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Background and Objectives: Clearances and the area under the plasma concentration–time curve extrapolated to infinity (AUC0–∞) of intravenous (IV) and oral midazolam and alfentanil are probes for hepatic and first-pass cytochrome P450 3A (CYP3A) activity, drug interactions, and phenotyping. Single-time plasma concentrations are also used as a proxy for clearance and AUC0–∞. Pupil diameter change is a noninvasive surrogate for plasma alfentanil. An ideal probe should have minimal intrasubject (interday) variability. Despite their widespread use, the intrasubject variability of CYP3A probes is not well characterized. This investigation determined the intrasubject (interday) variability of midazolam and alfentanil metrics of hepatic and first-pass CYP3A. Methods: Twelve volunteers were studied in a four-period protocol, with each period identical and separated by approximately 2 weeks. In each period, participants received 1 mg IV midazolam then 15 μg/kg IV alfentanil 1 h later. The next day, they received 3 mg oral midazolam then 60 μg/kg oral alfentanil. Plasma drug concentrations were determined by liquid chromatography-mass spectrometry (LCMS). Dark-adapted pupil diameters were measured coincident with blood sampling. Plasma concentrations and pupil effects (miosis) were analyzed using noncompartmental methods. The results were the coefficient of variation (%CV, mean ± SD) across four sessions in 12 participants. Results: For IV midazolam: AUC0–∞, clearance, and 5 h concentration, the %CVs were 12 ± 3, 12 ± 3, and 18 ± 8. For IV alfentanil AUC0–∞, clearance, 2 h concentration, and area under the effect curve from time zero to infinity (AUEC0–∞), the %CVs were 16 ± 5, 15 ± 4, 22 ± 7, and 50 ± 28. For oral midazolam AUC0–∞, clearance, and 5 h concentration, %CVs were 19 ± 5, 18 ± 4, and 28 ± 11. For oral alfentanil: AUC0–∞, clearance, 4 h concentration, and AUEC0–∞, %CVs were 20 ± 4, 21 ± 4, 42 ± 26, and 37 ± 14. Conclusions: Midazolam and alfentanil had comparable intrasubject variabilities of clearance and AUC0–∞. Single-time point metrics had greater intrasubject variability than AUC0–∞ and clearance. Miosis was a surrogate for alfentanil concentrations and provided real-time results, but intrasubject variability was greater than that of clearances and AUC0–∞. [ABSTRACT FROM AUTHOR]

Background: In clinical practice, the vast array of potential drug combinations necessitates swift and accurate assessments of pharmacokinetic drug–drug interactions (DDIs), along with recommendations for adjustments. Current methodologies for clinical DDI evaluations primarily rely on basic extrapolations from clinical trial data. However, these methods are limited in accuracy owing to their lack of a comprehensive consideration of various critical factors, including the inhibitory potency, dosage, and type of the inhibitor, as well as the metabolic fraction and intestinal availability of the substrate. Objective: This study aims to propose an efficient and accurate clinical pharmacokinetic-mediated DDI assessment tool, which comprehensively considers the effects of inhibitory potency and dosage of inhibitors, intestinal availability and fraction metabolized of substrates on DDI outcomes. Methods: This study focuses on DDIs caused by cytochrome P450 3A4 enzyme inhibition, utilizing extensive clinical trial data to establish a methodology to calculate the metabolic fraction and intestinal availability for substrates, as well as the concentration and inhibitory potency for inhibitors ( K i or k inact / K I ). These parameters were then used to predict the outcomes of DDIs involving 33 substrates and 20 inhibitors. We also defined the risk index for substrates and the potency index for inhibitors to establish a clinical DDI risk scale. The training set for parameter calculation consisted of 73 clinical trials. The validation set comprised 89 clinical DDI trials involving 53 drugs. which was used to evaluate the reliability of in vivo values of K i and k inact / K I , the accuracy of DDI predictions, and the false-negative rate of risk scale. Results: First, the reliability of the in vivo K i and k inact / K I values calculated in this study was assessed using a basic static model. Compared with values obtained from other methods, this study values showed a lower geometric mean fold error and root mean square error. Additionally, incorporating these values into the physiologically based pharmacokinetic-DDI model facilitated a good fitting of the C–t curves when the substrate's metabolic enzymes are inhibited. Second, area under the curve ratio predictions of studied drugs were within a 1.5 × margin of error in 81% of cases compared with clinical observations in the validation set. Last, the clinical DDI risk scale developed in this study predicted the actual risks in the validation set with only a 5.6% incidence of serious false negatives. Conclusions: This study offers a rapid and accurate approach for assessing the risk of pharmacokinetic-mediated DDIs in clinical practice, providing a foundation for rational combination drug use and dosage adjustments. [ABSTRACT FROM AUTHOR]

Background and Objectives: Trifluridine/tipiracil, registered for the treatment of patients with metastatic gastric and colorectal cancer, is a substrate and inhibitor for the organic cation transporter 2 (OCT2) and the multidrug and toxin extrusion protein 1 (MATE1), which raises the potential for drug–drug interactions with other OCT2/MATE1 modulators. Therefore, we prospectively examined the effect of an OCT2/MATE1 inhibitor (cimetidine) and substrate (metformin) on the pharmacokinetics of trifluridine. Methods: In this three-phase crossover study, patients with metastatic colorectal or gastric cancer were sequentially treated with trifluridine/tipiracil alone (phase A), trifluridine/tipiracil concomitant with metformin (phase B) and trifluridine/tipiracil concomitant with cimetidine (phase C). The primary endpoint was the relative difference in exposure of trifluridine assessed by the area under the curve from timepoint zero to infinity. A > 30% change in exposure was considered clinically relevant. A p-value of < 0.025 was considered significant because of a Bonferroni correction. Results: Eighteen patients were included in the analysis. Metformin did not significantly alter the exposure to trifluridine (− 12.6%; 97.5% confidence interval − 25.0, 1.8; p = 0.045). Cimetidine did alter the exposure to trifluridine significantly (+ 18.0%; 97.5% confidence interval 4.5, 33.3; p = 0.004), but this increase did not meet our threshold for clinical relevance. Metformin trough concentrations were not influenced by trifluridine/tipiracil. Conclusions: Our result suggests that the OCT2/MATE1 modulators cimetidine and metformin can be co-administered with trifluridine/tipiracil without clinically relevant effects on drug exposure. Clinical Trial Registration: NL8067 (registered 04-10-2019). [ABSTRACT FROM AUTHOR]

Polypharmacy is commonly employed in clinical settings. The potential risks of drug–drug interactions (DDIs) can compromise efficacy and pose serious health hazards. Integrating pharmacokinetics (PK) and pharmacodynamics (PD) models into DDIs research provides a reliable method for evaluating and optimizing drug regimens. With advancements in our comprehension of both individual drug mechanisms and DDIs, conventional models have begun to evolve towards more detailed and precise directions, especially in terms of the simulation and analysis of physiological mechanisms. Selecting appropriate models is crucial for an accurate assessment of DDIs. This review details the theoretical frameworks and quantitative benchmarks of PK and PD modeling in DDI evaluation, highlighting the establishment of PK/PD modeling against a backdrop of complex DDIs and physiological conditions, and further showcases the potential of quantitative systems pharmacology (QSP) in this field. Furthermore, it explores the current advancements and challenges in DDI evaluation based on models, emphasizing the role of emerging in vitro detection systems, high-throughput screening technologies, and advanced computational resources in improving prediction accuracy. [ABSTRACT FROM AUTHOR]

The renal secretion of many drugs is facilitated by membrane transporters, including organic cation transporter 2, multidrug and toxin extrusion protein 1/2-K and organic anion transporters 1 and 3. Inhibition of these transporters can reduce renal excretion of drugs and thereby pose a safety risk. Assessing the risk of inhibition of these membrane transporters by investigational drugs remains a key focus in the evaluation of drug–drug interactions (DDIs). Current methods to predict DDI risk are based on generating in vitro data followed by a clinical assessment using a recommended exogenous probe substrate for the individual drug transporter. More recently, monitoring plasma-based and urine-based endogenous biomarkers to predict transporter-mediated DDIs in early phase I studies represents a promising approach to facilitate, improve and potentially avoid conventional clinical DDI studies. This perspective reviews the evidence for use of these endogenous biomarkers in the assessment of renal transporter-mediated DDI, evaluates how endogenous biomarkers may help to expand the DDI assessment toolkit and offers some potential knowledge gaps. A conceptual framework for assessment that may complement the current paradigm of predicting the potential for renal transporter-mediated DDIs is outlined. [ABSTRACT FROM AUTHOR]

Introduction: The activity changes of cytochrome P450 (CYP450) enzymes, along with the complicated medication scenarios in diabetes mellitus (DM) patients, result in the unanticipated pharmacokinetics (PK), pharmacodynamics (PD), and drug–drug interactions (DDIs). Physiologically based pharmacokinetic (PBPK) modeling has been a useful tool for assessing the influence of disease status on CYP enzymes and the resulting DDIs. This work aims to develop a novel diabetic PBPK population model to facilitate the prediction of PK and DDI in DM patients. Methods: First, mathematical functions were constructed to describe the demographic and non-CYP physiological characteristics specific to DM, which were then incorporated into the PBPK model to quantify the net changes in CYP enzyme activities by comparing the PK of CYP probe drugs in DM versus non-DM subjects. Results: The results show that the enzyme activity is reduced by 32.3% for CYP3A4/5, 39.1% for CYP2C19, and 27% for CYP2B6, while CYP2C9 activity is enhanced by 38% under DM condition. Finally, the diabetic PBPK model was developed through integrating the DM-specific CYP activities and other parameters and was further used to perform PK simulations under 12 drug combination scenarios, among which 3 combinations were predicted to result in significant PK changes in DM, which may cause DDI risks in DM patients. Conclusions: The PBPK modeling applied herein provides a quantitative tool to assess the impact of disease factors on relevant enzyme pathways and potential disease-drug–drug-interactions (DDDIs), which may be useful for dosing regimen optimization and minimizing the DDI risks associated with the treatment of DM. [ABSTRACT FROM AUTHOR]

Background and Objectives: Encorafenib is a kinase inhibitor indicated for the treatment of patients with unresectable or metastatic melanoma or metastatic colorectal cancer, respectively, with selected BRAF V600 mutations. A clinical drug–drug interaction (DDI) study was designed to evaluate the effect of encorafenib on rosuvastatin, a sensitive substrate of OATP1B1/3 and breast cancer resistance protein (BCRP), and bupropion, a sensitive CYP2B6 substrate. Coproporphyrin I (CP-I), an endogenous substrate for OATP1B1, was measured in a separate study to deconvolute the mechanism of transporter DDI. Methods: DDI study participants received a single oral dose of rosuvastatin (10 mg) and bupropion (75 mg) on days − 7, 1, and 14 and continuous doses of encorafenib (450 mg QD) and binimetinib (45 mg BID) starting on day 1. The CP-I data were collected from participants in a phase 3 study who received encorafenib (300 mg QD) and cetuximab (400 mg/m2 initial dose, then 250 mg/m2 QW). Pharmacokinetic and pharmacodynamic analysis was performed using noncompartmental and compartmental methods. Results: Bupropion exposure was not increased, whereas rosuvastatin Cmax and area under the receiver operating characteristic curve (AUC) increased approximately 2.7 and 1.6-fold, respectively, following repeated doses of encorafenib and binimetinib. Increase in CP-I was minimal, suggesting that the primary effect of encorafenib on rosuvastatin is through BCRP. Categorization of statins on the basis of their metabolic and transporter profile suggests pravastatin would have the least potential for interaction when coadministered with encorafenib. Conclusion: The results from these clinical studies suggest that encorafenib does not cause clinically relevant CYP2B6 induction or inhibition but is an inhibitor of BCRP and may also inhibit OATP1B1/3 to a lesser extent. Based on these results, it may be necessary to consider switching statins or reducing statin dosage accordingly for coadministration with encorafenib. Clinical Trial Registration: ClinicalTrials.gov NCT03864042, registered 6 March 2019. [ABSTRACT FROM AUTHOR]

Nirmatrelvir is a potent and selective inhibitor of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease that is used as an oral antiviral coronavirus disease 2019 (COVID-19) treatment. To sustain unbound systemic trough concentrations above the antiviral in vitro 90% effective concentration value (EC90), nirmatrelvir is coadministered with 100 mg of ritonavir, a pharmacokinetic enhancer. Ritonavir inhibits nirmatrelvir's cytochrome P450 (CYP) 3A4-mediated metabolism which results in renal elimination becoming the primary route of nirmatrelvir elimination when dosed concomitantly. Nirmatrelvir exhibits absorption-limited nonlinear pharmacokinetics. When coadministered with ritonavir in patients with mild-to-moderate COVID-19, nirmatrelvir reaches a maximum concentration of 3.43 µg/mL (11.7× EC90) in approximately 3 h on day 5 of dosing, with a geometric mean day 5 trough concentration of 1.57 µg/mL (5.4× EC90). Drug interactions with nirmatrelvir/ritonavir (PAXLOVIDTM) are primarily attributed to ritonavir-mediated CYP3A4 inhibition, and to a lesser extent CYP2D6 and P-glycoprotein inhibition. Population pharmacokinetics and quantitative systems pharmacology modeling support twice daily dosing of 300 mg/100 mg nirmatrelvir/ritonavir for 5 days, with a reduced 150 mg/100 mg dose for patients with moderate renal impairment. Rapid clinical development of nirmatrelvir/ritonavir in response to the emerging COVID-19 pandemic was enabled by innovations in clinical pharmacology research, including an adaptive phase 1 trial design allowing direct to pivotal phase 3 development, fluorine nuclear magnetic resonance spectroscopy to delineate absorption, distribution, metabolism, and excretion profiles, and innovative applications of model-informed drug development to accelerate development. [ABSTRACT FROM AUTHOR]

Background and Objective: Early investigations into drug–drug interactions (DDIs) involving cytochrome P450 2C8 (CYP2C8) have highlighted the complexity of interactions between CYP2C8 substrate drugs, including montelukast, desloratadine, pioglitazone, repaglinide, and cerivastatin (the latter two being OATP1B1 substrates), and standardized CYP2C8 inhibitors such as clopidogrel (Clop) and gemfibrozil (Gem). These interactions have proven challenging to predict based solely on simple CYP inhibition. A hypothesis has emerged suggesting that these substrate drugs first distribute to UDP-glucuronosyltransferase (UGT) before undergoing oxidation by CYP2C8, resulting in bidirectional elimination. The process of drug distribution to UGT is believed to significantly impact these DDIs. This study aims to explore the intricate interplay between UGT and CYP2C8 in the context of DDIs involving CYP2C8 substrates affected by Clop and Gem. Methods: Plasma-level data for the unchanged drug and its metabolite, drawn from the respective literature, formed the basis of our analysis. We evaluated the enzymatic inhibitory activities of DDIs and utilized simulations to estimate plasma levels of the unchanged victim drug and its metabolite in each DDI. This was accomplished by employing a functional relationship that considered the fractional contributions of CYP2C8 and UGT to clearance, perpetrator-specific inhibitory activities against CYP2C8, and drug distribution to UGT. Results: Our findings emphasize the pivotal role of UGT-mediated distribution in the context of CYP2C8 substrate metabolism, particularly in the complex DDIs induced by Clop and Gem. In these DDIs, Gem exerts inhibitory effects on both UGT and CYP2C8, whereas Clop (specifically its metabolite, Clop-COOH) solely targets CYP2C8. Importantly, the inhibition of CYP2C8 by both Clop and Gem is achieved through a non-competitive mechanism, driven by the actions of their acyl-glucuronides. Clop and Gem exhibit inhibition activities accounting for 85% (pAi,CYP2C8 = 7) and 93% (pAi,CYP2C8 = 15), respectively. In contrast, Gem's inhibition of UGT is relatively modest (50%, pAi,UGT(d) = 2), and it operates through a non-specific, competitive process in drug distribution to UGT. Within this context, our UGT-CYP2C8 interplay model offers an accurate means of predicting the alterations resulting from DDIs, encompassing changes in plasma levels of the unchanged drug and its metabolites, as well as shifts in metabolite formation rates. Our analysis highlights the critical importance of considering the fractional contributions of CYP2C8 and UGT to the victim drug's clearance (fm,CYP2C8; fm,UGT) in DDI prediction. Furthermore, our examination of DDIs involving OATP1B1 substrate drugs underscores that accounting for the hepatic uptake transporters' role in the liver is superfluous in DDI prediction. Conclusion: These findings substantially enhance our comprehension of CYP2C8-mediated oxidation and DDIs, holding crucial implications for drug development and the planning of clinical trials involving these inhibitors. [ABSTRACT FROM AUTHOR]

Background: Ulotaront is a novel psychotropic agent with agonist activity at trace amine-associated receptor 1 (TAAR1) and 5-hydroxytryptamine type 1A (5-HT1A) receptors in phase III clinical development for the treatment of schizophrenia. Objective: This study aimed to investigate the effect of paroxetine, a strong cytochrome P450 (CYP) 2D6 inhibitor, on ulotaront pharmacokinetics (PK) in healthy volunteers. Methods: Subjects received a single oral dose of 25 mg ulotaront on Day 1 and an oral dose of 20 mg paroxetine once daily from Days 5 to 10 to achieve steady-state plasma paroxetine levels. On Day 11, subjects received another single oral dose of 25 mg ulotaront, with continued daily oral dosing of 20 mg paroxetine from Days 11 to 14. All 24 subjects were CYP2D6 normal metabolizers. Results: Coadministration of paroxetine increased ulotaront maximum observed plasma concentration (Cmax) and area under the plasma concentration-time curve from time zero to infinity (AUC∞) by 31% and 72%, respectively, and decreased ulotaront apparent clearance (CL/F) by approximately 42%. While coadministration of paroxetine increased AUC∞ of active but minor metabolite SEP-363854 by 32%, it had no effect on SEP-363854 Cmax, or on SEP-363854 to the ulotaront AUC from time zero to the last quantifiable concentration (AUClast) ratio. Based on the acceptable adverse event profile of ulotaront across previous phase II studies, the increase in ulotaront exposure is unlikely to be clinically meaningful. Conclusions: Weak drug–drug interactions were observed between ulotaront and the strong CYP2D6 inhibitor paroxetine; however, dose adjustment as a precondition when ulotaront is coadministered with strong CYP2D6 inhibitors or administered to CYP2D6 poor metabolizers should not be necessary. [ABSTRACT FROM AUTHOR]

Low molecular weight heparins (LMWH) and anti-Xa direct oral anti-coagulants (DOACs) are recommended for the long-term treatment of cancer-associated thrombosis (CAT) based on well-documented randomised controlled trials. Anti-Xa DOACs are viewed as a first choice for the treatment of patients with CAT. A large number of drug–drug interactions have been reported between DOACs and chemotherapy drugs, modifying circulating levels of DOAC leading to fears of increased bleeding risks or thrombotic recurrence. Progresses in anti-neoplastic therapies have improved the prognosis and the survival, thus increasing the prevalence of frail patients with cancer. However, since frailties tend to be excluded from large trials due to multiple co-morbidities, current guidelines are not fully applicable to this population. The management of these frail patients with CAT is particularly complex and requires a risk assessment on a case-by-case basis with specific focus on cancer, patient-related risk factors and drug–drug interactions. In this brief review we have identified age, co-morbidities and co-medications as key factors of frailty that require careful attention and we have developed a therapeutic decision algorithm to help clinicians optimising the use of anti-coagulants in patients with cancer with CAT, especially in case of anti-Xa DOACs concomitant medications. With the evaluation of the bleeding risk according to the type of cancer, and anticipating drug–drug interactions intensity, taking into account patient frailties allows the optimisation of the anti-coagulant choice. A systematic collaboration between oncologists, vascular pathology specialists and pharmacists is warranted to ensure an optimal patient management. Clinical studies are needed to determine the real impact of these interactions. [ABSTRACT FROM AUTHOR]

The treatment of patients infected with the hepatitis C virus (HCV) has been revolutionised by the development of direct-acting antiviral agents (DAAs) that target specific HCV proteins involved in viral replication. The first DAAs were associated with clinical problems such as adverse drug reactions and pharmacokinetic drug–drug interactions (DDIs). Current FDA/EMA-approved treatments are combinations of DAAs that simultaneously target the HCV N5A-protein, the HCV N5B-polymerase and the HCV NS3/4A-protease. Adverse events and DDIs are less likely with these DAA combinations but several DDIs of potential clinical significance remain. Much of the available information on the interaction of DAAs with CYP drug-metabolising enzymes and influx and efflux transporters is contained in regulatory summaries and is focused on DDIs of likely clinical importance. Important DDIs perpetrated by current DAAs include increases in the pharmacokinetic exposure to statins and dabigatran. Some mechanistic information can be deduced. Although the free concentrations of DAAs in serum are very low, a number of these DDIs are likely mediated by the inhibition of systemic influx transporters, especially OATP1B1/1B3. Other DDIs may arise by DAA-mediated inhibition of intestinal efflux transporters, which increases the systemic concentrations of some coadministered drugs. Conversely, DAAs are victims of DDIs mediated by cyclosporin, ketoconazole, omeprazole and HIV antiretroviral drug combinations, especially when boosted by ritonavir and, to a lesser extent, cobicistat. In addition, concurrent administration of inducers, such as rifampicin, carbamazepine and efavirenz, decreases exposure to some DAAs. Drug-drug interactions that increase the accumulation of HCV N3/4A-protease inhibitors like grazoprevir may exacerbate hepatic injury in HCV patients. Plain Language Summary: Direct-acting antiviral (DAA) drugs have revolutionised the treatment of patients with hepatitis C. Compared to the earlier agents, currently-approved DAA combinations have fewer adverse effects and are less likely to be associated with pharmacokinetic drug-drug interactions (DDIs). However, adverse events and DDIs still occur when DAAs are coadministered with certain drugs. In most cases DAAs likely perpetrate DDIs by inhibiting drug transporters. However, access to more detailed information on HCV DAAs as substrates and inhibitors of drug-metabolising enzymes and transporters, and the incidence of DDIs in target populations, would enhance the understanding of the significance of the likelihood of DDIs with DAAs. [ABSTRACT FROM AUTHOR]

Imeglimin (PXL008, EMD-387008, Twymeeg®) is a first-in-class novel oral hypoglycemic agent, launched in Japan, for the treatment of type 2 diabetes mellitus. Its mechanism of action targets mitochondrial bioenergetics to ameliorate insulin resistance and to enhance β-cell function. This review summarizes the properties underlying the pharmacokinetic profile of imeglimin, a small cationic drug belonging to the tetrahydrotriazine chemical class, with a complex mechanism of absorption involving an active transport through organic cation transporters (OCTs). Imeglimin absorption decreases when dose increases due to the saturation of the active uptake transport. Post absorption, imeglimin is rapidly and primarily distributed to organs and tissues, and has a half-life ranging from 9.03 to 20.2 h. Plasma protein binding of imeglimin is low, which explains the rapid distribution to the organs observed in all species. Imeglimin is excreted unchanged in urine, indicating a low extent of metabolism. Imeglimin is a substrate of multidrug and toxic compound extrusion (MATE) 2-K and a substrate and inhibitor of OCT1, OCT2, and MATE1. Clinical drug–drug interaction studies confirmed the absence of relevant clinical interaction with substrates or inhibitors of these transporters. Overall, the drug–drug interaction potential of imeglimin is low. Its pharmacokinetics profile has also been characterized in special populations, showing no influence of mild and moderate hepatic impairment but an impact of renal function on imeglimin renal clearance. Dosage adjustment is thus required in moderately and severely renally impaired patients. Imeglimin pharmacokinetics was shown to be insensitive to ethnicity and food intake and to have no effect on QTcF interval. [ABSTRACT FROM AUTHOR]

Background and Objective: Voriconazole is an important broad-spectrum anti-fungal drug with nonlinear pharmacokinetics. The aim of this single centre fixed-sequence open-label drug–drug interaction trial in healthy participants (N = 17) was to determine whether microdosed probe drugs for CYP3A and CYP2C19 reliably predict voriconazole clearance (CLVRZ). Methods: At baseline, a single oral microdose of the paradigm substrates midazolam (CYP3A) and omeprazole (CYP2C19) were given to estimate their clearances (CL). Thereafter, a single oral dose of voriconazole was administered (50, 100, 200 or 400 mg), followed by the microdosed probe drugs. Results: The clearances of midazolam (CLMDZ 790–2790 mL/min at baseline; 248–1316 mL/min during voriconazole) and omeprazole (CLOMZ 66.4–2710 mL/min at baseline; 30.1–1420 mL/min during voriconazole) were highly variable. CLMDZ [geometric mean ratio (GMR) 0.586 at 50 mg voriconazole decreasing to GMR 0.196 at 400 mg voriconazole] and CLOMZ (GMR 0.590 at 50 mg decreasing to GMR 0.166 at 400 mg) were reduced with higher voriconazole doses. CLMDZ was linearly correlated with CLVRZ (slope 1.458; adjusted R2 0.528) as was CLOMZ (slope 0.807; adjusted R2 0.898). Multiple linear regression resulted in an adjusted R2 of 0.997 for the relationship CLVRZ ~ log CLOMZ + log CLMDZ using data during voriconazole treatment and an adjusted R2 of 0.997 for the relationship CLVRZ ~ log CLOMZ + log CLMDZ + voriconazole dose, using baseline data for CLMDZ and CLOMZ. Conclusion: Microdosed midazolam and omeprazole accurately described and predicted total CLVRZ Trial Registration: EudraCT No: 2020-001017-20, registered on March 5th, 2020. DRKS: DRKS00022547, registered on August 6th, 2020. [ABSTRACT FROM AUTHOR]

IntroductionIt has been recognized that significant transporter interactions result in volume of distribution changes in addition to potential changes in clearance. For drugs that are not clinically significant transporter substrates, it is expected that drug-drug interactions would not result in any changes in volume of distribution.MethodsAn evaluation of this hypothesis proceeded via an extensive analysis of published intravenous metabolic drug-drug interactions, based on clinically recommended index substrates and inhibitors of major cytochrome P450 (CYP) isoforms.ResultsSeventy-two metabolic drug interaction studies were identified where volume of distribution at steady-state (Vss) values were available for the CYP index substrates caffeine (CYP1A2), metoprolol (CYP2D6), midazolam (CYP3A4), theophylline (CYP1A2), and tolbutamide (CYP2C9). Changes in exposure (area under the curve) up to 5.1-fold were observed; however, ratios of Vss changes have a range of 0.70-1.26, with one outlier displaying a Vss ratio of 0.57.DiscussionThese results support the widely held founding tenant of pharmacokinetics that clearance and Vss are independent parameters. Knowledge that Vss is unchanged in metabolic drug-drug interactions can be helpful in discriminating changes in clearance from changes in bioavailability (F) when only oral dosing data are available, as we have recently demonstrated. As Vss remains unchanged for intravenous metabolic drug-drug interactions, following oral dosing changes in Vss/F will reflect changes in F alone. This estimation of F change can subsequently be utilized to assess changes in clearance alone from calculations of apparent clearance. Utilization of this simple methodology for orally dosed drugs will have a significant impact on how drug-drug interactions are interpreted from drug development and regulatory perspectives.

Darolutamide is a next-generation androgen receptor signaling inhibitor (ARSI) currently approved for the treatment of nonmetastatic castration-resistant prostate cancer (nmCRPC) and metastatic hormone sensitive prostate cancer (mHSPC). Studies suggest that darolutamide also has the potential to be used to treat other stages of prostate cancer (PC), suggesting that its indications will broaden in the near future. Since ARSIs show similar efficacy for the treatment of PC, pharmacokinetic properties of these drugs and patient characteristics could help physicians decide which drug to select. This review provides an overview of the pharmacokinetic and pharmacodynamic properties of darolutamide. One of the most important pharmacological advantages of darolutamide is its low brain distribution and therefore limited seizure potential and central nervous system adverse effects. In addition, darolutamide has little drug–drug interaction potential and is unlikely to alter the exposure of other cytochrome P450 or P-glycoprotein substrates. Nevertheless, it may significantly increase the exposure of breast cancer resistant protein (BCRP) substrates. The limited solubility and bioavailability of darolutamide increases when taken together with food, regardless of the fat content. Darolutamide is excessively metabolized by oxidation and glucuronidation and excreted in the urine and feces. For this reason, dose reduction is required in patients with moderate and severe renal or severe hepatic impairment. Although no exposure–response relationship was observed with darolutamide, less advanced stages of PC showed better PSA response on treatment. Overall, darolutamide has some advantageous pharmacological properties that may lead to its preferred use, when broader registered, in selected patients across different disease stages. [ABSTRACT FROM AUTHOR]

Background and Objective: Spinal muscular atrophy (SMA) is a progressive neuromuscular disease caused by insufficient levels of survival motor neuron (SMN) protein. Risdiplam (EvrysdiTM) increases SMN protein and is approved for the treatment of SMA. Risdiplam has high oral bioavailability and is primarily eliminated through hepatic metabolism by flavin-containing monooxygenase3 (FMO3) and cytochrome P450 (CYP) 3A, by 75% and 20%, respectively. While the FMO3 ontogeny is critical input data for the prediction of risdiplam pharmacokinetics (PK) in children, it was mostly studied in vitro, and robust in vivo FMO3 ontogeny is currently lacking. We derived in vivo FMO3 ontogeny by mechanistic population PK modelling of risdiplam and investigated its impact on drug-drug interactions in children. Methods: Population and physiologically based PK (PPK and PBPK) modelling conducted during the development of risdiplam were integrated into a mechanistic PPK (Mech-PPK) model to estimate in vivo FMO3 ontogeny. A total of 10,205 risdiplam plasma concentration-time data from 525 subjects aged 2 months–61 years were included. Six different structural models were examined to describe the in vivo FMO3 ontogeny. Impact of the newly estimated FMO3 ontogeny on predictions of drug–drug interaction (DDI) in children was investigated by simulations for dual CYP3A-FMO3 substrates including risdiplam and theoretical substrates covering a range of metabolic fractions (fm) of CYP3A and FMO3 (fmCYP3A:fmFMO3 = 10%:90%, 50%:50%, 90%:10%). Results: All six models consistently predicted higher FMO3 expression/activity in children, reaching a maximum at the age of 2 years with an approximately threefold difference compared with adults. Different trajectories of FMO3 ontogeny in infants < 4 months of age were predicted by the six models, likely due to limited observations for this age range. Use of this in vivo FMO3 ontogeny function improved prediction of risdiplam PK in children compared to in vitro FMO3 ontogeny functions. The simulations of theoretical dual CYP3A-FMO3 substrates predicted comparable or decreased CYP3A-victim DDI propensity in children compared to adults across the range of fm values. Refinement of FMO3 ontogeny in the risdiplam model had no impact on the previously predicted low CYP3A-victim or -perpetrator DDI risk of risdiplam in children. Conclusion: Mech-PPK modelling successfully estimated in vivo FMO3 ontogeny from risdiplam data collected from 525 subjects aged 2 months–61 years. To our knowledge, this is the first investigation of in vivo FMO3 ontogeny by population approach using comprehensive data covering a wide age range. Derivation of a robust in vivo FMO3 ontogeny function has significant implications on the prospective prediction of PK and DDI in children for other FMO3 substrates in the future, as illustrated in the current study for FMO3 and/or dual CYP3A-FMO3 substrates. Clinical Trial Registry Numbers: NCT02633709, NCT03032172, NCT02908685, NCT02913482, NCT03988907. [ABSTRACT FROM AUTHOR]

Background: Mechanistic static pharmacokinetic (MSPK) models are simple, have fewer data requirements, and have broader applicability; however, they cannot use in vitro information and cannot distinguish the contributions of multiple cytochrome P450 (CYP) isoenzymes and the hepatic and intestinal first-pass effects appropriately. We aimed to establish a new MSPK analysis framework for the comprehensive prediction of drug interactions (DIs) to overcome these disadvantages. Methods: Drug interactions that occurred by inhibiting CYP1A2, CYP2C9, CYP2C19, CYP2D6, and CYP3A in the liver and CYP3A in the intestine were simultaneously analyzed for 59 substrates and 35 inhibitors. As in vivo information, the observed changes in the area under the concentration-time curve (AUC) and elimination half-life (t1/2), hepatic availability, and urinary excretion ratio were used. As in vitro information, the fraction metabolized (fm) and the inhibition constant (Ki) were used. The contribution ratio (CR) and inhibition ratio (IR) for multiple clearance pathways and hypothetical volume (VHyp) were inferred using the Markov Chain Monte Carlo (MCMC) method. Result: Using in vivo information from 239 combinations and in vitro 172 fm and 344 Ki values, changes in AUC, and t1/2 were estimated for all 2065 combinations, wherein the AUC was estimated to be more than doubled for 602 combinations. Intake-dependent selective intestinal CYP3A inhibition by grapefruit juice has been suggested. By separating the intestinal contributions, DIs after intravenous dosing were also appropriately inferred. Conclusion: This framework would be a powerful tool for the reasonable management of various DIs based on all available in vitro and in vivo information. [ABSTRACT FROM AUTHOR]

Background and objectiveGilteritinib is a novel, highly selective tyrosine kinase inhibitor approved in the USA, Canada, Europe, Brazil, Korea, and Japan for the treatment of FLT3 mutation-positive acute myeloid leukemia. This article describes the clinical pharmacokinetic profile of gilteritinib.MethodsThe pharmacokinetic profile of gilteritinib was assessed from five clinical studies.ResultsDose-proportional pharmacokinetics was observed following once-daily gilteritinib administration (dose range 20-450 mg). Median maximum concentration was reached 2-6 h following single and repeat dosing of gilteritinib; mean elimination half-life was 113 h. Elimination was primarily via feces. Exposure to gilteritinib was comparable under fasted and fed conditions. Gilteritinib is primarily metabolized via cytochrome P450 (CYP) 3A4; coadministration of gilteritinib with itraconazole (a strong P-glycoprotein inhibitor and CYP3A4 inhibitor) or rifampicin (a strong P-glycoprotein inducer and CYP3A inducer) significantly affected the gilteritinib pharmacokinetic profile. No clinically relevant interactions were observed when gilteritinib was coadministered with midazolam (a CYP3A4 substrate) or cephalexin (a multidrug and toxin extrusion 1 substrate). Unbound gilteritinib exposure was similar between subjects with hepatic impairment and normal hepatic function.ConclusionsGilteritinib exhibits a dose-proportional pharmacokinetic profile in healthy subjects and in patients with relapsed/refractory acute myeloid leukemia. Gilteritinib exposure is not significantly affected by food. Moderate-to-strong CYP3A inhibitors demonstrated a significant effect on gilteritinib exposure. Coadministration of gilteritinib with CYP3A4 or multidrug and toxin extrusion 1 substrates did not impact substrate concentrations. Unbound gilteritinib was comparable between subjects with hepatic impairment and normal hepatic function; dose adjustment is not warranted for patients with hepatic impairment.Clinical trial registrationNCT02014558, NCT02456883, NCT02571816.

Introduction: Metabolic inducers can expose people with polypharmacy to adverse health outcomes. A limited fraction of potential drug–drug interactions (DDIs) have been or can ethically be studied in clinical trials, leaving the vast majority unexplored. In the present study, an algorithm has been developed to predict the induction DDI magnitude, integrating data related to drug-metabolising enzymes. Methods: The area under the curve ratio (AUCratio) resulting from the DDI with a victim drug in the presence and absence of an inducer (rifampicin, rifabutin, efavirenz, or carbamazepine) was predicted from various in vitro parameters and then correlated with the clinical AUCratio (N = 319). In vitro data including fraction unbound in plasma, substrate specificity and induction potential for cytochrome P450s, phase II enzymes and uptake, and efflux transporters were integrated. To represent the interaction potential, the in vitro metabolic metric (IVMM) was generated by combining the fraction of substrate metabolised by each hepatic enzyme of interest with the corresponding in vitro fold increase in enzyme activity (E) value for the inducer. Results: Two independent variables were deemed significant and included in the algorithm: IVMM and fraction unbound in plasma. The observed and predicted magnitudes of the DDIs were categorised accordingly: no induction, mild, moderate, and strong induction. DDIs were assumed to be well classified if the predictions were in the same category as the observations, or if the ratio between these two was < 1.5-fold. This algorithm correctly classified 70.5% of the DDIs. Conclusion: This research presents a rapid screening tool to identify the magnitude of potential DDIs utilising in vitro data which can be highly advantageous in early drug development. [ABSTRACT FROM AUTHOR]

Background and Objective: CYP2C19-mediated drug interactions of acid-reducing agents are clinically important given the high possibility of concomitant administration with CYP2C19 substrates. This study aimed to evaluate the effect of tegoprazan on the pharmacokinetics (PK) of a CYP2C19 substrate, proguanil, compared with vonoprazan or esomeprazole. Methods: A two-part, randomized, open-label, two-sequence, three-period crossover study was conducted in 16 healthy CYP2C19 extensive metabolizers (eight subjects per part). In each period, a single oral dose of atovaquone/proguanil 250/100 mg was administered alone or co-administered with tegoprazan 50 mg, esomeprazole 40 mg (Part 1 only) or vonoprazan 20 mg (Part 2 only). The plasma and urine concentrations of proguanil and its metabolite, cycloguanil, were measured up to 48 h post-dose. PK parameters were calculated using a non-compartmental method and compared between administered alone and co-administered with tegoprazan, vonoprazan or esomeprazole. Results: Co-administration of tegoprazan did not significantly affect the systemic exposure of proguanil and cycloguanil. In contrast, co-administration of vonoprazan or esomeprazole increased proguanil systemic exposure and decreased cycloguanil systemic exposure, and the magnitude of the corresponding change was greater with esomeprazole co-administration than vonoprazan co-administration. Conclusion: Tegoprazan, unlike vonoprazan and esomeprazole, exhibited negligible CYP2C19-mediated PK interaction. It suggests that as an alternative to other acid-reducing agents, tegoprazan can be used concomitantly with CYP2C19 substrates in clinical settings. Trial Registration: Clinicaltrials.gov identifier: NCT04568772 (Registered on September 29, 2020). [ABSTRACT FROM AUTHOR]

Background and objective: Cilofexor is a selective farnesoid X receptor (FXR) agonist in development for the treatment of nonalcoholic steatohepatitis and primary sclerosing cholangitis. Our objective was to evaluate potential drug-drug interactions of cilofexor as a victim and as a perpetrator. Methods: In this Phase 1 study, healthy adult participants (n = 18–24 per each of the 6 cohorts) were administered cilofexor in combination with either perpetrators or substrates of cytochrome P-450 (CYP) enzymes and drug transporters. Results: In total, 131 participants completed the study. As a victim, cilofexor area under the curve (AUC) was 651%, 795%, and 175% when administered following single-dose cyclosporine (600 mg; organic anion transporting polypeptide [OATP]/P-glycoprotein [P-gp]/CYP3A inhibitor), single-dose rifampin (600 mg; OATP1B1/1B3 inhibitor), and multiple-dose gemfibrozil (600 mg twice daily [BID]; CYP2C8 inhibitor), respectively, compared with the administration of cilofexor alone. Cilofexor AUC was 33% when administered following multiple-dose rifampin (600 mg; OATP/CYP/P-gp inducer). Multiple-dose voriconazole (200 mg BID; CYP3A4 inhibitor) and grapefruit juice (16 ounces; intestinal OATP inhibitor) did not affect cilofexor exposure. As a perpetrator, multiple-dose cilofexor did not affect the exposure of midazolam (2 mg; CYP3A substrate), pravastatin (40 mg; OATP substrate), or dabigatran etexilate (75 mg; intestinal P-gp substrate), but atorvastatin (10 mg; OATP/CYP3A4 substrate) AUC was 139% compared with atorvastatin administered alone. Conclusion: Cilofexor may be coadministered with inhibitors of P-gp, CYP3A4, or CYP2C8 without the need for dose modification. Cilofexor may be coadministered with OATP, BCRP, P-gp, and/or CYP3A4 substrates—including statins—without dose modification. However, coadministration of cilofexor with strong hepatic OATP inhibitors, or with strong or moderate inducers of OATP/CYP2C8, is not recommended. [ABSTRACT FROM AUTHOR]

Background and Objective: Mechanistic static and dynamic physiologically based pharmacokinetic models are used in clinical drug development to assess the risk of drug–drug interactions (DDIs). Currently, the use of mechanistic static models is restricted to screening DDI risk for an investigational drug, while dynamic physiologically based pharmacokinetic models are used for quantitative predictions of DDIs to support regulatory filing. As physiologically based pharmacokinetic model development by sponsors as well as a review of models by regulators require considerable resources, we explored the possibility of using mechanistic static models to support regulatory filing, using representative cases of successful physiologically based pharmacokinetic submissions to the US Food and Drug Administration under different classes of applications. Methods: Drug–drug interaction predictions with mechanistic static models were done for representative cases in the different classes of applications using the same data and modelling workflow as described in the Food and Drug Administration clinical pharmacology reviews. We investigated the hypothesis that the use of unbound average steady-state concentrations of modulators as driver concentrations in the mechanistic static models should lead to the same conclusions as those from physiologically based pharmacokinetic modelling for non-dynamic measures of DDI risk assessment such as the area under the plasma concentration–time curve ratio, provided the same input data are employed for the interacting drugs. Results: Drug–drug interaction predictions of area under the plasma concentration–time curve ratios using mechanistic static models were mostly comparable to those reported in the Food and Drug Administration reviews using physiologically based pharmacokinetic models for all representative cases in the different classes of applications. Conclusions: The results reported in this study should encourage the use of models that best fit an intended purpose, limiting the use of physiologically based pharmacokinetic models to those applications that leverage its unique strengths, such as what-if scenario testing to understand the effect of dose staggering, evaluating the role of uptake and efflux transporters, extrapolating DDI effects from studied to unstudied populations, or assessing the impact of DDIs on the exposure of a victim drug with concurrent mechanisms. With this first step, we hope to trigger a scientific discussion on the value of a routine comparison of the two methods for regulatory submissions to potentially create a best practice that could help identify examples where the use of dynamic changes in modulator concentrations could make a difference to DDI risk assessment. [ABSTRACT FROM AUTHOR]

BackgroundInfliximab, a monoclonal antibody directed against tumor necrosis factor-α, is being evaluated as adjunctive therapy to intravenous immunoglobulin (IVIG) for treatment of young children with acute Kawasaki disease (KD).ObjectiveThe aim of this study was to develop a population pharmacokinetic (PopPK) model for infliximab in children with KD, and to evaluate the impact of covariates on infliximab disposition. Specifically, we wanted to investigate the effect of body weight and IVIG administration on PK parameters.MethodsIn the current PopPK analysis, 70 subjects with a median (interquartile range) age of 2.9 years (1.3-4.4) were included from two randomized controlled trials. Infliximab concentration-time data were best described by a two-compartment model with first-order elimination using non-linear mixed-effects modeling (NONMEM 7.3).ResultsThe clearance, volume of distribution of the central (V1) and peripheral (V2) compartment, and intercompartmental clearance estimates (95% confidence interval) from the PopPK analysis were 0.117 (0.091-0.134) L/day, 0.801 (0.545-0.960) L, 0.962 (0.733-1.759) L, and 0.692 (0.482-1.779) L/day, respectively. Allometric body weight was included on all parameters of the structural model and a covariate analysis revealed that administering infliximab after IVIG, as opposed to before, resulted in a 50% decrease in V2.ConclusionsOur study shows that the timing of infliximab administration relative to IVIG administration affects the disposition of the monoclonal antibody. These results may have important implications for other monoclonal antibodies administered in combination with IVIG for treating inflammatory diseases.

Corticosteroids (steroids) are commonly used concurrently with mycophenolic acid (MPA) as the first-line immunosuppression therapy for the prevention of rejection in solid organ transplantations. Steroids are also commonly administered with MPA in various autoimmune disorders such as systemic lupus erythematosus and idiopathic nephrotic syndrome. Despite various review articles having suggested the presence of pharmacokinetic interactions between MPA and steroids, definitive data have not yet been demonstrated. The aim of this Current Opinion is to critically evaluate the available clinical data and propose the optimal study design for characterising the MPA–steroid pharmacokinetic interactions. The PubMed and Embase databases were searched for relevant clinical articles in English as of September 29, 2022, where a total of 8 papers have been identified as supporting and 22 as non-supporting the purported drug interaction. To objectively evaluate the data, novel assessment criteria to effectively diagnose the interaction based on known MPA pharmacology were formulated, including the availability of independent control groups, prednisolone concentrations, MPA metabolite data, unbound MPA concentrations, and the characterisations of entero-hepatic recirculation and MPA renal clearance. Overall, the majority of the identified corticosteroid data were pertaining to prednisone or prednisolone. Our assessment indicated that no conclusive mechanistic data supporting the interaction are available in the current clinical literature, and further studies are required to quantify the effects/mechanisms of steroid-tapering or withdrawal on MPA pharmacokinetics. This current opinion provides justification for further translational investigations, as this particular drug interaction has the potential to exert significant adverse outcomes in patients prescribed MPA. [ABSTRACT FROM AUTHOR]

Protein kinase inhibitors share pharmacokinetic (PK) pathways among themselves. They are all metabolized by several cytochromes P450 (CYP). For most of them, CYP3A4 is the predominant metabolic pathway. However, their oral bioavailability differs. For example, the oral bioavailability of imatinib has been estimated at nearly 100%, but that of ibrutinib averages 3% due to its high hepatic first-pass effect. Overall, the smaller the oral bioavailability, the larger its interindividual PK variability. Indeed, for drugs with low oral bioavailability, the extent of their absorption is an additional cause (along with elimination variability) of differences in drug exposure among patients. The impact of drug–drug interaction (DDI) also differs between drugs with low or high oral bioavailability. We describe and explain why the impact of CYP3A4 inhibitors and inducers is much greater for protein kinase inhibitors with low oral bioavailability. The effect of food on protein kinase inhibitors and DDIs corresponding to plasma protein binding will also be considered. Finally, the benefits of these concepts in clinical practice (including therapeutic drug monitoring) will be discussed. Overall, our main objective was to apply fundamental PK concepts to understanding the main clinical issues of these oral anticancer drugs. [ABSTRACT FROM AUTHOR]

Background and Objective: In clinical practice, injectable drug combination (IDC) usually provides good therapeutic effects for patients. Numerous clinical studies have directly indicated that inappropriate IDC generates adverse drug events (ADEs). The clinical application of injections is increasing, and many injections lack relevant combination information. It is still a significant need for experienced clinical pharmacists to participate in evidence-based drug decision making, monitor medication safety, and manage drug interactions. Meanwhile, a large number of injection pairs and dosage combinations limit exhaustive screening. Here, we present a prediction framework, called DeepIDC, that can expediently screen the feasibility of IDCs using heterogeneous information with deep learning. This is the first specific prediction framework to identify IDCs. Methods: Since the interaction between the injected drugs may occur in the direct physical and chemical reactions at the time of mixing or may be the indirect interaction of their drug targets and pathways, we used molecular fingerprints, drug-target associations, and drug-pathway associations to convert injections into a string of digital vectors. Then, based on these injection vectors, we combined a bidirectional long short-term memory and a feed-forward neural network to build a prediction model for accurate and instructive prediction of IDC. Results: In three realistic evaluation scenarios, DeepIDC has achieved ideal prediction results. Furthermore, compared with the other five machine-learning methods, the proposed predictor is more efficient and robust. Among the top 30 potential IDCs of each IDC class predicted by DeepIDC, we found that 9 cases were experimentally verified in the literature or available on Drug.com. Conclusion: The information we extracted in vivo and in vitro can effectively characterize injectable drugs. DeepIDC developed based on deep learning algorithm provides a valuable unified framework for new IDC discovery, which can make up for the lack of IDC information and predict potential IDC events. [ABSTRACT FROM AUTHOR]

Olaparib, niraparib, rucaparib, and talazoparib are poly (ADP-ribose) polymerase (PARP) inhibitors approved for the treatment of ovarian, breast, pancreatic, and/or prostate cancer. Poly (ADP-ribose) polymerase inhibitors are potent inhibitors of the PARP enzymes with comparable half-maximal inhibitory concentrations in the nanomolar range. Olaparib and rucaparib are orally dosed twice a day, extensively metabolized by cytochrome P450 enzymes, and inhibitors of several enzymes and drug transporters with a high risk for drug–drug interactions. Niraparib and talazoparib are orally dosed once a day with a lower risk for niraparib and a minimal risk for talazoparib to cause drug–drug interactions. All four PARP inhibitors show moderate-to-high interindividual variability in plasma exposure. Higher exposure is associated with an increase in toxicity, mostly hematological toxicity. For talazoparib, exposure–efficacy relationships have been described, but for olaparib, niraparib, and rucaparib this relationship remains inconclusive. Further studies are required to investigate exposure–response relationships to improve dosing of PARP inhibitors, in which therapeutic drug monitoring could play an important role. In this review, we give an overview of the pharmacokinetic properties of the four PARP inhibitors, including considerations for patients with renal dysfunction or hepatic impairment, the effect of food, and drug–drug interactions. Furthermore, we focus on the pharmacodynamics and summarize the available exposure–efficacy and exposure–toxicity relationships. [ABSTRACT FROM AUTHOR]

Background and Objective: An ever-growing body of evidence supports the impact of cytokine modulation on the patient's phenotypic drug response. The aim of this systematic review was to analyze the clinical studies that assessed the pharmacokinetics of victim drugs of this drug–disease interaction in the presence of different scenarios of cytokine modulation in comparison with baseline conditions. Methods: We conducted a systematic review by searching the PubMed-MEDLINE database from inception until February 2022 to retrieve prospective and/or retrospective observational studies, population pharmacokinetic studies, phase I studies, and/or case series/reports that investigated the impact of cytokine modulation on the pharmacokinetic behavior of victim drugs. Only studies providing quantitative pharmacokinetic data of victim drugs by comparing normal status versus clinical conditions with documented cytokine modulation or by assessing the influence of anti-inflammatory biological agents on metabolism and/or transport of victim drugs were included. Results: Overall, 26 studies were included. Rheumatoid arthritis (6/26; 23.1%) and sepsis (5/26; 19.2%) were the two most frequently investigated pro-inflammatory clinical scenarios. The victim drug most frequently assessed was midazolam (14/26; 53.8%; as a probe for cytochrome P450 [CYP] 3A4). Cytokine modulation showed a moderate inhibitory effect on CYP3A4-mediated metabolism (area under the concentration–time curve increase and/or clearance decrease between 1.98-fold and 2.59-fold) and a weak-to-moderate inhibitory effect on CYP1A2, CYP2C9, and CYP2C19-mediated metabolism (in the area under the concentration–time curve increase or clearance decrease between 1.29-fold and 1.97-fold). Anti-interleukin-6 agents showed remarkable activity in counteracting downregulation of CYP3A4-mediated activity (increase in the area under the concentration–time curve between 1.75-fold and 2.56-fold). Conclusions: Cytokine modulation may cause moderate or weak-to-moderate downregulation of metabolism/transport of victim drugs, and this may theoretically have relevant clinical consequences. [ABSTRACT FROM AUTHOR]

Background and Objective: Pexidartinib is a novel oral small-molecule inhibitor that selectively targets colony-stimulating factor 1 receptor, KIT proto-oncogene receptor tyrosine kinase, and FMS-like tyrosine kinase 3 harboring an internal tandem duplication mutation. It is approved in the United States for the treatment of adult patients with symptomatic tenosynovial giant cell tumor (TGCT) associated with severe morbidity or functional limitations and not amenable to improvement with surgery. Pexidartinib in vitro data indicate the potential for absorption- and metabolism-related drug–drug interactions (DDIs). The objective was to present a comprehensive DDI risk assessment of agents that can impact pexidartinib exposure by altering its absorption and metabolism potentially affecting efficacy and safety of pexidartinib. Methods: Four open-label crossover studies were performed to assess the effects of a pH modifier (esomeprazole), a strong cytochrome P450 (CYP) 3A4 inhibitor (itraconazole), a strong CYP3A/5′-diphospho-glucuronosyltransferase (UGT) inducer (rifampin), and a UGT inhibitor (probenecid) on the single-dose pharmacokinetics of pexidartinib. In addition, a physiologically based pharmacokinetic model was developed to predict the effect of a moderate CYP3A4 inhibitor (fluconazole) and a moderate CYP3A inducer (efavirenz) on the pharmacokinetics of pexidartinib. Results: Co-administration of pexidartinib with esomeprazole modestly decreased pexidartinib exposure (maximum plasma concentration [Cmax], ng/mL: geometric mean ratio [90% confidence interval (CI)], 45.4% [36.8–55.9]; area under the drug plasma concentration–time curve from time 0 to infinity [AUC∞], ng•h/mL: geometric mean ratio [90% CI], 53.1% [47.4–59.3]), likely related to decreased solubility of pexidartinib at increased pH levels. As expected, the strong CYP3A4 inhibitor itraconazole increased pexidartinib exposure (Cmax, ng/mL: geometric mean ratio [90% CI], 148.3% [127.8–172.0]; AUC∞, ng•h/mL: geometric mean ratio [90% CI], 173.0% [160.7–186.3]) while the strong CYP3A/UGT inducer rifampin decreased exposure (Cmax, ng/mL: geometric mean ratio [90% CI], 67.1% [53.1–84.8]; AUC∞, ng•h/mL: geometric mean ratio [90% CI], 37.0% [30.6–44.8]). In addition, UGT inhibition increased pexidartinib exposure (Cmax, ng/mL: geometric mean ratio [90% CI], 105.8% [92.4–121.0]; AUC∞, ng•h/mL: geometric mean ratio [90% CI], 159.8% [143.4–178.0]), consistent with the fact that pexidartinib is a substrate of the UGT1A4 enzyme, which is responsible for the generation of the major metabolite, ZAAD-1006a. Conclusions: The physiologically based pharmacokinetic model predicted that a moderate CYP3A4 inhibitor and a moderate CYP3A inducer would produce modest increases and decreases, respectively, in pexidartinib exposure. These results provide a basis for pexidartinib dosing recommendations when administered concomitantly with drugs with drug–drug interaction potential, including dose adjustments when concomitant administration cannot be avoided. Clinical Trial Registration: Probenecid: phase I trial, NCT03138759, 3 May, 2017; esomeprazole, itraconazole, rifampin: phase I trials, not registered with ClinicalTrials.gov. [ABSTRACT FROM AUTHOR]

Background: Cytochrome P450 (CYP) 3A contributes to the metabolism of many approved drugs. CYP3A perpetrator drugs can profoundly alter the exposure of CYP3A substrates. However, effects of such drug-drug interactions are usually reported as maximum effects rather than studied as time-dependent processes. Identification of the time course of CYP3A modulation can provide insight into when significant changes to CYP3A activity occurs, help better design drug-drug interaction studies, and manage drug-drug interactions in clinical practice. Objective: We aimed to quantify the time course and extent of the in vivo modulation of different CYP3A perpetrator drugs on hepatic CYP3A activity and distinguish different modulatory mechanisms by their time of onset, using pharmacologically inactive intravenous microgram doses of the CYP3A-specific substrate midazolam, as a marker of CYP3A activity. Methods: Twenty-four healthy individuals received an intravenous midazolam bolus followed by a continuous infusion for 10 or 36 h. Individuals were randomized into four arms: within each arm, two individuals served as a placebo control and, 2 h after start of the midazolam infusion, four individuals received the CYP3A perpetrator drug: voriconazole (inhibitor, orally or intravenously), rifampicin (inducer, orally), or efavirenz (activator, orally). After midazolam bolus administration, blood samples were taken every hour (rifampicin arm) or every 15 min (remaining study arms) until the end of midazolam infusion. A total of 1858 concentrations were equally divided between midazolam and its metabolite, 1'-hydroxymidazolam. A nonlinear mixed-effects population pharmacokinetic model of both compounds was developed using NONMEM®. CYP3A activity modulation was quantified over time, as the relative change of midazolam clearance encountered by the perpetrator drug, compared to the corresponding clearance value in the placebo arm. Results: Time course of CYP3A modulation and magnitude of maximum effect were identified for each perpetrator drug. While efavirenz CYP3A activation was relatively fast and short, reaching a maximum after approximately 2–3 h, the induction effect of rifampicin could only be observed after 22 h, with a maximum after approximately 28–30 h followed by a steep drop to almost baseline within 1–2 h. In contrast, the inhibitory impact of both oral and intravenous voriconazole was prolonged with a steady inhibition of CYP3A activity followed by a gradual increase in the inhibitory effect until the end of sampling at 8 h. Relative maximum clearance changes were +59.1%, +46.7%, −70.6%, and −61.1% for efavirenz, rifampicin, oral voriconazole, and intravenous voriconazole, respectively. Conclusions: We could distinguish between different mechanisms of CYP3A modulation by the time of onset. Identification of the time at which clearance significantly changes, per perpetrator drug, can guide the design of an optimal sampling schedule for future drug-drug interaction studies. The impact of a short-term combination of different perpetrator drugs on the paradigm CYP3A substrate midazolam was characterized and can define combination intervals in which no relevant interaction is to be expected. Clinical Trial Registration: The trial was registered at the European Union Drug Regulating Authorities for Clinical Trials (EudraCT-No. 2013-004869-14). [ABSTRACT FROM AUTHOR]

Background and Objective: Tucatinib is approved for treatment of human epidermal growth factor receptor 2-positive metastatic breast cancer. Understanding potential drug-drug interactions (DDIs) informs proper dosing when co-administering tucatinib with other therapies. The aim of this study was to evaluate DDIs between tucatinib and metabolizing enzymes and transporters in healthy volunteers.Methods: Parts A-C assessed the impact of itraconazole (cytochrome P450 [CYP] 3A4 inhibitor), rifampin (CYP3A4/CYP2C8 inducer), or gemfibrozil (CYP2C8 inhibitor) on the pharmacokinetics of a single 300 mg dose of tucatinib administered orally and its primary metabolite, ONT-993. Parts D and E assessed the effect of steady-state tucatinib on the pharmacokinetics of repaglinide (CYP2C8 substrate), tolbutamide (CYP2C9 substrate), midazolam (CYP3A4 substrate), and digoxin (P-glycoprotein substrate).Results: Tucatinib area under the concentration-time curve from time 0 extrapolated to infinity (AUC0-inf) increased by ~ 1.3- and 3.0-fold with itraconazole and gemfibrozil, respectively, and decreased by 48% with rifampin, indicating that tucatinib is metabolized primarily by CYP2C8, and to a lesser extent via CYP3A. Tucatinib was a strong inhibitor of CYP3A (midazolam AUC0-inf increased 5.7-fold), a weak inhibitor of CYP2C8 and P-glycoprotein, and had no impact on CYP2C9-mediated metabolism in humans. Tucatinib was well tolerated, alone and with co-administered drugs.Conclusion: The potential DDIs identified here may be mitigated by avoiding concomitant use of tucatinib with strong CYP3A inducers, moderate CYP2C8 inducers, CYP3A substrates with a narrow therapeutic window (modifying substrate dose where concomitant use is unavoidable), and strong CYP2C8 inhibitors (decreasing tucatinib dose where concomitant use is unavoidable), or by reducing the dose of P-glycoprotein substrates with a narrow therapeutic window.Trial Registration: This trial (NCT03723395) was registered on October 29, 2018. [ABSTRACT FROM AUTHOR]

Background: Numerous drugs have the potential to be affected by cytochrome P450 (CYP) 2B6-mediated drug-drug interactions (DDIs).Objectives: In this work, we extend a static approach to the prediction of the extent of pharmacokinetics DDIs between substrates and inhibitors or inducers of CYP2B6.Methods: This approach is based on the calculation of two parameters (the contribution ratio [CR], representing the fraction of dose of the substrate metabolized via this pathway and the inhibitory or inducing potency of the perpetrator [IR or IC, respectively]) calculated from the area under the concentration-time curve (AUC) ratios obtained in in-vivo DDI studies.Results: Forty-eight studies involving 5 substrates, 11 inhibitors and 18 inducers of CYP2B6 (overall 15 inhibition and 33 induction studies) were divided into test and validation sets and considered for estimation of the parameters. The proposed approach demonstrated a fair accuracy for predicting the extent of DDI related to CYP2B6 inhibition and induction, all predictions related to the validation test (N = 18) being 50-200% of the observed ratios.Conclusions: This methodology can be used for proposing initial dose adaptations to be adopted, for example in clinical use or for designing DDI studies involving this enzyme. [ABSTRACT FROM AUTHOR]

Ritonavir-boosted nirmatrelvir (RBN) has been authorized recently in several countries as an orally active anti-SARS-CoV-2 treatment for patients at high risk of progressing to severe COVID-19 disease. Nirmatrelvir is the active component against the SARS-CoV-2 virus, whereas ritonavir, a potent CYP3A inhibitor, is intended to boost the activity of nirmatrelvir by increasing its concentration in plasma to ensure persistence of antiviral concentrations during the 12-hour dosing interval. RBN is involved in many clinically important drug-drug interactions both as perpetrator and as victim, which can complicate its use in patients treated with antiseizure medications (ASMs). Interactions between RBN and ASMs are bidirectional. As perpetrator, RBN may increase the plasma concentration of a number of ASMs that are CYP3A4 substrates, possibly leading to toxicity. As victims, both nirmatrelvir and ritonavir are subject to metabolic induction by concomitant treatment with potent enzyme-inducing ASMs (carbamazepine, phenytoin, phenobarbital and primidone). According to US and European prescribing information, treatment with these ASMs is a contraindication to the use of RBN. Although remdesivir is a valuable alternative to RBN, it may not be readily accessible in some settings due to cost and/or need for intravenous administration. If remdesivir is not an appropriate option, either bebtelovimab or molnupiravir may be considered. However, evidence about the clinical efficacy of bebtelovimab is still limited, and molnupiravir, the only orally active alternative, is deemed to have appreciably lower efficacy than RBN and remdesivir. [ABSTRACT FROM AUTHOR]

Background: Cariprazine is metabolised mainly by CYP3A4 and to a lesser extent by CYP2D6., Aim: This study aimed to evaluate the effects of erythromycin, a moderate cytochrome P450 (CYP)3A4 inhibitor, on the pharmacokinetics of cariprazine in male patients with schizophrenia, and to assess the influence of CYP2D6 phenotypes on cariprazine metabolism., Methods: Forty-two patients received oral doses of 1.5 mg cariprazine alone for 28 days (to reach steady state), followed by a co-administration of cariprazine 1.5 mg daily with erythromycin 500 mg twice daily (BID) and Enterol 250 mg BID for 21 days, followed by a 14-day post-treatment period. Blood samples were collected at predefined time points and analysed for cariprazine, its two active metabolites: desmethyl cariprazine (DCAR) and didesmethyl cariprazine (DDCAR), and erythromycin using validated high performance liquid chromatography-tandem mass spectrometry methods. CYP2D6 phenotypes were determined by genotyping. The pharmacokinetic parameters were calculated using non-compartmental analysis., Results: Erythromycin increased the area under the curve (AUC τ ) and peak concentration (C max ) of Total cariprazine (cariprazine + DCAR + DDCAR) by about 40-50% but did not affect the time to peak concentration (T max ). The CYP2D6 phenotypes had no substantial effect on the pharmacokinetics of cariprazine and its metabolites, either alone or in combination with erythromycin. Cariprazine was well tolerated and safe., Conclusion: The findings suggest that co-administration of cariprazine with moderate CYP3A4 inhibitors may require dose adjustment or monitoring; however, pharmacogenetic testing for CYP2D6 is not necessary for optimising cariprazine therapy., Trial Registration: Trial registration number (EudraCT Number): 2018-003721-28. Date of registration: 21-SEP-2018., (© 2024. The Author(s).)

Background and Objective: Firsocostat is an oral, liver-targeted inhibitor of acetyl-CoA carboxylase in clinical development for the treatment of metabolic dysfunction-associated steatohepatitis. This work evaluated the potential drug-drug interactions (DDIs) of firsocostat as a victim and as a perpetrator, to inform concomitant medication use., Methods: In this phase I study, healthy participants (n = 13-30 in each of four cohorts) received firsocostat alone or in combination with either victims or perpetrators of cytochrome P450 (CYP) enzymes and drug transporters to evaluate firsocostat as both a victim and perpetrator of DDIs, respectively., Results: Overall, 80 participants completed the study. As a victim of DDI, firsocostat plasma exposure (area under the plasma concentration-time curve [AUC] from 0 to infinity [AUC ∝ ]) was 19-fold, 22-fold, 63%, and 38% higher when administered with single-dose rifampin 600 mg (organic anion transporting polypeptide [OATP] 1B1/B3 inhibitor), single-dose cyclosporine A 600 mg (OATP/P-glycoprotein/CYP3A inhibitor), multiple-dose probenecid 500 mg twice daily (evaluated as a uridine diphosphate glucuronosyltransferase [UGT] inhibitor), and multiple-dose voriconazole 200 mg twice daily (CYP3A inhibitor), respectively, compared with the administration of firsocostat alone. As a perpetrator of DDI, multiple-dose administration of firsocostat did not affect the exposure of midazolam 2 mg (CYP3A substrate) or drospirenone/ethinylestradiol 3 mg/0.02 mg (combined oral contraceptive). Study treatments were well-tolerated and all adverse events were mild., Conclusions: Firsocostat can be administered with CYP3A and UGT inhibitors without dose adjustment. However, firsocostat should not be coadministered with strong OATP1B/3 inhibitors, such as rifampin and cyclosporine A. Firsocostat can be administered with CYP3A substrates or combined oral contraceptives without dose modification., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

This letter is a response to a recent review on the management of cancer-associated thrombosis (CAT). The authors suggest several additional considerations for CAT management. They emphasize the importance of considering frailty metrics and the holistic nature of frailty in treatment decisions. They also discuss the metabolism of direct oral anticoagulants (DOACs) and the impact of drug interactions on DOAC plasma exposure. Additionally, they refer readers to a post hoc analysis of a clinical trial that challenges the hypothesis of increased risk in CAT patients. Overall, the letter aims to deepen understanding of appropriate anticoagulation strategies in CAT. [Extracted from the article]

Background and Objective: A physiologically based pharmacokinetic (PBPK) modeling approach for esketamine and its metabolite noresketamine after esketamine intranasal administration was developed to aid the prediction of drug-drug interactions (DDIs) during the clinical development of esketamine nasal spray (SPRAVATO®). This article describes the development of the PBPK model to predict esketamine and noresketamine kinetics after intranasal administration of esketamine and its verification and application in the prediction of prospective DDIs with esketamine using models of index perpetrator and victim drugs.Methods: The intranasal PBPK (IN-PBPK) models for esketamine/noresketamine were constructed in Simcyp® v14.1 by combining the oral and intravenous esketamine PBPK models, with the dose divided in the ratio 57.7/42.3. Verification of the model was based on comparing the pharmacokinetics and DDI simulations with observed data in healthy volunteers.Results: The simulated and observed (171 healthy volunteers) plasma pharmacokinetic profiles of intranasal esketamine/noresketamine showed a good match. The relative contributions of different cytochromes P450 (CYPs), mainly CYP3A4 and CYP2B6, involved in esketamine/noresketamine clearance was captured correctly in the IN-PBPK model using the DDI clinical studies of intranasal esketamine with clarithromycin and rifampicin and a published DDI study of oral esketamine with ticlopidine. The induction potential of esketamine toward CYP3A4 was also well captured. Inhibition of intranasal esketamine in the presence of ticlopidine was predicted to be not clinically relevant. Different scenarios tested with esketamine as a CYP3A4 perpetrator of midazolam also predicted the absence of clinically relevant CYP3A4 interactions.Conclusion: This PBPK model of the intranasal route adequately described the pharmacokinetics and DDI of intranasal esketamine/noresketamine with potential perpetrator and victim drugs. This work was used to support regulatory submissions of SPRAVATO®. [ABSTRACT FROM AUTHOR]

Filgotinib (GS-6034, formerly GLPG0634; Jyseleca®) is an oral, preferential Janus kinase (JAK)-1 inhibitor. Preferential inhibition of JAK1 modulates a subset of proinflammatory cytokines within the JAK–signal transducer and activator of transcription pathway, which differ from those inhibited by JAK2 or JAK3. Filgotinib is absorbed extensively and rapidly after oral dosing and is metabolized by carboxylesterase isoform 2 to form its primary active metabolite, GS-829845. The primary metabolite has a similar JAK1 selectivity profile but reduced activity (by 10-fold) and increased systemic exposure (approximately 16- to 20-fold) compared with the parent compound. Both the parent and the metabolite demonstrate low binding to plasma proteins in humans (< 60%). Systemic exposures of filgotinib and its primary metabolite increase dose proportionally over a 50- to 200-mg once-daily dose range. Food does not affect the pharmacokinetics of filgotinib. Consistent with their terminal elimination half-lives (4.9–10.7 h for filgotinib and 19.6–27.3 h for the primary metabolite), steady state in plasma is reached by day 2 for filgotinib and day 4 for its metabolite. Filgotinib is mainly eliminated in the urine as the metabolite (> 80%). Intrinsic factors such as age, sex, race, mild renal impairment, and mild-to-moderate hepatic impairment have either no or minimal impact on the pharmacokinetics of filgotinib and its primary metabolite. Filgotinib has a low drug–drug interaction potential, without clinically significant interactions with commonly coadministered medications in patients with inflammatory diseases. Both filgotinib and its primary metabolite are substrates of P-glycoprotein (P-gp); however, coadministration with P-gp inhibitors and inducers does not affect filgotinib pharmacokinetics sufficiently to warrant dose adjustment. Neither filgotinib nor its primary metabolite affect the corrected QT interval (calculated using Fridericia's correction formula). Filgotinib is approved for the treatment of rheumatoid arthritis and ulcerative colitis in Europe, the UK, and Japan. [ABSTRACT FROM AUTHOR]

In recent years, many studies on population pharmacokinetics of linezolid have been conducted. This comprehensive review aimed to summarize population pharmacokinetic models of linezolid, by focusing on dosage optimization to maximize the probability of attaining a certain pharmacokinetic-pharmacodynamic parameter in special populations. We searched the PubMed and EMBASE databases for population pharmacokinetic analyses of linezolid using a parametric non-linear mixed-effect approach, including both observational and prospective trials. Of the 32 studies, 26 were performed in adults, four in children, and one in both adults and children. High between-subject variability was determined in the majority of the models, which was in line with the variability of linezolid concentrations previously detected in observational studies. Some studies found that patients with renal impairment, hepatic failure, advanced age, or low body weight had higher exposure and adverse reactions rates. In contrast, lower concentrations and therapeutic failure were associated with obese patients, young patients, and patients who had undergone renal replacement techniques. In critically ill patients, the inter-individual and intra-individual variability was even greater, suggesting that this population is at an even higher risk of underexposure and overexposure. Therapeutic drug monitoring may be warranted in a large proportion of patients given that the Monte Carlo simulations demonstrated that the one-size-fits-all labeled dosing of 600 mg every 12 h could lead to toxicity or therapeutic failure for high values of the minimum inhibitory concentration of the target pathogen. Further research on covariates, including renal function, hepatic function, and drug–drug interactions related to P-glycoprotein could help to explain variability and improve linezolid dosing regimens. [ABSTRACT FROM AUTHOR]

Background and Objective: Abrocitinib is a Janus kinase 1 inhibitor in development for the treatment of atopic dermatitis (AD). This work characterized orally administered abrocitinib population pharmacokinetics in healthy individuals, patients with psoriasis, and patients with AD and the effects of covariates on abrocitinib exposure. Methods: Abrocitinib concentration measurements (n = 6206) from 995 individuals from 11 clinical trials (seven phase I, two phase II, and two phase III) were analyzed, and a non-linear mixed-effects model was developed. Simulations of abrocitinib dose proportionality and steady-state accumulation of maximal plasma drug concentration (Cmax) and area under the curve (AUC) were conducted using the final model. Results: A two-compartment model with parallel zero- and first-order absorption, time-dependent bioavailability, and time- and dose-dependent clearance best described abrocitinib pharmacokinetics. Abrocitinib coadministration with rifampin resulted in lower exposure, whereas Asian/other race coadministration with fluconazole and fluvoxamine, inflammatory skin conditions (psoriasis/AD), and hepatic impairment resulted in higher exposure. After differences in body weight are accounted for, Asian participants demonstrated a 1.43- and 1.48-fold increase in Cmax and AUC, respectively. The overall distribution of exposures (Cmax and AUC) was similar in adolescents and adults after accounting for differences in total body weight. Conclusions: A population pharmacokinetics model was developed for abrocitinib that can be used to predict abrocitinib steady-state exposure in the presence of drug–drug interaction effects or intrinsic patient factors. Key covariates in the study population accounting for variability in abrocitinib exposures are Asian race and adolescent age, although these factors are not clinically meaningful. Clinical Trial Numbers: NCT01835197, NCT02163161, NCT02201524, NCT02780167, NCT03349060, NCT03575871, NCT03634345, NCT03637790, NCT03626415, NCT03386279, NCT03937258. Plain Language Summary: Abrocitinib is a drug approved in the UK and Japan for the treatment of atopic dermatitis. A population pharmacokinetic model for abrocitinib was developed based on data from 11 clinical trials that included 995 healthy individuals or patients with atopic dermatitis or psoriasis. Simulations of different patient factors, such as age, race, sex, body weight, liver function, and drug–drug interactions, were tested to examine differences in abrocitinib drug levels achieved in the body. The results of these simulations indicate that although there are some differences in abrocitinib exposure, no dose adjustments of abrocitinib are necessary based on these factors. [ABSTRACT FROM AUTHOR]

Drug-drug interactions were graded into four levels: red flags for contraindicated drug associations that may have deleterious consequences; amber for potential clinically relevant interactions manageable by close monitoring or dosage adjustment; yellow for interactions of weak intensity with no need for additional monitoring or dosage adjustment; and green for no clinically significant interaction expected. Table 2 provides the drug-drug interaction potential as well as dosing and monitoring recommendations for selected comedications. With the exception of vinblastine (inhibitor of arylacetamide deacetylase [[17]]) and paracetamol (inhibitor of arylacetamide deacetylase [[18]]), few drugs were shown to inhibit these enzymes and therefore rifapentine and isoniazid have a low potential to be impacted by other drugs. Drug-Drug Interaction Potential with Once-Weekly Isoniazid/Rifapentine (3HP) for the Treatment of Latent Tuberculosis Infection. [Extracted from the article]

Objectives: The aim of this study was to simulate the drug–drug interaction (DDI) between ritonavir-boosted atazanavir (ATV/r) and rifampicin (RIF) using physiologically based pharmacokinetic (PBPK) modelling, and to predict suitable dose adjustments for ATV/r for the treatment of people living with HIV (PLWH) co-infected with tuberculosis. Methods: A whole-body DDI PBPK model was designed using Simbiology 9.6.0 (MATLAB R2019a) and verified against reported clinical data for all drugs administered alone and concomitantly. The model contained the induction mechanisms of RIF and ritonavir (RTV), the inhibition effect of RTV for the enzymes involved in the DDI, and the induction and inhibition mechanisms of RIF and RTV on the uptake and efflux hepatic transporters. The model was considered verified if the observed versus predicted pharmacokinetic values were within twofold. Alternative ATV/r dosing regimens were simulated to achieve the trough concentration (Ctrough) clinical cut-off of 150 ng/mL. Results: The PBPK model was successfully verified according to the criteria. Simulation of different dose adjustments predicted that a change in regimen to twice-daily ATV/r (300/100 or 300/200 mg) may alleviate the induction effect of RIF on ATV Ctrough, with > 95% of individuals predicted to achieve Ctrough above the clinical cut-off. Conclusions: The developed PBPK model characterized the induction-mediated DDI between RIF and ATV/r, accurately predicting the reduction of ATV plasma concentrations in line with observed clinical data. A change in the ATV/r dosing regimen from once-daily to twice-daily was predicted to mitigate the effect of the DDI on the Ctrough of ATV, maintaining plasma concentration levels above the therapeutic threshold for most patients. [ABSTRACT FROM AUTHOR]

Background and objectivesIn the proximal tubule, basic drugs are transported from the renal cells to the tubule lumen through the concerted action of the H(+)/organic cation antiporters, multidrug and toxin extrusion (MATE) 1 and MATE2K. Dual inhibitors of the MATE transporters have been shown to have a clinically relevant effect on the pharmacokinetics of concomitantly administered basic drugs. However, the clinical impact of selective renal organic cation transport inhibition on the pharmacokinetics and pharmacodynamics of basic drugs, such as metformin, is unknown. This study sought to identify a selective MATE2K inhibitor in vitro and to determine its clinical impact on the pharmacokinetics and pharmacodynamics of metformin in healthy subjects.MethodsStrategic cell-based screening of 71 US Food and Drug Administration (FDA)-approved medications was conducted to identify selective inhibitors of renal organic cation transporters that are capable of inhibiting at clinically relevant concentrations. From this screen, nizatidine was identified and predicted to be a clinically potent and selective inhibitor of MATE2K-mediated transport. The effect of nizatidine on the pharmacokinetics and pharmacodynamics of metformin was evaluated in 12 healthy volunteers in an open-label, randomized, two-phase crossover drug-drug interaction (DDI) study.ResultsIn healthy volunteers, the MATE2K-selective inhibitor nizatidine significantly increased the apparent volume of distribution, half-life, and hypoglycemic activity of metformin. However, despite achieving unbound maximum concentrations greater than the in vitro inhibition potency (concentration of drug producing 50% inhibition [IC50]) of MATE2K-mediated transport, nizatidine did not affect the renal clearance (CLR) or net secretory clearance of metformin.ConclusionThis study demonstrates that a selective inhibition of MATE2K by nizatidine affected the apparent volume of distribution, tissue concentrations, and peripheral effects of metformin. However, nizatidine did not alter systemic concentrations or the CLR of metformin, suggesting that specific MATE2K inhibition may not be sufficient to cause renal DDIs with metformin.

Cladribine is a nucleoside analog that is phosphorylated in its target cells (B and T-lymphocytes) to its active triphosphate form (2-chlorodeoxyadenosine triphosphate). Cladribine tablets 10 mg (Mavenclad®), administered for up to 10 days per year in 2 consecutive years (3.5-mg/kg cumulative dose over 2 years), are used to treat patients with relapsing multiple sclerosis. Cladribine has been shown to be a substrate of various nucleoside transporters (NTs). Intestinal absorption and distribution of cladribine throughout the body appear to be essentially mediated by equilibrative NTs (ENTs) and concentrative NTs (CNTs), specifically by ENT1, ENT2, ENT4, CNT2 (low affinity), and CNT3. Other efficient transporters of cladribine are the ABC efflux transporters, specifically breast cancer resistance protein, which likely modulates the oral absorption and renal excretion of cladribine. A key transporter for the intracellular uptake of cladribine into B and T-lymphocytes is ENT1 with ancillary contributions of ENT2 and CNT2. Transporter-based drug interactions affecting absorption and target cellular uptake of a prodrug such as cladribine are likely to reduce systemic bioavailability and target cell exposure, thereby possibly hampering clinical efficacy. In order to manage optimized therapy, i.e., to ensure uncompromised target cell uptake to preserve the full therapeutic potential of cladribine, it is important that clinicians are aware of the existence of NT-inhibiting medicinal products, various lifestyle drugs, and food components. This article reviews the existing knowledge on inhibitors of NT, which may alter cladribine absorption, distribution, and uptake into target cells, thereby summarizing the existing knowledge on optimized methods of administration and concomitant drugs that should be avoided during cladribine treatment. [ABSTRACT FROM AUTHOR]

Background: Factor Xa inhibitors (FXaIs) are increasingly used without having sufficient drug–drug interaction data. Using a microdosed cocktail methodology could support filling the knowledge gap quickly. Methods: In a randomised crossover trial, we investigated the drug–drug interactions between six oral azole antifungals and a microdosed FXaI cocktail containing 25 µg rivaroxaban, 25 µg apixaban, and 50 µg edoxaban. Additionally, different enzyme activities were also monitored using a microdosed cocktail approach. The six different azole antifungals were administered in therapeutic doses over a 24 h period, while the microdosed cocktails were administered 1 h after administration of the azole antifungals. Results: Ketoconazole and posaconazole were the strongest perpetrators, showing similar increases as apixaban (area under the concentration–time curve ratio [AUCR] 1.64 and 1.62, respectively) and edoxaban (AUCR 2.08 and 2.1, respectively), whereas ketoconazole increased rivaroxaban 2.32-fold but only increased posaconazole 1.37-fold. All other azole antifungals showed less perpetrator effects on the FXaIs. Cytochrome P450 (CYP) 3A inhibition was confirmed using microdosed midazolam, with ketoconazole also the most potent perpetrator (8.42-fold). Conclusion: Drug–drug interactions for three victim drugs of the same drug class (FXaIs) with different clearance mechanisms can be studied using a microdosed cocktail approach. Using members of the azole antifungal drug class as perpetrators, multiple interactions can be studied in one trial, and a more detailed insight into the underlying interaction mechanisms is possible. Clinical Trial Registration: EudraCT number: 2017-004453-16. [ABSTRACT FROM AUTHOR]

Background and Objective: Cytochromes P450 (CYP) are the major enzymes involved in hepatic metabolism of drugs. Personalization of treatment in pediatrics is a major challenge, as it must not only take into account genetic, environmental, and physiological factors but also ontogeny. Published data in adults show that inflammation had an isoform-specific impact on CYP activities and we aimed to evaluate this impact in the pediatric population. Methods: Articles listed in PubMed through 7 January, 2021 that studied the impact of inflammation on CYP activities in pediatrics were included in this systematic review. Sources of inflammation, victim drugs (CYP involved), effect of drug–disease interactions, number and age of subjects, and study design were extracted. Results: Twenty-seven studies and case reports were included. The impact of inflammation on CYP activities appeared to be age dependent and isoform-specific, with some drug–disease interactions having significant pharmacokinetic and clinical impact. For example, midazolam clearance decreases by 70%, while immunosuppressant and theophylline concentrations increase three-fold and two-fold with intensive care unit admission and infection. Cytochrome P450 activity appears to return to baseline level when the disease is resolved. Conclusions: Studies that have assessed the impact of inflammation on CYP activity are lacking in pediatrics, yet it is a major factor to consider to improve drug efficacy or safety. The scarce current data show that the impact of inflammation is isoform and age dependent. An effort must be made to improve the understanding of the impact of inflammation on CYP activities in children to better individualize treatment. [ABSTRACT FROM AUTHOR]

Letermovir is a newly developed antiviral agent used for the prophylaxis of human cytomegalovirus infections in patients undergoing allogeneic hematopoietic cell transplantation. This novel anti-cytomegalovirus drug, used for the prophylaxis of cytomegalovirus reactivation until approximately 200 days after transplantation, effectively reduces the risk of clinically significant cytomegalovirus infection. No human counterpart exists for the terminase complex; letermovir is virus specific and lacks some toxicities previously observed with other anti-cytomegalovirus drugs, such as cytopenia and nephrotoxicity. The absolute bioavailability of letermovir in healthy individuals is estimated to be 94% based on a population-pharmacokinetic analysis. In contrast, oral administration of letermovir to patients undergoing hematopoietic cell transplantation results in lower exposure than that in healthy individuals. Renal or hepatic impairment does not influence the intrinsic clearance of letermovir. Co-administration of letermovir may alter the plasma concentrations of other drugs, including itself, as it acts as a substrate and inhibitor/inducer of several drug-metabolizing enzymes and transporters. In particular, attention should be paid to the drug-drug interactions between letermovir and calcineurin inhibitors or azole antifungal agents, which are commonly used in patients undergoing hematopoietic cell transplantation. This article reviews and summarizes the clinical pharmacokinetics and pharmacodynamics of letermovir, focusing on patients undergoing hematopoietic cell transplantation, healthy individuals, and specific patient subsets., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Background: Endogenous biomarkers are promising tools to assess transporter-mediated drug–drug interactions early in humans. Methods : We evaluated on a common and validated in vitro system the selectivity of 4-pyridoxic acid (PDA), homovanillic acid (HVA), glycochenodeoxycholate-3-sulphate (GCDCA-S) and taurine towards different renal transporters, including multidrug resistance-associated protein, and assessed the in vivo biomarker sensitivity towards the strong organic anion transporter (OAT) inhibitor probenecid at 500 mg every 6 h to reach close to complete OAT inhibition. Results : PDA and HVA were substrates of the OAT1/2/3, OAT4 (PDA only) and multidrug resistance-associated protein 4; GCDCA-S was more selective, having affinity only towards OAT3 and multidrug resistance-associated protein 2. Taurine was not a substrate of any of the investigated transporters under the in vitro conditions tested. Plasma exposure of PDA and HVA significantly increased and the renal clearance of GCDCA-S, PDA and HVA decreased; the magnitude of these changes was comparable to those of known clinical OAT probe substrates. PDA and GCDCA-S were the most promising endogenous biomarkers of the OAT pathway activity: PDA plasma exposure was the most sensitive to probenecid inhibition, and, in contrast, GCDCA-S was the most sensitive OAT biomarker based on renal clearance, with higher selectivity towards the OAT3 transporter. Conclusions: The current findings illustrate a clear benefit of measuring PDA plasma exposure during phase I studies when a clinical drug candidate is suspected to be an OAT inhibitor based on in vitro data. Subsequently, combined monitoring of PDA and GCDCA-S in both urine and plasma is recommended to tease out the involvement of OAT1/3 in the inhibition interaction. Clinical Trial Registration: EudraCT number: 2016-003923-49. [ABSTRACT FROM AUTHOR]

Background: Two pharmacokinetic/pharmacodynamic studies were conducted to evaluate the potential drug–drug interaction between elagolix, an oral gonadotropin-releasing hormone receptor antagonist, and an oral contraceptive (ethinylestradiol [EE] 0.035 mg and norgestimate 0.18/0.215/0.25 mg) or progestin-only contraceptive (norethindrone 0.35 mg) in healthy premenopausal women. Methods: These phase I studies used a two-period, sequential design, where period 1 included treatment with oral contraceptives, followed by period 2 with contraceptives coadministered with elagolix 150 mg once daily. Results: In study 1, pharmacokinetic exposures for EE in period 2 increased by 30% and the norgestimate metabolites decreased by approximately 15% when coadministered with elagolix. Mean hormone exposure appeared lower for follicle-stimulating hormone (FSH; 31%), luteinizing hormone (LH; 38%), and estradiol (E2; 16%). The percentage of women with consecutive progesterone (P) concentrations above 5 nmol/L was similar in both periods. Norethindrone pharmacokinetic exposures were comparable in both periods. The hormone exposure for LH and FSH was similar, and mean E2 exposure was 32% lower in period 2. The percentage of subjects with consecutive ovulatory P concentrations was also similar in both periods (study 2). Safety and tolerability profiles were unremarkable in both studies. Conclusions: Coadministration of elagolix 150 mg once daily with oral contraceptives containing EE and norgestimate, or norethindrone, resulted in small pharmacokinetic changes in the oral contraceptive components. Similar or lower FSH, LH, and E2 exposures were observed during coadministration, with ovulatory P concentrations also comparable in both periods. The pharmacodynamic profiles of the oral contraceptives were maintained when coadministered with elagolix. [ABSTRACT FROM AUTHOR]