Guidance Document on the Validation of (Quantitative) Structure-Activity Relationship [(Q)SAR] Models

,

doi:10.1787/9789264085442-en

Cited by 151 publications

(70 citation statements)

References 127 publications

(142 reference statements)

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“…13 A total of six PLS models have been developed. Here, we have used the spline option of the GFA algorithm in order to account for the presence of any non-linear relationship along with the linear variables.…”

Section: Developed Qsar Modelsmentioning

confidence: 99%

“…12 However, the in silico models should be developed in accordance with the guidelines of the Organization for Economic Cooperation and Development (OECD). 13 Vibrio fischeri (V. fischeri) is a Gram-negative, rod-shaped bacterium, and considered as an important member in a marine ecosystem.…”

Section: Introductionmentioning

confidence: 99%

“…We have also given serious attention to the applicability domain of the developed models during the prediction of external compounds as recommended by the OECD guidelines. 13 …”

Section: Introductionmentioning

confidence: 99%

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Development of predictive QSAR models for Vibrio fischeri toxicity of ionic liquids and their true external and experimental validation tests

et al. 2016

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Despite possessing an interesting chemical nature and tuneable physicochemical properties, ionic liquids (ILs) must have their ecotoxicity tested in order to be commercialized. The water solubility of ILs allows their easy access to the aquatic compartment of the ecosystem creating a potential hazard to aquatic organisms. Hence, it is relevant to design ionic liquids with lower toxicity while keeping the desired properties of interest. Considering the possibility of an enormous number of combinations of different cations and anions, a rational guidance for the structural design of ionic liquids is essential in order to prioritize the synthesis as well as testing of selected molecules only. Predictive in silico models, such as quantitative structure-activity relationship (QSAR) models, can play a pivotal role in exploring the important chemical attributes contributing to the response activity. These models may then lead to the design of novel ionic liquids. The present study aims at developing predictive QSAR models for the ecotoxicity of ionic liquids using the bacteria Vibrio fischeri as an indicator response species. Instead of a single model, here we have used multiple models to capture more complete structural information of ionic liquids for toxicity towards Vibrio fischeri. The derived chemical attributes have been implemented in designing new analogues, some of which have been synthesized and had their ecotoxicity tested for the same model organism. The predictive QSAR models reported here can be used for ecotoxicity prediction of new IL chemicals and for data-gap filling. Moreover, the synthesized low-toxic ILs could be considered for evaluation as well as for application in suitable processes serving the purpose of industry and academia.

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Section: Developed Qsar Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Development of predictive QSAR models for Vibrio fischeri toxicity of ionic liquids and their true external and experimental validation tests

et al. 2016

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“…[45] The model metrics were further assessed against the Organisation for Economic Co-operation and Development (OECD) guidelines for the calibration, internally validated (cross validation) and externally validated models (test set predictions). [26,46] For a dataset with n samples containing observations y 1 to y n , each associated with a predicted/modeled value f 1 to f n wherein y i is a sample within the dataset and f i is its associated predicted/modeled value, SS refers to sum of squares for the estimated (SS E ) and true values (SS T ) and summation is denoted by Σ:…”

Section: Model Performance Metricsmentioning

confidence: 99%

Cross‐Interaction Chromatography‐Based QSAR Model for Early‐Stage Screening to Facilitate Enhanced Developability of Monoclonal Antibody Therapeutics

Kizhedath

Karlberg

2019

Biotechnology Journal

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Monoclonal antibodies (mAbs) constitute a rapidly growing biopharmaceutical sector. However, their growth is impeded by developability issues such as polyspecificity and lack of solubility, which leads to attrition as well as manufacturing failures. In this study a multitool hybrid quantitative structure–activity relationship (QSAR) model development framework is described. This framework uses four novel datasets derived from the primary sequences of IgG1‐κ‐humanized mAbs with varying degrees of resolutions. Unsupervised pattern recognition is first performed on the descriptor sets to visualize any intrinsic property‐based clustering, followed by regression of descriptors against cross‐interaction chromatography (CIC) retention times. Model optimization is performed via unsupervised variable reduction followed by supervised variable selection. Finally, the models and datasets are benchmarked based on the regression model performance metrics such as R2, Q2, and RMSE. The results show that datasets containing localized descriptors rather than averaged value over the entire protein have better predictive performance of CIC retention behavior with R2 > 0.8 and RMSE < 0.3. Furthermore, the results indicate the physicochemical, electronic, and topological properties of hypervariable regions of antibodies that contribute most to the CIC retention times. The results of these studies could contribute to early‐stage screening and better design of mAbs.

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“…This leaving out and model building is continued until target activity/property of all compounds in the training is predicted. It is clear that Q 2 of a model can be lower than R 2 cal but according to the literature, a large difference between R 2 cal and Q 2 (bigger than 0.2-0.3) is a warning about occurrence of over-fitting [592,593]. According to the works of Golbraikh and Tropsha [594], a rule of thumb was proposed as the minimum acceptable criteria: Q 2 > 0.5 and R 2 > 0.…”

Section: Internal Validation By Cross Validationmentioning

confidence: 99%

Chemometrics tools in QSAR/QSPR studies: A historical perspective

Yousefinejad

Hemmateenejad

2015

Chemometrics and Intelligent Laboratory Systems

119

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Guidance Document on the Validation of (Quantitative) Structure-Activity Relationship [(Q)SAR] Models

Cited by 151 publications

References 127 publications

Development of predictive QSAR models for Vibrio fischeri toxicity of ionic liquids and their true external and experimental validation tests

Development of predictive QSAR models for Vibrio fischeri toxicity of ionic liquids and their true external and experimental validation tests

Cross‐Interaction Chromatography‐Based QSAR Model for Early‐Stage Screening to Facilitate Enhanced Developability of Monoclonal Antibody Therapeutics

Chemometrics tools in QSAR/QSPR studies: A historical perspective

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