Interactions between natural products and cancer treatments: underlying mechanisms and clinical importance.

Natural products, also referred to as dietary supplements, complementary and alternative medicines, and health or food supplements are widely used by people living with cancer. These products are predominantly self-selected and taken concurrently with cancer treatments with the intention of improving quality of life, immune function and reducing cancer symptoms and treatment side effects. Concerns have been raised that concurrent use may lead to interactions resulting in adverse effects and unintended treatment outcomes. This review provides an overview of the mechanisms by which these interactions can occur and the current evidence about specific clinically important natural product-drug interactions. Clinical studies investigating pharmacokinetic interactions provide evidence that negative treatment outcomes may occur when Hypericum perforatum, Grapefruit, Schisandra sphenanthera, Curcuma longa or Hydrastis canadensis are taken concurrently with common cancer treatments. Conversely, pharmacodynamic interactions between Hangeshashinto (TJ-14) and some cancer treatments have been shown to reduce the side effects of diarrhoea and oral mucositis. In summary, research in this area is limited and requires further investigation.

Audit of Data Sharing by Pharmaceutical Companies for Anticancer Medicines Approved by the US Food and Drug Administration.

Importance: Emerging policies drafted by the pharmaceutical industry indicate that they will transparently share clinical trial data. These data offer an unparalleled opportunity to advance evidence-based medicine and support decision-making. Objective: To evaluate the eligibility of independent, qualified researchers to access individual participant data (IPD) from oncology trials that supported US Food and Drug Administration (FDA) approval of new anticancer medicines within the past 10 years. Design, Setting, and Participants: In this quality improvement study, a cross-sectional analysis was performed of pivotal clinical trials whose results supported FDA-approved anticancer medicines between January 1, 2011, and June 30, 2021. These trials' results were identified from product labels. Exposures: Eligibility for IPD sharing was confirmed by identification of a public listing of the trial as eligible for sharing or by receipt of a positive response from the sponsor to a standardized inquiry. Main Outcomes and Measures: The main outcome was frequency of IPD sharing eligibility. Reasons for data sharing ineligibility were requested and collated, and company-, drug-, and trial-level subgroups were evaluated and presented using χ2 tests and forest plots. Results: During the 10-year period examined, 115 anticancer medicines were approved by the FDA on the basis of evidence from 304 pharmaceutical industry-sponsored trials. Of these trials, 136 (45%) were eligible for IPD sharing and 168 (55%) were not. Data sharing rates differed substantially among industry sponsors, with the most common reason for not sharing trial IPD being that the collection of long-term follow-up data was still ongoing (89 of 168 trials [53%]). Of the top 10 anticancer medicines by global sales, nivolumab, pembrolizumab, and pomalidomide had the lowest eligibility rates for data sharing (<10% of trials). Conclusions and Relevance: There has been a substantial increase in IPD sharing for industry-sponsored oncology trials over the past 5 years. However, this quality improvement study found that more than 50% of queried trials for FDA-approved anticancer medicines were ineligible for IPD sharing. Data accessibility would be substantially improved if, at the time of FDA registration of a medicine, all data that support the registration were made available.

Physiologically Based Pharmacokinetic Modeling Approaches for Patients With SARS-CoV-2 Infection: A Case Study With Imatinib.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, which causes coronavirus disease 2019 (COVID-19), manifests as mild respiratory symptoms to severe respiratory failure and is associated with inflammation and other physiological changes. Of note, substantial increases in plasma concentrations of α1 -acid-glycoprotein and interleukin-6 have been observed among patients admitted to the hospital with advanced SARS-CoV-2 infection. A physiologically based pharmacokinetic (PBPK) approach is a useful tool to evaluate and predict disease-related changes on drug pharmacokinetics. A PBPK model of imatinib has previously been developed and verified in healthy people and patients with cancer. In this study, the PBPK model of imatinib was successfully extrapolated to patients with SARS-CoV-2 infection by accounting for disease-related changes in plasma α1 -acid-glycoprotein concentrations and the potential drug interaction between imatinib and dexamethasone. The model demonstrated a good predictive performance in describing total and unbound imatinib concentrations in patients with SARS-CoV-2 infection. PBPK simulations highlight that an equivalent dose of imatinib may lead to substantially higher total drug concentrations in patients with SARS-CoV-2 infection compared to that in patients with cancer, while the unbound concentrations remain comparable between the 2 patient populations. This supports the notion that unbound trough concentration is a better exposure metric for dose adjustment of imatinib in patients with SARS-CoV-2 infection, compared to the corresponding total drug concentration. Potential strategies for refinement and generalization of the PBPK modeling approach in the patient population with SARS-CoV-2 are also provided in this article, which could be used to guide study design and inform dose adjustment in the future.

Physiologically based pharmacokinetic model predictions of natural product-drug interactions between goldenseal, berberine, imatinib and bosutinib.

PURPOSE: This study implements a physiologically based pharmacokinetic (PBPK) modelling approach to predict the effect of hydrastine and berberine, two major alkaloids present in goldenseal extract, on pharmacokinetics of imatinib and bosutinib. METHODS: PBPK models of hydrastine and berberine were developed in the Simcyp Simulator (version 17), integrating prior in vitro knowledge and published clinical pharmacokinetic data. The models account for reversible and irreversible (mechanism-based) inhibition of CYP3A enzymes as well as inhibition of the P-glycoprotein transporter. Inhibitory potencies of hydrastine and berberine on imatinib and bosutinib were estimated based on in vitro inhibition of metabolite formation. RESULTS: The PBPK models provided reliable estimates on the magnitude of interactions due to co-administration of goldenseal extract or high-dose berberine on substrates of CYP3A enzymes (midazolam, indinavir and cyclosporine) and P-glycoprotein (digoxin). PBPK simulations predicted a moderate twofold increase (5th to 95th percentiles of prediction of 1.4-3.1) in systemic exposure (AUC) of bosutinib when co-administered with clinically relevant doses of goldenseal extract. A high dose of berberine (300 mg thrice daily) was also expected to affect bosutinib exposure, albeit to a lesser extent than that predicted with goldenseal (AUC ratio of 1.3, 5th to 95th percentile: 1.1-1.6). Conversely, the corresponding effects on imatinib exposure are unlikely to be of clinical importance (predicted AUC ratios of 1.0-1.2). CONCLUSION: PBPK model-based predictions highlighted potential clinically significant interactions between goldenseal extract and bosutinib, but not imatinib. Dose adjustment may need to be considered if co-administration is desirable. These findings should be confirmed with optimally designed controlled drug interaction studies.

Physiologically-based pharmacokinetic model predictions of inter-ethnic differences in imatinib pharmacokinetics and dosing regimens.

AIMS: This study implements a physiologically-based pharmacokinetic (PBPK) modelling approach to investigate inter-ethnic differences in imatinib pharmacokinetics and dosing regimens. METHODS: A PBPK model of imatinib was built in the Simcyp Simulator (version 17) integrating in vitro drug metabolism and clinical pharmacokinetic data. The model accounts for ethnic differences in body size and abundance of drug-metabolising enzymes and proteins involved in imatinib disposition. Utility of this model for prediction of imatinib pharmacokinetics was evaluated across different dosing regimens and ethnic groups. The impact of ethnicity on imatinib dosing was then assessed based on the established range of trough concentrations (Css,min ). RESULTS: The PBPK model of imatinib demonstrated excellent predictive performance in describing pharmacokinetics and the attained Css,min in patients from different ethnic groups, shown by prediction differences that were within 1.25-fold of the clinically-reported values in published studies. PBPK simulation suggested a similar dose of imatinib (400-600 mg/d) to achieve the desirable range of Css,min (1000-3200 ng/mL) in populations of European, Japanese and Chinese ancestry. The simulation indicated that patients of African ancestry may benefit from a higher initial dose (600-800 mg/d) to achieve imatinib target concentrations, due to a higher apparent clearance (CL/F) of imatinib compared to other ethnic groups; however, the clinical data to support this are currently limited. CONCLUSION: PBPK simulations highlighted a potential ethnic difference in the recommended initial dose of imatinib between populations of European and African ancestry, but not populations of Chinese and Japanese ancestry.

Is there scope for better individualisation of anthracycline cancer chemotherapy?

Anthracyclines are used to treat solid and haematological cancers, particularly breast cancers, lymphomas and childhood cancers. Myelosuppression and cardiotoxicity are the primary toxicities that limit treatment duration and/or intensity. Cardiotoxicity, particularly heart failure, is a leading cause of morbidity and mortality in cancer survivors. Cumulative anthracycline dose is a significant predictor of cardiotoxicity risk, suggesting a role for anthracycline pharmacokinetic variability. Population pharmacokinetic modelling in children has shown that doxorubicin clearance in the very young is significantly lower than in older children, potentially contributing to their higher risk of cardiotoxicity. A model of doxorubicin clearance based on body surface area and age offers a patient-centred dose-adjustment strategy that may replace the current disparate initial-dose selection tools, providing a rational way to compensate for pharmacokinetic variability in children aged <7 years. Population pharmacokinetic models in adults have not adequately addressed older ages, obesity, hepatic and renal dysfunction, and potential drug-drug interactions to enable clinical application. Although candidate gene and genome-wide association studies have investigated relationships between genetic variability and anthracycline pharmacokinetics or clinical outcomes, there have been few clinically significant reproducible associations. Precision-dosing of anthracyclines is currently hindered by lack of clinically useful pharmacokinetic targets and models that predict cumulative anthracycline exposures. Combined with known risk factors for cardiotoxicity, the use of advanced echocardiography and biomarkers, future validated pharmacokinetic targets and predictive models could facilitate anthracycline precision dosing that truly maximises efficacy and provides individualised early intervention with cardioprotective therapies in patients at risk of cardiotoxicity.

Physiologically-Based Pharmacokinetic Predictions of the Effect of Curcumin on Metabolism of Imatinib and Bosutinib: In Vitro and In Vivo Disconnect.

PURPOSE: This study aimed to investigate the potential pharmacokinetic interactions between curcumin, imatinib and bosutinib, combining In Vitro and in silico methods. METHODS: In Vitro metabolism of imatinib and bosutinib were investigated in pooled human liver microsomes and recombinant CYP3A4 enzyme in the presence and absence of curcumin and curcumin glucuronide using an LC-MS/MS assay for N-desmethyl metabolites. A physiologically-based pharmacokinetic (PBPK) model for curcumin formulated as solid lipid nanoparticles (SLN) was constructed using In Vitro glucuronidation kinetics and published clinical pharmacokinetic data. The potential effects of curcumin coadministration on systemic exposures of imatinib and bosutinib were predicted in silico using PBPK simulations. RESULTS: Curcumin demonstrated potent reversible inhibition of cytochrome P450 (CYP)3A4-mediated N-demethylation of imatinib and bosutinib and CYP2C8-mediated metabolism of imatinib with inhibitory constants (ki,u) of ≤1.5 µmol. L-1. A confirmatory In Vitro study with paclitaxel, the 6α-hydroxylation of which is exclusively mediated by CYP2C8, was consistent with a potent inhibition of this enzyme by curcumin. Curcumin glucuronide also inhibited both CYP enzymes In Vitro, albeit to a lesser extent than that of curcumin. PBPK model simulations predicted that at recommended dosing regimens of SLN curcumin, coadministration would result in an increase in systemic exposures of imatinib and bosutinib of up to only 10%. CONCLUSION: A PBPK model for curcumin in a SLN formulation was successfully developed. Although curcumin possesses a strong In Vitro inhibitory activity towards CYP3A4 and CYP2C8 enzymes, its interactions with imatinib and bosutinib were unlikely to be of clinical importance due to curcumin's poor bioavailability.

Potential for pharmacokinetic interactions between Schisandra sphenanthera and bosutinib, but not imatinib: in vitro metabolism study combined with a physiologically-based pharmacokinetic modelling approach.

AIMS: This study aimed to investigate the potential interaction between Schisandra sphenanthera, imatinib and bosutinib combining in vitro and in silico methods. METHODS: In vitro metabolism of imatinib and bosutinib using recombinant enzymes and human liver microsomes were investigated in the presence and absence of Schisandra lignans. Physiologically-based pharmacokinetic (PBPK) models for the lignans accounting for reversible and mechanism-based inhibitions and induction of CYP3A enzymes were built in the Simcyp Simulator (version 17) and evaluated for their capability to predict interactions with midazolam and tacrolimus. Their potential effect on systemic exposures of imatinib and bosutinib were predicted using PBPK in silico simulations. RESULTS: Schisantherin A and schisandrol B, but not schisandrin A, potently inhibited CYP3A4-mediated metabolism of imatinib and bosutinib. All three compounds showed a strong reversible inhibition on CYP2C8 enzyme with ki of less than 0.5 µmol L-1 . The verified PBPK models were able to describe the increase in systemic exposure of midazolam and tacrolimus due to co-administration of S. sphenanthera, consistent with the reported changes in the corresponding clinical interaction study (AUC ratio of 2.0 vs 2.1 and 2.4 vs 2.1, respectively). The PBPK simulation predicted that at recommended dosing regimens of S. sphenanthera, co-administration would result in an increase in bosutinib exposure (AUC ratio 3.0) but not in imatinib exposure. CONCLUSION: PBPK models for Schisandra lignans were successfully developed. Interaction between imatinib and Schisandra lignans was unlikely to be of clinical importance. Conversely, S. sphenanthera at a clinically-relevant dose results in a predicted three-fold increase in bosutinib systemic exposure.

Wnt receptor gene FZD1 was associated with schizophrenia in genome-wide SNP analysis of the Australian Schizophrenia Research Bank cohort.

OBJECTIVES: Large-scale genetic analysis of common variation in schizophrenia has been a powerful approach to understanding this complex but highly heritable psychotic disorder. To further investigate loci, genes and pathways associated more specifically in the well-characterized Australian Schizophrenia Research Bank cohort, we applied genome-wide single-nucleotide polymorphism analysis in these three annotation categories. METHODS: We performed a case-control genome-wide association study in 429 schizophrenia samples and 255 controls. Post-genome-wide association study analyses were then integrated with genomic annotations to explore the enrichment of variation at the gene and pathway level. We also examine candidate single-nucleotide polymorphisms with potential function within expression quantitative trait loci and investigate overall enrichment of variation within tissue-specific functional regulatory domains of the genome. RESULTS: The strongest finding (p = 2.01 × 10-6, odds ratio = 1.82, 95% confidence interval = [1.42, 2.33]) in genome-wide association study was with rs10252923 at 7q21.13, downstream of FZD1 (frizzled class receptor 1). While this did not stand alone after correction, the involvement of FZD1 was supported by gene-based analysis, which exceeded the threshold for genome-wide significance (p = 2.78 × 10-6). CONCLUSION: The identification of FZD1, as an independent association signal at the gene level, supports the hypothesis that the Wnt signalling pathway is altered in the pathogenesis of schizophrenia and may be an important target for therapeutic development.

Optimization of a clofarabine-based drug combination regimen for the preclinical evaluation of pediatric acute lymphoblastic leukemia.

BACKGROUND: The aim of this study was to improve the predictive power of patient-derived xenografts (PDXs, also known as mouse avatars) to more accurately reflect outcomes of clofarabine-based treatment in pediatric acute lymphoblastic leukemia (ALL) patients. PROCEDURE: Pharmacokinetic (PK) studies were conducted using clofarabine at 3.5 to 15 mg/kg in mice. PDXs were established from relapsed/refractory ALL patients who exhibited good or poor responses to clofarabine. PDX engraftment and response to clofarabine (either as a single agent or in combinations) were assessed based on stringent objective response measures modeled after the clinical setting. RESULTS: In naïve immune-deficient NSG mice, we determined that a clofarabine dose of 3.5 mg/kg resulted in systemic exposures equivalent to those achieved in pediatric ALL patients treated with clofarabine-based regimens. This dose was markedly lower than the doses of clofarabine used in previously reported preclinical studies (typically 30-60 mg/kg) and, when scheduled consistent with the clinical regimen (daily × 5), resulted in 34-fold lower clofarabine exposures. Using a well-tolerated clofarabine/etoposide/cyclophosphamide combination regimen, we then found that the responses of PDXs better reflected the clinical responses of the patients from whom the PDXs were derived. CONCLUSIONS: This study has identified an in vivo clofarabine treatment regimen that reflects the clinical responses of relapsed/refractory pediatric ALL patients. This regimen could be used prospectively to identify patients who might benefit from clofarabine-based treatment. Our findings are an important step toward individualizing prospective patient selection for the use of clofarabine in relapsed/refractory pediatric ALL patients and highlight the need for detailed PK evaluation in murine PDX models.