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[Display omitted] The objective of the study was to show how mechanistic modelling can be used to characterize the skin absorption of Nimesulide (NIM) in both in vitro systems and in vivo subjects. A basic PBPK model for oral absorption to characterize the systemic disposition of NIM and MPML MechDermATM models for in vitro permeation and in vivo, topical absorption was developed and verified using published data. The developed models utilize drug physicochemical properties, formulation attributes and physiology information either collected from literature and/or from Simcyp databases (systems' data). Following the verification of the PBPK models virtual bioequivalence (VBE) trials were performed both at systemic and local exposure levels (dermis concentrations) to compare these formulations. A parameter sensitivity analysis was conducted to understand the impact of vehicle-related attributes on IVPT (in vitro permeation test) data. The vehicle-stratum corneum lipids partition coefficient in the formulation layer (Kp sc_lip:vehicle) was identified to be an appropriate parameter to take into account the differences in dermal absorption of marketed preparations based on the qualitative composition. Thus, this parameter was optimized for each marketed product based on the published in vitro data. After verification of the IVPT model, IVIVE was performed to assess the predictability of the model for studying the in vivo pharmacokinetics of NIM. The VBE analysis concluded that these formulations are bioequivalent at the level of systemic and local dermis exposure. To summarize, the study shows the use of modelling and simulation (M&S) tools to better understand the behaviour of formulations and their interaction with human physiology. [ABSTRACT FROM AUTHOR]

• Model-informed drug discovery and development (MID3) is poised to facilitate a major evolution in the way drugs R&D is conducted. • Employing computational models aids study design and interpretation and in some cases replaces actual experiments. • MID3 covers related approaches: physiologically based pharmacokinetics (PBPK), quantitative systems pharmacology/toxicology (QSP/T). • The true power of these techniques comes from their ability to integrate relevant in vitro , in vivo and ex vivo data. • It allows to predict drug potency, efficacy and safety to aid translation from nonclinical and clinical systems into the real-world situation of patients. 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. Used in combination, physiologically based pharmacokinetics, quantitative systems pharmacology and quantitative systems toxicology have the potential to improve prediction of the effective drug concentration and the associated beneficial and adverse effects, improving the chances of success in biopharma R&D. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Highlights: [•] Drug cardiotoxicity is a serious issue for all players in the market. [•] Currently used approaches for cardiac safety testing might not be adequately sufficient and cost-effective. [•] Translational tools based on the biophysically detailed, mathematical models enable in vitro–in vivo extrapolation. [•] It is especially important for situations where multiple ion channel are affected. [•] Empirical models describing variability in population level allow to account for the intra- and inter-individual variability. [Copyright &y& Elsevier]

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. © 2012 Wiley Periodicals, Inc. and the American Pharmacists Association J Pharm Sci 101:2584-2595, 2012 [ABSTRACT FROM AUTHOR]

Abstract: Palliative chemotherapy is one of the major parts among costs of cancer. An aging population, introduction of new technologies and increased patients’ healthcare expectations made the pharmacoeconomics analyses one of the mostly used tools in the decision making process. One of the main problems existing in pharmacoeconomics studies is the estimation of the effect. The aim of the study was to develop and validate on a real data artificial neural networks based systems for medical effect prediction and use them as a tool for modeling effect in pharmacoenomics analyses. Analysis was conducted on retrospective data from clinical records of non-small cell lung cancer in IIIB or IV (inoperative) stage patients treated with various therapy schemes. Logistic regression was used as the validation method. Based on the analysis of mean life duration (survival median) the output value in the database was transformed into a binary form. The threshold obtained after scientific, medical literature analysis was set at 35 weeks. Total classification rate as well as classification rate of both classes and AUROC value were used as a determinants of ANN’s effectiveness. Best obtained neural model results was 84% of correctly classified records with 76% and 89% of class 1 (survival >35 weeks) and class 0 (survival <35 weeks). The AUROC value was 0.82. After training with using whole dataset, numerical experiments were conducted. Tests with in silico chemotherapeutics replacement was done. The results confirms effectiveness of artificial neural networks based pharmacoeconomics models. [Copyright &y& Elsevier]

Summary: Objectives: The aim of this research was to analyze the antibacterial drug consumption pattern in a 1-million-plus strictly defined population in Poland. We assessed outpatient antibiotic sales (ATC J01) in relation to patient age and season of the year, and sought to determine the group of patients with the most frequent recurrence of bacterial diseases. Methods: The Lubuskie Regional Unit of the National Health Fund (NHF) and the Central Statistical Office (GUS) were the main sources of data. For the period 2002–2005, data on outpatient sales of antibiotics (ATC J01) in Lubuskie Province were collected and expressed in DDD (defined daily dose; World Health Organization anatomical therapeutic chemical (ATC)/DDD version 2006) per 1000 inhabitants per day (DID). Results: During the period 2002–2005, the average level of antibiotic use amounted to 19.8 DID in the Lubuskie population. During the 3-year period, 64.3% of the population was treated with antibiotics. This value varied for different age groups. Of the patients, 22.7% utilized 62.6% of a 3-year supply of antibiotic therapy in the province; a small 1.9% of the population used 10.0% of the supply. The seasonal variation of antibiotic consumption in different age groups showed a strong tendency to be flatter as the older age groups were analyzed. Conclusions: The data gained from the payers, i.e., the healthcare system, is a very valuable source of information for pharmacoepidemiological studies. Our study shows that seasonal variation in antibiotic use is strictly linked with the age of patients. Special attention should be paid to the relatively small group of patients that utilizes a significant percentage of the year''s supply. We have established an effective way to present recurrence data (a map showing the infectious disease incidence). This could be a very useful tool for comparing antibiotic consumption in different countries. [Copyright &y& Elsevier]

Gestational diabetes mellitus (GDM) is defined as any carbohydrates intolerance recognized first time during pregnancy. Obesity, maternal age, history of miscarriages and life style could be listed as the risk factors. Clinical studies prove that women with gestational diabetes have significantly different pregnancy characteristics and outcomes. For this reason, the possibility of correct diagnosis (especially for women with high risk factors) should be enabled to prevent serious consequences.The purpose of the study was to develop novel GDM diagnosis system based on artificial neural network (ANN). To depict neural data analysis tools, epidemiological database obtained from National Center for Health Statistics (NCHS), Center for Disease Control and Prevention (CDC) was used. Neural analysis was performed using own-written Nets2003 software [Farmacja Polska 54 (1998)]. The procedure was to use ANN as a flexible and advanced tool for modeling relationships between demographic factors and the risk of GDM. Best obtained results (ca. 70% of true positive diagnoses) were compared with logistic regression (56.3 of true positive diagnoses) a classical way of model in case of binary outcome (1-positive vs. 0-negative).The neural model was prepared and after validation and verification procedures was converted into the Java applet, which was subsequently published in the Internet (http://www.cyf-kr.edu.pl/~mfpolak). Such systems are often called ‘calculators’ and are getting more and more popular in the Web. The Java applet called ‘e-ANN’ was designed to handle vast variety of neural architectures. ANN is encoded into the ASCII configuration file, which could be easily updated, thus facilitating website management. [Copyright &y& Elsevier]

Abstract Older patients are generally not included in Phase 1 clinical trials despite being the population group who use the largest number of prescription medicines. Physiologically based pharmacokinetic (PBPK) modelling provides an understanding of the absorption and disposition of drugs in older patients. In this review, PBPK models used for the prediction of absorption and exposure of drugs after parenteral, oral and transdermal administration are discussed. Comparisons between predicted drug pharmacokinetics (PK) and observed PK are presented to illustrate the accuracy of the predictions by the PBPK models and their potential use in informing clinical trial design and dosage adjustments in older patients. In addition, a case of PBPK modelling of a bioequivalence study on two controlled release products is described, where PBPK predictions reproduced the study showing bioequivalence in healthy volunteers but not in older subjects with achlorhydria, indicating further utility in prospectively identifying challenges in bioequivalence studies. Graphical abstract Unlabelled Image [ABSTRACT FROM AUTHOR]

Modern model-based approaches to cardiac safety and efficacy assessment require accurate drug concentration-effect relationship establishment. Thus, knowledge of the active concentration of drugs in heart tissue is desirable along with inter-subject variability influence estimation. To that end, we developed a mechanistic physiologically based pharmacokinetic model of the heart. The models were described with literature-derived parameters and written in R, v.3.4.0. Five parameters were estimated. The model was fitted to amitriptyline and nortriptyline concentrations after an intravenous infusion of amitriptyline. The cardiac model consisted of 5 compartments representing the pericardial fluid, heart extracellular water, and epicardial intracellular, midmyocardial intracellular, and endocardial intracellular fluids. Drug cardiac metabolism, passive diffusion, active efflux, and uptake were included in the model as mechanisms involved in the drug disposition within the heart. The model accounted for inter-individual variability. The estimates of optimized parameters were within physiological ranges. The model performance was verified by simulating 5 clinical studies of amitriptyline intravenous infusion, and the simulated pharmacokinetic profiles agreed with clinical data. The results support the model feasibility. The proposed structure can be tested with the goal of improving the patient-specific model-based cardiac safety assessment and offers a framework for predicting cardiac concentrations of various xenobiotics. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

• Precision medicine is one of the pharmacotherapy development pathways. • The combination of 3D tablets printing with biopredictive dissolution and PBPK/PD modeling can help to progress towards MIPD. • The PBPK models allow for the virtual twin concept implementation. • There are still several challenges which need to be addressed and solved. 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. [ABSTRACT FROM AUTHOR]