Qualitative analysis of the role of metabolites in inhibitory drug-drug interactions: literature evaluation based on the metabolism and transport drug interaction database

Contribution analysis of metabolic tissues on systemic exposure of an active metabolite after oral administration of verapamil using a stable isotope-labeled compound

The present study illustrates the advantage of an isotope-IV study for the contribution analysis of metabolic tissues on systemic exposure of metabolites. A model parent drug, verapamil (VER), and its metabolite, norverapamil (Nor-VER), were used. This isotope-IV study used rats with and without the pre-treatment of the CYP inhibitor 1-aminobenzotriazole (ABT), was performed by the oral administration of VER (1 mg/kg) combined with the intravenous administration of stable isotope-labeled VER (VER-d6, 0.005 mg/kg). Plasma concentration profiles of both compounds and respective metabolites (Nor-VER, Nor-VER-d6) were then evaluated by LC-MSMS. VER oral availability was increased, and the systemic clearance decreased, in addition, the relative systemic exposure of Nor-VER and Nor-VER-d6 was increased by ABT pre-treatment. PK analyses revealed that, in ABT untreated rats, most Nor-VER in systemic circulation originated from the intestinal absorption process. ABT pre-treatment increased the contribution ratio to the systemic exposure of Nor-VER from the hepatic metabolism of systemically circulated VER, and decreased the contribution ratio of intestinal metabolism. These findings indicated that the isotope-IV study may be useful for considering the PK profile of metabolites.

Contributions of UDP-Glucuronosyltransferases to Human Hepatic and Intestinal Metabolism of Ticagrelor and Inhibition of UGTs and Cytochrome P450 Enzymes by Ticagrelor and its Glucuronidated Metabolite

Ticagrelor is the first reversibly binding, direct-acting, oral P2Y₁₂ receptor inhibitor. The contribution of UDP-glucuronosyltransferases (UGTs) enzymes to the metabolism of ticagrelor to its glucuronide conjugation, ticagrelor-O-glucuronide, in human liver microsomes (HLM) and human intestinal microsomes (HIM), was well characterized in the current study. The inhibition potential of human major UGTs by ticagrelor and ticagrelor-O-glucuronide was explored. The inhibitory effects of ticagrelor-O-glucuronide on cytochrome P450s (CYPs) enzymes were investigated as well. Ticagrelor glucuronidation exhibits substrate inhibition kinetics in both HLM and HIM with apparent K_m values of 5.65 and 2.52 μM, V_max values of 8.03 and 0.90 pmol min⁻¹·mg protein⁻¹, K_si values of 1,343.0 and 292.9 respectively. The in vitro intrinsic clearances (V_max/K_m) for ticagrelor glucuronidation by HLM and HIM were 1.42 and 0.36 μl min⁻¹·mg protein⁻¹, respectively. Study with recombinant human UGTs suggested that multiple UGT isoforms including UGT1A9, UGT1A7, UGT1A3, UGT1A4, UGT1A1, UGT2B7 and UGT1A8 are involved in the conversion of ticagrelor to ticagrelor-O-glucuronide with UGT1A9 showing highest catalytic activity. The results were further supported by the inhibition studies on ticagrelor glucuronidation with typical UGT inhibitors in pooled HLM and HIM. Little or no inhibition of UGT1A1, UGT1A3, UGT1A4, UGT1A6, UGT1A9 and UGT2B7 by ticagrelor and ticagrelor-O-glucuronide was noted. Ticagrelor-O-glucuronide also exhibited limited inhibitory effects toward CYP2C8, CYP2D6 and CYP3A4. In contrast, ticagrelor-O-glucuronide weakly inhibited CYP2B6, CYP2C9 and CYP2C19 activity with apparent IC₅₀ values of 45.0, 20.0 and 18.8 μM, respectively. The potential of ticagrelor-O-glucuronide to cause drug-drug interactions warrant further study.

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.

In Vitro Inhibition of Renal OCT2 and MATE1 Secretion by Antiemetic Drugs

The organic cation transporter 2 (OCT2) and multidrug and toxin extrusion protein 1 (MATE1) mediate the renal secretion of drugs. Recent studies suggest that ondansetron, a 5-HT₃ antagonist drug used to prevent nausea and vomiting, can inhibit OCT2- and MATE1-mediated transport. The purpose of this study was to test the ability of five 5-HT₃ antagonist drugs to inhibit the OCT2 and MATE1 transporters. The transport of the OCT2/MATE1 probe substrate ASP⁺ was assessed using two models: (1) HEK293 kidney cells overexpressing human OCT2 or MATE1, and (2) MDCK cells transfected with human OCT2 and MATE1. In HEK293 cells, the inhibition of ASP⁺ uptake by OCT2 listed in order of potency was palonosetron (IC₅₀: 2.6 μM) > ondansetron > granisetron > tropisetron > dolasetron (IC₅₀: 85.4 μM) and the inhibition of ASP⁺ uptake by MATE1 in order of potency was ondansetron (IC₅₀: 0.1 μM) > palonosetron = tropisetron > granisetron > dolasetron (IC₅₀: 27.4 μM). Ondansetron (0.5–20 μM) inhibited the basolateral-to-apical transcellular transport of ASP⁺ up to 64%. Higher concentrations (10 and 20 μM) of palonosetron, tropisetron, and dolasetron similarly reduced the transcellular transport of ASP⁺. In double-transfected OCT2-MATE1 MDCK cells, ondansetron at concentrations of 0.5 and 2.5 μM caused significant intracellular accumulation of ASP⁺. Taken together, these data suggest that 5-HT₃ antagonist drugs may inhibit the renal secretion of cationic drugs by interfering with OCT2 and/or MATE1 function.

Inhibitory effect of ketoconazole, quinidine and 1-aminobenzotriazole on pharmacokinetics of l-tetrahydropalmatine and its metabolite in rats

l-tetrahydropalmatine (l-THP) is mainly metabolised by CYP450 enzymes.This study was to investigate the possible effect of co-administered CYP inhibitors on the pharmacokinetics of l-THP and its metabolites in rats.An established LC-MS/MS method has been applied for the evaluation of drug-drug interaction between l-THP and CYP inhibitors. Following the administration of CYP inhibitors, a single dose of l-THP (9 mg/kg) was orally administrated.With regard to l-THP, the AUC_0-48 were significantly increased by 4.3, 3.79, and 11.39 folds, and C_max were increased by 4.74, 3.64, and 2.76 folds in the ketoconazole group (KET), quinidine group (QD), and 1-aminobenzotriazole group (ABT), respectively. KET and QD both significantly increased the AUC_0-48 of 2-DM and 2-DM-Glu by 1.38 ∼ 2.43 times, while C_max was significantly decreased by 41.3 and 78.0% in the ABT group, respectively. The C_max of 3-DM was reduced by 51.38, 48.02, and 63.31% after pre-treatment with KET, QD, and ABT, respectively, and C_max of 3-DM-Glu decreased correspondingly by 29.6, 22.1, and 58.0%.Results indicated that CYP inhibitors could markedly influence the systemic level of l-THP and its metabolites. To guarantee the safe use of l-THP, attention should be paid when l-THP was co-administered with CYP inhibitors, particularly with CYP3A4 and 2D6 inhibitors.

Prediction of Drug-Drug Interactions with Bupropion and Its Metabolites as CYP2D6 Inhibitors Using a Physiologically-Based Pharmacokinetic Model

The potential of inhibitory metabolites of perpetrator drugs to contribute to drug-drug interactions (DDIs) is uncommon and underestimated. However, the occurrence of unexpected DDI suggests the potential contribution of metabolites to the observed DDI. The aim of this study was to develop a physiologically-based pharmacokinetic (PBPK) model for bupropion and its three primary metabolites—hydroxybupropion, threohydrobupropion and erythrohydrobupropion—based on a mixed “bottom-up” and “top-down” approach and to contribute to the understanding of the involvement and impact of inhibitory metabolites for DDIs observed in the clinic. PK profiles from clinical researches of different dosages were used to verify the bupropion model. Reasonable PK profiles of bupropion and its metabolites were captured in the PBPK model. Confidence in the DDI prediction involving bupropion and co-administered CYP2D6 substrates could be maximized. The predicted maximum concentration (C_max) area under the concentration-time curve (AUC) values and C_max and AUC ratios were consistent with clinically observed data. The addition of the inhibitory metabolites into the PBPK model resulted in a more accurate prediction of DDIs (AUC and C_max ratio) than that which only considered parent drug (bupropion) P450 inhibition. The simulation suggests that bupropion and its metabolites contribute to the DDI between bupropion and CYP2D6 substrates. The inhibitory potency from strong to weak is hydroxybupropion, threohydrobupropion, erythrohydrobupropion, and bupropion, respectively. The present bupropion PBPK model can be useful for predicting inhibition from bupropion in other clinical studies. This study highlights the need for caution and dosage adjustment when combining bupropion with medications metabolized by CYP2D6. It also demonstrates the feasibility of applying the PBPK approach to predict the DDI potential of drugs undergoing complex metabolism, especially in the DDI involving inhibitory metabolites.

Estimation of Circulating Drug Metabolite Exposure in Human Using In Vitro Data and Physiologically Based Pharmacokinetic Modeling: Example of a High Metabolite/Parent Drug Ratio

(R)-4-((4-(((4-((tetrahydrofuran-3-yl)oxy)benzo[d]isoxazol-3-yl)oxy)methyl)piperidin-1-yl)methyl)tetrahydro-2H-pyran-4-ol (TBPT), a serotonin-4 receptor partial agonist, is metabolized to two metabolites: an N-dealkylation product [(R)-3-(piperidin-4-ylmethoxy)-4-((tetrahydrofuran-3-yl)oxy)benzo[d]isoxazole (M1)] and a cyclized oxazolidine structure [7-(((4-(((R)-tetrahydrofuran-3-yl)oxy)benzo[d]isoxazol-3-yl)oxy)methyl)octahydro-3H (M2)]. After administration of TBPT to humans the exposure to M1 was low and the exposure to M2 was high, relative to the parent drug, despite this being the opposite in vitro. In this study, projection of the plasma metabolite/parent (M/P) ratios for M1 and M2 was attempted using in vitro metabolism, binding, and permeability data in static and dynamic physiologically based pharmacokinetic (PBPK) models. In the static model, the fraction of parent clearance yielding the metabolite (which also required taking into account secondary metabolites of M1 and M2), the clearance of the metabolites and parent, and an estimate of the availability of the metabolites from the liver were combined to yield estimated parent/metabolite ratios of 0.32 and 23 for M1 and M2, respectively. PBPK modeling that used in vitro and physicochemical data input yielded estimates of 0.26 and 20, respectively. The actual values were 0.12 for M1/TBPT and 58 for M2/TBPT. Thus, the ratio for M1 was overpredicted, albeit at values less than unity. The ratio for M2/TBPT was underpredicted, and the high ratio of 58 may exceed a limiting ceiling of the approach. Nevertheless, when considered in the context of determining whether a potential circulating metabolite may be quantitatively important prior to administration of a drug for the first time to humans, the approaches succeeded in highlighting the importance of M2 (M/P ratio >> 1) relative to M1, despite M1 being much greater than M2 in vitro.

Discovery and development of pyrotinib: A novel irreversible EGFR/HER2 dual tyrosine kinase inhibitor with favorable safety profiles for the treatment of breast cancer

The discovery and development of a novel irreversible EGFR/HER2 dual tyrosine kinase inhibitor SHR1258 (pyrotinib) for the treatment of HER2-postive breast cancer is presented. The structure-activity relationship of lead series and their pharmacokinetic properties were evaluated to identify the potential candidates for further in vivo efficacy studies and preclinical safety assessments. Metabolic pathway and drug-drug interaction were also investigated in preclinical settings. In particular, major metabolites in human and animal species were assessed with regard to potential toxicity or off-target side effects. Overall, the potent and selective EGFR/HER2 dual inhibitor, pyrotinib, displayed robust anti-tumor effects on HER2-overexpressing xenograft models and sufficiently safety windows in animals as well as favorable pharmacokinetic properties in human, which substantially ensures current clinical development. Finally, recent advances of pyrotinib in clinical studies are highlighted with very encouraging outcomes in patients with HER2-postive advanced breast cancer.

Increase in the systemic exposure of primary metabolites of Midazolam in rat arising from CYP inhibition or hepatic dysfunction

The main purpose of this study is to demonstrate the possibility of increase in the systemic exposure of drug metabolites by CYP-inhibition or acute hepatitis. Midazolam (MDZ) was used as a model substrate of CYP3A and 1-aminobenzotriazole (ABT) was used as a CYP-inhibitor. After oral pretreatment with ABT, MDZ was intravenously injected to rats and the plasma profiles of MDZ and its primary metabolites, 1'-hydroxy MDZ and 4-hydroxy MDZ, were observed. In the ABT-pretreatment rats, plasma AUCs of both metabolites were much larger than those in control rats, demonstrating a higher systemic exposure of metabolites under CYP-inhibited condition. Furthermore, kinetic analysis revealed that the amount of both metabolites entered into the systemic circulation increased significantly (about 5-times). Increases in the systemic exposure of the primary metabolites of MDZ were also observed in the acute hepatitis rats induced by CCl₄-pretreatment. As underlying mechanisms, it was speculated that ABT inhibited the subsequent metabolism of primary metabolites of MDZ in the hepatocytes and enhanced their release to the systemic circulation. In vitro study with rat liver microsomes supported this speculation. In conclusion, this study showed the complexity of PK profiles of drug metabolites, which might lead to new aspects on their safety issue.

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.

A Pharmacokinetic Modeling Approach to Predict the Contribution of Active Metabolites to Human Efficacious Dose

A preclinical drug candidate, MRK-1 (Merck candidate drug parent compound), was found to elicit tumor regression in a mouse xenograft model. Analysis of samples from these studies revealed significant levels of two circulating metabolites, whose identities were confirmed by comparison with authentic standards using liquid chromatography-tandem mass spectrometry. These metabolites were found to have an in vitro potency similar to that of MRK-1 against the pharmacological target and were therefore thought to contribute to the observed efficacy. To predict this contribution in humans, a pharmacokinetic (PK) modeling approach was developed. At the mouse efficacious dose, the areas under the plasma concentration time curves (AUCs) of the active metabolites were normalized by their in vitro potency compared with MRK-1. These normalized metabolite AUCs were added to that of MRK-1 to yield a composite efficacious unbound AUC, expressed as "parent drug equivalents," which was used as the target AUC for predictions of the human efficacious dose. In vitro and preclinical PK studies afforded predictions of the PK of MRK-1 and the two active metabolites in human as well as the relative pathway flux to each metabolite. These were used to construct a PK model (Berkeley Madonna, version 8.3.18; Berkeley Madonna Inc., University of California, Berkeley, CA) and to predict the human dose required to achieve the target parent equivalent exposure. These predictions were used to inform on the feasibility of the human dose in terms of size, frequency, formulation, and likely safety margins, as well as to aid in the design of preclinical safety studies.

Quantitative Rationalization of Gemfibrozil Drug Interactions: Consideration of Transporters-Enzyme Interplay and the Role of Circulating Metabolite Gemfibrozil 1-O-β-Glucuronide

Gemfibrozil has been suggested as a sensitive cytochrome P450 2C8 (CYP2C8) inhibitor for clinical investigation by the U.S. Food and Drug Administration and the European Medicines Agency. However, gemfibrozil drug-drug interactions (DDIs) are complex; its major circulating metabolite, gemfibrozil 1-O-β-glucuronide (Gem-Glu), exhibits time-dependent inhibition of CYP2C8, and both parent and metabolite also behave as moderate inhibitors of organic anion transporting polypeptide 1B1 (OATP1B1) in vitro. Additionally, parent and metabolite also inhibit renal transport mediated by OAT3. Here, in vitro inhibition data for gemfibrozil and Gem-Glu were used to assess their impact on the pharmacokinetics of several victim drugs (including rosiglitazone, pioglitazone, cerivastatin, and repaglinide) by employing both static mechanistic and dynamic physiologically based pharmacokinetic (PBPK) models. Of the 48 cases evaluated using the static models, about 75% and 98% of the DDIs were predicted within 1.5- and 2-fold of the observed values, respectively, when incorporating the interaction potential of both gemfibrozil and its 1-O-β-glucuronide. Moreover, the PBPK model was able to recover the plasma profiles of rosiglitazone, pioglitazone, cerivastatin, and repaglinide under control and gemfibrozil treatment conditions. Analyses suggest that Gem-Glu is the major contributor to the DDIs, and its exposure needed to bring about complete inactivation of CYP2C8 is only a fraction of that achieved in the clinic after a therapeutic gemfibrozil dose. Overall, the complex interactions of gemfibrozil can be quantitatively rationalized, and the learnings from this analysis can be applied in support of future predictions of gemfibrozil DDIs.

Methylation and its role in the disposition of tanshinol, a cardiovascular carboxylic catechol from Salvia miltiorrhiza roots (Danshen)

AIM

Tanshinol is an important catechol in the antianginal herb Salvia miltiorrhiza roots (Danshen). This study aimed to characterize tanshinol methylation.

METHODS

Metabolites of tanshinol were analyzed by liquid chromatography/mass spectrometry. Metabolism was assessed in vitro with rat and human enzymes. The major metabolites were synthesized for studying their interactions with drug metabolizing enzymes and transporters and their vasodilatory properties. Dose-related tanshinol methylation and its influences on tanshinol pharmacokinetics were also studied in rats.

RESULTS

Methylation, preferentially in the 3-hydroxyl group, was the major metabolic pathway of tanshinol. In rats, tanshinol also underwent considerable 3-O-sulfation, which appeared to be poor in human liver. These metabolites were mainly eliminated via renal excretion, which involved tubular secretion mainly by organic anion transporter (OAT) 1. The methylated metabolites had no vasodilatory activity. Entacapone-impaired methylation did not considerably increase systemic exposure to tanshinol in rats. The saturation of tanshinol methylation in rat liver could be predicted from the Michaelis constant of tanshinol for catechol-O-methyltransferase (COMT). Tanshinol had low affinity for human COMT and OATs; its methylated metabolites also had low affinity for the transporters. Tanshinol and its major human metabolite (3-O-methyltanshinol) exhibited negligible inhibitory activities against human cytochrome P450 enzymes, organic anion transporting polypeptides 1B1/1B3, multidrug resistance protein 1, multidrug resistance-associated protein 2, and breast cancer resistance protein.

CONCLUSION

Tanshinol is mainly metabolized via methylation. Tanshinol and its major human metabolite have low potential for pharmacokinetic interactions with synthetic antianginal agents. This study will help define the risk of hyperhomocysteinemia related to tanshinol methylation.

Spectroscopic and molecular docking studies of the in vitro interaction between puerarin and cytochrome P450

Puerarin, an isoflavone glycoside extracted from Pueraria plants, has various medical functions. Cytochrome P450s (CYPs) are crucial phase I metabolizing enzymes, which have been spotlighted for their effects on drug metabolism. The interaction between puerarin and CYPs (CYP1A2 and CYP2D6) was investigated by fluorescence, UV-Vis and circular dichroism spectroscopies, as well as molecular docking, to explore the underlying mechanism under simulated physiological conditions. The molecular docking results indicated that puerarin interacted with CYPs mainly by hydrophobic force and hydrogen bonding. The fluorescences of CYPs were quenched statically. Binding constants (K_a) and number of binding sites (n) at different temperatures were calculated, with the results being consistent with those of molecular docking. At the same temperature, puerarin bound to CYP1A2 more weakly than it did to CYP2D6. UV-Vis and circular dichroism spectroscopies confirmed the micro-environmental and conformational changes of CYP1A2 and CYP2D6. The findings provide reliable evidence for clarifying the structures and functions of CYPs.

An industry perspective on tiered approach to the investigation of metabolites in drug development

Background: A tiered approach to drug metabolite measurement and identification is often used industry wide to fulfill regulatory requirements specified in recent US FDA and European Medicines Agency guidance. Although this strategy is structured in its intent it can be customized to address unique challenges which may arise during early and late drug development activities. These unconventional methods can be applied at any stage to facilitate metabolite characterization. Results: Two case studies are described NVS 1 and 2. NVS 1: plasma concentrations, measured using a radiolabeled MS-response factor exploratory method, were comparable to those from a validated bioanalytical method. The NVS 2 example showed how in vitro analysis helped to characterize an unexpectedly abundant circulating plasma metabolite M3. Conclusion: A tiered approach incorporating many aspects of conventional and flexible analytical methodologies can be pulled together to address regulatory questions surrounding drug metabolite characterization.

Metabolite identification studies on amiodarone in in vitro (rat liver microsomes, rat and human liver S9 fractions) and in vivo (rat feces, urine, plasma) matrices by using liquid chromatography with high-resolution mass spectrometry and multiple-stage mass spectrometry: characterization of the diquinone metabolite supposedly responsible for the drug's hepatotoxicity

RATIONALE

Several mechanisms have been anticipated for the toxicity of amiodarone, such as oxidative stress, lipid peroxidation, phospholipidosis, free radical generation, etc. Amiodarone is structurally similar to benzbromarone, an uricosuric agent, which was withdrawn from European markets due to its idiosyncratic hepatotoxicity. A proposed reason behind the toxicity of benzbromarone was the production of a reactive ortho-diquinone metabolite, which was found to form adducts with glutathione. Therefore, taking a clue that a similar diquinone metabolite of amiodarone may be the reason for its hepatotoxicity, metabolite identification studies were carried out on the drug using liquid chromatography/mass spectrometry (LC/MS) tools.

METHODS

The studies involved in vitro (rat liver microsomes, rat liver S9 fraction, human liver S9 fraction) and in vivo (rat feces, urine, plasma) models, wherein the samples were analyzed by employing LC/HRMS, LC/MS(n) and HDE-MS.

RESULTS AND CONCLUSIONS

A total of 26 metabolites of amiodarone were detected in the investigated in vitro and in vivo matrices. The suspected ortho-diquinone metabolite was one of them. The formation of the same might be an added reason for the hepatotoxicity shown by the drug.

Drug metabolites as cytochrome p450 inhibitors: a retrospective analysis and proposed algorithm for evaluation of the pharmacokinetic interaction potential of metabolites in drug discovery and development

Understanding drug-drug interactions (DDIs) is a key component of clinical practice ensuring patient safety and efficacy of medicines. The role of drug metabolites in DDIs is a developing area of science, and has been recently highlighted in a draft regulatory guidance. The guidance states that metabolites representing ≥25% of the parent drug's area under the plasma concentration/time curve and/or >10% of exposure of total drug-related material should trigger in vitro characterization of metabolites for cytochrome P450 inhibition and propensity for DDIs. The relationship between in vitro cytochrome P450 inhibitory potency, systemic exposure, and DDI potential of drug metabolites was examined using the Pfizer development database to identify compounds with pre-existing in vivo biotransformation data, where circulating metabolites were identified in humans. The database yielded 33 structurally diverse compounds with collectively 115 distinct circulating metabolites. Of these, 52% (60/115) achieved exposures >25% of parent drug levels as judged from mass balance/metabolite identification studies. It was noted that 14 metabolite standards for 12 parent drugs had been synthesized, monitored in clinical studies, and examined for cytochrome P450 inhibition. For the 14 metabolite/parent drug pairs, no clinically relevant DDIs were expected to occur against the major human cytochrome P450 isoforms. A review of the literature for parent/metabolite DDI information was also conducted to examine trends using a larger data set. Leveraging the analysis of both internal and literature-based data sets, an algorithm was devised for use in drug discovery/early development to assess cytochrome P450 inhibitory potential of drug metabolites and the propensity to cause a clinically relevant DDI.

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.

Drug-Drug Interactions within Protein Cavities Probed by Triplet-Triplet Energy Transfer

A new direct and noninvasive methodology based on transient absorption spectroscopy has been developed to probe the feasibility of drug-drug interactions within a common protein binding site. The simultaneous presence of (R)-cinacalcet (CIN) and (S)-propranolol (PPN) within human or bovine α1-acid glycoproteins (AAGs) is revealed by detection of (3)CIN* as the only transient species after laser flash photolysis of CIN/PPN/AAG mixtures at 308 nm. This is the result of triplet-triplet energy transfer from (3)PPN* to CIN, which requires close contact between the two drugs within the same biological compartment. Similar results are obtained with nabumetone and CIN as donor/acceptor partners. This new methodology can, in principle, be extended to a variety of drug/drug/biomolecule combinations.

Inhibition of CYP2C19 and CYP3A4 by omeprazole metabolites and their contribution to drug-drug interactions

The aim of this study was to evaluate the contribution of metabolites to drug-drug interactions (DDI) using the inhibition of CYP2C19 and CYP3A4 by omeprazole and its metabolites as a model. Of the metabolites identified in vivo, 5-hydroxyomeprazole, 5'-O-desmethylomeprazole, omeprazole sulfone, and carboxyomeprazole had a metabolite to parent area under the plasma concentration-time curve (AUC(m)/AUC(p)) ratio ≥ 0.25 when either total or unbound concentrations were measured after a single 20-mg dose of omeprazole in a cocktail. All of the metabolites inhibited CYP2C19 and CYP3A4 reversibly. In addition omeprazole, omeprazole sulfone, and 5'-O-desmethylomeprazole were time dependent inhibitors (TDI) of CYP2C19, whereas omeprazole and 5'-O-desmethylomeprazole were found to be TDIs of CYP3A4. The in vitro inhibition constants and in vivo plasma concentrations were used to evaluate whether characterization of the metabolites affected DDI risk assessment. Identifying omeprazole as a TDI of both CYP2C19 and CYP3A4 was the most important factor in DDI risk assessment. Consideration of reversible inhibition by omeprazole and its metabolites would not identify DDI risk with CYP3A4, and with CYP2C19, reversible inhibition values would only identify DDI risk if the metabolites were included in the assessment. On the basis of inactivation data, CYP2C19 and CYP3A4 inhibition by omeprazole would be sufficient to identify risk, but metabolites were predicted to contribute 30-63% to the in vivo hepatic interactions. Therefore, consideration of metabolites may be important in quantitative predictions of in vivo DDIs. The results of this study show that, although metabolites contribute to in vivo DDIs, their relative abundance in circulation or logP values do not predict their contribution to in vivo DDI risk.

Which metabolites circulate?

Characterization of the circulating metabolites for a new chemical entity in humans is essential for safety assessment, an understanding of their contributions to pharmacologic activities, and their potential involvement in drug-drug interactions. This review examines the abundance of metabolites relative to the total parent drug [metabolite-to-parent (M/P) ratio] from 125 drugs in relation to their structural and physicochemical characteristics, lipoidal permeability, protein binding, and fractional formation from parent (fm). Our analysis suggests that fm is the major determinant of total drug M/P ratio for amine, alcohol, N- and S-oxide, and carboxylic acid metabolites. Passage from the hepatocyte to systemic circulation does not appear to be limiting owing to the vast majority of metabolites formed being relatively lipid permeable. In some cases, active transport plays an important role in this process (e.g., carboxylic acid metabolites). Differences in total parent drug clearance and metabolite clearance are attenuated by the reduction in lipophilicity introduced by the metabolic step and resultant compensatory changes in unbound clearance and protein binding. A small subclass of these drugs (e.g., terfenadine) is unintentional prodrugs with very high parent drug clearance, resulting in very high M/P ratios. In contrast, arenol metabolites show a more complex relationship with fm due largely to the new metabolic routes (conjugation) available to the metabolite compared with the parent drug molecule. For these metabolites, a more thorough understanding of the elimination clearance of the metabolite is critical to discern the likelihood of whether the phenol will constitute a major circulating metabolite.

Sucralose, a synthetic organochlorine sweetener: overview of biological issues

Sucralose is a synthetic organochlorine sweetener (OC) that is a common ingredient in the world's food supply. Sucralose interacts with chemosensors in the alimentary tract that play a role in sweet taste sensation and hormone secretion. In rats, sucralose ingestion was shown to increase the expression of the efflux transporter P-glycoprotein (P-gp) and two cytochrome P-450 (CYP) isozymes in the intestine. P-gp and CYP are key components of the presystemic detoxification system involved in first-pass drug metabolism. The effect of sucralose on first-pass drug metabolism in humans, however, has not yet been determined. In rats, sucralose alters the microbial composition in the gastrointestinal tract (GIT), with relatively greater reduction in beneficial bacteria. Although early studies asserted that sucralose passes through the GIT unchanged, subsequent analysis suggested that some of the ingested sweetener is metabolized in the GIT, as indicated by multiple peaks found in thin-layer radiochromatographic profiles of methanolic fecal extracts after oral sucralose administration. The identity and safety profile of these putative sucralose metabolites are not known at this time. Sucralose and one of its hydrolysis products were found to be mutagenic at elevated concentrations in several testing methods. Cooking with sucralose at high temperatures was reported to generate chloropropanols, a potentially toxic class of compounds. Both human and rodent studies demonstrated that sucralose may alter glucose, insulin, and glucagon-like peptide 1 (GLP-1) levels. Taken together, these findings indicate that sucralose is not a biologically inert compound.

Importance of multi-p450 inhibition in drug-drug interactions: evaluation of incidence, inhibition magnitude, and prediction from in vitro data

Drugs that are mainly cleared by a single enzyme are considered more sensitive to drug-drug interactions (DDIs) than drugs cleared by multiple pathways. However, whether this is true when a drug cleared by multiple pathways is coadministered with an inhibitor of multiple P450 enzymes (multi-P450 inhibition) is not known. Mathematically, simultaneous equipotent inhibition of two elimination pathways that each contribute half of the drug clearance is equal to equipotent inhibition of a single pathway that clears the drug. However, simultaneous strong or moderate inhibition of two pathways by a single inhibitor is perceived as an unlikely scenario. The aim of this study was (i) to identify P450 inhibitors currently in clinical use that can inhibit more than one clearance pathway of an object drug in vivo and (ii) to evaluate the magnitude and predictability of DDIs caused by these multi-P450 inhibitors. Multi-P450 inhibitors were identified using the Metabolism and Transport Drug Interaction Database. A total of 38 multi-P450 inhibitors, defined as inhibitors that increased the AUC or decreased the clearance of probes of two or more P450s, were identified. Seventeen (45%) multi-P450 inhibitors were strong inhibitors of at least one P450, and an additional 12 (32%) were moderate inhibitors of one or more P450s. Only one inhibitor (fluvoxamine) was a strong inhibitor of more than one enzyme. Fifteen of the multi-P450 inhibitors also inhibit drug transporters in vivo, but such data are lacking on many of the inhibitors. Inhibition of multiple P450 enzymes by a single inhibitor resulted in significant (>2-fold) clinical DDIs with drugs that are cleared by multiple pathways such as imipramine and diazepam, while strong P450 inhibitors resulted in only weak DDIs with these object drugs. The magnitude of the DDIs between multi-P450 inhibitors and diazepam, imipramine, and omeprazole could be predicted using in vitro data with similar accuracy as probe substrate studies with the same inhibitors. The results of this study suggest that inhibition of multiple clearance pathways in vivo is clinically relevant, and the risk of DDIs with object drugs may be best evaluated in studies using multi-P450 inhibitors.

"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.

Risk assessment of mechanism-based inactivation in drug-drug interactions

Drug-drug interactions (DDIs) that occur via mechanism-based inactivation of cytochrome P450 are of serious concern. Although several predictive models have been published, early risk assessment of MBIs is still challenging. For reversible inhibitors, the DDI risk categorization using [I]/K(i) ([I], the inhibitor concentration; K(i), the inhibition constant) is widely used in drug discovery and development. Although a simple and reliable methodology such as [I]/K(i) categorization for reversible inhibitors would be useful for mechanism-based inhibitors (MBIs), comprehensive analysis of an analogous measure reflecting in vitro potency for inactivation has not been reported. The aim of this study was to evaluate whether the term λ/k(deg) (λ, first-order inactivation rate at a given MBI concentration; k(deg), enzyme degradation rate constant) would be useful in the prediction of the in vivo DDI risk of MBIs. Twenty-one MBIs with both in vivo area under the curve (AUC) change of marker substrates and in vitro inactivation parameters were identified in the literature and analyzed. The results of this analysis show that in vivo DDIs with >2-fold change of object drug AUC can be identified with the cutoff value of λ/k(deg) = 1, where unbound steady-state C(max) is used for inhibitor concentration. However, the use of total C(max) led to great overprediction of DDI risk. The risk assessment using λ/k(deg) coupled with unbound C(max) can be useful for the DDI risk evaluation of MBIs in drug discovery and development.

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

INTRODUCTION

Predictions of drug-drug interactions (DDIs) are commonly performed for single inhibitors, but interactions involving multiple inhibitors also frequently occur. Predictions of such interactions involving stereoisomer pairs, parent/metabolite combinations and simultaneously administered multiple inhibitors are increasing in importance. This review provides the framework for predicting inhibitory DDIs of multiple inhibitors with any combination of reversible inhibition mechanism.

AREAS COVERED

The review provides an overview of the reliability of the in vitro determined reversible inhibition mechanism. Furthermore, the article provides a method to predict DDIs for multiple reversible inhibitors that allows substituting the inhibition constant (K(i)) with an inhibitor affinity (IC(50)) value determined at S << K(M).

EXPERT OPINION

A better understanding and the prediction methods of DDIs, resulting from multiple inhibitors, are important. The inhibition mechanism of a reversible inhibitor is often equivocal across studies and unreliable. Determination of the K(i) requires the assignment of reversible inhibition mechanism but in vitro-to-in vivo prediction of DDI risk can be achieved for multiple inhibitors from estimates of the inhibitor affinity (IC(50)) only, regardless of the inhibition mechanism.

Assessment of algorithms for predicting drug-drug interactions via inhibition mechanisms: comparison of dynamic and static models

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT

The prediction of drug-drug interactions (DDIs) from in vitro data usually utilizes an average dosing interval estimate of inhibitor concentration in an equation-based static model. Simcyp®, a population-based ADME simulator, is becoming widely used for the prediction of DDIs and has the ability to incorporate the time-course of inhibitor concentration and hence generate a temporal profile of the inhibition process within a dynamic model.

WHAT THIS PAPER ADDS

Prediction of DDIs for 35 clinical studies incorporating a representative range of drug-drug interactions, with multiple studies across different inhibitors and victim drugs. Assessment of whether the inclusion of the time course of inhibition in the dynamic model improves prediction in comparison with the static model. Investigation of the impact of different inhibitor and victim drug parameters on DDI prediction accuracy including dosing time and the inclusion of active metabolites. Assessment of ability of the dynamic model to predict inter-individual variability in the DDI magnitude.

AIMS

Static and dynamic models (incorporating the time course of the inhibitor) were assessed for their ability to predict drug-drug interactions (DDIs) using a population-based ADME simulator (Simcyp®V8). The impact of active metabolites, dosing time and the ability to predict inter-individual variability in DDI magnitude were investigated using the dynamic model.

METHODS

Thirty-five in vivo DDIs involving azole inhibitors and benzodiazepines were predicted using the static and dynamic model; both models were employed within Simcyp for consistency in parameters. Simulations comprised of 10 trials with matching population demographics and dosage regimen to the in vivo studies. Predictive utility of the static and dynamic model was assessed relative to the inhibitor or victim drug investigated.

RESULTS

Use of the dynamic and static models resulted in comparable prediction success, with 71 and 77% of DDIs predicted within two-fold, respectively. Over 40% of strong DDIs (>five-fold AUC increase) were under-predicted by both models. Incorporation of the itraconazole metabolite into the dynamic model resulted in increased prediction accuracy of strong DDIs (80% within two-fold). Bias and imprecision in prediction of triazolam DDIs were higher in comparison with midazolam and alprazolam; >50% of triazolam DDIs were under-predicted regardless of the model used. Predicted inter-individual variability in the AUC ratio (coefficient of variation of 45%) was consistent with the observed variability (50%).

CONCLUSIONS

High prediction accuracy was observed using both the Simcyp dynamic and static models. The differences observed with the dose staggering and the incorporation of active metabolite highlight the importance of these variables in DDI prediction.

Physiologically-based pharmacokinetic modeling for absorption, transport, metabolism and excretion

The seminal paper on the liver physiologically-based pharmacokinetic (PBPK) model by Rowland et al. (J Pharmacokinet Biopharm 1:123-136, 1973) that described the influence of blood flow, intrinsic clearance, and binding on hepatic clearance had inspired further development of PBPK modeling of the liver, kidney and intestine as well as whole body. Shortly thereafter, a series of papers from Pang and Rowland compared the well-stirred and parallel-tube liver models and sparked further development on clearance concepts in the liver, including those described by the dispersion model. From 2005 onwards, several seminal papers by Rodgers and Rowland, in their recognition of the binding of molecules to tissue acidic and neutral phospholipids, improved the methodology in providing estimates of the tissue-to-plasma coefficient and rendering easy calculation of these hard-to-get constants. The improvement has strongly consolidated the basic premise on PBPK modeling and simulations and these basics have allowed scientists to focus on other important variables: membrane barriers, and transporter and enzyme and their heterogeneities that further impact drug disposition. In particular, the PBPK models have delved into sequential metabolism and futile cycling to illustrate how transporters and enzymes could affect the metabolism of drugs and metabolites. PBPK models that are especially pertinent to metabolite kinetics are being utilized in drug studies and risk assessment. These types of PBPK modeling reveal differences in kinetics between the formed vs. preformed metabolite, showing special considerations for membrane barriers, and the influence of competing pathways and competing organs.

Are circulating metabolites important in drug-drug interactions?: Quantitative analysis of risk prediction and inhibitory potency

The potential of metabolites to contribute to drug-drug interactions (DDIs) is not well defined. The aim of this study was to determine the quantitative role of circulating metabolites in inhibitory DDIs in vivo. The area under the plasma concentration-time curve (AUC) data related to at least one circulating metabolite was available for 71% of the 102 inhibitor drugs identified. Of the 80 metabolites characterized at steady state, 78% had AUCs >10% of that of the parent drug. A comparison of the inhibitor concentration/inhibition constant ([I]/K(i)) ratios of metabolites and the respective parent drugs showed that 17 of the 21 (80%) reversible inhibitors studied had metabolites that were likely to contribute to in vivo DDIs, with some metabolites predicted to have inhibitory effects greater than those of the parent drug. The in vivo drug interaction risks associated with amiodarone, bupropion, and sertraline could be identified from in vitro data only, when data pertaining to metabolites were included in the predictions. In conclusion, cytochrome P450 (CYP) inhibitors often have circulating metabolites that contribute to clinically observed CYP inhibition.

Accurate prediction of dose-dependent CYP3A4 inhibition by itraconazole and its metabolites from in vitro inhibition data

Inhibitory drug metabolites may contribute to drug-drug interactions (DDIs). The aim of this study was to determine the importance of inhibitory metabolites of itraconazole (ITZ) in in vivo cytochrome P450 (CYP) 3A4 inhibition. The pharmacokinetics of ITZ and midazolam (MDZ) were determined in six healthy volunteers in four sessions after administration of MDZ with and without oral ITZ. After doses of 50, 200, and 400 mg of ITZ, the clearance of orally administered MDZ decreased by 27, 74, and 83%, respectively. The in vivo half maximal inhibitory concentration (IC(50)) for ITZ ranged from 5 to 132 nmol/l in the six subjects. The metabolites of ITZ were estimated to account for ~50% of the total CYP3A4 inhibition, with the relative contribution increasing with time after ITZ dosing. Of the total of 18 interactions observed, 15 (84%) could be predicted within a twofold error margin, with improved accuracy observed when ITZ metabolites were included in the predictions. This study shows that the metabolites of ITZ contribute to CYP3A4 inhibition and need to be accounted for in quantitative rationalization of ITZ-mediated DDIs.

The role of metabolites in predicting drug-drug interactions: focus on irreversible cytochrome P450 inhibition

The irreversible inhibition of cytochrome P450 (CYP) enzymes can cause significant drug-drug interactions (DDIs). The formation of metabolites is fundamental for the inactivation of CYP enzymes. Of the 19 CYP enzyme inactivators for which the mechanism of action has been established, 10 have circulating metabolites, which are on the metabolic pathway to inactivation of the CYP enzyme. Because inactivation of CYP enzymes usually requires multiple metabolic steps, the prediction of interactions between metabolites and CYPs in vivo may require complex models and the availability of data generated in vitro from each metabolite. Data discussed in this review suggest that circulating metabolites are more important in CYP inhibition in vivo than has been acknowledged.

Physiologically based modelling and prediction of drug interactions

A major challenge for drug development and environmental or occupational health is the prediction of pharmacokinetic and pharmacodynamic interactions between drugs, natural chemicals or environmental contaminants. This article reviews briefly past developments in the area of physiologically based pharmacokinetic (PBPK) modelling of interactions. It also demonstrates a systems biology approach to the question, and the capabilities of new software tools to facilitate that development. Individual Systems Biology Markup Language models of metabolic pathways can now be automatically merged and coupled to a template PBPK pharmacokinetic model, using for example the GNU MCSim software. The global model generated is very efficient and able to simulate the interactions between a theoretically unlimited number of substances. Development time and the number of model parameter increase only linearly with the number of substances considered, even though the number of possible interactions increases exponentially.