Prediction of the effect of voriconazole on the pharmacokinetics of non-steroidal anti-inflammatory drugs

Use of modeling and simulation to predict the influence of triazole antifungal agents on the pharmacokinetics of zanubrutinib and acalabrutinib

Background: Bruton’s tyrosine kinase (BTK) inhibitors are commonly used in the targeted therapy of B-cell malignancies. It is reported that myelosuppression and fungal infections might occur during antitumor therapy of BTK inhibitors, therefore a combination therapy with triazole antifungals is usually required.

Objective: To evaluate the influence of different triazoles (voriconazole, fluconazole, itraconazole) on the pharmacokinetics of BTK inhibitors (zanubrutinib, acalabrutinib) and to quantify the drug-drug interactions (DDIs) between them.

Methods: The physiologically-based pharmacokinetic (PBPK) models were developed based on pharmacokinetic parameters and physicochemical data using Simcyp^® software. These models were validated using clinically observed plasma concentrations data which based on existing published studies. The successfully validated PBPK models were used to evaluate and predict potential DDIs between BTK inhibitors and different triazoles. BTK inhibitors and triazole antifungal agents were simulated by oral administration.

Results: Simulated plasma concentration-time profiles of the zanubrutinib, acalabrutinib, voriconazole, fluconazole, and itraconazole are consistent with the clinically observed profiles which based on existing published studies, respectively. The exposures of BTK inhibitors increase by varying degrees when co-administered with different triazole antifungals. At multiple doses regimen, voriconazole, fluconazole and itraconazole may increase the area under plasma concentration-time curve (AUC) of zanubrutinib by 127%, 81%, and 48%, respectively, and may increase the AUC of acalabrutinib by 326%, 119%, and 264%, respectively.

Conclusion: The PBPK models sufficiently characterized the pharmacokinetics of BTK inhibitors and triazole antifungals, and were used to predict untested clinical scenarios. Voriconazole exhibited the greatest influence on the exposures of BTK inhibitors. The dosage of zanubrutinib or acalabrutinib need to be reduced when co-administered with moderate CYP3A inhibitors.

Antifungal Drugs TDM: Trends and Update

PURPOSE

The increasing burden of invasive fungal infections results in growing challenges to antifungal (AF) therapeutic drug monitoring (TDM). This review aims to provide an overview of recent advances in AF TDM.

METHODS

We conducted a PubMed search for articles during 2016-2020 using "TDM" or "pharmacokinetics" or "drug-drug-interaction" with "antifungal," consolidated for each AF. Selection was limited to English language articles with human data on drug exposure.

RESULTS

More than 1000 articles matched the search terms. We selected 566 publications. The latest findings tend to confirm previous observations in real-life clinical settings. The pharmacokinetic variability related to special populations is not specific but must be considered. AF benefit-to-risk ratio, drug-drug interaction (DDI) profiles, and minimal inhibitory concentrations for pathogens must be known to manage at-risk situations and patients. Itraconazole has replaced ketoconazole in healthy volunteers DDI studies. Physiologically based pharmacokinetic modeling is widely used to assess metabolic azole DDI. AF prophylactic use was studied more for Aspergillus spp. and Mucorales in oncohematology and solid organ transplantation than for Candida (already studied). Emergence of central nervous system infection and severe infections in immunocompetent individuals both merit special attention. TDM is more challenging for azoles than amphotericin B and echinocandins. Fewer TDM requirements exist for fluconazole and isavuconazole (ISZ); however, ISZ is frequently used in clinical situations in which TDM is recommended. Voriconazole remains the most challenging of the AF, with toxicity limiting high-dose treatments. Moreover, alternative treatments (posaconazole tablets, ISZ) are now available.

CONCLUSIONS

TDM seems to be crucial for curative and/or long-term maintenance treatment in highly variable patients. TDM poses fewer cost issues than the drugs themselves or subsequent treatment issues. The integration of clinical pharmacology into multidisciplinary management is now increasingly seen as a part of patient care.

Use of Modeling and Simulation to Predict the Influence of Triazole Antifungal Agents on the Pharmacokinetics of Crizotinib

Crizotinib is used for the treatment of c-ros oncogene 1-positive advanced non-small-cell lung cancer. Triazole antifungal agents are widely used for invasive fungal infections in clinical practice. To predict the potential influence of different triazoles (voriconazole, fluconazole, and itraconazole) on the pharmacokinetics of crizotinib by modeling and simulation the physiologically based pharmacokinetic models were established and validated in virtual cancer subjects through Simcyp software based on the essential physicochemical properties and pharmacokinetic data collected. The validated physiologically based pharmacokinetic models were applied to predict the drug-drug interactions between crizotinib and different triazoles (voriconazole, fluconazole, or itraconazole) in patients with cancer. Crizotinib and triazole antifungal agents were administered orally. The predicted plasma concentration vs time profiles of crizotinib, voriconazole, fluconazole, and itraconazole showed good agreement with observed, respectively. The geometric mean area under the plasma concentration-time curve (AUC) of crizotinib was increased by 84%, 58%, and 79% when coadministered with voriconazole, fluconazole, or itraconazole at multiple doses, respectively. The drug-drug interaction results showed increased pharmacokinetic exposure (maximum plasma concentration and area under the plasma concentration-time curve) of crizotinib when coadministrated with different triazoles (voriconazole > itraconazole > fluconazole). Among the 3 triazoles, voriconazole exhibited the most significant influence on the pharmacokinetic exposure of crizotinib. In clinic, adverse drug reactions and toxicity related to crizotinib should be carefully monitored, and therapeutic drug monitoring for crizotinib is recommended to guide dosing and optimize treatment when coadministered with voriconazole, fluconazole, or itraconazole.

The Overview on the Pharmacokinetic and Pharmacodynamic Interactions of Triazoles

Second generation triazoles are widely used as first-line drugs for the treatment of invasive fungal infections, including aspergillosis and candidiasis. This class, along with itraconazole, voriconazole, posaconazole, and isavuconazole, is characterized by a broad range of activity, however, individual drugs vary considerably in safety, tolerability, pharmacokinetics profiles, and interactions with concomitant medications. The interaction may be encountered on the absorption, distribution, metabolism, and elimination (ADME) step. All triazoles as inhibitors or substrates of CYP isoenzymes can often interact with many drugs, which may result in the change of the activity of the drug and cause serious side effects. Drugs of this class should be used with caution with other agents, and an understanding of their pharmacokinetic profile, safety, and drug-drug interaction profiles is important to provide effective antifungal therapy. The manuscript reviews significant drug interactions of azoles with other medications, as well as with food. The PubMed and Google Scholar bases were searched to collect the literature data. The interactions with anticonvulsants, antibiotics, statins, kinase inhibitors, proton pump inhibitors, non-nucleoside reverse transcriptase inhibitors, opioid analgesics, benzodiazepines, cardiac glycosides, nonsteroidal anti-inflammatory drugs, immunosuppressants, antipsychotics, corticosteroids, biguanides, and anticoagulants are presented. We also paid attention to possible interactions with drugs during experimental therapies for the treatment of COVID-19.

Novel insights into the complex pharmacokinetics of voriconazole: a review of its metabolism

Voriconazole, a second-generation triazole frequently used for the prophylaxis and treatment of invasive fungal infections, undergoes complex metabolism mainly involving various (polymorphic) cytochrome P450 enzymes in humans. Although high inter- and intraindividual variability in voriconazole pharmacokinetics have been observed and the therapeutic range for this compound is relatively narrow, the metabolism of voriconazole has not been fully elucidated yet. The available literature data investigating the multiple different pathways and metabolites are extremely unbalanced and thus the absolute or relative contribution of the different pathways and enzymes involved in the metabolism of voriconazole remains uncertain. Furthermore, other factors such as nonlinear pharmacokinetics caused by auto-inhibition or -induction and polymorphisms of the metabolizing enzymes hinder safe and effective voriconazole dosing in clinical practice and have not yet been studied sufficiently. This review aimed at amalgamating the available literature on the pharmacokinetics of voriconazole in vitro and in vivo, with a special focus on metabolism in adults and children, in order to congregate an overall landscape of the current body of knowledge and identify knowledge gaps, opening the way towards further research in order to foster the understanding, towards better therapeutic dosing decisions.

The Pharmacokinetic Prediction of Cyclosporin A after Coadministration with Wuzhi Capsule

We aim to describe the influence of principal ingredients of Wuzhi capsule, schisandrin A (SIA) and schisantherin A (STA), on the pharmacokinetics of cyclosporin A (CsA) and to quantify the herb-drug interactions (HDIs) between SIA, STA, and CsA. CsA is a first-line immunosuppressant for anti-rejection therapy after solid organ transplantation, while narrow therapeutic window associated with strong hepatotoxicity largely limited its use. Wuzhi capsule, a liver-protective drug, was approved for coadministration with CsA to reduce the hepatotoxicity. There are few studies exploring HDIs of CsA when coadministered with Wuzhi capsule. The essential adjusted physicochemical data and pharmacokinetic parameters of SIA, STA, and CsA were collected. Then physiologically based pharmacokinetic (PBPK) models of SIA, STA, and CsA were built and verified in healthy subjects using Simcyp respectively. The refined PBPK models were used to estimate potential HDIs between CsA and SIA, STA. The simulated plasma concentration-time curves of CsA, SIA, and STA were in good accordance with the observed profiles respectively. CsA pharmacokinetics were improved after coadministration. After a single dose and multiple doses, the area under the plasma concentration-time curve (AUC) of CsA was increased by 47% and 226% when coadministered with STA, respectively, and by 8% and 36% when coadministered with SIA, respectively. PBPK models sufficiently described the pharmacokinetics of CsA, SIA, and STA. Compared with SIA, STA inhibited CsA metabolism to a greater extent. Our result revealed the dose of CsA can be reduced to maintain similar profile when used concomitantly with Wuzhi capsule.