@phdthesis{Solms2017,
  author    = {Solms, Alexander Maximilian},
  title     = {Integrating nonlinear mixed effects and physiologically-based modeling approaches for the analysis of repeated measurement studies},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-397070},
  school      = {Universit{\"a}t Potsdam},
  pages     = {x, 141},
  year      = {2017},
  abstract  = {During the drug discovery \& development process, several phases encompassing a number of preclinical and clinical studies have to be successfully passed to demonstrate safety and efficacy of a new drug candidate. As part of these studies, the characterization of the drug's pharmacokinetics (PK) is an important aspect, since the PK is assumed to strongly impact safety and efficacy. To this end, drug concentrations are measured repeatedly over time in a study population. The objectives of such studies are to describe the typical PK time-course and the associated variability between subjects. Furthermore, underlying sources significantly contributing to this variability, e.g. the use of comedication, should be identified. The most commonly used statistical framework to analyse repeated measurement data is the nonlinear mixed effect (NLME) approach. At the same time, ample knowledge about the drug's properties already exists and has been accumulating during the discovery \& development process: Before any drug is tested in humans, detailed knowledge about the PK in different animal species has to be collected. This drug-specific knowledge and general knowledge about the species' physiology is exploited in mechanistic physiological based PK (PBPK) modeling approaches -it is, however, ignored in the classical NLME modeling approach. Mechanistic physiological based models aim to incorporate relevant and known physiological processes which contribute to the overlying process of interest. In comparison to data--driven models they are usually more complex from a mathematical perspective. For example, in many situations, the number of model parameters outrange the number of measurements and thus reliable parameter estimation becomes more complex and partly impossible. As a consequence, the integration of powerful mathematical estimation approaches like the NLME modeling approach -which is widely used in data-driven modeling -and the mechanistic modeling approach is not well established; the observed data is rather used as a confirming instead of a model informing and building input. Another aggravating circumstance of an integrated approach is the inaccessibility to the details of the NLME methodology so that these approaches can be adapted to the specifics and needs of mechanistic modeling. Despite the fact that the NLME modeling approach exists for several decades, details of the mathematical methodology is scattered around a wide range of literature and a comprehensive, rigorous derivation is lacking. Available literature usually only covers selected parts of the mathematical methodology. Sometimes, important steps are not described or are only heuristically motivated, e.g. the iterative algorithm to finally determine the parameter estimates. Thus, in the present thesis the mathematical methodology of NLME modeling is systemically described and complemented to a comprehensive description, comprising the common theme from ideas and motivation to the final parameter estimation. Therein, new insights for the interpretation of different approximation methods used in the context of the NLME modeling approach are given and illustrated; furthermore, similarities and differences between them are outlined. Based on these findings, an expectation-maximization (EM) algorithm to determine estimates of a NLME model is described. Using the EM algorithm and the lumping methodology by Pilari2010, a new approach on how PBPK and NLME modeling can be combined is presented and exemplified for the antibiotic levofloxacin. Therein, the lumping identifies which processes are informed by the available data and the respective model reduction improves the robustness in parameter estimation. Furthermore, it is shown how apriori known factors influencing the variability and apriori known unexplained variability is incorporated to further mechanistically drive the model development. Concludingly, correlation between parameters and between covariates is automatically accounted for due to the mechanistic derivation of the lumping and the covariate relationships. A useful feature of PBPK models compared to classical data-driven PK models is in the possibility to predict drug concentration within all organs and tissue in the body. Thus, the resulting PBPK model for levofloxacin is used to predict drug concentrations and their variability within soft tissues which are the site of action for levofloxacin. These predictions are compared with data of muscle and adipose tissue obtained by microdialysis, which is an invasive technique to measure a proportion of drug in the tissue, allowing to approximate the concentrations in the interstitial fluid of tissues. Because, so far, comparing human in vivo tissue PK and PBPK predictions are not established, a new conceptual framework is derived. The comparison of PBPK model predictions and microdialysis measurements shows an adequate agreement and reveals further strengths of the presented new approach. We demonstrated how mechanistic PBPK models, which are usually developed in the early stage of drug development, can be used as basis for model building in the analysis of later stages, i.e. in clinical studies. As a consequence, the extensively collected and accumulated knowledge about species and drug are utilized and updated with specific volunteer or patient data. The NLME approach combined with mechanistic modeling reveals new insights for the mechanistic model, for example identification and quantification of variability in mechanistic processes. This represents a further contribution to the learn \& confirm paradigm across different stages of drug development. Finally, the applicability of mechanism--driven model development is demonstrated on an example from the field of Quantitative Psycholinguistics to analyse repeated eye movement data. Our approach gives new insight into the interpretation of these experiments and the processes behind.},
  language  = {en}
}
@phdthesis{Fuhrmann2018,
  author    = {Fuhrmann, Saskia},
  title     = {Physiologically-based pharmacokinetic and mechanism-based pharmacodynamic modelling of monoclonal antibodies with a focus on tumour targeting},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-418861},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xvii, 171},
  year      = {2018},
  abstract  = {Monoclonal antibodies (mAbs) are an innovative group of drugs with increasing clinical importance in oncology, combining high specificity with generally low toxicity. There are, however, numerous challenges associated with the development of mAbs as therapeutics. Mechanistic understanding of factors that govern the pharmacokinetics (PK) of mAbs is critical for drug development and the optimisation of effective therapies; in particular, adequate dosing strategies can improve patient quality life and lower drug cost. Physiologically-based PK (PBPK) models offer a physiological and mechanistic framework, which is of advantage in the context of animal to human extrapolation. Unlike for small molecule drugs, however, there is no consensus on how to model mAb disposition in a PBPK context. Current PBPK models for mAb PK hugely vary in their representation of physiology and parameterisation. Their complexity poses a challenge for their applications, e.g., translating knowledge from animal species to humans. In this thesis, we developed and validated a consensus PBPK model for mAb disposition taking into account recent insights into mAb distribution (antibody biodistribution coefficients and interstitial immunoglobulin G (IgG) pharmacokinetics) to predict tissue PK across several pre-clinical species and humans based on plasma data only. The model allows to a priori predict target-independent (unspecific) mAb disposition processes as well as mAb disposition in concentration ranges, for which the unspecific clearance (CL) dominates target-mediated CL processes. This is often the case for mAb therapies at steady state dosing. The consensus PBPK model was then used and refined to address two important problems: 1) Immunodeficient mice are crucial models to evaluate mAb efficacy in cancer therapy. Protection from elimination by binding to the neonatal Fc receptor is known to be a major pathway influencing the unspecific CL of both, endogenous and therapeutic IgG. The concentration of endogenous IgG, however, is reduced in immunodeficient mouse models, and this effect on unspecific mAb CL is unknown, yet of great importance for the extrapolation to human in the context of mAb cancer therapy. 2) The distribution of mAbs into solid tumours is of great interest. To comprehensively investigate mAb distribution within tumour tissue and its implications for therapeutic efficacy, we extended the consensus PBPK model by a detailed tumour distribution model incorporating a cell-level model for mAb-target interaction. We studied the impact of variations in tumour microenvironment on therapeutic efficacy and explored the plausibility of different mechanisms of action in mAb cancer therapy. The mathematical findings and observed phenomena shed new light on therapeutic utility and dosing regimens in mAb cancer treatment.},
  language  = {en}
}
@phdthesis{Fronton2014,
  author    = {Fronton, Ludivine},
  title     = {Modeling approaches to characterize the disposition of monoclonal antibodies},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-76537},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xxi, 133},
  year      = {2014},
  abstract  = {Monoclonal antibodies (mAbs) are engineered immunoglobulins G (IgG) used for more than 20 years as targeted therapy in oncology, infectious diseases and (auto-)immune disorders. Their protein nature greatly influences their pharmacokinetics (PK), presenting typical linear and non-linear behaviors. While it is common to use empirical modeling to analyze clinical PK data of mAbs, there is neither clear consensus nor guidance to, on one hand, select the structure of classical compartment models and on the other hand, interpret mechanistically PK parameters. The mechanistic knowledge present in physiologically-based PK (PBPK) models is likely to support rational classical model selection and thus, a methodology to link empirical and PBPK models is desirable. However, published PBPK models for mAbs are quite diverse in respect to the physiology of distribution spaces and the parameterization of the non-specific elimination involving the neonatal Fc receptor (FcRn) and endogenous IgG (IgGendo). The remarkable discrepancy between the simplicity of biodistribution data and the complexity of published PBPK models translates in parameter identifiability issues. In this thesis, we address this problem with a simplified PBPK model—derived from a hierarchy of more detailed PBPK models and based on simplifications of tissue distribution model. With the novel tissue model, we are breaking new grounds in mechanistic modeling of mAbs disposition: We demonstrate that binding to FcRn is indeed linear and that it is not possible to infer which tissues are involved in the unspecific elimination of wild-type mAbs. We also provide a new approach to predict tissue partition coefficients based on mechanistic insights: We directly link tissue partition coefficients (Ktis) to data-driven and species-independent published antibody biodistribution coefficients (ABCtis) and thus, we ensure the extrapolation from pre-clinical species to human with the simplified PBPK model. We further extend the simplified PBPK model to account for a target, relevant to characterize the non-linear clearance due to mAb-target interaction. With model reduction techniques, we reduce the dimensionality of the simplified PBPK model to design 2-compartment models, thus guiding classical model development with physiological and mechanistic interpretation of the PK parameters. We finally derive a new scaling approach for anatomical and physiological parameters in PBPK models that translates the inter-individual variability into the design of mechanistic covariate models with direct link to classical compartment models, specially useful for PK population analysis during clinical development.},
  language  = {en}
}