Human hepatocytes and cytochrome P450-selective inhibitors predict variability in human drug exposure more accurately than human recombinant P450s

Coadministration of bedaquiline and pyrifazimine reduce exposure to toxic metabolite N-desmethyl bedaquiline

Background: A new, effective anti-tuberculosis (TB) regimen containing bedaquiline (BDQ) and pyrifazimine (TBI-166) has been recommended for a phase IIb clinical trial. Preclinical drug-drug interaction (DDI) studies of the combination of BDQ and TBI-166 have been designed to support future clinical trials. In this study, we investigated whether a DDI between BDQ and TBI-166 affects the pharmacokinetics of BDQ. Methods: We performed in vitro quantification of the fractional contributions of the fraction of drug metabolism by individual CYP enzymes (f m) of BDQ and the inhibition potency of key metabolic pathways of TBI-166. Furthermore, we conducted an in vivo steady-state pharmacokinetics study in a murine TB model and healthy BALB/c mice. Results: The in vitro f m value indicated that the CYP3A4 pathway contributed more than 75% to BDQ metabolism to N-desmethyl-bedaquiline (M2), and TBI-166 was a moderate (IC50 2.65 µM) potential CYP3A4 inhibitor. Coadministration of BDQ and TBI-166 greatly reduced exposure to metabolite M2 (AUC0-t 76310 vs 115704 h ng/mL, 66% of BDQ alone), whereas the exposure to BDQ and TBI-166 did not changed. The same trend was observed both in healthy and TB model mice. The plasma concentration of M2 decreased significantly after coadministration of BDQ and TBI-166 and decreased further during treatment in the TB model. Conclusions: In conclusion, our results showed that the combination of BDQ and TBI-166 significantly reduced exposure to the toxic metabolite M2 by inhibiting the activity of the CYP3A4 pathway. The potential safety and efficacy benefits demonstrated by the TB treatment highly suggest that coadministration of BDQ and TBI-166 should be studied further.

Enhancing drug-drug Interaction Prediction by Integrating Physiologically-Based Pharmacokinetic Model with Fraction Metabolized by CYP3A4

BACKGROUND

Enhancing the precision of drug-drug interaction (DDI) prediction is essential for improving drug safety and efficacy. The aim is to identify the most effective fraction metabolized by CY3A4 (fm) for improving DDI prediction using physiologically based pharmacokinetic (PBPK) models.

RESEARCH DESIGN AND METHODS

The fm values were determined for 33 approved drugs using a human liver microsome for in vitro measurements and the ADMET Predictor software for in silico predictions. Subsequently, these fm values were integrated into PBPK models using the GastroPlus platform. The PBPK models, combined with a ketoconazole model, were utilized to predict AUCR (AUCcombo with ketoconazole/AUCdosing alone), and the accuracy of these predictions was evaluated by comparison with observed AUCR.

RESULTS

The integration of in vitro fm method demonstrates superior performance compared to the in silico fm method and fm of 100% method. Under the Guest-limits criteria, the integration of in vitro fm achieves an accuracy of 76%, while the in silico fm and fm of 100% methods achieve accuracies of 67% and 58%, respectively.

CONCLUSIONS

Our study highlights the importance of in vitro fm data to improve the accuracy of predicting DDIs and demonstrates the promising potential of in silico fm in predicting DDIs.

Defining the Selectivity of Chemical Inhibitors Used for Cytochrome P450 Reaction Phenotyping: Overcoming Selectivity Limitations with a Six-Parameter Inhibition Curve-Fitting Approach

The utility of chemical inhibitors in cytochrome P450 (CYP) reaction phenotyping is highly dependent on their selectivity and potency for their target CYP isoforms. In the present study, seventeen inhibitors of CYP1A2, 2B6, 2C8, 2C9, 2C19, 2D6, and 3A4/5 commonly used in reaction phenotyping were evaluated for their cross-enzyme selectivity in pooled human liver microsomes. The data were evaluated using a statistical desirability analysis to identify (1) inhibitors of superior selectivity for reaction phenotyping and (2) optimal concentrations for each. Among the inhibitors evaluated, α-naphthoflavone, furafylline, sulfaphenazole, tienilic acid, N-benzylnirvanol, and quinidine were most selective, such that their respective target enzymes were inhibited by ~95% without inhibiting any other CYP enzyme by more than 10%. Other commonly employed inhibitors, such as ketoconazole and montelukast, among others, were of insufficient selectivity to yield a concentration that could adequately inhibit their target enzymes without affecting other CYP enzymes. To overcome these shortcomings, an experimental design was developed wherein dose response data from a densely sampled multi-concentration inhibition curve are analyzed by a six-parameter inhibition curve function, allowing accounting of the inhibition of off-target CYP isoforms inhibition and more reliable determination of maximum targeted enzyme inhibition. The approach was exemplified using rosiglitazone N-demethylation, catalyzed by both CYP2C8 and 3A4, and was able to discern the off-target inhibition by ketoconazole and montelukast from the inhibition of the targeted enzyme. This methodology yields more accurate estimates of CYP contributions in reaction phenotyping. Significance Statement Isoform-selective chemical inhibitors are important tools for identifying and quantifying enzyme contributions as part of a CYP reaction phenotyping assessment for projecting drug-drug interactions. However, currently employed practices fail to adequately compensate for shortcomings in inhibitor selectivity and the resulting confounding impact on estimates of the CYP enzyme contribution to drug clearance. In this report, we describe a detailed IC50 study design with 6-parameter modeling approach that yields more accurate estimates of enzyme contribution.

An improved method for cytochrome p450 reaction phenotyping using a sequential qualitative-then-quantitative approach

Cytochrome P450 reaction phenotyping to determine the fraction of metabolism values (fm) for individual enzymes is a standard study in the evaluation of a new drug. However, there are technical challenges in these studies caused by shortcomings in the selectivity of P450 inhibitors and unreliable scaling procedures for recombinant P450 (rCYP) data. In this investigation, a two-step "qualitative-then-quantitative" approach to P450 reaction phenotyping is described. In the first step, each rCYP is tested qualitatively for potential to generate metabolites. In the second step, selective inhibitors for the P450s identified in step1 are tested for their effects on metabolism using full inhibition curves. Forty-eight drugs were evaluated in step 1 and there were no examples of missing an enzyme important to in vivo clearance. Five drugs (escitalopram, fluvastatin, pioglitazone, propranolol, and risperidone) were selected for full phenotyping in step2 to determine fm values, with findings compared to fm values estimated from single inhibitor concentration data and rCYP with intersystem-extrapolation-factor corrections. The two-step approach yielded fm values for major drug clearing enzymes that are close to those estimated from clinical data: escitalopram and CYP2C19 (0.42 vs 0.36-0.82), fluvastatin and CYP2C9 (0.76 vs 0.76), pioglitazone and CYP2C8 (0.72 vs 0.73), propranolol and CYP2D6 (0.68 vs 0.37-0.56) and risperidone and CYP2D6 (0.60 vs 0.66-0.88). Reaction phenotyping data generated in this fashion should offer better input to physiologically-based pharmacokinetic models for prediction of DDI and impact of genetic polymorphisms on drug clearance. The qualitative-then-quantitative approach is proposed as a replacement to standard reaction phenotyping strategies. Significance Statement P450 reaction phenotyping is important for projecting drug-drug interactions and interpatient variability in drug exposure. However, currently recommended practices can frequently fail to provide reliable estimates of the fractional contributions of specific P450 enzymes (fm) to drug clearance. In this report, we describe a two-step qualitative-then-quantitative reaction phenotyping approach that yields more accurate estimates of fm.

INTERSYSTEM EXTRAPOLATION FACTORS (ISEF) ARE SUBSTRATE-DEPENDENT FOR CYP3A4: IMPACT ON CYTOCHROME P450 REACTION PHENOTYPING

The use of intersystem extrapolation factors (ISEF) is required for the quantitative scaling of drug metabolism data generated in individually expressed cytochrome P450 enzymes when estimating fractional contribution to metabolism by P450 enzymes in vivo (f_m,_CYP). For successful prediction of f_m, ISEF values must be universal across all substrates for any individual enzyme. In this study, ISEF values were generated for ten CYP3A4 selective substrates using a common source of recombinant heterologously expressed CYP3A4 and a pool of human liver microsomes. The resulting ISEF values for CYP3A4 were substrate-dependent and ranged 8-fold, with the highest value generated from intrinsic clearance of midazolam depletion (0.36) and the lowest from quinidine depletion (0.044). Application of these ISEF values for estimation of the fractional contribution of CYP3A4 and CYP2C19 to omeprazole clearance yielded values that ranged from 0.21-0.63 and 0.37-0.79, respectively, as compared to back-extrapolated in vivo f_m values of 0.27 (CYP3A4) and 0.85 (CYP2C19) from clinical pharmacokinetic data. For risperidone, estimated f_m values for CYP3A4 and CYP2D6 ranged from 0.87-0.98 and 0.02-0.13, respectively, as compared to in vivo values of 0.36 (CYP3A4) and 0.63-0.88 (CYP2D6), showing that the importance of CYP3A4 was over-estimated and the importance of CYP2D6 under-estimated. Overall, these findings suggest that ISEF values for CYP3A4 can vary with the marker substrate used to derive them, thereby reducing the effectiveness of the approach of using metabolism data from rCYP3A4 with ISEF values for the prediction of f_m values in vivo. Significance Statement Intersystem extrapolation factors (ISEF) are utilized for assigning fractional contributions of individual enzymes to drug clearance (f_m) from drug metabolism data generated in recombinant P450s. The present data shows that ISEF values for cytochrome P4503A4 vary with the substrate. This can lead to variable and erroneous prediction of f_m.

A Fluidic Isolation-Assisted Homogeneous-Flow-Pressure Chip-Solid Phase Extraction-Mass Spectrometry System for Online Dynamic Monitoring of 25-Hydroxyvitamin D3 Biotransformation in Cells

It is well known that cell can response to various chemical and mechanical stimuli. Therefore, flow pressure variation induced by sample loading and elution should be small enough to ignore the physical impact on cells when we use a Chip-SPE-MS system for cells. However, most existent Chip-SPE-MS systems ignored the pressure alternation because it is extremely difficult to develop a homogeneous-flow-pressure hyphenated module. Herein, we developed an interesting fluidic isolation-assisted homogeneous-flow-pressure Chip-SPE-MS system and demonstrated that it is adequate for online high-throughput determination and quantification of the 25-hydroxyvitamin D₃ (25(OH)D₃) biotransformation in different cells. Briefly, the homogeneous ambient flow pressure is achieved by fluidic isolation between the cell culture channel and the SPE column, and an automatic sampling probe could accomplish the sample loading and dispensing to fulfill online pretreatment of the sample. Through this new system, the expression levels of 24,25-dihydroxyvitamin D₃ (24,25(OH)₂D₃) can be determined in real time with a detection limit of 2.54 nM. In addition, the results revealed that 25(OH)D₃ metabolic activity differed significantly between normal L-02 cells and cancerous HepG2 cells. Treatment of L-02 cells with a high dose of 25(OH)D₃ was found to increase significant formation of 24,25(OH)₂D₃, but this change was not apparent in HepG2 cells. The presented system promises to be a versatile tool for online accurate molecule biotransformation investigation and drug screening processes.

P450s under Restriction (PURE) Screen Using HepaRG and Primary Human Hepatocytes for Discovery of Novel HBV Antivirals

Herein is reported a novel screening paradigm PURE (P450s under restriction) for the identification and optimization of hits as part of a hepatitis B virus (HBV) antiviral discovery program. To closely represent in vivo hepatocytes, differentiated HepaRG cells (dHRGs) and primary human hepatocytes (PHHs) were used as the basis for an HBV infection system. However, a significant challenge arose during potency evaluation in using cultured dHRGs and PHHs as screening platforms because, as with hepatocytes in vivo, these cells express active cytochrome P450 enzymes and thus can metabolize test compounds. The observed antiviral effects may be the cumulative result of a dynamic pool of parent compound and metabolites thus confounding structure activity relationship (SAR) interpretation and subsequent optimization design initiatives. We show here that PURE methodology restricts metabolism of HBV-infected dHRGs and PHHs and thus provides highly informative potency data for decision-making on key representative antiviral compounds.

(-)-N-3-Benzylphenobarbital Is Superior to Omeprazole and (+)-N-3-Benzylnirvanol as a CYP2C19 Inhibitor in Suspended Human Hepatocytes

Early assessment of metabolism pathways of new chemical entities guides the understanding of drug-drug interactions. Selective enzyme inhibitors are indispensable in CYP reaction phenotyping. The most commonly applied CYP2C19 inhibitor, omeprazole, lacks selectivity. Two promising alternatives, (+)-N-3-benzylnirvanol and (-)-N-3-benzylphenobarbital, are already used as CYP2C19 inhibitors in some in vitro studies with suspended human hepatocytes. However, a full validation proving their suitability in terms of CYP and non-CYP selectivity has not been presented in literature. The present study provides a thorough comparison between omeprazole, (+)-N-3-benzylnirvanol, and (-)-N-3-benzylphenobarbital in terms of potency and selectivity and shows the superiority of (-)-N-3-benzylphenobarbital as a CYP2C19 inhibitor in suspended human hepatocytes. Furthermore, we evaluated the application of (-)-N-3-benzylphenobarbital to predict the in vivo contribution of CYP2C19 to drug metabolism [fraction metabolized (fm) of CYP2C19, fm_CYP2C19]. A set of 10 clinically used CYP2C19 substrates with reported in vivo fm_CYP2C19 data was evaluated. fm_CYP2C19, which was predicted using data from suspended human hepatocyte incubations, underestimated the in vivo fm_CYP2C19 The use of a different hepatocyte batch with a different CYP3A4/CYP2C19 activity ratio showed the impact of intrinsic CYP activities on the determination of fm_CYP2C19 Overall, this study confirms the selective CYP2C19 inhibition by (-)-N-3-benzylphenobarbital over other CYP isoforms (CYP1A2, CYP2B6, CYP2C8, CYP2C9, CYP2D6, and CYP3A4) and clinically relevant non-CYP enzymes [aldehyde oxidase, flavin-containing monooxygenase 3, N-acetyltransferase 2, uridine diphosphate glucuronosyltransferase (UGT) 1A1, UGT1A4, UGT2B7, UGT2B15] in suspended human hepatocytes. (-)-N-3-benzylphenobarbital is therefore the preferred CYP2C19 inhibitor to assess fm_CYP2C19 in suspended human hepatocytes in comparison with omeprazole and (+)-N-3-benzylnirvanol. SIGNIFICANCE STATEMENT: (-)-N-3-Benzylphenobarbital is a more potent and selective inhibitor of CYP2C19 in suspended human hepatocytes than omeprazole and (+)-N-3-benzylnirvanol. (-)-N-3-Benzylphenobarbital can be used to predict the fraction metabolized by CYP2C19 in suspended human hepatocytes.

Human hepatocytes and cytochrome P450-selective inhibitors predict variability in human drug exposure more accurately than human recombinant P450s

BACKGROUND AND PURPOSE

Drugs metabolically eliminated by several enzymes are less vulnerable to variable compound exposure in patients due to drug-drug interactions (DDI) or if a polymorphic enzyme is involved in their elimination. Therefore, it is vital in drug discovery to accurately and efficiently estimate and optimize the metabolic elimination profile.

EXPERIMENTAL APPROACH

CYP3A and/or CYP2D6 substrates with well described variability in vivo in humans due to CYP3A DDI and CYP2D6 polymorphism were selected for assessment of fraction metabolized by each enzyme (fmCYP ) in two in vitro systems: (i) human recombinant P450s (hrP450s) and (ii) human hepatocytes combined with selective P450 inhibitors. Increases in compound exposure in poor versus extensive CYP2D6 metabolizers and by the strong CYP3A inhibitor ketoconazole were mathematically modelled and predicted changes in exposure were compared with in vivo data.

KEY RESULTS

Predicted changes in exposure were within twofold of reported in vivo values using fmCYP estimated in human hepatocytes and there was a strong linear correlation between predicted and observed changes in exposure (r2  = 0.83 for CYP3A, r2  = 0.82 for CYP2D6). Predictions using fmCYP in hrP450s were not as accurate (r2  = 0.55 for CYP3A, r2  = 0.20 for CYP2D6).

CONCLUSIONS AND IMPLICATIONS

The results suggest that variability in human drug exposure due to DDI and enzyme polymorphism can be accurately predicted using fmCYP from human hepatocytes and CYP-selective inhibitors. This approach can be efficiently applied in drug discovery to aid optimization of candidate drugs with a favourable metabolic elimination profile and limited variability in patients.