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To understand and describe neurotoxicity mechanistically, we must first understand the processes and responses that occur within neuronal cell systems after the administration of a chemical. The dataset we present is a collection of experimental results from the literature that comprises various neurotoxic endpoints in human-derived in vitro models, allowing for easy data analysis. Currently available and free databases such as the EPA's ToxCast, which focuses on forecasting toxic health risks, are created by collecting reports on cytotoxicity testing and creating mathematical fits that could help predict the effects of a given chemical on various types of cells. We, in contrast, provide a smaller, raw, and heterogeneous dataset created solely of results on human-derived cell models that not only summarises the cytotoxic effects of certain substances but also creates a possibility for analysing the significance of the experimental set-up for the prediction of outcome.

Abstract The development of a generic drug product involves demonstrating that there is no significant difference in the rate and extent to which the active ingredient becomes available at the site of action, relative to the reference listed drug product. This remains challenging for many locally acting topical dermatological products because measuring the concentration of the active ingredient at the site of action in the skin may not be straightforward, and, in most instances, there are no established relationships between skin and plasma pharmacokinetic profiles. In recent years, the Office of Generic Drugs of the US Food and Drug Administration (FDA) established scientific research programs with the goal of enhancing patient access to high quality, affordable topical dermatological generics. A key strategy of these research programs was to leverage modeling and simulation methodologies that accelerate the development of these generics by facilitating alternative bioequivalence approaches for dermatological drug products. This report summarizes relevant insights and discussions from a 2021 FDA public workshop titled “Regulatory Utility of Mechanistic Modeling to Support Alternative Bioequivalence Approaches,” which illustrated how mechanistic modeling and simulation approaches can be utilized (and have been used) to inform generic drug product development and regulatory decisions during the assessment of generic drug applications submitted to the FDA.

In the field of the cosmetic industry, significant efforts are made to develop methods that are not only cost effective and time effective but are also environmentally friendly and cruelty free. Cosmetic tests using in vivo animal models are currently banned in the European Union. To fulfil regulatory requirements, new approach methodologies (NAMs) are implemented, and thereupon, in silico techniques have constantly acquired significance. This review aimed to show the general picture of the available computational methods and approaches, give some examples of their applications, present capabilities and limitations, and propose the way forward. The general information about in silico modelling and examples of its usage in the context of cosmetics and its legal regulation are presented. The review is divided with a focus on three endpoints of interest: (1) safety assessment, (2) exposure assessment, and (3) formulation characterization. With this comprehensive analysis, we try to answer the question as to whether we are using the opportunity.

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.

Abstract Physiologically‐based pharmacokinetic models combine knowledge about physiology, drug product properties, such as physicochemical parameters, absorption, distribution, metabolism, excretion characteristics, formulation attributes, and trial design or dosing regimen to mechanistically simulate drug pharmacokinetics (PK). The current work describes the development of a multiphase, multilayer mechanistic dermal absorption (MPML MechDermA) model within the Simcyp Simulator capable of simulating uptake and permeation of drugs through human skin following application of drug products to the skin. The model was designed to account for formulation characteristics as well as body site‐ and sex‐ population variability to predict local and systemic bioavailability. The present report outlines the structure and assumptions of the MPML MechDermA model and includes results from simulations comparing absorption at multiple body sites for two compounds, caffeine and benzoic acid, formulated as solutions. Finally, a model of the Feldene (piroxicam) topical gel, 0.5% was developed and assessed for its ability to predict both plasma and local skin concentrations when compared to in vivo PK data.

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.

Gastric mechanical stress often impacts drug dissolution from solid oral dosage forms, but in vitro experiments cannot recreate the substantial variability of gastric motility in a reasonable time. This study, for the first time, combines a novel dissolution apparatus with the design of experiments (DoE) and machine learning (ML) to overcome this obstacle. The workflow involves the testing of soft gelatin capsules in a set of fasted-state biorelevant dissolution experiments created with DoE. The dissolution results are used by an ML algorithm to build the classification model of the capsule’s opening in response to intragastric stress (IS) within the physiological space of timing and magnitude. Next, a random forest algorithm is used to model the further drug dissolution. The predictive power of the two ML models is verified with independent dissolution tests, and they outperform a polynomial-based DoE model. Moreover, the developed tool reasonably simulates over 50 dissolution profiles under varying IS conditions. Hence, we prove that our method can be utilized for the simulation of dissolution profiles related to the multiplicity of individual gastric motility patterns. In perspective, the developed workflow can improve virtual bioequivalence trials and the patient-centric development of immediate-release oral dosage forms.

Physiologically based pharmacokinetic and absorption modeling are being used by industry and regulatory bodies to address various scientifically challenging questions. While there is high confidence in the prediction of exposure for the BCS class I drugs administered as immediate-release formulations, in the case of prolonged-release formulations, special attention should be given to the input dissolution data. Our goal was to develop and verify a PBPK model for a BCS class I compound, ropinirole, and check the biopredictiveness of the dissolution data for the prolonged-release formulation administered by Parkinson’s patients. The model was built based on quality control dissolution data reported in the certificates of analysis and verified with the use of data derived from five clinical trial reports. The simulated pharmacokinetic parameters being within a two-fold range of the observed values confirmed acceptable model performance, in vivo relevance of the in vitro dissolution profiles, and indirectly indicated ropinirole stable release from the formulation in the patients’ gastro-intestinal tract. Ropinirole PBPK model will be used for exploring potential clinical scenarios while developing a new formulation.

In dieser Rezension wird Local political participation in Japan: A case study of Oita (2019) von Dani Daigle Kida 2019 besprochen. Dabei wird auf die beiden Stärken des Artikels eingegangen: (1) Triangulation qualitativer und quantitativer Methoden, (2) Konzentration auf den lokalen politischen Raum, der bisher nur wenig Aufmerksamkeit im Mainstream der politischen Partizipationsforschung erfahren hat. Im Zuge ihrer Feldforschung in Ōita liefert Kida wertvolle Erkenntnisse darüber, wie 'traditionelle' Partizipationsweisen bis heute existieren können, obwohl Parteienbindungen erodieren und Diskurse über ein stärkeres Einbinden von Bürger*innen in den politischen Prozess in Gesellschaft, Medien und wissenschaftlichen Kreisen spätestens seit der Heisei-Zeit geführt werden. Die Rezension endet mit dem Vorschlag, ein Konzept zu Partizipation zu verwenden, bei dem auch Tätigkeiten außerhalb des eng begriffenen politischen Raums berücksichtigt werden. Dadurch könnte eine Vielzahl an gesellschaftlich und letztlich auch politisch relevanten Aktivitäten im ländlichen Japan erfasst werden, die (zum Teil durchaus auch erfolgreiche) Versuche darstellen, die Gesellschaft oder lokale Entscheidungsfindungsprozesse zu beeinflussen.

Antazoline is an antihistaminic drug that is effective in the termination of paroxysmal atrial fibrillation. Despite its long presence in the market, antazoline’s ADME parameters and pharmacokinetic effects in humans are poorly characterized. The objective of this study was to fill this gap by generation of in vitro and in vivo data and the development of a physiologically based pharmacokinetic model describing antazoline and its main metabolite disposition. A set of ADME parameters for the antazoline and its hydroxy metabolite is provided based on literature data, QSAR predictions, in vitro binding and metabolic stability assays. These can be used to feed PBPK models. In our current work, the developed PBPK model simulating simultaneously the pharmacokinetic profile of antazoline and its metabolite was successfully verified against the available clinical data and the presented capability to account for the clinically observed variability. When used to feed the PD model (e.g., simulating ECG), concentration-time profiles predicted by the model enable the assessment of antazoline’s effect in various clinical scenarios with the possibility to account for population differences or CP mediated drug-drug interactions.

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

Spray drying is a single step process in which solutions or dispersions are converted into dry particles. It is widely used in pharmaceutical technology to produce inhalable particles. Dry particle behavior during inhalation, described as the emitted dose (ED) and fine particle fraction (FPF), is determined in vitro by standardized procedures. A large number of factors influencing the spray drying process and particle interaction makes it difficult to predict the final product properties in advance. This work presents the development of predictive models based on experimental data obtained by aerodynamic assessment of respirable dry powders. Developed models were tested according to the 10-fold cross-validation procedure and yielded good predictive ability. Both models were characterized by normalized root-mean-square error (NRMSE) below 8.50% and coefficient of determination (R2) above 0.90. Moreover, models were analyzed to establish a relationship between spray drying process parameters and the final product quality measures. Presented work describes the strategy of implementing the evolutionary algorithms in empirical model’s development. Obtained models can be applied as an expert system during pharmaceutical formulation development. The models have the potential for product optimization and a knowledge extraction to improve final quality of the drug.

A physiologically based pharmacokinetic (PBPK) model of selegiline (SEL), and its metabolites, was developed in silico to evaluate the disposition differences between healthy and special populations. SEL is metabolized to methamphetamine (MAP) and desmethyl selegiline (DMS) by several CYP enzymes. CYP2D6 metabolizes the conversion of MAP to amphetamine (AMP), while CYP2B6 and CYP3A4 predominantly mediate the conversion of DMS to AMP. The overall prediction error in simulated PK, using the developed PBPK model, was within 0.5–1.5-fold after intravenous and transdermal dosing in healthy and elderly populations. Simulation results generated in the special populations demonstrated that a decrease in cardiac output is a potential covariate that affects the SEL exposure in renally impaired (RI) and hepatic impaired (HI) subjects. A decrease in CYP2D6 levels increased the systemic exposure of MAP. DMS exposure increased due to a reduction in the abundance of CYP2B6 and CYP3A4 in RI and HI subjects. In addition, an increase in the exposure of the primary metabolites decreased the exposure of AMP. No significant difference between the adult and adolescent populations, in terms of PK, were observed. The current PBPK model predictions indicate that subjects with HI or RI may require closer clinical monitoring to identify any untoward effects associated with the administration of transdermal SEL patch.

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.

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 different preclinical biomarkers for QT prolongation effects 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.