UNLABELLED\nMETHODS\nRESULTS\nCONCLUSION\nBACKGROUND AND OBJECTIVES: Acute inflammation caused by infections or sepsis can impact pharmacokinetics. We used a model-based analysis to evaluate the... Show moreUNLABELLED\nMETHODS\nRESULTS\nCONCLUSION\nBACKGROUND AND OBJECTIVES: Acute inflammation caused by infections or sepsis can impact pharmacokinetics. We used a model-based analysis to evaluate the effect of acute inflammation as represented by interleukin-6 (IL-6) levels on drug clearance, focusing on renal glomerular filtration rate (GFR) and cytochrome P450 3A4 (CYP3A4)-mediated metabolism.\nA physiologically based model incorporating renal and hepatic drug clearance was implemented. Functions correlating IL-6 levels with GFR and in vitro CYP3A4 activity were derived and incorporated into the modeling framework. We then simulated treatment scenarios for hypothetical drugs by varying the IL-6 levels, the contribution of renal and hepatic drug clearance, and protein binding. The relative change in observed area under the concentration-time curve (AUC) was computed for these scenarios.\nInflammation showed opposite effects on drug exposure for drugs eliminated via the liver and kidney, with the effect of inflammation being inversely proportional to the extraction ratio (ER). For renally cleared drugs, the relative decrease in AUC was close to 30% during severe inflammation. For CYP3A4 substrates, the relative increase in AUC could exceed 50% for low-ER drugs. Finally, the impact of inflammation-induced changes in drug clearance is smaller for drugs with a larger unbound fraction.\nThis analysis demonstrates differences in the impact of inflammation on drug clearance for different drug types. The effects of inflammation status on pharmacokinetics may explain the inter-individual variability in pharmacokinetics in critically ill patients. The proposed model-based analysis may be used to further evaluate the effect of inflammation, i.e., by incorporating the effect of inflammation on other drug-metabolizing enzymes or physiological processes. Show less
Antimicrobial drugs constitute a fundamental part of modern medicine. The global rise in antimicrobial resistance poses a major threat to global health. Optimising antimicrobial treatment... Show moreAntimicrobial drugs constitute a fundamental part of modern medicine. The global rise in antimicrobial resistance poses a major threat to global health. Optimising antimicrobial treatment strategies in patients offers an important direction to address this challenge. In this thesis, we describe how quantitative characterisation of the drug, the pathogen, and the patients, and how these three factors interact, can help to achieve this goal. To this end, we used a combination of state-of-the-art in silico model-based approaches to analyse and integrate experimental data from in vitro models, and clinical data from healthy volunteers and patients. We developed models describing infection site drug exposure, antimicrobial resistance evolution, and host response biomarker dynamics. We explored the impact of infection on pulmonary pharmacokinetics, evolutionary-based treatment strategies, and the utility host response biomarker for treatment monitoring. The work in this thesis builds towards developing novel strategies to optimise antimicrobial treatments and showcases the importance on interdisciplinary collaborations. Show less
Healy, K.; Aulin, L.B.S.; Freij, U.; Ellerstad, M.; Brückle, L.; Hillmering, H.; ... ; Gustafsson, R. 2022
Prediction of antimicrobial target-site pharmacokinetics is of relevance to optimize treatment with antimicrobial agents. A physiologically based pharmacokinetic (PBPK) model framework was... Show morePrediction of antimicrobial target-site pharmacokinetics is of relevance to optimize treatment with antimicrobial agents. A physiologically based pharmacokinetic (PBPK) model framework was developed for prediction of pulmonary pharmacokinetics, including key pulmonary infection sites (i.e. the alveolar macrophages and the epithelial lining fluid).\nThe modelling framework incorporated three lung PBPK models: a general passive permeability-limited model, a drug-specific permeability-limited model and a quantitative structure-property relationship (QSPR)-informed perfusion-limited model. We applied the modelling framework to three fluoroquinolone antibiotics. Incorporation of experimental drug-specific permeability data was found essential for accurate prediction.\nIn the absence of drug-specific transport data, our QSPR-based model has generic applicability. Furthermore, we evaluated the impact of drug properties and pathophysiologically related changes on pulmonary pharmacokinetics. Pulmonary pharmacokinetics were highly affected by physiological changes, causing a shift in the main route of diffusion (i.e. paracellular or transcellular). Finally, we show that lysosomal trapping can cause an overestimation of cytosolic concentrations for basic compounds when measuring drug concentrations in cell homogenate.\nThe developed lung PBPK model framework constitutes a promising tool for characterization of pulmonary exposure of systemically administrated antimicrobials. Show less
Liu, F.; Aulin, L.B.S.; Kossen, S.S.A.; Cathalina, J.E.J.; Bremmer, M.; Foks, A.C.; ... ; Hasselt, J.G.C. van 2022
Sepsis is a life-threatening condition driven by the dysregulation of the host immune response to an infection. The complex and interacting mechanisms underlying sepsis remain not fully understood.... Show moreSepsis is a life-threatening condition driven by the dysregulation of the host immune response to an infection. The complex and interacting mechanisms underlying sepsis remain not fully understood. By integrating prior knowledge from literature using mathematical modelling techniques, we aimed to obtain a deeper mechanistic insight into sepsis pathogenesis and to evaluate promising novel therapeutic targets, with a focus on Toll-like receptor 4 (TLR4)-mediated pathways. A Boolean network of regulatory relationships was developed for key immune components associated with sepsis pathogenesis after TLR4 activation. Perturbation analyses were conducted to identify therapeutic targets associated with organ dysfunction or antibacterial activity. The developed model consisted of 42 nodes and 183 interactions. Perturbation analyses suggest that over-expression of tumour necrosis factor alpha (TNF-α) or inhibition of soluble receptor sTNF-R, tissue factor, and inflammatory cytokines (IFN-γ, IL-12) may lead to a reduced activation of organ dysfunction related endpoints. Over-expression of complement factor C3b and C5b led to an increase in the bacterial clearance related endpoint. We identified that combinatory blockade of IFN-γ and IL-10 may reduce the risk of organ dysfunction. Finally, we found that combining antibiotic treatment with IL-1β targeted therapy may have the potential to decrease thrombosis. In summary, we demonstrate how existing biological knowledge can be effectively integrated using Boolean network analysis for hypothesis generation of potential treatment strategies and characterization of biomarker responses associated with the early inflammatory response in sepsis. Show less
Liu, F.; Aulin, L.B.S.; Guo, T.; Krekels, E.H.J.; Moerland, M.; Graaf, P.H. van der; Hasselt, J.G.C. van 2022
Clinical studies in healthy volunteers challenged with lipopolysaccharide (LPS), a constituent of the cell wall of Gram-negative bacteria, represent a key model to characterize the Toll-like... Show moreClinical studies in healthy volunteers challenged with lipopolysaccharide (LPS), a constituent of the cell wall of Gram-negative bacteria, represent a key model to characterize the Toll-like receptor 4 (TLR4)-mediated inflammatory response. Here, we developed a mathematical modelling framework to quantitatively characterize the dynamics and inter-individual variability of multiple inflammatory biomarkers in healthy volunteer LPS challenge studies. Data from previously reported LPS challenge studies were used, which included individual-level time-course data for tumour necrosis factor alpha (TNF-alpha), interleukin 6 (IL-6), interleukin 8 (IL-8) and C-reactive protein (CRP). A one-compartment model with first-order elimination was used to capture the LPS kinetics. The relationships between LPS and inflammatory markers was characterized using indirect response (IDR) models. Delay differential equations were applied to quantify the delays in biomarker response profiles. For LPS kinetics, our estimates of clearance and volume of distribution were 35.7 L h(-1) and 6.35 L, respectively. Our model adequately captured the dynamics of multiple inflammatory biomarkers. The time delay for the secretion of TNF-alpha, IL-6 and IL-8 were estimated to be 0.924, 1.46 and 1.48 h, respectively. A second IDR model was used to describe the induced changes of CRP in relation to IL-6, with a delayed time of 4.2 h. The quantitative models developed in this study can be used to inform design of clinical LPS challenge studies and may help to translate preclinical LPS challenge studies to humans. Show less
Aulin, L.B.S.; Kleijburg, A.; Moerland, M.; Hasselt, J.G.C. van 2022
In this study, we describe the kinetics of a new potential inflammatory biomarker, presepsin, together with a panel of well-established biomarkers in a human endotoxemia study. We evaluated... Show moreIn this study, we describe the kinetics of a new potential inflammatory biomarker, presepsin, together with a panel of well-established biomarkers in a human endotoxemia study. We evaluated biomarker correlations and identified combinations that could hold valuable insights regarding the state of infection. Show less
Collateral sensitivity (CS), which arises when resistance to one antibiotic increases sensitivity toward other antibiotics, offers treatment opportunities to constrain or reverse the evolution of... Show moreCollateral sensitivity (CS), which arises when resistance to one antibiotic increases sensitivity toward other antibiotics, offers treatment opportunities to constrain or reverse the evolution of antibiotic resistance. The applicability of CS-informed treatments remains uncertain, in part because we lack an understanding of the generality of CS effects for different resistance mutations, singly or in combination. Here, we address this issue in the gram-positive pathogen Streptococcus pneumoniae by measuring collateral and fitness effects of clinically relevant gyrA and parC alleles and their combinations that confer resistance to fluoroquinolones. We integrated these results in a mathematical model that allowed us to evaluate how different in silico combination treatments impact the dynamics of resistance evolution. We identified common and conserved CS effects of different gyrA and parC alleles; however, the spectrum of collateral effects was unique for each allele or allelic pair. This indicated that allelic identity can impact the evolutionary dynamics of resistance evolution during monotreatment and combination treatment. Our model simulations, which included the experimentally derived antibiotic susceptibilities and fitness effects, and antibiotic-specific pharmacodynamics revealed that both collateral and fitness effects impact the population dynamics of resistance evolution. Overall, we provide evidence that allelic identity and interactions can have a pronounced impact on collateral effects to different antibiotics and suggest that these need to be considered in models examining CS-based therapies. Show less
Healy, K.; Freij, U.; Ellerstad, M.; Aulin, L.B.S.; Brückle, L.; Hillmering, H.; ... ; Gustafsson, R. 2022
Treatment failure of antibiotic therapy due to insufficient efficacy or occurrence of toxicity is a major clinical challenge, and is expected to become even more urgent with the global rise of... Show moreTreatment failure of antibiotic therapy due to insufficient efficacy or occurrence of toxicity is a major clinical challenge, and is expected to become even more urgent with the global rise of antibiotic resistance. Strategies to optimize treatment in individual patients are therefore of crucial importance. Currently, therapeutic drug monitoring plays an important role in optimizing antibiotic exposure to reduce treatment failure and toxicity. Biomarker-based strategies may be a powerful tool to further quantify and monitor antibiotic treatment response, and reduce variation in treatment response between patients. Host response biomarkers, such as CRP, procalcitonin, IL-6, and presepsin, could potentially carry significant information to be utilized for treatment individualization. To achieve this, the complex interactions among immune system, pathogen, drug, and biomarker need to be better understood and characterized. The purpose of this tutorial is to discuss the use and evidence of currently available biomarker-based approaches to inform antibiotic treatment. To this end, we also included a discussion on how treatment response biomarker data from preclinical, healthy volunteer, and patient-based studies can be further characterized using pharmacometric and system pharmacology based modeling approaches. As an illustrative example of how such modeling strategies can be used, we describe a case study in which we quantitatively characterize procalcitonin dynamics in relation to antibiotic treatments in patients with sepsis. Show less
Collateral sensitivity (CS)-based antibiotic treatments, where increased resistance to one antibiotic leads to increased sensitivity to a second antibiotic, may have the potential to limit the... Show moreCollateral sensitivity (CS)-based antibiotic treatments, where increased resistance to one antibiotic leads to increased sensitivity to a second antibiotic, may have the potential to limit the emergence of antimicrobial resistance. However, it remains unclear how to best design CS-based treatment schedules. To address this problem, we use mathematical modelling to study the effects of pathogen- and drug-specific characteristics for different treatment designs on bacterial population dynamics and resistance evolution. We confirm that simultaneous and one-day cycling treatments could supress resistance in the presence of CS. We show that the efficacy of CS-based cycling therapies depends critically on the order of drug administration. Finally, we find that reciprocal CS is not essential to suppress resistance, a result that significantly broadens treatment options given the ubiquity of one-way CS in pathogens. Overall, our analyses identify key design principles of CS-based treatment strategies and provide guidance to develop treatment schedules to suppress resistance. Show less
Aulin, L.B.S.; Koumans, C.I.M.; Haakman, Y.; Os, W. van; Kraakman, M.E.M.; Gooskens, J.; ... ; Hasselt, J.G.C. van 2021
Background and Objectives Teicoplanin is a highly protein-bound antibiotic, increasingly used to treat serious Gram-positive infections in critically ill children. Maturational and... Show moreBackground and Objectives Teicoplanin is a highly protein-bound antibiotic, increasingly used to treat serious Gram-positive infections in critically ill children. Maturational and pathophysiological intensive care unit-related changes often lead to altered pharmacokinetics. In this study, the objectives were to develop a pediatric population-pharmacokinetic model of unbound and total teicoplanin concentrations, to investigate the impact of plasma albumin levels and renal function on teicoplanin pharmacokinetics, and to evaluate the efficacy of the current weight-based dosing regimen. Methods An observational pharmacokinetic study was performed and blood samples were collected for quantification of unbound and total concentrations of teicoplanin after the first dose and in assumed steady-state conditions. A population-pharmacokinetic analysis was conducted using a standard sequential approach and Monte Carlo simulations were performed for a probability of target attainment analysis using previously published pharmacokinetic-pharmacodynamic targets. Results A two-compartment model with allometric scaling of pharmacokinetic parameters and non-linear plasma protein binding best described the data. Neither the inclusion of albumin nor the renal function significantly improved the model and no other covariates were supported for inclusion in the final model. The probability of target attainment analysis showed that the standard dosing regimen does not satisfactory attain the majority of the proposed targets. Conclusions We successfully characterized the pharmacokinetics of unbound and total teicoplanin in critically ill pediatric patients. The highly variable unbound fraction of teicoplanin could not be predicted using albumin levels, which may support the use of therapeutic drug monitoring of unbound concentrations. Poor target attainment was shown for the most commonly used dosing regimen, regardless of the pharmacokinetic-pharmacodynamic target evaluated. Show less
Aulin, L.B.S.; Valitalo, P.A.; Rizk, M.L.; Visser, S.A.G.; Rao, G.; Graaf, P.H. van der; Hasselt, J.G.C. van 2018
