Cancer biology, toxicology and alternative methods development go hand-in-hand

Systematic Organization of COVID-19 Data Supported by the Adverse Outcome Pathway Framework

Adverse Outcome Pathways (AOP) provide structured frameworks for the systematic organization of research data and knowledge. The AOP framework follows a set of key principles that allow for broad application across diverse disciplines related to human health, including toxicology, pharmacology, virology and medical research. The COVID-19 pandemic engages a great number of scientists world-wide and data is increasing with exponential speed. Diligent data management strategies are employed but approaches for systematically organizing the data-derived information and knowledge are lacking. We believe AOPs can play an important role in improving interpretation and efficient application of scientific understanding of COVID-19. Here, we outline a newly initiated effort, the CIAO project (https://www.ciao-covid.net/), to streamline collaboration between scientists across the world toward development of AOPs for COVID-19, and describe the overarching aims of the effort, as well as the expected outcomes and research support that they will provide.

Time-dependent re-organization of biological processes by the analysis of the dynamic transcriptional response of yeast cells to doxorubicin

Doxorubicin is an efficient chemotherapeutic reagent in the treatment of a variety of cancers. However, its underlying molecular mechanism is not fully understood and several severe side effects limit its application. In this study, the dynamic transcriptomic response of Saccharomyces cerevisiae cells to a doxorubicin pulse in a chemostat system was investigated to reveal the underlying molecular mechanism of this drug. The clustering of differentially and significantly expressed genes (DEGs) indicated that the response of yeast cells to doxorubicin is time dependent and may be classified as short-term, mid-term and long-term responses. The cells have started to reorganize their response after the first minute following the injection of the pulse. A modified version of Weighted Gene Co-expression Network Analysis (WGCNA) was used to cluster the positively correlated co-expression profiles, and functional enrichment analysis of these clusters was carried out. DNA replication and DNA repair processes were significantly affected and induced 60 minutes after exposure to doxorubicin. The response to oxidative stress was not identified as a significant term. A transcriptional re-organization of the metabolic pathways seems to be an early event and persists afterwards. The present study reveals for the first time that the RNA surveillance pathway, which is a post-transcriptional regulatory pathway, may be implicated in the short-term reaction of yeast cells to doxorubicin. Integration with regulome revealed the dynamic re-organization of the transcriptomic landscape. Fhl1p, Mbp1p, and Mcm1p were identified as primary regulatory factors responsible for tuning the differentially expressed genes.

NanoSolveIT Project: Driving nanoinformatics research to develop innovative and integrated tools for in silico nanosafety assessment

Nanotechnology has enabled the discovery of a multitude of novel materials exhibiting unique physicochemical (PChem) properties compared to their bulk analogues. These properties have led to a rapidly increasing range of commercial applications; this, however, may come at a cost, if an association to long-term health and environmental risks is discovered or even just perceived. Many nanomaterials (NMs) have not yet had their potential adverse biological effects fully assessed, due to costs and time constraints associated with the experimental assessment, frequently involving animals. Here, the available NM libraries are analyzed for their suitability for integration with novel nanoinformatics approaches and for the development of NM specific Integrated Approaches to Testing and Assessment (IATA) for human and environmental risk assessment, all within the NanoSolveIT cloud-platform. These established and well-characterized NM libraries (e.g. NanoMILE, NanoSolutions, NANoREG, NanoFASE, caLIBRAte, NanoTEST and the Nanomaterial Registry (>2000 NMs)) contain physicochemical characterization data as well as data for several relevant biological endpoints, assessed in part using harmonized Organisation for Economic Co-operation and Development (OECD) methods and test guidelines. Integration of such extensive NM information sources with the latest nanoinformatics methods will allow NanoSolveIT to model the relationships between NM structure (morphology), properties and their adverse effects and to predict the effects of other NMs for which less data is available. The project specifically addresses the needs of regulatory agencies and industry to effectively and rapidly evaluate the exposure, NM hazard and risk from nanomaterials and nano-enabled products, enabling implementation of computational 'safe-by-design' approaches to facilitate NM commercialization.

Toward Rigorous Materials Production: New Approach Methodologies Have Extensive Potential to Improve Current Safety Assessment Practices

Advanced material development, including at the nanoscale, comprises costly and complex challenges coupled to ensuring human and environmental safety. Governmental agencies regulating safety have announced interest toward acceptance of safety data generated under the collective term New Approach Methodologies (NAMs), as such technologies/approaches offer marked potential to progress the integration of safety testing measures during innovation from idea to product launch of nanomaterials. Divided in overall eight main categories, searchable databases for grouping and read across purposes, exposure assessment and modeling, in silico modeling of physicochemical structure and hazard data, in vitro high-throughput and high-content screening assays, dose-response assessments and modeling, analyses of biological processes and toxicity pathways, kinetics and dose extrapolation, consideration of relevant exposure levels and biomarker endpoints typify such useful NAMs. Their application generally agrees with articulated stakeholder needs for improvement of safety testing procedures. They further fit for inclusion and add value in nanomaterials risk assessment tools. Overall 37 of 50 evaluated NAMs and tiered workflows applying NAMs are recommended for considering safer-by-design innovation, including guidance to the selection of specific NAMs in the eight categories. An innovation funnel enriched with safety methods is ultimately proposed under the central aim of promoting rigorous nanomaterials innovation.

Toxic and Genomic Influences of Inhaled Nanomaterials as a Basis for Predicting Adverse Outcome

An immense variety of different types of engineered nanomaterials are currently being developed and increasingly applied to consumer products. Importantly, engineered nanomaterials may pose unexplored adverse health effects because of their small size. Particularly in occupational settings, the dustiness of certain engineered nanomaterials involves risk of inhalation and influences on lung function. These facts call for quick and cost-effective safety testing practices, such as that obtained through multiparametric high-throughput screening using cultured human lung cells. The predictive value of such in vitro-based testing depends partly on the effectiveness of coverage of the mechanisms underlying toxicity effects. The concept of adverse outcome pathways covers the array of causative effects starting from a molecular initiating event via cellular-, organ-, individual-, and population-level effects. Screening for adverse outcome pathway-related effects that drive the eventual toxic outcome provides a good basis for developing predictive testing methods and data-driven integrated testing strategies for hazard and risk assessment. Temporal and inherited genomic changes are likely to drive many adverse responses to engineered nanomaterials, such as multiwalled carbon nanotubes, of which one specific form has recently been evaluated as possibly carcinogenic. Here, we briefly describe current state-of-the-art strategies for analyzing and understanding genomic influences of engineered nanomaterial exposure, including the selected focus on lung disease, and strategies for using mechanistic knowledge to predict and prevent adverse outcome.

In silico cancer research towards 3R

BACKGROUND

Improving our understanding of cancer and other complex diseases requires integrating diverse data sets and algorithms. Intertwining in vivo and in vitro data and in silico models are paramount to overcome intrinsic difficulties given by data complexity. Importantly, this approach also helps to uncover underlying molecular mechanisms. Over the years, research has introduced multiple biochemical and computational methods to study the disease, many of which require animal experiments. However, modeling systems and the comparison of cellular processes in both eukaryotes and prokaryotes help to understand specific aspects of uncontrolled cell growth, eventually leading to improved planning of future experiments. According to the principles for humane techniques milestones in alternative animal testing involve in vitro methods such as cell-based models and microfluidic chips, as well as clinical tests of microdosing and imaging. Up-to-date, the range of alternative methods has expanded towards computational approaches, based on the use of information from past in vitro and in vivo experiments. In fact, in silico techniques are often underrated but can be vital to understanding fundamental processes in cancer. They can rival accuracy of biological assays, and they can provide essential focus and direction to reduce experimental cost.

MAIN BODY

We give an overview on in vivo, in vitro and in silico methods used in cancer research. Common models as cell-lines, xenografts, or genetically modified rodents reflect relevant pathological processes to a different degree, but can not replicate the full spectrum of human disease. There is an increasing importance of computational biology, advancing from the task of assisting biological analysis with network biology approaches as the basis for understanding a cell's functional organization up to model building for predictive systems.

CONCLUSION

Underlining and extending the in silico approach with respect to the 3Rs for replacement, reduction and refinement will lead cancer research towards efficient and effective precision medicine. Therefore, we suggest refined translational models and testing methods based on integrative analyses and the incorporation of computational biology within cancer research.

Ab initio chemical safety assessment: A workflow based on exposure considerations and non-animal methods

We describe and illustrate a workflow for chemical safety assessment that completely avoids animal testing. The workflow, which was developed within the SEURAT-1 initiative, is designed to be applicable to cosmetic ingredients as well as to other types of chemicals, e.g. active ingredients in plant protection products, biocides or pharmaceuticals. The aim of this work was to develop a workflow to assess chemical safety without relying on any animal testing, but instead constructing a hypothesis based on existing data, in silico modelling, biokinetic considerations and then by targeted non-animal testing. For illustrative purposes, we consider a hypothetical new ingredient x as a new component in a body lotion formulation. The workflow is divided into tiers in which points of departure are established through in vitro testing and in silico prediction, as the basis for estimating a safe external dose in a repeated use scenario. The workflow includes a series of possible exit (decision) points, with increasing levels of confidence, based on the sequential application of the Threshold of Toxicological (TTC) approach, read-across, followed by an "ab initio" assessment, in which chemical safety is determined entirely by new in vitro testing and in vitro to in vivo extrapolation by means of mathematical modelling. We believe that this workflow could be applied as a tool to inform targeted and toxicologically relevant in vitro testing, where necessary, and to gain confidence in safety decision making without the need for animal testing.

A transcriptomics data-driven gene space accurately predicts liver cytopathology and drug-induced liver injury

Predicting unanticipated harmful effects of chemicals and drug molecules is a difficult and costly task. Here we utilize a 'big data compacting and data fusion'-concept to capture diverse adverse outcomes on cellular and organismal levels. The approach generates from transcriptomics data set a 'predictive toxicogenomics space' (PTGS) tool composed of 1,331 genes distributed over 14 overlapping cytotoxicity-related gene space components. Involving ∼2.5 × 10⁸ data points and 1,300 compounds to construct and validate the PTGS, the tool serves to: explain dose-dependent cytotoxicity effects, provide a virtual cytotoxicity probability estimate intrinsic to omics data, predict chemically-induced pathological states in liver resulting from repeated dosing of rats, and furthermore, predict human drug-induced liver injury (DILI) from hepatocyte experiments. Analysing 68 DILI-annotated drugs, the PTGS tool outperforms and complements existing tests, leading to a hereto-unseen level of DILI prediction accuracy.

Inherent formulation issues of kinase inhibitors

The small molecular Kinase Inhibitor (smKI) drug class is very promising and rapidly expanding. All of these drugs are administered orally. The clear relationship between structure and function has led to drugs with a general low intrinsic solubility. The majority of the commercial pharmaceutical formulations of the smKIs are physical mixtures that are limited by the low drug solubility of a salt form. This class of drugs is therefore characterized by an impaired and variable bioavailability rendering them costly and their therapies suboptimal. New formulations are sparingly being reported in literature and patents. The presented data suggests that continued research into formulation design can help to develop more efficient and cost-effective smKI formulation. Moreover, it may also be of help in the future design of the formulations of new smKIs.

High throughput toxicity screening and intracellular detection of nanomaterials

With the growing numbers of nanomaterials (NMs), there is a great demand for rapid and reliable ways of testing NM safety—preferably using in vitro approaches, to avoid the ethical dilemmas associated with animal research. Data are needed for developing intelligent testing strategies for risk assessment of NMs, based on grouping and read‐across approaches. The adoption of high throughput screening (HTS) and high content analysis (HCA) for NM toxicity testing allows the testing of numerous materials at different concentrations and on different types of cells, reduces the effect of inter‐experimental variation, and makes substantial savings in time and cost. HTS/HCA approaches facilitate the classification of key biological indicators of NM‐cell interactions. Validation of in vitroHTS tests is required, taking account of relevance to in vivo results. HTS/HCA approaches are needed to assess dose‐ and time‐dependent toxicity, allowing prediction of in vivo adverse effects. Several HTS/HCA methods are being validated and applied for NM testing in the FP7 project NANoREG, including Label‐free cellular screening of NM uptake, HCA, High throughput flow cytometry, Impedance‐based monitoring, Multiplex analysis of secreted products, and genotoxicity methods—namely High throughput comet assay, High throughput in vitro micronucleus assay, and γH2AX assay. There are several technical challenges with HTS/HCA for NM testing, as toxicity screening needs to be coupled with characterization of NMs in exposure medium prior to the test; possible interference of NMs with HTS/HCA techniques is another concern. Advantages and challenges of HTS/HCA approaches in NM safety are discussed. WIREs Nanomed Nanobiotechnol 2017, 9:e1413. doi: 10.1002/wnan.1413

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Toward the Replacement of Animal Experiments through the Bioinformatics-driven Analysis of 'Omics' Data from Human Cell Cultures

This paper outlines the work for which Roland Grafström and Pekka Kohonen were awarded the 2014 Lush Science Prize. The research activities of the Grafström laboratory have, for many years, covered cancer biology studies, as well as the development and application of toxicity-predictive in vitro models to determine chemical safety. Through the integration of in silico analyses of diverse types of genomics data (transcriptomic and proteomic), their efforts have proved to fit well into the recently-developed Adverse Outcome Pathway paradigm. Genomics analysis within state-of-the-art cancer biology research and Toxicology in the 21st Century concepts share many technological tools. A key category within the Three Rs paradigm is the Replacement of animals in toxicity testing with alternative methods, such as bioinformatics-driven analyses of data obtained from human cell cultures exposed to diverse toxicants. This work was recently expanded within the pan-European SEURAT-1 project (Safety Evaluation Ultimately Replacing Animal Testing), to replace repeat-dose toxicity testing with data-rich analyses of sophisticated cell culture models. The aims and objectives of the SEURAT project have been to guide the application, analysis, interpretation and storage of 'omics' technology-derived data within the service-oriented sub-project, ToxBank. Particularly addressing the Lush Science Prize focus on the relevance of toxicity pathways, a 'data warehouse' that is under continuous expansion, coupled with the development of novel data storage and management methods for toxicology, serve to address data integration across multiple 'omics' technologies. The prize winners' guiding principles and concepts for modern knowledge management of toxicological data are summarised. The translation of basic discovery results ranged from chemical-testing and material-testing data, to information relevant to human health and environmental safety.

Variability in bioavailability of small molecular tyrosine kinase inhibitors

Small molecular tyrosine kinase inhibitors (smTKIs) are in the centre of the very quickly expanding area of personalized chemotherapy and oral applicability thereof. The number of drugs in this class is rapidly growing, with twenty current approvals by both the European Medicines Agency (EMA) and the Food and Drug Administration (FDA). The drugs are, however, generally characterized by a poor oral, and thus variable, bioavailability. This results in significant variation in plasma levels and exposure. The cause is a complex interplay of factors, including poor aqueous solubility, issued permeability, membrane transport and enzymatic metabolism. Additionally, food and drug-drug interactions can play a significant role. The issues related with an impaired bioavailability generally receive little attention. To the best of our knowledge, this article is the first to provide an overview of the factors that determine the bioavailability of the smTKIs.