@article{EhmannZollerMinichmayretal.2019,
  author    = {Ehmann, Lisa and Zoller, Michael and Minichmayr, Iris K. and Scharf, Christina and Huisinga, Wilhelm and Zander, Johannes and Kloft, Charlotte},
  title     = {Development of a dosing algorithm for meropenem in critically ill patients based on a population pharmacokinetic/pharmacodynamic analysis},
  series = {International journal of antimicrobial agents},
  volume    = {54},
  journal   = {International journal of antimicrobial agents},
  number    = {3},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0924-8579},
  doi       = {10.1016/j.ijantimicag.2019.06.016},
  pages     = {309 -- 317},
  year      = {2019},
  abstract  = {Effective antibiotic dosing is vital for therapeutic success in critically ill patients. This work aimed to develop an algorithm to identify appropriate meropenem dosing in critically ill patients. Population pharma-cokinetic (PK) modelling was performed in NONMEM (R) 7.3 based on densely sampled meropenem serum samples (n(patients) = 48; n(samples) =1376) and included a systematic analysis of 27 pre-selected covariates to identify factors influencing meropenem exposure. Using Monte Carlo simulations newly considering the uncertainty of PK parameter estimates, standard meropenem dosing was evaluated with respect to attainment of the pharmacokinetic/pharmacodynamic (PK/PD) target and was compared with alternative infusion regimens (short-term, prolonged, continuous; daily dose, 2000-6000 mg). Subsequently, a dosing algorithm was developed to identify appropriate dosing regimens. The two-compartment population PK model included three factors influencing meropenem pharmacokinetics: the Cockcroft-Gault creatinine clearance (CLCRCG ) on meropenem clearance; and body weight and albumin on the central and peripheral volume of distribution, respectively; of these, only CLCRCG was identified as a vital influencing factor on PK/PD target attainment. A three-level dosing algorithm was developed (considering PK parameter uncertainty), suggesting dosing regimens depending on renal function and the level (L) of knowledge about the infecting pathogen (L1, pathogen unknown; L2, pathogen known; L3((-MIC)), pathogen and susceptibility known; L3((+MIC)), MIC known). Whereas patients with higher CLCRCG and lower pathogen susceptibility required mainly intensified dosing regimens, lower than standard doses appeared sufficient for highly susceptible pathogens. In conclusion, a versatile meropenem dosing algorithm for critically ill patients is proposed, indicating appropriate dosing regimens based on patient- and pathogen-specific information. (C) 2019 Published by Elsevier B.V.},
  language  = {en}
}
@article{FalkenhagenKnoechelKloftetal.2023,
  author    = {Falkenhagen, Undine and Kn{\"o}chel, Jane and Kloft, Charlotte and Huisinga, Wilhelm},
  title     = {Deriving mechanism-based pharmacodynamic models by reducing quantitative systems pharmacology models},
  series = {CPT: Pharmacometrics \& Systems Pharmacology},
  volume    = {12},
  journal   = {CPT: Pharmacometrics \& Systems Pharmacology},
  number    = {4},
  publisher = {Wiley},
  address   = {Hoboken},
  issn      = {2163-8306},
  doi       = {10.1002/psp4.12903},
  pages     = {432 -- 443},
  year      = {2023},
  abstract  = {Quantitative systems pharmacology (QSP) models integrate comprehensive qualitative and quantitative knowledge about pharmacologically relevant processes. We previously proposed a first approach to leverage the knowledge in QSP models to derive simpler, mechanism-based pharmacodynamic (PD) models. Their complexity, however, is typically still too large to be used in the population analysis of clinical data. Here, we extend the approach beyond state reduction to also include the simplification of reaction rates, elimination of reactions, and analytic solutions. We additionally ensure that the reduced model maintains a prespecified approximation quality not only for a reference individual but also for a diverse virtual population. We illustrate the extended approach for the warfarin effect on blood coagulation. Using the model-reduction approach, we derive a novel small-scale warfarin/international normalized ratio model and demonstrate its suitability for biomarker identification. Due to the systematic nature of the approach in comparison with empirical model building, the proposed model-reduction algorithm provides an improved rationale to build PD models also from QSP models in other applications.},
  language  = {en}
}
@article{StueblerKloftHuisinga2023,
  author    = {St{\"u}bler, Sabine and Kloft, Charlotte and Huisinga, Wilhelm},
  title     = {Cell-level systems biology model to study inflammatory bowel diseases and their treatment options},
  series = {CPT: pharmacometrics \& systems pharmacology},
  volume    = {12},
  journal   = {CPT: pharmacometrics \& systems pharmacology},
  number    = {5},
  publisher = {Nature Publ. Group},
  address   = {London},
  issn      = {2163-8306},
  doi       = {10.1002/psp4.12932},
  pages     = {690 -- 705},
  year      = {2023},
  abstract  = {To help understand the complex and therapeutically challenging inflammatory bowel diseases (IBDs), we developed a systems biology model of the intestinal immune system that is able to describe main aspects of IBD and different treatment modalities thereof. The model, including key cell types and processes of the mucosal immune response, compiles a large amount of isolated experimental findings from literature into a larger context and allows for simulations of different inflammation scenarios based on the underlying data and assumptions. In the context of a large and diverse virtual IBD population, we characterized the patients based on their phenotype (in contrast to healthy individuals, they developed persistent inflammation after a trigger event) rather than on a priori assumptions on parameter differences to a healthy individual. This allowed to reproduce the enormous diversity of predispositions known to lead to IBD. Analyzing different treatment effects, the model provides insight into characteristics of individual drug therapy. We illustrate for anti-TNF-alpha therapy, how the model can be used (i) to decide for alternative treatments with best prospects in the case of nonresponse, and (ii) to identify promising combination therapies with other available treatment options.},
  language  = {en}
}