62 research outputs found
The role of interaction model in simulation of drug interactions and QT prolongation
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
Virtual thorough QT (TQT) trial-extrapolation of "In Vitro" cardiac safety data to "In Vivo" situation using multi-scale physiologically based ventricular cell-wall model exemplified with tolterodine and fesoterodine
QT interval prolongation typically assessed with dedicated clinical trials called thorough QT/QTc (TQT) studies is used as surrogate to identify the proarrhythmic risk of drugs albeit with criticism in terms of cost-effectiveness in establishing the actual risk of torsade de pointes (TdP). Quantitative systems toxicology and safety (QSTS) models have potential to quantitatively translate the in vitro cardiac safety data to clinical level including simulation of TQT trials. Virtual TQT simulations have been exemplified with use of two related drugs tolterodine and fesoterodine. The impact of bio-relevant concentration in plasma versus estimated heart tissue exposure on predictions was also assessed. Tolterodine and its therapeutically equipotent metabolite formed via CYP2D6 pathway, 5-HMT, inhibit multiple cardiac ion currents (IKr, INa, ICaL). The QSTS model was able to accurately simulate the QT prolongation at therapeutic and supra-therapeutic dose levels of tolterodine well within 95% confidence interval limits of observed data. The model was able to predict the QT prolongation difference between CYP2D6 extensive and poor metaboliser subject groups at both dose levels thus confirming the ability of the model to account for electrophysiologically active metabolite. The QSTS model was able to simulate the negligible QT prolongation observed with fesoterodine establishing that the 5-HMT does not prolong QT interval even though it is a blocker of hERG channel. With examples of TOL and FESO, we demonstrated the utility of the QSTS approaches to simulate virtual TQT trials, which in turn could complement and reduce the clinical studies or help optimise clinical trial designs
An Open-Access dataset of thorough QT studies results
Along with the current interest in changes of cardiovascular risk assessment strategy and
inclusion of in silico modelling into the applicable paradigm, the need for data has increased, both
for model generation and testing. Data collection is often time-consuming but an inevitable step in
the modelling process, requiring extensive literature searches and other identification of alternative
resources providing complementary results. The next step, namely data extraction, can also be
challenging. Here we present a collection of thorough QT/QTc (TQT) study results with detailed
descriptions of study design, pharmacokinetics, and pharmacodynamic endpoints. The presented
dataset provides information that can be further utilized to assess the predictive performance of
di erent preclinical biomarkers for QT prolongation e ects with the use of various modelling
approaches. As the exposure levels and population description are included, the study design and
characteristics of the study population can be recovered precisely in the simulation. Another possible
application of the TQT dataset is the analysis of drug characteristic/QT prolongation/TdP (torsade
de pointes) relationship after the integration of provided information with other databases and
tools. This includes drug cardiac safety classifications (e.g., CredibleMeds), Comprehensive in vitro
Proarrhythmia Assay (CiPA) compounds classification, as well as those containing information on
physico-chemical properties or absorption, distribution, metabolism, excretion (ADME) data like
PubChem or DrugBank
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
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
Real patient and its virtual twin : application of quantitative systems toxicology modelling in the cardiac safety assessment of citalopram
Abstract. 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 (IKr, IKs, ICaL); (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
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
- …