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Background and Objective: As a result of changes in physiology during pregnancy, the pharmacokinetics (PK) of drugs can be altered. It is unclear whether under- or overexposure occurs in pregnant cancer patients and thus also whether adjustments in dosing regimens are required. Given the severity of the malignant disease and the potentially high impact on both the mother and child, there is a high unmet medical need for adequate and tolerable treatment of this patient population. We aimed to develop and evaluate a semi-physiological enriched model that incorporates physiological changes during pregnancy into available population PK models developed from non-pregnant patient data., Methods: Gestational changes in plasma protein levels, renal function, hepatic function, plasma volume, extracellular water and total body water were implemented in existing empirical PK models for docetaxel, paclitaxel, epirubicin and doxorubicin. These models were used to predict PK profiles for pregnant patients, which were compared with observed data obtained from pregnant patients., Results: The observed PK profiles were well described by the model. For docetaxel, paclitaxel and doxorubicin, an overprediction of the lower concentrations was observed, most likely as a result of a lack of data on the gestational changes in metabolizing enzymes. For paclitaxel, epirubicin and doxorubicin, the semi-physiological enriched model performed better in predicting PK in pregnant patients compared with a model that was not adjusted for pregnancy-induced changes., Conclusion: By incorporating gestational changes into existing population pharmacokinetic models, it is possible to adequately predict plasma concentrations of drugs in pregnant patients which may inform dose adjustments in this population., (© 2023. The Author(s).)

Correlations between increasing concentrations of circulating tumor DNA (ctDNA) in plasma and disease progression have been shown. A nonlinear mixed effects model to describe the dynamics of epidermal growth factor receptor (EGFR) ctDNA data from patients with non-small cell lung cancer (NSCLC) combined with a parametric survival model were developed to evaluate the ability of these modeling techniques to describe ctDNA data. Repeated ctDNA measurements on L858R, exon19del, and T790M mutants were available from 54 patients with EGFR mutated NSCLC treated with erlotinib or gefitinib. Different dynamic models were tested to describe the longitudinal ctDNA concentrations of the driver and resistance mutations. Subsequently, a parametric time-to-event model for progression-free survival (PFS) was developed. Predicted L858R, exon19del, and T790M concentrations were used to evaluate their value as predictor for disease progression. The ctDNA dynamics were best described by a model consisting of a zero-order increase and first-order elimination (19.7/day, 95% confidence interval [CI] 14.9-23.6/day) of ctDNA concentrations. In addition, time-dependent development of resistance (5.0 × 10 -4 , 95% CI 2.0 × 10 -4 -7.0 × 10 -4 /day) was included in the final model. Relative change in L858R and exon19del concentrations from baseline was identified as most significant predictor of disease progression (p = 0.001). The dynamic model for L858R, exon19del, and T790M concentrations in ctDNA and time-to-event model adequately described the observed concentrations and PFS data in our clinical cohort. In addition, it was shown that nonlinear mixed effects modeling is a valuable method for the analysis of longitudinal and heterogeneous biomarker datasets obtained from clinical practice., (© 2022 The Authors. Clinical and Translational Science published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Introduction: ModraDoc006 is a novel docetaxel tablet formulation that is co-administrated with the cytochrome P450 3A4 and P-glycoprotein inhibitor ritonavir (r): ModraDoc006/r., Objectives: This study evaluated the effect of food consumed prior to administration of ModraDoc006/r on the pharmacokinetics of docetaxel and ritonavir., Methods: Patients with advanced solid tumours were enrolled in this randomized crossover study to receive ModraDoc006/r in a fasted state in week 1 and after a standardized high-fat meal in week 2 and vice versa. Pharmacokinetic sampling was conducted until 48 h after both study drug administrations. Docetaxel and ritonavir plasma concentrations were determined using liquid chromatography with tandem mass spectrometry. Safety was evaluated with the Common Terminology Criteria for Adverse Events, version 4.03., Results: In total, 16 patients completed the food-effect study. The geometric mean ratio (GMR) for the docetaxel area under the plasma concentration-time curve (AUC) 0-48 , AUC 0-inf and maximum concentration (C max ) were 1.11 (90% confidence interval [CI] 0.93-1.33), 1.19 (90% CI 1.00-1.41) and 1.07 (90% CI 0.81-1.42) in fed versus fasted conditions, respectively. For the ritonavir C max , the GMR was 0.79 (90% CI 0.69-0.90), whereas the AUC 0-48 and AUC 0-inf were bioequivalent. The most frequent treatment-related toxicities were grade ≤ 2 diarrhoea and fatigue. Hypokalaemia was the only observed treatment-related grade 3 toxicity., Conclusions: The docetaxel and ritonavir exposure were not bioequivalent, as consumption of a high-fat meal prior to administration of ModraDoc006/r resulted in a slightly higher docetaxel exposure and lower ritonavir C max . Since docetaxel exposure is the only clinically relevant parameter in our patient population, the overall conclusion is that combined ModraDoc006 and ritonavir treatment may be slightly affected by concomitant intake of a high-fat meal. In view of the small effect, it is most likely that the intake of a light meal will not affect the systemic exposure to docetaxel. CLINICALTRIALS., Gov Identifier: NCT03147378, date of registration: May 10 2017.

Introduction Oral formulations of docetaxel have successfully been developed as an alternative for intravenous administration. Co-administration with the enzyme inhibitor ritonavir boosts the docetaxel plasma exposure. In dose-escalation trials, the maximum tolerated doses for two different dosing regimens were established and dose-limiting toxicities (DLTs) were recorded. The aim of current analysis was to develop a pharmacokinetic (PK)-toxicodynamic (TOX) model to quantify the relationship between docetaxel plasma exposure and DLTs. Methods A total of 85 patients was included in the current analysis, 18 patients showed a DLT in the four-week observation period. A PK-TOX model was developed and simulations based on the PK-TOX model were performed. Results The final PK-TOX model was characterized by an effect compartment representing the toxic effect of docetaxel, which was linked to the probability of developing a DLT. Simulations of once-weekly, once-daily 60 mg and once-weekly, twice-daily 30 mg followed by 20 mg of oral docetaxel suggested that 14% and 34% of patients, respectively, would have a probability >25% to develop a DLT in a four-week period. Conclusions A PK-TOX model was successfully developed. This model can be used to evaluate the probability of developing a DLT following treatment with oral docetaxel and ritonavir in different dosing regimens.

In recent years new targeted small molecule kinase inhibitors have become available for pediatric patients with cancer. Relationships between drug exposure and treatment response have been established for several of these drugs in adults. Following these exposure-response relationships, pharmacokinetic (PK) target minimum plasma rug concentration at the end of a dosing interval (C min ) values to guide therapeutic drug monitoring (TDM) in adults have been proposed. Despite the fact that variability in PK may be even larger in pediatric patients, TDM is only sparsely applied in pediatric oncology. Based on knowledge of the PK, mechanism of action, molecular driver, and pathophysiology of the disease, we bridge available data on the exposure-efficacy relationship from adults to children and propose target C min values to guide TDM for the pediatric population. Dose adjustments in individual pediatric patients can be based on these targets. Nevertheless, further research should be performed to validate these targets., (© 2020 The Authors Clinical Pharmacology & Therapeutics © 2020 American Society for Clinical Pharmacology and Therapeutics.)

Background: For oral anticancer drugs, trough concentration (Cmin) is usually used as a target in therapeutic drug monitoring (TDM). Recording of Cmin is highly challenging in outpatients, in whom there is typically a variability in sample collection time after dosing. Various methods are used to estimate Cmin from the collected samples. This simulation study aimed to evaluate the performance of 3 different methods in estimating the Cmin of 4 oral anticancer drugs for which TDM is regularly performed., Methods: Plasma concentrations of abiraterone, dabrafenib, imatinib, and pazopanib at a random time (Ct,sim) and at the end of the dosing interval (Cmin,sim) were simulated from population pharmacokinetic models including 1000 patients, and the values were converted into simulated observed concentrations (Ct,sim,obs and Cmin,sim,obs) by adding a residual error. From Ct, sim,obs, Cmin was predicted (Cmin,pred) by the Bayesian estimation (method 1), taking the ratio of the Ct,sim,obs and typical population concentration and multiplying this ratio with the typical population value of Cmin,sim (method 2), and log-linear extrapolation (method 3). Target attainment was assessed by comparing Cmin,pred with the proposed pharmacokinetic targets related to efficacy and calculating the positive predictive and negative predictive values., Results: The mean relative prediction error and root mean squared relative prediction error results showed that method 3 was out-performed by method 1 and 2. Target attainment was adequately predicted by all 3 methods (the respective positive predictive value of method 1, 2, and 3 was 92.1%, 92.5%, and 93.1% for abiraterone; 87.3%, 86.9%, and 99.1% for dabrafenib; 79.3%, 79.3%, and 75.9% for imatinib; and 72.5%, 73.5%, and 67.6% for pazopanib), indicating that dose adjustments were correctly predicted., Conclusions: Both method 1 and 2 provided accurate and precise individual Cmin,pred values. However, method 2 was easier to implement than method 1 to guide individual dose adjustments in TDM programs.

Oral administration of docetaxel is an attractive alternative for conventional intravenous (IV) administration. The low bioavailability of docetaxel, however, hinders the application of oral docetaxel in the clinic. The aim of the current study was to develop a population pharmacokinetic (PK) model for docetaxel and ritonavir based on the phase 1 studies and to support drug development of this combination treatment. PK data were collected from 191 patients who received IV docetaxel and different oral docetaxel formulations (drinking solution, ModraDoc001 capsule, and ModraDoc006 tablet) coadministered with ritonavir. A PK model was first developed for ritonavir. Subsequently, a semiphysiological PK model was developed for docetaxel, which incorporated the inhibition of docetaxel metabolism by ritonavir. The uninhibited intrinsic clearance of docetaxel was estimated based on data on IV docetaxel as 1980 L/h (relative standard error, 11%). Ritonavir coadministration extensively inhibited the hepatic metabolism of docetaxel to 9.3%, which resulted in up to 12-fold higher docetaxel plasma concentrations compared to oral docetaxel coadministered without ritonavir. In conclusion, a semiphysiological PK model for docetaxel and ritonavir was successfully developed. Coadministration of ritonavir resulted in increased plasma concentrations of docetaxel after administration of the oral formulations of ModraDoc. Furthermore, the oral ModraDoc formulations showed lower variability in plasma concentrations between and within patients compared to the drinking solution. Comparable exposure could be reached with the oral ModraDoc formulations compared to IV administration., (© 2019, The American College of Clinical Pharmacology.)

A liquid chromatography-tandem mass spectrometry assay was developed and validated for the nine oral anticancer agents alectinib, cobimetinib, lenvatinib, nintedanib, osimertinib, palbociclib, ribociclib, vismodegib and vorinostat in order to support therapeutic drug monitoring (TDM). The assay was based on reversed-phase chromatography coupled with tandem mass spectrometry operating in the positive ion mode. The assay was validated based on the guidelines on bioanalytical methods by the US Food and Drug Administration and European Medicines Agency. The method was validated over a linear range of 10-200 ng/mL for alectinib, lenvatinib, nintedanib and vismodegib; 50-1000 ng/mL for cobimetinib and palbociclib; 100-2000 ng/mL for osimertinib; 5.00-100 ng/mL for ribociclib; 25-500 ng/mL for vorinostat. Intra-assay and inter-assay bias was within ±20% for all analytes at the lower limit of quantification and within ±15% at remaining concentrations. Stability experiments showed that osimertinib is unstable in the biomatrix and should be shipped on dry-ice and stored at -20 °C until analysis. All other compounds were stable in the biomatrix. The described TDM method was successfully validated and applied for TDM in patients treated with these KIs., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Aim: Pharmacokinetics of small molecule kinase inhibitors (KIs) used in cancer treatment may alter with increasing age, but results are conflicting. This study aims to compare exposure to KIs between older and younger patients (≥70 and <70 years) in clinical practice., Methods: KI plasma concentrations of routinely treated patients were measured using validated assays. Calculated trough concentrations were compared in both age groups. For KIs with a clinically meaningful target concentration (erlotinib, imatinib, pazopanib, sunitinib and vemurafenib), influence of older age on target attainment was assessed., Results: We analysed 616 samples from 454 patients (median age: 61; range 20-93 years), treated with dabrafenib (n = 105), erlotinib (n = 49), imatinib (n = 165), pazopanib (n = 63), sunitinib (n = 87), trametinib (n = 95) and vemurafenib (n = 52). Older age did not significantly influence exposure to erlotinib, imatinib, pazopanib, sunitinib, trametinib and vemurafenib. Elderly patients had significantly higher dabrafenib trough concentrations than younger patients (P = 0.02; 62 ng ml -1 (coefficient of variation [CV] 41%), vs. 53 ng ml -1 (CV 46%), respectively). For KIs with a predefined target concentration, 68% of older and 61% of younger patients reached target., Conclusions: In this real-world study, exposure to most included KIs was comparable in older and younger patients, except for dabrafenib, which showed higher exposure in older patients. In the absence of an absolute target for this KI, clinical relevance remains unclear. For all other included KIs, our data suggest no clinically relevant influence of older age on KI exposure., (© 2018 The British Pharmacological Society.)

Introduction: Paediatric dose-finding studies are challenging to perform due to ethical reasons, the limited number of available patients and restricted number of blood samples. In certain cases, the adult pharmacokinetic (PK) exposure can be used as target for dose finding in paediatrics. The aim of this study was to investigate the performance of a paediatric phase I dose-finding clinical trial in silico., Methods: Using an adult pharmacokinetic model, clinical trial simulations were performed to determine the power of a proposed clinical trial design. Power was defined as the fraction of 1000 trials with an area under the plasma concentration-time curve at steady-state (AUC 0-24,SS ) within ±20% of the adult geometric mean AUC 0-24,SS . Different scenarios were compared to optimise the design of the trial. To show the potential of this framework for similar compounds, the current simulation method was also evaluated with adult and paediatric data from literature on sunitinib., Results: At the starting dose of 300 mg/m 2 , the power of the trial design was 66.9%. Power did not improve by dose escalation to 350 mg/m 2 (65.3%). Power increased to 78.9% with inclusion of 10 patients per trial. Paediatric sunitinib PK data were adequately predicted from adult data with a mean prediction error of 1.80%., Conclusion: The performance of PK-based clinical trials in paediatrics can be predicted and optimised through PK modelling and simulation. Application of this approach enables clinical trials in paediatrics to be performed as efficiently as possible while protecting the child from unnecessary harm., (Copyright © 2017 Elsevier Ltd. All rights reserved.)