Lars Kuepfer
RWTH Aachen University
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Publication
Featured researches published by Lars Kuepfer.
Molecular Systems Biology | 2007
Robert Schuetz; Lars Kuepfer; Uwe Sauer
To which extent can optimality principles describe the operation of metabolic networks? By explicitly considering experimental errors and in silico alternate optima in flux balance analysis, we systematically evaluate the capacity of 11 objective functions combined with eight adjustable constraints to predict 13C‐determined in vivo fluxes in Escherichia coli under six environmental conditions. While no single objective describes the flux states under all conditions, we identified two sets of objectives for biologically meaningful predictions without the need for further, potentially artificial constraints. Unlimited growth on glucose in oxygen or nitrate respiring batch cultures is best described by nonlinear maximization of the ATP yield per flux unit. Under nutrient scarcity in continuous cultures, in contrast, linear maximization of the overall ATP or biomass yields achieved the highest predictive accuracy. Since these particular objectives predict the system behavior without preconditioning of the network structure, the identified optimality principles reflect, to some extent, the evolutionary selection of metabolic network regulation that realizes the various flux states.
Nature Biotechnology | 2007
Lars Kuepfer; Matthias Peter; Uwe Sauer; Jörg Stelling
Systems biology iteratively combines experimentation with mathematical modeling. However, limited mechanistic knowledge, conflicting hypotheses and scarce experimental data severely hamper the development of predictive mechanistic models in many areas of biology. Even under such high uncertainty, we show here that ensemble modeling, when combined with targeted experimental analysis, can unravel key operating principles in complex cellular pathways. For proof of concept, we develop a library of mechanistically alternative dynamic models for the highly conserved target-of-rapamycin (TOR) pathway of Saccharomyces cerevisiae. In contrast to the prevailing view of a de novo assembly of type 2A phosphatases (PP2As), our integrated computational and experimental analysis proposes a specificity factor, based on Tap42p-Tip41p, for PP2As as the key signaling mechanism that is quantitatively consistent with all available experimental data. Beyond revising our picture of TOR signaling, we expect ensemble modeling to help elucidate other insufficiently characterized cellular circuits.
Computers in Biology and Medicine | 2016
Lars Ole Schwen; André Homeyer; Michael Schwier; Uta Dahmen; Olaf Dirsch; Arne Schenk; Lars Kuepfer; Tobias Preusser; Andrea Schenk
Many physiological processes and pathological conditions in livers are spatially heterogeneous, forming patterns at the lobular length scale or varying across the organ. Steatosis, a common liver disease characterized by lipids accumulating in hepatocytes, exhibits heterogeneity at both these spatial scales. The main goal of the present study was to provide a method for zonated quantification of the steatosis patterns found in an entire mouse liver. As an example application, the results were employed in a pharmacokinetics simulation. For the analysis, an automatic detection of the lipid vacuoles was used in multiple slides of histological serial sections covering an entire mouse liver. Lobuli were determined semi-automatically and zones were defined within the lobuli. Subsequently, the lipid content of each zone was computed. The steatosis patterns were found to be predominantly periportal, with a notable organ-scale heterogeneity. The analysis provides a quantitative description of the extent of steatosis in unprecedented detail. The resulting steatosis patterns were successfully used as a perturbation to the liver as part of an exemplary whole-body pharmacokinetics simulation for the antitussive drug dextromethorphan. The zonated quantification is also applicable to other pathological conditions that can be detected in histological images. Besides being a descriptive research tool, this quantification could perspectively complement diagnosis based on visual assessment of histological images.
Archives of Toxicology | 2017
Christoph Thiel; Henrik Cordes; Isabel Conde; José V. Castell; Lars M. Blank; Lars Kuepfer
Understanding central mechanisms underlying drug-induced toxicity plays a crucial role in drug development and drug safety. However, a translation of cellular in vitro findings to an actual in vivo context remains challenging. Here, physiologically based pharmacokinetic (PBPK) modeling was used for in vivo contextualization of in vitro toxicity data (PICD) to quantitatively predict in vivo drug response over time by integrating multiple levels of biological organization. Explicitly, in vitro toxicity data at the cellular level were integrated into whole-body PBPK models at the organism level by coupling in vitro drug exposure with in vivo drug concentration–time profiles simulated in the extracellular environment within the organ. PICD was exemplarily applied on the hepatotoxicant azathioprine to quantitatively predict in vivo drug response of perturbed biological pathways and cellular processes in rats and humans. The predictive accuracy of PICD was assessed by comparing in vivo drug response predicted for rats with observed in vivo measurements. To demonstrate clinical applicability of PICD, in vivo drug responses of a critical toxicity-related pathway were predicted for eight patients following acute azathioprine overdoses. Moreover, acute liver failure after multiple dosing of azathioprine was investigated in a patient case study by use of own clinical data. Simulated pharmacokinetic profiles were therefore related to in vivo drug response predicted for genes associated with observed clinical symptoms and to clinical biomarkers measured in vivo. PICD provides a generic platform to investigate drug-induced toxicity at a patient level and thus may facilitate individualized risk assessment during drug development.
Antimicrobial Agents and Chemotherapy | 2016
Henrik Cordes; Christoph Thiel; Hélène Eloise Aschmann; Vanessa Baier; Lars M. Blank; Lars Kuepfer
ABSTRACT Due to its high early bactericidal activity, isoniazid (INH) plays an essential role in tuberculosis treatment. Genetic polymorphisms of N-acetyltransferase type 2 (NAT2) cause a trimodal distribution of INH pharmacokinetics in slow, intermediate, and fast acetylators. The success of INH-based chemotherapy is associated with acetylator and patient health status. Still, a standard dose recommended by the FDA is administered regardless of acetylator type or immune status, even though adverse effects occur in 5 to 33% of all patients. Slow acetylators have a higher risk of development of drug-induced toxicity, while fast acetylators and immune-deficient patients face lower treatment success rates. To mechanistically assess the trade-off between toxicity and efficacy, we developed a physiologically based pharmacokinetic (PBPK) model describing the NAT2-dependent pharmacokinetics of INH and its metabolites. We combined the PBPK model with a pharmacodynamic (PD) model of antimycobacterial drug effects in the lungs. The resulting PBPK/PD model allowed the simultaneous simulation of treatment efficacies at the site of infection and exposure to toxic metabolites in off-target organs. Subsequently, we evaluated various INH dosing regimens in NAT2-specific immunocompetent and immune-deficient virtual populations. Our results suggest the need for acetylator-specific dose adjustments for optimal treatment outcomes. A reduced dose for slow acetylators substantially lowers the exposure to toxic metabolites and thereby the risk of adverse events, while it maintains sufficient treatment efficacies. Vice versa, intermediate and fast acetylators benefit from increased INH doses and a switch to a twice-daily administration schedule. Our analysis outlines how PBPK/PD modeling may be used to design and individualize treatment regimens.
Archives of Toxicology | 2018
Lars Kuepfer; Olivia Clayton; Christoph Thiel; Henrik Cordes; Ramona Nudischer; Lars M. Blank; Vanessa Baier; Stephane Heymans; Florian Caiment; Adrian Roth; David A. Fluri; Jens M. Kelm; José V. Castell; Nathalie Selevsek; Ralph Schlapbach; Hector C. Keun; James Hynes; Ugis Sarkans; Hans Gmuender; Ralf Herwig; Steven Niederer; Johannes Schuchhardt; Matthew Segall; Jos Kleinjans
Lars Kuepfer1 · Olivia Clayton2 · Christoph Thiel1 · Henrik Cordes1 · Ramona Nudischer2 · Lars M. Blank1 · Vanessa Baier1 · Stephane Heymans3,4 · Florian Caiment5 · Adrian Roth2 · David A. Fluri6 · Jens M. Kelm6 · José Castell7 · Nathalie Selevsek8 · Ralph Schlapbach8 · Hector Keun9 · James Hynes10 · Ugis Sarkans11 · Hans Gmuender12 · Ralf Herwig13 · Steven Niederer14 · Johannes Schuchhardt15 · Matthew Segall16 · Jos Kleinjans5
PLOS Computational Biology | 2017
Christoph Thiel; Henrik Cordes; Lorenzo Fabbri; Hélène Eloise Aschmann; Vanessa Baier; Ines Smit; Francis Atkinson; Lars M. Blank; Lars Kuepfer
Drug-induced toxicity is a significant problem in clinical care. A key problem here is a general understanding of the molecular mechanisms accompanying the transition from desired drug effects to adverse events following administration of either therapeutic or toxic doses, in particular within a patient context. Here, a comparative toxicity analysis was performed for fifteen hepatotoxic drugs by evaluating toxic changes reflecting the transition from therapeutic drug responses to toxic reactions at the cellular level. By use of physiologically-based pharmacokinetic modeling, in vitro toxicity data were first contextualized to quantitatively describe time-resolved drug responses within a patient context. Comparatively studying toxic changes across the considered hepatotoxicants allowed the identification of subsets of drugs sharing similar perturbations on key cellular processes, functional classes of genes, and individual genes. The identified subsets of drugs were next analyzed with regard to drug-related characteristics and their physicochemical properties. Toxic changes were finally evaluated to predict both molecular biomarkers and potential drug-drug interactions. The results may facilitate the early diagnosis of adverse drug events in clinical application.
npj Systems Biology and Applications | 2018
Henrik Cordes; Christoph Thiel; Vanessa Baier; Lars M. Blank; Lars Kuepfer
Drug-induced perturbations of the endogenous metabolic network are a potential root cause of cellular toxicity. A mechanistic understanding of such unwanted side effects during drug therapy is therefore vital for patient safety. The comprehensive assessment of such drug-induced injuries requires the simultaneous consideration of both drug exposure at the whole-body and resulting biochemical responses at the cellular level. We here present a computational multi-scale workflow that combines whole-body physiologically based pharmacokinetic (PBPK) models and organ-specific genome-scale metabolic network (GSMN) models through shared reactions of the xenobiotic metabolism. The applicability of the proposed workflow is illustrated for isoniazid, a first-line antibacterial agent against Mycobacterium tuberculosis, which is known to cause idiosyncratic drug-induced liver injuries (DILI). We combined GSMN models of a human liver with N-acetyl transferase 2 (NAT2)-phenotype-specific PBPK models of isoniazid. The combined PBPK-GSMN models quantitatively describe isoniazid pharmacokinetics, as well as intracellular responses, and changes in the exometabolome in a human liver following isoniazid administration. Notably, intracellular and extracellular responses identified with the PBPK-GSMN models are in line with experimental and clinical findings. Moreover, the drug-induced metabolic perturbations are distributed and attenuated in the metabolic network in a phenotype-dependent manner. Our simulation results show that a simultaneous consideration of both drug pharmacokinetics at the whole-body and metabolism at the cellular level is mandatory to explain drug-induced injuries at the patient level. The proposed workflow extends our mechanistic understanding of the biochemistry underlying adverse events and may be used to prevent drug-induced injuries in the future.Systems toxicology: linking pharmacokinetics to endogenous metabolismThe genotype of a patient determines the extent of drug-induced metabolic perturbations on the endogenous cellular network of the liver. A team around Lars Kuepfer at Germany’s RWTH Aachen University developed a computational workflow that links drug pharmacokinetics at the whole-body level with a cellular network of the liver. The authors used the competitive cofactor and energy demands in endogenous and drug metabolism to establish a multi-scale model for the antibiotic isoniazid. Their model quantitatively describes how isoniazid pharmacokinetics alter the intracellular liver biochemistry and the utilization of extracellular metabolites in different patient genotypes. The study outlines how a mechanistic understanding of genotype-dependent drug-induced metabolic perturbations may help to explain diverging incidence rates of toxic events in different patient subgroups. This could reduce the occurrence of toxic side effects during drug treatments in the future.
Methods of Molecular Biology | 2014
Lars Kuepfer
Stoichiometric models describe cellular biochemistry with systems of linear equations. The models which are fundamentally based on the steady-state assumption are comparatively easy to construct and can be applied to networks up to genome scale. Fluxes are inherent variables in stoichiometric models and linear optimization can be used to identify intracellular flux distributions. Great caution, however, has to be paid to the selection of the specific objective function which inevitably implies the existence of a specific global cellular rationale. On the other hand, stoichiometric models provide an analytical platform for contextualization of experimental data. Equally important, the stoichiometric models can be used for structural analyses of metabolic networks as such supporting for example rational model-driven strategies in metabolic engineering.
npj Systems Biology and Applications | 2018
Christoph Thiel; Ines Smit; Vanessa Baier; Henrik Cordes; Brigida Fabry; Lars M. Blank; Lars Kuepfer
A quantitative analysis of dose–response relationships is essential in preclinical and clinical drug development in order to optimize drug efficacy and safety, respectively. However, there is a lack of quantitative understanding about the dynamics of pharmacological drug–target interactions in biological systems. In this study, a quantitative systems pharmacology (QSP) approach is applied to quantify the drug efficacy of cyclooxygenase-2 (COX-2) and 5-lipoxygenase (5-LOX) inhibitors by coupling physiologically based pharmacokinetic models, at the whole-body level, with affected biological networks, at the cellular scale. Both COX-2 and 5-LOX are key enzymes in the production of inflammatory mediators and are known targets in the design of anti-inflammatory drugs. Drug efficacy is here evaluated for single and appropriate co-treatment of diclofenac, celecoxib, zileuton, and licofelone by quantitatively studying the reduction of prostaglandins and leukotrienes. The impact of rifampicin pre-treatment on prostaglandin formation is also investigated by considering pharmacokinetic drug interactions with diclofenac and celecoxib, finally suggesting optimized dose levels to compensate for the reduced drug action. Furthermore, a strong correlation was found between pain relief observed in patients as well as celecoxib- and diclofenac-induced decrease in prostaglandins after 6 h. The findings presented reveal insights about drug-induced modulation of cellular networks in a whole-body context, thereby describing complex pharmacokinetic/pharmacodynamic behavior of COX-2 and 5-LOX inhibitors in therapeutic situations. The results demonstrate the clinical benefit of using QSP to predict drug efficacy and, hence, encourage its use in future drug discovery and development programs.Systems pharmacology: PBPK/PD modeling at the whole-body scaleDrug efficacy is governed by both pharmacokinetics at the whole-body level and pharmacodynamic responses at the cellular scale. A team around Lars Kuepfer at Germany’s RWTH Aachen University applied a quantitative systems pharmacology (QSP) approach to assess the therapeutic potential for a set of drugs in the treatment of pain and inflammatory diseases. To this end, the authors integrated a cellular network model of arachidonic acid metabolism into physiologically based pharmacokinetic models to obtain a quantitative understanding about the dynamics of pharmacological drug–target interactions. The integrated multiscale model was used to address prototypical questions of drug development programs such as dose finding, drug–drug interactions and assessment of therapeutic efficacy. The results of the study demonstrate the benefits of using QSP in future drug development programs.