Can 3D Printed Tablets Be Bioequivalent and How to Test It: A PBPK Model Based Virtual Bioequivalence Study for Ropinirole Modified Release Tablets

As the field of personalized dosing develops, the pharmaceutical manufacturing industry needs to offer flexibility in terms of tailoring the drug release and strength to the individual patient's needs. One of the promising tools which have such capacity is 3D printing technology. However, manufacturing small batches of drugs for each patient might lead to huge test burden, including the need to conduct bioequivalence trials of formulations to support the change of equipment or strength. In this paper we demonstrate how to use 3D printing in conjunction with virtual bioequivalence trials based on physiologically based pharmacokinetic (PBPK) modeling. For this purpose, we developed 3D printed ropinirole formulations and tested their bioequivalence with the reference product Polpix. The Simcyp simulator and previously developed ropinirole PBPK model were used for the clinical trial simulations. The Weibull-fitted dissolution profiles of test and reference formulations were used as inputs for the model. The virtual bioequivalence trials were run using parallel design. The study power of 80% was reached using 125 individuals. The study demonstrated how to use PBPK modeling in conjunction with 3D printing to test the virtual bioequivalence of newly developed formulations. This virtual experiment demonstrated the bioequivalence of one of the newly developed formulations with a reference product available on a market.

3D printing combined with biopredictive dissolution and PBPK/PD modeling optimization and personalization of pharmacotherapy: Are we there yet?

Precision medicine requires selecting the appropriate dosage regimen for a patient using the right drug, at the right time. Model-Informed Precision Dosing (MIPD) is a concept suggesting utilization of model-based prediction methods for optimizing the treatment benefit-harm balance, based on individual characteristics of the patient, disease, treatment method, and other factors. Here, we discuss a theoretical workflow comprising several elements, beginning from the physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) models, through 3D printed tablets with the model proposed dose, information range and flow, and the patient themselves. We also describe each of these elements, and the connection between them, highlighting challenges and potential obstacles.

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.

Data on ADME parameters of bisphenol A and its metabolites for use in physiologically based pharmacokinetic modelling

The paper presents the collection of physicochemical parameters of bisphenol A (BPA) and its sulfate (BPAS) and glucuronide (BPAG) conjugates, accompanied by data characterizing their absorption, distribution, metabolism and excretion (ADME) behavior following oral administration of BPA. The data were collected from open literature sources and publicly available databases. Additionally, data calculated by using the MarvinSketch 18.30.0 software or predicted by relevant QSAR models built in Simcyp® Simulator were also used. All data were analysed and are fit for purpose if necessary to ensure a reliable prediction of pharmacokinetics of BPA and its conjugates. The data selection process and reasoning for fitting is provided to allow critical assessment and to ensure data transparency. Finally, the sensitivity analysis was performed to assess the influence of the selected parameters on the PBPK model predictions.

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.

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.

An open-access data set of pig skin anatomy and physiology for modelling purposes

The use of animal as opposed to human skin for in vitro permeation testing (IVPT) is an alternative, which can reduce logistical and economic issues. However, this surrogate also has ethical considerations and may not provide an accurate estimation of dermal absorption in humans due to physiological differences. The current project aimed to provide a detailed repository for the anatomical and physiological parameters of porcine skin, with the aim of parametrizing the Multi-phase Multi-layer Mechanistic Dermal Absorption (MPML MechDermA) Model in the Simcyp Simulator. The MPML MechDermA Model is a physiologically based pharmacokinetic (PBPK) model that accounts for the physiology and geometry of skin in a mechanistic mathematical modelling framework. The database provided herein contains information on 14 parameters related to porcine skin anatomy and physiology, namely, skin surface pH, number of stratum corneum (SC) layers, SC thickness, corneocyte thickness, corneocyte dimensions (length and width), volume fraction of water in corneocyte (where SC is divided into four parts with different water contents), intercellular lipid thickness, viable epidermis thickness, dermis thickness, hair follicle and hair shaft diameter, hair follicle depth and hair follicle density. The collected parameters can be used to parameterize PBPK models, which could be further utilized to bridge the gap between animal and human studies with interspecies extrapolation or to predict dermatokinetic properties typically assessed in IVPT experiments. Database URL: https://data.mendeley.com/datasets/mwz9xv4cpd/1.

Correction to: Open-access database of literature derived drug-related Torsade de Pointes cases

An amendment to this paper has been published and can be accessed via the original article.

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 K_d and two B_max values: K_d (1) = 0.319 µg L⁻¹ and B_max (1) = 0.240 μg g⁻¹, and K_d (2) = 34.6 µg L⁻¹ and B_max (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.

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.

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.

From probiotics to psychobiotics - the gut-brain axis in psychiatric disorders

This review aims to present a comprehensive state-of-the-art analysis of the bidirectional crosstalk between gut microbiota and the central nervous system (CNS). The literature concerning the potential effects of gut microbiota on psychiatric disorders through neural pathways comprising the 'gut-brain axis' were gathered. In addition, the influence of probiotics and prebiotics and dairy-rich diets combined with the intake of probiotics and prebiotics on gut microbiota and the subsequent relationship with brain function was reviewed. However, a meta-analysis on the effectiveness of probiotic supplementation in psychiatric disorders is lacking. Therefore, a systematic search of PubMed, Embase, Cochrane Central Register of Controlled Trials, and ClinicalTrials.gov databases from January 1969 to December 2019 was conducted. It led to the identification of a total of 844 research articles. Of these, a total of 23 studies met the meta-analysis criteria. Statistical analysis revealed that there was no significant difference in the symptoms of schizophrenia, stress, and anxiety between probiotic and placebo groups, post-intervention. Probiotic administration reduced depressive symptoms among patients with depression in a statistically significant manner (standardised mean difference (SMD) = -0.87; 95% confidence interval (95% confidence interval): -1.66, -0.99; P=0.03). Further evidence from larger and more rigorous studies with longer duration of probiotic administration, as well as well-defined populations, homogenous probiotic intervention and outcome measures, are needed to clarify the potential therapeutic effects of probiotics on psychiatric symptoms. Based on the current literature, it seems that not all probiotic-/prebiotic-/dairy-rich diet-based treatments exhibited a psychobiotic effect on the CNS. Among the parameters determining the success of the given treatment, the most significant were probiotic composition (multi-strain formulation), the quantity of ingested psychobiotics and the duration of the study.

Better prediction of the local concentration-effect relationship: the role of physiologically based pharmacokinetics and quantitative systems pharmacology and toxicology in the evolution of model-informed drug discovery and development

Model-informed drug discovery and development (MID3) is an umbrella term under which sit several computational approaches: quantitative systems pharmacology (QSP), quantitative systems toxicology (QST) and physiologically based pharmacokinetics (PBPK). QSP models are built using mechanistic knowledge of the pharmacological pathway focusing on the putative mechanism of drug efficacy; whereas QST models focus on safety and toxicity issues and the molecular pathways and networks that drive these adverse effects. These can be mediated through exaggerated on-target or off-target pharmacology, immunogenicity or the physiochemical nature of the compound. PBPK models provide a mechanistic description of individual organs and tissues to allow the prediction of the intra- and extra-cellular concentration of the parent drug and metabolites under different conditions. Information on biophase concentration enables the prediction of a drug effect in different organs and assessment of the potential for drug-drug interactions. Together, these modelling approaches can inform the exposure-response relationship and hence support hypothesis generation and testing, compound selection, hazard identification and risk assessment through to clinical proof of concept (POC) and beyond to the market.

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.

Real Patient and its Virtual Twin: Application of Quantitative Systems Toxicology Modelling in the Cardiac Safety Assessment of Citalopram

A quantitative systems toxicology (QST) model for citalopram was established to simulate, in silico, a 'virtual twin' of a real patient to predict the occurrence of cardiotoxic events previously reported in patients under various clinical conditions. The QST model considers the effects of citalopram and its most notable electrophysiologically active primary (desmethylcitalopram) and secondary (didesmethylcitalopram) metabolites, on cardiac electrophysiology. The in vitro cardiac ion channel current inhibition data was coupled with the biophysically detailed model of human cardiac electrophysiology to investigate the impact of (i) the inhibition of multiple ion currents (I_Kr, I_Ks, I_CaL); (ii) the inclusion of metabolites in the QST model; and (iii) unbound or total plasma as the operating drug concentration, in predicting clinically observed QT prolongation. The inclusion of multiple ion channel current inhibition and metabolites in the simulation with unbound plasma citalopram concentration provided the lowest prediction error. The predictive performance of the model was verified with three additional therapeutic and supra-therapeutic drug exposure clinical cases. The results indicate that considering only the hERG ion channel inhibition of only the parent drug is potentially misleading, and the inclusion of active metabolite data and the influence of other ion channel currents should be considered to improve the prediction of potential cardiac toxicity. Mechanistic modelling can help bridge the gaps existing in the quantitative translation from preclinical cardiac safety assessment to clinical toxicology. Moreover, this study shows that the QST models, in combination with appropriate drug and systems parameters, can pave the way towards personalised safety assessment.

Humans Vary, So Cardiac Models Should Account for That Too!

The utilization of mathematical modeling and simulation in drug development encompasses multiple mathematical techniques and the location of a drug candidate in the development pipeline. Historically speaking they have been used to analyze experimental data (i.e., Hill equation) and clarify the involved physical and chemical processes (i.e., Fick laws and drug molecule diffusion). In recent years the advanced utilization of mathematical modeling has been an important part of the regulatory review process. Physiologically based pharmacokinetic (PBPK) models identify the need to conduct specific clinical studies, suggest specific study designs and propose appropriate labeling language. Their application allows the evaluation of the influence of intrinsic (e.g., age, gender, genetics, disease) and extrinsic [e.g., dosing schedule, drug-drug interactions (DDIs)] factors, alone or in combinations, on drug exposure and therefore provides accurate population assessment. A similar pathway has been taken for the assessment of drug safety with cardiac safety being one the most advanced examples. Mechanistic mathematical model-informed safety evaluation, with a focus on drug potential for causing arrhythmias, is now discussed as an element of the Comprehensive in vitro Proarrhythmia Assay. One of the pillars of this paradigm is the use of an in silico model of the adult human ventricular cardiomyocyte to integrate in vitro measured data. Existing examples (in vitro—in vivo extrapolation with the use of PBPK models) suggest that deterministic, epidemiological and clinical data based variability models can be merged with the mechanistic models describing human physiology. There are other methods available, based on the stochastic approach and on population of models generated by randomly assigning specific parameter values (ionic current conductance and kinetic) and further pruning. Both approaches are briefly characterized in this manuscript, in parallel with the drug-specific variability.

Thorough QT (TQT) studies: concordance with torsadogenesis and an evolving cardiac safety testing paradigm

Since 2005, when the International Conference on Harmonisation (ICH) E14 guideline was adopted, no drug has been withdrawn because of QTc prolongation or torsade de pointes arrhythmia. There are, however, costs associated with this success. In addition to the time and money invested, thorough QT (TQT) studies have limited the efficiency of the drug development pipeline. In this paper, we discuss the relevance of TQT trials as a tool for proarrhythmic risk prediction as a part of the debate regarding their usefulness.

Virtual Clinical Trial Toward Polytherapy Safety Assessment: Combination of Physiologically Based Pharmacokinetic/Pharmacodynamic-Based Modeling and Simulation Approach With Drug-Drug Interactions Involving Terfenadine as an Example

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.

Drug-drug interactions and QT prolongation as a commonly assessed cardiac effect - comprehensive overview of clinical trials

Background

Proarrhythmia assessment is one of the major concerns for regulatory bodies and pharmaceutical industry. ICH guidelines recommending preclinical tests have been established in attempt to eliminate the risk of drug-induced arrhythmias. However, in the clinic, arrhythmia occurrence is determined not only by the inherent property of a drug to block ion currents and disturb electrophysiological activity of cardiac myocytes, but also by many other factors modifying individual risk of QT prolongation and subsequent proarrhythmia propensity. One of those is drug-drug interactions. Since polypharmacy is a common practice in clinical settings, it can be anticipated that there is a relatively high risk that the patient will receive at least two drugs mutually modifying their proarrhythmic potential and resulting either in triggering the occurrence or mitigating the clinical symptoms. The mechanism can be observed either directly at the pharmacodynamic level by competing for the molecular targets, or indirectly by modifying the physiological parameters, or at the pharmacokinetic level by alteration of the active concentration of the victim drug.

Methods

This publication provides an overview of published clinical studies on pharmacokinetic and/or pharmacodynamic drug-drug interactions in humans and their electrophysiological consequences (QT interval modification). Databases of PubMed and Scopus were searched and combinations of the following keywords were used for Title, Abstract and Keywords fields: interaction, coadministration, combination, DDI and electrocardiographic, QTc interval, ECG. Only human studies were included. Over 4500 publications were retrieved and underwent preliminary assessment to identify papers accordant with the topic of this review. 76 papers reporting results for 96 drug combinations were found and analyzed.

Results

The results show the tremendous variability of drug-drug interaction effects, which makes one aware of complexity of the problem, and suggests the need for assessment of an additional risk factors and careful ECG monitoring before administration of drugs with anticipated QT prolongation.

Conclusions

DDIs can play significant roles in drugs’ cardiac safety, as evidenced by the provided examples. Assessment of the pharmacodynamic effects of the drug interactions is more challenging as compared to the pharmacokinetic due to the significant diversity in the endpoints which should be analyzed specifically for various clinical effects. Nevertheless, PD components of DDIs should be accounted for as PK changes alone do not allow to fully explain the electrophysiological effects in clinic situations.

Electronic supplementary material

The online version of this article (doi:10.1186/s40360-016-0053-1) contains supplementary material, which is available to authorized users.

Computer-based prediction of the drug proarrhythmic effect: problems, issues, known and suspected challenges

It is likely that computer modelling and simulations will become an element of comprehensive cardiac safety testing. Their role would be primarily the integration and the interpretation of previously gathered data. There are still unanswered questions and issues which we list and describe below. They include sources of data used for the development of the models as well as data utilized as input information, which can come from the in vitro studies and the quantitative structure-activity relationship models. The pharmacokinetics of the drugs in question play a crucial role as their active concentration should be considered, yet the question remains where is the right place to assess it. The pharmacodynamic angle includes complications coming from multiple drugs (i.e. active metabolites) acting in parallel as well as the type of interaction with (potentially) multiple affected channels. Once established, the model and the methodology of its use should be further validated, optimistically against individual data reported at the clinical level as the physiological, anatomical, and genetic parameters play a crucial role in the drug-triggered arrhythmia induction. All the abovementioned issues should be at least considered and-hopefully-resolved, to properly utilize the mathematical models for a cardiac safety assessment.

How in vitro influences in silico utilized for the prediction of in vivo - pilot study of the drug-induced pro-arrhythmic potency prediction

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.

In vitro-in vivo extrapolation of drug-induced proarrhythmia predictions at the population level

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.

Tox-database.net: a curated resource for data describing chemical triggered in vitro cardiac ion channels inhibition

BACKGROUND

Drugs safety issues are now recognized as being factors generating the most reasons for drug withdrawals at various levels of development and at the post-approval stage. Among them cardiotoxicity remains the main reason, despite the substantial effort put into in vitro and in vivo testing, with the main focus put on hERG channel inhibition as the hypothesized surrogate of drug proarrhythmic potency. The large interest in the IKr current has resulted in the development of predictive tools and informative databases describing a drug's susceptibility to interactions with the hERG channel, although there are no similar, publicly available sets of data describing other ionic currents driven by the human cardiomyocyte ionic channels, which are recognized as an overlooked drug safety target.

DISCUSSION

The aim of this database development and publication was to provide a scientifically useful, easily usable and clearly verifiable set of information describing not only IKr (hERG), but also other human cardiomyocyte specific ionic channels inhibition data (IKs, INa, ICa).

SUMMARY

The broad range of data (chemical space and in vitro settings) and the easy to use user interface makes tox-database.net a useful tool for interested scientists. DATABASE URL: http://tox-database.net.

Predictive model for L-type channel inhibition: multichannel block in QT prolongation risk assessment

Evaluation of the proarrhythmic potential of an investigated compound is now an integral element of the safety profile required for the approval of new drugs. The human ether-à-go-go-related gene (hERG) channel blocking potency is regarded as a surrogate marker of the proarrhythmic risk at the early stages of the research and development process. However, there is no straight correlation between QT prolongation and TdP occurrence probability, and hERG inhibition potential can be an inadequate predictor of QT prolongation. The L-type calcium channel plays a pivotal role in cardiomyocytes' physiology. Thus the main aim of this study was to develop a predictive model for drug-triggered CaL channel inhibition and also the assessment of drug-multichannel interaction effects on the heart rate-corrected QT interval. The data set, consisting of 123 records describing in vitro experimental settings, measured IC₅₀ values and calculated physico-chemical properties for 72 various chemicals, was collected. The models were tested in a modified 10-fold cross-validation procedure. The generalization ability of the best model was as follows: root mean squared error (RMSE) = 1.10, normalized root mean squared error (NRMSE) = 16.09%. Out of the 10 most important variables, 5 described conditions of the in vitro experiments thus their description and experiment's conditions standardization might be the key to the models better performance. The simulations performed with the ToxComp system showed that the hERG block alone causes concentration-dependent QT prolongation, whereas when multichannel block is regarded, the effect could be reversed. For that reason, the multichannel interaction of tested compounds should be taken into consideration, in order to make the proarrhythmic risk assessment more reliable.

Slow delayed rectifying potassium current (IKs ) - analysis of the in vitro inhibition data and predictive model development

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.

Evaluation of patients' adherence to statins in Poland

INTRODUCTION

High efficacy of statin therapy in primary and secondary prevention of coronary heart disease (CHD) has been confirmed in numerous clinical trials. However, studies' results do not reflect patient benefits in everyday clinical practice. Observed discrepancies are the result of patients' failure to comply with medical recommendations regarding both non-pharmacological and pharmacological treatment. Non-compliance can lead to serious health and economic consequences.

OBJECTIVES

The purpose of the presented study was the retrospective analysis of compliance in patients treated with statins on the basis of Polish reimbursement data from the National Health Fund (NFZ).

METHODS

Medication Possession Ratio (MPR) was used for the compliance assessment; proportion of patients achieving the minimum level of compliance which ensure a satisfactory clinical response was also evaluated. Number of days to discontinuation was calculated in order to estimate patient persistence representing the time over which a patient continues to fill a prescription.

RESULTS

The results of the analysis indicate that only 12% of the population of patients treated with statins demonstrates proper level of both compliance and persistence. Statistically significant differences were found among different patient groups; however, in all the groups, compliance was lower than the minimal level required for clinical benefits.

CONCLUSIONS

The study revealed poor compliance among patients receiving statin prescriptions for hyperlipidaemia treatment. We presume that the most important factor for non-adherence can be lack of proper patient education, thus all patients on statin treatment should receive sufficient attention, supervision and better information.

Collation, assessment and analysis of literature in vitro data on hERG receptor blocking potency for subsequent modeling of drugs' cardiotoxic properties

The assessment of the torsadogenic potency of a new chemical entity is a crucial issue during lead optimization and the drug development process. It is required by the regulatory agencies during the registration process. In recent years, there has been a considerable interest in developing in silico models, which allow prediction of drug-hERG channel interaction at the early stage of a drug development process. The main mechanism underlying an acquired QT syndrome and a potentially fatal arrhythmia called torsades de pointes is the inhibition of potassium channel encoded by hERG (the human ether-a-go-go-related gene). The concentration producing half-maximal block of the hERG potassium current (IC(50)) is a surrogate marker for proarrhythmic properties of compounds and is considered a test for cardiac safety of drugs or drug candidates. The IC(50) values, obtained from data collected during electrophysiological studies, are highly dependent on experimental conditions (i.e. model, temperature, voltage protocol). For the in silico models' quality and performance, the data quality and consistency is a crucial issue. Therefore the main objective of our work was to collect and assess the hERG IC(50) data available in accessible scientific literature to provide a high-quality data set for further studies.