aging men and women in the absence of severe comorbidities.

Dual EGFR and ABL Tyrosine Kinase Inhibitor Treatment in a Patient with Concomitant EGFR-Mutated Lung Adenocarcinoma and BCR-ABL1-Positive CML

Tyrosine kinase inhibitor (TKI) combination is expected to increase in the era of precision medicine. TKI combination may be required to treat double primary cancers, each having a targetable gene, or to treat a single malignancy with multiple targetable genes. Here, we demonstrate the first report of dual EGFR and ABL TKI treatment in a patient with concomitant EGFR-mutated lung adenocarcinoma and BCR-ABL1-positive chronic myeloid leukemia (CML). A 60-year-old man with an 8-year history of CML was diagnosed as advanced EGFR-mutated lung adenocarcinoma. Complete molecular response of CML had been achieved by imatinib, and ABL-TKI had been switched to nilotinib four years previously due to muscle cramps. We discontinued nilotinib and started afatinib. Although partial response of lung adenocarcinoma was achieved, cytogenetic relapse of CML was observed following nilotinib discontinuation. We applied the previously described framework of cytochrome P450 3A4-mediated oral drug-drug interactions and selected gefitinib and nilotinib to treat both malignancies. We effectively and safely administered this combination for seven months. The present report is the first to demonstrate the safety and efficacy of dual EGFR and ABL TKI treatment in a patient with concomitant EGFR-mutated lung adenocarcinoma and CML.

Prevalence of drug-drug interaction in atrial fibrillation patients based on a large claims data

This study aimed to compare and determine the prevalence of drug–drug interaction (DDI) and bleeding rate in atrial fibrillation (AF) patients receiving anticoagulants in a clinical setting. We used large claims data of AF patients obtained from the Japan Medical Data Center. The prevalence of DDIs and cases leading to bleeding events were surveyed clinically relevant DDIs extracted from 1) reported from a spontaneous adverse event reporting system (Japanese Adverse Drug Events Report system; JADER) ≥4 patients; 2) DDIs cited in the package inserts of each anticoagulant (each combination assessed according to “Drug interaction 2015” list; 3) warfarin and quinolone antibiotics DDIs. DDIs were categorized the mechanisms for pharmacokinetic DDI (Cytochrome P450 (CYP) or transporter etc. that modulate blood concentration of anticoagulants)/pharmacodynamic DDI (combination with similar pharmacological actions) or both in the analysis for each patients’ prescriptions obtained from a claims data. AF patients were compared between cases with and without bleeding after administered of anticoagulants. Bleeding was observed in 220/3290 (6.7%) AF patients. The bleeding rate in patients with both pharmacokinetic and pharmacodynamic DDI mechanisms (26.3%) was higher than that in patients with either mechanism (8.6% and 9.2%, respectively) or without DDIs (4.9%). The odds ratio for bleeding in AF patients with both of pharmacokinetic and pharmacodynamic was (7.18 [4.69–11.00], p<0.001). Our study concluded multi mechanism based DDIs leads serious outcome as compared to that of single mechanism based DDIs in AF patients. We determined the prevalence and frequency of bleeding for anticoagulant-related DDIs. To manage DDIs, both pharmacokinetic and pharmacodynamic DDI mechanisms should be closely monitored for initial symptoms of bleeding within the first 3 months.

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.

In vitro-in vivo correlation of the drug-drug interaction potential of antiretroviral HIV treatment regimens on CYP1A1 substrate riociguat

Objectives: Riociguat is a soluble guanylate cyclase stimulator licensed for the treatment of pulmonary arterial hypertension (PAH), a potentially fatal complication of human immunodeficiency virus infection. This study investigated the inhibitory potency of selected antiretroviral regimens on the metabolic clearance of riociguat.Methods: The inhibitory potential of the components of six antiretroviral combinations (ATRIPLA® (efavirenz/emtricitabine/tenofovir disoproxil), COMPLERA® (rilpivirine/emtricitabine/tenofovir disoproxil), STRIBILD® (elvitegravir/cobicistat/emtricitabine/tenofovir disoproxil), TRIUMEQ® (abacavir/dolutegravir/lamivudine), and two ritonavir-boosted regimens) on riociguat metabolism were evaluated in recombinant human CYP1A1 and CYP3A4 as well as in human hepatocytes exhibiting both CYP1A1 and CYP3A4 activity. In vitro-in vivo correlation was performed between calculated and observed increases in riociguat exposure in vivo.Results: Using both in vitro systems, the predicted increase in exposure of riociguat was highest with components of TRIUMEQ® followed by COMPLERA®, ATRIPLA®, STRIBILD®, and the ritonavir-boosted regimens. Further experiments in human hepatocytes confirmed CYP1A1 to be the predominant enzyme in the metabolic clearance of riociguat.Conclusion: Antiretroviral treatment containing the potent CYP1A1 inhibitor abacavir had the greatest impact on riociguat metabolic clearance. The impact of comedications containing only strong CYP3A4 inhibitors e.g. ritonavir was less pronounced, suggesting a benefit of riociguat over PAH-targeting medications with contraindications for use with strong CYP3A4 inhibitors.

Pharmacokinetics of the Novel, Selective, Non-steroidal Mineralocorticoid Receptor Antagonist Finerenone in Healthy Volunteers: Results from an Absolute Bioavailability Study and Drug-Drug Interaction Studies In Vitro and In Vivo

BACKGROUND AND OBJECTIVES

Finerenone is a selective, non-steroidal mineralocorticoid receptor antagonist. In vivo and in vitro studies were performed to assess absolute bioavailability of finerenone, the effect of metabolic enzyme inhibitors on the pharmacokinetics of finerenone and its metabolites, the quantitative contribution of the involved enzymes cytochrome P450 (CYP) 3A4 and CYP2C8 and the relevance of gut wall versus liver metabolism.

METHODS

The pharmacokinetics, safety and tolerability of finerenone (1.25-10 mg orally or 0.25-1.0 mg intravenously) were evaluated in healthy male volunteers in four crossover studies. Absolute bioavailability was assessed in volunteers receiving finerenone orally and by intravenous infusion (n = 15) and the effects of erythromycin (n = 15), verapamil (n = 13) and gemfibrozil (n = 16) on finerenone pharmacokinetics were investigated. Finerenone was also incubated with cryopreserved human hepatocytes in vitro in the presence of erythromycin, verapamil or gemfibrozil.

RESULTS

Finerenone absolute bioavailability was 43.5% due to first-pass metabolism in the gut wall and liver. The geometric mean AUC_0-∞ ratios of finerenone (drug + inhibitor/drug alone) were 3.48, 2.70 and 1.10 with erythromycin, verapamil and gemfibrozil, respectively. The contribution ratio of CYP3A4 to the metabolic clearance of finerenone derived from these values was 0.88-0.89 and was consistent with estimations based on in vitro data, with the remaining metabolic clearance due to CYP2C8 involvement.

CONCLUSION

Finerenone is predominantly metabolized by CYP3A4 in the gut wall and liver. Increases in systemic exposure upon concomitant administration of inhibitors of this isoenzyme are predictable and consistent with in vitro data. Inhibition of CYP2C8, the second involved metabolic enzyme, has no relevant effect on finerenone in vivo.

Assessment of Pharmacokinetic Drug-Drug Interactions in Humans: In Vivo Probe Substrates for Drug Metabolism and Drug Transport Revisited

Pharmacokinetic parameters of selective probe substrates are used to quantify the activity of an individual pharmacokinetic process (PKP) and the effect of perpetrator drugs thereon in clinical drug-drug interaction (DDI) studies. For instance, oral caffeine is used to quantify hepatic CYP1A2 activity, and oral dagibatran etexilate for intestinal P-glycoprotein (P-gp) activity. However, no probe substrate depends exclusively on the PKP it is meant to quantify. Lack of selectivity for a given enzyme/transporter and expression of the respective enzyme/transporter at several sites in the human body are the main challenges. Thus, a detailed understanding of the role of individual PKPs for the pharmacokinetics of any probe substrate is essential to allocate the effect of a perpetrator drug to a specific PKP; this is a prerequisite for reliably informed pharmacokinetic models that will allow for the quantitative prediction of perpetrator effects on therapeutic drugs, also in respective patient populations not included in DDI studies.

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.

[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.

Notable Drug-Drug Interaction Between Etizolam and Itraconazole in Poor Metabolizers of Cytochrome P450 2C19

In this study, impact of a polymorphism of CYP2C19 on drug-drug interaction (DDI) was examined for etizolam. The effect of itraconazole (a strong CYP3A inhibitor) on the pharmacokinetics of etizolam (a substrate of CYP2C19 and CYP3A) was assessed in both extensive metabolizers (EMs) and poor metabolizers (PMs) of CYP2C19. Sixteen participants (8 EMs and 8 PMs) received a single oral dose of etizolam (0.25 mg) on day 1. The participants ingested itraconazole (200 mg twice a day) on days 2-5. On day 5, participants received an oral dose of etizolam (0.25 mg) again. Before coadministration of itraconazole (day 1), the area under the time-plasma concentration curve from time zero to infinity (AUC_∞ ) of etizolam was higher in PMs than in EMs (2.65-fold, P < .01). Coadministration of itraconazole increased the AUC_∞ of etizolam 1.66-fold and 2.34-fold in EMs and PMs, respectively (day 5). Consequently, AUC_∞ was 6.18-fold higher in PMs with itraconazole than that in EMs without itraconazole. The increase by itraconazole was larger in PMs (P < .01). In heterozygous EMs (hEMs), AUC_∞ was simulated to be 2.56-fold higher with itraconazole than that in EMs without itraconazole. We found that in vitro measurements of fraction metabolized (f_m ) using the liver microsome prepared from PM donors would be helpful to predict polymorphism-dependent DDIs. These results suggest that the PMs and hEMs of a polymorphic CYP would be at higher risk of DDIs relative to EMs for drugs metabolized by both polymorphic and nonpolymorphic CYPs such as etizolam.

Simulations of Cytochrome P450 3A4-Mediated Drug-Drug Interactions by Simple Two-Compartment Model-Assisted Static Method

In order to predict cytochrome P450 3A4 (CYP3A4)-mediated drug-drug interactions (DDIs), a simple 2-compartment model-assisted, overall inhibition activity (A_i,overall) method was derived based on 2 concepts. One concept was that the increase in blood victim level and fold increase in the area under the blood victim level curve produced by DDI are determined entirely by A_i,overall, the hepatic availability of the victim and fraction of urinary excreted unchanged victim, where A_i,overall is determined by the perpetrator-specific CYP isoform inhibition activities (A_i,CYPs, DDI predictor-1) and victim-specific fractional CYP isoform contributions (f_m,CYPs, predictor-2). The other concept was that a DDI can be bridged to other DDIs, so that any possible DDI produced by a given victim or a given perpetrator can be predicted by using these predictors. The A_i,CYP3A4s of 12 common CYP3A4 inhibitors were able to be determined and shown to be useful for the prediction of CYP3A4-mediated DDIs wherein victims were metabolized by multiple CYP isoforms. Additionally, it was demonstrated that f_m,CYP values with high confidence can be estimated by bridging DDIs produced by the same victim and different perpetrators. This bridging approach will accelerate prediction of DDIs produced by new chemical entities from the existing DDI database.

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.

Modulation of Hepatic Cytochrome P450 Enzymes by Curcumin and its Pharmacokinetic Consequences in Sprague-dawley Rats

Background:

Curcumin (CUR) is a polyphenolic component derived from an herbal remedy and dietary spice turmeric (Curcuma longa).

Objective:

The aim of this study was to investigate inhibitory effects of CUR on in vitro cytochrome P450 (CYP) activity and in vivo pharmacokinetic consequences of single CUR dose in rats.

Materials and Methods:

An in vitro CYP inhibition study in rat liver microsomes (RLM) was conducted using probe substrates for CYPs. Then, an in vivo pharmacokinetics of intravenous buspirone (BUS), a probe substrate for CYP3A, was studied with the concurrent administration of oral CUR in rats.

Results:

In the in vitro CYP inhibition study, CUR inhibited the CYP3A-mediated metabolism of testosterone (TES) with a half maximal inhibitory concentration of 11.0 ± 3.3 μM. However, the impact of a single oral CUR dose on the pharmacokinetics of BUS in rats is limited, showing that CUR cannot function as an inhibitor for CYP3A-mediated drug metabolism in vivo.

Conclusion:

To the best of our knowledge, our results are the first reported data regarding the inhibition of in vitro CYP3A-mediated metabolism of TES and the in vivo impact of a single CUR dose on the pharmacokinetics of BUS in rats. Further study is required to draw a confirmative conclusion on whether CUR can be a clinically relevant CYP3A4 inhibitor.

SUMMARY

CUR can inhibit the in vitro CYP3A-mediated metabolism of TES in RLM. However, the impact of a single oral CUR dose on the pharmacokinetics of BUS in rats is limited, showing that CUR cannot function as an inhibitor for CYP3A-mediated drug metabolism in vivo.