Prediction of free imatinib concentrations based on total plasma concentrations in patients with gastrointestinal stromal tumours

Development of an hollow fiber solid phase microextraction method for the analysis of unbound fraction of imatinib and N-desmethyl imatinib in human plasma

Therapeutic drug monitoring (TDM) of imatinib (IM) in cancer therapy offers the potential to improve treatment efficacy while minimizing toxicity. There was a significant correlation between unbound concentration and clinical response and toxicity, compared with total plasma concentrations, and the quantification of unbound IM and its metabolite, N-desmethyl imatinib (NDI) are of interest for TDM. However, traditional unbound drug separation methods have shortcomings, especially are susceptible to non-specific binding (NSB) of drugs to the polymer-constructed components of filter membranes, which are difficult to avoid at present. Hence it is necessary to developed a reliable separation method for the analysis of the unbound fraction of IM and NDI in TDM. We developed and validated an hollow fiber solid phase microextraction (HF-SPME) method coupled with high-performance liquid chromatography tandem mass spectrometry (HPLC-MS/MS) that to measure unbound IM and NDI concentration in human plasma. It used the NSB phenomenon and solve the NSB problem. The preparation procedure only involves a common vortex and ultrasonication without dilution of samples and modification of membrane. A total of 50 chronic myeloid leukemia (CML) patients were enrolled in our study. The relationship between the unbound and total concentrations for IM and NDI, as well as the concentration ratios of NDI to IM in 50 clinical plasma samples were investigated. The extraction recovery is high to 95.5-106 % with validation parameters for the methodological results were all excellent. There were both a poor linear relationship between the unbound and total concentrations for IM (r2=0.504) and NDI (r2=0.201) in 50 clinical plasma samples. The unbound concentration ratios of NDI to IM varied widely in CML patients. The determination of unbound IM and NDI concentration is meaningful and necessary. The developed HF-SPME method is simple, accurate and precise that could be used to measure unbound IM and NDI concentration in clinical TDM.

Whole Body Physiologically Based Pharmacokinetic Model to Explain A Patient With Drug-Drug Interaction Between Voriconazole and Flucloxacillin

BACKGROUND AND OBJECTIVES

Voriconazole administered concomitantly with flucloxacillin may result in subtherapeutic plasma concentrations as shown in a patient with Staphylococcus aureus sepsis and a probable pulmonary aspergillosis. After switching our patient to posaconazole, therapeutic concentrations were reached. The aim of this study was to first test our hypothesis that flucloxacillin competes with voriconazole not posaconazole for binding to albumin ex vivo, leading to lower total concentrations in plasma.

METHODS

A physiologically based pharmacokinetic (PBPK) model was then applied to predict the mechanism of action of the drug-drug interaction (DDI). The model included non-linear hepatic metabolism and the effect of a severe infectious disease on cytochrome P450 (CYP) enzymes activity.

RESULTS

The unbound voriconazole concentration remained unchanged in plasma after adding flucloxacillin, thereby rejecting our hypothesis of albumin-binding site competition. The PBPK model was able to adequately predict the plasma concentration of both voriconazole and posaconazole over time in healthy volunteers. Upregulation of CYP3A4, CYP2C9, and CYP2C19 through the pregnane X receptor (PXR) gene by flucloxacillin resulted in decreased voriconazole plasma concentrations, reflecting the DDI observations in our patient. Posaconazole metabolism was not affected, or was only limitedly affected, by the changes through the PXR gene, which agrees with the observed plasma concentrations within the target range in our patient.

CONCLUSIONS

Ex vivo experiments reported that the unbound voriconazole plasma concentration remained unchanged after adding flucloxacillin. The PBPK model describes the potential mechanism driving the drug-drug and drug-disease interaction of voriconazole and flucloxacillin, highlighting the large substantial influence of flucloxacillin on the PXR gene and the influence of infection on voriconazole plasma concentrations, and suggests a more limited effect on other triazoles.

Physiologically Based Pharmacokinetic Modelling to Predict Imatinib Exposures in Cancer Patients with Renal Dysfunction: A Case Study

Imatinib is mainly metabolised by CYP3A4 and CYP2C8 and is extensively bound to α-acid glycoprotein (AAG). A physiologically based pharmacokinetic (PBPK) model for imatinib describing the CYP3A4-mediated autoinhibition during multiple dosing in gastrointestinal stromal tumor patients with normal renal function was previously reported. After performing additional verification, the PBPK model was applied to predict the exposure of imatinib after multiple dosing in cancer patients with varying degrees of renal impairment. In agreement with the clinical data, there was a positive correlation between AAG levels and imatinib exposure. A notable finding was that for recovery of the observed data in cancer patients with moderate RI (CrCL 20 to 39 mL/min), reductions of hepatic CYP3A4 and CYP2C8 abundances, which reflect the effects of RI, had to be included in the simulations. This was not the case for mild RI (CrCL 40 to 50 mL/min). The results support the finding of the clinical study, which demonstrated that both AAG levels and the degree of renal impairment are key components that contribute to the interpatient variability associated with imatinib exposure. As indicated in the 2020 FDA draft RI guidance, PBPK modelling could be used to support an expanded inclusion of patients with RI in clinical studies.

Pharmacokinetics and pharmacodynamics of imatinib for optimal drug repurposing from cancer to COVID-19

INTRODUCTION

In the randomized double-blind placebo-controlled CounterCOVID study, oral imatinib treatment conferred a positive clinical outcome and a signal for reduced mortality in COVID-19 patients. High concentrations of alpha-1 acid glycoprotein (AAG) were observed in these patients and were associated with increased total imatinib concentrations.

AIMS

This post-hoc study aimed to compare the difference in exposure following oral imatinib administration in COVID-19 patients to cancer patients and assess assocations between pharmacokinetic (PK) parameters and pharmacodynamic (PD) outcomes of imatinib in COVID-19 patients. We hypothesize that a relatively higher drug exposure of imatinib in severe COVID-19 patients leads to improved pharmacodynamic outcome parameters.

METHODS

648 total concentration plasma samples obtained from 168 COVID-19 patients were compared to 475 samples of 105 cancer patients, using an AAG-binding model. Total trough concentration at steady state (Ct_trough) and total average area under the concentration-time curve (AUCt_ave) were associated with ratio between partial oxygen pressure and fraction of inspired oxygen (P/F), WHO ordinal scale (WHO-score) and liberation of oxygen supplementation (O₂lib). Linear regression, linear mixed effects models and time-to-event analysis were adjusted for possible confounders.

RESULTS

AUCt_ave and Ct_trough were respectively 2.21-fold (95%CI 2.07-2.37) and 1.53-fold (95%CI 1.44-1.63) lower for cancer compared to COVID-19 patients. Ct_trough, not AUCt_ave, associated significantly with P/F (β=-19,64; p-value=0.014) and O₂lib (HR 0.78; p-value= 0.032), after adjusting for sex, age, neutrophil-lymphocyte ratio, dexamethasone concomitant treatment, AAG and baseline P/F-and WHO-score. Ct_trough, but not AUCt_ave associated significantly with WHO-score. These results suggest an inverse relationship between PK-parameters, Ct_trough and AUCt_ave, and PD outcomes.

CONCLUSION

COVID-19 patients exhibit higher total imatinib exposure compared to cancer patients, attributed to differences in plasma protein concentrations. Higher imatinib exposure in COVID-19 patients did not associate with improved clinical outcomes. Ct_trough and AUCt_ave inversely associated with some PD-outcomes, which may be biased by disease course, variability in metabolic rate and protein binding. Therefore, additional PKPD analyses into unbound imatinib and its main metabolite may better explain exposure-response.

Population pharmacokinetic modelling of imatinib in healthy subjects receiving a single dose of 400 mg

Purpose

Imatinib is indicated for treatment of CML, GIST, etc. The population pharmacokinetics (popPK) of imatinib in patients under long-term treatment are reported in literature. Data obtained from bioequivalence trials for healthy subjects were used to evaluate the influence of demographic and pharmacogenetic factors on imatinib pharmacokinetics (PK) in a collective without concurrent drugs, organ dysfunction, inflammation etc. In addition, the differences in PK between the healthy subjects and a patient cohort was examined to identify possible disease effects.

Methods

26 volunteers were administered orally with single dose of 400 mg imatinib. 16–19 plasma samples per volunteer were collected from 0.5 up to 72 h post-dose. The popPK was built and post hoc estimates were compared with previously published PK parameters evaluated by non-compartmental analysis in the same cohort. The predictivity of the model for data collected from 40 patients with gastrointestinal stromal tumors at steady state was evaluated.

Results

The popPK was best described by a two-compartment transit model with first-order elimination. No significant covariates were identified, probably due to the small cohort and the narrow range of demographic covariates; CYP3A5 phenotypes appeared to have some influence on the clearance of imatinib. Good agreement between non-compartment and popPK analyses was observed with the differences of the geometric means/ median of PK estimates below 10%. The model indicated lower clearance for patients compared to healthy volunteers (p value < 0.01).

Conclusion

The two-compartment transit model adequately describes the absorption and distribution of imatinib in healthy volunteers. For patients, a lower clearance of imatinib compared to healthy volunteer was estimated by the model. The model can be applied for dose individualization based on trough concentrations assuming no significant differences in absorption between patients and healthy volunteers.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00280-022-04454-y.

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.

Therapeutic Drug Monitoring and Individualized Medicine of Dasatinib: Focus on Clinical Pharmacokinetics and Pharmacodynamics

Dasatinib is an oral second-generation tyrosine kinase inhibitor known to be used widely in Philadelphia chromosome-positive (Ph+) chronic myeloid leukemia (CML) and Ph+ acute lymphoblastic leukemia (ALL). Notably, although a high pharmacokinetic variability in patients and an increased risk of pleural effusion are attendant, fixed dosing remains standard practice. Retrospective studies have suggested that dasatinib exposure may be associated with treatment response (efficacy/safety). Therapeutic drug monitoring (TDM) is gradually becoming a practical tool to achieve the goal of individualized medicine for patients receiving targeted drugs. With the help of TDM, these patients who maintain response while have minimum adverse events may achieve long-term survival. This review summaries current knowledge of the clinical pharmacokinetics variation, exposure-response relationships and analytical method for individualized dosing of dasatinib, in particular with respect to therapeutic drug monitoring. In addition, it highlights the emerging insights into several controversial issues in TDM of dasatinib, with the aim of presenting up-to-date evidence for clinical decision-making and insights for future studies.

Integrated exposure-response analysis of efficacy and safety of lurbinectedin to support the dose regimen in small-cell lung cancer

Purpose

These exposure–response (E–R) analyses integrated lurbinectedin effects on key efficacy and safety variables in relapsed SCLC to determine the adequacy of the dose regimen of 3.2 mg/m² 1-h intravenous infusion every 3 weeks (q3wk).

Methods

Logistic models and Cox regression analyses were applied to correlate lurbinectedin exposure metrics (AUC_tot and AUC_u) with efficacy and safety endpoints: objective response rate (ORR) and overall survival (OS) in SCLC patients (n = 99) treated in study B-005 with 3.2 mg/m² q3wk, and incidence of grade 4 (G4) neutropenia and grade 3–4 (G ≥ 3) thrombocytopenia in a pool of cancer patients from single-agent phase I to III studies (n = 692) treated at a wide range of doses. A clinical utility index was used to assess the appropriateness of the selected dose.

Results

Effect of lurbinectedin AUC_u on ORR best fitted to a sigmoid-maximal response (E_max) logistic model, where E_max was dependent on chemotherapy-free interval (CTFI). Cox regression analysis with OS found relationships with both CTFI and AUC_u. An E_max logistic model for G4 neutropenia and a linear logistic model for G ≥ 3 thrombocytopenia, which retained platelets and albumin at baseline and body surface area, best fitted to AUC_tot and AUC_u. AUC_u between approximately 1000 and 1700 ng·h/L provided the best benefit/risk ratio, and the dose of 3.2 mg/m² provided median AUC_u of 1400 ng·h/L, thus maximizing the proportion of patients within that lurbinectedin target exposure range.

Conclusions

The relationships evidenced in this integrated E–R analysis support a favorable benefit-risk profile for lurbinectedin 3.2 mg/m² q3wk.

Trial registration

Clinicaltrials.gov: NCT02454972; registered May 27, 2015.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00280-021-04366-3.

External evaluation of population pharmacokinetic models of imatinib in adults diagnosed with chronic myeloid leukaemia

AIMS

Imatinib is considered the standard first-line treatment in newly diagnosed patients with chronic-phase myeloid leukaemia (CML). Several imatinib population pharmacokinetic (popPK) models have been developed. However, their predictive performance has not been well established when extrapolated to different populations. Therefore, this study aimed to perform an external evaluation of available imatinib popPK models developed mainly in adult patients, and to evaluate the improvement in individual model-based predictions through Bayesian forecasting computed by each model at different treatment occasions.

METHODS

A literature review was conducted through PubMed and Scopus to identify popPK models. Therapeutic drug monitoring data collected in adult CML patients treated with imatinib was used for external evaluation, including prediction- and simulated-based diagnostics together with Bayesian forecasting analysis.

RESULTS

Fourteen imatinib popPK studies were included for model-performance evaluation. A total of 99 imatinib samples were collected from 48 adult CML patients undergoing imatinib treatment with a minimum of one plasma concentration measured at steady-state between January 2016 and December 2020. The model proposed by Petain et al showed the best performance concerning prediction-based diagnostics in the studied population. Bayesian forecasting demonstrated a significant improvement in predictive performance at the second visit. Inter-occasion variability contributed to reducing bias and improving individual model-based predictions.

CONCLUSIONS

Imatinib popPK studies developed in Caucasian subjects including α1-acid glycoprotein showed the best model performance in terms of overall bias and precision. Moreover, two imatinib samples from different visits appear sufficient to reach an adequate model-based individual prediction performance trough Bayesian forecasting.

Elevated acute phase proteins affect pharmacokinetics in COVID-19 trials: Lessons from the CounterCOVID - imatinib study

This study aimed to determine whether published pharmacokinetic (PK) models can adequately predict the PK profile of imatinib in a new indication, such as coronavirus disease 2019 (COVID‐19). Total (bound + unbound) and unbound imatinib plasma concentrations obtained from 134 patients with COVID‐19 participating in the CounterCovid study and from an historical dataset of 20 patients with gastrointestinal stromal tumor (GIST) and 85 patients with chronic myeloid leukemia (CML) were compared. Total imatinib area under the concentration time curve (AUC), maximum concentration (C_max) and trough concentration (C_trough) were 2.32‐fold (95% confidence interval [CI] 1.34–3.29), 2.31‐fold (95% CI 1.33–3.29), and 2.32‐fold (95% CI 1.11–3.53) lower, respectively, for patients with CML/GIST compared with patients with COVID‐19, whereas unbound concentrations were comparable among groups. Inclusion of alpha1‐acid glycoprotein (AAG) concentrations measured in patients with COVID‐19 into a previously published model developed to predict free imatinib concentrations in patients with GIST using total imatinib and plasma AAG concentration measurements (AAG‐PK‐Model) gave an estimated mean (SD) prediction error (PE) of −20% (31%) for total and −7.0% (56%) for unbound concentrations. Further covariate modeling with this combined dataset showed that in addition to AAG; age, bodyweight, albumin, CRP, and intensive care unit admission were predictive of total imatinib oral clearance. In conclusion, high total and unaltered unbound concentrations of imatinib in COVID‐19 compared to CML/GIST were a result of variability in acute phase proteins. This is a textbook example of how failure to take into account differences in plasma protein binding and the unbound fraction when interpreting PK of highly protein bound drugs, such as imatinib, could lead to selection of a dose with suboptimal efficacy in patients with COVID‐19.

Therapeutic drug monitoring in oncology: International Association of Therapeutic Drug Monitoring and Clinical Toxicology consensus guidelines for imatinib therapy

Although therapeutic drug monitoring (TDM) is an important tool in guiding drug dosing for other areas of medicine including infectious diseases, cardiology, psychiatry and transplant medicine, it has not gained wide acceptance in oncology. For imatinib and other tyrosine kinase inhibitors, a flat dosing approach is utilised for management of oral chemotherapy. There are many published studies examining the correlation of blood concentrations with clinical effects of imatinib. The International Association of Therapeutic Drug Monitoring and Clinical Toxicology (IATDMCT) determined that there was a need to examine the published literature regarding utility of TDM in imatinib therapy and to develop consensus guidelines for TDM based on the available data. This article summarises the scientific evidence regarding TDM of imatinib, as well as the consensus guidelines developed by the IATDMCT.

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 (C_ss,min ).

RESULTS

The PBPK model of imatinib demonstrated excellent predictive performance in describing pharmacokinetics and the attained C_ss,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 C_ss,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.

Data-driven personalization of a physiologically based pharmacokinetic model for caffeine: A systematic assessment

Physiologically based pharmacokinetic (PBPK) models have been proposed as a tool for more accurate individual pharmacokinetic (PK) predictions and model‐informed precision dosing, but their application in clinical practice is still rare. This study systematically assesses the benefit of using individual patient information to improve PK predictions. A PBPK model of caffeine was stepwise personalized by using individual data on (1) demography, (2) physiology, and (3) cytochrome P450 (CYP) 1A2 phenotype of 48 healthy volunteers participating in a single‐dose clinical study. Model performance was benchmarked against a caffeine base model simulated with parameters of an average individual. In the first step, virtual twins were generated based on the study subjects' demography (height, weight, age, sex), which implicated the rescaling of average organ volumes and blood flows. The accuracy of PK simulations improved compared with the base model. The percentage of predictions within 0.8‐fold to 1.25‐fold of the observed values increased from 45.8% (base model) to 57.8% (Step 1). However, setting physiological parameters (liver blood flow determined by magnetic resonance imaging, glomerular filtration rate, hematocrit) to measured values in the second step did not further improve the simulation result (59.1% in the 1.25‐fold range). In the third step, virtual twins matching individual demography, physiology, and CYP1A2 activity considerably improved the simulation results. The percentage of data within the 1.25‐fold range was 66.15%. This case study shows that individual PK profiles can be predicted more accurately by considering individual attributes and that personalized PBPK models could be a valuable tool for model‐informed precision dosing approaches in the future.

Drug Exposure to Establish Pharmacokinetic-Response Relationships in Oncology

In the oncology field, understanding the relationship between the dose administered and the exerted effect is particularly important because of the narrow therapeutic index associated with anti-cancer drugs and the high interpatient variability. Therefore, in this review, we provide a critical perspective of the different methods of characterising treatment exposure in the oncology setting. The increasing number of modelling applications in oncology reflects the applicability and the impact of pharmacometrics on all phases of the drug development process and patient management as well. Pharmacometric modelling is a worthy component within the current paradigm of model-based drug development, but pharmacometric modelling techniques are also accessible for the clinician in the optimisation of current oncology therapies. Consequently, the application of population models in a hospital setting by generating close collaborations between physicians and pharmacometricians is highly recommended, providing a systematic means of developing and assessing model-based metrics as 'drivers' for various responses to treatments, which can then be evaluated as predictors for treatment success. Characterising the key determinants of variability in exposure is of particular importance for anticancer agents, as efficacy and toxicity are associated with exposure. We present the different strategies to describe and predict drug exposure that can be applied depending on the data available, with the objective of obtaining the most useful information in the patients' favour throughout the full drug cycle. Therefore, the objective of the present article is to review the different approaches used to characterise a patient's exposure to oncology drugs, which will result in a better understanding of the time course of the response and the magnitude of interpatient variability.

ABCG2 Single Nucleotide Polymorphism Affects Imatinib Pharmacokinetics in Lower Alpha-1-Acid Glycoprotein Levels in Humans

Imatinib is transported extracellularly by ABCB1 and ABCG2 efflux transporters and bound to alpha-1-acid glycoprotein (AGP) in the bloodstream. However, the clinical and pharmacokinetic effects of ABCB1 and ABCG2 on imatinib were inconsistent in the previous literature and have not been confirmed. Therefore, in the present study, we explored the effects of the ABCG2 and ABCB1 genetic polymorphisms on imatinib pharmacokinetics in association with plasma AGP levels in healthy subjects. Twenty-seven healthy individuals were recruited, genotyped for ABCG2 and ABCB1, and given a single oral dose of 400 mg imatinib. Plasma imatinib concentrations were measured and its pharmacokinetics was assessed with respect to ABCG2 (c.421C>A and c.34G>A) and ABCB1 (c.1236C>T, c.2677C>T/A, and c.3435C>T) genotypes, and plasma AGP levels. AGP levels showed a strong positive correlation with imatinib pharmacokinetics. ABCG2 c.421C>A single nucleotide polymorphism showed a statistically significant effect on imatinib pharmacokinetics in low plasma AGP levels groups (<80 mg/dl); subjects with high plasma AGP levels (n = 5, ≥80 mg/dl) were excluded. The results indicate that plasma AGP levels and ABCG2 polymorphisms modulated imatinib pharmacokinetics; however, the effects of the ABCG2 transporter was masked at high plasma AGP levels.

Therapeutic Drug Monitoring of Targeted Anticancer Protein Kinase Inhibitors in Routine Clinical Use: A Critical Review

BACKGROUND

Therapeutic response to oral targeted anticancer protein kinase inhibitors (PKIs) varies widely between patients, with insufficient efficacy of some of them and unacceptable adverse reactions of others. There are several possible causes for this heterogeneity, such as pharmacokinetic (PK) variability affecting blood concentrations, fluctuating medication adherence, and constitutional or acquired drug resistance of cancer cells. The appropriate management of oncology patients with PKI treatments thus requires concerted efforts to optimize the utilization of these drug agents, which have probably not yet revealed their full potential.

METHODS

An extensive literature review was performed on MEDLINE on the PK, pharmacodynamics, and therapeutic drug monitoring (TDM) of PKIs (up to April 2019).

RESULTS

This review provides the criteria for determining PKIs suitable candidates for TDM (eg, availability of analytical methods, observational PK studies, PK-pharmacodynamics relationship analysis, and randomized controlled studies). It reviews the major characteristics and limitations of PKIs, the expected benefits of TDM for cancer patients receiving them, and the prerequisites for the appropriate utilization of TDM. Finally, it discusses various important practical aspects and pitfalls of TDM for supporting better implementation in the field of cancer treatment.

CONCLUSIONS

Adaptation of PKIs dosage regimens at the individual patient level, through a rational TDM approach, could prevent oncology patients from being exposed to ineffective or unnecessarily toxic drug concentrations in the era of personalized medicine.

The Steps to Therapeutic Drug Monitoring: A Structured Approach Illustrated With Imatinib

Pharmacometric methods have hugely benefited from progress in analytical and computer sciences during the past decades, and play nowadays a central role in the clinical development of new medicinal drugs. It is time that these methods translate into patient care through therapeutic drug monitoring (TDM), due to become a mainstay of precision medicine no less than genomic approaches to control variability in drug response and improve the efficacy and safety of treatments. In this review, we make the case for structuring TDM development along five generic questions: 1) Is the concerned drug a candidate to TDM? 2) What is the normal range for the drug's concentration? 3) What is the therapeutic target for the drug's concentration? 4) How to adjust the dosage of the drug to drive concentrations close to target? 5) Does evidence support the usefulness of TDM for this drug? We exemplify this approach through an overview of our development of the TDM of imatinib, the very first targeted anticancer agent. We express our position that a similar story shall apply to other drugs in this class, as well as to a wide range of treatments critical for the control of various life-threatening conditions. Despite hurdles that still jeopardize progress in TDM, there is no doubt that upcoming technological advances will shape and foster many innovative therapeutic monitoring methods.

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.

Free prednisolone pharmacokinetics predicted from total concentrations in patients with inflammatory - immunonologic conditions

Prednisone is an anti-inflammatory drug widely used in internal medicine and rheumatology, but dosing remains empirical. The active metabolite of prednisone is free prednisolone. The aim of this work was to build a population pharmacokinetic (PK) model that can predict free prednisolone concentrations in patients with inflammatory/immunologic conditions.A total of 107 patients from the department of internal medicine of Cochin hospital provided 343 observations. Blood samples drawn for biological analyses were used for drug determination. Total plasma prednisolone concentrations were measured by liquid chromatography-mass spectrometry, and the data were modelled using Monolix. The pharmacokinetics was ascribed a one-compartment open model with three transit compartments standing for the absorption and metabolism process. The model used predicts free concentrations that served to derive total concentrations given published binding constants. Only size parameters influenced the pharmacokinetics. Free prednisolone CL_U /F and V_U /F, scaled allometrically on lean body weight, were, respectively, 26.7 L/h and 94.3 L for 50 kg LBW. CL_U /F interindividual variability was 0.20. The additive and proportional residual variabilities were, respectively, 4.3 µg/L and 0.20. The results point out some dosing issues, that is the possibility of under- or over-dosage in thin or overweight patients respectively.

Label-Free Quantification of Anticancer Drug Imatinib in Human Plasma with Surface Enhanced Raman Spectroscopy

Therapeutic drug monitoring (TDM) for anticancer drug imatinib has been suggested as the best way to improve the treatment response and minimize the risk of adverse reactions in chronic myelogenous leukemia (CML) and gastrointestinal stromal tumor (GIST) patients. TDM of oncology treatments with standard analytical methods, such as liquid chromatography-tandem mass spectrometry (LC-MS/MS) is, however, complex and demanding. This paper proposes a new method for quantitation of imatinib in human plasma, based on surface enhanced raman spectroscopy (SERS) and multivariate calibration using partial least-squares regression (PLSR). The best PLSR model was obtained with three latent variables in the range from 123 to 5000 ng/mL of imatinib, providing a standard error of prediction (SEP) of 510 ng/mL. The method was validated in accordance with international guidelines, through the estimate of figures of merit, such as precision, accuracy, systematic error, analytical sensitivity, limits of detection, and quantitation. Moreover, the feasibility and clinical utility of this approach have also been verified using real plasma samples taken from deidentified patients. The results were in good agreement with a clinically validated LC-MS/MS method. The new SERS method presented in this preliminary work showed simplicity, short analysis time, good sensitivity, and could be considered a promising platform for TDM of imatinib treatment in a point-of-care setting.

Determination of unbound fraction of pazopanib in vitro and in cancer patients reveals albumin as the main binding site

INTRODUCTION

Pazopanib exhibits wide inter-patient pharmacokinetic variability which may contribute to differences in treatment outcome. Unbound drug concentrations are believed to be more relevant to pharmacological responses than total concentrations. Thus it is desirable to evaluate pazopanib binding on plasma proteins and different factors potentially affecting this process.

METHODS

An equilibrium dialysis method coupled with UPLC-MS/MS assay has been optimized and validated for the determination of pazopanib unbound fraction (fu%) in human plasma. Pazopanib binding in the plasma of healthy volunteers and in isolated protein solutions was investigated. The unbound fraction was determined for 24 cancer patients treated daily with pazopanib.

RESULTS

We found that pazopanib was extensively bound in human plasma (>99.9 %) with a mean fu% value of 0.0106 ± 0.0013 % at 40 μg/mL. Protein binding was concentration independent over a clinically relevant range of concentrations. In isolated protein solutions, pazopanib at 40 μg/mL was mainly bound to albumin (40 g/L) and to a lesser extent to α1-acid glycoprotein (1 g/L) and low density lipoproteins (1.2 g/L), with a mean fu% of 0.0073 ± 0.0022 %, 0.992 ± 0.44 % and 7.4 ± 1.7 % respectively. Inter-patient variability (CV%) of fu% in cancer patients was limited (27.2 %). A correlation was observed between individual unbound fraction values and albuminemia.

CONCLUSIONS

Pazopanib exhibits extensive binding to plasma proteins in human plasma. Variable albumin concentrations, frequently observed in cancer patients, may affect pazopanib unbound fraction with implications for inter-patient variability in drug efficacy and toxicity.

Therapeutic drug monitoring of targeted anticancer therapy

New oral targeted anticancer therapies are revolutionizing cancer treatment by transforming previously deadly malignancies into chronically manageable conditions. Nevertheless, drug resistance, persistence of cancer stem cells, and adverse drug effects still limit their ability to stabilize or cure malignant diseases in the long term. Response to targeted anticancer therapy is influenced by tumor genetics and by variability in drug concentrations. However, despite a significant inter-patient pharmacokinetic variability, targeted anticancer drugs are essentially licensed at fixed doses. Their therapeutic use could however be optimized by individualization of their dosage, based on blood concentration measurements via the therapeutic drug monitoring (TDM). TDM can increase the probability of therapeutic responses to targeted anticancer therapies, and would help minimize the risk of major adverse reactions.

Clinical usefulness of therapeutic concentration monitoring for imatinib dosage individualization: results from a randomized controlled trial

PURPOSE

This study assessed whether a cycle of "routine" therapeutic drug monitoring (TDM) for imatinib dosage individualization, targeting an imatinib trough plasma concentration (C min) of 1,000 ng/ml (tolerance: 750-1,500 ng/ml), could improve clinical outcomes in chronic myelogenous leukemia (CML) patients, compared with TDM use only in case of problems ("rescue" TDM).

METHODS

Imatinib concentration monitoring evaluation was a multicenter randomized controlled trial including adult patients in chronic or accelerated phase CML receiving imatinib since less than 5 years. Patients were allocated 1:1 to "routine TDM" or "rescue TDM." The primary endpoint was a combined outcome (failure- and toxicity-free survival with continuation on imatinib) over 1-year follow-up, analyzed in intention-to-treat (ISRCTN31181395).

RESULTS

Among 56 patients (55 evaluable), 14/27 (52 %) receiving "routine TDM" remained event-free versus 16/28 (57 %) "rescue TDM" controls (P = 0.69). In the "routine TDM" arm, dosage recommendations were correctly adopted in 14 patients (median C min: 895 ng/ml), who had fewer unfavorable events (28 %) than the 13 not receiving the advised dosage (77 %; P = 0.03; median C min: 648 ng/ml).

CONCLUSIONS

This first target concentration intervention trial could not formally demonstrate a benefit of "routine TDM" because of small patient number and surprisingly limited prescriber's adherence to dosage recommendations. Favorable outcomes were, however, found in patients actually elected for target dosing. This study thus shows first prospective indication for TDM being a useful tool to guide drug dosage and shift decisions. The study design and analysis provide an interesting paradigm for future randomized TDM trials on targeted anticancer agents.

Plasma protein binding: from discovery to development

The importance of plasma protein binding (PPB) in modulating the effective drug concentration at pharmacological target sites has been the topic of significant discussion and debate amongst drug development groups over the past few decades. Free drug theory, which states that in absence of energy-dependent processes, after steady state equilibrium has been attained, free drug concentration in plasma is equal to free drug concentration at the pharmacologic target receptor(s) in tissues, has been used to explain pharmacokinetics/pharmacodynamics relationships in a large number of cases. Any sudden increase in free concentration of a drug could potentially cause toxicity and may need dose adjustment. Free drug concentration is also helpful to estimate the effective concentration of drugs that potentially can precipitate metabolism (or transporter)-related drug-drug interactions. Disease models are extensively validated in animals to progress a compound into development. Unbound drug concentration, and therefore PPB information across species is very informative in establishing safety margins and guiding selection of First in Human (FIH) dose and human efficacious dose. The scope of this review is to give an overview of reported role of PPB in several therapeutic areas, highlight cases where PPB changes are clinically relevant, and provide drug metabolism and pharmacokinetics recommendations in discovery and development settings.