Evaluation of drug-drug interactions for oncology therapies: in vitro-in vivo extrapolation model-based risk assessment

Evaluation of BCRP-Related DDIs Between Methotrexate and Cyclosporin A Using Physiologically Based Pharmacokinetic Modelling

BACKGROUND AND OBJECTIVE

This study provides a physiologically based pharmacokinetic (PBPK) model-based analysis of the potential drug-drug interaction (DDI) between cyclosporin A (CsA), a breast cancer resistance protein transporter (BCRP) inhibitor, and methotrexate (MTX), a putative BCRP substrate.

METHODS

PBPK models for CsA and MTX were built using open-source tools and published data for both model building and for model verification and validation. The MTX and CsA PBPK models were evaluated for their application in simulating BCRP-related DDIs. A qualification of an introduced empirical uniform in vitro scaling factor of Ki values for transporter inhibition by CsA was conducted by using a previously developed model of rosuvastatin (sensitive index BCRP substrate), and assessing if corresponding DDI ratios were well captured.

RESULTS

Within the simulated DDI scenarios for MTX in the presence of CsA, the developed models could capture the observed changes in PK parameters as changes in the area under the curve ratios (area under the curve during DDI/area under the curve control) of 1.30 versus 1.31 observed and the DDI peak plasma concentration ratios (peak plasma concentration during DDI/peak plasma concentration control) of 1.07 versus 1.28 observed. The originally reported in vitro Ki values of CsA were scaled with the uniform qualified scaling factor for their use in the in vivo DDI simulations to correct for the low intracellular unbound fraction of the CsA effector concentration. The resulting predicted versus observed ratios of peak plasma concentration and area under the curve DDI ratios with MTX were 0.82 and 0.99, respectively, indicating adequate model accuracy and choice of a scaling factor to capture the observed DDI.

CONCLUSIONS

All models have been comprehensively documented and made publicly available as tools to support the drug development and clinical research community and further community-driven model development.

A single-centre retrospective evaluation of potential drug-drug interactions in breast cancer patients undergoing CDK 4/6 inhibitors chemotherapy

INTRODUCTION

The utilization of CDK4/6 inhibitors has led to compromised survival rates for breast cancer patients. Consequently, certain treatment aspects, involving adherence and drug-to-drug interactions, are gaining prominence. To develop chemotherapy regimens that are both effective and efficient, our main objective was to thoroughly characterize the drug-drug interactions that occur between cyclin-dependent kinase inhibitors and concurrently prescribed medications in hospitalized breast cancer patients.

METHODS

In the current retrospective analysis, (January 2017 to January 2023), the baseline characteristics of patients under CDK4/6 chemotherapy were collected by reviewing the patient's medical records. Utilizing drug interactions checker softwares including Micromedex® online database system, Drugs.com® interaction checker, and UpToDate Lexicomp®, the potential for drug-drug interactions was further assessed.

RESULTS

In this retrospective analysis, total of 75 co-medications were prescribed along with palbociclib and ribociclib. Upon analysing all co-prescribed classes of drugs, the potential drug interactions of palbociclib and ribociclib with analgesics, acid-reducing agents, and statins occurred frequently in cancer patients. In the 21-patient cohort, 17 patients (80.95%), were found to be having prevalence of potential drug-drug interactions out of which 41.26% had major pharmacokinetic interactions, 42.85% were moderate ones, while 15.87% were pharmacodynamic interactions.

CONCLUSION

The retrospective analysis identified the potential risks associated with drug-drug interactions of cyclin-dependent kinase 4/6 inhibitors. Potentially, the application of drug interaction detectors could facilitate additional implementation of research specially designed for interventions aimed at enhancing patient care.

Beyond the Michaelis-Menten: Accurate Prediction of Drug Interactions through Cytochrome P450 3A4 Induction

The US Food and Drug Administration (FDA) guidance has recommended several model-based predictions to determine potential drug-drug interactions (DDIs) mediated by cytochrome P450 (CYP) induction. In particular, the ratio of substrate area under the plasma concentration-time curve (AUCR) under and not under the effect of inducers is predicted by the Michaelis-Menten (MM) model, where the MM constant ( K m $$ {K}_{\mathrm{m}} $$ ) of a drug is implicitly assumed to be sufficiently higher than the concentration of CYP enzymes that metabolize the drug ( E T $$ {E}_{\mathrm{T}} $$ ) in both the liver and small intestine. Furthermore, the fraction absorbed from gut lumen ( F a $$ {F}_{\mathrm{a}} $$ ) is also assumed to be one because F a $$ {F}_{\mathrm{a}} $$ is usually unknown. Here, we found that such assumptions lead to serious errors in predictions of AUCR. To resolve this, we propose a new framework to predict AUCR. Specifically, F a $$ {F}_{\mathrm{a}} $$ was re-estimated from experimental permeability values rather than assuming it to be one. Importantly, we used the total quasi-steady-state approximation to derive a new equation, which is valid regardless of the relationship between K m $$ {K}_{\mathrm{m}} $$ and E T $$ {E}_{\mathrm{T}} $$ , unlike the MM model. Thus, our framework becomes much more accurate than the original FDA equation, especially for drugs with high affinities, such as midazolam or strong inducers, such as rifampicin, so that the ratio between K m $$ {K}_{\mathrm{m}} $$ and E T $$ {E}_{\mathrm{T}} $$ becomes low (i.e., the MM model is invalid). Our work greatly improves the prediction of clinical DDIs, which is critical to preventing drug toxicity and failure.

Effect of fluconazole on the pharmacokinetics of a single dose of fedratinib in healthy adults

Purpose

Fedratinib is an orally administered Janus kinase (JAK) 2–selective inhibitor for the treatment of adult patients with intermediate-2 or high-risk primary or secondary myelofibrosis. In vitro, fedratinib is predominantly metabolized by cytochrome P450 (CYP) 3A4 and to a lesser extent by CYP2C19. Coadministration of fedratinib with CYP3A4 inhibitors is predicted to increase systemic exposure to fedratinib. This study evaluated the effect of multiple doses of the dual CYP3A4 and CYP2C19 inhibitor, fluconazole, on the pharmacokinetics of a single dose of fedratinib.

Methods

In this non-randomized, fixed-sequence, open-label study, healthy adult participants first received a single oral dose of fedratinib 100 mg on day 1. Participants then received fluconazole 400 mg on day 10 and fluconazole 200 mg once daily on days 11–23, with a single oral dose of fedratinib 100 mg on day 18. Pharmacokinetic parameters were calculated for fedratinib administered with and without fluconazole.

Results

A total of 16 participants completed the study and were included in the pharmacokinetic population. Coadministration of fedratinib with fluconazole increased maximum observed plasma concentration (C_max) and area under the plasma concentration–time curve from time 0 to the last quantifiable concentration (AUC_0–t) of fedratinib by 21% and 56%, respectively, compared with fedratinib alone. Single oral doses of fedratinib 100 mg administered with or without fluconazole were well tolerated.

Conclusions

Systemic exposure after a single oral dose of fedratinib was increased by up to 56% when fedratinib was coadministered with fluconazole compared with fedratinib alone.

Trial registry: Clinicaltrials.gov

NCT04702464.

Machine learning-based quantitative prediction of drug exposure in drug-drug interactions using drug label information

Many machine learning techniques provide a simple prediction for drug-drug interactions (DDIs). However, a systematically constructed database with pharmacokinetic (PK) DDI information does not exist, nor is there a machine learning model that numerically predicts PK fold change (FC) with it. Therefore, we propose a PK DDI prediction (PK-DDIP) model for quantitative DDI prediction with high accuracy, while constructing a highly reliable PK-DDI database. Reliable information of 3,627 PK DDIs was constructed from 3,587 drugs using 38,711 Food and Drug Administration (FDA) drug labels. This PK-DDIP model predicted the FC of the area under the time-concentration curve (AUC) within ± 0.5959. The prediction proportions within 0.8–1.25-fold, 0.67–1.5-fold, and 0.5–2-fold of the AUC were 75.77, 86.68, and 94.76%, respectively. Two external validations confirmed good prediction performance for newly updated FDA labels and FC from patients’. This model enables potential DDI evaluation before clinical trials, which will save time and cost.

Evaluation of the drug-drug interaction potential of treosulfan using a physiologically-based pharmacokinetic modelling approach

AIMS

The aim of this work is the development of a mechanistic physiologically-based pharmacokinetic (PBPK) model using in vitro to in vivo extrapolation to conduct a drug-drug interaction (DDI) assessment of treosulfan against two cytochrome p450 (CYP) isoenzymes and P-glycoprotein (P-gp) substrates.

METHODS

A PBPK model for treosulfan was developed de novo based on literature and unpublished clinical data. The PBPK DDI analysis was conducted using the U.S. Food and Drug Administration (FDA) DDI index drugs (probe substrates) midazolam, omeprazole and digoxin for CYP3A4, CYP2C19 and P-gp, respectively. Qualified and documented PBPK models of the probe substrates have been adopted from an open-source online model database.

RESULTS

The PBPK model for treosulfan, based on both in vitro and in vivo data, was able to predict the plasma concentration-time profiles and exposure levels of treosulfan applied for a standard conditioning treatment. Medium and low potentials for DDI on CYP3A4 (maximum area under the concentration-time curve ratio (AUCRmax  = 2.23) and CYP2C19 (AUCRmax  = 1.6) were predicted, respectively, using probe substrates midazolam and omeprazole. Treosulfan was not predicted to cause a DDI on P-gp.

CONCLUSION

Medicinal products with a narrow therapeutic index (eg, digoxin) that are substrates for CYP3A4, CYP2C19 or P-gp should not be given during treatment with treosulfan. However, considering the comprehensive treosulfan-based conditioning treatment schedule and the respective pharmacokinetic properties of the concomitantly used drugs (eg, half-life), the potential for interaction on all evaluated mechanisms would be low (AUCR < 1.25), if concomitantly administered drugs are dosed either 2 hours before or 8 hours after the 2-hour intravenous infusion of treosulfan.

Evaluation of the Expression Profile of Antibiotic-Induced Thrombocytopenia Using the Japanese Adverse Drug Event Report Database

Drug-induced thrombocytopenia (DITP) can be triggered by antibiotics; however, the details remain unclear. Here, we evaluated the expression profiles of DITP using the Japanese Adverse Drug Event Report (JADER) database. We analyzed reports of DITP between April 2004 and January 2021 from the JADER database. The reporting odds ratio (ROR) and 95% confidence interval (CI) were used to detect DITP signals. Factors thought to affect DITP, such as male sex and an age of at least 60 years, were added as covariates. We evaluated the time-to-onset profile and hazard type using the Weibull shape parameter. The JADER database contained 1,048,576 reports. Twelve of 60 antibiotics showed signals for DITP; the RORs (95% CIs) for ampicillin/sulbactam, ceftazidime, cefozopran, ciprofloxacin, fluconazole, fos-fluconazole, linezolid, pazufloxacin, piperacillin/tazobactam, teicoplanin, trimethoprim/sulfamethoxazole, and voriconazole were 1.75 (1.41-2.16), 1.77 (1.42-2.18), 1.35 (1.06-1.72), 2.56 (2.19-2.98), 1.93 (1.67-2.23), 2.08 (1.76-2.46), 5.29 (2.73-9.60), 1.92 (1.51-2.41), 1.54 (1.05-2.19), 1.47 (1.16-1.84), 1.92 (1.73-2.14), and 2.32 (1.59-3.30), respectively. In multiple logistic regression analysis, 7 and 6 antibiotics were detected for the factors age and male sex, respectively. The median times-to-onset of DITP for ciprofloxacin (oral treatment), fluconazole, linezolid, piperacillin/tazobactam, and trimethoprim/sulfamethoxazole were 91, 91, 11.5, 10, and 9 days, respectively. Furthermore, the 95% CI of the Weibull shape parameter β for these antibiotics was above and excluded 1, indicating that the antibiotics were the wear out failure type. We revealed the expression profiles of DITP following treatment with 12 antibiotics.

2020 FDA Drug-drug Interaction Guidance: A Comparison Analysis and Action Plan by Pharmaceutical Industrial Scientists

Background:

In January 2020, the US FDA published two final guidelines, one entitled “In vitro Drug Interaction Studies - Cytochrome P450 Enzyme- and Transporter-Mediated Drug Interactions Guidance for Industry” and the other entitled “Clinical Drug Interaction Studies - Cytochrome P450 Enzyme- and Transporter-Mediated Drug Interactions Guidance for Industry”. These were updated from the 2017 draft in vitro and clinical DDI guidance.

Methods:

This study is aimed to provide an analysis of the updates along with a comparison of the DDI guidelines published by the European Medicines Agency (EMA) and Japanese Pharmaceuticals and Medical Devices Agency (PMDA) along with the current literature.

Results:

The updates were provided in the final FDA DDI guidelines and explained the rationale of those changes based on the understanding from research and literature. Furthermore, a comparison among the FDA, EMA, and PMDA DDI guidelines are presented in Tables 1, 2 and 3.

Conclusion:

The new 2020 clinical DDI guidance from the FDA now has even higher harmonization with the guidance (or guidelines) from the EMA and PMDA. A comparison of DDI guidance from the FDA 2017, 2020, EMA, and PMDA on CYP and transporter based DDI, mathematical models, PBPK, and clinical evaluation of DDI is presented in this review.

Unveiling the Impact of Morphine on Tamoxifen Metabolism in Mice in vivo

Background: Tamoxifen is used to treat breast cancer and cancer recurrences. After administration, tamoxifen is converted into two more potent antitumor compounds, 4OH-tamoxifen and endoxifen by the CYP3A4/5 and 2D6 enzymes in human. These active compounds are inactivated by the same UDP-glucuronosyltransferase isoforms as those involved in the metabolism of morphine. Importantly, cancer-associated pain can be treated with morphine, and the common metabolic pathway of morphine and tamoxifen suggests potential clinically relevant interactions.

Methods: Mouse liver microsomes were used to determine the impact of morphine on 4OH-tamoxifen metabolism in vitro. For in vivo experiments, female mice were first injected with tamoxifen alone and then with tamoxifen and morphine. Blood was collected, and LC-MS/MS was used to quantify tamoxifen, 4OH-tamoxifen, N-desmethyltamoxifen, endoxifen, 4OH-tamoxifen-glucuronide, and endoxifen-glucuronide.

Results:In vitro, we found increased K_m values for the production of 4OH-tamoxifen-glucuronide in the presence of morphine, suggesting an inhibitory effect on 4OH-tamoxifen glucuronidation. Conversely, in vivo morphine treatment decreased 4OH-tamoxifen levels in the blood while dramatically increasing the formation of inactive metabolites 4OH-tamoxifen-glucuronide and endoxifen-glucuronide.

Conclusions: Our findings emphasize the need for caution when extrapolating results from in vitro metabolic assays to in vivo drug metabolism interactions. Importantly, morphine strongly impacts tamoxifen metabolism in mice. It suggests that tamoxifen efficiency could be reduced when both drugs are co-administered in a clinical setting, e.g., to relieve pain in breast cancer patients. Further studies are needed to assess the potential for tamoxifen-morphine metabolic interactions in humans.

Safety and Tolerability of Epidermal Growth Factor Receptor (EGFR) Tyrosine Kinase Inhibitors in Oncology

Tyrosine kinase inhibitors (TKIs) that target epidermal growth factor receptor (EGFR) have dramatically improved progression-free survival in non-small-cell lung cancer (NSCLC) patients who carry sensitizing EGFR-activating mutations and in patients with breast and pancreatic cancers. However, EGFR-TKIs are associated with significant and disabling undesirable effects that adversely impact on quality of life and compliance. These effects include dermatological reactions, diarrhoea, hepatotoxicity, stomatitis, interstitial lung disease and ocular toxicity. Each individual EGFR-TKI is also associated with additional adverse effect(s) that are not shared widely by the other members of its class. Often, these effects call for dose reduction, treatment discontinuation or pharmacotherapeutic intervention. Since dermatological effects result from on-target effects on wild-type EGFR, rash is often considered to be a biomarker of efficacy. A number of studies have reported better outcomes in patients with skin reactions compared with those without. This has led to a 'dosing-to-rash' strategy to optimize therapeutic outcomes. Although conceptually attractive, there is currently insufficient evidence-based support for this strategy. While skin reactions following EGFR-TKIs are believed to result from an effect on wild-type EGFR, their efficacy is related to effects on mutant variants of EGFR. It is noteworthy that newer EGFR-TKIs that spare wild-type EGFR are associated with fewer dermatological reactions. Furthermore, secondary mutations such as T790M in exon 20 often lead to development of resistance to the clinical activity and efficacy of first- and second-generation EGFR-TKIs. This has stimulated the search for later-generations of EGFR-TKIs with the ability to overcome this resistance and with greater target selectivity to spare wild-type EGFR in expectations of an improved safety profile. However, available data reviewed herein indicate that not only are these newer agents associated with the aforementioned adverse effects typical of earlier agents, but they are also susceptible to resistance due to tertiary mutations, most frequently C797S. At least three later-generation EGFR-TKIs, canertinib, naquotinib and rociletinib, have been discontinued from further development in NSCLC following concerns about their safety and risk/benefit.

Effects of Δ9-tetrahydrocannabinol on irinotecan-induced clinical effects in rats

Because of the great interest for research on the potential use of cannabis preparations as co-medication for alleviation of toxic effects in cancer management, we investigated the influence of Δ⁹-tetrahydrocannabinol (Δ⁹-THC) to modulate irinotecan (CPT-11)-induced toxicity. Male Wistar rats were treated either with a single irinotecan intraperitoneal dose, 100 mg/kg body-weight (b.w.), or with irinotecan in combination with THC (7 mg/kg b.w., p.o., administered repeatedly for 1, 3 and 7 days). Serial blood samples were obtained up to seven days after dosing and were analyzed for complete blood count and biochemical parameters (liver enzymes, creatinine, inflammatory markers, and lipid status). Serial urine samples were collected in the first 24 h to monitor the time-course of THC metabolite 11-nor-9-carboxy-Δ⁹-THC (THC-COOH) excretion with concomitant irinotecan treatment or without. Both irinotecan and irinotecan + Δ⁹-THC administration caused moderate leukopenia but a greater decrease in leukocyte count was observed in the irinotecan + Δ⁹-THC treated compared to the single irinotecan suggesting higher cytotoxic effects in combined treatment. Irinotecan treatment induced elevation of aspartate aminotransferase (AST) in rats without diarrheal symptoms and without an increase in circulating pro-inflammatory mediators. Interestingly, the elevation of AST was not observed in the irinotecan + Δ⁹-THC group. The median creatinine-corrected urinary THC-COOH concentration was higher in the irinotecan + THC group compared to the THC-only group in a time-dependent manner, suggesting a possible early interaction between cannabinoids and irinotecan. Further studies are needed to investigate the role of cannabinoids particularly on hematological toxicity, irinotecan metabolism and their role as a possible modifiable factor among irinotecan-treated hosts.

In Silico Evaluation of Pharmacokinetic Optimization for Antimitogram-Based Clinical Trials

Antimitograms are prototype in vitro tests for evaluating chemotherapeutic efficacy using patient-derived primary cancer cells. These tests might help optimize treatment from a pharmacodynamic standpoint by guiding treatment selection. However, they are technically challenging and require refinements and trials to demonstrate benefit to be widely used. In this study, we performed simulations aimed at exploring how to validate antimitograms and how to complement them by pharmacokinetic optimization. A generic model of advanced cancer, including pharmacokinetic-pharmacodynamic monitoring, was used to link dosing schedules with progression-free survival (PFS), as built from previously validated modules. This model was used to explore different possible situations in terms of pharmacokinetic variability, pharmacodynamic variability, and antimitogram performance. The model recapitulated tumor dynamics and standalone therapeutic drug monitoring efficacy consistent with published clinical results. Simulations showed that combining pharmacokinetic and pharmacodynamic optimization should increase PFS in a synergistic fashion. Simulated data were then used to compute required clinical trial sizes, which were 30% to 90% smaller when pharmacokinetic optimization was added to pharmacodynamic optimization. This improvement was observed even when pharmacokinetic optimization alone exhibited only modest benefit. Overall, our work illustrates the synergy derived from combining antimitograms with therapeutic drug monitoring, permitting a disproportionate reduction of the trial size required to prove a benefit on PFS. Accordingly, we suggest that strategies with benefits too small for standalone clinical trials could be validated in combination in a similar manner.Significance: This work offers a method to reduce the number of patients needed for a clinical trial to prove the hypothesized benefit of a drug to progression-free survival, possibly easing opportunities to evaluate combinations. Cancer Res; 78(7); 1873-82. ©2018 AACR.

Cytochrome P450 3A selectively affects the pharmacokinetic interaction between erlotinib and docetaxel in rats

Erlotinib as a first-line drug is used in non-small cell lung cancer (NSCLC) patients with sensitive EGFR mutations, while resistance to this drug will occur after several years of treatment. Therefore, the microtubule disturber docetaxel is introduced as combined regimen in clinical trials. This report investigated the potentials and mechanisms of drug-drug interaction (DDI) between erlotinib and docetaxel using wild type (WT) and Cyp3a1/2 knockout (KO) rats. The erlotinib O-demethylation and docetaxel hydroxylation reactions in the absence or the presence of another drug were analyzed in vitro via the assay of rat liver microsomes. In whole animal studies, erlotinib and docetaxel were given to WT and KO rats individually or jointly, and the pharmacokinetic profiles of these two drugs were analyzed and compared among different groups. The results showed that docetaxel not only inhibited the CYP3A-mediated biotransformation of erlotinib in vitro, but also significantly increased the maximum concentration and systemic exposure of erlotinib in vivo in WT rats. In contrast, the DDI was significantly attenuated in KO rats. On the other hand, erlotinib did not influence docetaxel either in vitro biotransformation or in vivo pharmacokinetic behaviors. These results exhibited the potentials of erlotinib-docetaxel interaction and indicated that the CYP3A played the perpetrating role of docetaxel on erlotinib in rats. A better understanding of this DDI with CYP3A may help the regulation of the use of these two drugs, avoid potential problems, and adjust dose carefully and early in clinic.

Pharmacogenomic Characterization and Isobologram Analysis of the Combination of Ascorbic Acid and Curcumin-Two Main Metabolites of Curcuma longa-in Cancer Cells

Curcuma longa has long been used in China and India as anti-inflammatory agent to treat a wide variety of conditions and also as a spice for varied curry preparations. The chemoprofile of the Curcuma species exhibits the presence of varied phytochemicals with curcumin being present in all three species but AA only being shown in C. longa. This study explored the effect of a curcumin/AA combination on human cancer cell lines. The curcumin/AA combination was assessed by isobologram analysis using the Loewe additivity drug interaction model. The drug combination showed additive cytotoxicity toward CCRF-CEM and CEM/ADR5000 leukemia cell lines and HCT116p53^+/+ and HCT116p53^−/− colon cancer cell line, while the glioblastoma cell lines U87MG and U87MG.ΔEGFR showed additive to supra-additive cytotoxicity. Gene expression profiles predicting sensitivity and resistance of tumor cells to induction by curcumin and AA were determined by microarray-based mRNA expressions, COMPARE, and hierarchical cluster analyses. Numerous genes involved in transcription (TFAM, TCERG1, RGS13, C11orf31), apoptosis-regulation (CRADD, CDK7, CDK19, CD81, TOM1) signal transduction (NR1D2, HMGN1, ABCA1, DE4ND4B, TRIM27) DNA repair (TOPBP1, RPA2), mRNA metabolism (RBBP4, HNRNPR, SRSF4, NR2F2, PDK1, TGM2), and transporter genes (ABCA1) correlated with cellular responsiveness to curcumin and ascorbic acid. In conclusion, this study shows the effect of the curcumin/AA combination and identifies several candidate genes that may regulate the response of varied cancer cells to curcumin and AA.

Toward standardized reporting of drug interactions: the READI checklist for anecdotal reports

Anecdotal reports contribute 30% of the literature on adverse drug reactions and interactions. However, the quality of such reports has not been uniformly high. Standardized reporting of clinical studies is of increasing interest, including the CARE guidelines on reporting anecdotal cases in general. Although there are guidelines on evaluating and managing drug-drug interactions, there are none recommending methods for reporting suspected drug interactions. Here, based on published guidelines for reporting suspected adverse drug reactions, I propose a checklist for reporting details of suspected drug interactions, the REporting Anecdotal Drug Interactions (READI) checklist, hoping to stimulate discussion and improve reporting of suspected drug interactions. The checklist includes items relating, among others, to the patient affected, the drugs involved, and the outcome.