Future directions in drug-drug interaction evaluations: Industry perspective on the ICH M12 guidance.

Inhibition of Intestinal OATP2B1 by the Calcium Receptor Antagonist Ronacaleret Results in a Significant Drug-Drug Interaction by Causing a 2-Fold Decrease in Exposure of Rosuvastatin.

Verinurad is a selective uric acid transporter 1 (URAT1) inhibitor in development for the treatment of chronic kidney disease and heart failure. In humans, two major acyl glucuronide metabolites have been identified: direct glucuronide M1 and N-oxide glucuronide M8. Using in vitro systems recommended by regulatory agencies, we evaluated the interactions of verinurad, M1, and M8 with major drug-metabolizing enzymes and transporters and the potential for clinically relevant drug-drug interactions (DDIs). The IC50 for inhibition of CYP2C8, CYP2C9, and CYP3A4/5 for verinurad was ≥14.5 µM, and maximum free plasma concentration (Iu,max)/IC50 was <0.02 at the anticipated therapeutic Cmax and therefore not considered a DDI risk. Verinurad was not an inducer of CYP1A2, CYP2B6, or CYP3A4/5. Verinurad was identified as a substrate of the hepatic uptake transporter organic anion-transporting polypeptide (OATP) 1B3. Since verinurad hepatic uptake involved both active and passive transport, there is a low risk of clinically relevant DDIs with OATP, and further study is warranted. Verinurad was a substrate of the efflux transporters P-glycoprotein (P-gp) and breast cancer resistance protein (BCRP), and renal transporter organic anion transporter 1 (OAT1), although it is not considered a DDI risk in vivo because of dose-proportional pharmacokinetics (P-gp and BCRP) and limited renal excretion of verinurad (OAT1). M1 and M8 were substrates of multidrug resistance-associated protein (MRP) 2 and MRP4 and inhibitors of MRP2. Apart from verinurad being a substrate of OATP1B3 in vitro, the potential for clinically relevant DDIs involving verinurad and its metabolites as victims or perpetrators of metabolizing enzymes or drug transporters is considered low. SIGNIFICANCE STATEMENT: Drug transporters and metabolizing enzymes have an important role in the absorption and disposition of a drug and its metabolites. Using in vitro systems recommended by regulatory agencies, we determined that, apart from verinurad being a substrate of organic anion-transporting polypeptide 1B3, the potential for clinically relevant drug-drug interactions involving verinurad and its metabolites M1 and M8 as victims or perpetrators of metabolizing enzymes or drug transporters is considered low.

Physiologically based pharmacokinetic modeling of aldehyde oxidase drug-drug interactions mediated by erlotinib.

The Role of Coproporphyrins As Endogenous Biomarkers for Organic Anion Transporting Polypeptide 1B Inhibition-Progress from 2016 to 2023.

Significant increases in detection capability for assessment of organic anion transporting polypeptide-mediated drug-drug interaction in cynomolgus monkeys by modifying pretreatment of Coproporphyrin I and III, and glycochenodeoxycholate-3-sulfate in plasma

Role of Transporters in Drug Development.

Bemnifosbuvir is a novel oral guanosine nucleotide prodrug candidate for the treatment of chronic hepatitis C virus infection. Potential drug-drug interactions (DDIs) of bemnifosbuvir as a substrate or perpetrator with regard to ATP-binding cassette (ABC) and solute carrier (SLC) transporters were evaluated in vitro and in clinical studies. Bemnifosbuvir was demonstrated in vitro as a substrate and inhibitor of the ABC transporters' P-glycoprotein (P-gp), as an inhibitor of the breast cancer resistance protein (BCRP), as well as a weak inhibitor of SLC transporters, including organic anion transporting polypeptide 1B1 (OATP1B1). Phase 1 studies in healthy participants were subsequently conducted to assess the clinical significance of transporter-mediated DDI potentials of bemnifosbuvir as a precipitant using digoxin and rosuvastatin as P-gp and BCRP/OATP1B1 index substrates, respectively. A single dose of 0.25 mg digoxin or 10 mg rosuvastatin was administered alone and with 1100 mg bemnifosbuvir, either simultaneously or staggered. Simultaneous administration of a single dose of 1100 mg bemnifosbuvir increased total plasma exposure of both drugs by less than 20%, and transiently increased the peak plasma exposure of digoxin and rosuvastatin by 78% and 40%, respectively. Staggered dosing reduced the magnitude of changes in peak exposure to digoxin and rosuvastatin. No serious adverse events or drug discontinuations were observed. Dose adjustments are therefore unlikely for drugs that are substrates of P-gp or BCRP/OAT1B1 when coadministered with bemnifosbuvir, and staggered dosing may further reduce any DDI risk.

BackgroundCommon comorbidities associated with gout, including obesity, hypertension, diabetes, and chronic kidney disease, may confer a greater risk of drug-drug interaction (DDI) through both polypharmacy and disease-associated alterations in drug...

Static and dynamic (PBPK) prediction models were applied to estimate the drug-drug interaction (DDI) risk of AZD2066. The predictions were compared to the results of an in vivo cocktail study. Various in vivo measures for tolbutamide as a probe agent for cytochrome P450 2C9 (CYP2C9) were also compared. In vitro inhibition data for AZD2066 were obtained using human liver microsomes and CYP-specific probe substrates. DDI prediction was performed using PBPK modelling with the SimCYP simulator™ or static model. The cocktail study was an open label, baseline, controlled interaction study with 15 healthy volunteers receiving multiple doses of AD2066 for 12days. A cocktail of single doses of 100mg caffeine (CYP1A2 probe), 500mg tolbutamide (CYP2C9 probe), 20mg omeprazole (CYP2C19 probe) and 7.5mg midazolam (CYP3A probe) was simultaneously applied at baseline and during the administration of AZD2066. Bupropion as a CYP2B6 probe (150mg) and 100mg metoprolol (CYP2D6 probe) were administered on separate days. The pharmacokinetic parameters for the probe drugs and their metabolites in plasma and urinary recovery were determined. In vitro AZD2066 inhibited CYP1A2, CYP2B6, CYP2C9, CYP2C19 and CYP2D6. The static model predicted in vivo interaction with predicted AUC ratio values of >1.1 for all CYP (except CYP3A4). The PBPK simulations predicted no risk for clinical relevant interactions. The cocktail study showed no interaction for the CYP2B6 and CYP2C19 enzymes, a possible weak inhibition of CYP1A2, CYP2C9 and CYP3A4 activities and a slight inhibition (29%) of CYP2D6 activity. The tolbutamide phenotyping metrics indicated that there were significant correlations between CLform and AUCTOL, CL, Aemet and LnTOL24h. The MRAe in urine showed no correlation to CLform. DDI prediction using the static approach based on total concentration indicated that AZD20066 has a potential risk for inhibition. However, no DDI risk could be predicted when a more in vivo-like dynamic prediction method with the PBPK with SimCYP™ software based on early human PK data was used and more parameters (i.e. free fraction in plasma, no DDI risk) were taken into account. The clinical cocktail study showed no or low risks for clinical relevant DDI interactions. Our findings are in line with the hypothesis that the dynamic prediction method predicts DDI in vivo in humans better than the static model based on total plasma concentrations.

Stimulatory effect on the transport mediated by organic anion transporting polypeptide 2B1

Sunitinib, a CYP3A4 substrate, is standard of care treatment in metastatic renal cell carcinoma (mRCC) and is administered orally as a single dose of 50 mg, in a 4 weeks on/2 weeks off regimen. Frequently, dose reduction is necessary because of toxicity, as is the association of comedication to treat side effects. In addition, existing comorbidities in these patients necessitate the intake of various classes of chronic medication. Only limited data are available on the risk of drug-drug interactions (DDI). The objective of our paper was to evaluate prescribed dose, comedication, risk of drug-drug interactions and outcome among patients with mRCC treated with sunitinib. A single-centre, retrospective analysis was performed for patients with mRCC treated with sunitinib. The drug interaction databases 'Clinical Pharmacology' and 'Lexicomp' were used to screen for possible interactions. The hospital files of 36 patients with mRCC were evaluated. Twenty-two patients received sunitinib as first-line treatment. Progression-free survival (PFS) in this first-line group was longer for patients that started with full-dose sunitinib (21·1 months; n = 12) than for patients started on reduced dose (3·5 months; n = 10). In the whole group of 36 patients, an average of 6·8 comedications was taken. Possible pharmacodynamic drug-drug interactions were most frequently found (47%) and reported as major interactions (QT prolongation). Risk of pharmacokinetic interactions due to co-administration of CYP inhibitors, CYP inducers, CYP substrates and PgP substrates was reported for 8%, 11%, 53% and 19%, respectively. These interactions were reported as major or moderate. Patients with mRCC under treatment with sunitinib at a reduced starting dose had a decreased PFS compared with patients started with full-dose sunitinib. Due to adverse drug reactions and comorbidity, patients under sunitinib, a CYP3A4 substrate, took an average of 6·8 comedications provoking an important risk of major-to-moderate drug-drug interactions. With the help of a multidisciplinary team, avoidance of drug-drug interactions could be obtained. Moreover, serial ECG monitoring is recommended for patients at high risk of QT prolongation.

Considerations from the Innovation and Quality Induction Working Group in Response to Drug-Drug Interaction Guidance from Regulatory Agencies: Guidelines on Model Fitting and Recommendations on Time Course for In Vitro Cytochrome P450 Induction Studies Including Impact on Drug Interaction Risk Assessment.

Opioid Metabolism

Introduction: It is widely accepted that current practice of polypharmacy inevitably increases the incidence of drug–drug interactions (DDIs). Serious DDIs are a major liability for new molecular entities entering the pharmaceutical market. Various strategies are employed to avoid problematic compounds for clinical development. Progress made with reversible CYP DDIs has prompted a switch to study and model time-dependent inhibition and induction interactions.Areas covered: An overview of popular experimental practices is presented with discussion of techniques and algorithms used to analyse the clinical DDI risk. Emphasis is placed on the transition from early, simple static equations, via more complex net mechanistic, static models to dynamic approaches involving multiple perpetrators and metabolites, simultaneous inhibition and induction.Expert opinion: Inclusion of the more conservative terms for parameters required for DDI evaluation may eliminate promising chemical space, encourages poor practice and hampers innovation. Breakthroughs have originated from understanding of ‘outliers’ from such analyses where CYP enzyme–transporter interplay may be involved. The role of key transporters in drug disposition is firmly established as the chemistry required to address new targets deviates from traditional ‘drug-like’ space. Attempts to model more complex interactions for substrates of both CYP enzymes and drug transporters are still in their infancy and will benefit from dynamic modelling.

The use of statins is widespread across disease areas because many patients have comorbidities. Given that these drugs have become common as comedications, it is essential to have an understanding of the potential risks of drug-drug interactions (DDIs) between statins and candidate drugs in development. Although the hepatic uptake transporter organic anion-transporting polypeptide 1B1 (OATP1B1) is known to play a substantial role in statin-related DDI risk, other transporters and metabolizing enzymes can also be involved. Consequently, a holistic approach to risk assessment is required, tailored to each statin. Using evidence from pharmacogenetics, DDIs, and literature on absorption, distribution, metabolism, and elimination (ADME) in humans, this review identifies pathways that contribute the most to, and are therefore the most critical to, the disposition of each statin. It also provides an understanding of the expected theoretical maximum increase in systemic exposure if the disposition of a statin is inhibited. Finally, on a statin-by-statin basis, we propose in vitro inhibition studies that should be routinely conducted during drug development so as to better assess DDI risk.