A combined experimental and computational framework to evaluate the behavior of therapeutic cells for peripheral nerve regeneration

Recent studies have explored the potential of tissue‐mimetic scaffolds in encouraging nerve regeneration. One of the major determinants of the regenerative success of cellular nerve repair constructs (NRCs) is the local microenvironment, particularly native low oxygen conditions which can affect implanted cell survival and functional performance. In vivo, cells reside in a range of environmental conditions due to the spatial gradients of nutrient concentrations that are established. Here we evaluate in vitro the differences in cellular behavior that such conditions induce, including key biological features such as oxygen metabolism, glucose consumption, cell death, and vascular endothelial growth factor secretion. Experimental measurements are used to devise and parameterize a mathematical model that describes the behavior of the cells. The proposed model effectively describes the interactions between cells and their microenvironment and could in the future be extended, allowing researchers to compare the behavior of different therapeutic cells. Such a combinatorial approach could be used to accelerate the clinical translation of NRCs by identifying which critical design features should be optimized when fabricating engineered nerve repair conduits.

PK-DB: pharmacokinetics database for individualized and stratified computational modeling

A multitude of pharmacokinetics studies have been published. However, due to the lack of an open database, pharmacokinetics data, as well as the corresponding meta-information, have been difficult to access. We present PK-DB (https://pk-db.com), an open database for pharmacokinetics information from clinical trials. PK-DB provides curated information on (i) characteristics of studied patient cohorts and subjects (e.g. age, bodyweight, smoking status, genetic variants); (ii) applied interventions (e.g. dosing, substance, route of application); (iii) pharmacokinetic parameters (e.g. clearance, half-life, area under the curve) and (iv) measured pharmacokinetic time-courses. Key features are the representation of experimental errors, the normalization of measurement units, annotation of information to biological ontologies, calculation of pharmacokinetic parameters from concentration-time profiles, a workflow for collaborative data curation, strong validation rules on the data, computational access via a REST API as well as human access via a web interface. PK-DB enables meta-analysis based on data from multiple studies and data integration with computational models. A special focus lies on meta-data relevant for individualized and stratified computational modeling with methods like physiologically based pharmacokinetic (PBPK), pharmacokinetic/pharmacodynamic (PK/PD), or population pharmacokinetic (pop PK) modeling.

In silico prediction of dermal absorption of pesticides - an evaluation of selected models against results from in vitro testing

Current guidance for the estimation of dermal absorption (DA) of pesticides recommends the use of default values, read-across of information between formulations and in vitro testing. While QSARs exist to estimate percutaneous absorption, their use is currently not encouraged. Therefore, the potential of publicly available models for DA estimation was investigated based on data from 564 human in vitro DA experiments on pesticides. The classic Potts Guy model, the correction of Cleek Bunge for highly lipophilic chemicals, the mechanistic model of Mitragotri, and the COSMOS model were used to estimate the permeability coefficient k_p. Different approaches were explored to calculate the percentage of external dose absorbed. IH SkinPerm was examined as stand-alone model. The models generally failed to accurately predict experimental values. For 30-40% of the predictions, there was overestimation by one order of magnitude. Three models underpredicted >10% of the cases, the remaining models <5%. DA of hydrophilic substances was typically underpredicted. Overprediction was more prominent for solid preparations and suspensions. The molecular weight, irritation potential and skin thickness did not correlate with the models' predictivity. Of the models investigated, IH SkinPerm performed best with 38% of the predictions within one order of magnitude and 2% underpredicted cases.

A unifying mathematical model of lipid droplet metabolism reveals key molecular players in the development of hepatic steatosis

The liver responds to elevated plasma concentrations of free fatty acids (FFAs) with an enhanced uptake of FFAs and their esterification to triacylglycerol (TAG). On the long term, this may result in massive hepatic TAG accumulation called steatosis hepatitis. In hepatocytes, the poor water-soluble TAG is packed in specialized organelles: Lipid droplets (LDs) serving as transient cellular deposit and lipoproteins (LPs) transporting TAG and cholesterol esters to extra-hepatic tissues. The dynamics of these organelles is controlled by a variety of regulatory surface proteins (RSPs). Assembly and export of VLDLs are mainly regulated by the microsomal transfer protein (MTP) and apoprotein B100. Formation and lipolysis of LDs are regulated by several RSPs. The best studied regulators belong to the PAT (Perilipin/Adipophilin/TIP47) and CIDE families. Knockdown or overexpression of SRPs may significantly affect the total number and size distribution of LDs. Intriguingly, a large cell-to-cell heterogeneity with respect to the number and size of LDs has been found in various cell types including hepatocytes. These findings suggest that the extent of cellular lipid accumulation is determined not only by the imbalance between lipid supply and utilization but also by variations in the expression of RSPs and metabolic enzymes. To better understand the relative regulatory impact of individual processes involved in the cellular TAG turnover, we developed a comprehensive kinetic model encompassing the pathways of the fatty acid and triglyceride metabolism and the main molecular processes governing the dynamics of LDs. The model was parametrized such that a large number of experimental in vitro and in vivo findings are correctly recapitulated. A control analysis of the model revealed that variations in the activity of FFA uptake, diacylglycerol acyltransferase (DGAT) 2, and adipose triglyceride lipase (ATGL) have the strongest influence on the cellular TAG level. We used the model to simulate LD size distributions in human hepatoma cells and hepatocytes exposed to a challenge with FFAs. A random fold change by a factor of about two in the activity of RSPs was sufficient to reproduce the large diversity of droplet size distributions observed in individual cells. Under the premise that the same extent of variability of RSPs holds for the intact organ, our model predicts variations in the TAG content of individual hepatocytes by a factor of about 3-6 depending on the nutritional regime. Taken together, our modeling approach integrates numerous experimental findings on individual processes in the cellular TAG metabolism and LD dynamics metabolism to a consistent state-of-the-art dynamic network model that can be used to study how changes in the external conditions or systemic parameters will affect the TAG content of hepatocytes.