Physiology based pharmacokinetic (PBPK) modeling and simulation is a useful method for prediction of biodistribution of both macromolecules and small molecules. It can enhance our understanding of the underlying mechanisms of biodistribution and hence may help in rational design of macromolecules used as diagnostic and therapeutic agents. In this review we discuss PBPK modeling and simulation of a radiolabelled Monoclonal Antibody ((111)In-DOTA-hAFP31 IgG) ("MAB") in mice without tumor and in a human with tumor. This study is part of Xemet Co.'s effort to develop a more accurate and reliable PBPK model and simulation platform, which is applicable both for small molecules and macromolecules. The simulated results were fitted to experimental time series data by varying parameters which were not fixed a priori. It was demonstrated that the PBPK model describes the main features of the pharmacokinetics of the studied systems. It was also shown that simulation can be used for evaluating the parameters of the system and scaling up the pharmacokinetics of MAB from mice to man. We identified several areas of improvement and further development needed to improve the accuracy of PBPK simulation for MAB and other macromolecules. It was concluded that the transvascular permeabilities are the most important parameters and more research is needed to enable prediction of permeabilities from molecular characteristics of macromolecules. It would also be necessary to understand better and describe with a more detailed model the microstructure of the tumor and to measure or predict the antigen concentration in tumor. Non-specific, non-saturable binding in other organs/tissues should be understood better and the kinetic constants of the binding should be measured experimentally. Although the metabolism and clearance were neglected in this study they need to be included in more detailed studies. Also the intracellular trafficking of macromolecules, which was not included in this study, shall be included in the more accurate models.

Traditional recommendation systems have limited possibilities to optimise business value in editorial decision making in news production, as they select the recommendations only from the content whose production has been decided editorially in the daily news process or content from existing content inventories. This paper explores an approach to use predictive analytics to make it possible to optimise story assignment and editing in daily editorial work based on selected business objectives already before publishing. In this case study exploration, we use the 'constructive approach' as a method to provide solutions to concrete business problems with a scientific approach. We contribute by experimenting a novel method combining elements from several scientific domains like strategic management and system dynamics. We conclude that with language analysis using recurrent neural networks, we were able to predict the success of a news story published on a digital channel in a way that fulfils the 'weak market test' criteria of the constructive approach. A company with whom the model was developed considered it valuable enough to decide to move it from exploration to be further developed and used in real news production.