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Aims: To catalogue the perpetrators of CYP-mediated pharmacokinetic drug-drug interactions (PK-DDIs) using clinically relevant criteria, and to compare this with an analogous catalogue., Methods: Candidate inhibitors and inducers of CYP1A2, CYP2C9, CYP2C19, CYP2D6 and CYP3A ('perpetrators') were evaluated using published clinical pharmacokinetic interaction studies. Studies were selected on the basis of ≥six human subjects, use of a validated in vivo probe substrate for the CYP enzyme, and clinically relevant dosing. Inhibitors were described according to the FDA classifications of strong, moderate or weak, whereas inducers were classified as major (≥twofold decrease in AUC) or weak (

PURPOSE: The introduction of COVID-19 therapies containing ritonavir has markedly expanded the scope of use for this medicine. As a strong cytochrome P450 3A4 inhibitor, the use of ritonavir is associated with a high drug interaction risk. There are currently no data to inform clinician regarding the likely magnitude and duration of interaction between ritonavir-containing COVID-19 therapies and small-molecule kinase inhibitors (KIs) in patients with cancer. METHODS: Physiologically based pharmacokinetic modeling was used to conduct virtual clinical trials with a parallel group study design in the presence and absence of ritonavir (100 mg twice daily for 5 days). The magnitude and time course of changes in KI exposure when coadministered with ritonavir was evaluated as the primary outcome. RESULTS: Dosing of ritonavir resulted in a > 2-fold increase in steady-state area under the plasma concentration-time curve and maximal concentration for six of the 10 KIs. When the KI was coadministered with ritonavir, dose reductions to between 10% and 75% of the original dose were required to achieve an area under the plasma concentration-time curve within 1.25-fold of the value in the absence of ritonavir. CONCLUSION: To our knowledge, this study provides the first data to assist clinicians' understanding of the drug interaction risk associated with administering ritonavir-containing COVID-19 therapies to patients with cancer who are currently being treated with KIs. These data may support clinicians to make more informed dosing decisions for patients with cancer undergoing treatment with KIs who require treatment with ritonavir-containing COVID-19 antiviral therapies. These data may support clinicians to make more informed dosing decisions for patients with cancer undergoing treatment with kinase inhibitors who require treatment with ritonavir-containing COVID-19 antiviral therapies. [ABSTRACT FROM AUTHOR]

Drug-drug interactions (DDIs) arising from the perturbation of drug metabolising enzyme activities represent both a clinical problem and a potential economic loss for the pharmaceutical industry. DDIs involving glucuronidated drugs have historically attracted little attention and there is a perception that interactions are of minor clinical relevance. This review critically examines the scope and aetiology of DDIs that result in altered exposure of glucuronidated drugs. Interaction mechanisms, namely inhibition and induction of UDP-glucuronosyltransferase (UGT) enzymes and the potential interplay with drug transporters, are reviewed in detail, as is the clinical significance of known DDIs. Altered victim drug exposure arising from modulation of UGT enzyme activities is relatively common and, notably, the incidence and importance of UGT induction as a DDI mechanism is greater than generally believed. Numerous DDIs are clinically relevant, resulting in either loss of efficacy or an increased risk of adverse effects, necessitating dose individualisation. Several generalisations relating to the likelihood of DDIs can be drawn from the known substrate and inhibitor selectivities of UGT enzymes, highlighting the importance of comprehensive reaction phenotyping studies at an early stage of drug development. Further, rigorous assessment of the DDI liability of new chemical entities that undergo glucuronidation to a significant extent has been recommended recently by regulatory guidance. Although evidence-based approaches exist for the in vitro characterisation of UGT enzyme inhibition and induction, the availability of drugs considered appropriate for use as 'probe' substrates in clinical DDI studies is limited and this should be a research priority. [ABSTRACT FROM AUTHOR]

Major advances in the characterization of uridine diphosphate (UDP)-glucuronosyltransferase (UGT) enzyme substrate and inhibitor selectivities and the development of experimental paradigms to investigate xenobiotic glucuronidation in vitro now permit the prediction of a range of drug-glucuronidation parameters in humans. In particular, the availability of substrate and inhibitor “probes” for the major hepatic drug metabolizing UGTs together with batteries of recombinant enzymes allow the reaction phenotyping of drug glucuronidation reactions. Additionally, in vitro experimental approaches and scaling strategies have been successfully applied to the quantitative prediction of in vivo clearance via glucuronidation and drug-drug interaction potential. [ABSTRACT FROM AUTHOR]

Aims Using the fluconazole–zidovudine (AZT) interaction as a model, to determine whether inhibition of UDP–glucuronosyltransferase (UGT) catalysed drug metabolism in vivo could be predicted quantitatively from in vitro kinetic data generated in the presence and absence bovine serum albumin (BSA). Methods Kinetic constants for AZT glucuronidation were generated using human liver microsomes (HLM) and recombinant UGT2B7, the principal enzyme responsible for AZT glucuronidation, as the enzyme sources with and without fluconazole. K i values were used to estimate the decrease in AZT clearance in vivo. Results Addition of BSA (2%) to incubations decreased the K m values for AZT glucuronidation by 85–90% for the HLM (923 ± 357 to 91 ± 9 µm) and UGT2B7 (478–70 µm) catalysed reactions, with little effect on Vmax. Fluconazole, which was shown to be a selective inhibitor of UGT2B7, competitively inhibited AZT glucuronidation by HLM and UGT2B7. Like the K m, BSA caused an 87% reduction in the K i for fluconazole inhibition of AZT glucuronidation by HLM (1133 ± 403 to 145 ± 36 µm) and UGT2B7 (529 to 73 µm). K i values determined for fluconazole using HLM and UGT2B7 in the presence (but not absence) of BSA predicted an interaction in vivo. The predicted magnitude of the interaction ranged from 41% to 217% of the reported AUC increase in patients, depending on the value of the in vivo fluconazole concentration employed in calculations. Conclusions K i values determined under certain experimental conditions may quantitatively predict inhibition of UGT catalysed drug glucuronidation in vivo. [ABSTRACT FROM AUTHOR]

Objective: Macrolide antibiotics are mechanism based inactivators of CYP3A enzymes that exhibit varying degrees of inhibitory potency. Our aim was to predict quantitatively the drug-drug interaction (DDI) potential of five macrolides from in vitro studies using testosterone as the CYP3A substrate, and to compare the predictions generated from human liver microsomal and recombinant CYP3A4 data. Methods: The in vitro kinetic constants of CYP3A inactivation (KI and kinact) were estimated by varying the time of pre-incubation and the concentration of troleandomycin, erythromycin, clarithromycin, roxithromycin or azithromycin. CYP3A activity was determined from the measurement of testosterone 6β-hydroxylation with human liver microsomes (HLM) and recombinant CYP3A4 as the enzyme sources. The mechanism-based pharmacokinetic model was fitted with inactivation data to predict the increase in oral area under the plasma concentration-time curve (AUC) for midazolam. Results: All five macrolides inactivated testosterone 6β-hydroxylation by HLM and recombinant CYP3A4 with kinact values in the range of 0.023 to 0.058 min-1. The potency of inactivation (KI) was higher using recombinant CYP3A4 as the enzyme source. The oral AUCs for midazolam were predicted from HLM data to increase 16.6, 5.3, 4.6, 1.6 and 1.2-fold due to the inhibition of metabolic clearance by troleandomycin, erythromycin, clarithromycin, roxithromycin and azithromycin, respectively. These results are within the range of the AUC ratios reported for clinical DDI studies. The predicted AUC increases generated using recombinant CYP3A4 overestimated the magnitude of the DDIs. Conclusions: The DDI potential of five macrolide antibiotics was quantitatively predicted from in vitro studies using testosterone as the CYP3A substrate with HLM as the enzyme source. [ABSTRACT FROM AUTHOR]

A letter to the editor is presented about drug-drug interactions involving macrolides.