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.

Effect of Nigella sativa oil on pharmacokinetics and pharmacodynamics of gliclazide in rats.

Nigella sativa oil (NSO) has been used widely for its putative anti-hyperglycemic activity. However, little is known about its potential effect on the pharmacokinetics and pharmacodynamics of antidiabetic drugs, including gliclazide. This study aimed to investigate herb-drug interactions between gliclazide and NSO in rats. Plasma concentrations of gliclazide (single oral and intravenous dose of 33 and 26.4 mg/kg, respectively) in the presence and absence of co-administration with NSO (52 mg/kg per oral) were quantified in healthy and insulin resistant rats (n = 5 for each group). Physiological and treatment-related factors were evaluated as potential influential covariates using a population pharmacokinetic modeling approach (NONMEM version 7.4). Clearance, volume of distribution and bioavailability of gliclazide were unaffected by disease state (healthy or insulin resistant). The concomitant administration of NSO resulted in higher systemic exposures of gliclazide by modulating bioavailability (29% increase) and clearance (20% decrease) of the drug. A model-independent analysis highlighted that pre-treatment with NSO in healthy rats was associated with a higher glucose lowering effect by up to 50% compared with that of gliclazide monotherapy, but not of insulin resistant rats. Although a similar trend in glucose reductions was not observed in insulin resistant rats, co-administration of NSO improved the sensitivity to insulin of this rat population. Natural product-drug interaction between gliclazide and NSO merits further evaluation of its clinical importance.

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.

Pharmacokinetic assessment of constituents of Boswellia serrata, pine bark extracts, curcumin in combination including methylsulfonylmethane in healthy volunteers.

OBJECTIVES: Dietary supplements are increasingly used by people with osteoarthritis. Boswellia serrata extract, curcumin, pine bark extract and methylsulfonylmethane have been identified as having the largest effects for symptomatic relief in a systematic review. It is important to understand whether any pharmacokinetic interactions are among the major constituents of these supplements so as to provide information when considering the combination use of these supplements. The aim of this study was to investigate the pharmacokinetics of the constituents alone and in combination. METHODS: This study was a randomized, open-label, single-dose, four-treatment, four-period, crossover study with 1-week washout. The pharmacokinetics of the constituents of these supplements when dosed in combination with methylsulfonylmethane were compared to being administered alone. Plasma samples were obtained over 24 h from 16 healthy participants. Eight major constituents were analysed using a validated ultra-high-performance liquid chromatography-tandem mass spectrometry assay. KEY FINDINGS: The pharmacokinetics of each constituent was characterized, and there were no significant differences in the pharmacokinetic profiles of the constituents when administered as a combination, relative to the constituents when administered alone (P > 0.05). CONCLUSIONS: These data suggest that interactions between the major constituents of this supplement combination are unlikely and therefore could be investigated to manage patients with osteoarthritis without significant concerns for possible pharmacokinetic interactions.

Physiologically Based Pharmacokinetic Modelling of Hyperforin to Predict Drug Interactions with St John's Wort.

BACKGROUND AND OBJECTIVES: 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. METHODS: 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. RESULTS: 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). CONCLUSION: 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.