In Vitro Simulation of the Fasted Gastric Conditions and Their Variability to Elucidate Contrasting Properties of the Marketed Dabigatran Etexilate Pellet-Filled Capsules and Loose Pellets.

Variability of the gastrointestinal tract is rarely reflected in in vitro test protocols but often turns out to be crucial for the oral dosage form performance. In this study, we present a generation method of dissolution profiles accounting for the variability of fasted gastric conditions. The workflow featured 20 biopredictive tests within the physiological variability. The experimental array was constructed with the use of the design of experiments, based on three parameters: gastric pH and timings of the intragastric stress event and gastric emptying. Then, the resulting dissolution profiles served as a training data set for the dissolution process modeling with the machine learning algorithms. This allowed us to generate individual dissolution profiles under a customizable gastric pH and motility patterns. For the first time ever, we used the method to successfully elucidate dissolution properties of two dosage forms: pellet-filled capsules and bare pellets of the marketed dabigatran etexilate product Pradaxa. We showed that the dissolution of capsules was triggered by mechanical stresses and thus was characterized by higher variability and a longer dissolution onset than observed for pellets. Hence, we proved the applicability of the method for the in vitro and in silico characterization of immediate-release dosage forms and, potentially, for the improvement of in vitro-in vivo extrapolation.

Development and verification of mechanistic vaginal absorption and metabolism model to predict systemic exposure after vaginal ring and gel application.

AIMS: The current work describes the development of mechanistic vaginal absorption and metabolism model within Simcyp Simulator to predict systemic concentrations following vaginal application of ring and gel formulations. METHODS: Vaginal and cervix physiology parameters were incorporated in the model development. The study highlights the model assumptions including simulation results comparing systemic concentrations of 5 different compounds, namely, dapivirine, tenofovir, lidocaine, ethinylestradiol and etonogestrel, administered as vaginal ring or gel. Due to lack of data, the vaginal absorption parameters were calculated based on assumptions or optimized. The model uses release rate/in vitro release profiles with formulation characteristics to predict drug mass transfer across vaginal tissue into the systemic circulation. RESULTS: For lidocaine and tenofovir vaginal gel, the predicted to observed AUC0-t and Cmax ratios were well within 2-fold error limits. The average fold error (AFE) and absolute AFE indicating bias and precision of predictions range from 0.62 to 1.61. For dapivirine, the pharmacokinetic parameters are under and overpredicted in some studies due to lack of formulation composition details and relevance of release rate used in ring model. The predicted to observed AUC0-t and Cmax ratios were well within 2-fold error limits for etonogestrel and ethinylestradiol vaginal ring (AFEs and absolute AFEs from 0.84 to 1.83). CONCLUSION: The current study provides first of its kind physiologically based pharmacokinetic framework integrating physiology, population and formulation data to carry out in silico mechanistic vaginal absorption studies, with the potential for virtual bioequivalence assessment in the future.

Physiologically based modelling of dermal absorption and kinetics of consumer-relevant chemicals: A case study with exposure to bisphenol A from thermal paper.

Bisphenol A (BPA) is one of the best studied industrial chemicals in terms of exposure, toxicity, and toxicokinetics. This renders it an ideal candidate to exploit the recent advancements in physiologically based pharmacokinetic (PBPK) modelling to support risk assessment of BPA specifically, and of other consumer-relevant hazardous chemicals in general. Using the exposure from thermal paper as a case scenario, this study employed the multi-phase multi-layer mechanistic dermal absorption (MPML MechDermA) model available in the Simcyp® Simulator to simulate the dermal toxicokinetics of BPA at local and systemic levels. Sensitivity analysis helped to identify physicochemical and physiological factors influencing the systemic exposure to BPA. The iterative modelling process was as follows: (i) development of compound files for BPA and its conjugates, (ii) setting-up of a PBPK model for intravenous administration, (iii) extension for oral administration, and (iv) extension for exposure via skin (i.e., hand) contact. A toxicokinetic study involving hand contact to BPA-containing paper was used for model refinement. Cumulative urinary excretion of total BPA had to be employed for dose reconstruction. PBPK model performance was verified using the observed serum BPA concentrations. The predicted distribution across the skin compartments revealed a depot of BPA in the stratum corneum (SC). These findings shed light on the role of the SC to act as temporary reservoir for lipophilic chemicals prior to systemic absorption, which inter alia is relevant for the interpretation of human biomonitoring data and for establishing the relationship between external and internal measures of exposure.

In Vitro/In Vivo Translation of Synergistic Combination of MDM2 and MEK Inhibitors in Melanoma Using PBPK/PD Modelling: Part III.

The development of in vitro/in vivo translational methods and a clinical trial framework for synergistically acting drug combinations are needed to identify optimal therapeutic conditions with the most effective therapeutic strategies. We performed physiologically based pharmacokinetic-pharmacodynamic (PBPK/PD) modelling and virtual clinical trial simulations for siremadlin, trametinib, and their combination in a virtual representation of melanoma patients. In this study, we built PBPK/PD models based on data from in vitro absorption, distribution, metabolism, and excretion (ADME), and in vivo animals' pharmacokinetic-pharmacodynamic (PK/PD) and clinical data determined from the literature or estimated by the Simcyp simulator (version V21). The developed PBPK/PD models account for interactions between siremadlin and trametinib at the PK and PD levels. Interaction at the PK level was predicted at the absorption level based on findings from animal studies, whereas PD interaction was based on the in vitro cytotoxicity results. This approach, combined with virtual clinical trials, allowed for the estimation of PK/PD profiles, as well as melanoma patient characteristics in which this therapy may be noninferior to the dabrafenib and trametinib drug combination. PBPK/PD modelling, combined with virtual clinical trial simulation, can be a powerful tool that allows for proper estimation of the clinical effect of the above-mentioned anticancer drug combination based on the results of in vitro studies. This approach based on in vitro/in vivo extrapolation may help in the design of potential clinical trials using siremadlin and trametinib and provide a rationale for their use in patients with melanoma.

Artificial Intelligence That Predicts Sensitizing Potential of Cosmetic Ingredients with Accuracy Comparable to Animal and In Vitro Tests-How Does the Infotechnomics Compare to Other "Omics" in the Cosmetics Safety Assessment?

The aim of the current study was to develop an in silico model to predict the sensitizing potential of cosmetic ingredients based on their physicochemical characteristics and to compare the predictions with historical animal data and results from "omics"-based in vitro studies. An in silico model was developed with the use of WEKA machine learning software fed with physicochemical and structural descriptors of haptens and trained with data from published epidemiological studies compiled into estimated odds ratio (eOR) and estimated attributable risk (eAR) indices. The outcome classification was compared to the results of animal studies and in vitro tests. Of all the models tested, the best results were obtained for the Naive Bayes classifier trained with 24 physicochemical descriptors and eAR, which yielded an accuracy of 86%, sensitivity of 80%, and specificity of 90%. This model was subsequently used to predict the sensitizing potential of 15 emerging and less-studied haptens, of which 7 were classified as sensitizers: cyclamen aldehyde, N,N-dimethylacrylamide, dimethylthiocarbamyl benzothiazole sulphide, geraniol hydroperoxide, isobornyl acrylate, neral, and prenyl caffeate. The best-performing model (NaiveBayes eAR, 24 parameters), along with an alternative model based on eOR (Random Comittee eOR, 17 parameters), are available for further tests by interested readers. In conclusion, the proposed infotechnomics approach allows for a prediction of the sensitizing potential of cosmetic ingredients (and possibly also other haptens) with accuracy comparable to historical animal tests and in vitro tests used nowadays. In silico models consume little resources, are free of ethical concerns, and can provide results for multiple chemicals almost instantly; therefore, the proposed approach seems useful in the safety assessment of cosmetics.

Mechanistic Modeling of In Vitro Skin Permeation and Extrapolation to In Vivo for Topically Applied Metronidazole Drug Products Using a Physiologically Based Pharmacokinetic Model.

Physiologically based pharmacokinetic (PBPK) modeling has increasingly been employed in dermal drug development and regulatory assessment, providing a framework to integrate relevant information including drug and drug product attributes, skin physiology parameters, and population variability. The current study aimed to develop a stepwise modeling workflow with knowledge gained from modeling in vitro skin permeation testing (IVPT) to describe in vivo exposure of metronidazole locally in the stratum corneum following topical application of complex semisolid drug products. The initial PBPK model of metronidazole in vitro skin permeation was developed using infinite and finite dose aqueous metronidazole solution. Parameters such as stratum corneum lipid-water partition coefficient (Ksclip/water) and stratum corneum lipid diffusion coefficient (Dsclip) of metronidazole were optimized using IVPT data from simple aqueous solutions (infinite) and MetroGel (10 mg/cm2 dose application), respectively. The optimized model, when parameterized with physical and structural characteristics of the drug products, was able to accurately predict the mean cumulative amount permeated (cm2/h) and flux (µg/cm2/h) profiles of metronidazole following application of different doses of MetroGel and MetroCream. Thus, the model was able to capture the impact of differences in drug product microstructure and metamorphosis of the dosage form on in vitro metronidazole permeation. The PBPK model informed by IVPT study data was able to predict the metronidazole amount in the stratum corneum as reported in clinical studies. In summary, the proposed model provides an enhanced understanding of the potential impact of drug product attributes in influencing in vitro skin permeation of metronidazole. Key kinetic parameters derived from modeling the metronidazole IVPT data improved the predictions of the developed PBPK model of in vivo local metronidazole concentrations in the stratum corneum. Overall, this work improves our confidence in the proposed workflow that accounts for drug product attributes and utilizes IVPT data toward improving predictions from advanced modeling and simulation tools.

Utilization of mechanistic modelling and simulation to analyse fenspiride proarrhythmic potency - Role of physiological and other non-drug related parameters.

WHAT IS KNOWN AND OBJECTIVE: Fenspiride, a drug that had been used for decades for the treatment of respiratory diseases, was recently withdrawn from the market due to the potential risk of QT prolongation and proarrhythmia. This is the first such withdrawal for many years and hence poses a question whether such risk could have been predicted and to what degree non-drug-specific parameters play a role in the reported QT prolongation and cases of TdP. The study aim was to test various 'what-if' scenarios to assess the influence of age, gender, heart rate, and plasma potassium concentration on QT interval prolongation due to various doses of fenspiride with the use of mechanistic mathematical modelling. METHODS: Concentration-time profiles were simulated with the use of a PBPK model developed based on published physico-chemical data, data from in vitro ADME experiments, and in vivo PK study results. Pharmacodynamic effect, that is, drug-triggered pseudoECG signal modification was simulated using a biophysically detailed model of human cardiac myocytes. Analysis of the qNet metric was also performed to classify proarrhythmic risk related to fenspiride. RESULTS: In the simulation study, arrhythmia was not observed even in the 'what-if' scenarios with extreme exposure, age, heart rate, and plasma potassium concentration. The qNet metric value positioned fenspiride in the intermediate risk class. WHAT IS NEW AND CONCLUSION: It can be hypothesized that the clinically observed arrhythmia cases were not directly caused by fenspiride alone but a combination of multiple factors, including comedications.

In Vitro/In Vivo Translation of Synergistic Combination of MDM2 and MEK Inhibitors in Melanoma Using PBPK/PD Modelling: Part I.

Translation of the synergy between the Siremadlin (MDM2 inhibitor) and Trametinib (MEK inhibitor) combination observed in vitro into in vivo synergistic efficacy in melanoma requires estimation of the interaction between these molecules at the pharmacokinetic (PK) and pharmacodynamic (PD) levels. The cytotoxicity of the Siremadlin and Trametinib combination was evaluated in vitro in melanoma A375 cells with MTS and RealTime-Glo assays. Analysis of the drug combination matrix was performed using Synergy and Synergyfinder packages. Calculated drug interaction metrics showed high synergy between Siremadlin and Trametinib: 23.12%, or a 7.48% increase of combined drug efficacy (concentration-independent parameter ß from Synergy package analysis and concentration-dependent δ parameter from Synergyfinder analysis, respectively). In order to select the optimal PD interaction parameter which may translate observed in vitro synergy metrics into the in vivo setting, further PK/PD studies on cancer xenograft animal models coupled with PBPK/PD modelling are needed.

In Vitro/In Vivo Translation of Synergistic Combination of MDM2 and MEK Inhibitors in Melanoma Using PBPK/PD Modelling: Part II.

The development of in vitro/in vivo translational methods for synergistically acting drug combinations is needed to identify the most effective therapeutic strategies. We performed PBPK/PD modelling for siremadlin, trametinib, and their combination at various dose levels and dosing schedules in an A375 xenografted mouse model (melanoma cells). In this study, we built models based on in vitro ADME and in vivo PK/PD data determined from the literature or estimated by the Simcyp Animal simulator (V21). The developed PBPK/PD models allowed us to account for the interactions between siremadlin and trametinib at PK and PD levels. The interaction at the PK level was described by an interplay between absorption and tumour disposition levels, whereas the PD interaction was based on the in vitro results. This approach allowed us to reasonably estimate the most synergistic and efficacious dosing schedules and dose levels for combinations of siremadlin and trametinib in mice. PBPK/PD modelling is a powerful tool that allows researchers to properly estimate the in vivo efficacy of the anticancer drug combination based on the results of in vitro studies. Such an approach based on in vitro and in vivo extrapolation may help researchers determine the most efficacious dosing strategies and will allow for the extrapolation of animal PBPK/PD models into clinical settings.

How circadian variability of the heart rate and plasma electrolytes concentration influence the cardiac electrophysiology - model-based case study.

The circadian rhythm of cardiac electrophysiology is dependent on many physiological and biochemical factors. Provided, that models describing the circadian patterns of cardiac activity and/or electrophysiology which have been verified to the acceptable level, modeling and simulation can give answers to many of heart chronotherapy questions. The aim of the study was to assess the performance of the circadian models implemented in Cardiac Safety Simulator v 2.2 (Certara, Sheffield, UK) (CSS), as well as investigate the influence ofcircadian rhythms on the simulation results in terms of cardiac safety. The simulations which were run in CSS accounted for inter-individual and intra-individual variability. Firstly, the diurnal variations in QT interval length in a healthy population were simulated accounting for heart rate (HR) circadian changes alone, or with concomitant diurnal variations of plasma ion concentrations. Next, tolterodine was chosen as an exemplary drug for PKPD modelling exercise to assess the role of circadian rhythmicity in the prediction of drug effects on QT interval. The results of the simulations were in line with clinical observations, what can serve as a verification of the circadian models implemented in CSS. Moreover, the results have suggested that the circadian variability of the electrolytes balance is the main factor influencing QT circadian pattern. The fluctuation of ion concentration increases the intra-subject variability of predicted drug-triggered QT corrected for HR (QTc) prolongation effect and, in case of modest drug effect on QTc interval length, allows to capture this effect.

Magnetic Core-Shell Molecularly Imprinted Nano-Conjugates for Extraction of Antazoline and Hydroxyantazoline from Human Plasma-Material Characterization, Theoretical Analysis and Pharmacokinetics.

The aim of this study was to develop magnetic molecularly imprinted nano-conjugate sorbent for effective dispersive solid phase extraction of antazoline (ANT) and its metabolite, hydroxyantazoline (ANT-OH) in analytical method employing liquid chromatography coupled with mass spectrometry method. The core-shell material was characterized in terms of adsorption properties, morphology and structure. The heterogeneous population of adsorption sites towards ANT-OH was characterized by two Kd and two Bmax values: Kd (1) = 0.319 µg L-1 and Bmax (1) = 0.240 µg g-1, and Kd (2) = 34.6 µg L-1 and Bmax (2) = 5.82 µg g-1. The elemental composition of magnetic sorbent was as follows: 17.55, 37.33, 9.14, 34.94 wt% for Si, C, Fe and O, respectively. The extraction protocol was optimized, and the obtained results were explained using theoretical analysis. Finally, the analytical method was validated prior to application to pharmacokinetic study in which the ANT was administrated intravenously to three healthy volunteers. The results prove that the novel sorbent could be useful in extraction of ANT and ANT-OH from human plasma and that the analytical strategy could be a versatile tool to explain a potential and pharmacological activity of ANT and ANT-OH.

Combining an in silico proarrhythmic risk assay with a tPKPD model to predict QTc interval prolongation in the anesthetized guinea pig assay.

Human-based in silico models are emerging as important tools to study the effects of integrating inward and outward ion channel currents to predict clinical proarrhythmic risk. The aims of this study were 2-fold: 1) Evaluate the capacity of an in silico model to predict QTc interval prolongation in the in vivo anesthetized cardiovascular guinea pig (CVGP) assay for new chemical entities (NCEs) and; 2) Determine if a translational pharmacokinetic/pharmacodynamic (tPKPD) model can improve the predictive capacity. In silico simulations for NCEs were performed using a population of human ventricular action potential (AP) models. PatchXpress® (PX) or high throughput screening (HTS) ion channel data from respectively n = 73 and n = 51 NCEs were used as inputs for the in silico population. These NCEs were also tested in the CVGP (n = 73). An M5 pruned decision tree-based regression tPKPD model was used to evaluate the concentration at which an NCE is liable to prolong the QTc interval in the CVGP. In silico results successfully predicted the QTc interval prolongation outcome observed in the CVGP with an accuracy/specificity of 85%/73% and 75%/77%, when using PX and HTS ion channel data, respectively. Considering the tPKPD predicted concentration resulting in QTc prolongation (EC5%) increased accuracy/specificity to 97%/95% using PX and 88%/97% when using HTS. Our results support that human-based in silico simulations in combination with tPKPD modeling can provide correlative results with a commonly used early in vivo safety assay, suggesting a path toward more rapid NCE assessment with reduced resources, cycle time, and animal use.

Characterization of In Vitro and In Vivo Metabolism of Antazoline Using Liquid Chromatography-Tandem Mass Spectrometry.

Antazoline (ANT) was recently shown to be an effective and safe antiarrhythmic drug in the termination of atrial fibrillation. However, the drug is still not listed in clinical guidelines. No data on ANT metabolism in humans is available. We used liquid chromatography coupled with tandem mass spectrometry to identify and characterize metabolites of ANT. We analyzed plasma of volunteers following a single intravenous administration of 100 mg of ANT mesylate and in in vitro cultures of human hepatocytes. We revealed that ANT was transformed into at least 15 metabolites and we investigated the role of cytochrome P450 isoforms. CYP2D6 was the main one involved in the fast metabolism of ANT. The biotransformation of ANT by CYP2C19 was much slower. The main Phase I metabolite was M1 formed by the removal of phenyl and metabolite M2 with hydroxyl in the para position of phenyl. Glucuronidation was the leading Phase II metabolism. Further study on pharmacokinetics of the metabolites would allow us to better understand the activity profile of ANT and to predict its potential clinical applications. Ultimately, further investigation of the activity profile of the new hydroxylated M2 metabolite of ANT might result in an active substance with a different pharmacological profile than the parent molecule, and potentially a new drug candidate.

Drug interaction at hERG channel: In vitro assessment of the electrophysiological consequences of drug combinations and comparison against theoretical models.

Drugs carry a proarrhythmic risk, which gets even greater when they are used in combination. In vitro assessment of the proarrhythmic potential of drugs is limited to one compound and thus neglects the potential of drug-drug interactions, including those involving active metabolites. Here we present the results of an in vitro study of potential drug-drug interactions at the level of the hERG channel for the combination of up to three compounds: loratadine, desloratadine and ketoconazole. Experiments were performed at room temperature on an automated patch-clamp device CytoPatch 2, with the use of heterogeneously, stably transfected HEK cells. Single drugs, pairs and triplets were used. The results provided as the inhibition of the IKr current for pairs were compared against the calculated theoretical interaction. Models applied to calculate the combined effect of inhibitory actions of simultaneously given drugs include: (1) simple additive model with a maximal inhibition limit of 1 (all channels blocked in 100%); (2) Bliss independence; and (3) Loewe additivity. The observed IC50 values for loratadine, desloratadine and ketoconazole were 5.15, 1.95 and 0.74 µm respectively. For the combination of drugs tested in pairs, the effect was concentration dependent. In lower concentrations, the synergistic effect was observed, while for the highest tested concentrations it was subadditive. To triple the effect, it was subadditive regardless of concentrations. The square root of sum of squares of differences between the observed and predicted total inhibition was calculated to assess the theoretical interaction models. For most of the drugs, the allotopic model offered the best fit.

Drug-physiology interaction and its influence on the QT prolongation-mechanistic modeling study.

The current study is an example of drug-disease interaction modeling where a drug induces a condition which can affect the pharmacodynamics of other concomitantly taken drugs. The electrophysiological effects of hypokaliemia and heart rate changes induced by the antiasthmatic drugs were simulated with the use of the cardiac safety simulator. Biophysically detailed model of the human cardiac physiology-ten Tusscher ventricular cardiomyocyte cell model-was employed to generate pseudo-ECG signals and QTc intervals for 44 patients from four clinical studies. Simulated and observed mean QTc values with standard deviation (SD) for each reported study point were compared and differences were analyzed with Student's t test (α = 0.05). The simulated results reflected the QTc interval changes measured in patients, as well as their clinically observed interindividual variability. The QTc interval changes were highly correlated with the change in plasma potassium both in clinical studies and in the simulations (Pearson's correlation coefficient > 0.55). The results suggest that the modeling and simulation approach could provide valuable quantitative insight into the cardiological effect of the potassium and heart rate changes caused by electrophysiologically inactive, non-cardiological drugs. This allows to simulate and predict the joint effect of several risk factors for QT prolongation, e.g., drug-dependent QT prolongation due to the ion channels inhibition and the current patient physiological conditions.

Physiologically based pharmacokinetic-quantitative systems toxicology and safety (PBPK-QSTS) modeling approach applied to predict the variability of amitriptyline pharmacokinetics and cardiac safety in populations and in individuals.

The physiologically based pharmacokinetic (PBPK) models allow for predictive assessment of variability in population of interest. One of the future application of PBPK modeling is in the field of precision dosing and personalized medicine. The aim of the study was to develop PBPK model for amitriptyline given orally, predict the variability of cardiac concentrations of amitriptyline and its main metabolite-nortriptyline in populations as well as individuals, and simulate the influence of those xenobiotics in therapeutic and supratherapeutic concentrations on human electrophysiology. The cardiac effect with regard to QT and RR interval lengths was assessed. The Emax model to describe the relationship between amitriptyline concentration and heart rate (RR) length was proposed. The developed PBPK model was used to mimic 29 clinical trials and 19 cases of amitriptyline intoxication. Three clinical trials and 18 cases were simulated with the use of PBPK-QSTS approach, confirming lack of cardiotoxic effect of amitriptyline in therapeutic doses and the increase in heart rate along with potential for arrhythmia development in case of amitriptyline overdose. The results of our study support the validity and feasibility of the PBPK-QSTS modeling development for personalized medicine.

Quantitative approach for cardiac risk assessment and interpretation in tuberculosis drug development.

Cardiotoxicity is among the top drug safety concerns, and is of specific interest in tuberculosis, where this is a known or potential adverse event of current and emerging treatment regimens. As there is a need for a tool, beyond the QT interval, to quantify cardiotoxicity early in drug development, an empirical decision tree based classifier was developed to predict the risk of Torsades de pointes (TdP). The cardiac risk algorithm was developed using pseudo-electrocardiogram (ECG) outputs derived from cardiac myocyte electromechanical model simulations of increasing concentrations of 96 reference compounds which represented a range of clinical TdP risk. The algorithm correctly classified 89% of reference compounds with moderate sensitivity and high specificity (71 and 96%, respectively) as well as 10 out of 12 external validation compounds and the anti-TB drugs moxifloxacin and bedaquiline. The cardiac risk algorithm is suitable to help inform early drug development decisions in TB and will evolve with the addition of emerging data.

Utilizing postmortem drug concentrations in mechanistic modeling and simulation of cardiac effects: a proof of concept study with methadone.

Methadone-related poisoning has been found to be the leading and increasing cause of death among intoxication cases in several countries. Aside from respiratory depression, methadone is known to cause QT-prolongation, which may lead to sudden cardiac death. Concentrations in heart tissue should be more accurate for estimating cardiotoxic effects. The aim of this study was to investigate whether the effect of methadone on the QT-interval could be simulated and whether the concentrations in heart tissues allowed for better prediction of the Bazett corrected QT-interval (QTcB). A predictive performance study was conducted using the simulation platform Cardiac Safety Simulator to mimic five literature studies using their described study conditions. Both free and total plasma and heart concentrations were investigated using two different in silico models: the O'Hara-Rudy (ORD) model and the 10 Tusscher (TNNP) model. The results showed that the QTcB of methadone was best predicted either with total plasma using the TNNP model or with free plasma using the ORD model. The ORD model was highly sensitive to the total heart concentrations, resulting in overprediction of the QTcB. The TNNP model also overpredicted the QTcB, but to a lesser degree than the ORD model. Furthermore, due to a low baseline QTcB, the ORD model underpredicted the QTcB for both the free plasma and free heart concentrations. In conclusion, it is possible to simulate the cardiac effects of methadone, yet several elements influence the approach uncertainty including but not limited to biophysically details model of cardiac electrophysiology, exposure data, and input parameters.

Early assessment of proarrhythmic risk of drugs using the in vitro data and single-cell-based in silico models: proof of concept.

BACKGROUND AND PURPOSE: To determine the predictive performance of in silico models using drug-specific preclinical cardiac electrophysiology data to investigate drug-induced arrhythmia risk (e.g. Torsade de pointes (TdP)) in virtual human subjects. EXPERIMENTAL APPROACH: To assess drug proarrhythmic risk, we used a set of in vitro electrophysiological measurements describing ion channel inhibition triggered by the investigated drugs. The Cardiac Safety Simulator version 2.0 (CSS; Simcyp, Sheffield, UK) platform was used to simulate human left ventricular cardiac myocyte action potential models. RESULTS: This study shows the impact of drug concentration changes on particular ionic currents by using available experimental data. The simulation results display safety threshold according to drug concentration threshold and log (threshold concentration/ effective therapeutic plasma concentration (ETPC)). CONCLUSION AND IMPLICATIONS: We reproduced the underlying biophysical characteristics of cardiac cells resulted in effects of drugs associated with cardiac arrhythmias (action potential duration (APD) and QT prolongation and TdP) which were observed in published 3D simulations, yet with much less computational burden.

Enhanced QSAR models for drug-triggered inhibition of the main cardiac ion currents.

The currently changing cardiac safety testing paradigm suggests, among other things, a shift towards using in silico models of cellular electrophysiology and assessment of a concomitant block of multiple ion channels. In this study, a set of four enhanced QSAR models have been developed: for the rapid delayed rectifying potassium current (IKr), slow delayed rectifying potassium current (IKs), peak sodium current (INa) and late calcium current (ICaL), predicting ion currents changes for the specific in vitro experiment from the 2D structure of the compounds. The models are a combination of both in vitro study parameters and physico-chemical descriptors, which is a novel approach in drug-ion channels interactions modeling. Their predictive power assessed in the enhanced, more demanding than standard procedure, 10-fold cross validation was reasonably high. Rough comparison with published pure in silico hERG interaction models shows that the quality of the model predictions does not differ from other models available in the public domain, however, it takes its advantage in accounting for inter-experimental settings variability. Developed models are implemented in the Cardiac Safety Simulator, a commercially available platform enabling the in vitro-in vivo extrapolation of the drugs proarrhythmic effect and ECG simulation. A more comprehensive assessment of the effects of the compounds on ion channels allows for making more informed decisions regarding the risk - and thus avoidance - of exclusion of potentially safe and effective drugs.