Cancer Biology, Toxicology and Alternative Methods Development Go Hand‐in‐Hand

Abstract

Toxicological research faces the challenge of integrating knowledge from diverse fields and novel technological developments generally in the biological and medical sciences. We discuss herein the fact that the multiple facets of cancer research, including discovery related to mechanisms, treatment and diagnosis, overlap many up and coming interest areas in toxicology, including the need for improved methods and analysis tools. Common to both disciplines, in vitro and in silico methods serve as alternative investigation routes to animal studies. Knowledge on cancer development helps in understanding the relevance of chemical toxicity studies in cell models, and many bioinformatics-based cancer biomarker discovery tools are also applicable to computational toxicology. Robotics-aided, cell-based, high-throughput screening, microscale immunostaining techniques and gene expression profiling analyses are common tools in cancer research, and when sequentially combined, form a tiered approach to structured safety evaluation of thousands of environmental agents, novel chemicals or engineered nanomaterials. Comprehensive tumour data collections in databases have been translated into clinically useful data, and this concept serves as template for computer-driven evaluation of toxicity data into meaningful results. Future 'cancer research-inspired knowledge management' of toxicological data will aid the translation of basic discovery results and chemicals-and materials-testing data to information relevant to human health and environmental safety.Assessing the intrinsic toxicological properties of environmental agents is central to defining hazard within the paradigm of human risk assessment used now for decades [1]. A constantly increasing number of chemicals and engineered nanomaterials (ENMs) emphasize the need of applying novel tools and screening technologies outside of the standard, both lengthy and expensive, rodent toxicological tests traditionally used in such work [1][2][3][4][5][6][7][8][9][10][11][12][13]. Especially considered for the future innovation of novel ENMs, safety evaluations should be integrated proactively and efficiently already in the material and product development phase [9,12]. Summarized under a concept termed 'Toxicity Testing in the 21st Century' or 'Tox21', biochemical and cell-based in vitro assays coupled with bioinformatics and modelling-driven in silico assays are now considered key to transforming toxicology from a previously animal-based testing practice into a computational science built on systems biology [2,4,6,[9][10][11]13]. The resulting novel research field is variably termed 'systems toxicology', 'toxicogenomics' or 'computational toxicology' [3,4,8,14,15]. Such effort typically relies on informatics-driven and modelling-based analyses of results from several experimental systems and data-rich technologies for measuring pools of biological molecules, such as mRNAs, the overall aim being to interdependently analyse toxicity data and profiling results for understanding...

Variability in bioavailability of small molecular tyrosine kinase inhibitors

“…Source: EPARs and FDA label texts, additional sources indicated where appropriate. 1 No formal studies done. 2 Food serves as gastrointestinal protection.…”

“…In the first place new agents are designed fulfilling the requirements for personalized medicine. The advancement of techniques such as (cell-based) high throughput screening and the diverse possibilities in molecular modeling have lead to a therapeutic target-based drug discovery regime [1,2]. Along with the evolution of synthetical methods, compounds are found that are highly specific and demonstrate great affinity for molecular targets [3][4][5][6].…”

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

“…Accordingly, various modelling approaches have analysed ‘omics data sets to generate biomarker signatures or to characterize mechanisms of toxicity at a system-wide level, but suffer from the high dimensionality of omics data relative to sample number as well as problems in scaling across experimental systems (for example, hepatocyte cultures to liver) or species (for example, rat to human)111213141516. Complicating predictive biomarker discovery, different genes and gene families will unlikely have similar dose response, so dose-dependent transitions will influence the classification of (toxic) modes-of-action1718.…”

“…The US Broad Institute Connectivity Map (CMap) data set has thousands of gene expression profiles of mostly FDA approved drugs and has been used to connect small molecules, genes and diseases (‘connectivity mapping’) to define biologically similar compounds, including for the purpose of identifying toxic modes of action11152526272829. The US National Cancer Institute (NCI) 60 tumour cell line screen includes results on GI 50 (50% growth inhibition), total growth inhibition (TGI), and LC 50 (50% lethal concentration) for many compounds tested in the major CMap cell lines30.…”

Enriching Nanomaterials Omics Data: An Integration Technique to Generate Biological Descriptors

“…Cancer research in particular, is the area where a wealth of diverse omics data are produced, and one that has introduced integration methodologies for all available information to draw a more complete view of the biological mechanisms of the disease. Along these lines, a recently published review presents a cancer research‐inspired pipeline that applies high‐throughput and high‐content profiling technologies integrated with omics profiling for assessing toxicity of chemicals and engineered nanomaterials . The authors emphasize how, in analogy to biomarker discovery in cancer research, toxicology's aim is to identify genes or pathways from the “toxome” that could predict specific toxicity effects.…”