CIIPro: a new read-across portal to fill data gaps using public large-scale chemical and biological data

Russo, Daniel P.; Kim, Marlene T.; Wang, Wenyi; Pinolini, Daniel C.; Shende, Sunil; Strickland, Judy; Hartung, Thomas; Zhu, Hao

doi:10.1093/bioinformatics/btw640

Cited by 29 publications

(35 citation statements)

References 11 publications

(21 reference statements)

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“…To form a training set of compounds for modeling, public in vitro bioassay data for 7,385 compounds were extracted from PubChem (https://pubchem.ncbi.nlm.nih.gov/) using an automatic data mining portal (http://ciipro.rutgers.edu/) (Russo et al. 2017; Zhang et al.…”

Section: Methodsmentioning

confidence: 99%

Nonanimal Models for Acute Toxicity Evaluations: Applying Data-Driven Profiling and Read-Across

Russo

Strickland

Karmaus

et al. 2019

Environ Health Perspect

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Background:Low-cost, high-throughput in vitro bioassays have potential as alternatives to animal models for toxicity testing. However, incorporating in vitro bioassays into chemical toxicity evaluations such as read-across requires significant data curation and analysis based on knowledge of relevant toxicity mechanisms, lowering the enthusiasm of using the massive amount of unstructured public data.Objective:We aimed to develop a computational method to automatically extract useful bioassay data from a public repository (i.e., PubChem) and assess its ability to predict animal toxicity using a novel bioprofile-based read-across approach.Methods:A training database containing 7,385 compounds with diverse rat acute oral toxicity data was searched against PubChem to establish in vitro bioprofiles. Using a novel subspace clustering algorithm, bioassay groups that may inform on relevant toxicity mechanisms underlying acute oral toxicity were identified. These bioassays groups were used to predict animal acute oral toxicity using read-across through a cross-validation process. Finally, an external test set of over 600 new compounds was used to validate the resulting model predictivity.Results:Several bioassay clusters showed high predictivity for acute oral toxicity (positive prediction rates range from 62–100%) through cross-validation. After incorporating individual clusters into an ensemble model, chemical toxicants in the external test set were evaluated for putative acute toxicity (positive prediction rate equal to 76%). Additionally, chemical fragment–in vitro–in vivo relationships were identified to illustrate new animal toxicity mechanisms.Conclusions:The in vitro bioassay data-driven profiling strategy developed in this study meets the urgent needs of computational toxicology in the current big data era and can be extended to develop predictive models for other complex toxicity end points. https://doi.org/10.1289/EHP3614

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Section: Methodsmentioning

confidence: 99%

Nonanimal Models for Acute Toxicity Evaluations: Applying Data-Driven Profiling and Read-Across

Russo

Strickland

Karmaus

et al. 2019

Environ Health Perspect

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“…The confidence score is an estimate of the reliability of the calculated biological similarity, the higher the score, the more reliable the biological similarity value. The confidence score represents the number of assays that have results for both chemicals in a given pair but gives less weight to the assays that only have inactive results for both chemicals (discussed in more detail in Russo et al, 2017). Biological nearest neighbors are then calculated by the WEBS tool by setting suitable parameter cutoffs for both the biological similarity and the confidence scores.…”

Section: Toxreadmentioning

confidence: 99%

“…chemicals in the training set structurally similar to the target chemical in the test set on the basis of MACCS keys) of the predicted chemicals can be visualised in a similarity plot (Figure 10 reflects an example). Biological nearest neighbours are presented on the right hand side of the plot whereas chemical nearest neighbours are on the left of the target chemicals' predicted activity (Russo et al, 2017).…”

Section: Toxreadmentioning

confidence: 99%

Navigating through the minefield of read-across tools: A review of in silico tools for grouping

Patlewicz

Helman

Pradeep

et al. 2017

Computational Toxicology

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Read-across is a popular data gap filling technique used within analogue and category approaches for regulatory purposes. In recent years there have been many efforts focused on the challenges involved in read-across development, its scientific justification and documentation. Tools have also been developed to facilitate read-across development and application. Here, we describe a number of publicly available read-across tools in the context of the category/analogue workflow and review their respective capabilities, strengths and weaknesses. No single tool addresses all aspects of the workflow. We highlight how the different tools complement each other and some of the opportunities for their further development to address the continued evolution of read-across.

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“…To extract the biological features from chemical representations in QSAR, there have been attempted to use biochemical assay information as descriptors [21]- [23]. Recently, experimental in vivo or in vitro test results have been introduced as descriptors to fill missing biological values [24]- [26].…”

Section: Introductionmentioning

confidence: 99%

Extension of pQSAR: Ensemble Model Generated by Random Forest and Partial Least Squares Regressions

et al. 2020

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Quantitative structure-activity relationship (QSAR) regression models are mathematical ones which relate the structural properties of chemicals to the potencies of the biological activities of the chemicals. In QSAR models, the physical and chemical information of the molecules is encoded into quantitative numbers called descriptors. Recently, experimental test results (profiles) have been used as descriptors of chemicals. Profile QSAR 2.0 (pQSAR) model suggested by Martin et. al, is a multitask, two step machine learning prediction method with a combination of random forest regressions (RFRs) and partial least squares regression (PLSR). In pQSAR model, one fills the profile table's missing values with RFRs and then builds PLSR using the profile predictions. Note that in the second step of the pQSAR method, PLSR's predictor variables are profiles; so activity values, and the response variables are also activity values. Thus we can use the PLSRs to update the profile table and then repeat the second step. In this work, we propose an extended model of pQSAR generated by RFRs and PLSRs. Experiment of updating the given full initially predicted profile table by two kinds of prediction models, RFRs and PLSRs, has been conducted iteratively for the PKIS and ChEMBL data sets. Even though prediction performance of individual combination of RFRs and PLSRs varies, the average of the all possible predicted profile tables for given iteration shows better performance. This ensemble model has better prediction performance in sense of Pearson's R 2 compared to that of the pQSAR model.

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CIIPro: a new read-across portal to fill data gaps using public large-scale chemical and biological data

Cited by 29 publications

References 11 publications

Nonanimal Models for Acute Toxicity Evaluations: Applying Data-Driven Profiling and Read-Across

Nonanimal Models for Acute Toxicity Evaluations: Applying Data-Driven Profiling and Read-Across

Navigating through the minefield of read-across tools: A review of in silico tools for grouping

Extension of pQSAR: Ensemble Model Generated by Random Forest and Partial Least Squares Regressions

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