Although anti-cancer treatments have significantly advanced over the past decades, obstacles to accomplishing successful treatment still exist. The occurrence of treatment resistance is one of the... Show moreAlthough anti-cancer treatments have significantly advanced over the past decades, obstacles to accomplishing successful treatment still exist. The occurrence of treatment resistance is one of the major factors that limit the long-lasting efficacy of anti-cancer treatment. Additionally, substantial variability in pharmacokinetics (PK) / pharmacodynamics (PD) of anti-cancer drugs also challenges successful oncology treatment. Therefore, gaining knowledge of and ultimately better suppressing evolutionary resistance development during treatment, and applying personalized treatment are desired to improve anti-cancer treatment. In this thesis, we have applied quantitative modeling approaches to address these needs, aiming for improved treatment for oncology patients. Our work demonstrated that with the quantitative models, the evolutionary progression of tumors could be characterized and predicted, accounting for interactions among heterogeneous tumor cells and supported by mutant gene variants detected in circulating tumor DNA (ctDNA). In addition, we developed population PK /PD models which enabled quantitative description of the PK and PD of anti-cancer drugs and corresponding variabilities in real-world patients. The developed models have been further applied to support the identification of optimal treatment strategies and guide individualized treatment for oncology patients. Show less
Purpose: Machine Learning (ML) algorithms represent an interesting alternative to maximum a posteriori Bayesian estimators (MAP-BE) for tacrolimus AUC estimation, but it is not known if training an... Show morePurpose: Machine Learning (ML) algorithms represent an interesting alternative to maximum a posteriori Bayesian estimators (MAP-BE) for tacrolimus AUC estimation, but it is not known if training an ML model using a lower number of full pharmacokinetic (PK) profiles (="true" reference AUC) provides better performances than using a larger dataset of less accurate AUC estimates. The objectives of this study were: to develop and benchmark ML algorithms trained using full PK profiles to estimate MeltDose (R)-tacrolimus individual AUCs using 2 or 3 blood concentrations; and to compare their performance to MAP-BE. Methods: Data from liver (n = 113) and kidney (n = 97) transplant recipients involved in MeltDose-tacrolimus PK studies were used for the training and evaluation of ML algorithms. "True" AUC0-24 h was calculated for each patient using the trapezoidal rule on the full PK profile. ML algorithms were trained to estimate tacrolimus true AUC using 2 or 3 blood concentrations. Performances were evaluated in 2 external sets of 16 (renal) and 48 (liver) transplant patients. Results: Best estimation performances were obtained with the MARS algorithm and the following limited sampling strategies (LSS): predose (0), 8, and 12 h post-dose (rMPE = -1.28%, rRMSE = 7.57%), or 0 and 12 h (rMPE = -1.9%, rRMSE = 10.06%). In the external dataset, the performances of the final ML algorithms based on two samples in kidney (rMPE = -3.1%, rRMSE = 11.1%) or liver transplant recipients (rMPE = -3.4%, rRMSE = 9.86%) were as good as or better than those of MAP-BEs based on three time points. Conclusion: The MARS ML models developed using "true" MeltDose (R)-tacrolimus AUCs yielded accurate individual estimations using only two blood concentrations. Show less
Otto, M.E.; Bergmann, K.R.; Jacobs, G.; Esdonk, M.J. van 2021
Purpose The recent repurposing of ketamine as treatment for pain and depression has increased the need for accurate population pharmacokinetic (PK) models to inform the design of new clinical... Show morePurpose The recent repurposing of ketamine as treatment for pain and depression has increased the need for accurate population pharmacokinetic (PK) models to inform the design of new clinical trials. Therefore, the objectives of this study were to externally validate available PK models on (S)-(nor)ketamine concentrations with in-house data and to improve the best performing model when necessary. Methods Based on predefined criteria, five models were selected from literature. Data of two previously performed clinical trials on (S)-ketamine administration in healthy volunteers were available for validation. The predictive performances of the selected models were compared through visual predictive checks (VPCs) and calculation of the (root) mean (square) prediction errors (ME and RMSE). The available data was used to adapt the best performing model through alterations to the model structure and re-estimation of inter-individual variability (IIV). Results The model developed by Fanta et al. (Eur J Clin Pharmacol 71:441-447, 2015) performed best at predicting the (S)-ketamine concentration over time, but failed to capture the (S)-norketamine C-max correctly. Other models with similar population demographics and study designs had estimated relatively small distribution volumes of (S)-ketamine and thus overpredicted concentrations after start of infusion, most likely due to the influence of circulatory dynamics and sampling methodology. Model predictions were improved through a reduction in complexity of the (S)-(nor)ketamine model and re-estimation of IIV. Conclusion The modified model resulted in accurate predictions of both (S)-ketamine and (S)-norketamine and thereby provides a solid foundation for future simulation studies of (S)-(nor)ketamine PK in healthy volunteers after (S)-ketamine infusion. Show less
Purpose To develop and validate a population pharmacokinetic model of ciprofloxacin intravenously in critically ill patients, and determine target attainment to provide guidance for more effective... Show morePurpose To develop and validate a population pharmacokinetic model of ciprofloxacin intravenously in critically ill patients, and determine target attainment to provide guidance for more effective regimens. Methods Non-linear mixed-effects modelling was used for the model development and covariate analysis. Target attainment of an integral AUC(0-24)/MIC >= 100 for different MICs was calculated for standard dosing regimens. Monte Carlo simulations were performed to define the probability of target attainment (PTA) of several dosing regimens. Results A total of 204 blood samples were collected from 42 ICU patients treated with ciprofloxacin 400-1200 mg/day, with median values for age of 66 years, APACHE II score of 22, BMI of 26 kg/m(2), and eGFR of 58.5 mL/min/1.73 m(2). The median integral AUC(0-24) and integral C-max were 29.9 mg center dot h/L and 3.1 mg/L, respectively. Ciprofloxacin pharmacokinetics were best described by a two-compartment model. We did not find any significant covariate to add to the structural model. The proportion of patients achieving the target integral AUC(0-24)/MIC >= 100 were 61.9% and 16.7% with MICs of 0.25 and 0.5 mg/L, respectively. Results of the PTA simulations suggest that a dose of >= 1200 mg/day is needed to achieve sufficient integral AUC(0-24)/MIC ratios. Conclusions The model described the pharmacokinetics of ciprofloxacin in ICU patients adequately. No significant covariates were found and high inter-individual variability of ciprofloxacin pharmacokinetics in ICU patients was observed. The poor target attainment supports the use of higher doses such as 1200 mg/day in critically ill patients, while the variability of inter-individual pharmacokinetics parameters emphasizes the need for therapeutic drug monitoring to ensure optimal exposure. Show less
Recently a framework was presented to assess whether pediatric covariate models for clearance can be extrapolated between drugs sharing elimination pathways, based on extraction ratio, protein... Show moreRecently a framework was presented to assess whether pediatric covariate models for clearance can be extrapolated between drugs sharing elimination pathways, based on extraction ratio, protein binding, and other drug properties. Here we evaluate when a pediatric covariate function for midazolam clearance can be used to scale clearance of other CYP3A substrates. A population PK model including a covariate function for clearance was developed for midazolam in children aged 1–17 years. Commonly used CYP3A substrates were selected and using the framework, it was assessed whether the midazolam covariate function accurately scales their clearance. For eight substrates, reported pediatric clearance values were compared numerically and graphically with clearance values scaled using the midazolam covariate function. For sildenafil, clearance values obtained with population PK modeling based on pediatric concentration-time data were compared with those scaled with the midazolam covariate function. According to the framework, a midazolam covariate function will lead to systemically accurate clearance scaling (absolute prediction error (PE) < 30%) for CYP3A substrates binding to albumin with an extraction ratio between 0.35 and 0.65 when binding < 10% in adults, between 0.05 and 0.55 when binding > 90%, and with an extraction ratio ranging between these values when binding between 10 and 90%. Scaled clearance values for eight commonly used CYP3A substrates were reasonably accurate (PE < 50%). Scaling of sildenafil clearance was accurate (PE < 30%). We defined for which CYP3A substrates a pediatric covariate function for midazolam clearance can accurately scale plasma clearance in children. This scaling approach may be useful for CYP3A substrates with scarce or no available pediatric PK information. Show less
Anti-thymocyte globulin (ATG) and alemtuzumab are both used in hematopoietic cell transplantation (HCT) to prevent graft-versus-host-disease (GvHD) and graft failure. Main toxicities include... Show moreAnti-thymocyte globulin (ATG) and alemtuzumab are both used in hematopoietic cell transplantation (HCT) to prevent graft-versus-host-disease (GvHD) and graft failure. Main toxicities include absent or slow immune reconstitution. This thesis aims to develop evidence based dosing regimens for both agents. We found that current weight-based dosing of ATG and alemtuzumab lead to highly biased exposures across the different age groups in the pediatric population. Furthermore, ATG clearance was not found to increase with increasing body weight in patients over 50 kg (i.e. adolescents and adults). Timely CD4+ T-cell immune reconstitution after HCT is essential for reducing viral reactivations and relapse following HCT, and thereby improves survival chances. High exposure to ATG after infusion of the graft diminishes chances for CD4+ T-cell reconstitution. Therefore, exposure to ATG has a major impact on the clinical outcomes including survival following HCT in children and adults. We conclude that individualizing dosing and timing of ATG potentially makes HCT a safer and more effective treatment option, and will lead to improved survival chances. Individualized dosing regimens for ATG in children have been designed based on the results in this thesis, and are currently being evaluated in prospective clinical trials for efficacy and safety. Show less
Guglieri-Lopez, B.; Perez-Pitarch, A.; Moes, D.J.A.R.; Porta-Oltra, B.; Climente-Marti, M.; Guchelaar, H.J.; Merino-Sanjuan, M. 2017
The main objective of the investigations was to explore the PK/PD correlations of fluvoxamine, as a prototype for the Selective Serotonin Reuptake Inhibitors (SSRIs). In the various investigations,... Show moreThe main objective of the investigations was to explore the PK/PD correlations of fluvoxamine, as a prototype for the Selective Serotonin Reuptake Inhibitors (SSRIs). In the various investigations, a spectrum of different biomarkers was used, each reflecting a specific process on the causal path between drug administration and response. The effects of fluvoxamine have been explored in six investigation steps; from the relatively simple description of the pharmacokinetics of fluvoxamine in plasma and brain to the more complex relationships with the effects on SERT occupancy, 5-HT and 5-HIAA levels and REM sleep. In the PCA study, a categorical PK/PD model was proposed for the effects of fluvoxamine on PCA induced behavioral effects as a kind of intermediary biomarker. There appeared to be important aspects in the PK/PD relationships of fluvoxamine, which was already indicated in the well-known delayed therapeutic effect of SSRIs. The cascading PK/PD model enables the prediction of the effects of functional adaptation upon long-term administration. For SSRIs, adaptation may occur at various levels in the biological system. The various studied biomarkers provide a basis to determine at which level of the biological system functional adaptation occurs (i.e. target site distribution, target expression, turnover of neurotransmitters, transduction mechanisms). Show less
