Physiologically Based Pharmacokinetic (PBPK) Model Predictions of Disease Mediated Changes in Drug Disposition in Patients with Nonalcoholic Fatty Liver Disease (NAFLD).

PURPOSE: This study was designed to verify a virtual population representing patients with nonalcoholic fatty liver disease (NAFLD) to support the implementation of a physiologically based pharmacokinetic (PBPK) modeling approach for prediction of disease-related changes in drug pharmacokinetics. METHODS: A virtual NAFLD patient population was developed in GastroPlus (v.9.8.2) by accounting for pathophysiological changes associated with the disease and proteomics-informed alterations in the abundance of metabolizing enzymes and transporters pertinent to drug disposition. The NAFLD population model was verified using exemplar drugs where elimination is influenced predominantly by cytochrome P450 (CYP) enzymes (chlorzoxazone, caffeine, midazolam, pioglitazone) or by transporters (rosuvastatin, 11C-metformin, morphine and the glucuronide metabolite of morphine). RESULTS: PBPK model predictions of plasma concentrations of all the selected drugs and hepatic radioactivity levels of 11C-metformin were consistent with the clinically-observed data. Importantly, the PBPK simulations using the virtual NAFLD population model provided reliable estimates of the extent of changes in key pharmacokinetic parameters for the exemplar drugs, with mean predicted ratios (NAFLD patients divided by healthy individuals) within 0.80- to 1.25-fold of the clinically-reported values, except for midazolam (prediction-fold difference of 0.72). CONCLUSION: A virtual NAFLD population model within the PBPK framework was successfully developed with good predictive capability of estimating disease-related changes in drug pharmacokinetics. This supports the use of a PBPK modeling approach for prediction of the pharmacokinetics of new investigational or repurposed drugs in patients with NAFLD and may help inform dose adjustments for drugs commonly used to treat comorbidities in this patient population.

Application of physiologically based pharmacokinetic modeling to understand real-world outcomes in patients receiving imatinib for chronic myeloid leukemia.

We aimed to use physiologically based pharmacokinetic (PBPK) modeling and simulation to predict imatinib steady-state plasma exposure in patients with chronic myeloid leukemia (CML) to investigate variability in outcomes. A validated imatinib PBPK model (Simcyp Simulator) was used to predict imatinib AUCss , Css,min and Css,max for patients with CML (n = 68) from a real-world retrospective observational study. Differences in imatinib exposure were evaluated based on clinical outcomes, (a) Early Molecular Response (EMR) achievement and (b) occurrence of grade ≥3 adverse drug reactions (ADRs), using the Kruskal-Wallis rank sum test. Sensitivity analyses explored the influence of patient characteristics and drug interactions on imatinib exposure. Simulated imatinib exposure was significantly higher in patients who achieved EMR compared to patients who did not (geometric mean AUC0-24,ss 51.2 vs. 42.7 µg h mL-1 , p < 0.05; Css,min 1.1 vs. 0.9 µg mL-1 , p < 0.05; Css,max 3.4 vs. 2.8 µg mL-1 , p < 0.05). Patients who experienced grade ≥3 ADRs had a significantly higher simulated imatinib exposure compared to patients who did not (AUC0-24,ss 56.1 vs. 45.9 µg h mL-1 , p < 0.05; Css,min 1.2 vs. 1.0 µg mL-1 , p < 0.05; Css,max 3.7 vs. 3.0 µg mL-1 , p < 0.05). Simulations identified a range of patient (sex, age, weight, abundance of hepatic CYP2C8 and CYP3A4, α1 -acid glycoprotein concentrations, liver and kidney function) and medication-related factors (dose, concomitant CYP2C8 modulators) contributing to the inter-individual variability in imatinib exposure. Relationships between imatinib plasma exposure, EMR achievement and ADRs support the rationale for therapeutic drug monitoring to guide imatinib dosing to achieve optimal outcomes in CML.

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.

Considerations for Physiologically Based Modeling in Liver Disease: From Nonalcoholic Fatty Liver (NAFL) to Nonalcoholic Steatohepatitis (NASH).

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 NAFLD-mediated 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.

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.

Investigating bile acid-mediated cholestatic drug-induced liver injury using a mechanistic model of multidrug resistance protein 3 (MDR3) inhibition.

Inhibition of the canalicular phospholipid floppase multidrug resistance protein 3 (MDR3) has been implicated in cholestatic drug-induced liver injury (DILI), which is clinically characterized by disrupted bile flow and damage to the biliary epithelium. Reduction in phospholipid excretion, as a consequence of MDR3 inhibition, decreases the formation of mixed micelles consisting of bile acids and phospholipids in the bile duct, resulting in a surplus of free bile acids that can damage the bile duct epithelial cells, i.e., cholangiocytes. Cholangiocytes may compensate for biliary increases in bile acid monomers via the cholehepatic shunt pathway or bicarbonate secretion, thereby influencing viability or progression to toxicity. To address the unmet need to predict drug-induced bile duct injury in humans, DILIsym, a quantitative systems toxicology model of DILI, was extended by representing key features of the bile duct, cholangiocyte functionality, bile acid and phospholipid disposition, and cholestatic hepatotoxicity. A virtual, healthy representative subject and population (n = 285) were calibrated and validated utilizing a variety of clinical data. Sensitivity analyses were performed for 1) the cholehepatic shunt pathway, 2) biliary bicarbonate concentrations and 3) modes of MDR3 inhibition. Simulations showed that an increase in shunting may decrease the biliary bile acid burden, but raise the hepatocellular concentrations of bile acids. Elevating the biliary concentration of bicarbonate may decrease bile acid shunting, but increase bile flow rate. In contrast to competitive inhibition, simulations demonstrated that non-competitive and mixed inhibition of MDR3 had a profound impact on phospholipid efflux, elevations in the biliary bile acid-to-phospholipid ratio, cholangiocyte toxicity, and adaptation pathways. The model with its extended bile acid homeostasis representation was furthermore able to predict DILI liability for compounds with previously studied interactions with bile acid transport. The cholestatic liver injury submodel in DILIsym accounts for several processes pertinent to bile duct viability and toxicity and hence, is useful for predictions of MDR3 inhibition-mediated cholestatic DILI in humans.

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.

Simultaneous HPLC Assay of Gliclazide and Ciprofloxacin in Plasma and its Implementation for Pharmacokinetic Study in Rats.

A simple and reliable high-performance liquid chromatography method for simultaneous quantitation of gliclazide and ciprofloxacin in plasma sample has been developed and validated. This method implements protein precipitation, a simple and practical pretreatment method by the addition of acetonitrile that gives a clean supernatant. The separation was carried out in a system consisted of a C18 column with acetonitrile and KH2PO4 (0.01 M, 0.1% v/v of triethylamine, pH 2.7) as the mobile phase in a gradient elution at a total flow-rate of 1 mL/min. Gliclazide and ciprofloxacin were quantitated using an ultraviolet detector set at wavelengths of 229 and 277 nm, respectively, which ensures optimal sensitivity for both compounds. This method possesses an excellent linearity at concentration ranges of 0.5-50 mg/L for gliclazide and 0.1-10 mg/L for ciprofloxacin. High within- and between-run accuracy for both gliclazide (% error of -8.00 to 0.45%) and ciprofloxacin (% error of -10.00 to 7.63%) were demonstrated. The intra- and inter-day precision (expressed as %CV) was <8 and 12% for gliclazide and ciprofloxacin, respectively. Both analytes were stable during storage and sample processing. The method reported in this study can be implemented for pharmacokinetic interaction study in rats.

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.

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.

Implementation of a Physiologically Based Pharmacokinetic Modeling Approach to Guide Optimal Dosing Regimens for Imatinib and Potential Drug Interactions in Paediatrics.

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/m2/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.

A Strategy to Refine the Phenotyping Approach and Its Implementation to Predict Drug Clearance: A Physiologically Based Pharmacokinetic Simulation Study.

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 (r2  = 0.60) compared to that of either probe drug alone. The clearance of atorvastatin and repaglinide had a strong correlation (r2  = 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.

Curcumin as a clinically-promising anti-cancer agent: pharmacokinetics and drug interactions.

INTRODUCTION: 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.