Evaluation of a New Molecular Entity as a Victim of Metabolic Drug-Drug Interactions-an Industry Perspective

Peppermint Essential Oil-Doped Hydroxyapatite Nanoparticles with Antimicrobial Properties

This study aimed at developing an antimicrobial material based on hydroxyapatite (HAp) and peppermint essential oil (P-EO) in order to stimulate the antimicrobial activity of hydroxyapatite. The molecular spectral features and morphology of the P-EO, HAp and hydroxyapatite coated with peppermint essential oil (HAp-P) were analyzed using Fourier-transform infrared (FTIR) spectroscopy and scanning electron microscopy (SEM). The coating of the HAp with the P-EO did not affect the ellipsoidal shape of the nanoparticles. The overlapping of IR bands of P-EO and HAp in the HAp-P spectrum determined the formation of the broad molecular bands that were observed in the spectral regions of 400-1000 cm-1 and 1000-1200 cm-1. The antibacterial activity of the P-EO, HAp and HAp-P were also tested against different Gram-positive bacteria (methicillin-resistant Staphylococcus aureus (MRSA) 388, S. aureus ATCC 25923, S. aureus ATCC 6538, E. faecium DSM 13590), Gram-negative bacteria (Escherichia coli ATCC 25922, E. coli C5, P. aeruginosa ATCC 27853, P. aeruginosa ATCC 9027) and a fungal strain of Candida parapsilosis. The results of the present study revealed that the antimicrobial activity of HAp-P increased significantly over that of HAp.

In Vitro and In Vivo Correlation of Hepatic Fraction of Metabolism by P450 in Dogs

1-Aminobenzotriazole (ABT) has been widely used as a nonspecific mechanism-based inhibitor of cytochrome P450 (P450) enzymes. It is extensively used in preclinical studies to determine the relative contribution of oxidative metabolism mediated by P450 in vitro and in vivo. The aim of present study was to understand the translation of fraction metabolized by P450 in dog hepatocytes to in vivo using ABT, for canagliflozin, known to be cleared by P450-mediated oxidation and UDP-glucuronosyltransferases-mediated glucuronidation, and 3 drug discovery project compounds mainly cleared by hepatic metabolism. In a dog hepatocyte, intrinsic clearance assay with and without preincubation of ABT, 3 Lilly compounds exhibited a wide range of fraction metabolized by P450. Subsequent metabolite profiling in dog hepatocytes demonstrated a combination of metabolism by P450 and UDP-glucuronosyltransferases. In vivo, dogs were pretreated with 50 mg/kg ABT or vehicle at 2 h before intravenous administration of canagliflozin and Lilly compounds. The areas under the concentration-time curve (AUC) were compared for the ABT-pretreated and vehicle-pretreated groups. The measured AUC_ABT/AUC_veh ratios were correlated to fraction of metabolism by P450 in dog hepatocytes, suggesting that in vitro ABT inhibition in hepatocytes is useful to rank order compounds for in vivo fraction of metabolism assessment.

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.

Translational Biomedical Informatics and Pharmacometrics Approaches in the Drug Interactions Research

Drug interaction is a leading cause of adverse drug events and a major obstacle for current clinical practice. Pharmacovigilance data mining, pharmacokinetic modeling, and text mining are computation and informatic tools on integrating drug interaction knowledge and generating drug interaction hypothesis. We provide a comprehensive overview of these translational biomedical informatics methodologies with related databases. We hope this review illustrates the complementary nature of these informatic approaches and facilitates the translational drug interaction research.

Analysis of Nonlinear Pharmacokinetics of a Highly Albumin-Bound Compound: Contribution of Albumin-Mediated Hepatic Uptake Mechanism

The cause of nonlinear pharmacokinetics (PK) (more than dose-proportional increase in exposure) of a urea derivative under development (compound A: anionic compound [pKa: 4.4]; LogP: 6.5; and plasma protein binding: 99.95%) observed in a clinical trial was investigated. Compound A was metabolized by CYP3A4, UGT1A1, and UGT1A3 with unbound K_m of 3.3-17.8 μmol/L. OATP1B3-mediated uptake of compound A determined in the presence of human serum albumin (HSA) showed that unbound K_m and V_max decreased with increased HSA concentration. A greater decrease in unbound K_m than in V_max resulted in increased uptake clearance (V_max/unbound K_m) with increased HSA concentration, the so-called albumin-mediated uptake. At 2% HSA concentration, unbound K_m was 0.00657 μmol/L. A physiologically based PK model assuming saturable hepatic uptake nearly replicated clinical PK of compound A. Unbound K_m for hepatic uptake estimated from the model was 0.000767 μmol/L, lower than the in vitro unbound K_m at 2% HSA concentration, whereas decreased K_m with increased concentration of HSA in vitro indicated lower K_m at physiological HSA concentration (4%-5%). In addition, unbound K_m values for metabolizing enzymes were much higher than unbound K_m for OATP1B3, indicating that the nonlinear PK of compound A is primarily attributed to saturated OATP1B3-mediated hepatic uptake of compound A.

Progress in Prediction and Interpretation of Clinically Relevant Metabolic Drug-Drug Interactions: a Minireview Illustrating Recent Developments and Current Opportunities

PURPOSE OF REVIEW

This review gives a perspective on the current "state of the art" in metabolic drug-drug interaction (DDI) prediction. We highlight areas of successful prediction and illustrate progress in areas where limits in scientific knowledge or technologies prevent us from having full confidence.

RECENT FINDINGS

Several examples of success are highlighted. Work done for bitopertin shows how in vitro and clinical data can be integrated to give a model-based understanding of pharmacokinetics and drug interactions. The use of interpolative predictions to derive explicit dosage recommendations for untested DDIs is discussed using the example of ibrutinib, and the use of DDI predictions in lieu of clinical studies in new drug application packages is exemplified with eliglustat and alectinib. Alectinib is also an interesting case where dose adjustment is unnecessary as the activity of a major metabolite compensates sufficiently for changes in parent drug exposure. Examples where "unusual" cytochrome P450 (CYP) and non-CYP enzymes are responsible for metabolic clearance have shown the importance of continuing to develop our repertoire of in vitro regents and techniques. The time-dependent inhibition assay using human hepatocytes suspended in full plasma allowed improved DDI predictions, illustrating the importance of continued in vitro assay development and refinement.

SUMMARY

During the past 10 years, a highly mechanistic understanding has been developed in the area of CYP-mediated metabolic DDIs enabling the prediction of clinical outcome based on preclinical studies. The combination of good quality in vitro data and physiologically based pharmacokinetic modeling may now be used to evaluate DDI risk prospectively and are increasingly accepted in lieu of dedicated clinical studies.

A Physiologically-Based Pharmacokinetic Modeling Approach To Predict Drug-Drug Interactions of Sonidegib (LDE225) with Perpetrators of CYP3A in Cancer Patients

Sonidegib (Odomzo) is an orally available Smoothened inhibitor for the treatment of advanced basal cell carcinoma. Sonidegib was found to be metabolized primarily by cytochrome P450 (CYP)3A in vitro. The effect of multiple doses of the strong CYP3A perpetrators, ketoconazole (KTZ) and rifampin (RIF), on sonidegib pharmacokinetics (PK) after a single 800 mg dose in healthy subjects was therefore assessed. These data were used to verify a physiologically-based pharmacokinetic (PBPK) model developed to 1) bridge the clinical drug-drug interaction (DDI) study of sonidegib with KTZ and RIF in healthy subjects to the marketed dose (200 mg) in patients 2) predict acute (14 days) versus long-term dosing of the perpetrators with sonidegib at steady state and 3) predict the effect of moderate CYP3A perpetrators on sonidegib exposure in patients. Treatment of healthy subjects with KTZ resulted in an increased sonidegib exposure of 2.25- and 1.49-fold (area under the curve_0-240h and maximal concentration respectively), and RIF decreased exposure by 72% and 54%, respectively. The model simulated the single- and/or multiple-dose PK of sonidegib (healthy subjects and patients) within ∼50% of observed values. The effect of KTZ and RIF on sonidegib in healthy subjects was also simulated well, and the predicted DDI in patients was slightly less and independent of sonidegib dose. At steady state, sonidegib was predicted to have a higher DDI magnitude with strong or moderate CYP3A perpetrators compared with a single dose. Different dosing regimens of sondigeb with the perpetrators were also simulated and provided guidance to the current dosing recommendations incorporated in the product label.

Impact of Probe Substrate Selection on Cytochrome P450 Reaction Phenotyping Using the Relative Activity Factor

Accurately assessing the contribution of cytochrome P450 (P450) isoforms to overall metabolic clearance is important for prediction of clinical drug-drug interactions (DDIs). The relative activity factor (RAF) approach in P450 reaction phenotyping assumes that the interaction between P450-selective probes and testing systems is the same as the interaction of drug candidate with those systems. To test this assumption, an intersystem clearance ratio (ICR) was created to evaluate the difference in values between RAF-scaled intrinsic clearance (CL_int) and measured CL_int in human liver microsomes (HLMs). The RAF value for CYP3A4 or CYP2C9 derived from a particular P450-selective probe reaction was applied to calculate RAF-scaled CL_int for other probe reactions of the same P450 isoform in a crossover manner and compared with the measured HLM CL_int When RAF derived from midazolam or nifedipine was used for CYP3A4, the ICR for testosterone 6β-hydroxylation was 31 and 25, respectively, suggesting significantly diverse interactions of CYP3A4 probes with the testing systems. Such ICR differences were less profound among probes for CYP2C9. In addition, these RAF values were applied to losartan and meloxicam, whose metabolism is mostly CYP2C9 mediated. Only using the RAF derived from testosterone for CYP3A4 produced the expected CYP2C9 contribution of 72%-87% and 47%-69% for metabolism of losartan and meloxicam, respectively. RAF derived from other CYP3A4 probes would have attributed predominantly to CYP3A4 and led to incorrect prediction of DDIs. Our study demonstrates a significant impact of probe substrate selection on P450 phenotyping using the RAF approach, and the ICR may provide a potential solution.

In vitro to in vivo extrapolation of the complex drug-drug interaction of bupropion and its metabolites with CYP2D6; simultaneous reversible inhibition and CYP2D6 downregulation

Bupropion is a widely used antidepressant and smoking cessation aid and a strong inhibitor of CYP2D6 in vivo. Bupropion is administered as a racemic mixture of R- and S-bupropion and has stereoselective pharmacokinetics. Four primary metabolites of bupropion, threo- and erythro-hydrobupropion and R,R- and S,S-OH-bupropion, circulate at higher concentrations than the parent drug and are believed to contribute to the efficacy and side effects of bupropion as well as to the CYP2D6 inhibition. However, bupropion and its metabolites are only weak inhibitors of CYP2D6 in vitro, and the magnitude of the in vivo drug-drug interactions (DDI) caused by bupropion cannot be explained by the in vitro data even when CYP2D6 inhibition by the metabolites is accounted for. The aim of this study was to quantitatively explain the in vivo CYP2D6 DDI magnitude by in vitro DDI data. Bupropion and its metabolites were found to inhibit CYP2D6 stereoselectively with up to 10-fold difference in inhibition potency between enantiomers. However, the reversible inhibition or active uptake into hepatocytes did not explain the in vivo DDIs. In HepG2 cells and in plated human hepatocytes bupropion and its metabolites were found to significantly downregulate CYP2D6 mRNA in a concentration dependent manner. The in vivo DDI was quantitatively predicted by significant down-regulation of CYP2D6 mRNA and reversible inhibition of CYP2D6 by bupropion and its metabolites. This study is the first example of a clinical DDI resulting from CYP down-regulation and first demonstration of a CYP2D6 interaction resulting from transcriptional regulation.

Recent advances in understanding hepatic drug transport

Cells need to strictly control their internal milieu, a function which is performed by the plasma membrane. Selective passage of molecules across the plasma membrane is controlled by transport proteins. As the liver is the central organ for drug metabolism, hepatocytes are equipped with numerous drug transporters expressed at the plasma membrane. Drug disposition includes absorption, distribution, metabolism, and elimination of a drug and hence multiple passages of drugs and their metabolites across membranes. Consequently, understanding the exact mechanisms of drug transporters is essential both in drug development and in drug therapy. While many drug transporters are expressed in hepatocytes, and some of them are well characterized, several transporters have only recently been identified as new drug transporters. Novel powerful tools to deorphanize (drug) transporters are being applied and show promising results. Although a large set of tools are available for studying transport in vitro and in isolated cells, tools for studying transport in living organisms, including humans, are evolving now and rely predominantly on imaging techniques, e.g. positron emission tomography. Imaging is an area which, certainly in the near future, will provide important insights into "transporters at work" in vivo.

Quantitative Prediction of Drug-Drug Interactions Involving Inhibitory Metabolites in Drug Development: How Can Physiologically Based Pharmacokinetic Modeling Help?

This subteam under the Drug Metabolism Leadership Group (Innovation and Quality Consortium) investigated the quantitative role of circulating inhibitory metabolites in drug-drug interactions using physiologically based pharmacokinetic (PBPK) modeling. Three drugs with major circulating inhibitory metabolites (amiodarone, gemfibrozil, and sertraline) were systematically evaluated in addition to the literature review of recent examples. The application of PBPK modeling in drug interactions by inhibitory parent-metabolite pairs is described and guidance on strategic application is provided.