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Computational Advances in Drug Safety: Systematic and Mapping Review of Knowledge Engineering Based Approaches

Pantelis Natsiavas, Andigoni Malousi, Cédric Bousquet, Marie-Christine Jaulent, Vassilis Koutkias
2019 Frontiers in Pharmacology  
Drug Safety (DS) is a domain with significant public health and social impact.  ...  Various exemplar applications were identified with promising results, e.g., improvement in Adverse Drug Reaction (ADR) prediction, detection of drug interactions, and novel ADE profiles related with specific  ...  Finally, a large-scale DDI prediction system relying on a large RDF knowledge base was developed upon vector-based as well as graph-based similarity metrics combined with terminological reasoning (Abdelaziz  ... 
doi:10.3389/fphar.2019.00415 pmid:31156424 pmcid:PMC6533857 fatcat:pljcyqcp6red7pdwa5burxgbx4

Automatic Filtering and Substantiation of Drug Safety Signals

Anna Bauer-Mehren, Erik M. van Mullingen, Paul Avillach, María del Carmen Carrascosa, Ricard Garcia-Serna, Janet Piñero, Bharat Singh, Pedro Lopes, José L. Oliveira, Gayo Diallo, Ernst Ahlberg Helgee, Scott Boyer (+5 others)
2012 PLoS Computational Biology  
We present a computational framework for the biological annotation of potential adverse drug reactions.  ...  Drug safety issues pose serious health threats to the population and constitute a major cause of mortality worldwide.  ...  Acknowledgments The authors wish to thank the NLMH for making UMLSH and MesHH available free of charge. Author Contributions  ... 
doi:10.1371/journal.pcbi.1002457 pmid:22496632 pmcid:PMC3320573 fatcat:brbigakv5fd4hfsvekgdtmopj4

Applying Semantic Web Technologies to Drug Safety Determination

S. Stephens, A. Morales, M. Quinlan
2006 IEEE Intelligent Systems  
Acknowledgments We thank the Oracle Spatial Development group for the implementation of the Oracle RDF Data Model. We also thank Otto Ritter of AstraZeneca for his assistance with this article.  ...  A 10 percent improvement in predicting failures before beginning large-scale Phase III clinical trials could save approximately $100 million in development costs. 1 In the face of these challenges, the  ...  of the inference process through XQuery, a standard XML technology.  ... 
doi:10.1109/mis.2006.2 fatcat:ugls4jsk2vcubihaytubtxjvye

Identification of drug candidates and repurposing opportunities through compound–target interaction networks

Anna Cichonska, Juho Rousu, Tero Aittokallio
2015 Expert Opinion on Drug Discovery  
Simon Anders for many useful discussions about different types of experimental assays and computational models.  ...  Here, we focus on computational models for drug compound identification using large-scale ligand--target interactions mapping in which the network perspective plays a central role.  ...  Computational prediction of compound--target interaction networks Network-based approaches to compound--target interaction inference hold a great promise to aid modern drug discovery.  ... 
doi:10.1517/17460441.2015.1096926 pmid:26429153 fatcat:vtz37pji6jcnlmcuw6k3v7t3kq

Recent developments in using mechanistic cardiac modelling for drug safety evaluation

Mark R. Davies, Ken Wang, Gary R. Mirams, Antonello Caruso, Denis Noble, Antje Walz, Thierry Lavé, Franz Schuler, Thomas Singer, Liudmila Polonchuk
2016 Drug Discovery Today  
) is a bold and welcome step in using computational tools for regulatory decision making.  ...  In this article we present how in silico cardiac modelling has matured into a decision making tool in drug discovery, contrast the different approaches being proposed and show the opportunities and challenges  ...  Such attempts to link cardiac safety endpoints across multiple scales have the potential to provide a more human-relevant assessment of proarrhythmic risk earlier in drug development.  ... 
doi:10.1016/j.drudis.2016.02.003 pmid:26891981 pmcid:PMC4909717 fatcat:jjgc7uchnzbwrpckuf22milgo4

An integrated approach for inference and mechanistic modeling for advancing drug development

Sergej V. Aksenov, Bruce Church, Anjali Dhiman, Anna Georgieva, Ramesh Sarangapani, Gabriel Helmlinger, Iya G. Khalil
2005 FEBS Letters  
This article discusses a two-pronged strategy for inferring biological interactions from large-scale multi-omic measurements and accounting for known biology via mechanistic dynamical simulations of pathways  ...  These approaches are already playing a role in driving drug development by providing a rational and systematic computational framework.  ...  Acknowledgments: The authors thank all of their colleagues at Gene Network Sciences with special thanks to Larry Felser, Basudev Chaudhuri, and Robert Miller for their invaluable software and computational  ... 
doi:10.1016/j.febslet.2005.02.012 pmid:15763567 fatcat:uttkmf2pwbf67hyeogh3kylqrm

Data Mining Methods to Detect Sentinel Associations and Their Application to Drug Safety Surveillance

Preciosa M. Coloma, Sandra de Bie
2014 Current Epidemiology Reports  
Such techniques have been used for a long time to support day-today operations of organizations handling large volumes of data, including banks, airlines, and retail organizations.  ...  This report provides an overview of data mining methods for detection of sentinel associations, with a specific focus on their applicability to surveillance of drug (or vaccine)related sentinel associations  ...  Additionally, drug-drug interactions may be identifiable.  ... 
doi:10.1007/s40471-014-0016-2 fatcat:pstsn5bxyzbntmis5varefm65m

Providing data science support for systems pharmacology and its implications to drug discovery

Thomas Hart, Lei Xie
2016 Expert Opinion on Drug Discovery  
deconvolution and personalized adverse drug reaction prediction.  ...  Areas covered-This paper critically reviews the impact of three fundamental concepts of data science on systems pharmacology: similarity inference, overfitting avoidance, and disentangling causality from  ...  Acknowledgements We sincerely thank the editor and the reviewers for their constructive suggestions This work was supported by the National Library of Medicine of the National Institute of Health under  ... 
doi:10.1517/17460441.2016.1135126 pmid:26689499 pmcid:PMC4988863 fatcat:ol5ra4b2efewtiietrqhcocqle

Bridging the Data Gap From in vitro Toxicity Testing to Chemical Safety Assessment Through Computational Modeling

Qiang Zhang, Jin Li, Alistair Middleton, Sudin Bhattacharya, Rory B. Conolly
2018 Frontiers in Public Health  
Chemical toxicity testing is moving steadily toward a human cell and organoid-based in vitro approach for reasons including scientific relevancy, efficiency, cost, and ethical rightfulness.  ...  Inferring human health risk from chemical exposure based on in vitro testing data is a challenging task, facing various data gaps along the way.  ...  As an essential computational modeling component in the TT21C framework, toxicokinetic IVIVE is finding its applications in both environmental chemical risk assessment and drug development based on results  ... 
doi:10.3389/fpubh.2018.00261 pmid:30255008 pmcid:PMC6141783 fatcat:fq4q3goptzfmzidi7lkjc2wqne

The role of drug profiles as similarity metrics: applications to repurposing, adverse effects detection and drug–drug interactions

Santiago Vilar, George Hripcsak
2016 Briefings in Bioinformatics  
In this article, we focus on the description of biological profiles for drugs that can be used as a system to compare similarity and create methods to predict and analyze drugs' actions.  ...  Explosion of the availability of big data sources along with the development in computational methods provides a useful framework to study drugs' actions, such as interactions with pharmacological targets  ...  The authors described a computational system to predict drug-target interactions on a network of 621 approved drugs and 893 target proteins by using different drug similarity inference methods, including  ... 
doi:10.1093/bib/bbw048 pmid:27273288 pmcid:PMC6078166 fatcat:yeoxzrbacnbuplvfppmazr6ziq

Quantitative Systems Pharmacology Models for a New International Cardiac Safety Regulatory Paradigm: An Overview of the Ci PA in silico Modeling Approach

Zhihua Li, Christine Garnett, David G. Strauss
2019 CPT: Pharmacometrics & Systems Pharmacology  
risk prediction.  ...  As a relatively new discipline, quantitative systems pharmacology has seen a significant increase in the application and utility of drug development.  ...  Although for some QSP model applications such as PBPK, an individual model is developed for a specific drug, other QSP models are intended to be used for many different drugs, similar to the CiPA TdP risk-prediction  ... 
doi:10.1002/psp4.12423 pmid:31044559 pmcid:PMC6617836 fatcat:o4zof63norhuxfvrtivrqgkg2e

Signal Detection in Pharmacovigilance: A Review of Informatics-driven Approaches for the Discovery of Drug-Drug Interaction Signals in Different Data Sources

Heba Ibrahim, A. Abdo, Ahmed M. El Kerdawy, A. Sharaf Eldin
2021 Artificial Intelligence in the Life Sciences  
The objective of this article is to review the application of informatics-driven approaches in the pharmacovigilance field with focus on drug-drug interaction (DDI) safety signal discovery using various  ...  This review is substantiated based on the fact that DDIs are contributing to a public health problem represented in 6-30% adverse drug event occurrences.  ...  In 2014 [111] , Cheng et al., adopted a heterogeneous network-assisted inference (HNAI) framework for large-scale prediction of ligand-receptor DDIs.  ... 
doi:10.1016/j.ailsci.2021.100005 fatcat:m324w243gfflxpiyq3g2qt7znq

Quantitative systems toxicology

Peter Bloomingdale, Conrad Housand, Joshua F. Apgar, Bjorn L. Millard, Donald E. Mager, John M. Burke, Dhaval K. Shah
2017 Current Opinion in Toxicology  
For the past 80 years, toxicity testing has consisted of evaluating the adverse effects of drugs in animals to predict human health risks. The U.S.  ...  The vision aims to reduce the use of animals and drug development costs through the integration of computational modeling and in vitro experimental methods that evaluates the perturbation of toxicity-related  ...  Cardiovascular safety Cardiovascular safety concerns are the leading cause of drugs withdrawn from the US market and a large reason for attrition in drug development [20] .  ... 
doi:10.1016/j.cotox.2017.07.003 pmid:29308440 pmcid:PMC5754001 fatcat:yynxvkpxtrfx5p5rvgu5ovj6nu

Data mining methodologies for pharmacovigilance

Mei Liu, Michael E. Matheny, Yong Hu, Hua Xu
2012 SIGKDD Explorations  
This article provides a general overview of the current computational methodologies applied for PhV at different stages of drug development and concludes with future directions and challenges.  ...  Thus, for medication safety, ADR monitoring is required for each drug throughout its life cycle, including early stages of drug design, different phases of clinical trials, and postmarketing surveillance  ...  [26] proposed a method that predicted pharmacological effects from chemical structures and then used the effect similarity to infer drug-target interactions. Hammann et al.  ... 
doi:10.1145/2408736.2408742 fatcat:kkrx4ixd6fhf5lwnjqme3lynqi

Predictive Systems Toxicology [chapter]

Narsis A. Kiani, Ming-Mei Shang, Hector Zenil, Jesper Tegner
2018 Msphere  
Yet, QSARs require a large dataset to produce robust statistics, which makes the framework less useful in applications where data is limited.  ...  SA based models flourished in toxicity prediction in almost all types of toxic endpoint [10, 11]. Several expert systems are available for toxicity prediction based on pre-built rules and SAs, e.g.  ...  In a more recent effort, machine-learning approaches have been used for larger-scale predictions of drug-target interactions.  ... 
doi:10.1007/978-1-4939-7899-1_25 pmid:29934910 fatcat:67btyybpxrc7dgu2d74fav7inq
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