TOXIF.PTML models in toxicology, ecotoxicology, and environmental sciences

Classic Machine Learning (ML) models can be transformed into read-across Quantitative Structure-Toxicity Relationships (QSTR). These are multi-objective models (multi-output models able to optimize multiple properties IC50, CC50, TC50, etc.). They are also multi-level (proteins, cells, organisms, ecosystem, etc.) and multi-label models (multi-species, cell lines, techniques, sensors, etc.). One way to do that is using Information Fusion Perturbation Theory Machine Learning (IFPTML) algorithms coined by us as (TOXIF.PTML). See some papers from CHEM.PTML LAB in this area. At follows we summarize for you some of the papers about the more recent/interesting TOXIF.PTML models developed by our group. Read more about IFPTML models in our group in newsletter: https://www.dhirubhai.net/newsletters/chem-ptml-lab-newsletter-6924816402038071296/

<EU> Jarraian, gure taldeak garatu dituen TOXIF.PTML ereduei buruzko artikuluo batzuk laburbiltzen dizuegu. Irakurri gehiago IFPTML ereduei buruz gure CHEM.PTML taldeko buletinean:

<SP> A continuación, resumimos algunos artículos sobre los modelos TOXIF.PTML que nuestro equipo ha desarrollado. Lea más sobre los modelos IFPTML en nuestro boletín.

Paper 1.

CHEM.PTML LAB NEWS: American Chemical Society Published: Multi-Endpoint Acute hashtag#Toxicity hashtag#Assessment of Organic Compounds Using Large-Scale hashtag#MachineLearning Modeling. by Amir Daghighi, Gerardo M. Casanola-Martin, Kweeni Iduoku, Hrvoje Kusic, Humbert G. Díaz, Prof.,?and Bakhtiyor Rasulev, Prof.*

Linkedin post: https://www.dhirubhai.net/posts/humbertgdiaz_toxicity-assessment-machinelearning-activity-7201301903639588866-f2bz?utm_source=share&utm_medium=member_desktop

See Prof. Rasulev's linkedin announcing post: https://lnkd.in/dRSrhV5S, Thank y'all!!! ??

Citation: Environ. Sci. Technol.?2024, May 26, doi: 10.1021/acs.est.4c01017.

Paper link: https://doi.org/10.1021/acs.est.4c01017

Abstract: In recent years, alternative animal testing methods such as computational and machine learning approaches have become increasingly crucial for toxicity testing. However, the complexity and scarcity of available biomedical data challenge the development of predictive models. Combining nonlinear machine learning together with multicondition descriptors offers a solution for using data from various assays to create a robust model. This work applies multicondition descriptors (MCDs) to develop a QSTR (Quantitative Structure–Toxicity Relationship) model based on a large toxicity data set comprising more than 80,000 compounds and 59 different end points (122,572 data points). The prediction capabilities of developed single-task multi-end point machine learning models as well as a novel data analysis approach with the use of Convolutional Neural Networks (CNN) are discussed. The results show that using MCDs significantly improves the model and using them with CNN-1D yields the best result (R2train = 0.93, R2ext = 0.70). Several structural features showed a high level of contribution to the toxicity, including van der Waals surface area (VSA), number of nitrogen-containing fragments (nN+), presence of S–P fragments, ionization potential, and presence of C–N fragments. The developed models can be very useful tools to predict the toxicity of various compounds under different conditions, enabling quick toxicity assessment of new compounds.

Paper 2.

CHEM.PTML LAB PAPER: Elsevier Published our Work: hashtag#Prediction of acute hashtag#Toxicity of hashtag#Pesticides for?Americamysis bahia?using linear and nonlinear hashtag#QSTR hashtag#modelling approaches. Authors: Karel Dieguez Santana (a), Manuel Mesias Nachimba-Mayanchi (b), Amilkar Yudier Puris (c), Roldán Torres Gutiérrez (d), Humbert G. Díaz, Prof. (e, f).

Linkedin post: https://www.dhirubhai.net/posts/humbertgdiaz_prediction-toxicity-pesticides-activity-6965353668577488896-LixV?utm_source=share&utm_medium=member_desktop

Citation: Environ. Res. (2022) 113984, doi: 10.1016/j.envres.2022.113984.

Paper link: https://doi.org/10.1016/j.envres.2022.113984

Abstract: Globally, pesticides are toxic substances with wide applications. However, the widespread use of pesticides has received increasing attention from regulatory agencies due to their various acute and chronic effects on multiple organisms. In this study, Quantitative Structure-Toxicity Relationship (QSTR) models were established using Multiple Linear Regression (MLR) and five Machine Learning (ML) algorithms to predict pesticide toxicity in Americamysis bahia. The most in?uential descriptors included in the MLR model are RBF, JGI2, nCbH, nRCOOR, nRSR, nPO4 and ‘Cl-090’, with positive contributions to the dependent variable (negative decimal logarithm of median lethal concentration at 96-h). The Random Forest (RF) regression model was superior amongst the five ML models. We observed higher values of R2 (0.812) and lower values of RMSE (0.595) and MAE (0.462) in the cross-validation training set and external validation set. Similarly, this study had a high level of fitness and was internally robust and externally predictive compared to models presented in similar studies. The results suggest that the developed QSTR models are suitable for reliably predicting the aquatic toxicity of structurally diverse pesticides and can be used for screening, prioritising new pesticides, filling data gaps and overcoming the limitations of in vivo and in vitro tests.

Affiliations: (a) UPV/EHU :: FCT-ZTF, Universidad del País Vasco/Euskal Herriko Unibertsitatea, hashtag#BasqueCountry, (b) Universidad Técnica Estatal de Quevedo, (c) Universidad Regional Amazónica Ikiam hashtag#Ecuador, (d) Fundación Biofísica Bizkaia / Biofisika Bizkaia Fundazioa, (f) Ikerbasque, Basque Foundation for Science.

Paper 3.

Computational tool for risk assessment of nanomaterials: novel QSTR-perturbation model for simultaneous prediction of ecotoxicity and cytotoxicity of uncoated and coated nanoparticles under multiple experimental conditions. by Kleandrova VV, Luan F, González-Díaz H, Juan M. Ruso , Alejandro Speck-Planche , Natalia Cordeiro

Citation: Environ Sci Technol. 2014 Dec 16;48(24):14686-94. doi: 10.1021/es503861x. Epub 2014 Nov 21.PMID: 25384130.

Paper link: https://pubs.acs.org/doi/10.1021/es503861x

Abstract: Nanomaterials have revolutionized modern science and technology due to their multiple applications in engineering, physics, chemistry, and biomedicine. Nevertheless, the use and manipulation of nanoparticles (NPs) can bring serious damages to living organisms and their ecosystems. For this reason, ecotoxicity and cytotoxicity assays are of special interest in order to determine the potential harmful effects of NPs. Processes based on ecotoxicity and cytotoxicity tests can significantly consume time and financial resources. In this sense, alternative approaches such as quantitative structure–activity/toxicity relationships (QSAR/QSTR) modeling have provided important insights for the better understanding of the biological behavior of NPs that may be responsible for causing toxicity. Until now, QSAR/QSTR models have predicted ecotoxicity or cytotoxicity separately against only one organism (bioindicator species or cell line) and have not reported information regarding the quantitative influence of characteristics other than composition or size. In this work, we developed a unified QSTR-perturbation model to simultaneously probe ecotoxicity and cytotoxicity of NPs under different experimental conditions, including diverse measures of toxicities, multiple biological targets, compositions, sizes and conditions to measure those sizes, shapes, times during which the biological targets were exposed to NPs, and coating agents. The model was created from 36488 cases (NP–NP pairs) and exhibited accuracies higher than 98% in both training and prediction sets. The model was used to predict toxicities of several NPs that were not included in the original data set. The results of the predictions suggest that the present QSTR-perturbation model can be employed as a highly promising tool for the fast and efficient assessment of ecotoxicity and cytotoxicity of NPs.

Paper 4.

Computational ecotoxicology: simultaneous prediction of ecotoxic effects of nanoparticles under different experimental conditions. by Kleandrova VV, Luan F, González-Díaz H, Ruso JM, Melo A, Speck-Planche A, Cordeiro MN.

Citation: Environ Int. 2014 Dec;73:288-94. doi: 10.1016/j.envint.2014.08.009. Epub 2014 Aug 29.PMID: 25173945

Paper link: https://doi.org/10.1016/j.envint.2014.08.009

Abstract: Nanotechnology has brought great advances to many fields of modern science. A manifold of applications of nanoparticles have been found due to their interesting optical, electrical, and biological/chemical properties. However, the potential toxic effects of nanoparticles to different ecosystems are of special concern nowadays. Despite the efforts of the scientific community, the mechanisms of toxicity of nanoparticles are still poorly understood. Quantitative-structure activity/toxicity relationships (QSAR/QSTR) models have just started being useful computational tools for the assessment of toxic effects of nanomaterials. But most QSAR/QSTR models have been applied so far to predict ecotoxicity against only one organism/bio-indicator such as Daphnia magna. This prevents having a deeper knowledge about the real ecotoxic effects of nanoparticles, and consequently, there is no possibility to establish an efficient risk assessment of nanomaterials in the environment. In this work, a perturbation model for nano-QSAR problems is introduced with the aim of simultaneously predicting the ecotoxicity of different nanoparticles against several assay organisms (bio-indicators), by considering also multiple measures of ecotoxicity, as well as the chemical compositions, sizes, conditions under which the sizes were measured, shapes, and the time during which the diverse assay organisms were exposed to nanoparticles. The QSAR-perturbation model was derived from a database containing 5520 cases (nanoparticle–nanoparticle pairs), and it was shown to exhibit accuracies of ca. 99% in both training and prediction sets. In order to demonstrate the practical applicability of our model, three different nickel-based nanoparticles (Ni) with experimental values reported in the literature were predicted. The predictions were found to be in very good agreement with the experimental evidences, confirming that Ni-nanoparticles are not ecotoxic when compared with other nanoparticles. The results of this study thus provide a single valuable tool toward an efficient prediction of the ecotoxicity of nanoparticles under multiple experimental conditions.