Bayesian data assimilation to support informed decision-making in individualized chemotherapy

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 computational 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

Transport and magnetic properties of La_(1-x)Ca_xMnO_3-films (0.1<x<0.9)

By laser ablation we prepared thin films of the colossal magnetoresistive compound La_(1-x)Ca_xMnO_3 with doping levels 0.1<x<0.9 on MgO substrates. X-ray diffraction revealed epitaxial growth and a systematic decrease of the lattice constants with doping. The variation of the transport and magnetic properties in this doping series was investigated by SQUID magnetization and electrical transport measurements. For the nonmetallic samples resistances up to 10^13 Ohm have been measured with an electrometer setup. While the transport data indicate polaronic transport for the metallic samples above the Curie temperature the low doped ferromagnetic insulating samples show a variable range hopping like transport at low temperature.Comment: 2 pages, 3 EPS figures, LT22 Proceedings to appear in Physica

A continued learning approach for model-informed precision dosing: Updating models in clinical practice

Model-informed precision dosing (MIPD) is a quantitative dosing framework that combines prior knowledge on the drug-disease-patient system with patient data from therapeutic drug/ biomarker monitoring (TDM) to support individualized dosing in ongoing treatment. Structural models and prior parameter distributions used in MIPD approaches typically build on prior clinical trials that involve only a limited number of patients selected according to some exclusion/inclusion criteria. Compared to the prior clinical trial population, the patient population in clinical practice can be expected to also include altered behavior and/or increased interindividual variability, the extent of which, however, is typically unknown. Here, we address the question of how to adapt and refine models on the level of the model parameters to better reflect this real-world diversity. We propose an approach for continued learning across patients during MIPD using a sequential hierarchical Bayesian framework. The approach builds on two stages to separate the update of the individual patient parameters from updating the population parameters. Consequently, it enables continued learning across hospitals or study centers, because only summary patient data (on the level of model parameters) need to be shared, but no individual TDM data. We illustrate this continued learning approach with neutrophil-guided dosing of paclitaxel. The present study constitutes an important step toward building confidence in MIPD and eventually establishing MIPD increasingly in everyday therapeutic use

Reinforcement learning and Bayesian data assimilation for model‐informed precision dosing in oncology

Model-informed precision dosing (MIPD) using therapeutic drug/biomarker monitoring offers the opportunity to significantly improve the efficacy and safety of drug therapies. Current strategies comprise model-informed dosing tables or are based on maximum a posteriori estimates. These approaches, however, lack a quantification of uncertainty and/or consider only part of the available patient-specific information. We propose three novel approaches for MIPD using Bayesian data assimilation (DA) and/or reinforcement learning (RL) to control neutropenia, the major dose-limiting side effect in anticancer chemotherapy. These approaches have the potential to substantially reduce the incidence of life-threatening grade 4 and subtherapeutic grade 0 neutropenia compared with existing approaches. We further show that RL allows to gain further insights by identifying patient factors that drive dose decisions. Due to its flexibility, the proposed combined DA-RL approach can easily be extended to integrate multiple end points or patient-reported outcomes, thereby promising important benefits for future personalized therapies

Mobile Stress Management Applications: An Affordance-Theoretic Perspective on the Adoption and Use

Chronic stress is a burden on mental and physical health. Despite the development and effectiveness of mobile stress management applications, their adoption and continued use remain low. Given that research revealed systematic differences in usage behavior among user types, we aim to investigate what drives these differences. We extend the affordance perspective and argue that accounting for psychological needs, actualized affordances, and actualization costs across different user types provides a deeper understanding of the factors driving the adoption and use of mobile stress management applications. The qualitative interview study of our mixed-methods study reveals eight affordances, eight actualization costs, and initial evidence for systematic differences among the user types. The quantitative questionnaire study will uncover the psychological needs, actualized affordances, and perceived actualization costs of the six user types. This work contributes a new theoretical perspective to overcome the gap in the adoption and usage of mobile stress management applications

Visualization of High-Dimensional Combinatorial Catalysis Data

The role of various techniques for visualization of high-dimensional data is demonstrated in the context of combinatorial high-throughput experimentation (HTE). Applying visualization tools, we identify which constituents of catalysts are associated with final products in a huge combinatorially generated data set of heterogeneous catalysts, and catalytic activity regions are identified with respect to pentanary composition spreads of catalysts. A radial visualization scheme directly visualizes pentanary composition spreads in two-dimensional (2D) space and catalytic activity of a final product by combining high-throughput results from five slate libraries. A glyph plot provides many possibilities for visualizing high-dimensional data with interactive tools. For catalyst discovery and lead optimization, this work demonstrates how large multidimensional catalysis data sets are visualized in terms of quantitative composition activity relationships (QCAR) to effectively identify the relevant key role of compositions (i.e., lead compositions) of catalysts