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

Phenotypic Models of Drug-Drug-Gene Interactions Mediated by Cytochrome Drug-Metabolizing Enzymes

Genetic polymorphisms in drug metabolizing enzymes and drug-drug interactions are major sources of inadequate drug exposure and ensuing adverse effects or insufficient responses. The current challenge in assessing drug-drug gene interactions (DDGIs) for the development of precise dose adjustment recommendation systems is to take into account both simultaneously. Here, we analyze the static models of DDGI from in vivo data and focus on the concept of phenoconversion to model inhibition and genetic polymorphisms jointly. These models are applicable to datasets where pharmacokinetic information is missing and are being used in clinical support systems and consensus dose adjustment guidelines. We show that all such models can be handled by the same formal framework, and that models that differ at first sight are all versions of the same linear phenoconversion model. This model includes the linear pharmacogenetic and inhibition models as special cases. We highlight present challenges in this endeavor and the open issues for future research in developing DDGI models for recommendation systems.

Impact of the use of a drug-drug interaction checker on pharmacist interventions involving well-known strong interactors

OBJECTIVES

Several drug-drug interaction (DDI) checkers such as DDI-Predictor have been developed to detect and grade DDIs. DDI-Predictor gives an estimate of the magnitude of an interaction based on the ratio of areas under the curve. The objective of the present study was to analyse the frequencies of DDIs involving well-known strong interactors such as rifampicin and selective serotonin reuptake inhibitors (SSRIs), as reported by a clinical pharmacy team using DDI-Predictor, and the pharmacist intervention acceptance rate.

METHODS

The pharmacist intervention rate and the physician acceptance rate were calculated for DDIs involving rifampicin or the SSRIs fluoxetine, paroxetine, duloxetine and sertraline. The rates were compared with a bilateral χ2 test or Fisher's exact test.

RESULTS

Of the 284 DDIs recorded, 38 (13.4%) involved rifampicin and 78 (27.5%) involved SSRIs. The pharmacist intervention rate differed significantly (68.4% for rifampicin vs 48.8% for SSRIs; p=0.045) but the physician acceptance rate did not (84.6% for rifampicin vs 81.6% for SSRIs; p=1). Pharmaceutical interventions for SSRIs were more frequent when the ratio of the area under the drug concentration versus time curve in DDI-Predictor was >2. Pharmacists were more likely to issue a pharmacist intervention for DDIs involving rifampicin because of a high perceived risk of treatment failure and were less likely to issue a pharmacist intervention for DDIs involving an SSRI, except when the suspected interaction was strong.

CONCLUSIONS

DDI checkers can help pharmacists to manage DDIs involving strong interactors. DDIs involving strong inhibitors versus a strong inducer differ with regard to their intervention and acceptance rates, notably due to the estimation of the magnitude of the DDI.

Clinical Trial Data-Driven Risk Assessment of Drug-Drug Interactions: A Rapid and Accurate Decision-Making Tool

BACKGROUND

In clinical practice, the vast array of potential drug combinations necessitates swift and accurate assessments of pharmacokinetic drug-drug interactions (DDIs), along with recommendations for adjustments. Current methodologies for clinical DDI evaluations primarily rely on basic extrapolations from clinical trial data. However, these methods are limited in accuracy owing to their lack of a comprehensive consideration of various critical factors, including the inhibitory potency, dosage, and type of the inhibitor, as well as the metabolic fraction and intestinal availability of the substrate.

OBJECTIVE

This study aims to propose an efficient and accurate clinical pharmacokinetic-mediated DDI assessment tool, which comprehensively considers the effects of inhibitory potency and dosage of inhibitors, intestinal availability and fraction metabolized of substrates on DDI outcomes.

METHODS

This study focuses on DDIs caused by cytochrome P450 3A4 enzyme inhibition, utilizing extensive clinical trial data to establish a methodology to calculate the metabolic fraction and intestinal availability for substrates, as well as the concentration and inhibitory potency for inhibitors ( K i ork inact / K I ). These parameters were then used to predict the outcomes of DDIs involving 33 substrates and 20 inhibitors. We also defined the risk index for substrates and the potency index for inhibitors to establish a clinical DDI risk scale. The training set for parameter calculation consisted of 73 clinical trials. The validation set comprised 89 clinical DDI trials involving 53 drugs. which was used to evaluate the reliability of in vivo values of K i andk inact / K I , the accuracy of DDI predictions, and the false-negative rate of risk scale.

RESULTS

First, the reliability of the in vivo K i andk inact / K I values calculated in this study was assessed using a basic static model. Compared with values obtained from other methods, this study values showed a lower geometric mean fold error and root mean square error. Additionally, incorporating these values into the physiologically based pharmacokinetic-DDI model facilitated a good fitting of the C-t curves when the substrate's metabolic enzymes are inhibited. Second, area under the curve ratio predictions of studied drugs were within a 1.5 × margin of error in 81% of cases compared with clinical observations in the validation set. Last, the clinical DDI risk scale developed in this study predicted the actual risks in the validation set with only a 5.6% incidence of serious false negatives.

CONCLUSIONS

This study offers a rapid and accurate approach for assessing the risk of pharmacokinetic-mediated DDIs in clinical practice, providing a foundation for rational combination drug use and dosage adjustments.

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.

The Potential Therapeutic Application of Simvastatin for Brain Complications and Mechanisms of Action

Statins are common drugs that are clinically used to reduce elevated plasma cholesterol levels. Based on their solubility, statins are considered to be either hydrophilic or lipophilic. Amongst them, simvastatin has the highest lipophilicity to facilitate its ability to cross the blood-brain barrier. Recent studies have suggested that simvastatin could be a promising therapeutic option for different brain complications and diseases ranging from brain tumors (i.e., medulloblastoma and glioblastoma) to neurological disorders (i.e., Alzheimer's disease, Parkinson's disease, and Huntington's disease). Specific mechanisms of disease amelioration, however, are still unclear. Independent studies suggest that simvastatin may reduce the risk of developing certain neurodegenerative disorders. Meanwhile, other studies point towards inducing cell death in brain tumor cell lines. In this review, we outline the potential therapeutic effects of simvastatin on brain complications and review the clinically relevant molecular mechanisms in different cases.

Atypical kinetics of cytochrome P450 enzymes in pharmacology and toxicology

Atypical kinetics are observed in metabolic reactions catalyzed by cytochrome P450 enzymes (P450). Yet, this phenomenon is regarded as experimental artifacts in some instances despite increasing evidence challenging the assumptions of typical Michaelis-Menten kinetics. As P450 play a major role in the metabolism of a wide range of substrates including drugs and endogenous compounds, it becomes critical to consider the impact of atypical kinetics on the accuracy of estimated kinetic and inhibitory parameters which could affect extrapolation of pharmacological and toxicological implications. The first half of this book chapter will focus on atypical non-Michaelis-Menten kinetics (e.g. substrate inhibition, biphasic and sigmoidal kinetics) as well as proposed underlying mechanisms supported by recent insights in mechanistic enzymology. In particular, substrate inhibition kinetics in P450 as well as concurrent drug inhibition of P450 in the presence of substrate inhibition will be further discussed. Moreover, mounting evidence has revealed that despite the high degree of sequence homology between CYP3A isoforms (i.e. CYP3A4 and CYP3A5), they have the propensities to exhibit vastly different susceptibilities and potencies of mechanism-based inactivation (MBI) with a common drug inhibitor. These experimental observations pertaining to the presence of these atypical isoform- and probe substrate-specific complexities in CYP3A isoforms by several clinically-relevant drugs will therefore be expounded and elaborated upon in the second half of this book chapter.

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.

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

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

Does DDI-Predictor Help Pharmacists to Detect Drug-Drug Interactions and Resolve Medication Issues More Effectively?

The characterization of drug-drug interactions (DDIs) may require the use of several different tools, such as the thesaurus issued by our national health agency (i.e., ANSM), the metabolic pathways table from the Geneva University Hospital (GUH), and DDI-Predictor (DDI-P). We sought to (i) compare the three tools’ respective abilities to detect DDIs in routine clinical practice and (ii) measure the pharmacist intervention rate (PIR) and physician acceptance rate (PAR) associated with the use of DDI-P. The three tools’ respective DDI detection rates (in %) were measured. The PIRs and PARs were compared by using the area under the curve ratio given by DDI-P (R_AUC) and applying a chi-squared test. The DDI detection rates differed significantly: 40.0%, 76.5%, and 85.2% for ANSM (The National Agency for the Safety of Medicines and Health Products), GUH and DDI-P, respectively (p < 0.0001). The PIR differed significantly according to the DDI-P’s R_AUC: 90.0%, 44.2% and 75.0% for R_AUC ≤ 0.5; R_AUC 0.5–2 and R_AUC > 2, respectively (p < 0.001). The overall PAR was 85.1% and did not appear to depend on the R_AUC category (p = 0.729). Our results showed that more pharmacist interventions were issued when details of the strength of the DDI were available. The three tools can be used in a complementary manner, with a view to refining medication adjustments.

Inhibition and Induction by Poziotinib of Different Rat Cytochrome P450 Enzymes In Vivo and in an In Vitro Cocktail Method

Poziotinib is an orally active, irreversible, pan-HER tyrosine kinase inhibitor used to treat non-small cell lung cancer, breast cancer, and gastric cancer. Poziotinib is currently under clinical investigation, and understanding its drug-drug interactions is extremely important for its future development and clinical application. The cocktail method is most suitable for evaluating the activity of cytochrome P450 enzymes (CYPs). As poziotinib is partially metabolized by CYPs, cocktail probes are used to study the interaction between drugs metabolized by each CYP subtype. Midazolam, bupropion, dextromethorphan, tolbutamide, chlorzoxazone, phenacetin, and their metabolites were used to examine the effects of poziotinib on the activity of cyp1a2, 2b1, 2d1, 2c11, 2e1, and 3a1/2, respectively. The in vitro experiment was carried out by using rat liver microsomes (RLMs), whereas the in vivo experiment involved the comparison of the pharmacokinetic parameters of the probes after co-administration with poziotinib to rats to those of control rats treated with only probes. UPLC-MS/MS was used to detect the probes and their metabolites in rat plasma and rat liver microsomes. The in vitro results revealed that the half-maximal inhibitory concentration values of bupropion and tolbutamide in RLMs were 8.79 and 20.17 μM, respectively, indicating that poziotinib showed varying degrees of inhibition toward cyp2b1 and cyp2c11. Poziotinib was a competitive inhibitor of cyp2b1 and cyp2c11, with Ki values of 16.18 and 17.66 μM, respectively. No time- or concentration-dependence of inhibition by poziotinib was observed toward cyp2b1 and cyp2c11 in RLMs. Additionally, no obvious inhibitory effects were observed on the activity of cyp1a2, cyp2d1, cyp2e1, and cyp3a1/2. In vivo analysis revealed that bupropion, tolbutamide, phenacetin, and chlorzoxazone showed significantly different pharmacokinetic parameters after administration (p < 0.05); there was no significant difference in the pharmacokinetic parameters of dextromethorphan and midazolam. These results show that poziotinib inhibited cyp2b1 and cyp2c11, but induced cyp1a2 and cyp2e1 in rats. Thus, poziotinib inhibited cyp2b1 and cyp2c11 activity in rats, suggesting the possibility of interactions between poziotinib and these CYP substrates and the need for caution when combining them in clinical settings.

Evaluation of acacetin inhibition potential against cytochrome P450 in vitro and in vivo

Acacetin is a natural flavonoid that is widely distributed in plants and possesses numerous pharmacological activities. The aim of the present study was to investigate the effects of acacetin on the activities of the cytochrome P450 family members CYP1A2, CYP2B1, CYP2C11, CYP2D1, CYP2E1, and CYP3A2 in rat liver microsomes in vitro and rats in vivo to evaluate potential herb-drug interactions by using a cocktail approach. Phenacetin, bupropion, tolbutamide, dextromethorphan, chlorzoxazone, and midazolam were chosen as the probe substrates. An ultra-performance liquid chromatography-tandem mass spectrometry method was developed for the simultaneous detection of the probe substrates and their metabolites. In vitro, the mode of acacetin inhibition of CYP2B1, CYP2C11, and CYP2E1 was competitive, while mixed inhibition was observed for CYP1A2 and CYP3A2. The Ki values in this study were less than 8.32 μM. In vivo, the mixed probe substrates were administered by gavage after daily intraperitoneal injection with 50 mg/kg acacetin or saline for 2 weeks. The main pharmacokinetic parameters, area under the plasma concentration-time curve (AUC), plasma clearance (CL), and maximum plasma concentration (C_max) of the probe substrates were significantly different in the experimental group than in the control group. Overall, the in vitro and in vivo results indicated that acacetin would be at high risk to cause toxicity and drug interactions via cytochrome P450 inhibition.

The Effect of Grapefruit Juice on the Pharmacokinetics of Tadalafil in Rats

We developed and validated a novel, sensitive, selective, and inexpensive high-performance liquid chromatography (HPLC) method for the determination of tadalafil in rats plasma and to investigate the effect of grapefruit juice on the pharmacokinetics of tadalafil in rats. The ZORBAX Eclipse XDB-C18 (4.6 × 150 mm, 5 μm) chromatography column can be used to separate tadalafil and carbamazepine (internal standard, IS). A mixture of acetonitrile-0.2% trifluoroacetic acid-water (48 : 10 : 42, V/V/V) was used as the mobile phase with a flow rate of 1.0 mL/min. The column temperature was set at 35.0°C. The detection wavelength was set at 286 nm. The tadalafil was extracted by ethyl acetate from plasma at the alkaline condition. 12 healthy male Sprague-Dawley (SD) rats were randomly divided into two groups, Group A (experimental group, received grapefruit juice 5 mL/kg for 7 days) and Group B (control group, received normal saline for 7 days). All the rats were given a single dose of tadalafil (5 mg/kg) after the last administration. The main pharmacokinetic parameters were calculated by DAS 2.0 software. Under the conditions of this experiment, the plasma concentrations of tadalafil in the range of 10–2000 ng/ml had a good linear relationship. The intra- and interday precision for tadalafil in plasma were less than 15%, and the relative recovery rate was good at low, medium, and high QC levels. The C_max of tadalafil in the control group and the experimental group was (725.89 ± 161.59) ng/mL and (1271.60 ± 179.31) ng/mL, t_1/2 was (9.28 ± 2.07) h and (11.70 ± 1.47) h, AUC _(0-t) was (7399.61 ± 696.85) ng·h/mL and (9586.52 ± 2048.81) ng·h/mL, and AUC_(0-∞) was (7995.50 ± 707.23) ng·h/mL and (10639.43 ± 2235.94) ng·h/mL, respectively. Results show that the C_max of tadalafil in group A was 75.17% higher than that in group B, the Vz/F was also reduced, and the t_1/2 was increased by 2.42 h. The developed HPLC–DAD method for the determination of tadalafil in rats plasma was accurate, reproducible, specific, and it was found to be suitable for the pharmacokinetics of tadalafil and food-drug interactions. Grapefruit juice can inhibit the metabolism of tadalafil and increase the exposure of tadalafil in rats.

Association between clinically relevant toxicities of pazopanib and sunitinib and the use of weak CYP3A4 and P-gp inhibitors

PURPOSE

Sunitinib and pazopanib, two tyrosine kinase inhibitors (TKI), may be targets of potential pharmacokinetic drug-drug interactions (P-PK-DDIs). While strong cytochrome P4503A (CYP3A4) inhibitors or inducers should cause a clinically relevant modification in plasma TKI concentrations, the effect of weak inhibitors is unknown. The objective of this study was to evaluate the association between weak P-PK-DDI and clinically relevant toxicity in real life.

PATIENTS AND METHODS

This was a single-center retrospective study including patients treated with sunitinib or pazopanib for any malignancies, for whom a PK-DDI analysis was performed before starting TKI. The primary endpoint was the correlation between P-PK-DDIs and a dose decrease after 1 month of treatment. The secondary endpoint was the correlation between PK-DDIs and drug withdrawal due to toxicity.

RESULTS

Seventy-six patients were assessed. A P-PK-DDI with weak CYP3A4 or P-gp inhibition was found in 14 patients. In patients with P-PK-DDI or without, the dose was reduced during the first month in 57.1% and 17.7% (p = 0.003) and the drug withdrawn in 42.8% and 11.3% (p = 0.011), respectively. In multivariate analysis, a significant correlation was found between P-PK-DDI (CYP3A4 and P-gp inhibitors) and dose reduction, and between drug withdrawal and PK-DDI (CYP3A4 inhibitors).

CONCLUSION

P-PK-DDI was correlated with dose reduction and drug withdrawal due to toxicity. The causality of this relationship warrants to be assessed; therefore, therapeutic drug monitoring is necessary in patients treated with TKI.

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.

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.

Evaluation of the inhibition potential of plumbagin against cytochrome P450 using LC-MS/MS and cocktail approach

Plumbagin (5-hydroxy-2-methyl-1,4-naphthoquinone), a natural naphthoquinone compound isolated from roots of Plumbago zeylanica L., has drawn a lot of attention for its plenty of pharmacological properties including antidiabetes and anti-cancer. The aim of this study was to investigate the effects of plumbagin on CYP1A2, CYP2B1/6, CYP2C9/11, CYP2D1/6, CYP2E1 and CYP3A2/4 activities in human and rat liver and evaluate the potential herb-drug interactions using the cocktail approach. All CYP substrates and their metabolites were analyzed using high-performance liquid chromatography-tandem mass spectrometry (LC-MS/MS). Plumbagin presented non-time-dependent inhibition of CYP activities in both human and rat liver. In humans, plumbagin was not only a mixed inhibitor of CYP2B6, CYP2C9, CYP2D6, CYP2E1 and CYP3A4, but also a non-competitive inhibitor of CYP1A2, with Ki values no more than 2.16 μM. In rats, the mixed inhibition of CYP1A2 and CYP2D1, and competitive inhibition for CYP2B1, CYP2C11 and CYP2E1 with Ki values less than 9.93 μM were observed. In general, the relatively low Ki values of plumbagin in humans would have a high potential to cause the toxicity and drug interactions involving CYP enzymes.