Abstract
Awareness of drug interactions involving opioids is critical for patient treatment as they are common therapeutics used in numerous care settings, including both chronic and disease-related pain. Not only do opioids have narrow therapeutic indexes and are extensively used, but they have the potential to cause severe toxicity. Opioids are the classical pain treatment for patients who suffer from moderate to severe pain. More importantly, opioids are often prescribed in combination with multiple other drugs, especially in patient populations who typically are prescribed a large drug regimen. This review focuses on the current knowledge of common opioid drug–drug interactions (DDIs), focusing specifically on hydrocodone, oxycodone, and morphine DDIs. The DDIs covered in this review include pharmacokinetic DDI arising from enzyme inhibition or induction, primarily due to inhibition of cytochrome p450 enzymes (CYPs). However, opioids such as morphine are metabolized by uridine-5’-diphosphoglucuronosyltransferases (UGTs), principally UGT2B7, and glucuronidation is another important pathway for opioid-drug interactions. This review also covers several pharmacodynamic DDI studies as well as the basics of CYP and UGT metabolism, including detailed opioid metabolism and the potential involvement of metabolizing enzyme gene variation in DDI. Based upon the current literature, further studies are needed to fully investigate and describe the DDI potential with opioids in pain and related disease settings to improve clinical outcomes for patients.
SIGNIFICANCE STATEMENT A review of the literature focusing on drug–drug interactions involving opioids is important because they can be toxic and potentially lethal, occurring through pharmacodynamic interactions as well as pharmacokinetic interactions occurring through inhibition or induction of drug metabolism.
Introduction
Opioid analgesics are widely used in clinical practice for a wide variety of pain management plans for both chronic and acute pain. It is estimated that, in the United States, 50 million adults suffer from chronic pain and 20 million adults have high impact chronic pain (Dahlhamer et al., 2018; Zelaya et al., 2020; Yong et al., 2022), with chronic pain prevalence in older populations higher as compared to younger populations (Johannes et al., 2010; Larsson et al., 2017; Dahlhamer et al., 2018; Zelaya et al., 2020). Although there are numerous treatment options for chronic pain, it is estimated that over 8 million people use opioids for long-term chronic pain management (Reuben et al., 2015; Dowell et al., 2016; Bohnert et al., 2018; Hales et al., 2020; https://www.cdc.gov/drugoverdose/rxrate-maps/state2020.html; Dowell et al., 2022). The cost of chronic pain has been estimated to be $635 billion in annual medical costs, disability, and loss of productivity (Institute of Medicine (US) Committee on Advancing Pain Research, 2011). Hydrocodone, oxycodone, and morphine are among the most widely prescribed or used opioid analgesics (https://nida.nih.gov/publications/drugfacts/prescription-opioids; https://www.cdc.gov/opioids/basics/index.html). All three opioids have been used in the clinic for decades and many studies have been performed focusing on the efficacy of their analgesic properties and side effects, as well as on their metabolism both in vitro and in vivo (Cone and Darwin, 1978; Otton et al., 1993; Coffman et al., 1997; Kaplan et al., 1997; Coffman et al., 1998; Stone et al., 2003; Hutchinson et al., 2004; Lalovic et al., 2004; Adams and Ahdieh, 2005; Lalovic et al., 2006; Kapil et al., 2015). From these studies, pharmacokinetic and pharmacodynamic profiles of these opioids can be gleaned to provide their best efficacy in clinical practice.
A drug’s pharmacokinetic and pharmacodynamic profile can be altered by polypharmacy - i.e., the concomitant use of more than one drug –which can lead to potentially harmful drug–drug interactions (DDI). Such alterations in drug pharmacokinetics or pharmacodynamics can lead to adverse drug events (ADE) that can alter drug efficacy and/or toxicity. Pharmacodynamic DDIs can lead to either enhanced or decreased pharmacological action of the object drug (Overholser and Foster, 2011; Niu et al., 2019). Pharmacokinetic DDIs lead to changes in bioavailability and altered production of active or inactive metabolites (Smith, 2009).
Opioids are central nervous system (CNS) depressants and act upon opioid receptors (mu, delta, and kappa) that are found in the brain, spinal cord, and the periphery (Hersh et al., 2007). Most commonly, opioids have a higher affinity for the mu opioid receptor (MOR) (Theriot et al., 2023). MOR subtypes arise due to splice variants; MOR 1 is associated with analgesia and dependence; MOR 2 is associated with respiratory depression, miosis, and constipation; and MOR 3 is associated with vasodilation (Trescot et al., 2008; Valentino and Volkow, 2018; Dhaliwal and Gupta, 2023). When drugs with similar pharmacological actions are taken together this can lead to pharmacodynamic DDI potentially causing ADE. For example, taking two drugs that both cause depression of the CNS can cause respiratory depression and even death.
An important mechanism underlying pharmacokinetic DDI includes the interaction of a precipitant drug with the metabolizing enzymes that catalyze the biotransformation of the object drug (Smith, 2009). Metabolic enzymes are divided into phase I and phase II metabolism, with the cytochrome P450 enzymes (CYP) the major superfamily within the phase I metabolic enzymes and the UDP-glucuronosyltransferases (UGT) the major phase II enzyme superfamily. Most opioids are metabolized by both the CYP and UGT family of enzymes (Trescot et al., 2008), with several, including codeine, hydrocodone, oxycodone, tramadol, and morphine, metabolized primarily by CYP3A4, CYP2D6 and UGT2B7 (Cone et al., 1978; Yue et al., 1991a,b; Otton et al., 1993; Pöyhiä et al., 1993; Hagen et al., 1995; Caraco et al., 1996a,b; Coffman et al., 1997; Paar et al., 1997; Coffman et al., 1998; Green et al., 1998; Subrahmanyam et al., 2001; Donnelly et al., 2002; Shapiro and Shear, 2002; Zheng et al., 2002; Stone et al., 2003; Benetton et al., 2004; Hutchinson et al., 2004; Lalovic et al., 2004; Zheng et al., 2004; Baldacci and Thormann, 2006; Lalovic et al., 2006; Madadi and Koren, 2008; Ohno et al., 2008; Jenkins et al., 2009; Nieminen et al., 2009; Cone et al., 2013a,b; Barakat et al., 2014; Elder et al., 2014; Kurogi et al., 2014; DePriest et al., 2016a,b; Romand et al., 2017; Shen et al., 2019). Approximately 25% and 50% of all pharmaceutical drugs are substrates of CYP2D6 and CYP3A4, respectively (Bertz and Granneman, 1997; Evans and Relling, 1999; Ingelman-Sundberg, 2005; Ingelman-Sundberg and Rodriguez-Antona, 2005; Trescot et al., 2008; Zhou, 2009; Zanger and Schwab, 2013), while UGT2B7 is a major UGT isoform that is responsible for catalyzing the biotransformation of numerous xenobiotics and endogenous substances (Bhasker et al., 2000; Tukey and Strassburg, 2000; Stingl et al., 2014; Shen et al., 2019). Since pharmacokinetic DDI can occur by dysregulated drug metabolism, it is important to study potential DDI to characterize the severity of the drug reaction for clinical practice.
This review focuses on three commonly prescribed opioids in the non-cancer chronic pain setting, hydrocodone, oxycodone and morphine, and their respective metabolism and known DDIs. Opioid metabolism is summarized along with the implications of potential DDI due to changes in opioid pharmacokinetics and metabolism. This review includes a description of known associated DDIs and their adverse effects on metabolic pathways and patient response and toxicity. The pharmaceutical drugs identified in this review as altering opioid metabolism can be condensed into general treatment categories. The most common treatments conferred by the drugs that lead to pharmacokinetic DDIs with opioids are antifungals, antibiotics, antivirals, anti-depressants/anti-psychotics, chemotherapeutic/anti-cancer, anticonvulsant, and sedatives.
Furthermore, the classes of drugs identified in this review involved in these DDIs include azoles, protease inhibitors, pharmacokinetic enhancers, non-nucleoside reverse transcriptase inhibitors, quinolones, macrolides, chloramphenicol, ketolides, antimycobacterial, monoamine oxidase inhibitors, selective serotonin reuptake inhibitors, uni- and tricyclic antidepressants, serotonin-norepinephrine reuptake inhibitors, butyrophenones, phenothiazines, atypical second generation antipsychotics, iminostilbenes, benzodiazepines, hydantoins, barbiturates, kinase inhibitors, antiestrogens, poly (ADP-ribose) polymerase inhibitors, isocitrate dehydrogenase-1 inhibitors, among others. Depending on the patient and diagnosis, it is possible that multiples of these precipitant drugs and opioids could be co-prescribed together. Therefore, in addition to patients treated for pain, these DDIs can potentially occur in a wide variety of patient populations outside of typical pain patients.
Methods
Relevant literature was reviewed using PubMed (last searched on December 12, 2022) and the search terms ‘hydrocodone’, ‘morphine’, and ‘oxycodone’. Each search term was also combined with the following additional search terms, ‘metabolism,’ ‘active metabolites,’ ‘drug interaction,’ and ‘drug–drug interaction.’ Searches were filtered for language (English, and text type: free full text), with all studies involving pharmacokinetic DDIs, pharmacodynamic DDIs, and clinical DDIs included in the review. The following studies were excluded from the results: studies showing neutral DDIs, studies in which the opioid was the precipitant drug causing a DDIs, and transporter-related DDIs. These search parameters were used to compile a comprehensive in vitro and in vivo dataset to summarize both pharmacodynamic and pharmacokinetic drug–drug interactions involving the commonly prescribed opioids, hydrocodone, oxycodone, and morphine.
Results
Metabolism and Activity of Hydrocodone, Oxycodone, and Morphine and their Metabolites
Hydrocodone Metabolism
Greater than 50% of the total hydrocodone dose is metabolized by CYP-mediated phase 1 metabolism (Cone and Darwin, 1978; Cone et al., 1978; Park et al., 1982; Otton et al., 1993; Hutchinson et al., 2004; Barakat et al., 2012; Valtier and Bebarta, 2012). Hydrocodone is a substrate of both CYP2D6 and CYP3A4/5, with it metabolized by CYP2D6 via O-demethylation to its active metabolite, hydromorphone (Otton et al., 1993; Hutchinson et al., 2004), and by CYP3A4 through N-demethylation to a major inactive metabolite, norhydrocodone (see Fig. 1) (Hutchinson et al., 2004). As CYP3A4 and CYP3A5 are highly homologous and metabolize the same substrates (Guengerich, 2005; Daly, 2006; Tseng et al., 2014), the contribution of CYP3A5 to hydrocodone metabolism is still unclear. Hydrocodone, hydromorphone, and norhydrocodone undergo further metabolism by glucuronidation and reduction to minor metabolites (Fig. 1) (Cone and Darwin, 1978; Cone et al., 1978). Hydrocodone has been suggested to be a prodrug in some instances, as the active metabolite, hydromorphone, exhibits a greater analgesic effect than its parent molecule (Trescot et al., 2008). However, previous studies have shown that in the absence of CYP2D6, hydrocodone dosing still elicits analgesic activity, suggesting that hydrocodone has its own analgesic properties (Kaplan et al., 1997; Tomkins et al., 1997). Even though hydromorphone has been shown to have 10- to 33-fold higher affinity to mu-opioid receptors (Hennies et al., 1988; Chen et al., 1991) and greater analgesic potency than morphine when administered subcutaneously (Jaffe JH, 1990), individuals who are poor metabolizers (PM) for CYP2D6 do not exhibit different responses compared to extensive metabolizers (EM) with equivalent hydrocodone dosing (Kaplan et al., 1997; Kapil et al., 2015). This is likely explained by the relatively low abundance of hydromorphone compared to the parent drug in the plasma of individuals taking hydrocodone [with observed plasma levels of hydromorphone at ∼3%–5% of the hydrocodone dose (Cone and Darwin, 1978; Cone et al., 1978; Coller et al., 2009; Hao et al., 2011; Valtier and Bebarta, 2012; Langman et al., 2013; Darwish et al., 2015; Kapil et al., 2015)] and possibly due to the higher rate at which hydrocodone enters the brain as compared to hydromorphone (Kaplan et al., 1997; Schaefer et al., 2017) .
Schematic of hydrocodone metabolism.
Hydromorphone is further metabolized through reduction to 6α-and 6β-hydromorphol which undergoes subsequent glucuronidation by UGT2B7 (Coffman et al., 1998) to form hydromorphone-3-glucuronide (Cone et al., 1978; Wright et al., 2001; Trescot et al., 2008) (Fig. 1). Although hydromorphone has low blood brain barrier (BBB) penetration, it is also given as a stand-alone analgesic because of its high opioid effects (Hagen et al., 1995; Wright et al., 2001; Drewes et al., 2013; Landolf et al., 2020).
Oxycodone Metabolism
The metabolism of oxycodone is similar to that of hydrocodone. The primary oxidative pathway for oxycodone is by N-demethylation by CYP3A4/5 to form noroxycodone, an inactive metabolite [see Fig. 2 (Weinstein and Gaylord, 1979; Lalovic et al., 2004, 2006)]. Lalovic et al., found that CYP3A5 is active in oxycodone metabolism (Lalovic et al., 2004), which is consistent with many previous studies demonstrating that most CYP3A4 substrates may also be metabolized by CYP3A5 due to their high sequence homology (Guengerich, 2005; Daly, 2006; Tseng et al., 2014). However, the actual contribution of CYP3A5 (vs. CYP3A4) to oxycodone metabolism has not been firmly established (Naito et al., 2011; Tseng et al., 2014). Oxycodone can also be O-demethylated to oxymorphone, an active metabolite, via CYP2D6 (Otton et al., 1993). Noroxycodone has weak antinociceptive effects and is thus considered inactive in comparison to oxymorphone (Weinstein and Gaylord, 1979; Leow and Smith, 1994; Stamer et al., 2013). Oxymorphone and noroxycodone are further metabolized to noroxymorphone by CYP3A4/5 and CYP2D6, respectively (Lalovic et al., 2004, 2006). Oxycodone also undergoes glucuronidation by UGT2B7 and minimally by UGT2B4 (Moore et al., 2003; Romand et al., 2017). Similar to that observed for hydromorphone, oxymorphone can undergo glucuronidation via UGT2B7 to form oxymorphone-3-glucuronide (Coffman et al., 1998; Adams and Ahdieh, 2005; Lalovic et al., 2006). However, the enzyme(s) involved in the glucuronidation of noroxycodone to noroxycodone-glucuronide have not been characterized (Huddart et al., 2018). Oxycodone, noroxycodone, and oxymorphone are also metabolized by keto-reduction to form α and β- oxycodol, oxymorphol, and noroxycodol, respectively [Fig. 2; (Moore et al., 2003; Baldacci and Thormann, 2005; Lalovic et al., 2006)].
Schematic of oxycodone metabolism.
Oxycodone exhibits its own analgesic effects (Lalovic et al., 2006) as inhibition of CYP2D6 did not attenuate its antinociceptive or opioid side effects, and oxycodone readily crosses the BBB (Cleary et al., 1994; Kaiko et al., 1996; Heiskanen et al., 1998; Boström et al., 2005, 2006, 2008; Okura et al., 2008; Lemberg et al., 2010; Drewes et al., 2013). Oxycodone is 1.5 times more potent than morphine when given by oral administration ((WHO), 2018), but they are both considered as medium potency (Eddy and Lee, 1959; Beaver et al., 1977; Thompson et al., 2004; Drewes et al., 2013; Cone et al., 2013a). Oxycodone’s active metabolite, oxymorphone, exhibits a more potent mu-opioid receptor affinity as compared to morphine after parenteral administration and oxymorphone has a 40-fold higher affinity for the mu-opioid receptor as compared to its parent drug, oxycodone and is 10 times more potent than oxycodone when given by intravenous administration (Chen et al., 1991; Lalovic et al., 2006; Babalonis et al., 2021). However, the overall contribution of oxymorphone to the analgesic efficacy of oxycodone is not fully understood (Lalovic et al., 2006; Cone et al., 2013a). Some studies have shown that oxymorphone contributes little analgesic efficacy during oxycodone administration, possibly due to its low relative abundance or its lower BBB permeability (Heiskanen et al., 1998; Lalovic et al., 2006; Zwisler et al., 2009; Lemberg et al., 2010). In contrast, multiple studies have shown that oxymorphone has long lasting analgesic effects with minimal side effects when administered independently (Gimbel and Ahdieh, 2004; Gimbel et al., 2005; Hale et al., 2005; Aqua et al., 2007), and it is prescribed alone as an effective analgesic typically in cancer pain management and obstetrics (Inturrisi, 2002).
Morphine Metabolism
The major metabolic pathway for morphine is via glucuronidation by UGT2B7 to form its major metabolite, morphine-3-glucuronide, and its minor metabolite, morphine-6-glucuronide (Fig. 3) (Coffman et al., 1997, 1998; Donnelly et al., 2002; Stone et al., 2003; Ohno et al., 2008). UGT1A1 and UGT1A8 have also been implicated as playing a role in morphine-6-glucuronide formation (Ohno et al., 2008), while morphine-3-glucuronide formation can also be catalyzed by UGT1A3 and UGT1A8 (Green et al., 1998; Cheng et al., 1999; Stone et al., 2003). Morphine is also N-demethylated by CYP3A4 and CYP2C8 to form a minor metabolite, normorphine (Projean et al., 2003), which can be further metabolized to two glucuronide metabolites (Yeh et al., 1977). Morphine can also be metabolized to minor sulfate metabolites by SULT1A3 [Fig. 3; (Andersson et al., 2014; Kurogi et al., 2014)].
Schematic of morphine metabolism.
Morphine-6-glucuronide is considered to be active and may partially contribute to morphine’s analgesic effect (Frances et al., 1992; Portenoy et al., 1992; Klepstad et al., 2000; Kilpatrick and Smith, 2005; Lötsch, 2005; Wittwer and Kern, 2006) given its high affinity for both mu opioid receptors [mu 1 and mu 2; (Pasternak et al., 1987; Frances et al., 1992)]. Several studies have shown that the potency of morphine-6-glucuronide is equal to or more active than morphine depending on the route of administration (Paul et al., 1989; Osborne et al., 1990; Frances et al., 1992; Kilpatrick and Smith, 2005; Wittwer and Kern, 2006; Ohno et al., 2008). However, several studies suggest that the contribution of morphine-6-glucuronide to morphine’s analgesic effects is likely small due to it accounting for only 10% of circulating metabolite in plasma (Hasselström and Säwe, 1993; Lötsch et al., 1996; Andersen et al., 2003; Ing Lorenzini et al., 2012) and because it does not easily cross the BBB as compared to morphine (Frances et al., 1992; Bickel et al., 1996; Wandel et al., 2002; Drewes et al., 2013; Seleman et al., 2014). A systematic review found the weighted mean ratios in serum of morphine and its glucuronide metabolites were 6 (range = 0.2–15) for morphine-3-glucuronide:morphine and 0.9 (range = 0.03–2.6) for morphine-6-glucuronide:morphine in patients with normal renal function given intravenous morphine (Faura et al., 1998). However, some studies suggest that morphine-6-glucuronide may have a major role in morphine analgesia (Hanna et al., 1990; Osborne et al., 1990, 1992), although the analgesic efficacy was found to not be dependent on morphine-6-glucuronide plasma concentration (Osborne et al., 1992). Morphine-6-glucuronide can be administered as its own medication and is well tolerated (Osborne et al., 1992; Penson et al., 2002).
In contrast, morphine-3-glucuronide does not exhibit analgesic effects (Shimomura et al., 1971; Christensen and Jørgensen, 1987; Oguri et al., 1987; Pasternak et al., 1987; Qian-Ling et al., 1992; Wittwer and Kern, 2006) and was shown to antagonize the analgesic effects of both morphine and morphine-6-glucuronide in both mice and rats (Qian-Ling et al., 1992; Christrup, 1997; Faura et al., 1997). Studies have shown that morphine-3-glucuronide causes neuroexcitatory effects such as hyperalgesia, allodynia, and myoclonus (Andersen et al., 2003; Roeckel et al., 2017).
Pharmacodynamic Drug–Drug Interactions
Pharmacodynamic drug interactions involving opioids typically occur when opioids are taken in conjunction with other CNS depressants due to polypharmacy, potentially leading to life-threatening ADE (Prostran et al., 2016; Matos et al., 2020). Pharmacodynamic drug interactions can either be additive, synergistic, or antagonistic (Pérez-Mañá et al., 2018; Niu et al., 2019). An example of an additive or synergistic interaction would be concomitantly taking an opioid with a CNS depressant, such as a benzodiazepine, which could lead to respiratory depression and potentially death (Mirakbari et al., 2003; Dowell et al., 2016; Hwang et al., 2016; Bingham et al., 2020). Since both opioids and CNS drugs have narrow therapeutic indexes, close physician monitoring of the patient should occur under these circumstances, adjusting the dose as necessary to ensure an ADE does not occur. Generally, opioids should not be prescribed or taken together with CNS depressants, such as benzodiazepines, antipsychotics, muscle relaxers, or tranquilizers (FDA, 2017). While there is an overall lack of clinical information investigating the pharmacodynamic interaction and side effects of opioids with these drug classes (Jones et al., 2012; Leonard and Kangas, 2020), there is substantial evidence suggesting an increase in overdose when opioids and benzodiazepines are combined (Park et al., 2015; Bachhuber et al., 2016; Dasgupta et al., 2016; Sun et al., 2017; Dowell et al., 2022; https://nida.nih.gov/research-topics/opioids/benzodiazepines-opioids#). An example of an antagonistic interaction would be the administration of naloxone after an opioid overdose, in which naloxone reverses the effects of the opioid overdose (Boom et al., 2012; Dunne, 2018). Pharmacodynamic DDI can be beneficial if medications are prescribed deliberately and safely (Niu et al., 2019). Furthermore, depending on the pharmacodynamic interaction (additive/synergistic vs antagonistic) smaller amounts of opioids may need to be prescribed as their analgesic effects may still be effective if taken concurrently with other drugs (Pick, 1997; Niu et al., 2019). Specific examples of pharmacodynamic interactions can be found in Table 1.
In vivo and in vitro studies performed to examine potential drug–drug interactions between precipitant drugs and either hydrocodone, morphine, or oxycodone
Drug–Drug Interactions Where Opioids are the Object Drug
Mu-receptor agonists, specifically opioids, typically have a narrow therapeutic range (Grönlund et al., 2010a,b). Thus, at normal dosages, opioid taken concomitantly with a drug that could potentially inhibit opioid metabolism could potentially lead to adverse drug events. Conversely, taking a concomitant drug that is an inducer of opioid metabolism could potentially lead to subtherapeutic efficacy. Furthermore, prodrugs may exhibit the opposite effect, where if prodrug metabolism is inhibited, there would be subtherapeutic efficacy, and if it is induced there is a higher risk of adverse drug events due to higher plasma concentrations. Altered dosing schedules may be needed to maintain safe therapeutic plasma concentrations of the opioid in the presence of either an enzyme inhibitor or inducer. Therapeutic monitoring may become of increased importance if a known inducer or inhibitor of metabolizing enzyme is prescribed with a drug that is a substrate of an enzyme involved in opioid metabolism to ensure that prescribed opioids are within their therapeutic window.
Genotype Influence on Metabolism
For personalized approaches to patient care and the prescribing of prescription drugs, it is important to consider individual genotypes for key metabolizing enzymes when prescribing concomitant drugs that are known to be inhibitors or inducers of the key metabolizing enzymes. The major metabolizing CYP enzymes 3A4/5, 2C9, 2D6, 2E1, 1A2, and 2C19 (Shapiro and Shear, 2002), as well as UGT2B7, the major enzyme important in morphine metabolism (Bhasker et al., 2000; Shen et al., 2019), all exhibit high-prevalence polymorphisms potentially important in DDI.
CYP2D6
CYP2D6 has multiple single nucleotide polymorphisms (SNPs) that lead to functional variants of the enzyme and individuals can be categorized into different CP2D6 genotype groups based on the functionality of their CYP2D6 genotypes. Those that have two normal function alleles of CYP2D6 are EM, and these include the *1, *2, and *35 allelic variants (Bradford, 2002; Gaedigk et al., 2018, 2020, 2021). Individuals who have two nonfunctioning, one less functional allele and one nonfunctioning allele, or deleted genes, are PM and include the *3, *4, *5, *6, *7, and *8 variants (Gaedigk et al., 2018, 2020, 2021). Intermediate metabolizers (IM) are those individuals either homozygous for less functional alleles or have one normal function allele and one less function allele or deleted gene. The *9, *10, *17 and *41 alleles are associated with the IM phenotype (Gaedigk et al., 2018, 2020, 2021). Individuals who take a drug that is a CYP2D6 inhibitor or inducer can experience phenoconversion from an EM to PM metabolizer phenotype, potentially altering clinical response by affecting the metabolic clearance of the victim drug and potentially leading to an ADE (Shah and Smith, 2015). Conversely, an individual exhibiting the EM phenotype can exhibit an ultra-rapid metabolizer (UM) phenotype if they are taking a drug that induces transcription of the drug metabolizing enzyme of interest (Shah and Smith, 2015). As CYP2D6 is highly polymorphic, therapeutic monitoring may be necessary, with dosage adjustments based upon an individual’s genotype to ensure optimal plasma drug concentration, especially for those drugs with a narrow therapeutic index, and this may be particularly true for opioid dosing.
CYP3A4
Similar to CYP2D6, the CYP3A4 gene also exhibits numerous SNPs; however, many of these alleles have yet to show variation in CYP3A4 activity in vivo (Westlind-Johnsson et al., 2006). EM phenotype individuals are those that have any of the CYP3A4 *1 allelic sub-variants (Gonzalez et al., 1988; Gaedigk et al., 2018, 2020, 2021). The CYP3A4 PM phenotype is considered when the rare *20 allelic variant is present (Westlind-Johnsson et al., 2006). CYP3A4 alleles considered to have decreased enzymatic function in vivo include the *18A and *22 alleles, and these are linked to individuals with the CYP3A4 IM phenotype (Dai et al., 2001; Kang et al., 2009; Elens et al., 2011a,b; Wang et al., 2011a; Gaedigk et al., 2018, 2020, 2021). Genetic variation in the CYP3A4 enzyme is particularly important when considering potential DDIs since CYP3A4 accounts for approximately 50% of all drug metabolism (Trescot et al., 2008).
UGT2B7
The most prevalent UGT2B7 SNP is at codon 268 (His>Tyr) (approximately 50% prevalence in Caucasians) and it has been associated with varying functionalities depending on the substrate (Lazarska et al., 2018). The UGT2B7*2 variant has been shown to exhibit either increased activity or decreased activity depending on the drug examined (Thibaudeau et al., 2006; Bélanger et al., 2009; Wang et al., 2011b). Other UGT2B7 polymorphisms include less prevalent (i.e., minor allele frequency <3%) synonymous, nonsynonymous, and promoter SNPs (Bhasker et al., 2000; Wang et al., 2018b), but their effect on UGT2B7 activity or expression and overall drug metabolism has been much less studied.
Table 2 lists known inhibitors and inducers of the metabolizing enzymes CYP2D6, CYP3A4, and UGT2B7, which are the major enzymes involved in hydrocodone, oxycodone, and morphine metabolism. Known inhibitors of these metabolizing enzymes could potentially lead to changes in pharmacokinetic disposition, such as increased exposure to a given substrate or, conversely, lead to a decrease in exposure of an active metabolite after a prodrug is given. This increase in exposure could potentially increase efficacy or lead to accumulation of active drug, potentially resulting in toxicity or adverse drug events, or lead to inefficacy due to poor metabolism of a prodrug resulting in little to no formation of the active metabolite. In contrast, inducers of these metabolizing enzymes could potentially lead to sub-therapeutic levels of active drug decreasing the drug’s efficacy. There have been relatively few clinical trials examining the effects of CYP2D6 or CYP3A4 inhibitors in concomitant administration with hydrocodone (Kapil et al., 2015). The majority of inhibition studies have been performed in vitro, primarily in human liver microsomes or human hepatocytes to determine potential DDI. In contrast, there have been extensive studies performed to examine the potential drug-drug interactions with oxycodone and morphine both in vitro and in vivo. These include in vitro mechanistic studies as well as in vivo human clinical trials to identify pharmacokinetic and pharmacodynamic changes. Briefly, 8 DDI were identified involving hydrocodone, including some that were in the same study, 21 involving oxycodone, and 30 involving morphine. A few major DDI studies are described below for each opioid; these include studies that show significant DDI including pharmacokinetic and drug–gene interactions. In vitro DDI studies are not described in depth. Other known DDI that were not described in detail below include other trials as well as in vitro studies. These can be found in Table 1.
Known inducers and inhibitors of three major opioid metabolizing enzymes (adapted from Drug Interactions Flockhart TableTM from the Department of Medicine Clinical Pharmacology Division at Indiana University for CYP2D6 and CYP3A4 (Flockhart et al., 2021) and the Food and Drug Administration Drug Development and Drug Interactions Table of Substrates, Inhibitors and Inducers (FDA, 2022).
Hydrocodone
In a randomized controlled trial, the effects of paroxetine (20 mg), a known time-dependent CYP2D6 inhibitor, was investigated when co-administered with a once daily 20 mg extended-release hydrocodone tablet (Kapil et al., 2015). As compared to placebo-treated controls, there was a decrease in the area under the curve (AUC) of the active metabolite hydromorphone in the paroxetine-treated group (0.64 ng·h/L vs 3.8 ng·h/L). However, the maximum concentration (Cmax), time at maximum concentration, and half-life of hydrocodone were similar regardless of the presence of paroxetine. The AUC and Cmax ranges after paroxetine exposure were within their predetermined range of 80%–125% (FDA, 2020), suggesting that paroxetine did not significantly alter hydrocodone exposure. Few adverse effects were reported, including headache, nausea, and diarrhea, and there was no significant difference in reported side effects between the placebo and treatment groups (Kapil et al., 2015). Even though hydromorphone is pharmacologically active, due to its relatively low plasma levels after hydrocodone administration, the inhibition of its formation is not expected to heavily impact hydrocodone efficacy and pharmacodynamic properties (Kapil et al., 2015).This is consistent with the fact that CYP2D6-mediated O-demethylation accounts for 3% of total hydrocodone metabolism (as compared to CYP3A4 mediated N-demethylation which accounts for 40% of its metabolism (Cone and Darwin, 1978; Cone et al., 1978) and is therefore not expected to heavily impact hydrocodone efficacy and pharmacodynamic properties (Kapil et al., 2015).
A phase I clinical trial was performed to determine if DDI were observed for hydrocodone when co-administered with the treatment regimen for hepatitis C virus (termed 3D) consisting of ombitasvir/paritaprevir/ritonavir and dabusavir. This concomitant administration led to a 27% and 90% increase in Cmax and AUC of hydrocodone, respectively, in the 3D group as compared to placebo controls. The increase in hydrocodone exposure is likely due to ritonavir inhibition of CYP3A4. The researchers recommended a 50% dose reduction in hydrocodone to account for the increase in hydrocodone exposure with concomitant administration with the 3D regimen to avoid potential adverse drug events (Polepally et al., 2016). Future studies will be required to determine how the inhibition of this major metabolic pathway will affect the pharmacodynamics of hydrocodone.
A single case study involving a white male with chronic pain participating in a 2-day protocol was performed to determine if cannabis added to a hydrocodone/acetaminophen regimen could detect pharmacodynamic or pharmacokinetic drug–drug interactions. For both days, the male took his prescribed hydrocodone/acetaminophen regimen (½ tablet of 7.5 mg/325 mg combination) with the addition of smoking one pre-rolled cannabis cigarette (0.5 g; 22.17% THC; 0.12% CBD) on day 2. On day 2, hydrocodone plasma levels observed were lower than on day 1, with pharmacokinetic analysis indicating a more rapid absorption of hydrocodone. The participant also reported lower pain, and this may be explained by the more rapid absorption of hydrocodone in the presence of cannabis (Bindler et al., 2022).
Another single case study involved a 5-year-old girl suffering from respiratory tract/ear infections (Madadi et al., 2010). The girl had been prescribed valproic acid since birth to treat her for seizures (250 mg twice per day) and was prescribed hydrocodone 1 mg/ml, one teaspoon three times/day for 5 days) and clarithromycin for her infection (Madadi et al., 2010). After 24 hours of using newly prescribed hydrocodone and clarithromycin, the child was found unresponsive and pronounced dead at the hospital, with postmortem tests revealing high plasma hydrocodone levels (0.14 µg/ml) with undetectable levels of plasma hydromorphone (<0.008 µg/ml). Genetic testing revealed that the patient has one functionally impaired CYP2D6 allele (*41) and one normal function (*2A) allele (Madadi et al., 2010), suggesting that she was an intermediate-to-poor metabolizer of CYP2D6 substrates like hydrocodone, resulting in decreased hydromorphone formation. Furthermore, clarithromycin is a known inhibitor of CYP3A4 (Rodrigues et al., 1997). Thus, both major metabolic pathways of hydrocodone were impaired either due to a drug-gene interaction (CYP2D6) or chemical inhibition (CYP3A4), leading to increased plasma levels of hydrocodone (Madadi et al., 2010). This case highlights the importance of drug-gene interactions and the complex interplay of DDI involving drug–gene interactions. It is becoming increasingly important to be aware of individual patient drug metabolizing enzyme genotypes to avoid harmful side effects when prescribing medications.
In vitro studies focusing on hydrocodone DDI found significant inhibition of hydromorphone formation in the presence of quinidine and furafylline through CYP2D6 inhibition (Hutchinson et al., 2004). Hutchinson et al., also found significant inhibition of norhydrocodone in the presence of ketoconazole and troleandomycin through CYP3A4 inhibition (Hutchinson et al., 2004). These data further suggest that the prescription of hydrocodone with either CYP2D6 or CYP3A4 inhibitors should be avoided.
Oxycodone
There have been three randomized controlled trials examining the effects of paroxetine, a CYP2D6 inhibitor, on the pharmacokinetics of oxycodone. In one of these trials, chronic pain patients were administered oxycodone tailored to each patient need (range 20 mg–320 mg/day) along with 20 mg/day of paroxetine (dose-corrected plasma oxycodone concentrations of patients were similar, but patients were also on other comedications for their chronic conditions; (Lemberg et al., 2010). Compared with placebo controls, paroxetine increased the mean dose-adjusted AUC and Cmax of oxycodone by 19% and 23%, respectively. Paroxetine also increased the noroxycodone AUC and Cmax by 100% and 102%, respectively, as compared to the placebo group. The oxymorphone Cmax and AUC were decreased with paroxetine treatment by 57% and 67% (Lemberg et al., 2010) and noroxymorphone plasma concentrations were also decreased by paroxetine treatment. Several adverse effects were reported during concomitant treatment, including headaches, drowsiness, dizziness, and nausea/vomiting. Interestingly, the analgesic effect of oxycodone was not significantly altered with paroxetine treatment, suggesting that CYP2D6 inhibition may not be of major clinical significance and that oxymorphone formation may not be central to oxycodone’s analgesic effect (Lemberg et al., 2010).
Two other randomized control trials investigated the effect of paroxetine alone or in tandem with the CYP3A4 inhibitor, itraconazole, on oxycodone pharmacokinetics. In an initial study by Gronlund et al., only minimal changes in AUC and Cmax for oxycodone was observed after oral oxycodone administration for the paroxetine-treated group as compared to placebo controls (Grönlund et al., 2010a). However, oxymorphone plasma concentrations decreased by 44% while the AUC for noroxycodone increased by 68% in the presence of paroxetine, suggesting that the inhibition of CYP2D6-mediated oxymorphone formation by paroxetine could potentially result in shunting of oxycodone metabolism to the CYP3A4-mediated formation of noroxycodone. In contrast, administration of both paroxetine and itraconazole led to 1.8- to 1.9-fold increases in Cmax and AUC for oxycodone, with corresponding decreases in the AUC of both oxymorphone and noroxycodone. The same researchers performed a similar randomized study but with intravenous oxycodone administration (Grönlund et al., 2011a). Similar to that observed in their earlier oral oxycodone administration study, paroxetine inhibited oxymorphone formation but with little change in oxycodone pharmacokinetics. With the administration of both paroxetine and itraconazole, there was a significant 2-fold increase in oxycodone exposure as well as inhibition of both oxymorphone and noroxycodone formation. Together, these data are consistent with an effective inhibition of both CYP2D6- and CYP3A4-mediated oxycodone metabolism by the paroxetine-itraconazole combination. While the data further suggested that the inhibition of CYP3A4 may be more clinically relevant to potential oxycodone DDI as compared to CYP2D6 inhibition, the studies by Gronlund et al., did not investigate the effect of itraconazole alone on oxycodone pharmacokinetics. Interestingly, the inhibition of both enzymes failed to cause significant changes in the analgesic effects manifested by oxycodone in the two studies after a single dose of oxycodone, suggesting that with repeated oxycodone administration, the decrease in oxycodone clearance observed in this study can result in accumulated levels of oxycodone leading to adverse side effects (Grönlund et al., 2011a). Saari et al., found that the coadministration of itraconazole (oral) and oxycodone (intravenous) led to decreased plasma clearance by 32% and increased AUC of oxycodone by 51%. Similarly, the AUC of orally administered oxycodone increased 1.44-fold in the presence of itraconazole and the Cmax increased by 45% (Saari et al., 2010). The AUC of the CYP3A4 metabolite, noroxycodone was decreased by 49% and oxymorphone was increased by 359% (Saari et al., 2010).
In a non-randomized controlled trial, subjects were administered oxycodone (0.2 mg/kg) and 20 mg of paroxetine (Kummer et al., 2011). Again, no significant effect on oxycodone pharmacokinetics was observed in the paroxetine group as compared to the placebo control group, with both the AUC and Cmax of oxycodone minimally affected by paroxetine treatment.
Two randomized controlled trials investigated the effect of the CYP3A4 inhibitor, ketoconazole, on the pharmacokinetics of oxycodone. Samer et al., found that after co-administration, the noroxycodone Cmax decreased by 80%, oxymorphone AUC exposure and Cmax increased 3.5-fold and 1.5-fold, respectively, and the oxycodone AUC increased by 1.8-fold. The oxymorphone increase suggests metabolic shunting towards the CYP2D6 pathway after inhibition of CYP3A4 (Samer et al., 2010a,b). Similarly, Kummer et al., found that co-administration of ketoconazole and oxycodone led to an increase in oxycodone AUC and Cmax by 146% and 77%, respectively. They also observed a decrease in Cmax for noroxycodone; however, they did not observe any effect on oxymorphone. The administration of ketoconazole did influence oxycodone pharmacodynamics (Kummer et al., 2011). Thus, this DDI could potentially be of clinical importance due to the changes observed in pharmacokinetics as well as pharmacodynamics of oxycodone.
A clinical trial examined the effects of rifampin, a CYP3A4 inducer, on the pharmacokinetics of either oral or intravenous administration of oxycodone. Rifampin was found to reduce the oxycodone AUC by 53% and 86% in the oral and intravenous administration studies, respectively, with increases in the AUC ratios of the oxycodone metabolite, noroxycodone, to parent oxycodone by 2.4- and 7.6-fold for intravenous and oral administration, respectively. Rifampin increased first pass metabolism of oxycodone, and the reported analgesic effects were also decreased. Therefore, concomitant administration of rifampin with oxycodone may lead to a clinically significant DDI due to potentiation of CYP3A4 activity, and dose adjustments should be made accordingly (Nieminen et al., 2009).
In a case study, a patient who was taking both rifampin and oxycodone exhibited low levels of urinary oxycodone, suggesting that rifampin-induced CYP3A4 metabolism resulted in high levels of oxycodone elimination via one of its major metabolites (Lee et al., 2006). In another case study, a patient taking both rifampin and oxycodone (1120 mg) exhibited uncontrolled pain. Researchers believe the patient showed oxycodone resistance that was most likely caused by subtherapeutic levels of oxycodone present due to the induction of CYP3A4 by rifampin leading to large increases in the production of the nonactive metabolite, noroxycodone (Sakamoto et al., 2017). The patient was switched to morphine and pain was adequately tolerated with a typical dose (Sakamoto et al., 2017). Taken together, the combination of rifampin and oxycodone results in a clinically significant DDI that needs to be monitored.
The inhibition of oxycodone metabolism has also been studied extensively in vitro. The majority of these studies have found either inhibition of both CYP2D6 and CYP3A4 mediated metabolism of oxycodone, or inhibition of one or the other metabolic pathways in human liver microsomes. These studies suggest that further investigation of these potential DDI is needed in vivo and further suggest that the co-administration of CYP2D6 or CYP3A4 inhibitors with oxycodone should be avoided. Further detail can be found in Table 1.
Morphine
A randomized control trial was performed to examine the effects of methadone on the pharmacodynamics and pharmacokinetics of morphine (Doverty et al., 2001). The study was conducted in patients who were on methadone maintenance treatment and were administered (intravenously) morphine in a two-step system to ensure steady state plasma morphine levels were reached. In a preliminary analysis, morphine clearance was decreased in the methadone group compared to placebo controls. Pain tolerance tests revealed that individuals in the methadone group were cross tolerant to morphine analgesia. Clinically, the findings suggested that a higher dose of morphine will be needed in individuals who are taking methadone to have the same analgesic effects (Doverty et al., 2001). These findings suggest that there is a pharmacodynamic interaction occurring such that morphine analgesia is not as effective in methadone patients compared to non-methadone patients. Further studies are needed to examine this potential pharmacodynamic interaction and their clinical impacts on pharmacotherapies prescribed for acute pain, especially in the methadone patient population.
In another study, Gelston et al. found that with coadministration of methadone and codeine, there was a 2.4- and 3.5-fold decrease in morphine-3-glucuronide and morphine-6-glucuronide formation, respectively. Interestingly, morphine plasma levels were unaltered (Gelston et al., 2012) due possibly to a continued metabolism of codeine to morphine. The decrease in morphine glucuronide levels has been attributed to methadone inhibition of UGT2B7 and UGT2B4 (Gelston et al., 2012). The inhibition of UGT2B7 is likely the cause of the decreased clearance observed (Doverty et al., 2001) with coadministration of methadone with morphine. Morrish et al., found that both methadone enantiomers inhibit morphine-3-glucuronide and morphine-6-glucuronide formation in human liver microsome samples, further suggesting that methadone inhibits UGT2B7-mediated metabolism of morphine (Morrish et al., 2006).
A randomized controlled trial was performed to determine the effects of rifampin on morphine pharmacokinetics (Fromm et al., 1997). The AUCs of both the morphine-3-glucuronide and morphine-6-glucuronide were altered with rifampin treatment (morphine-3-glucuronide = 3412 pmol·h/ml before vs 4327 pmol·h/ml after rifampin treatment; morphine-6-glucuronide = 665 pmol·h/ml before vs 537 pmol·h/ml after rifampin treatment). Serum morphine concentrations as well as the AUC were also significantly reduced (AUC = 132 pmol·h/ml before rifampin treatment vs 97 pmol·h/ml after rifampin treatment). This corresponded with increases in serum normorphine levels and decreased urinary recovery of both morphine glucuronides. The decrease in morphine AUC and serum concentrations could be due to rifampin induction of CYP3A4 (Fromm et al., 1997; Kliewer and Willson, 2002; Matsuda et al., 2002; Gashaw et al., 2003; Glaeser et al., 2005), resulting in increases in normorphine formation and decreases in the formation of other metabolites. However, normorphine formation is a relatively minor morphine metabolism pathway, suggesting that another unknown mechanism may also be playing a role in the observed DDI between the two agents (Fromm et al., 1997).
In another study, cancer patients were administered oral morphine solution for five days for pain control. Each patient was monitored for morphine plasma concentration after the administration of clomipramine or amitriptyline (25 or 50 mg daily) (Ventafridda et al., 1990). It was found that morphine AUC increased 2-fold in the presence of clomipramine and that the morphine AUC increased 1.29-fold in the presence of amitriptyline. Furthermore, these two drugs caused an apparent increase in morphine-induced analgesia in a potentially addictive fashion. Both drugs are known to potentiate the serotoninergic activity of morphine (Samanin and Valzelli, 1972; Ventafridda et al., 1990). An in vitro study showing that both amitriptyline and clomipramine inhibited UGT2B7 metabolism of morphine and formation of morphine-3-glucuronide and morphine-6-glucuronide in human liver microsomes further corroborated these findings (Wahlström et al., 1994).
In a randomized controlled trial examining the potential effects of diclofenac on the pharmacokinetics of morphine in patients after surgery, a statistically significant decrease in hourly morphine use, morphine plasma concentration, and morphine-6-glucuronide plasma levels was observed with concomitant use of diclofenac (Tighe et al., 1999). Interestingly, the peak time of analgesia was delayed, and morphine-6-glucuronide plasma concentrations did not decrease until five hours into the study even though morphine consumption decreased (Tighe et al., 1999). The underlying mechanism for this DDI is unknown and future mechanistic studies will be needed to better understand the elevated morphine-6-glucuronide levels with decreasing morphine consumption.
Numerous in vitro studies have been performed to investigate the potential inhibitory effects of many drugs on morphine metabolism. These studies are described in Table 1 and show that UGT2B7 inhibition leads to decreases in morphine-3-glucuronide and morphine-6-glucuronide formation. Results from one study suggest that inhibition of UGT2B7-mediated morphine metabolism by mefenamic acid would be predicted to lead to a 40% increase in the morphine AUC (Uchaipichat et al., 2022). These in vitro studies highlight the importance of studying UGT-mediated DDIs as they are typically not investigated as readily as CYP-mediated DDIs.
Discussion
In summary, hydrocodone is metabolized through both phase one and phase two metabolism, with CYP2D6, CYP3A4, and UGT2B7 all playing a major role (Otton et al., 1993; Hutchinson et al., 2004). The major metabolite formed is norhydrocodone and is inactive (Trescot et al., 2008). A minor pathway catalyzed primarily by CYP2D6 forms the active metabolite, hydromorphone. Hydromorphone is itself used clinically as an opioid and is further metabolized by UGT2B7 to an active metabolite, hydromorphone-3-glucuronide (Coffman et al., 1998). CYP2D6 is highly polymorphic and functional allelic variants can result in varying phenotypes of EM (two normal functioning wild-type alleles), IM (some loss of function usually by having two IM alleles or one EM and IM/PM allele), UM (either increased function or multiple copy number of the wild-type allele), and PM (very low functioning enzyme of complete loss of function, usually with two low functioning alleles) (Bradford, 2002). Individuals who are normally EM may potentially exhibit a PM phenotype if one or more metabolizing enzymes are inhibited. In contrast, EM individuals may exhibit a UM phenotype if the concomitant drug is an inducer of one or more of these enzymes. While CYP3A4 exhibits numerous polymorphisms, studies have found that most of these variants do not lead to changes in enzyme function (Westlind-Johnsson et al., 2006). UGT2B7 also has numerous SNPs leading to altered functionality, although there is some debate as to whether they are functional (Bhasker et al., 2000; Lazarska et al., 2018; Wang et al., 2018a). Relatively few in vivo studies have been performed examining potential DDIs between hydrocodone and various other drugs. This review focused on two known DDIs, inhibition of CYP2D6 by paroxetine and inhibition of CYP3A4 by ritonavir. Greater exposure to hydrocodone was observed with ritonavir as CYP3A4 is the major metabolic pathway, and this may be a clinically significant DDI. Inhibition of CYP2D6 likely does not cause a clinically significant DDI with hydrocodone. This is likely due to the smaller fraction metabolized by CYP2D6 for hydrocodone, which is roughly <20%. It is thought that inhibition of CYP2D6 is unlikely to cause a DDI in vivo as an increase in parent drug concentrations by 20% does not qualify as a DDI as recommended by the FDA (victim drug with inhibitor AUC is >125% AUC of victim drug alone) (FDA, 2020). Clinical recommendations for potential in vivo drug–drug interactions involving hydrocodone are listed in Table 3.
Clinical suggestions to manage opioid drug-drug interactions
Oxycodone is metabolized primarily by CYP2D6 to its minor yet active metabolite, oxymorphone (Childers et al., 1979; Chen et al., 1991; Otton et al., 1993). N-demethylation by CYP3A4 produces the major metabolite noroxycodone (Weinstein and Gaylord, 1979; Leow and Smith, 1994; Stamer et al., 2013). Oxycodone and its major and minor metabolites undergo further metabolism by UGT2B7 (Coffman et al., 1998; Adams and Ahdieh, 2005; Lalovic et al., 2006). The oxycodone DDIs discussed in detail in this review cover a small portion of the known DDIs that involve oxycodone. Paroxetine and ketoconazole both increased oxycodone exposure, likely through inhibition of CYP2D6 and CYP3A4, respectively. Inhibition of CYP2D6 led to minimal increases in oxycodone exposure and are thus likely not a clinically significant DDI (Grönlund et al., 2010a, 2011a; Lemberg et al., 2010; Kummer et al., 2011). Inhibition of CYP3A4 by ketoconazole or induction by rifampin indicated how important CYP3A4-mediated DDIs would be for the efficacy and safety of oxycodone (Nieminen et al., 2009; Grönlund et al., 2010a). Inhibition of CYP3A4 led to a significant increase in oxycodone exposure and induction of CYP3A4 led to a significant decrease in oxycodone exposure (Nieminen et al., 2009; Grönlund et al., 2010a, 2011a). Therefore, clinically relevant DDIs may occur with drugs that interact with CYP3A4 and possibly not with CYP2D6; clinical recommendations on these DDIs are listed in Table 3.
Morphine is metabolized mainly by UGT2B7 to its major metabolite, morphine-3-glucuronide, as well as its minor active metabolite, morphine-6-glucuronide (Oguri et al., 1987; Pasternak et al., 1987; Coffman et al., 1997, 1998; Donnelly et al., 2002; Stone et al., 2003; Wittwer and Kern, 2006). Morphine is also metabolized by CYP3A4, although this is a minor pathway to form normorphine (Projean et al., 2003). A few known morphine DDIs were discussed in this review, including rifampin, diclofenac, and methadone. Rifampin led to an induction of CYP3A4, increased clearance of morphine, and increased formation of normorphine (Fromm et al., 1997). The effects of methadone were less clear as it appeared that there was a decrease in morphine clearance, yet the patients appeared to be cross tolerant to morphine suggesting an increase in morphine dose would be needed (Gelston et al., 2012). Tighe et al., also found interesting results demonstrating that there were decreases in plasma morphine levels and an increase in morphine-6-glucuronide levels, even with decreases in morphine dosing (Tighe et al., 1999). From these studies, it is not clear if UGT2B7 inhibition would cause clinically significant DDI with morphine, and further investigations are needed to examine the effect of UGT2B7 inhibition or induction on potential DDI with morphine. Clinical guidance on the potential DDI involving morphine are listed in Table 3.
Exposure to a known inducer or inhibitor of a metabolizing enzyme could potentially be exacerbated depending on an individual’s genotype for these enzymes. Future studies are needed to examine the role of drug–gene interactions with major opioids on the potential for adverse drug interaction to occur, especially with an at-risk population, such as those with high polypharmacy. For example, a PM individual may experience less severe effects if a DDI should occur when the perpetrator drug is a drug metabolism inhibitor due to the smaller change in the fraction metabolized of the victim drug being metabolized. Conversely, if a PM phenotype individual is induced, then the therapeutic efficacy may significantly decrease. Alternatively, an individual who has a UM phenotype and takes a perpetrator drug that inhibits metabolism of the victim drug may experience significant adverse effects; if the perpetrator drug induces metabolism of the victim drug, that individual may only exhibit minimal effects due again to a small change in the fraction metabolized of the victim drug being metabolized. Opioid DDI studies incorporating genotype as a potential confounder will be an important focus for future studies. Opioids are the first line treatment for moderate to severe pain; however, there are risks involved with their use, including potential ADE and risk of addiction. Physicians will need to incorporate potential opioid DDI information to improve clinical care by altering dosing regimens, using alternative treatments, and close therapeutic monitoring when prescribing in combination with drugs that may alter opioid metabolism and efficacy.
Limitations
In this review, we did not consider potential or known DDIs in which the opioid of interest is the precipitant. The focus of the study was to summarize the known and potential DDIs that can lead to changes in the selected opioid metabolism as the object drug that can then lead to ADEs. In future studies, it will be important to identify the DDIs that are a result of the opioid being the precipitant and altering the metabolism of another drug taken concomitantly with the opioid to have more comprehensive knowledge of drug profiles. Identifying as many potential DDI combinations as possible will increase therapeutic knowledge and improve patient care. Another limitation in this review is that only three opioids were discussed, focusing on those used widely in medical practice (https://nida.nih.gov/publications/drugfacts/prescription-opioids). There are numerous other opioids that are also commonly used, more potent, and have a larger addiction potential compared to the three discussed in this review. In addition, there is increased interest in the role of transporters and the effects of their inhibition or upregulation on the pharmacokinetic and pharmacodynamics of opioids as well as other drugs. However, potential drug–drug interactions occurring due to inhibition of opioid transporters were not examined in this review.
Conclusions
Studies focusing on opioid metabolism and potential DDIs are important for clinical use and improved patient care, especially in patient populations with high polypharmacy. Concomitant use of a metabolic enzyme inhibitor or inducer could potentially cause adverse effects by altering pharmacokinetic disposition and pharmacodynamics of a given drug. The evidence provided in this review show that significant DDI can occur between major opioids and certain drugs that affect the metabolism of those opioids. The DDI can be detrimental not only in themselves but also when they occur in vulnerable populations that have high polypharmacy use. We hypothesize that such DDI could potentially be further exacerbated by the existence of functional genotypes in metabolizing enzyme genes and further insight into a patient’s pharmacogenetic profile could provide clinicians with additional information for precision medicine as it applies to opioid dosing and regimen, especially in patient populations with high polypharmacy.
Data Availability
This article contains no datasets generated or analyzed during the current study.
Authorship Contributions
Wrote or contributed to the writing of the manuscript: Coate, Lazarus.
Footnotes
- Received March 24, 2023.
- Accepted August 21, 2023.
This work was supported by National Institutes of Health (NIH) National Institute on Drug Abuse (NIDA) [Grant F31-DA056197] (to S.C.).
The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants, or patents received or pending, or royalties.
Abbreviations
- ADE
- adverse drug events
- AUC
- area under the curve
- BBB
- blood brain barrier
- Cmax
- maximum plasma concentration
- CNS
- central nervous system
- CYP
- cytochrome P450
- DDI
- drug–drug interactions
- EM
- extensive metabolizer
- IM
- intermediate metabolizer
- MOR
- mu opioid receptor
- PM
- poor metabolizer
- SNP
- single nucleotide polymorphism
- UM
- ultrarapid metabolizer
- UGT
- uridine-5’-diphosphoglucuronsyltransferase
- Copyright © 2023 by The Author(s)
This is an open access article distributed under the CC BY-NC Attribution 4.0 International license.
