Integrating Multiple Evidence Sources to Predict Adverse Drug Reactions Based on a Systems Pharmacology Model

Ds, Cao; Xiao, Nan; Yj, Li; Wb, Zeng; Yz, Liang; Ap, Lu; Qi, Xu; Af, Chen

doi:10.1002/psp4.12002

Cited by 32 publications

(12 citation statements)

References 36 publications

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“…To predict the potential toxicological effects of thousands of environmental chemicals, including drugs and drug candidates in early stage of drug development, alternative strategies are required to supplement traditional toxicity testing methods. A number of in silico approaches have been developed recently to predict adverse drug reactions using available public datasets of drugs 7 – 9 . Prediction models were built using chemical structure 10 – 12 , protein target information 13 , 14 , phenotypic data 7 , 15 , or combinations of different data types on drugs, with the application of various machine learning methods.…”

Section: Introductionmentioning

confidence: 99%

Expanding biological space coverage enhances the prediction of drug adverse effects in human using in vitro activity profiles

Huang

Xia

Sakamuru

et al. 2018

Sci Rep

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In vitro assay data have recently emerged as a potential alternative to traditional animal toxicity studies to aid in the prediction of adverse effects of chemicals on humans. Here we evaluate the data generated from a battery of quantitative high-throughput screening (qHTS) assays applied to a large and diverse collection of chemicals, including approved drugs, for their capacity in predicting human toxicity. Models were built with animal in vivo toxicity data, in vitro human cell-based assay data, as well as in combination with chemical structure and/or drug-target information to predict adverse effects observed for drugs in humans. Interestingly, we found that the models built with the human cell-based assay data performed close to those of the models based on animal in vivo toxicity data. Furthermore, expanding the biological space coverage of assays by including additional drug-target annotations was shown to significantly improve model performance. We identified a small set of targets, which, when added to the current suite of in vitro human cell-based assay data, result in models that greatly outperform those built with the existing animal toxicity data. Assays can be developed for this set of targets to screen compounds for construction of robust models for human toxicity prediction.

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Section: Introductionmentioning

confidence: 99%

Expanding biological space coverage enhances the prediction of drug adverse effects in human using in vitro activity profiles

Huang

Xia

Sakamuru

et al. 2018

Sci Rep

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“…The similarity-based approaches predict ADRs by looking for molecules that are structurally similar to the existing drugs [23, 40, 41]. Though they are relatively simple to implement, these methods are less effective if the existing and predicted drugs are diverse in structure.…”

Section: Related Workmentioning

confidence: 99%

Predicting adverse drug reactions through interpretable deep learning framework

et al. 2018

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BackgroundAdverse drug reactions (ADRs) are unintended and harmful reactions caused by normal uses of drugs. Predicting and preventing ADRs in the early stage of the drug development pipeline can help to enhance drug safety and reduce financial costs.MethodsIn this paper, we developed machine learning models including a deep learning framework which can simultaneously predict ADRs and identify the molecular substructures associated with those ADRs without defining the substructures a-priori.ResultsWe evaluated the performance of our model with ten different state-of-the-art fingerprint models and found that neural fingerprints from the deep learning model outperformed all other methods in predicting ADRs. Via feature analysis on drug structures, we identified important molecular substructures that are associated with specific ADRs and assessed their associations via statistical analysis.ConclusionsThe deep learning model with feature analysis, substructure identification, and statistical assessment provides a promising solution for identifying risky components within molecular structures and can potentially help to improve drug safety evaluation.

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“…More importantly, systematic investigation of generated knowledge in both the chemical and biological knowledge spaces is required, especially in the scenarios of identifying both new targets and their potential ligands, discovering potential biomarkers for complex diseases, understanding the mechanism of interactions, and discovering new regulatory mechanism etc. [ 3 – 8 ]. Therefore, it is very necessary to build informatics platforms for unified data or knowledge representation that can integrate the existing efforts from both communities.…”

Section: Introductionmentioning

confidence: 99%

PyBioMed: a python library for various molecular representations of chemicals, proteins and DNAs and their interactions

Dong¹,

Yao²,

Zhang³

et al. 2018

J Cheminform

113

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Background With the increasing development of biotechnology and informatics technology, publicly available data in chemistry and biology are undergoing explosive growth. Such wealthy information in these data needs to be extracted and transformed to useful knowledge by various data mining methods. Considering the amazing rate at which data are accumulated in chemistry and biology fields, new tools that process and interpret large and complex interaction data are increasingly important. So far, there are no suitable toolkits that can effectively link the chemical and biological space in view of molecular representation. To further explore these complex data, an integrated toolkit for various molecular representation is urgently needed which could be easily integrated with data mining algorithms to start a full data analysis pipeline.ResultsHerein, the python library PyBioMed is presented, which comprises functionalities for online download for various molecular objects by providing different IDs, the pretreatment of molecular structures, the computation of various molecular descriptors for chemicals, proteins, DNAs and their interactions. PyBioMed is a feature-rich and highly customized python library used for the characterization of various complex chemical and biological molecules and interaction samples. The current version of PyBioMed could calculate 775 chemical descriptors and 19 kinds of chemical fingerprints, 9920 protein descriptors based on protein sequences, more than 6000 DNA descriptors from nucleotide sequences, and interaction descriptors from pairwise samples using three different combining strategies. Several examples and five real-life applications were provided to clearly guide the users how to use PyBioMed as an integral part of data analysis projects. By using PyBioMed, users are able to start a full pipelining from getting molecular data, pretreating molecules, molecular representation to constructing machine learning models conveniently.ConclusionPyBioMed provides various user-friendly and highly customized APIs to calculate various features of biological molecules and complex interaction samples conveniently, which aims at building integrated analysis pipelines from data acquisition, data checking, and descriptor calculation to modeling. PyBioMed is freely available at http://projects.scbdd.com/pybiomed.html.

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Integrating Multiple Evidence Sources to Predict Adverse Drug Reactions Based on a Systems Pharmacology Model

Cited by 32 publications

References 36 publications

Expanding biological space coverage enhances the prediction of drug adverse effects in human using in vitro activity profiles

Expanding biological space coverage enhances the prediction of drug adverse effects in human using in vitro activity profiles

Predicting adverse drug reactions through interpretable deep learning framework

PyBioMed: a python library for various molecular representations of chemicals, proteins and DNAs and their interactions

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