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Xu B, Cui W, Tao L, Yang L, Zhao X. Risk mitigation strategy and mechanism analysis of neonicotinoid pesticides on earthworms. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 347:123719. [PMID: 38458525 DOI: 10.1016/j.envpol.2024.123719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Revised: 03/03/2024] [Accepted: 03/04/2024] [Indexed: 03/10/2024]
Abstract
Neonicotinoid insecticides (NNIs) are a new class of widely used insecticides with certain risks to non-target organisms, like earthworms. The gray correlation method was used to calculate the comprehensive risk effect value of acute toxicity (LC50) and bioaccumulation (logKow) of NNIs on earthworms. A comprehensive effects three-dimensional quantitative structure-activity relationship (3D-QSAR) model was constructed, using NNIs molecular structures and the comprehensive effect value as the independent and dependent variables, respectively. One of the representatives guadipyr (GUA) was selected as the template molecule for the molecular design and modification. A total of 63 NNIs alternatives were designed with a reduced comprehensive value higher than 10%, and as high as 42%. After screening, 15 NNIs alternatives were screened with decreased acute toxicity to earthworms, bioaccumulation effects and improved functional property. The calculated primary acute risk quotient of earthworms shows that the designed NNIs alternatives have lower earthworm risks (reduction of 70.48-99.99%). Results also found that the electronic, geometric and topological parameters of NNIs are the key descriptors that affect NNIs alternatives' toxicity. The number of hydrophobic interaction amino acid residues in NNIs molecules also contributes to the acute toxicity and the bioaccumulation of NNIs alternatives on earthworms. This study aims to design and screen functionally improved and environmentally friendly NNIs alternatives that have low risk to earthworms and provide theoretical methods and new ideas for the risk control and development of pesticides represented by NNIs.
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Affiliation(s)
- Bohan Xu
- College of Resources and Environment, Jilin Agricultural University, Changchun, 130118, China.
| | - Weihan Cui
- College of Resources and Environment, Jilin Agricultural University, Changchun, 130118, China.
| | - Li Tao
- College of Resources and Environment, Jilin Agricultural University, Changchun, 130118, China.
| | - Luze Yang
- College of Resources and Environment, Jilin Agricultural University, Changchun, 130118, China.
| | - Xingmin Zhao
- College of Resources and Environment, Jilin Agricultural University, Changchun, 130118, China.
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2
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Maeshima T, Yoshida S, Watanabe M, Itagaki F. Prediction model for milk transfer of drugs by primarily evaluating the area under the curve using QSAR/QSPR. Pharm Res 2023; 40:711-719. [PMID: 36720832 PMCID: PMC10036427 DOI: 10.1007/s11095-023-03477-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Accepted: 01/25/2023] [Indexed: 02/02/2023]
Abstract
PURPOSE Information on milk transferability of drugs is important for patients who wish to breastfeed. The purpose of this study is to develop a prediction model for milk-to-plasma drug concentration ratio based on area under the curve (M/PAUC). The quantitative structure-activity/property relationship (QSAR/QSPR) approach was used to predict compounds involved in active transport during milk transfer. METHODS We collected M/P ratio data from literature, which were curated and divided into M/PAUC ≥ 1 and M/PAUC < 1. Using the ADMET Predictor® and ADMET Modeler™, we constructed two types of binary classification models: an artificial neural network (ANN) and a support vector machine (SVM). RESULTS M/P ratios of 403 compounds were collected, M/PAUC data were obtained for 173 compounds, while 230 compounds only had M/Pnon-AUC values reported. The models were constructed using 129 of the 173 compounds, excluding colostrum data. The sensitivity of the ANN model was 0.969 for the training set and 0.833 for the test set, while the sensitivity of the SVM model was 0.971 for the training set and 0.667 for the test set. The contribution of the charge-based descriptor was high in both models. CONCLUSIONS We built a M/PAUC prediction model using QSAR/QSPR. These predictive models can play an auxiliary role in evaluating the milk transferability of drugs.
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Affiliation(s)
- Tae Maeshima
- Department of Clinical & Pharmaceutical Sciences, Faculty of Pharma Science, Teikyo University, Itabashi-Ku, Tokyo, 173-8605, Japan
| | - Shin Yoshida
- Department of Clinical & Pharmaceutical Sciences, Faculty of Pharma Science, Teikyo University, Itabashi-Ku, Tokyo, 173-8605, Japan
| | - Machiko Watanabe
- Department of Clinical & Pharmaceutical Sciences, Faculty of Pharma Science, Teikyo University, Itabashi-Ku, Tokyo, 173-8605, Japan
| | - Fumio Itagaki
- Department of Clinical & Pharmaceutical Sciences, Faculty of Pharma Science, Teikyo University, Itabashi-Ku, Tokyo, 173-8605, Japan.
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3
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Vilas-Boas SM, da Costa MC, Coutinho JAP, Ferreira O, Pinho SP. Octanol–Water Partition Coefficients and Aqueous Solubility Data of Monoterpenoids: Experimental, Modeling, and Environmental Distribution. Ind Eng Chem Res 2022. [DOI: 10.1021/acs.iecr.1c04196] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Sérgio M. Vilas-Boas
- Centro de Investigação de Montanha (CIMO), Instituto Politécnico de Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal
- CICECO − Aveiro Institute of Materials, Department of Chemistry, University of Aveiro, 3810-193 Aveiro, Portugal
- School of Chemical Engineering (FEQ), University of Campinas (UNICAMP), 13083-852 Campinas, Brazil
| | - Mariana C. da Costa
- School of Chemical Engineering (FEQ), University of Campinas (UNICAMP), 13083-852 Campinas, Brazil
| | - João A. P. Coutinho
- CICECO − Aveiro Institute of Materials, Department of Chemistry, University of Aveiro, 3810-193 Aveiro, Portugal
| | - Olga Ferreira
- Centro de Investigação de Montanha (CIMO), Instituto Politécnico de Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal
| | - Simão P. Pinho
- Centro de Investigação de Montanha (CIMO), Instituto Politécnico de Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal
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Rombouts FJR, Hsiao CC, Bache S, De Cleyn M, Heckmann P, Leenaerts J, Martinéz-Lamenca C, Van Brandt S, Peschiulli A, Vos A, Gijsen HJM. Modulating physicochemical properties of tetrahydropyridine-2-amine BACE1 inhibitors with electron-withdrawing groups: A systematic study. Eur J Med Chem 2022; 228:114028. [PMID: 34920170 DOI: 10.1016/j.ejmech.2021.114028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Revised: 11/20/2021] [Accepted: 11/26/2021] [Indexed: 11/18/2022]
Abstract
A common challenge for medicinal chemists is to reduce the pKa of strongly basic groups' conjugate acids into a range that preserves the desired effects, usually potency and/or solubility, but avoids undesired effects like high volume of distribution (Vd), limited membrane permeation, and off-target binding to, notably, the hERG channel and monoamine receptors. We faced this challenge with a 3,4,5,6-tetrahydropyridine-2-amine scaffold harboring an amidine, a key structural component of potential inhibitors of BACE1, the rate-limiting enzyme in the production of Aβ species that make up amyloid plaques in Alzheimer's disease. In our endeavor to balance potency with desirable properties to achieve brain penetration, we introduced a diverse set of groups in beta position of the amidine that modulate logD, PSA and pKa. Given the synthetic challenge to prepare these highly functionalized warheads, we first developed a design flow including predicted physicochemical parameters which allowed us to select only the most promising candidates for synthesis. For this we evaluated a set of commercial packages to predict physicochemical properties, which can guide medicinal chemists in their endeavors to modulate pKa values of amidine and amine bases.
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Affiliation(s)
| | - Chien-Chi Hsiao
- Janssen Research & Development, Turnhoutseweg 30, B-2340, Beerse, Belgium
| | - Solène Bache
- Janssen Research & Development, Turnhoutseweg 30, B-2340, Beerse, Belgium
| | - Michel De Cleyn
- Janssen Research & Development, Turnhoutseweg 30, B-2340, Beerse, Belgium
| | - Pauline Heckmann
- Janssen Research & Development, Turnhoutseweg 30, B-2340, Beerse, Belgium
| | - Jos Leenaerts
- Janssen Research & Development, Turnhoutseweg 30, B-2340, Beerse, Belgium
| | | | - Sven Van Brandt
- Janssen Research & Development, Turnhoutseweg 30, B-2340, Beerse, Belgium
| | - Aldo Peschiulli
- Janssen Research & Development, Turnhoutseweg 30, B-2340, Beerse, Belgium
| | - Ann Vos
- Janssen Research & Development, Turnhoutseweg 30, B-2340, Beerse, Belgium
| | - Harrie J M Gijsen
- Janssen Research & Development, Turnhoutseweg 30, B-2340, Beerse, Belgium
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5
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Bartels M, van Osdol W, Le Merdy M, Chappelle A, Kuhl A, West R. In silico predictions of absorption of MDI substances after dermal or inhalation exposures to support a category based read-across assessment. Regul Toxicol Pharmacol 2022; 129:105117. [PMID: 35017021 DOI: 10.1016/j.yrtph.2022.105117] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 12/28/2021] [Accepted: 01/06/2022] [Indexed: 01/08/2023]
Abstract
Methylenediphenyl diisocyanate (MDI) substances used polyurethane production can range from their simplest monomeric forms (e.g., 4,4'-MDI) to mixtures of the monomers with various homologues, homopolymer, and prepolymer derivatives. The relative dermal or inhalation absorption of 39 constituents of these substances in human were predicted using the GastroPlus® program. Predicted dermal uptake and absorption of the three MDI monomers from an acetone vehicle was 84-86% and 1.4-1.5%, respectively, with lower uptake and absorption predicted for the higher MW analogs. Lower absorption was predicted from exposures in a more lipophilic vehicle (1-octanol). Modeled inhalation exposures afforded the highest pulmonary absorption for the MDI monomers (38-54%), with 3-27% for the MW range of 381-751, and <0.1% for the remaining, higher MW derivatives. Predicted oral absorption, representing mucociliary transport, ranged from 5 to 10% for the MDI monomers, 10-25% for constituents of MW 381-751, and ≤3% for constituents with MW > 900. These in silico evaluations should be useful in category-based, worst-case, Read-Across assessments for MDI monomers and modified MDI substances for potential systemic effects. Predictions of appreciable mucociliary transport may also be useful to address data gaps in oral toxicity testing for this category of compounds.
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Affiliation(s)
| | | | | | - Anne Chappelle
- International Isocyanate Institute, Mountain Lakes, NJ, USA
| | - Adam Kuhl
- Huntsman LLC, The Woodlands, Texas, USA
| | - Robert West
- International Isocyanate Institute, Mountain Lakes, NJ, USA
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Hoshino Y, Yoshioka H, Hisaka A. Comparison of Predictions by BCS, rDCS and Machine Learning for the Effect of Food on Oral Drug Absorption Based on Features Calculated In silico. AAPS J 2021; 24:10. [PMID: 34893922 DOI: 10.1208/s12248-021-00664-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Accepted: 10/23/2021] [Indexed: 11/30/2022] Open
Abstract
In this study, observed food effects of 473 drugs were categorized into positive, negative, or no effects and compared with the predictions made by machine learning (ML), the Biopharmaceutics Classification System (BCS) and refined Developability Classification System (rDCS). All methods used primarily in silico estimates for prediction, and for ML, four algorithms were evaluated using nested cross-validation to select important information from 371 features calculated based on the chemical structure. Approximately 18 features, including estimated solubility in biorelevant media, were selected as important, and the random forest classifier was the best among four algorithms with 36.6% error rate (ER) and 10.8% opposite prediction rate (OPR). The prediction by rDCS utilizing solubility in a biorelevant medium was somewhat inferior, but not by much; 41.0% ER and 11.4% OPR. Compared with these two methods, the prediction by BCS was inferior; 54.5% ER and 21.4% OPR. ER was improved modestly by using measured features instead of in silico estimates when BCS was applied to a subset of 151 drugs (46.4% from 55.0%). ML and rDCS predicted the food effects of the same subset using in silico estimates with ERs of 37.7% and 42.4%, respectively, suggesting that the predictions by ML and rDCS using in silico features are similar or more accurate than those by BCS using measured features. These results suggest that ML was useful in revealing essential features from complex information and, together with rDCS, is effective in predicting food effects during drug development, including early drug discovery.
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Affiliation(s)
- Yusuke Hoshino
- Clinical Pharmacology and Pharmacometrics, Graduate School of Pharmaceutical Sciences, Chiba University, 1-8-1, Inohana, Chuo-ku, Chiba-shi, Chiba, 260-8675, Japan.,Toxicology & Pharmacokinetics Research, Central Research Laboratories, Zeria Pharmaceutical Co., Ltd, 2512-1 Numagami, Oshikiri, Kumagaya-shi, Saitama, 360-0111, Japan
| | - Hideki Yoshioka
- Department of Clinical Medicine, Faculty of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba-shi, Ibaraki, 305-8575, Japan
| | - Akihiro Hisaka
- Clinical Pharmacology and Pharmacometrics, Graduate School of Pharmaceutical Sciences, Chiba University, 1-8-1, Inohana, Chuo-ku, Chiba-shi, Chiba, 260-8675, Japan.
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Hussain S, Le TTY, Tsay RY, Lin SY. Solubility determination of surface-active components from dynamic surface tension data. J IND ENG CHEM 2020. [DOI: 10.1016/j.jiec.2020.09.019] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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8
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Liu Y, Mapa MST, Sprando RL. Liver toxicity of anthraquinones: A combined in vitro cytotoxicity and in silico reverse dosimetry evaluation. Food Chem Toxicol 2020; 140:111313. [PMID: 32240702 DOI: 10.1016/j.fct.2020.111313] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Revised: 03/25/2020] [Accepted: 03/27/2020] [Indexed: 02/07/2023]
Abstract
Anthraquinones are found in a variety of consumer products such as dietary supplements, traditional Chinese medicines, and drugs. Along with their widespread use, potential safety concerns have emerged, especially liver toxicity. Therefore, there is a need to conduct rapid and inexpensive safety assessment for anthraquinones due to a lack of animal and human toxicological data. Here, a combined in vitro cytotoxicity and in silico reverse dosimetry approach was adopted to consider the potential human liver toxicity of 16 anthraquinones and derivatives. First, cytotoxicity (EC50) in two human liver cell lines (HepG2/C3A and HuH-7) was measured under two conditions (single and repeated dosing, 72 h). Second, toxic doses (Dtox) required to yield plasma steady-state concentrations (Css) equal to in vitro EC50 values were predicted by reverse dosimetry simulation using a PBPK model. Finally, Dtox was compared to literature-derived estimated daily intake (EDI) of anthraquinones to assess safety. Among the 16 anthraquinones, rhein was identified as a potential hepatotoxicant due to a combination of cytotoxicity, plasma concentration, and daily intake level. These in vitro and in silico findings provide preliminary data and guidance for further animal and clinical studies to confirm liver toxicity of anthraquinones.
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Affiliation(s)
- Yitong Liu
- Division of Toxicology, Office of Applied Research and Safety Assessment, Center for Food Safety and Applied Nutrition, U.S. Food and Drug Administration, Laurel, MD, USA.
| | - Mapa S T Mapa
- Division of Toxicology, Office of Applied Research and Safety Assessment, Center for Food Safety and Applied Nutrition, U.S. Food and Drug Administration, Laurel, MD, USA
| | - Robert L Sprando
- Division of Toxicology, Office of Applied Research and Safety Assessment, Center for Food Safety and Applied Nutrition, U.S. Food and Drug Administration, Laurel, MD, USA
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Duchowicz PR. QSPR studies on water solubility, octanol-water partition coefficient and vapour pressure of pesticides. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2020; 31:135-148. [PMID: 31842624 DOI: 10.1080/1062936x.2019.1699602] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Accepted: 11/27/2019] [Indexed: 06/10/2023]
Abstract
The assessment of the environmental fate and (eco)toxicological effects of pesticide compounds is of crucial importance. The present review is focused on Quantitative Structure-Property Relationships (QSPR) applications on three environmentally relevant physicochemical properties of pesticides, which can be used for assessing their environmental partition and transport, as well as exposure potential namely water solubility, octanol-water partition coefficient and vapour pressure. This article revises various interesting QSPR applications with special emphasis on studies developed during the 2009-2019 period.
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Affiliation(s)
- P R Duchowicz
- Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas (INIFTA), CONICET, UNLP, La Plata, Argentina
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10
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Birch H, Redman AD, Letinski DJ, Lyon DY, Mayer P. Determining the water solubility of difficult-to-test substances: A tutorial review. Anal Chim Acta 2019; 1086:16-28. [DOI: 10.1016/j.aca.2019.07.034] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2019] [Revised: 07/04/2019] [Accepted: 07/18/2019] [Indexed: 11/29/2022]
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11
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Fioressi SE, Bacelo DE, Rojas C, Aranda JF, Duchowicz PR. Conformation-independent quantitative structure-property relationships study on water solubility of pesticides. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2019; 171:47-53. [PMID: 30594756 DOI: 10.1016/j.ecoenv.2018.12.056] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2018] [Revised: 12/14/2018] [Accepted: 12/19/2018] [Indexed: 06/09/2023]
Abstract
Water solubility is a key physicochemical parameter in pesticide control and regulation, although sometimes its experimental determination is not an easy task. In this study, we present Quantitative Structure-Property Relationships (QSPRs) for predicting the water solubility at 20 °C of 1211 approved heterogeneous pesticide compounds, collected from the online Pesticides Properties Data Base (PPDB). Validated and generally applicable Multivariable Linear Regression (MLR) models were established, including molecular descriptors carrying constitutional and topological aspects of the analyzed compounds. The most representative descriptors were selected from the exploration of a large number of about 18,000 structural variables. A hybrid approach that involves a molecular descriptor, a fingerprint, and a flexible descriptor showed the best predictive performance.
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Affiliation(s)
- Silvina E Fioressi
- Departamento de Química, Facultad de Ciencias Exactas y Naturales, Universidad de Belgrano, Villanueva 1324, CP 1426, Buenos Aires, Argentina.
| | - Daniel E Bacelo
- Departamento de Química, Facultad de Ciencias Exactas y Naturales, Universidad de Belgrano, Villanueva 1324, CP 1426, Buenos Aires, Argentina
| | - Cristian Rojas
- Facultad de Ciencia y Tecnología, Universidad del Azuay, Av. 24 de Mayo 7-77 y Hernán Malo, Cuenca, Ecuador
| | - José F Aranda
- Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas (INIFTA), CONICET, UNLP, Diag. 113 y 64, C.C. 16, Sucursal 4, 1900 La Plata, Argentina
| | - Pablo R Duchowicz
- Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas (INIFTA), CONICET, UNLP, Diag. 113 y 64, C.C. 16, Sucursal 4, 1900 La Plata, Argentina.
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12
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Raevsky OA, Grigorev VY, Polianczyk DE, Raevskaja OE, Dearden JC. Aqueous Drug Solubility: What Do We Measure, Calculate and QSPR Predict? Mini Rev Med Chem 2019; 19:362-372. [PMID: 30058484 DOI: 10.2174/1389557518666180727164417] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2018] [Revised: 07/06/2018] [Accepted: 07/20/2018] [Indexed: 01/07/2023]
Abstract
Detailed critical analysis of publications devoted to QSPR of aqueous solubility is presented in the review with discussion of four types of aqueous solubility (three different thermodynamic solubilities with unknown solute structure, intrinsic solubility, solubility in physiological media at pH=7.4 and kinetic solubility), variety of molecular descriptors (from topological to quantum chemical), traditional statistical and machine learning methods as well as original QSPR models.
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Affiliation(s)
- Oleg A Raevsky
- Department of Computer-Aided Molecular Design, Institute of Physiologically Active Compounds, Russian Academy of Science, Chernogolovka, Russian Federation
| | - Veniamin Y Grigorev
- Department of Computer-Aided Molecular Design, Institute of Physiologically Active Compounds, Russian Academy of Science, Chernogolovka, Russian Federation
| | - Daniel E Polianczyk
- Department of Computer-Aided Molecular Design, Institute of Physiologically Active Compounds, Russian Academy of Science, Chernogolovka, Russian Federation
| | - Olga E Raevskaja
- Department of Computer-Aided Molecular Design, Institute of Physiologically Active Compounds, Russian Academy of Science, Chernogolovka, Russian Federation
| | - John C Dearden
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, United Kingdom
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