Curcumin has been extensively studied for its anti-cancer properties. While a diverse array of in vitro and preclinical research support the prospect of curcumin use as an anti-cancer therapeutic, ...most human studies have failed to meet the intended clinical expectation. Poor systemic availability of orally-administered curcumin may account for this disparity. Areas covered: This descriptive review aims to concisely summarise available clinical studies investigating curcumin pharmacokinetics when administered in different formulations. A critical analysis of pharmacokinetic- and pharmacodynamic-based interactions of curcumin with concomitantly administered drugs is also provided. Expert opinion: The encouraging clinical results of curcumin administration are currently limited to people with colorectal cancer, given that sufficient curcumin concentrations persist in colonic mucosa. Higher parent curcumin systemic exposure, which can be achieved by several newer formulations, has important implications for optimal treatment of cancers other than those in gastrointestinal tract. Curcumin-drug pharmacokinetic interactions are also almost exclusively in the enterocytes, owing to extensive first pass metabolism and poor curcumin bioavailability. Greater scope of these interactions, i.e. modulation of the systemic elimination of co-administered drugs, may be expected from more-bioavailable curcumin formulations. Further studies are still warranted, especially with newer formulations to support the inclusion of curcumin in cancer therapy regimens.
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.
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.
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 (5
th
to 95
th
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, 5
th
to 95
th
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.
Long-term use of imatinib is effective and well-tolerated in children with chronic myeloid leukaemia (CML) yet defining an optimal dosing regimen for imatinib in younger patients is a challenge. The ...potential interactions between imatinib and coadministered drugs in this "special" population also remains largely unexplored. This study implements a physiologically based pharmacokinetic (PBPK) modeling approach to investigate optimal dosing regimens and potential drug interactions with imatinib in the paediatric population. A PBPK model for imatinib was developed in the Simcyp Simulator (version 17) utilizing
,
drug metabolism, and
pharmacokinetic data and verified using an independent set of published clinical pharmacokinetic data. The model was then extrapolated to children and adolescents (aged 2-18 years) by incorporating developmental changes in organ size and maturation of drug-metabolising enzymes and plasma protein responsible for imatinib disposition. The PBPK model described imatinib pharmacokinetics in adult and paediatric populations and predicted drug interaction with carbamazepine, a cytochrome P450 (CYP)3A4 and 2C8 inducer, with a good accuracy (evaluated by visual inspections of the simulation results and predicted pharmacokinetic parameters that were within 1.25-fold of the clinically observed values). The PBPK simulation suggests that the optimal dosing regimen range for imatinib is 230-340 mg/m
/d in paediatrics, which is supported by the recommended initial dose for treatment of childhood CML. The simulations also highlighted that children and adults being treated with imatinib have similar vulnerability to CYP modulations. A PBPK model for imatinib was successfully developed with an excellent performance in predicting imatinib pharmacokinetics across age groups. This PBPK model is beneficial to guide optimal dosing regimens for imatinib and predict drug interactions with CYP modulators in the paediatric population.
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.
Herb-drug interactions with St John's wort (SJW) have been widely studied in numerous clinical studies. The objective of this study was to develop and evaluate a physiologically based pharmacokinetic ...(PBPK) model for hyperforin (the constituent of SJW responsible for interactions), which has the potential to provide unique insights into SJW interactions and allow prediction of the likely extent of interactions with SJW compared to published interaction reports.
A PBPK model of hyperforin accounting for the induction of cytochrome P450 (CYP) 3A, CYP2C9 and CYP2C19 was developed in the Simcyp
Simulator (version 17) and verified using published, clinically observed pharmacokinetic data. The predictive performance of this model based on the prediction fold-difference (expressed as the ratio of predicted and clinically observed change in systemic exposure of drug) was evaluated across a range of CYP substrates.
The verified PBPK model predicted the change in victim drug exposure due to the induction by SJW (expressed as area under the plasma concentration-time curve (AUC) ratio) within 1.25-fold (0.80-1.25) of that reported in clinical studies. The PBPK simulation indicated that the unbound concentration of hyperforin in the liver was far lower than in the gut (enterocytes). Simulations revealed that induction of intestinal CYP enzymes by hyperforin was found to be more pronounced than the corresponding increase in liver CYP activity (15.5- vs. 1.1-fold, respectively, at a hyperforin dose of 45 mg/day).
In the current study, a PBPK model for hyperforin was successfully developed, with a predictive capability for the interactions of SJW with different CYP3A, CYP2C9 and CYP2C19 substrates. This PBPK model is valuable to predict the extent of herb-drug interactions with SJW and help design the clinical interaction studies, particularly for new drugs and previously unstudied clinical scenarios.
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.
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.
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