In vitro-to-in vivo predictions of drug-drug interactions involving multiple reversible inhibitors

Inhibition of human UDP-glucuronosyltransferase enzyme by ripretinib: Implications for drug-drug interactions

Ripretinib, a tyrosine kinase inhibitor (TKI), is the first FDA approved fourth-line therapy for adults with advanced gastrointestinal stromal tumor (GIST). Studies have shown that several TKIs for treating GIST were potent inhibitors of human UDP- glucosyltransferase (UGTs) enzymes. However, whether ripretinib affects the activity of UGTs remains unclear. The aim of this study was to investigate the effects of ripretinib on major UGT isoforms, as well as to evaluate its potential drug-drug interactions (DDIs) risk caused by the inhibition of UGTs activities. The inhibitory effects and inhibition modes of ripretinib on UGTs were systematically evaluated using high-performance liquid chromatography (HPLC) and enzyme kinetic studies, respectively. Our data showed that ripretinib exhibited potent inhibition against UGT1A1, UGT1A3, UGT1A4, UGT1A7 and UGT1A8. Enzyme kinetic studies indicated that ripretinib was not only a competitive inhibitor of UGT1A1, UGT1A4 and UGT1A7, but also a noncompetitive inhibitor of UGT1A3, as well as a mixed inhibitor of UGT1A8. The prediction results of in vitro-in vivo extrapolation (IVIVE) demonstrated that ripretinib might bring the potential risk of DDIs when combined with substrates of UGT1A1, UGT1A3, UGT1A4, UGT1A7 or UGT1A8. Therefore, special attention should be paid when ripretinib is used in conjunction with other drugs metabolized by UGTs to avoid risk of DDIs in clinic.

Do Inhibitory Metabolites Impact DDI Risk Assessment? Analysis of in vitro and in vivo Data from NDA Reviews Between 2013 and 2018

Evaluating the potential of new drugs and their metabolites to cause drug-drug interactions (DDIs) is critical for understanding drug safety and efficacy. Although multiple analyses of proprietary metabolite testing data have been published, no systematic analyses of metabolite data collected according to current testing criteria have been conducted. To address this knowledge gap, 120 new molecular entities approved between 2013 and 2018 were reviewed. Comprehensive data on metabolite-to-parent area under the curve ratios (AUC_M /AUC_P ), inhibitory potency of parent and metabolites, and clinical DDIs were collected. Sixty-four percent of the metabolites quantified in vivo had AUC_M /AUC_P  ≥ 0.25 and 75% of these metabolites were tested for cytochrome P450 (CYP) inhibition in vitro, resulting in 15 metabolites with potential DDI risk identification. Although 50% of the metabolites with AUC_M /AUC_P  < 0.25 were also tested in vitro, none of them showed meaningful CYP inhibition potential. The metabolite percentage of plasma total radioactivity cutoff of ≥ 10% did not appear to add value to metabolite testing strategies. No relationship between metabolite versus parent drug polarity and inhibition potency was observed. Comparison of metabolite and parent maximum concentration (C_max ) divided by inhibition constant (K_i ) values suggested that metabolites can contribute to in vivo DDIs and, hence, quantitative prediction of clinical DDI magnitude may require both parent and metabolite data. This systematic analysis of metabolite data for newly approved drugs supports an AUC_M /AUC_P cutoff of ≥ 0.25 to warrant metabolite in vitro CYP screening to adequately characterize metabolite inhibitory DDI potential and support quantitative DDI predictions.

Inhibition and induction of CYP enzymes in humans: an update

The cytochrome P450 (CYP) enzyme family is the most important enzyme system catalyzing the phase 1 metabolism of pharmaceuticals and other xenobiotics such as herbal remedies and toxic compounds in the environment. The inhibition and induction of CYPs are major mechanisms causing pharmacokinetic drug–drug interactions. This review presents a comprehensive update on the inhibitors and inducers of the specific CYP enzymes in humans. The focus is on the more recent human in vitro and in vivo findings since the publication of our previous review on this topic in 2008. In addition to the general presentation of inhibitory drugs and inducers of human CYP enzymes by drugs, herbal remedies, and toxic compounds, an in-depth view on tyrosine-kinase inhibitors and antiretroviral HIV medications as victims and perpetrators of drug–drug interactions is provided as examples of the current trends in the field. Also, a concise overview of the mechanisms of CYP induction is presented to aid the understanding of the induction phenomena.

Abemaciclib Inhibits Renal Tubular Secretion Without Changing Glomerular Filtration Rate

Abemaciclib, an inhibitor of cyclin dependent kinases 4 and 6, is indicated for metastatic breast cancer treatment. Reversible increases in serum creatinine levels of ~15-40% over baseline have been observed following abemaciclib dosing. This study assessed the in vitro and clinical inhibition of renal transporters by abemaciclib and its metabolites using metformin (a clinically relevant transporter substrate), in a clinical study that quantified glomerular filtration and iohexol clearance. In vitro, abemaciclib inhibited metformin uptake by organic cation transporter 2, multidrug and toxin extrusion (MATE)1, and MATE2-K transporters with a half-maximal inhibitory concentration of 0.4-3.8 μM. Clinically, abemaciclib significantly increased metformin exposure but did not significantly affect measured glomerular filtration rate, serum neutrophil gelatinase-associated lipocalin (NGAL), serum cystatin-C, or the urinary markers of kidney tubular injury, NGAL and kidney injury molecule-1.

Accurate Estimation of In Vivo Inhibition Constants of Inhibitors and Fraction Metabolized of Substrates with Physiologically Based Pharmacokinetic Drug-Drug Interaction Models Incorporating Parent Drugs and Metabolites of Substrates with Cluster Newton Method

The accurate estimation of "in vivo" inhibition constants (K i) of inhibitors and fraction metabolized (f m) of substrates is highly important for drug-drug interaction (DDI) prediction based on physiologically based pharmacokinetic (PBPK) models. We hypothesized that analysis of the pharmacokinetic alterations of substrate metabolites in addition to the parent drug would enable accurate estimation of in vivo K i and f m Twenty-four pharmacokinetic DDIs caused by P450 inhibition were analyzed with PBPK models using an emerging parameter estimation method, the cluster Newton method, which enables efficient estimation of a large number of parameters to describe the pharmacokinetics of parent and metabolized drugs. For each DDI, two analyses were conducted (with or without substrate metabolite data), and the parameter estimates were compared with each other. In 17 out of 24 cases, inclusion of substrate metabolite information in PBPK analysis improved the reliability of both K i and f m Importantly, the estimated K i for the same inhibitor from different DDI studies was generally consistent, suggesting that the estimated K i from one study can be reliably used for the prediction of untested DDI cases with different victim drugs. Furthermore, a large discrepancy was observed between the reported in vitro K i and the in vitro estimates for some inhibitors, and the current in vivo K i estimates might be used as reference values when optimizing in vitro-in vivo extrapolation strategies. These results demonstrated that better use of substrate metabolite information in PBPK analysis of clinical DDI data can improve reliability of top-down parameter estimation and prediction of untested DDIs.

Physiologically Based Pharmacokinetic Model of All-trans-Retinoic Acid with Application to Cancer Populations and Drug Interactions

All-trans retinoic acid (atRA) is a front-line treatment of acute promyelocytic leukemia (APL). Due to its activity in regulating the cell cycle, it has also been evaluated for the treatment of other cancers. However, the efficacy of atRA has been limited by atRA inducing its own metabolism during therapy, resulting in a decrease of atRA exposure during continuous dosing. Frequent relapse occurs in patients receiving atRA monotherapy. In an attempt to combat therapy resistance, inhibitors of atRA metabolism have been developed. Of these, ketoconazole and liarozole have shown some benefits, but their usage is limited by side effects and low potency toward the cytochrome P450 26A1 isoform (CYP26A1), the main atRA hydroxylase. We determined the pharmacokinetic basis of therapy resistance to atRA and tested whether the complex disposition kinetics of atRA could be predicted in healthy subjects and in cancer patients in the presence and absence of inhibitors of atRA metabolism using physiologically based pharmacokinetic (PBPK) modeling. A PBPK model of atRA disposition was developed and verified in healthy individuals and in cancer patients. The population-based PBPK model of atRA disposition incorporated saturable metabolic clearance of atRA, induction of CYP26A1 by atRA, and the absorption and distribution kinetics of atRA. It accurately predicted the changes in atRA exposure after continuous dosing and when coadministered with ketoconazole and liarozole. The developed model will be useful in interpretation of atRA disposition and efficacy, design of novel dosing strategies, and development of next-generation atRA metabolism inhibitors.

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.

An S-warfarin and AZD1981 interaction: in vitro and clinical pilot data suggest the N-deacetylated amino acid metabolite as the primary perpetrator

AIM

AZD1981 is an orally bioavailable chemoattractant receptor-homologous molecule expressed on Th2 cells (CRTh2) receptor antagonist progressed to phase II trials for the treatment of allergic asthma. Previously performed in vitro human hepatocyte incubations identified N-deacetylated AZD1981 as a primary metabolite. We report on metabolite exposure from a clinical excretion balance, on in vitro studies performed to determine the likelihood of a metabolite-dependent drug-drug interaction (DDI) and on a clinical warfarin DDI study. The aim was to demonstrate that N-deacetylated AZD1981 is responsible for the observed interaction.

METHODS

The excretion and biotransformation of [¹⁴ C]-AZD1981 were studied in healthy male volunteers, and subsequently in vitro cytochrome P450 (CYP) inhibition and hepatocyte uptake investigations were carried out with metabolites and the parent drug. A clinical DDI study using coadministered twice-daily 100 mg and 400 mg AZD1981 with 25 mg warfarin was performed.

RESULTS

The excretion balance study showed N-deacetylated AZD1981 to be the most abundant metabolite present in plasma. In vitro data revealed the metabolite to be a weak CYP2C9 time-dependent inhibitor, subject to more active hepatic uptake than the parent molecule. Clinically, the S-warfarin area under the plasma concentration-time curve increased, on average, 1.4-fold [95% confidence interval (CI) 1.22, 1.50] and 2.4-fold (95% CI 2.11, 2.64) after 100 mg (n = 13) and 400 mg (n = 11) AZD1981 administration, respectively. In vitro CYP inhibition and hepatocyte uptake data were used to explain the interaction.

CONCLUSIONS

N-deacetylated AZD1981 can be added to the small list of drug metabolites reported as sole contributors to clinical drug-drug interactions, with weak time-dependent inhibition exacerbated by efficient hepatic uptake being the cause.

Application of Static Models to Predict Midazolam Clinical Interactions in the Presence of Single or Multiple Hepatitis C Virus Drugs

Asunaprevir (ASV), daclatasvir (DCV), and beclabuvir (BCV) are three drugs developed for the treatment of chronic hepatitis C virus infection. Here, we evaluated the CYP3A4 induction potential of each drug, as well as BCV-M1 (the major metabolite of BCV), in human hepatocytes by measuring CYP3A4 mRNA alteration. The induction responses were quantified as induction fold (mRNA fold change) and induction increase (mRNA fold increase), and then fitted with four nonlinear regression algorithms. Reversible inhibition and time-dependent inhibition (TDI) on CYP3A4 activity were determined to predict net drug-drug interactions (DDIs). All four compounds were CYP3A4 inducers and inhibitors, with ASV demonstrating TDI. The curve-fitting results demonstrated that fold increase is a better assessment to determine kinetic parameters for compounds inducing weak responses. By summing the contribution of each inducer, the basic static model was able to correctly predict the potential for a clinically meaningful induction signal for single or multiple perpetrators, but with over prediction of the magnitude. With the same approach, the mechanistic static model improved the prediction accuracy of DCV and BCV when including both induction and inhibition effects, but incorrectly predicted the net DDI effects for ASV alone or triple combinations. The predictions of ASV or the triple combination could be improved by only including the induction and reversible inhibition but not the ASV CYP3A4 TDI component. Those results demonstrated that static models can be applied as a tool to help project the DDI risk of multiple perpetrators using in vitro data.

Chiral Plasma Pharmacokinetics and Urinary Excretion of Bupropion and Metabolites in Healthy Volunteers

Bupropion, widely used as an antidepressant and smoking cessation aid, undergoes complex metabolism to yield numerous metabolites with unique disposition, effect, and drug-drug interactions (DDIs) in humans. The stereoselective plasma and urinary pharmacokinetics of bupropion and its metabolites were evaluated to understand their potential contributions to bupropion effects. Healthy human volunteers (n = 15) were administered a single oral dose of racemic bupropion (100 mg), which was followed by collection of plasma and urine samples and determination of bupropion and metabolite concentrations using novel liquid chromatography-tandem mass spectrometry assays. Time-dependent, elimination rate-limited, stereoselective pharmacokinetics were observed for all bupropion metabolites. Area under the plasma concentration-time curve from zero to infinity ratios were on average approximately 65, 6, 6, and 4 and Cmax ratios were approximately 35, 6, 3, and 0.5 for (2R,3R)-/(2S,3S)-hydroxybupropion, R-/S-bupropion, (1S,2R)-/(1R,2S)-erythrohydrobupropion, and (1R,2R)-/(1S,2S)-threohydrobupropion, respectively. The R-/S-bupropion and (1R,2R)-/(1S,2S)-threohydrobupropion ratios are likely indicative of higher presystemic metabolism of S- versus R-bupropion by carbonyl reductases. Interestingly, the apparent renal clearance of (2S,3S)-hydroxybupropion was almost 10-fold higher than that of (2R,3R)-hydroxybupropion. The prediction of steady-state pharmacokinetics demonstrated differential stereospecific accumulation [partial area under the plasma concentration-time curve after the final simulated bupropion dose (300-312 hours) from 185 to 37,447 nM⋅h] and elimination [terminal half-life of approximately 7-46 hours] of bupropion metabolites, which may explain observed stereoselective differences in bupropion effect and DDI risk with CYP2D6 at steady state. Further elucidation of bupropion and metabolite disposition suggests that bupropion is not a reliable in vivo marker of CYP2B6 activity. In summary, to our knowledge, this is the first comprehensive report to provide novel insight into mechanisms underlying bupropion disposition by detailing the stereoselective pharmacokinetics of individual bupropion metabolites, which will enhance clinical understanding of bupropion's effects and DDIs with CYP2D6.

Stereoselective inhibition of CYP2C19 and CYP3A4 by fluoxetine and its metabolite: implications for risk assessment of multiple time-dependent inhibitor systems

Recent guidance on drug-drug interaction (DDI) testing recommends evaluation of circulating metabolites. However, there is little consensus on how to quantitatively predict and/or assess the risk of in vivo DDIs by multiple time-dependent inhibitors (TDIs) including metabolites from in vitro data. Fluoxetine was chosen as the model drug to evaluate the role of TDI metabolites in DDI prediction because it is a TDI of both CYP3A4 and CYP2C19 with a circulating N-dealkylated inhibitory metabolite, norfluoxetine. In pooled human liver microsomes, both enantiomers of fluoxetine and norfluoxetine were TDIs of CYP2C19, (S)-norfluoxetine was the most potent inhibitor with time-dependent inhibition affinity constant (KI) of 7 μM, and apparent maximum time-dependent inhibition rate (k(inact,app)) of 0.059 min(-1). Only (S)-fluoxetine and (R)-norfluoxetine were TDIs of CYP3A4, with (R)-norfluoxetine being the most potent (K(I) = 8 μM, and k(inact,app) = 0.011 min(-1)). Based on in-vitro-to-in-vivo predictions, (S)-norfluoxetine plays the most important role in in vivo CYP2C19 DDIs, whereas (R)-norfluoxetine is most important in CYP3A4 DDIs. Comparison of two multiple TDI prediction models demonstrated significant differences between them in in-vitro-to-in-vitro predictions but not in in-vitro-to-in-vivo predictions. Inclusion of all four inhibitors predicted an in vivo decrease in CYP2C19 (95%) and CYP3A4 (60-62%) activity. The results of this study suggest that adequate worst-case risk assessment for in vivo DDIs by multiple TDI systems can be achieved by incorporating time-dependent inhibition by both parent and metabolite via simple addition of the in vivo time-dependent inhibition rate/cytochrome P450 degradation rate constant (λ/k(deg)) values, but quantitative DDI predictions will require a more thorough understanding of TDI mechanisms.

"Drugs on oxygen": an update and perspective on the role of cytochrome P450 testing in pharmacology

Low hit rates for lead compounds and high attrition remain a major problem for drug development. The reasons for compound failure range from poor pharmacokinetics to toxic metabolites and adverse drug interactions; all of which are frequently mediated by cytochrome P450-dependent monooxygenases (CYPs). However, despite some 30 years of assay development and refinement, CYP metabolism remains a critical issue during drug development. While current testing strategies succeed in characterizing single substance toxicity, they are challenged by practical issues such as assay standardization or complex scenarios such as multidrug usage. This editorial summarizes where we stand and highlights the major challenges we face with CYPs in drug development today. The article also tries to spell out the future direction of CYP testing. The latter will depend on the extended inclusion of polypharmacy into testing strategies, as well as on our capability to make use of upcoming complex in vitro test systems and their inclusion into tiered testing strategies.