@misc{WeisseMiddletonHuisinga2010,
  author    = {Weiße, Andrea Y. and Middleton, Richard H. and Huisinga, Wilhelm},
  title     = {Quantifying uncertainty, variability and likelihood for ordinary differential equation models},
  series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {894},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-43134},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-431340},
  pages     = {12},
  year      = {2010},
  abstract  = {Background In many applications, ordinary differential equation (ODE) models are subject to uncertainty or variability in initial conditions and parameters. Both, uncertainty and variability can be quantified in terms of a probability density function on the state and parameter space. Results The partial differential equation that describes the evolution of this probability density function has a form that is particularly amenable to application of the well-known method of characteristics. The value of the density at some point in time is directly accessible by the solution of the original ODE extended by a single extra dimension (for the value of the density). This leads to simple methods for studying uncertainty, variability and likelihood, with significant advantages over more traditional Monte Carlo and related approaches especially when studying regions with low probability. Conclusions While such approaches based on the method of characteristics are common practice in other disciplines, their advantages for the study of biological systems have so far remained unrecognized. Several examples illustrate performance and accuracy of the approach and its limitations.},
  language  = {en}
}
@misc{KrippendorffOyarzunHuisinga2012,
  author    = {Krippendorff, Ben-Fillippo and Oyarz{\´u}n, Diego A. and Huisinga, Wilhelm},
  title     = {Predicting the F(ab)-mediated effect of monoclonal antibodies in vivo by combining cell-level kinetic and pharmacokinetic modelling},
  series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {958},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-43105},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-431051},
  pages     = {125 -- 139},
  year      = {2012},
  abstract  = {Cell-level kinetic models for therapeutically relevant processes increasingly benefit the early stages of drug development. Later stages of the drug development processes, however, rely on pharmacokinetic compartment models while cell-level dynamics are typically neglected. We here present a systematic approach to integrate cell-level kinetic models and pharmacokinetic compartment models. Incorporating target dynamics into pharmacokinetic models is especially useful for the development of therapeutic antibodies because their effect and pharmacokinetics are inherently interdependent. The approach is illustrated by analysing the F(ab)-mediated inhibitory effect of therapeutic antibodies targeting the epidermal growth factor receptor. We build a multi-level model for anti-EGFR antibodies by combining a systems biology model with in vitro determined parameters and a pharmacokinetic model based on in vivo pharmacokinetic data. Using this model, we investigated in silico the impact of biochemical properties of anti-EGFR antibodies on their F(ab)-mediated inhibitory effect. The multi-level model suggests that the F(ab)-mediated inhibitory effect saturates with increasing drug-receptor affinity, thereby limiting the impact of increasing antibody affinity on improving the effect. This indicates that observed differences in the therapeutic effects of high affinity antibodies in the market and in clinical development may result mainly from Fc-mediated indirect mechanisms such as antibody-dependent cell cytotoxicity.},
  language  = {en}
}
@misc{MaierHartungdeWiljesetal.2020,
  author    = {Maier, Corinna and Hartung, Niklas and de Wiljes, Jana and Kloft, Charlotte and Huisinga, Wilhelm},
  title     = {Bayesian Data Assimilation to Support Informed Decision Making in Individualized Chemotherapy},
  series = {Postprints der Universit{\"a}t Potsdam : Mathematisch Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam : Mathematisch Naturwissenschaftliche Reihe},
  number    = {827},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-44550},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-445500},
  pages     = {14},
  year      = {2020},
  abstract  = {An essential component of therapeutic drug/biomarker monitoring (TDM) is to combine patient data with prior knowledge for model-based predictions of therapy outcomes. Current Bayesian forecasting tools typically rely only on the most probable model parameters (maximum a posteriori (MAP) estimate). This MAP-based approach, however, does neither necessarily predict the most probable outcome nor does it quantify the risks of treatment inefficacy or toxicity. Bayesian data assimilation (DA) methods overcome these limitations by providing a comprehensive uncertainty quantification. We compare DA methods with MAP-based approaches and show how probabilistic statements about key markers related to chemotherapy-induced neutropenia can be leveraged for more informative decision support in individualized chemotherapy. Sequential Bayesian DA proved to be most computationally efficient for handling interoccasion variability and integrating TDM data. For new digital monitoring devices enabling more frequent data collection, these features will be of critical importance to improve patient care decisions in various therapeutic areas.},
  language  = {en}
}
@article{MaierHartungdeWiljesetal.2020,
  author    = {Maier, Corinna and Hartung, Niklas and de Wiljes, Jana and Kloft, Charlotte and Huisinga, Wilhelm},
  title     = {Bayesian Data Assimilation to Support Informed Decision Making in Individualized Chemotherapy},
  series = {CPT: Pharmacometrics \& Systems Pharmacology},
  volume    = {XX},
  journal   = {CPT: Pharmacometrics \& Systems Pharmacology},
  publisher = {Nature Publ. Group},
  address   = {London},
  issn      = {2163-8306},
  doi       = {10.1002/psp4.12492},
  pages     = {12},
  year      = {2020},
  abstract  = {An essential component of therapeutic drug/biomarker monitoring (TDM) is to combine patient data with prior knowledge for model-based predictions of therapy outcomes. Current Bayesian forecasting tools typically rely only on the most probable model parameters (maximum a posteriori (MAP) estimate). This MAP-based approach, however, does neither necessarily predict the most probable outcome nor does it quantify the risks of treatment inefficacy or toxicity. Bayesian data assimilation (DA) methods overcome these limitations by providing a comprehensive uncertainty quantification. We compare DA methods with MAP-based approaches and show how probabilistic statements about key markers related to chemotherapy-induced neutropenia can be leveraged for more informative decision support in individualized chemotherapy. Sequential Bayesian DA proved to be most computationally efficient for handling interoccasion variability and integrating TDM data. For new digital monitoring devices enabling more frequent data collection, these features will be of critical importance to improve patient care decisions in various therapeutic areas.},
  language  = {en}
}