@article{HetheyHartungWangorschetal.2021,
author = {Hethey, Christoph Philipp and Hartung, Niklas and Wangorsch, Gaby and Weisser, Karin and Huisinga, Wilhelm},
title = {Physiology-based toxicokinetic modelling of aluminium in rat and man},
series = {Archives of toxicology : official journal of EUROTOX},
volume = {95},
journal = {Archives of toxicology : official journal of EUROTOX},
number = {9},
publisher = {Springer},
address = {Berlin ; Heidelberg},
issn = {0340-5761},
doi = {10.1007/s00204-021-03107-y},
pages = {2977 -- 3000},
year = {2021},
abstract = {A sufficient quantitative understanding of aluminium (Al) toxicokinetics (TK) in man is still lacking, although highly desirable for risk assessment of Al exposure. Baseline exposure and the risk of contamination severely limit the feasibility of TK studies administering the naturally occurring isotope Al-27, both in animals and man. These limitations are absent in studies with Al-26 as a tracer, but tissue data are limited to animal studies. A TK model capable of inter-species translation to make valid predictions of Al levels in humans-especially in toxicological relevant tissues like bone and brain-is urgently needed. Here, we present: (i) a curated dataset which comprises all eligible studies with single doses of Al-26 tracer administered as citrate or chloride salts orally and/or intravenously to rats and humans, including ultra-long-term kinetic profiles for plasma, blood, liver, spleen, muscle, bone, brain, kidney, and urine up to 150 weeks; and (ii) the development of a physiology-based (PB) model for Al TK after intravenous and oral administration of aqueous Al citrate and Al chloride solutions in rats and humans. Based on the comprehensive curated Al-26 dataset, we estimated substance-dependent parameters within a non-linear mixed-effect modelling context. The model fitted the heterogeneous Al-26 data very well and was successfully validated against datasets in rats and humans. The presented PBTK model for Al, based on the most extensive and diverse dataset of Al exposure to date, constitutes a major advancement in the field, thereby paving the way towards a more quantitative risk assessment in humans.},
language = {en}
}
@article{HartungWahlRastogietal.2021,
author = {Hartung, Niklas and Wahl, Martin and Rastogi, Abhishake and Huisinga, Wilhelm},
title = {Nonparametric goodness-of-fit testing for parametric covariate models in pharmacometric analyses},
series = {CPT: pharmacometrics \& systems pharmacology},
volume = {10},
journal = {CPT: pharmacometrics \& systems pharmacology},
number = {6},
publisher = {Nature Publ. Group},
address = {London},
issn = {2163-8306},
doi = {10.1002/psp4.12614},
pages = {564 -- 576},
year = {2021},
abstract = {The characterization of covariate effects on model parameters is a crucial step during pharmacokinetic/pharmacodynamic analyses. Although covariate selection criteria have been studied extensively, the choice of the functional relationship between covariates and parameters, however, has received much less attention. Often, a simple particular class of covariate-to-parameter relationships (linear, exponential, etc.) is chosen ad hoc or based on domain knowledge, and a statistical evaluation is limited to the comparison of a small number of such classes. Goodness-of-fit testing against a nonparametric alternative provides a more rigorous approach to covariate model evaluation, but no such test has been proposed so far. In this manuscript, we derive and evaluate nonparametric goodness-of-fit tests for parametric covariate models, the null hypothesis, against a kernelized Tikhonov regularized alternative, transferring concepts from statistical learning to the pharmacological setting. The approach is evaluated in a simulation study on the estimation of the age-dependent maturation effect on the clearance of a monoclonal antibody. Scenarios of varying data sparsity and residual error are considered. The goodness-of-fit test correctly identified misspecified parametric models with high power for relevant scenarios. The case study provides proof-of-concept of the feasibility of the proposed approach, which is envisioned to be beneficial for applications that lack well-founded covariate models.},
language = {en}
}
@article{MaierWiljesHartungetal.2022,
author = {Maier, Corinna Sabrina and Wiljes, Jana de and Hartung, Niklas and Kloft, Charlotte and Huisinga, Wilhelm},
title = {A continued learning approach for model-informed precision dosing},
series = {CPT: pharmacometrics \& systems pharmacology},
volume = {11},
journal = {CPT: pharmacometrics \& systems pharmacology},
number = {2},
publisher = {London},
address = {Nature Publ. Group},
issn = {2163-8306},
doi = {10.1002/psp4.12745},
pages = {185 -- 198},
year = {2022},
abstract = {Model-informed precision dosing (MIPD) is a quantitative dosing framework that combines prior knowledge on the drug-disease-patient system with patient data from therapeutic drug/ biomarker monitoring (TDM) to support individualized dosing in ongoing treatment. Structural models and prior parameter distributions used in MIPD approaches typically build on prior clinical trials that involve only a limited number of patients selected according to some exclusion/inclusion criteria. Compared to the prior clinical trial population, the patient population in clinical practice can be expected to also include altered behavior and/or increased interindividual variability, the extent of which, however, is typically unknown. Here, we address the question of how to adapt and refine models on the level of the model parameters to better reflect this real-world diversity. We propose an approach for continued learning across patients during MIPD using a sequential hierarchical Bayesian framework. The approach builds on two stages to separate the update of the individual patient parameters from updating the population parameters. Consequently, it enables continued learning across hospitals or study centers, because only summary patient data (on the level of model parameters) need to be shared, but no individual TDM data. We illustrate this continued learning approach with neutrophil-guided dosing of paclitaxel. The present study constitutes an important step toward building confidence in MIPD and eventually establishing MIPD increasingly in everyday therapeutic use.},
language = {en}
}
@article{HartungBorghardt2020,
author = {Hartung, Niklas and Borghardt, Jens Markus},
title = {A mechanistic framework for a priori pharmacokinetic predictions of orally inhaled drugs},
series = {PLoS Computational Biology : a new community journal},
volume = {16},
journal = {PLoS Computational Biology : a new community journal},
number = {12},
publisher = {PLoS},
address = {San Fransisco},
issn = {1553-734X},
doi = {10.1371/journal.pcbi.1008466},
pages = {24},
year = {2020},
abstract = {Author summary
The use of orally inhaled drugs for treating lung diseases is appealing since they have the potential for lung selectivity, i.e. high exposure at the site of action -the lung- without excessive side effects. However, the degree of lung selectivity depends on a large number of factors, including physiochemical properties of drug molecules, patient disease state, and inhalation devices. To predict the impact of these factors on drug exposure and thereby to understand the characteristics of an optimal drug for inhalation, we develop a predictive mathematical framework (a "pharmacokinetic model"). In contrast to previous approaches, our model allows combining knowledge from different sources appropriately and its predictions were able to adequately predict different sets of clinical data. Finally, we compare the impact of different factors and find that the most important factors are the size of the inhaled particles, the affinity of the drug to the lung tissue, as well as the rate of drug dissolution in the lung. In contrast to the common belief, the solubility of a drug in the lining fluids is not found to be relevant. These findings are important to understand how inhaled drugs should be designed to achieve best treatment results in patients.
The fate of orally inhaled drugs is determined by pulmonary pharmacokinetic processes such as particle deposition, pulmonary drug dissolution, and mucociliary clearance. Even though each single process has been systematically investigated, a quantitative understanding on the interaction of processes remains limited and therefore identifying optimal drug and formulation characteristics for orally inhaled drugs is still challenging. To investigate this complex interplay, the pulmonary processes can be integrated into mathematical models. However, existing modeling attempts considerably simplify these processes or are not systematically evaluated against (clinical) data. In this work, we developed a mathematical framework based on physiologically-structured population equations to integrate all relevant pulmonary processes mechanistically. A tailored numerical resolution strategy was chosen and the mechanistic model was evaluated systematically against data from different clinical studies. Without adapting the mechanistic model or estimating kinetic parameters based on individual study data, the developed model was able to predict simultaneously (i) lung retention profiles of inhaled insoluble particles, (ii) particle size-dependent pharmacokinetics of inhaled monodisperse particles, (iii) pharmacokinetic differences between inhaled fluticasone propionate and budesonide, as well as (iv) pharmacokinetic differences between healthy volunteers and asthmatic patients. Finally, to identify the most impactful optimization criteria for orally inhaled drugs, the developed mechanistic model was applied to investigate the impact of input parameters on both the pulmonary and systemic exposure. Interestingly, the solubility of the inhaled drug did not have any relevant impact on the local and systemic pharmacokinetics. Instead, the pulmonary dissolution rate, the particle size, the tissue affinity, and the systemic clearance were the most impactful potential optimization parameters. In the future, the developed prediction framework should be considered a powerful tool for identifying optimal drug and formulation characteristics.},
language = {en}
}
@article{GomezHartung2018,
author = {Gomez, Christophe and Hartung, Niklas},
title = {Stochastic and deterministic models for the metastatic emission process},
series = {Cancer Systems Biology},
volume = {1711},
journal = {Cancer Systems Biology},
publisher = {Humana Press Inc.},
address = {Totowa},
isbn = {978-1-4939-7493-1},
issn = {1064-3745},
doi = {10.1007/978-1-4939-7493-1_10},
pages = {193 -- 224},
year = {2018},
abstract = {Although the detection of metastases radically changes prognosis of and treatment decisions for a cancer patient, clinically undetectable micrometastases hamper a consistent classification into localized or metastatic disease. This chapter discusses mathematical modeling efforts that could help to estimate the metastatic risk in such a situation. We focus on two approaches: (1) a stochastic framework describing metastatic emission events at random times, formalized via Poisson processes, and (2) a deterministic framework describing the micrometastatic state through a size-structured density function in a partial differential equation model. Three aspects are addressed in this chapter. First, a motivation for the Poisson process framework is presented and modeling hypotheses and mechanisms are introduced. Second, we extend the Poisson model to account for secondary metastatic emission. Third, we highlight an inherent crosslink between the stochastic and deterministic frameworks and discuss its implications. For increased accessibility the chapter is split into an informal presentation of the results using a minimum of mathematical formalism and a rigorous mathematical treatment for more theoretically interested readers.},
language = {en}
}
@article{MelinParraGuillenHartungetal.2018,
author = {Melin, Johanna and Parra-Guillen, Zinnia Patricia and Hartung, Niklas and Huisinga, Wilhelm and Ross, Richard J. and Whitaker, Martin J. and Kloft, Charlotte},
title = {Predicting Cortisol Exposure from Paediatric Hydrocortisone Formulation Using a Semi-Mechanistic Pharmacokinetic Model Established in Healthy Adults},
series = {Clinical Pharmacokinetics},
volume = {57},
journal = {Clinical Pharmacokinetics},
number = {4},
publisher = {Springer},
address = {Northcote},
issn = {0312-5963},
doi = {10.1007/s40262-017-0575-8},
pages = {515 -- 527},
year = {2018},
abstract = {Background and objective Optimisation of hydrocortisone replacement therapy in children is challenging as there is currently no licensed formulation and dose in Europe for children under 6 years of age. In addition, hydrocortisone has non-linear pharmacokinetics caused by saturable plasma protein binding. A paediatric hydrocortisone formulation, Infacort (R) oral hydrocortisone granules with taste masking, has therefore been developed. The objective of this study was to establish a population pharmacokinetic model based on studies in healthy adult volunteers to predict hydrocortisone exposure in paediatric patients with adrenal insufficiency. Methods Cortisol and binding protein concentrations were evaluated in the absence and presence of dexamethasone in healthy volunteers (n = 30). Dexamethasone was used to suppress endogenous cortisol concentrations prior to and after single doses of 0.5, 2, 5 and 10 mg of Infacort (R) or 20 mg of Infacort (R)/hydrocortisone tablet/hydrocortisone intravenously. A plasma protein binding model was established using unbound and total cortisol concentrations, and sequentially integrated into the pharmacokinetic model. Results Both specific (non-linear) and non-specific (linear) protein binding were included in the cortisol binding model. A two-compartment disposition model with saturable absorption and constant endogenous cortisol baseline (Baseline (cort),15.5 nmol/L) described the data accurately. The predicted cortisol exposure for a given dose varied considerably within a small body weight range in individuals weighing < 20 kg. Conclusions Our semi-mechanistic population pharmacokinetic model for hydrocortisone captures the complex pharmacokinetics of hydrocortisone in a simplified but comprehensive framework. The predicted cortisol exposure indicated the importance of defining an accurate hydrocortisone dose to mimic physiological concentrations for neonates and infants weighing < 20 kg.},
language = {en}
}
@article{EhmannZollerMinichmayretal.2017,
author = {Ehmann, Lisa and Zoller, Michael and Minichmayr, Iris K. and Scharf, Christina and Maier, Barbara and Schmitt, Maximilian V. and Hartung, Niklas and Huisinga, Wilhelm and Vogeser, Michael and Frey, Lorenz and Zander, Johannes and Kloft, Charlotte},
title = {Role of renal function in risk assessment of target non-attainment after standard dosing of meropenem in critically ill patients},
series = {Critical care},
volume = {21},
journal = {Critical care},
publisher = {BioMed Central},
address = {London},
issn = {1466-609X},
doi = {10.1186/s13054-017-1829-4},
pages = {14},
year = {2017},
abstract = {Background: Severe bacterial infections remain a major challenge in intensive care units because of their high prevalence and mortality. Adequate antibiotic exposure has been associated with clinical success in critically ill patients. The objective of this study was to investigate the target attainment of standard meropenem dosing in a heterogeneous critically ill population, to quantify the impact of the full renal function spectrum on meropenem exposure and target attainment, and ultimately to translate the findings into a tool for practical application. Methods: A prospective observational single-centre study was performed with critically ill patients with severe infections receiving standard dosing of meropenem. Serial blood samples were drawn over 4 study days to determine meropenem serum concentrations. Renal function was assessed by creatinine clearance according to the Cockcroft and Gault equation (CLCRCG). Variability in meropenem serum concentrations was quantified at the middle and end of each monitored dosing interval. The attainment of two pharmacokinetic/pharmacodynamic targets (100\% T->MIC, 50\% T->4xMIC) was evaluated for minimum inhibitory concentration (MIC) values of 2 mg/L and 8 mg/L and standard meropenem dosing (1000 mg, 30-minute infusion, every 8 h). Furthermore, we assessed the impact of CLCRCG on meropenem concentrations and target attainment and developed a tool for risk assessment of target non-attainment. Results: Large inter-and intra-patient variability in meropenem concentrations was observed in the critically ill population (n = 48). Attainment of the target 100\% T->MIC was merely 48.4\% and 20.6\%, given MIC values of 2 mg/L and 8 mg/L, respectively, and similar for the target 50\% T->4xMIC. A hyperbolic relationship between CLCRCG (25-255 ml/minute) and meropenem serum concentrations at the end of the dosing interval (C-8h) was derived. For infections with pathogens of MIC 2 mg/L, mild renal impairment up to augmented renal function was identified as a risk factor for target non-attainment (for MIC 8 mg/L, additionally, moderate renal impairment). Conclusions: The investigated standard meropenem dosing regimen appeared to result in insufficient meropenem exposure in a considerable fraction of critically ill patients. An easy-and free-to-use tool (the MeroRisk Calculator) for assessing the risk of target non-attainment for a given renal function and MIC value was developed.},
language = {en}
}
@article{MelinHartungParraGuillenetal.2019,
author = {Melin, Johanna Stina Elisabet and Hartung, Niklas and Parra-Guillen, Zinnia Patricia and Whitaker, Martin J. and Ross, Richard J. and Kloft, Charlotte},
title = {The circadian rhythm of corticosteroid-binding globulin has little impact on cortisol exposure after hydrocortisone dosing},
series = {Clinical endocrinology},
volume = {91},
journal = {Clinical endocrinology},
number = {1},
publisher = {Wiley},
address = {Hoboken},
issn = {0300-0664},
doi = {10.1111/cen.13969},
pages = {33 -- 40},
year = {2019},
abstract = {Context Optimization of hydrocortisone replacement therapy is important to prevent under- and over dosing. Hydrocortisone pharmacokinetics is complex as circulating cortisol is protein bound mainly to corticosteroid-binding globulin (CBG) that has a circadian rhythm. Objective A detailed analysis of the CBG circadian rhythm and its impact on cortisol exposure after hydrocortisone administration. Design and Methods CBG was measured over 24 hours in 14 healthy individuals and, employing a modelling and simulation approach using a semi-mechanistic hydrocortisone pharmacokinetic model, we evaluated the impact on cortisol exposure (area under concentration-time curve and maximum concentration of total cortisol) of hydrocortisone administration at different clock times and of the changing CBG concentrations. Results The circadian rhythm of CBG was well described with two cosine terms added to the baseline of CBG: baseline CBG was 21.8 mu g/mL and interindividual variability 11.9\%; the amplitude for the 24 and 12 hours cosine functions were relatively small (24 hours: 5.53\%, 12 hours: 2.87\%) and highest and lowest CBG were measured at 18:00 and 02:00, respectively. In simulations, the lowest cortisol exposure was observed after administration of hydrocortisone at 23:00-02:00, whereas the highest was observed at 15:00-18:00. The differences between the highest and lowest exposure were minor (<= 12.2\%), also regarding the free cortisol concentration and free fraction (<= 11.7\%). Conclusions Corticosteroid-binding globulin has a circadian rhythm but the difference in cortisol exposure is <= 12.2\% between times of highest and lowest CBG concentrations; therefore, hydrocortisone dose adjustment based on time of dosing to adjust for the CBG concentrations is unlikely to be of clinical benefit.},
language = {en}
}
@article{HartungHuynhGaudyMarquesteetal.2017,
author = {Hartung, Niklas and Huynh, Cecilia T. -K. and Gaudy-Marqueste, Caroline and Flavian, Antonin and Malissen, Nausicaa and Richard-Lallemand, Marie-Aleth and Hubert, Florence and Grob, Jean-Jacques},
title = {Study of metastatic kinetics in metastatic melanoma treated with B-RAF inhibitors: Introducing mathematical modelling of kinetics into the therapeutic decision},
series = {PLoS one},
volume = {12},
journal = {PLoS one},
publisher = {PLoS},
address = {San Fransisco},
issn = {1932-6203},
doi = {10.1371/journal.pone.0176080},
pages = {11},
year = {2017},
abstract = {Background Evolution of metastatic melanoma (MM) under B-RAF inhibitors (BRAFi) is unpredictable, but anticipation is crucial for therapeutic decision. Kinetics changes in metastatic growth are driven by molecular and immune events, and thus we hypothesized that they convey relevant information for decision making. Patients and methods We used a retrospective cohort of 37 MM patients treated by BRAFi only with at least 2 close CT-scans available before BRAFi, as a model to study kinetics of metastatic growth before, under and after BRAFi. All metastases (mets) were individually measured at each CT-scan. From these measurements, different measures of growth kinetics of each met and total tumor volume were computed at different time points. A historical cohort permitted to build a reference model for the expected spontaneous disease kinetics without BRAFi. All variables were included in Cox and multistate regression models for survival, to select best candidates for predicting overall survival. Results Before starting BRAFi, fast kinetics and moreover a wide range of kinetics (fast and slow growing mets in a same patient) were pejorative markers. At the first assessment after BRAFi introduction, high heterogeneity of kinetics predicted short survival, and added independent information over RECIST progression in multivariate analysis. Metastatic growth rates after BRAFi discontinuation was usually not faster than before BRAFi introduction, but they were often more heterogeneous than before. Conclusions Monitoring kinetics of different mets before and under BRAFi by repeated CT-scan provides information for predictive mathematical modelling. Disease kinetics deserves more interest},
language = {en}
}
@article{HartungBenaryWolfetal.2017,
author = {Hartung, Niklas and Benary, Uwe and Wolf, Jana and Kofahl, Bente},
title = {Paracrine and autocrine regulation of gene expression by Wnt-inhibitor Dickkopf in wild-type and mutant hepatocytes},
series = {BMC systems biology},
volume = {11},
journal = {BMC systems biology},
publisher = {BioMed Central},
address = {London},
issn = {1752-0509},
doi = {10.1186/s12918-017-0470-9},
pages = {16},
year = {2017},
abstract = {Background: Cells are able to communicate and coordinate their function within tissues via secreted factors. Aberrant secretion by cancer cells can modulate this intercellular communication, in particular in highly organised tissues such as the liver. Hepatocytes, the major cell type of the liver, secrete Dickkopf (Dkk), which inhibits Wnt/beta-catenin signalling in an autocrine and paracrine manner. Consequently, Dkk modulates the expression of Wnt/beta-catenin target genes. We present a mathematical model that describes the autocrine and paracrine regulation of hepatic gene expression by Dkk under wild-type conditions as well as in the presence of mutant cells. Results: Our spatial model describes the competition of Dkk and Wnt at receptor level, intra-cellular Wnt/beta-catenin signalling, and the regulation of target gene expression for 21 individual hepatocytes. Autocrine and paracrine regulation is mediated through a feedback mechanism via Dkk and Dkk diffusion along the porto-central axis. Along this axis an APC concentration gradient is modelled as experimentally detected in liver. Simulations of mutant cells demonstrate that already a single mutant cell increases overall Dkk concentration. The influence of the mutant cell on gene expression of surrounding wild-type hepatocytes is limited in magnitude and restricted to hepatocytes in close proximity. To explore the underlying molecular mechanisms, we perform a comprehensive analysis of the model parameters such as diffusion coefficient, mutation strength and feedback strength. Conclusions: Our simulations show that Dkk concentration is elevated in the presence of a mutant cell. However, the impact of these elevated Dkk levels on wild-type hepatocytes is confined in space and magnitude. The combination of inter-and intracellular processes, such as Dkk feedback, diffusion and Wnt/beta-catenin signal transduction, allow wild-type hepatocytes to largely maintain their gene expression.},
language = {en}
}