Simple Evaluation Method for CYP3A4 Induction from Human Hepatocytes: The Relative Factor Approach with an Induction Detection Limit Concentration Based on the Emax Model

3D spheroid HepaRG and fluorescent biphasic tracer for CYP3A4-mediated antibiotic interaction monitoring in sepsis

Cytochrome P450 3A4 (CYP3A4) is a crucial enzyme in the metabolism of xenobiotics, particularly in drug metabolism interactions (DDIs), making it a significant factor in clinical drug use. However, current assay techniques are both laborious and costly, making it difficult to construct a high-throughput monitoring method that can be used in conjunction with the clinic. This poses certain safety hazards for drug combination. Therefore, it is crucial to develop a synchronized monitoring method for the inhibition and induction of CYP3A4. In this study, we utilized 3D culture technology to develop a HepaRG cells spheroid model. The CYP450 and transporter expression, the albumin secretion, and urea synthesis capacity characteristics were analyzed. The NEN probe was utilized as a tracer molecule for CYP3A4. The fluorescence intensity of metabolites was characterized by laser confocal technique to determine the inhibition and expression of CYP3A4 in the HepaRG cell spheroid model by the antibiotics for sepsis. The results indicate that the HepaRG sphere model successfully possessed the physiological phenotype of the liver, which could be used for drug interaction monitoring. Through positive drug testing, NEN probe was able to achieve bidirectional characterization of CYP3A4 induction and inhibition. The monitoring method described in this paper was successfully applied to drug interaction monitoring of commonly used antibiotics in sepsis patients, which is a convenient and rapid monitoring method. The proposed method offers a new strategy for monitoring CYP3A4-mediated drug-drug interactions with a high-throughput assay, which will help to improve the safety of clinical drug combination.

Enzyme-mediated drug-drug interactions: a review of in vivo and in vitro methodologies, regulatory guidance, and translation to the clinic

Enzyme-mediated pharmacokinetic drug-drug interactions can be caused by altered activity of drug metabolizing enzymes in the presence of a perpetrator drug, mostly via inhibition or induction. We identified a gap in the literature for a state-of-the art detailed overview assessing this type of DDI risk in the context of drug development. This manuscript discusses in vitro and in vivo methodologies employed during the drug discovery and development process to predict clinical enzyme-mediated DDIs, including the determination of clearance pathways, metabolic enzyme contribution, and the mechanisms and kinetics of enzyme inhibition and induction. We discuss regulatory guidance and highlight the utility of in silico physiologically-based pharmacokinetic modeling, an approach that continues to gain application and traction in support of regulatory filings. Looking to the future, we consider DDI risk assessment for targeted protein degraders, an emerging small molecule modality, which does not have recommended guidelines for DDI evaluation. Our goal in writing this report was to provide early-career researchers with a comprehensive view of the enzyme-mediated pharmacokinetic DDI landscape to aid their drug development efforts.

Applying Physiologically Based Pharmacokinetic Modeling to Interpret Carbamazepine's Nonlinear Pharmacokinetics and Its Induction Potential on Cytochrome P450 3A4 and Cytochrome P450 2C9 Enzymes

Carbamazepine (CBZ) is commonly prescribed for epilepsy and frequently used in polypharmacy. However, concerns arise regarding its ability to induce the metabolism of other drugs, including itself, potentially leading to the undertreatment of co-administered drugs. Additionally, CBZ exhibits nonlinear pharmacokinetics (PK), but the root causes have not been fully studied. This study aims to investigate the mechanisms behind CBZ's nonlinear PK and its induction potential on CYP3A4 and CYP2C9 enzymes. To achieve this, we developed and validated a physiologically based pharmacokinetic (PBPK) parent-metabolite model of CBZ and its active metabolite Carbamazepine-10,11-epoxide in GastroPlus®. The model was utilized for Drug-Drug Interaction (DDI) prediction with CYP3A4 and CYP2C9 victim drugs and to further explore the underlying mechanisms behind CBZ's nonlinear PK. The model accurately recapitulated CBZ plasma PK. Good DDI performance was demonstrated by the prediction of CBZ DDIs with quinidine, dolutegravir, phenytoin, and tolbutamide; however, with midazolam, the predicted/observed DDI AUClast ratio was 0.49 (slightly outside of the two-fold range). CBZ's nonlinear PK can be attributed to its nonlinear metabolism caused by autoinduction, as well as nonlinear absorption due to poor solubility. In further applications, the model can help understand DDI potential when CBZ serves as a CYP3A4 and CYP2C9 inducer.

Prediction of ROS1 and TRKA/B/C occupancy in plasma and cerebrospinal fluid for entrectinib alone and in DDIs using physiologically based pharmacokinetic (PBPK) modeling approach

PURPOSE

Entrectinib (ENT) is a potent c-ros oncogene 1(ROS1) and neurotrophic tyrosine receptor kinase (NTRKA/B/C) inhibitor. To determine the optimum dosage of ENT using ROS1 and NTRKA/B/C occupancy in plasma and cerebrospinal fluid (CSF) in drug-drug interactions (DDIs), physiologically-based pharmacokinetic (PBPK) models for healthy subjects and cancer population were developed for ENT and M5 (active metabolite).

METHODS

The PBPK models were built using the modeling parameters of ENT and M5 that were mainly derived from the published paper on the ENT PBPK model, and then validated by the observed pharmacokinetics (PK) in plasma and CSF from healthy subjects and patients.

RESULTS

The PBPK model showed that AUC, Cmax, and Ctrough ratios between predictions and observations are within the range of 0.5-2.0, except that the M5 AUC ratio is slightly above 2.0 (2.34). Based on the efficacy (> 75% occupancy for ROS1 and NTRKA/B/C) and safety (AUC < 160 μM·h and Cmax < 8.9 μM), the appropriate dosing regimens were identified. The appropriate dosage is 600 mg once daily (OD) when administered alone, reduced to 200 mg and 400 mg OD with itraconazole and fluconazole, respectively. ENT is not recommended for co-administration with rifampicin or efavirenz, but is permitted with fluvoxamine or dexamethasone.

CONCLUSION

The PBPK models can serve as a powerful approach to predict ENT concentration as well as ROS1 and NTRKA/B/C occupancy in plasma and CSF.

Prediction of drug-drug interactions between roflumilast and CYP3A4/1A2 perpetrators using a physiologically-based pharmacokinetic (PBPK) approach

This study aimed to develop a physiologically-based pharmacokinetic (PBPK) model to predict changes in the pharmacokinetics (PK) and pharmacodynamics (PD, PDE4 inhibition) of roflumilast (ROF) and ROF N-oxide when co-administered with eight CYP3A4/1A2 perpetrators. The population PBPK model of ROF and ROF N-oxide has been successfully developed and validated based on the four clinical PK studies and five clinical drug-drug interactions (DDIs) studies. In PK simulations, every ratio of prediction to observation for PK parameters fell within the range 0.7 to 1.5. In DDI simulations, except for tow peak concentration ratios (Cmax) of ROF with rifampicin (prediction: 0.63 vs. observation: 0.19) and with cimetidine (prediction: 1.07 vs. observation: 1.85), the remaining predicted ratios closely matched the observed ratios. Additionally, the PBPK model suggested that co-administration with the three perpetrators (cimetidine, enoxacin, and fluconazole) may use with caution, with CYP3A4 strong inhibitor (ketoconazole and itraconazole) or with dual CYP3A41A2 inhibitor (fluvoxamine) may reduce to half-dosage or use with caution, while co-administration with CYP3A4 strong or moderate inducer (rifampicin, efavirenz) should avoid. Overall, the present PBPK model can provide recommendations for adjusting dosing regimens in the presence of DDIs.

Beyond the Michaelis-Menten: Accurate Prediction of Drug Interactions through Cytochrome P450 3A4 Induction

The US Food and Drug Administration (FDA) guidance has recommended several model-based predictions to determine potential drug-drug interactions (DDIs) mediated by cytochrome P450 (CYP) induction. In particular, the ratio of substrate area under the plasma concentration-time curve (AUCR) under and not under the effect of inducers is predicted by the Michaelis-Menten (MM) model, where the MM constant ( K m $$ {K}_{\mathrm{m}} $$ ) of a drug is implicitly assumed to be sufficiently higher than the concentration of CYP enzymes that metabolize the drug ( E T $$ {E}_{\mathrm{T}} $$ ) in both the liver and small intestine. Furthermore, the fraction absorbed from gut lumen ( F a $$ {F}_{\mathrm{a}} $$ ) is also assumed to be one because F a $$ {F}_{\mathrm{a}} $$ is usually unknown. Here, we found that such assumptions lead to serious errors in predictions of AUCR. To resolve this, we propose a new framework to predict AUCR. Specifically, F a $$ {F}_{\mathrm{a}} $$ was re-estimated from experimental permeability values rather than assuming it to be one. Importantly, we used the total quasi-steady-state approximation to derive a new equation, which is valid regardless of the relationship between K m $$ {K}_{\mathrm{m}} $$ and E T $$ {E}_{\mathrm{T}} $$ , unlike the MM model. Thus, our framework becomes much more accurate than the original FDA equation, especially for drugs with high affinities, such as midazolam or strong inducers, such as rifampicin, so that the ratio between K m $$ {K}_{\mathrm{m}} $$ and E T $$ {E}_{\mathrm{T}} $$ becomes low (i.e., the MM model is invalid). Our work greatly improves the prediction of clinical DDIs, which is critical to preventing drug toxicity and failure.

Discovery of CH7057288 as an Orally Bioavailable, Selective, and Potent pan-TRK Inhibitor

Kinase fusions involving tropomyosin receptor kinases (TRKs) have been proven to act as strong oncogenic drivers and are therefore recognized as attractive therapeutic targets. We screened an in-house kinase-focused library and identified a promising hit compound with a unique tetracyclic scaffold. Compound 1 showed high TRK selectivity but moderate cell growth inhibitory activity as well as a potential risk of inducing CYP3A4. In this report, chemical modification intended to improve TRK inhibition and avoid CYP3A4 induction enabled us to identify an orally bioavailable, selective, and potent TRK inhibitor 7.

Differential Impacts of Phenol Red on Benzo[a]pyrene and Dexamethasone-Modified Cytochrome P450s in Human Cancer Cells

&lt;b&gt;Background and Objective:&lt;/b&gt; Phenol red, the pH indicator in cell culture media, influences the expression of cytochrome P450s (CYPs) in cell lines. This study aimed to examine how phenol red modified CYP induction by benzo[&lt;i&gt;a&lt;/i&gt;]pyrene and dexamethasone in human hepatocarcinoma (HepG2), colorectal adenocarcinoma (Caco-2) and choriocarcinoma (BeWo) cells. &lt;b&gt;Materials and Methods:&lt;/b&gt; The cells (1×10&lt;sup&gt;5&lt;/sup&gt; cells/well in a 24-well plate) were incubated with benzo[&lt;i&gt;a&lt;/i&gt;]pyrene (0.1, 1 and 10 μM) or dexamethasone (1, 5 and 10 μM) in either phenol red or phenol red-free media for 24 hrs. The mRNA expression of CYPs was determined by Real-Time Polymerase Chain Reaction (RT/qPCR). &lt;b&gt;Results:&lt;/b&gt; Phenol red enhanced expression of benzo[&lt;i&gt;a&lt;/i&gt;]pyrene-induced CYP1A2 inHepG2 and BeWo cells and suppressed benzo[&lt;i&gt;a&lt;/i&gt;]pyrene-induced CYP2A6 expression in HepG2 and Caco-2 cells, benzo[&lt;i&gt;a&lt;/i&gt;]pyrene induced CYP2B6 expression in HepG2 cells and benzo[&lt;i&gt;a&lt;/i&gt;]pyrene- and dexamethasone-induced CYP3A4 expression in HepG2 and Caco-2 cells. The expression of CYP3A5 was affected differently in HepG2 and Caco-2 cell lines. Phenol red enhanced benzo[&lt;i&gt;a&lt;/i&gt;]pyrene- and dexamethasone-induced CYP3A5 expression in Caco-2 cells but suppressed benzo[&lt;i&gt;a&lt;/i&gt;]pyrene- and dexamethasone-induced CYP3A5 expression in HepG2 cells. &lt;b&gt;Conclusion:&lt;/b&gt; Phenol red differentially influenced expression of benzo[&lt;i&gt;a&lt;/i&gt;]pyrene- and dexamethasone-induced CYP1A2, CYP2A6, CYP2B6, CYP3A4 and CYP3A5 mRNAs in HepG2, Caco-2 and BeWo cells. Therefore, the inclusion of phenol red in cell culture media is of concern in studies of drug and xenobiotic metabolism via CYPs in human cell line models.