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The aim of the study was to simulate the possible clinical situation of the drug overdose and to assess its electrophysiological consequences, and thus to move the M&S approach to the patient's bedside level. Cardiological consequences of citalopram overdose were simulated with use of the ToxComp system. Physiology of the 36-yo woman, 33 hours post suicidal ingestion, with high serum citalopram (477 ng/mL) and its metabolite desmethylcitalopram (123.2 ng/mL) concentration was simulated. Ionic current inhibition data were either taken from the literature or predicted with use of the extended QSAR models. Total ionic currents inhibition was simulated as the sum of inhibitions. Plasma ions concentrations and heart rate rhythm were taken from the clinical report (Tarabar 2008). QTc [ms] value with Fridericia correction was the endpoint. Four scenarios were tested assuming that either IKr current only or IKr+IKs currents are affected with various heart rates. The comparison of the observed vs. simulated QTcF values for four tested scenarios was as follows: (1) IKr/102 bpm — 524/469; (2) IKr/150 bpm — 515/511; (3) IKr+IKs/102 bpm — 524/487; (4) IKr+IKs/150 bpm — 515/524. ToxComp system predicted the electrophysiological consequences of citalopram overdose. Results indirectly suggest the role of the drug triggered multiple currents inhibition instead of dealing with hERG channel only. [ABSTRACT FROM PUBLISHER]

The project aims to develop the systems biology driven, modeling and simulation based, easy to use for non-modelers platform for the drugs proarrhythmic potency assessment at the population level. Platform realizes the in vitro-in vivo extrapolation approach thus the input data comes from the patch clamp in vitro ionic currents inhibition studies. Covariates describing variability in population include cardiomyocyte, heart and plasma physiological parameters which are correlated with basic demographic information (age, gender). Simulated output data includes action potential and ECG derivatives where APD50, APD90 and QTc are analyzed as the endpoints. System was implemented with use of the Java/JavaFX technology and is available both on line and off line and distributed under the GPLv3 license. Its main role include clinical trials optimization and waiving, helping with the go-no go decision making. [ABSTRACT FROM PUBLISHER]

The study aimed to assess the usefulness of the modeling and simulation approach for the proarrhythmic potency of the non-cardiological drugs prediction. Ability to mimic population effect with use of the virtual population generator on the QTc value was tested. ToxComp version 1.5 (www.tox-comp.net) with ten Tuscher human cardiomyocyte model (1D string -Epi/M/Endo — 20/30/50%) was used to mimic the clinical study and formoterol was chosen as a model drug (Lecaillon study — 8 males/4 females, mean age[SD] — 24[6]). Simulation time was set to 10 seconds and the QTc [ms] value with Fridericia correction was chosen as an endpoint. Obtained simulation results were compared with clinical study outcomes. None of the differences between observed and predicted values were statistically significant (t-test). Current study proves that such phenomena can be predicted with use of the mathematical models working on the population level. [ABSTRACT FROM PUBLISHER]

The aim of this work was to create extended QSAR model of the relationship between sodium channel blocking activity of the particular compound and its chemical structure together with the in vitro assay conditions. Artificial neural networks (ANNs) were chosen as modeling tools. Chemoinformatics software was used for calculation of the molecular descriptors describing the structure of the interest. Drug concentration causing 50% of the channel inhibition (IC50) was used as the modeling endpoint. The data was based on the literature search and consisted of 38 drugs and 108 records. Initial number of inputs was 110 and during the sensitivity analysis was reduced to 20. ANNs models were optimized in the extended 10-fold cross-validation scheme yielding RMSE = 0.68, NRMSE = 20.7% and R2= 0.35. Best models were ANNs ensembles combining three ANNs with their outputs averaged as a collective output of the system. [ABSTRACT FROM PUBLISHER]