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This review aims to present and compare various Torsade de pointes propensity classification schemes of drugs that are publicly available from many scientific sources. We have also tracked and listed the compounds that were differently categorized. Additionally, we would like to draw attention to the need for establishing a general, standardized classification of a drug's proarrhythmic propensity. This is especially important in the current drug development process because of the changing paradigm of drug cardiac safety assessment., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

A Quantitative Systems Pharmacology approach was utilized to predict the cardiac consequences of drug-drug interaction (DDI) at the population level. The Simcyp in vitro-in vivo correlation and physiologically based pharmacokinetic platform was used to predict the pharmacokinetic profile of terfenadine following co-administration of the drug. Electrophysiological effects were simulated using the Cardiac Safety Simulator. The modulation of ion channel activity was dependent on the inhibitory potential of drugs on the main cardiac ion channels and a simulated free heart tissue concentration. ten Tusscher's human ventricular cardiomyocyte model was used to simulate the pseudo-ECG traces and further predict the pharmacodynamic consequences of DDI. Consistent with clinical observations, predicted plasma concentration profiles of terfenadine show considerable intra-subject variability with recorded C max values below 5 ng/mL for most virtual subjects. The pharmacokinetic and pharmacodynamic effects of inhibitors were predicted with reasonable accuracy. In all cases, a combination of the physiologically based pharmacokinetic and physiology-based pharmacodynamic models was able to differentiate between the terfenadine alone and terfenadine + inhibitor scenario. The range of QT prolongation was comparable in the clinical and virtual studies. The results indicate that mechanistic in vitro-in vivo correlation can be applied to predict the clinical effects of DDI even without comprehensive knowledge on all mechanisms contributing to the interaction., (Copyright © 2016 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.)

Cardiac safety is an issue causing early terminations at various stages of drug development. Efforts are put into the elimination of false negatives as well as false positives resulting from the current testing paradigm. In silico approaches offer mathematical system and data description from the ion current, through cardiomyocytes level, up to incorporation of inter-individual variability at the population level. The article aims to review three main modelling and simulation approaches, i.e. "top-down" which refers to models built on the observed data, "bottom-up", which stands for a mechanistic description of human physiology, and "middle-out" which combines both strategies. Modelling and simulation is a well-established tool in the assessment of drug proarrhythmic potency with an impact on research and development as well as on regulatory decisions, and it is certainly here to stay. What is more, the shift to systems biology and physiology-based models makes the cardiac effect more predictable.

Computational modelling is a cornerstone of Comprehensive In Vitro Proarrhythmia Assay and is re-increasingly being used in drug development. Electrophysiological effects of drug-drug interactions can be predicted in silico, e.g. with the use of in vitro cardiac ion channel data, PK profiles and human ventricular cardiomyocyte models. There are, however, several approaches with different assumptions used to assess the combined effect of multiple drugs, and there is no agreed standard interaction model. The aim of this study was to assess whether the choice of the drug-drug interaction (DDI) model (Bliss independence, Loewe additivity, or simple sum) influences the results of QT interval simulation trial. The Simcyp Simulator version 12.1 (Simcyp Ltd. [part of Certara], Sheffield, UK) and Cardiac Safety Simulator 2.0 (Simcyp Ltd. [part of Certara], Sheffield, UK) were used to simulate results of 8 virtual trials mimicking clinical studies and generate individual QTc data. The combined effect of inhibitory actions of drugs which were given simultaneously was calculated with use of three different interaction models. The PD effect of DDI was assessed and the differences between mean observed and mean predicted ΔQTcB values for terfenadine interactions were not statistically significant in all but one cases. Differences between the three DDI models are not statistically significant, implying that the choice of the DDI model, in the case of lack of synergy or antagonism, is irrelevant to the average predicted effect at the clinical level. However, in some cases, it can influence the verdict on combinatorial therapy safety for individual patients., Competing Interests: SP is an employee of Simcyp (part of Certara). Human and Animal Rights and Informed Consent This article does not contain any studies with human or animal subjects performed by any of the authors.

Little is known about the uptake of drugs into the human heart, although it is of great importance nowadays, when science desires to predict tissue level behavior rather than to measure it. Although the drug concentration in cardiac tissue seems a better predictor for physiological and electrophysiological changes than its level in plasma, knowledge of this value is very limited. Tissue to plasma partition coefficients (Kp) come to rescue since they characterize the distribution of a drug among tissues as being one of the input parameters in physiologically based pharmacokinetic (PBPK) models. The article reviews cardiac surgery and forensic medical studies to provide a reference for drug concentrations in human cardiac tissue. Firstly, the focus is on whether a drug penetrates into heart tissue at a therapeutic level; the provided values refer to antibiotics, antifungals and anticancer drugs. Drugs that directly affect cardiomyocyte electrophysiology are another group of interest. Measured levels of amiodarone, digoxin, perhexiline and verapamil in different sites in human cardiac tissue where the compounds might meet ion channels, gives an insight into how these more lipophilic drugs penetrate the heart. Much data are derived from postmortem studies and they provide insight to the cardiac distribution of more than 200 drugs. The analysis depicts potential problems in defining the active concentration location, what may indirectly suggest multiple mechanisms involved in the drug distribution within the heart. Copyright © 2015 John Wiley & Sons, Ltd., (Copyright © 2015 John Wiley & Sons, Ltd.)

Different batches of atorvastatin, represented by two immediate release formulation designs, were studied using a novel dynamic dissolution apparatus, simulating stomach and small intestine. A universal dissolution method was employed which simulated the physiology of human gastrointestinal tract, including the precise chyme transit behavior and biorelevant conditions. The multicompartmental dissolution data allowed direct observation and qualitative discrimination of the differences resulting from highly pH dependent dissolution behavior of the tested batches. Further evaluation of results was performed using IVIVC/IVIVR development. While satisfactory correlation could not be achieved using a conventional deconvolution based-model, promising results were obtained through the use of a nonconventional approach exploiting the complex compartmental dissolution data.

The current drug cardiac safety risk assessment paradigm is about to be changed. The discussed modifications cover clinical as well as pre-clinical sides. As for the latter, the pre-clinical assessment, it is planned to be based on the analysis of the drug-triggered multiple ion currents inhibition. Considering the variability in the in vitro patch clamp studies results, it would be of benefit to assess how these apparatus- and protocol-dependent differences influence the risk prediction and, eventually, the decision making. Four compounds, namely dextromethorphan, ketoconazole, terfenadine, and quinidine were screened for hERG inhibition with an automated patch clamp apparatus (CytoPatch(TM)2). The results were then compared against the literature published data, and after being complemented with information about other current inhibitions and effective therapeutic plasma concentration, utilized for the in silico based safety assessment. Two endpoints were used: (1) the concentration dependent potential to induce early afterdepolarizations in the simulated action potential and (2) the arrhythmia-like disruption in the simulated pseudo-ECG signals. Data analysis results prove that IC50 values, describing the inhibition potential, significantly differ among studies, and the choice of input data can greatly influence the in silico based safety assessment and thus the decision making process.

Correlation of the thickness of the left ventricular posterior wall (LVPWd) with various parameters, including age, gender, weight and height, was investigated in this study using regression models. Multicenter derived database comprised over 4,000 healthy individuals. The developed models were further utilized in the in vitro-in vivo (IVIV) translation of the drug cardiac safety data with use of the mathematical model of human cardiomyocytes operating at the virtual healthy population level. LVPWd was assumed to be equivalent to the length of one-dimensional string of virtual cardiomyocyte cells which was presented, as other physiological factors, to be a parameter influencing the simulated pseudo-ECG (pseudoelectrocardiogram), QTcF and ∆QTcF, both native and modified by exemplar drug (disopyramide) after I Kr current disruption. Simulation results support positive correlation between the LVPWd and QTcF/∆QTc. Developed models allow more detailed description of the virtual population and thus inter-individual variability influence on the drug cardiac safety.

Quantitative prediction of food effects (FE) upon drug pharmacokinetics, including population variability, in advance of human trials may help with trial design by optimising the number of subjects and sampling times when a clinical study is warranted or by negating the need for conduct of clinical studies. Classification and rule-based systems such as the BCS and BDDCS and statistical QSARs are widely used to anticipate the nature of FE in early drug development. However, their qualitative rather than quantitative nature makes them less appropriate for assessing the magnitude of FE. Moreover, these approaches are based upon drug properties alone and are not appropriate for estimating potential formulation-specific FE on modified or controlled release products. In contrast, physiologically-based mechanistic models can consider the scope and interplay of a range of physiological changes after food intake and, in combination with appropriate in vitro drug- and formulation-specific data, can make quantitative predictions of formulation-specific FE including the inter-individual variability of such effects. Herein the Advanced Dissolution, Absorption and Metabolism (ADAM) model is applied to the prediction of formulation-specific FE for BCS/BDDCS Class II drug and CYP3A4 substrate nifedipine using as far as possible only in vitro data. Predicted plasma concentration profiles of all three studied formulations under fasted and fed states are within 2-fold of clinically observed profiles. The % prediction error (%PE) in fed-to-fasted ratio of Cmax and AUC were less than 5% for all formulations except for the Cmax of Nifedicron (%PE=-29.6%). This successful case study should help to improve confidence in the use of mechanistic physiologically-based models coupled with in vitro data for the anticipation of FE in advance of in vivo studies. However, it is acknowledged that further studies with drugs/formulations exhibiting a wide range of properties are required to further validate this methodology., (Copyright © 2013 Elsevier B.V. All rights reserved.)

Background: Many drugs (belonging to different chemical groups) have the potential for QT interval prolongation associated with ionic channel blockade in the cardiomyocyte membrane. Due to the fact that this phenomenon is linked to a higher risk of TdP, the ability to predict its scale is one of the most important outcomes of cardiotoxicity assessment of new agents., Methods: With use of the Cardiac Safety Simulator (CSS), the effect of six antipsychotic drugs was predicted in silico. Separate simulations were carried out for each studied population taking the drug. The aim of this study was to predict both the mean values of delta QTc and the results range. To be able to observe individual variability after drug administration, each patient was randomly assigned to the individual drug concentration. Also, appropriate diversity in heart rate, plasma electrolytes concentrations, morphometric parameters of ventricular myocytes, and one common hERG polymorphism frequency in population were added., Results: Analyzing the results of simulation with Student's t-test, in five of six cases, there were no statistically significant differences between observed and predicted mean values. The diversity of results in all populations studied, however, was not fully reconstructed., Discussion: The model was able to accurately reproduce the average effect of the drug on the length when the phenomenon is associated purely with blocking of ionic channels. Nevertheless, the problem of variability in the population and its effect on the QT interval requires further study., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

Drug cardiotoxicity is a serious issue for patients, regulators, pharmaceutical companies and health service payers because they are all affected by its consequences. Despite the wide range of data they generate, existing approaches for cardiac safety testing might not be adequate and sufficiently cost-effective, probably as a result of the complexity of the problem. For this reason, translational tools (based on biophysically detailed, mathematical models) allowing for in vitro-in vivo extrapolation are gaining increasing interest. This current review describes approaches that can be used for cardiac safety assessment at the population level, by accounting for various sources of variability including kinetics of the compound of interest., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

Purpose: The human body is known to be composed of 24 elements, among which potassium, sodium and calcium are considered to be essential. These necessary components play a significant physiological role which includes regulation of the electrical and mechanical action of the heart. Abnormal concentration of the above-mentioned ions, i.e. water-electrolyte imbalance, may result in cardiac arrhythmias, muscle contraction disorders, disturbances of neuronal activity and influences the drugs activity., Methods: The study aimed at gathering and analyzing results of publicly available research which reported serum concentration of these ions. This information, together with an additional collection of data (gender, age, height, weight, measurement method), is presented in table form attached as supplementary material., Results: The serum ions concentrations means weighted by the study-specific sample sizes indicated statistically significant differences between males and females for all ions - K+ 4.21 and 4.09, Na+ 140.1 and 138.17, Ca2+ 2.42 and 2.31 respectively)., Conclusions: Obtained results correspond with the current laboratory reference values. As potassium, sodium and calcium follow the circadian rhythm, publications reporting serum concentration values were also collected and presented. Further studies are planned to describe such phenomenon in a form of the statistical model.

The excitable cell membranes contain ion channels that allow the ions passage through the specific pores via a passive process. Assessment of the inhibition of the IKr (hERG) current is considered to be the main target during the drug development process, although there are other ionic currents for which drug-triggered modification can either potentiate or mask hERG channel blockade. Information describing the results of in vitro studies investigating the chemical-IKs current interactions has been developed in the current study. Based on the publicly available data sources, 145 records were collected. The final list of publications consists of 64 positions and refers to 106 different molecules connected with IKs current inhibition, with at least one IC50 value measured. Ultimately, 98 of the IC50 values expressed as absolute values were gathered. For 36 records the IC50 was expressed as a relative value. For the 11 remaining records, the inhibition was not clearly expressed. Based on the collected data the predictive models for the IC50 estimation were developed with the use of various algorithms. The extended Quantitative Structure-Activity Relationships (QSAR) methodology was applied and the in vitro research settings were included as independent variables, apart from the physico-chemical descriptors calculated with the use of the Marvin Calculator Plugins. The root mean squared error and normalized root mean squared error values for the best model (an expert system based on two independent artificial neural networks) were 0.86 and 14.04%, respectively. The model was further built into the ToxComp system, the ToxIVIVE tool specialized for cardiotoxicity assessment of drugs., (Copyright © 2012 John Wiley & Sons, Ltd.)

Estimation of systemic exposure after absorption of any xenobiotic from the skin is very important in development of dermal pharmaceutical products as well as assessing un-intended exposures due to cosmetic products or environmental and occupational compounds. Historically, animal models have been used to evaluate dermal drug absorption before conducting human trials. However, occasional disparity between the animal and human data plus rising public interest and regulatory requirements to reduce animal usage in research combined with high cost and time-consuming attributes of animal experiments have prompted many academic and industrial researchers to develop economically viable and scientifically robust in silico and in vitro methods to assess dermal drug absorption. There are a number of in silico models available in literature from quantitative structure-activity relationship to semi-mechanistic to physiologically based mechanistic models. Nonetheless, to the best of our knowledge, so far, there has been no attempt to combine mechanistic skin absorption model with database of physiological variability to simulate the inter- and intra-individual variability observed in human trials. Thus, we report here mechanistic dermal absorption model with formulation, stratum corneum, viable epidermis-dermis and blood compartments along with datab"ase of human dermal physiological variability including gender, ethnic and site of application variations. The developed model is incorporated into the Simcyp simulator which is a 'bottom-up' platform and database for mechanistic modelling and simulation of the drug disposition process using full body physiologically based pharmacokinetics model. The built model is validated using the clinical pharmacokinetic data from five different topical formulations of diclofenac. The effect of penetration enhancers, site of application, gender and ethnic variations were incorporated to simulate the clinical trials. The applied mechanistic dermal absorption model when combined with skin physiological database was able to recover well the observed clinical pharmacokinetics and population variability in all the five validation studies., (Copyright © 2012 Wiley Periodicals, Inc.)

Electrical phenomena located within the plasma membrane of the mammalian cardiac cells are connected with the cells' main physiological functions--signals processing and contractility. They were extensively studied and described mathematically in so-called Hodgkin-Huxley paradigm. One of the physiological parameters, namely cell electric capacitance, has not been analyzed in-depth. The aim of the study was to validate the mechanistic model describing the capacitive properties of cells, based on a collected experimental dataset which describes the electric capacitance of human ventricular myocytes. The gathered data was further utilized for developing an empirical correlation between a healthy individual's age and cardiomyocyte electric capacitance.

The presented study aims to assess the possibility of simulating changes in cardiac cell electrophysiology due to K897T polymorphism in the Caucasian population. In the first part of the experiment, the parameters of the equations describing channel gating were fitted to the experimental data. Then, the action potentials of midmyocardial cells of 100 individuals were simulated in the in vitro - in vivo extrapolation system - ToxComp. Mean APD90 for the entire simulated population is 352.05 ms (SD = 21.69 ms). Mean APD90 for the 80 individuals with the WT version of the hERG gene and for the 20 K897T homo- and heterozygotes is respectively 349.08 ms (SD = 21.09 ms) and 363.95ms (SD = 20.41 ms). The ToxComp system can be useful in predicting the impact of genetic variability on drug triggered cardiac cell electrophysiology interference.

The goal of this study was to analyze antibacterial drug prescribing in the 1-million-plus, strictly defined population in Poland. For the 2002-2005 years, data from National Health Fund on outpatient purchasing of antibiotics in Lubuskie's Province were collected and expressed in DDD per 1-thousand inhabitants per day (DID). In the period 2002-2005 the average rate of antibiotics purchasing was 19.8 DID. During the 3-year period, at least 64.3% of the population had prescribed antibiotics. 22.7% of patients purchased 62.6% of a three-year total purchasing of antibacterial drugs in the province. A tiny 1.9% of the population purchased 10.0% of the total. From the different age-group, the study showed that special attention should be paid to two different and relatively small groups of patients - those utilizing significant percentage of the year's supply, and group of 5-9 year-old children. Seasonal variation in antibiotic prescribing was strictly linked with the age of patients. Pulmonary diseases were indentified as a factor considerably elevating antibacterial drugs purchasing frequency. A useful way to present recurring patterns in this frequency has been proposed.

Pharmacoeconomics research identifies, measures, and compares the costs (resources consumed) and consequences of medical products and services, where at least one of the compared alternatives is pharmacotherapy. Pharmacoeconomics has been designed to enable the decision maker to identify the preferred choice among existing alternatives. The decisions are often important for the patients' lives on the one hand, and for payers on the other (where the payer is understood as the institution responsible for financial resources allocation). One of the most commonly used types of pharmacoeconomic analysis is cost-effectiveness analysis. Two different alternatives can be compared using cost-effectiveness analysis if only their medical and clinical consequences could be measured in similar units (clinical or physical parameters). The aim of the project is to use an artificial neural network (ANN) for medical effect prediction, which could help in the extrapolation of pharmacoeconomic analysis' results. To depict neural data analysis tools, a database containing 100 non-small cell lung cancer (NSCLC) patients in non-operative IIIB and IV stage has been used. Each patient was described using 30 factors (i.e. sex, age, anticancer drugs dosage) and, as an output value, the expected survival time was established. The role of the ANN system was to predict the patient's survival time based on the above mentioned information. Binary values were tested as outcomes. Positive values (coded as 1) meant that patient survival time would be equal to or longer than 35 weeks. Negative values (coded as 0) meant that the patient survival time would be shorter than 35 weeks. Binary values were obtained using a threshold, which based on the mean survival time of patients derived from literature. Back-propagation as well as fuzzy-logic neural networks were applied. A 10-fold cross validation method was used to obtain the appropriate models. Final results were compared with the generic, logistic regression-based model. The best prediction score of the ANN model was 82%; higher than logistic regression prediction rate.

Hypertension is the most common cause of death. Therefore it is recognized as a major civilization disease next to diabetes, hyperuricemia, asthma etc. The objective was to use artificial neural networks (ANNs) to handle demographic data and to produce system of hypertension risk prediction. Database used in the development of hypertension risk model was obtained from CDC (BRFSS--Behavioral Risk Factor Surveillance System). Screening for optimal ANN architecture was performed among various backpropagation and fuzzy neural networks with use of 10-fold cross-validation scheme. Single ANNs as well as experts committees were tested. Best results were found to be around 75%--expressed as total classification rate. Java applet was designed to be the interface between ANN system and end user. Spreadsheet form was chosen to facilitate navigation and used by healthcare non-specialists. Free of charge Internet publication is expected soon at the address [url: see text].

The following publication include results of thinking the matter over usefulness some computer, medical databases as well as Internet drug information resources. Quantity of medical software and Internet portals is not equal to quality, so it is necessary to order and classify medical electronic tools. It was bring MICROMEDEX databases system to readers attention, as a model, reference tool helpful in computer aided medicine.