QSAR Models for Predicting in Vivo Aquatic Toxicity of Chlorinated Alkanes to Fish

Bond-based quadratic indices, new TOMOCOMD-CARDD molecular descriptors, and linear discriminant analysis (LDA) were used to discover novel lead trichomonacidals. The obtained LDA-based quantitative structure-activity relationships (QSAR) models, using nonstochastic and stochastic indices, were able to classify correctly 87.91% (87.50%) and 89.01% (84.38%) of the chemicals in training (test) sets, respectively. They showed large Matthews correlation coefficients of 0.75 (0.71) and 0.78 (0.65) for the training (test) sets, correspondingly. Later, both models were applied to the virtual screening of 21 chemicals to find new lead antitrichomonal agents. Predictions agreed with experimental results to a great extent because a correct classification for both models of 95.24% (20 of 21) of the chemicals was obtained. Of the 21 compounds that were screened and synthesized, 2 molecules (chemicals G-1, UC-245) showed high to moderate cytocidal activity at the concentration of 10 μg/ml, another 2 compounds (G-0 and CRIS-148) showed high cytocidal activity only at the concentration of 100 μg/ml, and the remaining chemicals (from CRIS-105 to CRIS-153, except CRIS-148) were inactive at these assayed concentrations. Finally, the best candidate, G-1 (cytocidal activity of 100% at 10 μg/ml) was in vivo assayed in ovariectomized Wistar rats achieving promising results as a trichomonacidal drug-like compound. (Journal of Biomolecular Screening 2008:785-794).

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Evaluation of CADASTER QSAR Models for the Aquatic Toxicity of (Benzo)triazoles and Prioritisation by Consensus Prediction

Alternatives to Laboratory Animals ◽

10.1177/026119291304100107 ◽

2013 ◽

Vol 41 (1) ◽

pp. 49-64 ◽

Cited By ~ 5

Author(s):

Stefano Cassani ◽

Simona Kovarich ◽

Ester Papa ◽

Partha Pratim Roy ◽

Magnus Rahmberg ◽

...

Keyword(s):

Aquatic Toxicity ◽

Consensus Prediction ◽

Qsar Models

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QSAR-ANALYSIS OF POLYSUBSTITUTED FUNCTIONALIZED AMINOTHIAZOLES WITH ANTIHYPERTENSIVE ACTIVITY

International Journal of Medicine and Medical Research ◽

10.11603/ijmmr.2413-6077.2019.2.10898 ◽

2020 ◽

Vol 5 (2) ◽

pp. 98-104

Author(s):

I. V. Drapak

Keyword(s):

Theoretical Basis ◽

Molecular Descriptors ◽

De Novo ◽

Antihypertensive Drugs ◽

Biologically Active ◽

Antihypertensive Activity ◽

Activity Parameter ◽

Qsar Analysis ◽

Qsar Models

Background. QSAR analysis is an important tool for the identification of pharmacophore fragments in biologically active substances and helps optimize the search for new effective drugs. Objective. The aim of the study was to determine the molecular descriptors for QSAR analysis of polysubstituted functionalized aminothiazoles as a theoretical basis for purposeful search de novo of potential antihypertensive drugs among the investigated compounds. Methods. Calculation of molecular descriptors and QSAR-models creation was carried out using the Hyper-Chem 7.5 and BuildQSAR packages. Results. The calculation of a number of molecular descriptors (electronic, steric, geometric, energy) was performed for 15 new polysubstituted functionalized aminothiazoles, with established in vivo antihypertensive activity. According to the calculated molecular descriptors and antihypertensive activity parameter, the QSAR models were derived НА = a + b ∙ X1 + c ∙ X2 + d ∙ X3 , where the activity parameter НА is antihypertensive activity and X1, X2, X3 are molecular descriptors. Conclusion. The study of ‘the structure - antihypertensive activity’ relationship for polysubstituted functionalized aminothiazoles was carried out. QSAR analysis revealed that volume, area, lipophilicity, dipole moment, refractivity, polarization of the molecule and energy of the lowest unoccupied molecular orbital have the most significant effect on antihypertensive activity. It was suggested that the attained QSAR-models may have antihypertensive activity within abovementioned row of compounds and can be considered as theoretical basis for de novo design of new potential antihypertensive drugs.

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QSAR prioritization of chemical inventories for endocrine disruptor testing

Pure and Applied Chemistry ◽

10.1351/pac200375112389 ◽

2003 ◽

Vol 75 (11-12) ◽

pp. 2389-2396 ◽

Cited By ~ 14

Author(s):

P. Schmieder ◽

Ovanes Mekenyan ◽

Steven Bradbury ◽

G. Veith

Keyword(s):

Binding Affinity ◽

Endocrine Disruption ◽

Flexible Structures ◽

3D Qsar ◽

In Vitro Assays ◽

First Choice ◽

Qsar Models ◽

Order Of Magnitude

Binding affinity between chemicals and the estrogen receptor (ER) serves as an indicator of the potential to cause endocrine disruption through this receptor-mediated endocrine pathway. Estimating ER-binding affinity is, therefore, one strategic approach to reducing the costs of screening chemicals for potential risks of endocrine disruption. While measuring ER binding with in vitro assays may be the first choice in prioritizing chemicals for additional in vitro or in vivo estrogenicity testing, the time and costs associated with screening thousands of chemicals is prohibitive. Recent advances in 3D modeling of the reactivity of flexible structures make in silico methods for estimating ER binding possible. One technique, the common reactivity pattern (COREPA) approach, was applied to development of reactivity patterns for ER relative binding affinity based on global nucleophilicity, interatomic distances between nucleophilic sites, and local electron donor capability of the nucleophilic sites. The reactivity patterns provided descriptor profiles for order-of-magnitude RBA ranges of training set chemicals. An exploratory expert system was subsequently developed to predict RBA and rank chemicals with respect to potential estrogenicity. A strategy is presented for extending initial exploratory 3D QSAR models beyond current training sets to increase applicability to more diverse structures in large chemical inventories.

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Rainbow trout primary epidermal cell proliferation as an indicator of aquatic toxicity: an in vitro/in vivo exposure comparison

Aquatic Toxicology ◽

10.1016/s0166-445x(01)00269-7 ◽

2002 ◽

Vol 60 (1-2) ◽

pp. 43-59 ◽

Cited By ~ 19

Author(s):

Michael Kilemade ◽

Maria Lyons-Alcantara ◽

Tina Rose ◽

Richard Fitzgerald ◽

Carmel Mothersill

Keyword(s):

Cell Proliferation ◽

Rainbow Trout ◽

Epidermal Cell ◽

Aquatic Toxicity ◽

Epidermal Cell Proliferation

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Gaultheria leucocarpa var. yunnanensis for Treating Rheumatoid Arthritis—An Assessment Combining Machine Learning–Guided ADME Properties Prediction, Network Pharmacology, and Pharmacological Assessment

Frontiers in Pharmacology ◽

10.3389/fphar.2021.704040 ◽

2021 ◽

Vol 12 ◽

Author(s):

Xiuhuan Wang ◽

Youyi Sun ◽

Ling Ling ◽

Xueyang Ren ◽

Xiaoyun Liu ◽

...

Keyword(s):

Rheumatoid Arthritis ◽

Machine Learning ◽

Methyl Salicylate ◽

Molecular Networks ◽

Network Pharmacology ◽

Rheumatic Arthritis ◽

Qsar Models ◽

Adme Properties

Background: Dianbaizhu (Gaultheria leucocarpa var. yunnanensis), a traditional Chinese/ethnic medicine (TC/EM), has been used to treat rheumatoid arthritis (RA) for a long time. The anti–rheumatic arthritis fraction (ARF) of G. yunnanensis has significant anti-inflammatory and analgesic activities and is mainly composed of methyl salicylate glycosides, flavonoids, organic acids, and others. The effective ingredients and rudimentary mechanism of ARF remedying RA have not been elucidated to date.Purpose: The aim of the present study is to give an insight into the effective components and mechanisms of Dianbaizhu in ameliorating RA, based on the estimation of the absorption, distribution, metabolism, and excretion (ADME) properties, analysis of network pharmacology, and in vivo and in vitro validations.Study design and methods: The IL-1β–induced human fibroblast-like synoviocytes of RA (HFLS-RA) model and adjuvant-induced arthritis in the rat model were adopted to assess the anti-RA effect of ARF. The components in ARF were identified by using UHPLC-LTQ-Orbitrap-MSn. The quantitative structure–activity relationship (QSAR) models were developed by using five machine learning algorithms, alone or in combination with genetic algorithms for predicting the ADME properties of ARF. The molecular networks and pathways presumably referring to the therapy of ARF on RA were yielded by using common databases and visible software, and the experimental validations of the key targets conducted in vitro.Results: ARF effectively relieved RA in vivo and in vitro. The five optimized QSAR models that were developed showed robustness and predictive ability. The characterized 48 components in ARF had good biological potency. Four key signaling pathways were obtained, which were related to both cytokine signaling and cell immune response. ARF suppressed IL-1β–induced expression of EGFR, MMP 9, IL2, MAPK14, and KDR in the HFLS-RA .Conclusions: ARF has good druggability and high exploitation potential. Methyl salicylate glycosides and flavonoids play essential roles in attuning RA. ARF may partially attenuate RA by regulating the expression of multi-targets in the inflammation–immune system. These provide valuable information to rationalize ARF and other TC/EMs in the treatment of RA.

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Review of the Aquatic Toxicity, Estrogenic Responses and Bioaccumulation of Alkylphenols and Alkylphenol Polyethoxylates

Water Quality Research Journal ◽

10.2166/wqrj.1999.005 ◽

1999 ◽

Vol 34 (1) ◽

pp. 123-178 ◽

Cited By ~ 229

Author(s):

Mark R. Servos

Keyword(s):

Chronic Toxicity ◽

Aquatic Toxicity ◽

Aquatic Organisms ◽

Test Methods ◽

Endocrine Function ◽

Toxicity Tests ◽

Few Data ◽

Available Information

Abstract A review of the available information on the toxicity and bioaccumulation of alkyphenols (AP) and their polyethoxylates (APE) and polyethoxycarboxylates (APEC) was conducted in support of their assessment as Priority Substances under the Canadian Environmental Protection Act. This included an examination of the acute and chronic toxicity of these compounds in a wide variety of aquatic organisms as well as an examination of their potential effects on endocrine function in fish and aquatic invertebrates. Although the data in the literature are scattered among many species, different test methods and chemicals, there is a consistent pattern in the toxicity. Nonylphenol (NP) and octylphenol (OP) are both acutely toxic to fish (17-3000 µg/L), invertebrates (20-3000 µg/L) and algae (27-2500 µg/L). In chronic toxicity tests no observable effect concentrations (NOEC) are as low as 6 mg/L in fish and 3.7 µg/L in invertebrates. There is an increase in the toxicity of both NPEs and OPEs with decreasing EO chain length. NPECs and OPECs are less toxic than corresponding APEs and have acute toxicities similar to APEs with 6-9 EO units. APs and APEs bind to the estrogen receptor resulting in the expression of several responses both in vitro and in vivo, including the induction of vitellogenin. The threshold for vitellogenin induction in fish is 10 µg/L for NP and 3 µg/L for OF. APEs also affect the growth of testes, alter normal steroid metabolism, disrupt smoltificaton and cause intersex (ova-testes) in fish. The available literature suggests that the ability of AP and APEs to bioaccumulate in aquatic biota in the environment is low to moderate, BCFs and BAFs in biota, including algae, plant, invertebrates and fish range from 0.9 to 3400. Although there are relatively few data available for OP or OPEs, their potential to bioaccumulate is expected to be similar to that of corresponding NP and NPEs.

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