Araştırma Makalesi
BibTex RIS Kaynak Göster

Integrative In Silico Toxicity Assessment of Chlorfenapyr Using AI-Driven Platforms

Yıl 2025, Cilt: 5 Sayı: 1, 44 - 52, 27.06.2025

Öz

Chlorfenapyr is a pyrrole-class pesticide with a unique mechanism of action that disrupts mitochondrial oxidative phosphorylation. Despite its broad-spectrum insecticidal use, publicly available toxicological data on chlorfenapyr remain limited, particularly regarding organ-specific and long-term effects. To address this data gap, the present study implements a multi-model in silico toxicity assessment using three AI-based platforms—SwissADME, ProTox-II, and ADMETlab 2.0—to predict key toxicokinetic and toxicodynamic properties from the compound’s SMILES representation. Physicochemical and pharmacokinetic parameters such as molecular weight, lipophilicity, gastrointestinal absorption, and cytochrome P450 inhibition were consistently predicted across platforms. However, notable discrepancies emerged in blood–brain barrier (BBB) permeability and hepatotoxicity outcomes. Acute toxicity was estimated with a predicted LD₅₀ of 55 mg/kg (Class 3), while organ-specific risks included neurotoxicity and respiratory toxicity. Both platforms highlighted mitochondrial membrane potential disruption and oxidative stress pathways as probable mechanisms of toxicity. Toxicophore analysis further revealed substructures associated with non-genotoxic carcinogenicity, aquatic toxicity, and poor biodegradability, raising environmental safety concerns. By combining complementary model outputs, this AI-supported approach allows for scalable, reproducible, and ethically favorable screening of chemical hazards. The findings demonstrate that multi-endpoint in silico toxicology workflows can effectively identify early warning signals of compound toxicity and guide future experimental priorities—particularly for chemicals like chlorfenapyr, where experimental data are scarce and regulatory insight is urgently needed.

Kaynakça

  • G. T. Comstock, H. Nguyen, A. Bronstein, and L. Yip, “Chlorfenapyr poisoning: a systematic review,” Clin Toxicol (Phila), vol. 62, no. 7, pp. 412–424, Jul. 2024, doi: 10.1080/15563650.2024.2367658.
  • S. S. Shinde, P. S. Giram, P. S. Wakte, and S. S. Bhusari, “ADMET tools in the digital era: Applications and limitations,” Adv Pharmacol, vol. 103, pp. 65–80, 2025, doi: 10.1016/bs.apha.2025.01.004.
  • L. Peltason and J. Bajorath, “Systematic computational analysis of structure-activity relationships: concepts, challenges and recent advances,” Future Med Chem, vol. 1, no. 3, pp. 451–466, Jun. 2009, doi: 10.4155/fmc.09.41.
  • A. Daina, O. Michielin, and V. Zoete, “SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules,” Sci Rep, vol. 7, p. 42717, Mar. 2017, doi: 10.1038/srep42717.
  • P. Banerjee, A. O. Eckert, A. K. Schrey, and R. Preissner, “ProTox-II: a webserver for the prediction of toxicity of chemicals,” Nucleic Acids Res, vol. 46, no. W1, pp. W257–W263, Jul. 2018, doi: 10.1093/nar/gky318.
  • G. Xiong et al., “ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties,” Nucleic Acids Res, vol. 49, no. W1, pp. W5–W14, Jul. 2021, doi: 10.1093/nar/gkab255.
  • D. F. El Sherif et al., “The binary mixtures of lambda-cyhalothrin, chlorfenapyr, and abamectin, against the house fly larvae, Musca domestica L.,” Molecules, vol. 27, no. 10, p. 3084, 2022.
  • S. Ohnuki, S. Tokishita, M. Kojima, and S. Fujiwara, “Effect of chlorpyrifos‐exposure on the expression levels of CYP genes in Daphnia magna and examination of a possibility that an up‐regulated clan 3 CYP , CYP360A8 , reacts with pesticides,” Environmental Toxicology, vol. 39, no. 6, pp. 3641–3653, Jun. 2024, doi: 10.1002/tox.24224.
  • D. Xu et al., “Expression reduction and a variant of a P450 gene mediate chlorpyrifos resistance in Tetranychus urticae Koch,” Journal of Advanced Research, 2024, Accessed: Apr. 08, 2025. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S209012322400417X
  • E. T. Huang et al., “Predicting blood–brain barrier permeability of molecules with a large language model and machine learning,” Scientific Reports, vol. 14, no. 1, p. 15844, 2024.
  • Z. Wang et al., “In Silico Prediction of Blood–Brain Barrier Permeability of Compounds by Machine Learning and Resampling Methods,” ChemMedChem, vol. 13, no. 20, pp. 2189–2201, Oct. 2018, doi: 10.1002/cmdc.201800533.
  • L. Liu et al., “Prediction of the Blood–Brain Barrier (BBB) Permeability of Chemicals Based on Machine-Learning and Ensemble Methods,” Chem. Res. Toxicol., vol. 34, no. 6, pp. 1456–1467, Jun. 2021, doi: 10.1021/acs.chemrestox.0c00343.
  • M. S. Kamel et al., “Microwave-Assisted Synthesis, Biological Activity Evaluation, Molecular Docking, and ADMET Studies of Some Novel Pyrrolo [2,3-b] Pyrrole Derivatives,” Molecules, vol. 27, no. 7, Art. no. 7, Jan. 2022, doi: 10.3390/molecules27072061.
  • Y. Ren et al., “Unravelling the polytoxicology of chlorfenapyr on non-target HepG2 cells: The involvement of mitochondria-mediated programmed cell death and DNA damage,” Molecules, vol. 27, no. 17, p. 5722, 2022.
  • L. Wang et al., “Insecticide chlorfenapyr confers induced toxicity in human cells through mitochondria-dependent pathways of apoptosis,” Ecotoxicology and Environmental Safety, vol. 289, p. 117502, 2025.
  • R. K. Abdel-Razik and N. A. Hamed, “Chlorfenapyr induce oxidative phosphorylation deficiency in exposed rat and the quinoa effective role,” Alexandria Science Exchange Journal, vol. 42, no. 4, pp. 809–822, 2021.
  • P. Huang, X. Yan, B. Yu, X. He, L. Lu, and Y. Ren, “A comprehensive review of the current knowledge of chlorfenapyr: synthesis, mode of action, resistance, and environmental toxicology,” Molecules, vol. 28, no. 22, p. 7673, 2023.
  • A. Leskovac and S. Petrović, “Pesticide use and degradation strategies: food safety, challenges and perspectives,” Foods, vol. 12, no. 14, p. 2709, 2023.
  • P. Kubiak-Hardiman, S. A. Haughey, J. Meneely, S. Miller, K. Banerjee, and C. T. Elliott, “Identifying Gaps and Challenges in Global Pesticide Legislation that Impact the Protection of Consumer Health: Rice as a Case Study,” Expo Health, vol. 15, no. 3, pp. 597–618, Sep. 2023, doi: 10.1007/s12403-022-00508-x.
  • N. Donley et al., “Pesticides and environmental injustice in the USA: root causes, current regulatory reinforcement and a path forward,” BMC Public Health, vol. 22, no. 1, p. 708, Dec. 2022, doi: 10.1186/s12889-022-13057-4.
  • K. Friedrich, G. R. da Silveira, J. C. Amazonas, A. do M. Gurgel, V. E. S. de Almeida, and M. Sarpa, “International regulatory situation of pesticides authorized for use in Brazil: potential for damage to health and environmental impacts,” Cadernos de Saúde Pública, vol. 37, p. e00061820, 2021.
  • K. N. Ganesh et al., “Green Chemistry: A Framework for a Sustainable Future,” Environ. Sci. Technol., vol. 55, no. 13, pp. 8459–8463, Jul. 2021, doi: 10.1021/acs.est.1c03762.
  • T. Hartung, “Artificial intelligence as the new frontier in chemical risk assessment,” Frontiers in Artificial Intelligence, vol. 6, p. 1269932, 2023.
  • Z. Lin and W.-C. Chou, “Machine learning and artificial intelligence in toxicological sciences,” Toxicological Sciences, vol. 189, no. 1, pp. 7–19, 2022.
  • S. Ghosh and K. Roy, “Quantitative read-across structure-activity relationship (q-RASAR): A novel approach to estimate the subchronic oral safety (NOAEL) of diverse organic chemicals in rats,” Toxicology, vol. 505, p. 153824, 2024.
  • R. N. Ram, D. Gadaleta, and T. E. Allen, “The role of ‘big data’and ‘in silico’New Approach Methodologies (NAMs) in ending animal use–A commentary on progress,” Computational Toxicology, vol. 23, p. 100232, 2022.

Yapay Zekâ Tabanlı Platformlarla Chlorfenapyr'in Bütüncül In Silico Toksisite Değerlendirmesi

Yıl 2025, Cilt: 5 Sayı: 1, 44 - 52, 27.06.2025

Öz

Chlorfenapyr, mitokondriyal oksidatif fosforilasyonu bozan özgün etki mekanizmasına sahip pirrol sınıfı bir pestisittir. Geniş spektrumlu insektisit olarak yaygın kullanımına rağmen, chlorfenapyr’in toksikolojik profiline dair veriler sınırlıdır; özellikle organ spesifik etkiler ve uzun vadeli toksisite açısından önemli bilgi boşlukları bulunmaktadır. Bu boşluğu doldurmak amacıyla, bu çalışma chlorfenapyr bileşiğinin SMILES koduna dayalı olarak SwissADME, ProTox-II ve ADMETlab 2.0 gibi üç yapay zekâ destekli platform kullanılarak çok modeli bir in silico toksisite değerlendirmesi gerçekleştirmiştir. Moleküler ağırlık, lipofiliklik, gastrointestinal emilim ve sitokrom P450 inhibisyonu gibi fizikokimyasal ve farmakokinetik parametreler, platformlar arasında tutarlı biçimde öngörülmüştür. Ancak, kan-beyin bariyeri geçirgenliği ve hepatotoksisite tahminlerinde belirgin tutarsızlıklar gözlemlenmiştir. Akut toksisite, tahmini LD₅₀ değeri 55 mg/kg (Sınıf 3) olarak değerlendirilmiştir; organ düzeyinde toksik etkiler arasında ise nörotoksisite ve solunum toksisitesi öne çıkmaktadır. Her iki platform da mitokondriyal membran potansiyeli bozulması ve oksidatif stres yollarının olası toksisite mekanizmaları olduğunu vurgulamıştır. Toksikofor analizi, genotoksik olmayan karsinojenite, sucul toksisite ve biyobozunmazlık ile ilişkili alt yapılar ortaya koyarak çevresel güvenlik açısından da endişelere işaret etmektedir. Tamamlayıcı modellerin çıktılarının birleştirilmesi, bu yapay zekâ destekli yaklaşımı kimyasal tehlike taramalarında ölçeklenebilir, tekrarlanabilir ve etik açıdan uygun hale getirmektedir. Bulgular, çok uçlu in silico toksikoloji iş akışlarının, bileşik toksisitesine dair erken uyarı sinyallerini etkin biçimde saptayabileceğini ve özellikle chlorfenapyr gibi deneysel verisi kısıtlı kimyasallar için gelecekteki araştırma önceliklerini belirlemede yol gösterici olabileceğini göstermektedir.

Kaynakça

  • G. T. Comstock, H. Nguyen, A. Bronstein, and L. Yip, “Chlorfenapyr poisoning: a systematic review,” Clin Toxicol (Phila), vol. 62, no. 7, pp. 412–424, Jul. 2024, doi: 10.1080/15563650.2024.2367658.
  • S. S. Shinde, P. S. Giram, P. S. Wakte, and S. S. Bhusari, “ADMET tools in the digital era: Applications and limitations,” Adv Pharmacol, vol. 103, pp. 65–80, 2025, doi: 10.1016/bs.apha.2025.01.004.
  • L. Peltason and J. Bajorath, “Systematic computational analysis of structure-activity relationships: concepts, challenges and recent advances,” Future Med Chem, vol. 1, no. 3, pp. 451–466, Jun. 2009, doi: 10.4155/fmc.09.41.
  • A. Daina, O. Michielin, and V. Zoete, “SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules,” Sci Rep, vol. 7, p. 42717, Mar. 2017, doi: 10.1038/srep42717.
  • P. Banerjee, A. O. Eckert, A. K. Schrey, and R. Preissner, “ProTox-II: a webserver for the prediction of toxicity of chemicals,” Nucleic Acids Res, vol. 46, no. W1, pp. W257–W263, Jul. 2018, doi: 10.1093/nar/gky318.
  • G. Xiong et al., “ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties,” Nucleic Acids Res, vol. 49, no. W1, pp. W5–W14, Jul. 2021, doi: 10.1093/nar/gkab255.
  • D. F. El Sherif et al., “The binary mixtures of lambda-cyhalothrin, chlorfenapyr, and abamectin, against the house fly larvae, Musca domestica L.,” Molecules, vol. 27, no. 10, p. 3084, 2022.
  • S. Ohnuki, S. Tokishita, M. Kojima, and S. Fujiwara, “Effect of chlorpyrifos‐exposure on the expression levels of CYP genes in Daphnia magna and examination of a possibility that an up‐regulated clan 3 CYP , CYP360A8 , reacts with pesticides,” Environmental Toxicology, vol. 39, no. 6, pp. 3641–3653, Jun. 2024, doi: 10.1002/tox.24224.
  • D. Xu et al., “Expression reduction and a variant of a P450 gene mediate chlorpyrifos resistance in Tetranychus urticae Koch,” Journal of Advanced Research, 2024, Accessed: Apr. 08, 2025. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S209012322400417X
  • E. T. Huang et al., “Predicting blood–brain barrier permeability of molecules with a large language model and machine learning,” Scientific Reports, vol. 14, no. 1, p. 15844, 2024.
  • Z. Wang et al., “In Silico Prediction of Blood–Brain Barrier Permeability of Compounds by Machine Learning and Resampling Methods,” ChemMedChem, vol. 13, no. 20, pp. 2189–2201, Oct. 2018, doi: 10.1002/cmdc.201800533.
  • L. Liu et al., “Prediction of the Blood–Brain Barrier (BBB) Permeability of Chemicals Based on Machine-Learning and Ensemble Methods,” Chem. Res. Toxicol., vol. 34, no. 6, pp. 1456–1467, Jun. 2021, doi: 10.1021/acs.chemrestox.0c00343.
  • M. S. Kamel et al., “Microwave-Assisted Synthesis, Biological Activity Evaluation, Molecular Docking, and ADMET Studies of Some Novel Pyrrolo [2,3-b] Pyrrole Derivatives,” Molecules, vol. 27, no. 7, Art. no. 7, Jan. 2022, doi: 10.3390/molecules27072061.
  • Y. Ren et al., “Unravelling the polytoxicology of chlorfenapyr on non-target HepG2 cells: The involvement of mitochondria-mediated programmed cell death and DNA damage,” Molecules, vol. 27, no. 17, p. 5722, 2022.
  • L. Wang et al., “Insecticide chlorfenapyr confers induced toxicity in human cells through mitochondria-dependent pathways of apoptosis,” Ecotoxicology and Environmental Safety, vol. 289, p. 117502, 2025.
  • R. K. Abdel-Razik and N. A. Hamed, “Chlorfenapyr induce oxidative phosphorylation deficiency in exposed rat and the quinoa effective role,” Alexandria Science Exchange Journal, vol. 42, no. 4, pp. 809–822, 2021.
  • P. Huang, X. Yan, B. Yu, X. He, L. Lu, and Y. Ren, “A comprehensive review of the current knowledge of chlorfenapyr: synthesis, mode of action, resistance, and environmental toxicology,” Molecules, vol. 28, no. 22, p. 7673, 2023.
  • A. Leskovac and S. Petrović, “Pesticide use and degradation strategies: food safety, challenges and perspectives,” Foods, vol. 12, no. 14, p. 2709, 2023.
  • P. Kubiak-Hardiman, S. A. Haughey, J. Meneely, S. Miller, K. Banerjee, and C. T. Elliott, “Identifying Gaps and Challenges in Global Pesticide Legislation that Impact the Protection of Consumer Health: Rice as a Case Study,” Expo Health, vol. 15, no. 3, pp. 597–618, Sep. 2023, doi: 10.1007/s12403-022-00508-x.
  • N. Donley et al., “Pesticides and environmental injustice in the USA: root causes, current regulatory reinforcement and a path forward,” BMC Public Health, vol. 22, no. 1, p. 708, Dec. 2022, doi: 10.1186/s12889-022-13057-4.
  • K. Friedrich, G. R. da Silveira, J. C. Amazonas, A. do M. Gurgel, V. E. S. de Almeida, and M. Sarpa, “International regulatory situation of pesticides authorized for use in Brazil: potential for damage to health and environmental impacts,” Cadernos de Saúde Pública, vol. 37, p. e00061820, 2021.
  • K. N. Ganesh et al., “Green Chemistry: A Framework for a Sustainable Future,” Environ. Sci. Technol., vol. 55, no. 13, pp. 8459–8463, Jul. 2021, doi: 10.1021/acs.est.1c03762.
  • T. Hartung, “Artificial intelligence as the new frontier in chemical risk assessment,” Frontiers in Artificial Intelligence, vol. 6, p. 1269932, 2023.
  • Z. Lin and W.-C. Chou, “Machine learning and artificial intelligence in toxicological sciences,” Toxicological Sciences, vol. 189, no. 1, pp. 7–19, 2022.
  • S. Ghosh and K. Roy, “Quantitative read-across structure-activity relationship (q-RASAR): A novel approach to estimate the subchronic oral safety (NOAEL) of diverse organic chemicals in rats,” Toxicology, vol. 505, p. 153824, 2024.
  • R. N. Ram, D. Gadaleta, and T. E. Allen, “The role of ‘big data’and ‘in silico’New Approach Methodologies (NAMs) in ending animal use–A commentary on progress,” Computational Toxicology, vol. 23, p. 100232, 2022.
Toplam 26 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Modelleme ve Simülasyon, Yapay Zeka (Diğer)
Bölüm Araştırma Makalesi
Yazarlar

Ünzile Yaman 0000-0002-4493-3684

Gönderilme Tarihi 8 Nisan 2025
Kabul Tarihi 16 Haziran 2025
Yayımlanma Tarihi 27 Haziran 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 5 Sayı: 1

Kaynak Göster

IEEE Ü. Yaman, “Integrative In Silico Toxicity Assessment of Chlorfenapyr Using AI-Driven Platforms”, Journal of Artificial Intelligence and Data Science, c. 5, sy. 1, ss. 44–52, 2025.