Evaluation of the drug-drug interaction potential of brigatinib using a physiologically-based pharmacokinetic modeling approach.

Brigatinib is an oral anaplastic lymphoma kinase (ALK) inhibitor approved for the treatment of ALK-positive metastatic non-small cell lung cancer. In vitro studies indicated that brigatinib is primarily metabolized by CYP2C8 and CYP3A4 and inhibits P-gp, BCRP, OCT1, MATE1, and MATE2K. Clinical drug-drug interaction (DDI) studies with the strong CYP3A inhibitor itraconazole or the strong CYP3A inducer rifampin demonstrated that CYP3A-mediated metabolism was the primary contributor to overall brigatinib clearance in humans. A physiologically-based pharmacokinetic (PBPK) model for brigatinib was developed to predict potential DDIs, including the effect of moderate CYP3A inhibitors or inducers on brigatinib pharmacokinetics (PK) and the effect of brigatinib on the PK of transporter substrates. The developed model was able to predict clinical DDIs with itraconazole (area under the plasma concentration-time curve from time 0 to infinity [AUC∞] ratio [with/without itraconazole]: predicted 1.86; observed 2.01) and rifampin (AUC∞ ratio [with/without rifampin]: predicted 0.16; observed 0.20). Simulations using the developed model predicted that moderate CYP3A inhibitors (e.g., verapamil and diltiazem) may increase brigatinib AUC∞ by ~40%, whereas moderate CYP3A inducers (e.g., efavirenz) may decrease brigatinib AUC∞ by ~50%. Simulations of potential transporter-mediated DDIs predicted that brigatinib may increase systemic exposures (AUC∞) of P-gp substrates (e.g., digoxin and dabigatran) by 15%-43% and MATE1 substrates (e.g., metformin) by up to 29%; however, negligible effects were predicted on BCRP-mediated efflux and OCT1-mediated uptake. The PBPK analysis results informed dosing recommendations for patients receiving moderate CYP3A inhibitors (40% brigatinib dose reduction) or inducers (up to 100% increase in brigatinib dose) during treatment, as reflected in the brigatinib prescribing information.

Supplementing clinical lactation studies with PBPK modeling to inform drug therapy in lactating mothers: Prediction of primaquine exposure as a case example.

Evaluating the safety of primaquine (PQ) during breastfeeding requires an understanding of its pharmacokinetics (PKs) in breast milk and its exposure in the breastfed infant. Physiologically-based PK (PBPK) modeling is primed to assess the complex interplay of factors affecting the exposure of PQ in both the mother and the nursing infant. A published PBPK model for PQ describing the metabolism by monoamine oxidase A (MAO-A; 90% contribution) and cytochrome P450 2D6 (CYP2D6; 10%) in adults was applied to predict the exposure of PQ in mothers and their breastfeeding infants. Plasma exposures following oral daily dosing of 0.5 mg/kg in the nursing mothers in a clinical lactation study were accurately captured, including the observed ranges. Reported infant daily doses based on milk data from the clinical study were used to predict the exposure of PQ in breastfeeding infants greater than or equal to 28 days. On average, the predicted exposures were less than or equal to 0.13% of the mothers. Furthermore, in simulations involving neonates less than 28 days, PQ exposures remain less than 0.16% of the mothers. Assuming that MAO-A increases slowly with age, the predicted relative exposure of PQ remains low in neonates (<0.46%). Thus, the findings of our study support the recommendation made by the authors who reported the results of the clinical lactation study, that is, that when put into context of safety data currently available in children, PQ should not be withheld in lactating women as it is unlikely to cause adverse events in breastfeeding infants greater than or equal to 28 days old.

A physiologically based pharmacokinetic - pharmacodynamic modelling approach to predict incidence of neutropenia as a result of drug-drug interactions of paclitaxel in cancer patients.

Paclitaxel is the backbone of standard chemotherapeutic regimens used in a number of malignancies and is frequently given with concomitant medications. Newly developed oncolytic agents, including tyrosine kinase inhibitors are often shown to be CYP3A4 and P-gp inhibitors. The aim of this study was to develop a PBPK model for intravenously administered paclitaxel in order to predict the incidence of neutropenia and to estimate the DDI potential as a victim drug. The dose-dependent effects on paclitaxel plasma protein binding, volume of distribution and drug clearance were considered for dose levels of 80 mg/m2, 135 mg/m2 and 175 mg/m2. A pharmacodynamics model that incorporate the impact of paclitaxel on the neutrophil was developed. The relative metabolic clearance via CYP3A4 and CYP2C8, the renal clearance as well as P-gp mediated biliary clearance were incorporated in the model in order to assess the neutropenia in the presence of DDI. The developed PBPK-PD model was able to recover the drop in neutrophils observed after the administration of 175mg/m2 of paclitaxel over a 3-h duration. The mean nadir observed was 1.9 × 109 neutrophils/L and was attained after 10 days of treatment, and a fraction of 47% of the population was predicted to have at some point a neutropenia including 12% with severe neutropenia. In the case of concomitant administration of ketoconazole, 39% of the population was predicted to suffer from severe neutropenia. In summary, PBPK-PD modeling allows a priori prediction of DDIs and safety events involving complex combination therapies which are often utilized in an oncology setting.

Assessing pharmacokinetic differences in Caucasian and East Asian (Japanese, Chinese and Korean) populations driven by CYP2C19 polymorphism using physiologically-based pharmacokinetic modelling.

Understanding the influence of ethnicity on drug exposure is key to patient safety and could minimize repetitive clinical studies. This analysis aimed to evaluate the ability of physiologically-based pharmacokinetic modelling to predict exposure of CYP2C19 substrates (lansoprazole, (es)citalopram, voriconazole) across Caucasian and East Asian populations. CYP2C19 abundance levels in Japanese and Chinese populations have been re-assessed based on clinical evidence. Model performance in each population was evaluated by predicted-over-observed AUC ratios and comparison of observed data with simulated plasma concentration profiles. Exposures in 84.4% (76 out of 90) of the clinical studies were predicted within 1.5-fold of observed values. The reported concentration-time profiles were well-captured within the 90% prediction intervals. With specified CYP2C19 phenotype, PBPK modelling is capable to predict systemic exposure of drugs largely metabolized by CYP2C19 in different ethnic populations. This study demonstrated PBPK modelling can be applied to assess genotype-dependent exposure difference across ethnicities.

Application of permeability-limited physiologically-based pharmacokinetic models: part I-digoxin pharmacokinetics incorporating P-glycoprotein-mediated efflux.

A prerequisite for the prediction of the magnitude of P-glycoprotein (P-gp)-mediated drug-drug interactions between digoxin and P-gp inhibitors (e.g. verapamil and its metabolite norverapamil) or P-gp inducers (e.g. rifampicin) is a predictive pharmacokinetic model for digoxin itself. Thus, relevant in vitro metabolic, transporter and inhibitory data incorporated into permeability-limited models, such as the "advanced dissolution, absorption and metabolism" (ADAM) module and the permeability-limited liver (PerL) module, integrated with a mechanistic physiologically-based pharmacokinetic (PBPK) model such as that of the Simcyp Simulator (version 12.2) are necessary. Simulated concentration-time profiles of digoxin generated using the developed model were consistent with observed data across 31 independent studies [13 intravenous single dose (SD), 12 per oral SD and six multiple dose studies]. The fact that predicted tmax (time of maximum plasma concentration observed) and Cmax (maximum plasma concentration observed) of oral digoxin were similar to observed values indicated that the relative contributions of permeation and P-gp-mediated efflux in the model were appropriate. There was no indication of departure from dose proportionality over the dose range studied (0.25-1.5 mg). All dose normalised area under the plasma concentration-time curve profiles (AUCs) for the 0.25, 0.5, 0.75 and 1 mg doses resembled each other. Thus, PBPK modelling in conjunction with mechanistic absorption and distribution models and reliable in vitro transporter data can be used to assess the impact of dose on P-gp-mediated efflux (or otherwise).

Application of permeability-limited physiologically-based pharmacokinetic models: part II - prediction of P-glycoprotein mediated drug-drug interactions with digoxin.

Digoxin is the recommended substrate for assessment of P-glycoprotein (P-gp)-mediated drug-drug interactions (DDIs) in vivo. The overall aim of our study was to investigate the inhibitory potential of both verapamil and norverapamil on the P-gp-mediated efflux of digoxin in both gut and liver. Therefore, a physiologically-based pharmacokinetic (PBPK) model for verapamil and its primary metabolite was developed and validated through the recovery of observed clinical plasma concentration data for both moieties and the reported interaction with midazolam, albeit a cytochrome P450 3A4-mediated DDI. The validated inhibitor model was then used in conjunction with the model developed previously for digoxin. The range of values obtained for the 10 trials indicated that increases in area under the plasma concentration-time curve (AUC) profiles and maximum plasma concentration observed (Cmax ) values of digoxin following administration of verapamil were more comparable with in vivo observations, when P-gp inhibition by the metabolite, norverapamil, was considered as well. The predicted decrease in AUC and Cmax values of digoxin following administration of rifampicin because of P-gp induction was 1.57- (range: 1.42-1.77) and 1.62-fold (range: 1.53-1.70), which were reasonably consistent with observed values of 1.4- and 2.2-fold, respectively. This study demonstrates the application of permeability-limited models of absorption and distribution within a PBPK framework together with relevant in vitro data on transporters to assess the clinical impact of modulated P-gp-mediated efflux by drugs in development.

Predicting drug-drug interactions: application of physiologically based pharmacokinetic models under a systems biology approach.

The development of in vitro-in vivo extrapolation (IVIVE), a 'bottom-up' approach, to predict pharmacokinetic parameters and drug-drug interactions (DDIs) has accelerated mainly due to an increase in the understanding of the multiple mechanisms involved in these interactions and the availability of appropriate in vitro systems that act as surrogates for delineating various elements of the interactions relevant to absorption, distribution, metabolism and elimination. Recent advances in the knowledge of the population variables required for IVIVE (demographic, anatomical, genetic and physiological parameters) have also contributed to the appreciation of the sources of variability and wider use of this approach for different scenarios within the pharmaceutical industry. Initially, the authors present an overview of the integration of IVIVE into 'static' and 'dynamic' models for the quantitative prediction of DDIs. The main purpose of this review is to discuss the application of IVIVE in conjunction with physiologically based pharmacokinetic modeling under a systems biology approach to characterize the potential DDIs in individual patients, including those who cannot be investigated in formal clinical trials for ethical reasons. In addition, we address the issues related to the prediction of complex DDIs involving the inhibition of cytochrome P- and transporter-mediated activities through multiple drugs.

Application of in vitro-in vivo extrapolation (IVIVE) and physiologically based pharmacokinetic (PBPK) modelling to investigate the impact of the CYP2C8 polymorphism on rosiglitazone exposure.

PURPOSE: To predict the impact of the CYP2C8 3 genotype on rosiglitazone exposure in the absence and presence of trimethoprim. METHODS: Prior in vitro and in vivo information for rosiglitazone and trimethoprim were collated from the literature. Specifically, data on the frequency of the different allelic forms of CYP2C8 and their metabolic activity for rosiglitazone were incorporated into a physiologically-based pharmacokinetic (PBPK) model within the Simcyp Simulator (V11.1) to predict differences in the relative exposure of rosiglitazone according to CYP2C8 3 genotype in a virtual population. RESULTS: Following multiple doses of 8 mg rosiglitazone, the predicted mean AUC(0-24) was 37 % lower in CYP2C8 3 homozygotes compared with wildtype homozygotes (p < 0.001), which was consistent with the 36 % lower value observed in vivo (p < 0.001) Kirchheiner et al. (Clin Pharmacol Ther 80:657-667, 2006). Predicted median AUC ratios of rosiglitazone in the presence and absence of trimethoprim ranged from 1.35 to 1.66 for ten virtual trials of subjects with the CYP2C8 1/1 genotype, which included the observed value of 1.42. In subjects with the CYP2C8 1/3 genotype, the predicted AUC ratios for all trials were higher than the observed value of 1.18 Kirchheiner et al. (Clin Pharmacol Ther 80:657-667, 2006). CONCLUSIONS: Investigating the drug interactions in individuals with rare allelic forms of drug metabolising enzymes is fraught with many practical problems. Current study demonstrates the utility of prior in vitro metabolism data from such allelic forms to predict the relative exposure of a drug as a function of genotype. However, in vitro inhibition data obtained in one allelic variant (e.g. CYP2C8 1) may not be adequate to predict the in vivo interactions in another allele (e.g. CYP2C8 3), since the inhibitory characteristics of perpetrator might be different in each allelic variant in the same way as that of metabolism of the victim drug by such variants of the enzyme.

Cytochrome p450 turnover: regulation of synthesis and degradation, methods for determining rates, and implications for the prediction of drug interactions.

In vivo enzyme levels are governed by the rates of de novo enzyme synthesis and degradation. A current lack of consensus on values of the in vivo turnover half-lives of human cytochrome P450 (CYP) enzymes places a significant limitation on the accurate prediction of changes in drug concentration-time profiles associated with interactions involving enzyme induction and mechanism (time)-based inhibition (MBI). In the case of MBI, the full extent of inhibition is also sensitive to values of enzyme turnover half-life. We review current understanding of CYP regulation, discuss the pros and cons of various in vitro and in vivo approaches used to estimate the turnover of specific CYPs and, by simulation, consider the impact of variability in estimates of CYP turnover on the prediction of enzyme induction and MBI in vivo. In the absence of consensus on values for the in vivo turnover half-lives of key CYPs, a sensitivity analysis of predictions of the pharmacokinetic effects of enzyme induction and MBI to these values should be an integral part of the modelling exercise, and the selective use of values should be avoided.

Prediction of intestinal first-pass drug metabolism.

Despite a lower content of many drug metabolising enzymes in the intestinal epithelium compared to the liver (e.g. intestinal CYP3A abundance in the intestine is 1% that of the liver), intestinal metabolic extraction may be similar to or exceed hepatic extraction. Modelling of events on first-pass through the intestine requires attention to the complex interplay between passive permeability, active transport, binding, relevant blood flows and the intrinsic activity and capacity of enzyme systems. We have compared the predictive accuracy of the "well-stirred" gut model with that of the "Q(Gut)" model. The former overpredicts the fraction escaping first-pass gut metabolism; the latter improves the predictions by accounting for interplay between permeability and metabolism.

Theoretical assessment of a new experimental protocol for determining kinetic values describing mechanism (time)-based enzyme inhibition.

We have shown previously that the conventional experimental protocol (CEP) used to characterise mechanism-based enzyme inhibition (MBI) of drug metabolism in vitro may introduce substantial bias in estimates of the relevant kinetic parameters. The aim of this study was to develop and assess, by computer simulation, an alternative, mechanistically-based experimental protocol (MEP). This protocol comprises three parts viz. assessment of the metabolism of the mechanism-based enzyme inactivator (MBEI), of its ability to participate in competitive inhibition and its ability to cause time-dependent inhibition. Thus, values of the maximum inactivation rate constant (k(inact)), the inactivator concentration associated with half-maximal rate of inactivation (K(I)), the partition ration (r), and the reversible inhibition constant (K(i)) of the MBEI are determined by nonlinear optimization of the experimental data using a model that allows for metabolism of both probe substrate and MBEI, the time-course of inactivation of the enzyme, and reversible inhibition of the metabolism of both probe substrate and MBEI. Sensitivity analysis is used to estimate the degree of confidence in the final parameter values. Virtual experiments using the MEP and the CEP were simulated, applying starting kinetic parameters reported for 16 known MBEIs. In the presence of simulated experimental error (5% CV), the MEP recovered accurate estimates of the kinetic values for all compounds, while estimates using the CEP were less accurate and less precise. The MEP promises to improve consistency in the determination of in vitro measures of MBI and, thereby, the quantitative assessment of its in vivo consequences.

The effect of chronic tobacco smoking on arterial stiffness.

AIMS: Cardiovascular disease caused by smoking is related to the pathophysiological burden placed on the vascular endothelium. We studied the effect of chronic cigarette smoking on arterial wave reflection (study 1) and smoking cessation on pulse wave analysis (study 2). METHODS: Fifty smokers and 50 age- and sex-matched nonsmokers participated in study 1. Study 2 recruited 20 volunteers from the stop smoking clinic at the Royal Hallamshire Hospital, Sheffield, UK. Systemic augmentation index (AIx) and carotid-femoral pulse wave velocity (PWV) were measured using the SphygmoCor system. Brachial blood pressure (BP) (Omron 705-CP-E), AIx and PWV were recorded at a single visit in study 1. Study 2 measured these variables on 'quit day' and 4 weeks later. RESULTS: In study 1, AIx was significantly higher in smokers than in nonsmokers (median 17.25 vs. 11.75%, P = 0.004). Multiple regression analysis showed a significant correlation between AIx and age, diastolic BP, smoking status (P < 0.001), blood glucose (P = 0.045) and weight (P = 0.049). In study 2, AIx significantly reduced after 4 weeks of abstinence in successful quitters (n = 10) compared with relapsed smokers (n = 4) (median 5.0 vs.- 9.5; P = 0.013). PWV did not reach significance in either study. CONCLUSIONS: Chronic tobacco smoking is associated with endothelial dysfunction and increased AIx in subjects of a wide age range free from additional cardiovascular risk factors, which is partially reversible after 4 weeks of smoking cessation.

Kinetic values for mechanism-based enzyme inhibition: assessing the bias introduced by the conventional experimental protocol.

The in vitro characterisation of a mechanism-based enzyme inactivator (MBEI) includes determination of the maximum inactivation rate constant (k(inact)), the inactivator concentration that produces half-maximal rate of inactivation (K(I)), and the partition ratio (r). Conventional experimental protocols (CEPs) assume insignificant metabolism of the MBEI during the "pre-incubation" stage and negligible inactivation of enzyme during the "incubation" stage. The aim of this study was to evaluate the bias in the estimation of kinetic values as a consequence of these assumptions. Ranges of values of k(inact), K(I), and r for reported MBEIs were collated and data for 27 virtual compounds were generated by combining the median, high and low values of each parameter. The kinetics of the virtual compounds and of four reported MBEIs were simulated under CEP, but taking account of enzyme inactivation, metabolism of the MBEI and the probe substrate, and their interaction at relevant stages. The differences between the estimated and starting kinetic values reflect the bias introduced by the CEP in the absence of experimental error. Despite simulating a stringent experimental procedure, 19% of the estimated kinetic values of the 27 virtual MBEIs had greater than 100% bias. Simulations relating to two of the actual MBEIs indicated no bias in k(inact) and 8-33% bias in K(I). However, the bias in K(I) values of the two other compounds exceeded 98% and corresponding bias in k(inact) was greater than 300%. Thus, CEP may introduce substantial bias in estimated kinetic values for mechanism-based inhibition, and the validity of some of the reported kinetic parameters may be questionable.

Coronary risk versus cardiovascular risk for treatment decisions in mild hypertension.

BACKGROUND: British guidelines recommend treatment for mild hypertension at a cardiovascular (CVD) risk threshold of 20% over 10 years. However, treatment is targeted at the equivalent coronary (CHD) risk of 15% over 10 years. We examined the relationship between CHD and CVD risk in men and women with mild hypertension and assessed the accuracy of using a 10-year CHD risk threshold of 15% to identify patients at a 10-year CVD risk > or = 20%. DESIGN: Cross-sectional survey of England in 1998. METHODS: We identified 5588 subjects aged 35-74 years free of cardiovascular disease with complete data for risk assessment. Of these, 1364 (24.4%) had mild hypertension (systolic pressure 140-159 mmHg or diastolic pressure 90-99 mmHg). The Framingham functions were used to estimate CHD and CVD event risk for each individual. RESULTS: At a 10-year CHD risk of 15%, the corresponding 10-year CVD risk for men and women, respectively was 20% and 21% in those aged < 55 years, and 24% and 25% in those aged > or = 55 years. Using a 10-year CHD risk threshold of 15% to identify patients at a 10-year CVD risk > or = 20% had high specificity (>96%) in all four groups. For men and women respectively, the sensitivity was 73% (62-84%) and 62% (35-88%) in younger subjects, and 89% (85-93%) and 47% (38-56%) in older subjects. CONCLUSION: Using a 10-year CHD risk of 15% to target patients at a 10-year CVD risk > or = 20% was reasonably accurate for men but missed about 50% of women eligible for antihypertensive treatment.

Should we treat high-normal blood pressure?

OBJECTIVES: To examine the risk of cardiovascular disease associated with high-normal blood pressure in English adults and estimate the proportion of these individuals who are at high cardiovascular risk. DESIGN AND SETTING: Cross-sectional survey of England in 1998. PARTICIPANTS: Nationally representative sample of 12,341 individuals aged 18-80 years living in private households in England. MAIN OUTCOME MEASURE: Percentage of individuals with high-normal blood pressure who have cardiovascular disease, diabetes mellitus or a 10-year cardiovascular event risk of at least 20%. RESULTS: Of the 12,341 participants, 2413 (19.6%) had high-normal blood pressure. About 5.3% of those aged 18-80 years with high-normal blood pressure had cardiovascular disease or diabetes, and a further 7.6% were at a predicted cardiovascular event risk of at least 20% over 10 years. The mean predicted risk was 8.7% for men and 6.3% for women in the high-normal blood pressure category. The majority of men aged 61-80 years were at high cardiovascular risk. On average, men and women with high-normal blood pressure had a greater incidence of cardiovascular disease and diabetes mellitus, and a greater predicted mean cardiovascular risk than those with normal blood pressure. Extending antihypertensive treatment to individuals with high-normal blood pressure who are at high cardiovascular risk would involve treating an additional 2.5% of the English population aged 18-80 years. CONCLUSION: These findings support the view that individuals with high-normal blood pressure at high risk for cardiovascular disease should be targeted for blood pressure-lowering treatment.