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.

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 in silico, in vitro drug metabolism, and in vivo 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 2 /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.

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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.

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.

Nonalcoholic fatty liver disease (NAFLD), representing a clinical spectrum ranging from nonalcoholic fatty liver (NAFL) to nonalcoholic steatohepatitis (NASH), is rapidly evolving into a global pandemic. Patients with NAFLD are burdened with high rates of metabolic syndrome-related comorbidities resulting in polypharmacy. Therefore, it is crucial to gain a better understanding of NAFLD-mediated changes in drug disposition and efficacy/toxicity. Despite extensive clinical pharmacokinetic data in cirrhosis, current knowledge concerning pharmacokinetic alterations in NAFLD, particularly at different stages of disease progression, is relatively limited. In vitro-to-in vivo extrapolation coupled with physiologically based pharmacokinetic and pharmacodynamic (IVIVE-PBPK/PD) modeling offers a promising approach for optimizing pharmacologic predictions while refining and reducing clinical studies in this population. Use of IVIVE-PBPK to predict intra-organ drug concentrations at pharmacologically relevant sites of action is particularly advantageous when it can be linked to pharmacodynamic effects. Quantitative systems pharmacology/toxicology (QSP/QST) modeling can be used to translate pharmacokinetic and pharmacodynamic data from PBPK/PD models into clinically relevant predictions of drug response and toxicity. In this review, a detailed summary of NAFLDmediated alterations in human physiology relevant to drug absorption, distribution, metabolism, and excretion (ADME) is provided. The application of literature-derived physiologic parameters and ADME-associated protein abundance data to inform virtual NAFLD population development and facilitate PBPK/PD, QSP, and QST predictions is discussed along with current limitations of these methodologies and knowledge gaps. The proposed methodologic framework offers great potential for meaningful prediction of pharmacological outcomes in patients with NAFLD and can inform both drug development and clinical practice for this 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.

The phenotyping approach to predict drug metabolism activity is often hampered by a lack of correlation between the probe and the drug of interest. In this article, we present a strategy to refine the phenotyping approach based on a physiologically based pharmacokinetic simulation (implemented in Simcyp Simulator version 17) using previously published models. The apparent clearance (CL/F) of erlotinib was better predicted by the sum of caffeine and i.v. midazolam CL/F (r 2 = 0.60) compared to that of either probe drug alone. The clearance of atorvastatin and repaglinide had a strong correlation (r 2 = 0.70 and 0.63, respectively) with that of pitavastatin (a SLCO1B1 probe). Use of multiple probes for drugs that are predominantly metabolized by more than one cytochrome P450 (CYP) enzyme should be considered. In a case in which hepatic uptake transporters play a significant role in the disposition of a drug, the pharmacokinetic of a transporter probe will provide better predictions of the drug clearance.