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International Journal of Molecular... Jul 2022Pollution is defined as the presence in or introduction of a substance into the environment that has harmful or poisonous effects [...].
Pollution is defined as the presence in or introduction of a substance into the environment that has harmful or poisonous effects [...].
Topics: Biomarkers; Risk Assessment; Toxicogenetics
PubMed: 35955413
DOI: 10.3390/ijms23158280 -
Trends in Pharmacological Sciences Feb 2019Toxicogenomics (TGx) has contributed significantly to toxicology and now has great potential to support moves towards animal-free approaches in regulatory decision... (Review)
Review
Toxicogenomics (TGx) has contributed significantly to toxicology and now has great potential to support moves towards animal-free approaches in regulatory decision making. Here, we discuss in vitro TGx systems and their potential impact on risk assessment. We raise awareness of the rapid advancement of genomics technologies, which generates novel genomics features essential for enhanced risk assessment. We specifically emphasize the importance of reproducibility in utilizing TGx in the regulatory setting. We also highlight the role of machine learning (particularly deep learning) in developing TGx-based predictive models. Lastly, we touch on the topics of how TGx approaches could facilitate adverse outcome pathways (AOP) development and enhance read-across strategies to further regulatory application. Finally, we summarize current efforts to develop TGx for risk assessment and set out remaining challenges.
Topics: Animal Testing Alternatives; Animals; Humans; Machine Learning; Reproducibility of Results; Risk Assessment; Toxicogenetics
PubMed: 30594306
DOI: 10.1016/j.tips.2018.12.001 -
Biomedicine & Pharmacotherapy =... Jul 2023More information about a person's genetic makeup, drug response, multi-omics response, and genomic response is now available leading to a gradual shift towards... (Review)
Review
More information about a person's genetic makeup, drug response, multi-omics response, and genomic response is now available leading to a gradual shift towards personalized treatment. Additionally, the promotion of non-animal testing has fueled the computational toxicogenomics as a pivotal part of the next-gen risk assessment paradigm. Artificial Intelligence (AI) has the potential to provid new ways analyzing the patient data and making predictions about treatment outcomes or toxicity. As personalized medicine and toxicogenomics involve huge data processing, AI can expedite this process by providing powerful data processing, analysis, and interpretation algorithms. AI can process and integrate a multitude of data including genome data, patient records, clinical data and identify patterns to derive predictive models anticipating clinical outcomes and assessing the risk of any personalized medicine approaches. In this article, we have studied the current trends and future perspectives in personalized medicine & toxicology, the role of toxicogenomics in connecting the two fields, and the impact of AI on personalized medicine & toxicology. In this work, we also study the key challenges and limitations in personalized medicine, toxicogenomics, and AI in order to fully realize their potential.
Topics: Humans; Artificial Intelligence; Precision Medicine; Toxicogenetics; Algorithms; Technology
PubMed: 37121152
DOI: 10.1016/j.biopha.2023.114784 -
Advances in Clinical and Experimental... Aug 2023The majority of Americans, accounting for 51% of the population, take 2 or more drugs daily. Unfortunately, nearly 100,000 people die annually as a result of adverse...
The majority of Americans, accounting for 51% of the population, take 2 or more drugs daily. Unfortunately, nearly 100,000 people die annually as a result of adverse drug reactions (ADRs), making it the 4th most common cause of mortality in the USA. Drug-drug interactions (DDls) and their impact on patients represent critical challenges for the healthcare system. To reduce the incidence of ADRs, this study focuses on identifying DDls using a machine-learning approach. Drug-related information was obtained from various free databases, including DrugBank, BioGRID and Comparative Toxicogenomics Database. Eight similarity matrices between drugs were created as covariates in the model in order to assess their infiuence on DDls. Three distinct machine learning algorithms were considered, namely, logistic regression (LR), extreme Gradient Boosting (XGBoost) and neural network (NN). Our study examined 22 notable drugs and their interactions with 841 other drugs from DrugBank. The accuracy of the machine learning approaches ranged from 68% to 78%, while the F1 scores ranged from 78% to 83%. Our study indicates that enzyme and target similarity are the most significant parameters in identifying DDls. Finally, our data-driven approach reveals that machine learning methods can accurately predict DDls and provide additional insights in a timely and cost-effective manner.
Topics: Humans; Drug Interactions; Drug-Related Side Effects and Adverse Reactions; Algorithms; Databases, Factual; Machine Learning
PubMed: 37589227
DOI: 10.17219/acem/169852 -
Frontiers in Genetics 2019
PubMed: 30891065
DOI: 10.3389/fgene.2019.00165 -
Molecular Omics Aug 2018The toxicogenomics field aims to understand and predict toxicity by using 'omics' data in order to study systems-level responses to compound treatments. In recent years... (Review)
Review
The toxicogenomics field aims to understand and predict toxicity by using 'omics' data in order to study systems-level responses to compound treatments. In recent years there has been a rapid increase in publicly available toxicological and 'omics' data, particularly gene expression data, and a corresponding development of methods for its analysis. In this review, we summarize recent progress relating to the analysis of RNA-Seq and microarray data, review relevant databases, and highlight recent applications of toxicogenomics data for understanding and predicting compound toxicity. These include the analysis of differentially expressed genes and their enrichment, signature matching, methods based on interaction networks, and the analysis of co-expression networks. In the future, these state-of-the-art methods will likely be combined with new technologies, such as whole human body models, to produce a comprehensive systems-level understanding of toxicity that reduces the necessity of in vivo toxicity assessment in animal models.
Topics: Animals; Databases, Genetic; Drug Discovery; Gene Expression Profiling; Gene Expression Regulation; Gene Regulatory Networks; Humans; Pharmacogenomic Testing; Systems Biology; Toxicity Tests; Toxicogenetics
PubMed: 29917034
DOI: 10.1039/c8mo00042e -
F1000Research 2021Nanotoxicology is a relatively new field of research concerning the study and application of nanomaterials to evaluate the potential for harmful effects in parallel with...
Nanotoxicology is a relatively new field of research concerning the study and application of nanomaterials to evaluate the potential for harmful effects in parallel with the development of applications. Nanotoxicology as a field spans materials synthesis and characterisation, assessment of fate and behaviour, exposure science, toxicology / ecotoxicology, molecular biology and toxicogenomics, epidemiology, safe and sustainable by design approaches, and chemoinformatics and nanoinformatics, thus requiring scientists to work collaboratively, often outside their core expertise area. This interdisciplinarity can lead to challenges in terms of interpretation and reporting, and calls for a platform for sharing of best-practice in nanotoxicology research. The F1000Research Nanotoxicology collection, introduced via this editorial, will provide a place to share accumulated best practice, via original research reports including no-effects studies, protocols and methods papers, software reports and living systematic reviews, which can be updated as new knowledge emerges or as the domain of applicability of the method, model or software is expanded. This editorial introduces the Nanotoxicology Collection in . The aim of the collection is to provide an open access platform for nanotoxicology researchers, to support an improved culture of data sharing and documentation of evolving protocols, biological and computational models, software tools and datasets, that can be applied and built upon to develop predictive models and move towards nanotoxicology and nanoinformatics. Submissions will be assessed for fit to the collection and subjected to the F1000Research open peer review process.
Topics: Nanostructures; Research Design; Software
PubMed: 34853679
DOI: 10.12688/f1000research.75113.1 -
Frontiers in Bioscience (Landmark... Oct 2022Environmental toxicogenomics aims to collect, analyze and interpret data on changes in gene expression and protein activity resulting from exposure to toxic substances... (Review)
Review
Environmental toxicogenomics aims to collect, analyze and interpret data on changes in gene expression and protein activity resulting from exposure to toxic substances using high-performance omics technologies. Molecular profiling methods such as genomics, transcriptomics, proteomics, metabolomics, and bioinformatics techniques, permit the simultaneous analysis of a multitude of gene variants in an organism exposed to toxic agents to search for genes prone to damage, detect patterns and mechanisms of toxicity, and identify specific gene expression profiles that can provide biomarkers of exposure and risk. Compared to previous approaches to measuring molecular changes caused by toxicants, toxicogenomic technologies can improve environmental risk assessment while reducing animal studies. We discuss the prospects and limitations of converting omic datasets into valuable information, focusing on assessing the risks of mixed toxic substances to the environment and human health.
Topics: Animals; Humans; Toxicogenetics; Genomics; Proteomics; Computational Biology; Metabolomics
PubMed: 36336867
DOI: 10.31083/j.fbl2710294 -
Frontiers in Genetics 2020Phthalates are esters of phthalic acid which are used in cosmetics and other daily personal care products. They are also used in polyvinyl chloride (PVC) plastics to... (Review)
Review
Phthalates are esters of phthalic acid which are used in cosmetics and other daily personal care products. They are also used in polyvinyl chloride (PVC) plastics to increase durability and plasticity. Phthalates are not present in plastics by covalent bonds and thus can easily leach into the environment and enter the human body by dermal absorption, ingestion, or inhalation. Several and studies suggest that phthalates can act as endocrine disruptors and cause moderate reproductive and developmental toxicities. Furthermore, phthalates can pass through the placental barrier and affect the developing fetus. Thus, phthalates have ubiquitous presence in food and environment with potential adverse health effects in humans. This review focusses on studies conducted in the field of toxicogenomics of phthalates and discusses possible transgenerational and multigenerational effects caused by phthalate exposure during any point of the life-cycle.
PubMed: 32435260
DOI: 10.3389/fgene.2020.00405 -
Protein & Cell May 2018Inter-individual heterogeneity in drug response is a serious problem that affects the patient's wellbeing and poses enormous clinical and financial burdens on a societal... (Review)
Review
Inter-individual heterogeneity in drug response is a serious problem that affects the patient's wellbeing and poses enormous clinical and financial burdens on a societal level. Pharmacogenomics has been at the forefront of research into the impact of individual genetic background on drug response variability or drug toxicity, and recently the gut microbiome, which has also been called the second genome, has been recognized as an important player in this respect. Moreover, the microbiome is a very attractive target for improving drug efficacy and safety due to the opportunities to manipulate its composition. Pharmacomicrobiomics is an emerging field that investigates the interplay of microbiome variation and drugs response and disposition (absorption, distribution, metabolism and excretion). In this review, we provide a historical overview and examine current state-of-the-art knowledge on the complex interactions between gut microbiome, host and drugs. We argue that combining pharmacogenomics and pharmacomicrobiomics will provide an important foundation for making major advances in personalized medicine.
Topics: Anti-Infective Agents; Biodiversity; Humans; Microbiota; Pharmacogenetics; Precision Medicine; Toxicogenetics
PubMed: 29705929
DOI: 10.1007/s13238-018-0547-2