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Recent EMA (final) and FDA (draft) drug interaction guidances proposed that human circulating metabolites should be investigated in vitro for their drug-drug interaction (DDI) potential if present at ≥ 25% of parent AUC (FDA) or ≥25% parent and ≥10% of total drug-related AUC (EMA). To examine the application of these regulatory recommendations, a g...
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... the authors focused on identifying compounds (within the 137 most prescribed drugs) that have metabolites that could cause DDI that was not predicted by the parent in vitro P450 inhibition properties. A total of 42 of these 137 drugs overlapped with the drugs analyzed by Isoherranen et al. (2009) (129 named drugs) and Yeung et al. (2011) (102 named drugs). The available data on in vitro P450 inhibition by parent drugs and their abundant metabolites (generally $25% of the parent AUC and/or $10% of the total AUC) and in vivo inhibition from clinical studies were collected as follows. ...
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... collected parameters (along with other pertinent information, e.g., dose) for all 137 drugs are shown in Supplemental Table 1. Based on the in vitro and in vivo parent DDI data, the drugs were divided into five categories using the criteria described below (see Table 1 and Fig. ...
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... shown in Table 1, a total of 102 drugs belong to categories 1-4 and 35 drugs are in the unassigned category. The predictability of the parent in vitro DDI data for in vivo DDI is depicted in Fig. 2 for drugs belonging to categories 1-4. There are 48 drugs in category 1 (true negatives), 10 drugs in category 3 (false negatives), 26 drugs in category 4 (true positives), and 18 drugs in category 2 (false positives). Therefore, based on the parent [I]/K i (in vitro) and in vivo DDI data, the true negatives are 83% (48 of 58 drugs in ...
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... importantly, two of these amine metabolites (from escitalopram and amiodarone) are confirmed to be more potent P450 inhibitors than the respective parent drug. Alkylamine metabolites that inactivate P450 are predominantly secondary alkylamines except for norfluoxetine (a primary alkylamine, Hanson et al., 2010), which was shown to inactivate multiple P450 isoforms (Lutz et al., 2013). Historically, the quasi-irreversible inhibition of CYP450 by secondary alkylamines is thought to occur via a reaction sequence involving N- dealkylation to primary alkylamines, which can be further N- hydroxylated to hydroxylamines, followed by further oxidation and dehydrogenation to nitroso derivatives (Fig. 3). ...
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... the quasi-irreversible inhibition of CYP450 by secondary alkylamines is thought to occur via a reaction sequence involving N- dealkylation to primary alkylamines, which can be further N- hydroxylated to hydroxylamines, followed by further oxidation and dehydrogenation to nitroso derivatives (Fig. 3). An alternative pathway was recently reported in the formation of nitroso metabolites involving exclusively N-hydroxylation instead of N-dealkylation of secondary alkylamine drugs (Hanson et al., 2010). Regardless of the reaction sequence, it is the nitroso metabolites that bind to the ferrous form of the prosthetic heme iron of P450 with high affinity via coordinate bonds and cause quasi-irreversible inactivation of the enzyme (Franklin, 1991;Kalgutkar et al., 2007). ...
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Context: Friedelin is a triterpenoid with several biological activities. However, the affects of Friedelin on the activity of human liver cytochrome P450 (CYP) enzymes remains unclear.
Objective: This study investigates the inhibitory effects of Friedelin on the major human liver CYP isoforms (CYP3A4, 1A2, 2A6, 2E1, 2D6, 2C9, 2C19 and 2C8).
Materia...
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... While most of the compounds were selective to a singular CYP isoform, compounds such as ticlopidine, fluvoxamine, and amiodarone were evaluated across multiple CYPs based on reported drug interaction studies. Additionally, for compounds such as gemfibrozil and amiodarone, where metabolites are known to exhibit TDI (Yu et al., 2015), inhibition kinetics of the metabolite were also generated. Reversible and time-dependent inhibition kinetic parameters along with free fraction in microsomes are listed in Table 2 and the % control activity vs incubation time and k obs vs [I] plots for each drug are shown in Supp. ...
In vitro time-dependent inhibition (TDI) kinetic parameters for cytochrome P450 (CYP) 1A2, 2B6, 2C8, 2C9, 2C19, and 2D6, were determined in pooled human liver microsomes for 19 drugs (and 2 metabolites) for which clinical drug-drug interactions (DDI) are known. In vitro TDI data were incorporated into the projection of the magnitude of DDIs using mechanistic static models and Simcyp®. Results suggest that for the mechanistic static model, use of estimated average unbound exit concentration of the inhibitor from the liver resulted in a successful prediction of observed magnitude of clinical DDIs and was similar to Simcyp®. Overall, predictions of DDI magnitude (i.e., fold increase in AUC of a CYP-specific marker substrate) were within 2-fold of actual values. Geometric mean-fold errors were 1.7 and 1.6 for static and dynamic models, respectively. Projections of DDI from both models were also highly correlated to each other (r2 = 0.92). This investigation demonstrates that DDI can be reliably predicted from in vitro TDI data generated in HLM for several CYP enzymes. Simple mechanistic static model equations as well as more complex dynamic PBPK models can be employed in this process. Significance Statement Cytochrome P450 time-dependent inhibitors (TDI) can cause drug-drug interactions (DDI). An ability to reliably assess the potential for a new drug candidate to cause DDI is essential during drug development. In this report, TDI data for 19 drugs (and 2 metabolites) were measured and used in static and dynamic models to reliably project the magnitude of DDI resulting from inhibition of CYP1A2, 2B6, 2C8, 2C9, 2C19, and 2D6.
... (Schadt et al., 2018). In the case of major metabolites (observed in vitro incubations and/or in circulation in animals and human), particularly ones observed from oxidative metabolism, accessing their corresponding authentic synthetic standards is important, since it allows additional insights into their contribution to the primary pharmacology (biologically active metabolites) as well as off-target (change in selectivity against receptor or enzyme sub-types, undesired activity for human ether-a-go-go gene product and other ion channels, etc.) and drug-drug interaction (potential to inhibit or induce human drug metabolizing enzymes such as CYPs) profiles (Yeung et al., 2011;Yu and Tweedie 2013;Yu et al., 2015). ...
Over the past decades, the number of scientists trained in departments dedicated to traditional medicinal chemistry, biotransformation and/or chemical toxicology have seemingly declined. Yet, there remains a strong demand for such specialized skills in the pharmaceutical industry, particularly within drug metabolism/pharmacokinetics (DMPK) departments. In this position paper, the members of the Biotransformation, Mechanisms, and Pathways Focus Group (BMPFG) steering committee reflect on the diverse roles and responsibilities of scientists trained in the biotransformation field in pharmaceutical companies and contract research organizations. The BMPFG is affiliated with the International Society for the Study of Xenobiotics (ISSX) and was specifically created to promote the exchange of ideas pertaining to topics of current and future interest involving the metabolism of xenobiotics (including drugs). The authors also delve into the relevant education and diverse training skills required to successfully nurture the future cohort of industry biotransformation scientists and guide them toward a rewarding career path. The ability of scientists with a background in biotransformation and organic chemistry to creatively solve complex drug metabolism problems encountered during research and development efforts on both small and large molecular modalities is exemplified in five relevant case studies. Finally, the authors stress the importance and continued commitment to training the next generation of biotransformation scientists who are not only experienced in the metabolism of conventional small molecule therapeutics, but are also equipped to tackle emerging challenges associated with new drug discovery modalities including peptides, protein degraders, and antibodies. SIGNIFICANCE STATEMENT: Biotransformation and mechanistic drug metabolism scientists are critical to advancing chemical entities through discovery and development, yet the number of scientists academically trained for this role is on the decline. This position paper highlights the continuing demand for biotransformation scientists and the necessity of nurturing creative ways to train them and guarantee the future growth of this field.
... 27 The European Medicines Agency (EMA) introduced their guidance for metabolite testing in 2012, but unlike the FDA, the EMA recommends metabolite testing when AUC M /AUC P >0.25 while metabolite also comprises >10% of total drug-related material in plasma, 28 although we are unaware of a publication of the rationale. Several additional decision trees and strategies have been suggested for metabolite testing, 27,29,30 but the supporting data were limited. It was proposed 29 that CYP inhibition risk of a metabolite could be predicted based on the parent inhibition through calculation of an R met value, where R met is the metabolite C max divided by K i,metabolite or K i,parent /4 as an estimate for K i,metabolite if the metabolite has not yet been synthesized. ...
... The International Consortium for Innovation and Quality in Pharmaceutical Development (IQ consortium) suggested that an R met ≥0.1 or metabolites having AUC M /AUC P ≥1 warrants in vitro inhibition testing. 30 There is a notable lack of systematic analyses on whether testing of metabolites according to the different proposed criteria by regulatory agencies increase the number of false negative and/or false positive DDI determinations and whether the different workflows result in divergent outcomes. As such, the exact impact of metabolite testing on DDI risk assessment remains unknown. ...
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 (AUCM/AUCP), inhibitory potency of parent and metabolites, and clinical drug‐drug interactions (DDI) were collected. 64% of the metabolites quantified in vivo had AUCM/AUCP≥25% and 75% of these metabolites were tested for cytochrome P450 (CYP) inhibition in vitro, resulting in 15 metabolites with potential DDI risk identification. While 50% of the metabolites with AUCM/AUCP<25% were also tested in vitro, none of them showed meaningful CYP inhibition potential. The metabolite % 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 (Cmax) divided by inhibition constant Ki 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 AUCM/AUCP cutoff of ≥25% to warrant metabolite in vitro CYP screening to adequately characterize metabolite inhibitory DDI potential and support quantitative DDI predictions.
... The various types of MBI have been thoroughly reviewed, and are illustrated in Figure 24 using some typical structural alerts (acetylene derivatives, aliphatic or aromatic amines, and 1,3-benzodioxoles). 14,[79][80][81] In vitro, MBI is concentration-, NADPH-, and time-dependent, and different assay procedures make it possible to distinguish among the different above-mentioned inactivation mechanisms. In any case, MBI is irreversible in vivo, so that the loss of catalytic activity can only be restored by de novo synthesis of the enzyme. ...
Tyrosine kinase inhibitors (TKI) are small heterocyclic molecules targeting transmembrane and cytoplasmic tyrosine kinases that have met with considerable success in clinical oncology. TKI are associated with toxicities including liver injury that may be serious and even life‐threatening. Many of them require warnings in drug labeling against liver injury, and five of them have Black Box Warning (BBW) labels. Although drug‐induced liver injury is a matter of clinical and industrial concern, little is known about the underlying mechanisms that likely involve reactive metabolites (RM). RM are electrophiles or radicals originating from the metabolic activation of particular functional groups, known as structural alerts or toxicophores. RM are able to covalently bind to proteins and macromolecules, causing cellular damage and even cell death. If the adducted protein is the enzyme involved in RM formation, time‐dependent inhibition of the enzyme—also called mechanism‐based inhibition (MBI) or inactivation—can occur and lead to pharmacokinetic drug‐drug interactions. To mitigate RM liabilities, common practice in drug development includes avoiding structural alerts and assessing RM formation via RM trapping screens with soft and hard nucleophiles (glutathione, potassium cyanide, and methoxylamine) in liver microsomes. RM‐positive derivatives are further optimized to afford drug candidates with blocked or minimized bioactivation potential. However, different structural alerts are still commonly used scaffolds in drug design, including in TKI structures. This review focuses on the current state of knowledge of the relations among TKI structures, bioactivation pathways, RM characterization, and hepatotoxicity and cytochrome P450 MBI in vitro.
... In addition to general and genetic toxicities, there has also been a focus on circulating metabolites and their potential to cause or contribute to drug-drug interactions (EMA, 2012;FDA, 2012;FDA, 2017). Such cases are relatively infrequent (Yeung et al., 2011;Callegari et al., 2013;Yu and Tweedie, 2013;Yu et al., 2015). However in one instance the inhibition of cerivastatin clearance (mediated by CYP2C8 and OATP1B1) by the glucuronide metabolite of gemfibrozil led to tragic consequences (Staffa et al., 2002;Shitara et al., 2004). ...
Since the introduction of MIST guidance by FDA in 2008 there have been major changes in the experimental methods for the identification and quantification of metabolites, ways to evaluate coverage of metabolites, and the timing of critical clinical and non-clinical studies to generate these information. In this cross-industry article we discuss how the increased focus on human drug metabolites and their potential contribution to safety and drug-drug interactions has influenced the approaches taken by industry for the identification and quantitation of human drug metabolites. Before the MIST guidance was issued, the method of choice for generating comprehensive metabolite profile was radiochromatography. The MIST guidance increased the focus on human drug metabolites and their potential contribution to safety and drug-drug interactions and led to changes in the practices of drug metabolism scientists. In addition, the guidance suggested that human metabolism studies should also be accelerated which has led to more frequent determination of human metabolite profiles from multiple ascending dose clinical studies. Generating a comprehensive and quantitative profile of human metabolites has become a more urgent task. This, together with technological advances, led to a general shift of the focus towards earlier human metabolism studies, using high resolution mass spectrometry, and to a reduction in animal radiolabel ADME studies. The changes induced by the MIST guidance are highlighted by six case studies included herein, reflecting different stages of implementation of the MIST guidance within the pharmaceutical industry.
... 9 There are now noted examples where circulating metabolites may have partially or fully contributed to the observed clinical DDIs. [9][10][11][12][13] As a result, recent regulatory guidance recommends investigation of the role of metabolites in clinical DDIs. Specifically, both the European Medical Agency (EMA) and US Food and Drug Administration (FDA) 14,15 have proposed criteria based on the relative exposure of metabolite and parent drug in systemic circulation. ...
... The objectives of this scholarship group were: first, to understand the frequency of cases where metabolite(s) significantly contributed to DDIs, and second, to assess current practices for metabolite in vitro inhibition studies in drug development settings. 12 Of the DDIs reviewed by the MDSG, several drugs (including gemfibrozil, sertraline, bupropion, and amiodarone) were identified with "surprise" DDIs: examples in which in vivo CYP inhibition was not predicted by in vitro CYP inhibition data. For these examples, metabolites were proposed to contribute to the in vivo CYP inhibition. ...
... However, the major circulating metabolites of these drugs inhibit enzymes and transporters in vitro, which could explain the disconnect. 12 We evaluated these drugs (amiodarone, gemfibrozil, and sertraline) using PBPK modeling to mechanistically understand the quantitative contribution of the metabolites to the DDIs; additionally, we reviewed the literature for other examples of metaboliteprecipitated DDIs (Supplemental Table 2). Some common themes emerged across the three case studies reviewed. ...
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.
... AMIO, itself, appears to be a fairly weak in vitro inhibitor of these enzymes (Kobayashi et al., 1998;Ohyama et al., 2000), which raises the possibility that inhibitory metabolites play a more direct role than the parent drug. In fact, a recent literature review identified AMIO as one of only five out of 137 total pharmaceuticals to cause a metabolism-dependent clinical DDI judged to be due entirely to an inhibitory metabolite(s), with little to no contribution of the parent drug (Yu et al., 2015). Therefore, a more complete analysis of AMIO-P450 inhibition should provide a useful case study in helping to determine which future drugs are more at risk of a metabolism-dependent DDI caused by inhibitory metabolites. ...
IC50 shift and time dependent inhibition (TDI) experiments were carried out to measure the ability of amiodarone (AMIO) and its circulating human metabolites to reversibly and irreversibly inhibit CYP1A2, CYP2C9, CYP2D6 and CYP3A4 activities in human liver microsomes. [I]u/Ki,u values were calculated and used to predict in vivo AMIO drug-drug interactions (DDIs) for pharmaceuticals metabolized by these four enzymes. Based on these values, the minor metabolite di-desethylamiodarone (DDEA) is predicted to be the major cause of DDIs with xenobiotics primarily metabolized by CYP1A2, CYP2C9 or CYP3A4, while AMIO and its mono-desethyl derivative (MDEA) are the most likely cause of interactions involving inhibition of CYP2D6 metabolism. AMIO drug interactions predicted from the reversible inhibition of the four P450 activities were found to be in good agreement with the magnitude of reported clinical DDIs with lidocaine, warfarin, metoprolol and simvastatin. TDI experiments showed DDEA to be a potent inactivator of CYP1A2 (KI = 0.46 μM, kinact = 0.030 min(-1)), while MDEA was a moderate inactivator of both CYP2D6 (KI = 2.7 μM, kinact = 0.018 min(-1)) and CYP3A4 (KI = 2.6 μM, kinact = 0.016 min(-1)). For DDEA and MDEA, mechanism-based inactivation appears to occur through formation of a metabolic intermediate (MI) complex. Additional metabolic studies strongly suggest that CYP3A4 is the primary enzyme involved in the metabolism of AMIO to both MDEA and DDEA. In summary, these studies demonstrate both the diversity of likely inhibitory mechanisms with AMIO and the need to consider metabolites as the 'culprit' in inhibitory P450-based DDIs.
The American Society for Pharmacology and Experimental Therapeutics.
... The current European Medicines Agency (EMA) and U.S. Food and Drug Administration (FDA) guidelines on DDIs recommend in vitro investigation of metabolite's interaction potential if present at $25% of the parent area under the plasma concentration-time curve (AUC) (CDER, 2012) or $25% of the parent AUC and $10% of the total drug-related AUC (CHMP, 2012). Under the auspices of the Drug Metabolism Leadership Group of the Innovative and Quality Consortium (IQ-DMLG), the Metabolite-mediated DDI Scholarship Group (MDSG)-tasked to examine the application of these regulatory recommendations-was formed with scientists representing 18 pharmaceutic companies (Yu et al., 2015). Based on the analysis of 137 frequently prescribed drugs, the MDSG concluded that the risk for unexpected clinical DDI as a result of not assessing in vitro cytochrome P450 (P450) inhibition by metabolite(s) is low, which is consistent with earlier reports (Yeung et al., 2011;Callegari et al., 2013). ...
... Furthermore, the DDI risk in vivo when metabolites inhibit both uptake transport and metabolism is expected to be large. So the early risk assessment of metabolite-mediated DDIs should consider structural alerts for TDI, in vitro interaction potential against enzymes and transporters, and systemic exposure of both parent and metabolites (Callegari et al., 2013;Yu et al., 2015). The resulting data can then be integrated in the static or PBPK models for DDI predictions, as described here. ...
Gemfibrozil has been suggested as a sensitive cytochrome P-450 (CYP)2C8 inhibitor for clinical investigation by the US Food and Drug Administration and the European Medicines Agency. However, gemfibrozil drug-drug interactions (DDIs) are complex as 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 (OATP)1B1 in vitro. Additionally, parent and metabolite also inhibit renal transport mediated by organic anion transporter 3. Here, in vitro inhibition data for gemfibrozil and Gem-Glu were utilized 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 following 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.
The American Society for Pharmacology and Experimental Therapeutics.
