Modeling Doxorubicin Pharmacokinetics in Multiple Myeloma Suggests Mechanism of Drug Resistance.

OBJECTIVE: Multiple myeloma (MM) is a plasma cell malignancy often treated with chemotherapy drugs. Among these, doxorubicin (DOXO) is commonly employed, sometimes in combined-drug therapies, but it has to be optimally administered in order to maximize its efficacy and reduce possible side effects. To support DOXO studies and treatment optimization, here we propose an experimental/modeling approach to establish a model describing DOXO pharmacokinetics (PK) in MM cells. METHODS: A series of in vitro experiments were performed in MM1R and MOLP-2 cells. DOXO was administered at two dosages (200 nM, 450 nM) at [Formula: see text] = 0 and removed at [Formula: see text] = 3 hrs. Intracellular DOXO concentration was measured via fluorescence microscopy during both drug uptake ([Formula: see text] = 0-3 hrs) and release phases ([Formula: see text] = 3-8 hrs). Four PK candidate models were identified, and were compared and selected based on their ability to describe DOXO data and numerical parameter identification. RESULTS: The most parsimonious model consists of three compartments describing DOXO distribution between the extracellular space, the cell cytoplasm and the nucleus, and defines the intracellular DOXO efflux rate through a Hill function, simulating a threshold/saturation drug resistance mechanism. This model predicted DOXO data well in all the experiments and provided precise parameter estimates (mean ± standard deviation coefficient of variation: 15.8 ± 12.2%). CONCLUSIONS: A reliable PK model describing DOXO uptake and release in MM cells has been successfully developed. SIGNIFICANCE: The proposed PK model, once integrated with DOXO pharmacodynamics, has the potential of allowing the study and the optimization of DOXO treatment strategies in MM.

Emotion Recognition from Physiological Signals Collected with a Wrist Device and Emotional Recall.

Implementing affective engineering in real-life applications requires the ability to effectively recognize emotions using physiological measurements. Despite being a widely researched topic, there seems to be a lack of systems that translate results from data collected in a laboratory setting to higher technology readiness levels. In this paper, we delve into the feasibility of emotion recognition beyond controlled laboratory environments. For this reason, we create a minimally-invasive experimental setup by combining emotional recall via autobiographical emotion memory tasks with a user-friendly Empatica wristband measuring blood volume pressure, electrodermal activity, skin temperature, and acceleration. We employ standard practices of feature-based supervised learning and specifically use support vector machines to explore subject dependency through various segmentation methods. We collected data from 45 participants. After preprocessing, using a data set of 134 segments from 40 participants, the accuracy of the classifier after 10-fold cross-validation was barely better than random guessing (36% for four emotions). However, when extracting multiple segments from each emotion task per participant using 10-fold cross-validation (i.e., including subject-dependent data in the training set), the classification rate increased to up to 75% for four emotions but was still as low as 32% for leave-one-subject-out cross-validation (i.e., subject-independent training). We conclude that highly subject-dependent issues might pose emotion recognition.