Proposal of a Safe and Effective Study Design for CYP3A-Mediated Drug-Drug Interactions

Physiologically Based Pharmacokinetic Modeling to Predict Drug-Drug Interactions of Soticlestat as a Victim of CYP Induction and Inhibition, and as a Perpetrator of CYP and P-Glycoprotein Inhibition

Soticlestat (TAK-935) is a cholesterol 24-hydroxylase inhibitor. A physiologically-based pharmacokinetic model has been developed to predict potential soticlestat drug-drug interactions (DDIs) using the Simcyp v20 Population-based Simulator and verified with data from single-/multiple-rising-dose and clinical DDI studies. Simulated area under the plasma concentration-time curve from 0 to infinity (AUC0-inf) and maximal drug concentration (Cmax) based on the model were generally within 2-fold of observed values for all soticlestat doses. Model-simulated versus observed AUC0-inf and Cmax geometric mean ratios (GMRs) for soticlestat with/without itraconazole (potent cytochrome P450 [CYP] 3A inhibitor), and mefenamic acid (potent UDP glucuronosyltransferase [UGT] 1A9 inhibitor) were ≤1.10-fold. As soticlestat is primarily metabolized by UGT enzymes and Simulator v20 incorporates rifampin's induction of CYP3A only, the model underpredicted soticlestat's DDI with rifampin. However, with user-defined rifampin UGT induction, the predicted AUC0-inf GMR was within 1.5-fold of the observed value, meeting the 2-fold acceptance criteria. Hence, the model was appropriate for evaluating DDIs with CYP3A inhibitors and inducers not evaluated in clinical DDI studies; all predicted DDIs were low/not clinically relevant (<50% impact on exposure). Furthermore, no clinically significant DDIs were predicted following coadministration of soticlestat with sensitive CYP2C8, CYP2C9, CYP2C19, CYP3A4, and P-glycoprotein substrates.

Pharmacokinetic Boosting of Calcineurin Inhibitors in Transplantation: Pros, Cons, and Perspectives

ABSTRACT

The concept of pharmacokinetic (PK) boosting of calcineurin inhibitors (CNI) emerged after the FDA approval of cyclosporine-A. Several studies followed, and the proof of concept was well established by the late 1990s. This also continued for the next blockbuster immunosuppressant, tacrolimus. The driver for such research was an endeavor to save costs, as both drugs were expensive due to patent protection. Two CYP inhibitors, ketoconazole and diltiazem, have been extensively studied in this context and continue to be prescribed off-label along with the CNI. It has been observed that using ketoconazole reduces the dose requirement of tacrolimus by about 50% and 30% with diltiazem, which is in conformity with their pharmacological actions. Off-label co-prescription of these drugs with CNI is often encountered in low and middle-income countries. The foremost reason cited is economic. This article collates the evidence from the clinical studies that evaluate the PK-boosting effects of CNI and also reviews the gaps in the current evidence base. The current knowledge prevents the transplant community from making meaningful inferences about the risks and benefits of such strategies. Although the PK-boosting strategy can lead to serious adverse events, emerging evidence suggests that it may be advantageous for individuals with high CNI dose requirements. Hence, PK boosting may be an unmet need in the therapeutics of CNI. Nevertheless, there are several unanswered questions surrounding such use, and therefore, this merits testing in well-designed clinical studies. Moreover, drugs with better safer profiles and a history of successful PK boosting may be considered for evaluation with CNI.

Evaluation of drug-drug interactions of a novel potent FLT3 inhibitor SKLB1028 in healthy subjects

SKLB1028 is a novel multi-target protein kinase inhibitor under investigation for the treatment of FLT3-ITD mutated acute myeloid leukemia. Based on the preclinical characterization of SKLB1028 metabolism, three drug-drug interaction clinical studies were performed to investigate the effects of itraconazole, rifampin (CYP3A4 inhibitor and inducer, respectively), and gemfibrozil (CYP2C8 inhibitor) on the metabolism of SKLB1028. Fourteen healthy Chinese male subjects were enrolled in each study. In Study 1, subjects were administered a single dose of SKLB1028 (100 mg on days 1 and 11) and multiple doses of itraconazole (200 mg twice daily on day 8 and 200 mg once daily from days 9 to 18). Itraconazole was given with a loading dose on Day 8 and the total administration of itraconazole was 11 days. In Study 2, subjects were administered a single dose of SKLB1028 (100 mg on days 1 and 12) and multiple doses of gemfibrozil (600 mg twice daily from days 8 to 19). In Study 3, subjects were administered a single dose of SKLB1028 (150 mg on days 1 and 15) and multiple doses of rifampin (600 mg once daily from day 8 to 22). Itraconazole increased the AUC and Cmax of SKLB1028 by approximately 28% and 41%, respectively. Compared to the single drug, co-administration with gemfibrozil increased the AUC of SKLB1028 by ~26% and the Cmax by ~21%. Co-administration with rifampin reduced the AUC of SKLB1028 by ~30%, while the Cmax did not change significantly. All treatments were well tolerated in all three studies.

An open-label study to explore the optimal design of CYP3A drug-drug interaction clinical trials in healthy Chinese people

A drug-drug interaction (DDI) trial of cytochrome P450 3A (CYP3A) is a necessary part of early-phase trials of drugs mainly metabolized by this enzyme, but CYP3A DDI clinical trials do not have a standard design, especially for Chinese people. We aimed to offer specific recommendations for CYP3A DDI clinical trial design. This was an open, three-cycle, self-controlled study. Healthy subjects were given different administration strategies of CYP3A4 perpetrators. In each cycle, blood samples were collected before and within 24 h after the administration of midazolam, the CYP3A indicator substrate. The plasma concentrations of midazolam and 1-hydroxymidazolam was obtained using liquid chromatography tandem mass spectrometry assay. For CYP3A inhibition, itraconazole exposure with a loading dose could increase the exposure of midazolam by 3.21-fold based on maximum plasma concentration (Cmax), 8.37-fold based on area under the curve Pharmacology Research & Perspectives for review only from zero to the time point (AUC0-t), and 11.22-fold based on area under the curve from zero to infinity (AUC0-∞). The data were similar for itraconazole pretreatment without a loading dose. For CYP3A induction, the exposure of rifampin for 7 days decreased the plasma concentration of midazolam ~0.27-fold based on Cmax, ~0.18-fold based on AUC0-t, and ~0.18-fold based on AUC0-∞. Midazolam exposure did not significantly change when the pretreatment of rifampin increased to 14 days. This study showed that itraconazole pretreatment for 3 days without a loading dose was enough for CYP3A inhibition, and pretreatment with rifampin for 7 days could induce near-maximal CYP3A levels.

The Use of Microdosing for In vivo Phenotyping of Cytochrome P450 Enzymes: Where Do We Stand? A Narrative Review

Cytochrome P450 (CYP) enzymes play a central role in the elimination of approximately 80% of all clinically used drugs. Differences in CYP enzyme activity between individuals can contribute to interindividual variability in exposure and, therefore, treatment outcome. In vivo CYP enzyme activity could be determined with phenotyping. Currently, (sub)therapeutic doses are used for in vivo phenotyping, which can lead to side effects. The use of microdoses (100 µg) for in vivo phenotyping for CYP enzymes could overcome the limitations associated with the use of (sub)therapeutic doses of substrates. The aim of this review is to provide a critical overview of the application of microdosing for in vivo phenotyping of CYP enzymes. A literature search was performed to find drug-drug interaction studies of CYP enzyme substrates that used microdoses of the respective substrates. A substrate was deemed sensitive to changes in CYP enzyme activity when the pharmacokinetics of the substrate significantly changed during inhibition and induction of the enzyme. On the basis of the currently available evidence, the use of microdosing for in vivo phenotyping for subtypes CYP1A2, CYP2C9, CYP2D6, and CYP2E1 is not recommended. Microdosing can be used for the in vivo phenotyping of CYP2C19 and CYP3A. The recommended microdose phenotyping test for CYP2C19 is measuring the omeprazole area-under-the-concentration-time curve over 24 h (AUC0-24) after administration of a single 100 µg dose. CYP3A activity could be best determined with a 0.1-75 µg dose of midazolam, and subsequently measuring AUC extrapolated to infinity (AUC∞) or clearance. Moreover, there are two metrics available for midazolam using a limited sampling strategy: AUC over 10 h (AUC0-10) and AUC from 2 to 4 h (AUC2-4).

Pharmacokinetics, safety, and efficacy of cedirogant from phase I studies in healthy participants and patients with chronic plaque psoriasis

Cedirogant is an inverse agonist of retinoic acid-related orphan receptor gamma thymus (RORγt) developed for the treatment of moderate to severe chronic plaque psoriasis. Here, we report the results from two phase I studies in which the pharmacokinetics (PK), safety, and efficacy of cedirogant in healthy participants and patients with moderate to severe chronic plaque psoriasis were evaluated. The studies consisted of single (20-750 mg) and multiple (75-375 mg once-daily [q.d.]) ascending dose designs, with effect of food and itraconazole on cedirogant exposure also evaluated. Safety and PK were evaluated for both healthy participants and psoriasis patients, and efficacy was assessed in psoriasis patients. Following single and multiple doses, cedirogant mean terminal half-life ranged from 16 to 28 h and median time to reach maximum plasma concentration ranged from 2 to 5 h across both populations. Cedirogant plasma exposures were dose-proportional after single doses and less than dose-proportional from 75 to 375 mg q.d. doses. Steady-state concentrations were achieved within 12 days. Accumulation ratios ranged from approximately 1.2 to 1.8 across tested doses. Food had minimal effect and itraconazole had limited impact on cedirogant exposure. No discontinuations or serious adverse events due to cedirogant were recorded. Psoriasis Area and Severity Index (PASI) and Self-Assessment of Psoriasis Symptoms (SAPS) assessments demonstrated numerical improvement with treatment of cedirogant 375 mg q.d. compared with placebo. The PK, safety, and efficacy profiles of cedirogant supported advancing it to phase II clinical trial in psoriasis patients.

Studies on the inhibitory effect of isavuconazole on flumatinib metabolism in vitro and in vivo

As the validated agent for the treatment of chronic myelogenous leukemia (CML), flumatinib is a novel oral tyrosine kinase inhibitor (TKI) with higher potency and selectivity for BCR-ABL1 kinase compared to imatinib. Many patients experience aspergillosis infection and they may start using isavuconazole, which is an inhibitor of CYP3A4. However, there is no study on their interaction in vitro and in vivo. In the present study, the concentrations of flumatinib and its major metabolite M1 were rapidly determined using an stable ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) method. The half-maximal inhibitory concentration (IC₅₀) was 6.66 μM in human liver microsomes (HLM), while 0.62 μM in rat liver microsomes (RLM) and 2.90 μM in recombinant human CYP3A4 (rCYP3A4). Furthermore, the mechanisms of inhibition of flumatinib in human liver microsomes, rat liver microsomes and rCYP3A4 by isavuconazole were mixed. Moreover, ketoconazole, posaconazole, and isavuconazole showed more potent inhibitory effects than itraconazole, fluconazole, and voriconazole on HLM-mediated flumatinib metabolism. In pharmacokinetic experiments of rats, it was observed that isavuconazole could greatly change the pharmacokinetic parameters of flumatinib, including AUC_(0-t), AUC_(0-∞), C_max and CLz/F, but had no effect on the metabolism of M1. According to the results of in vitro and in vivo studies, the metabolism of flumatinib was inhibited by isavuconazole, suggesting that isavuconazole may raise the plasma concentration of flumatinib. Thus, it is important to take special care of the interactions between flumatinib and isavuconazole in clinical applications.

MecDDI: Clarified Drug-Drug Interaction Mechanism Facilitating Rational Drug Use and Potential Drug-Drug Interaction Prediction

Drug-drug interactions (DDIs) are a major concern in clinical practice and have been recognized as one of the key threats to public health. To address such a critical threat, many studies have been conducted to clarify the mechanism underlying each DDI, based on which alternative therapeutic strategies are successfully proposed. Moreover, artificial intelligence-based models for predicting DDIs, especially multilabel classification models, are highly dependent on a reliable DDI data set with clear mechanistic information. These successes highlight the imminent necessity to have a platform providing mechanistic clarifications for a large number of existing DDIs. However, no such platform is available yet. In this study, a platform entitled "MecDDI" was therefore introduced to systematically clarify the mechanisms underlying the existing DDIs. This platform is unique in (a) clarifying the mechanisms underlying over 1,78,000 DDIs by explicit descriptions and graphic illustrations and (b) providing a systematic classification for all collected DDIs based on the clarified mechanisms. Due to the long-lasting threats of DDIs to public health, MecDDI could offer medical scientists a clear clarification of DDI mechanisms, support healthcare professionals to identify alternative therapeutics, and prepare data for algorithm scientists to predict new DDIs. MecDDI is now expected as an indispensable complement to the available pharmaceutical platforms and is freely accessible at: https://idrblab.org/mecddi/.

Lycopene, Mesoporous Silica Nanoparticles and Their Association: A Possible Alternative against Vulvovaginal Candidiasis?

Commonly found colonizing the human microbiota, Candida albicans is a microorganism known for its ability to cause infections, mainly in the vulvovaginal region known as vulvovaginal candidiasis (VVC). This pathology is, in fact, one of the main C. albicans clinical manifestations, changing from a colonizer to a pathogen. The increase in VVC cases and limited antifungal therapy make C. albicans an increasingly frequent risk in women's lives, especially in immunocompromised patients, pregnant women and the elderly. Therefore, it is necessary to develop new therapeutic options, especially those involving natural products associated with nanotechnology, such as lycopene and mesoporous silica nanoparticles. From this perspective, this study sought to assess whether lycopene, mesoporous silica nanoparticles and their combination would be an attractive product for the treatment of this serious disease through microbiological in vitro tests and acute toxicity tests in an alternative in vivo model of Galleria mellonella. Although they did not show desirable antifungal activity for VVC therapy, the present study strongly encourages the use of mesoporous silica nanoparticles impregnated with lycopene for the treatment of other human pathologies, since the products evaluated here did not show toxicity in the in vivo test performed, being therefore, a topic to be further explored.

Involvement of Transporters in Intestinal Drug-Drug Interactions of Oral Targeted Anticancer Drugs Assessed by Changes in Drug Absorption Time

(1) Background: Oral targeted anticancer drugs are victims of presystemic pharmacokinetic drug−drug interactions (DDI). Identification of the nature of these DDIs, i.e., enzyme-based or/and transporter-based, is challenging, since most of these drugs are substrates of intestinal and/or hepatic cytochrome P-450 enzymes and of intestinal membrane transporters. (2) Methods: Variations in mean absorption time (MAT) between DDIs and control period (MAT ratios < 0.77 or >1.30) have been proposed to implicate transporters in DDIs at the intestinal level. This methodology has been applied to a large set of oral targeted anticancer drugs (n = 54, involved in 77 DDI studies), from DDI studies available either in the international literature and/or in publicly accessible FDA files. (3) Results: Significant variations in MAT were evidenced in 33 DDI studies, 12 of which could be explained by modulation of an efflux transporter. In 21 DDI studies, modulation of efflux transporters could not explain the MAT variation, suggesting a possible relevant role of influx transporters in the intestinal absorption. (4) Conclusions: This methodology allows one to suggest the involvement of intestinal transporters in DDIs, and should be used in conjunction with in vitro methodologies to help understanding the origin of DDIs.

Investigation of a Data Split Strategy Involving the Time Axis in Adverse Event Prediction Using Machine Learning

Adverse events are a serious issue in drug development, and many prediction methods using machine learning have been developed. The random split cross-validation is the de facto standard for model building and evaluation in machine learning, but care should be taken in adverse event prediction because this approach does not strictly match the real-world situation. The time split, which uses the time axis, is considered suitable for real-world prediction. However, the differences in model performance obtained using the time and random splits are not clear due to the lack of comparable studies. To understand the differences, we compared the model performance between the time and random splits using nine types of compound information as input, eight adverse events as targets, and six machine learning algorithms. The random split showed higher area under the curve values than did the time split for six of eight targets. The chemical spaces of the training and test datasets of the time split were similar, suggesting that the concept of applicability domain is insufficient to explain the differences derived from the splitting. The area under the curve differences were smaller for the protein interaction than for the other datasets. Subsequent detailed analyses suggested the danger of confounding in the use of knowledge-based information in the time split. These findings indicate the importance of understanding the differences between the time and random splits in adverse event prediction and suggest that appropriate use of the splitting strategies and interpretation of results are necessary for the real-world prediction of adverse events. We provide the analysis code and datasets used in the present study at https://github.com/mizuno-group/AE_prediction.

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.

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.

Perpetrator Characteristics of Azole Antifungal Drugs on Three Oral Factor Xa Inhibitors Administered as a Microdosed Cocktail

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.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40262-021-01051-9.