ARSIM (1966) RTECS NUMBER-TF0525000-VG-Chemical Toxicity Database. Agricultural Research Service, USDA Information Memorandum (Beltsville, MD 20705) 20:7. https://www.drugfuture.com/toxic/q93-q400.html

Arya J, Bist R (2022) The diverse ways to determine experimental dose in animals. HPMIJ 5:21–24. https://doi.org/10.15406/hpmij.2022.05.00202

Article  Google Scholar 

Bajgar J (2004) Organophosphates/nerve agent poisoning: mechanism of action, diagnosis, prophylaxis, and treatment. In: Makowski GS (ed) Advances in clinical chemistry. Elsevier, Amsterdam, pp 151–216

Google Scholar 

Balali-Mood M, Balali-Mood B, Balali-Mood K (2017) Nerve agents. In: Brent J, Burkhart K, Dargan P et al (eds) Critical care toxicology: diagnosis and management of the critically poisoned patient. Springer International Publishing, Cham, pp 2655–2682

Chapter  Google Scholar 

Banerjee P, Dehnbostel FO, Preissner R (2018a) Prediction is a balancing act: importance of sampling methods to balance sensitivity and specificity of predictive models based on imbalanced chemical data sets. Front Chem. https://doi.org/10.3389/fchem.2018.00362

Article  PubMed  PubMed Central  Google Scholar 

Banerjee P, Eckert AO, Schrey AK, Preissner R (2018b) ProTox-II: a webserver for the prediction of toxicity of chemicals. Nucleic Acids Res 46:W257–W263. https://doi.org/10.1093/nar/gky318

Article  PubMed  PubMed Central  Google Scholar 

Barnes JM, Denz FA (1954) The reaction of rats to diets containing octamethyl pyrophosphoramide (schradan) and 00-diethyl-s-ethylmercaptoethanol thiophosphate (“Systox”). Occup Environ Med 11:11–19. https://doi.org/10.1136/oem.11.1.11

Article  Google Scholar 

Bolt HM (2023) Sarin: a never-ending story. Arch Toxicol 97:1–2. https://doi.org/10.1007/s00204-022-03417-9

Article  PubMed  Google Scholar 

Bolt HM, Hengstler JG (2020) The rapid development of computational toxicology. Arch Toxicol 94:1371–1372. https://doi.org/10.1007/s00204-020-02768-5

Article  PubMed  PubMed Central  Google Scholar 

Borba JVB, Alves VM, Braga RC et al (2022) STopTox: an in silico alternative to animal testing for acute systemic and topical toxicity. Environ Health Perspect 130:027012. https://doi.org/10.1289/EHP9341

Article  PubMed  PubMed Central  Google Scholar 

CDC, NIOSH (1994a) CDC—Immediately Dangerous to Life or Health Concentrations (IDLH): Dichlorvos—NIOSH Publications and Products. https://www.cdc.gov/niosh/idlh/62737.html

CDC, NIOSH (1994b) CDC—Immediately Dangerous to Life or Health Concentrations (IDLH): Parathion—NIOSH Publications and Products. https://www.cdc.gov/niosh/idlh/56382.html

Chavan S, Friedman R, Nicholls IA (2015) Acute toxicity-supported chronic toxicity prediction: a k-nearest neighbor coupled read-across strategy. Int J Mol Sci 16:11659–11677. https://doi.org/10.3390/ijms160511659

Article  PubMed  PubMed Central  Google Scholar 

Cheng F, Li W, Zhou Y et al (2012) Admetsar: a comprehensive source and free tool for assessment of chemical admet properties. J Chem Inf Model 52:3099–3105. https://doi.org/10.1021/ci300367a

Article  PubMed  Google Scholar 

Crofts PC (1958) Compounds containing carbon–phosphorus bonds. Q Rev Chem Soc 12:341–366. https://doi.org/10.1039/QR9581200341

Article  Google Scholar 

Diauudin FN, Rashid JIA, Knight VF et al (2019) A review of current advances in the detection of organophosphorus chemical warfare agents based biosensor approaches. Sensing Bio-Sensing Res 26:100305. https://doi.org/10.1016/j.sbsr.2019.100305

Article  Google Scholar 

Diaza RG, Manganelli S, Esposito A et al (2015) Comparison of in silico tools for evaluating rat oral acute toxicity. SAR QSAR Environ Res 26:1–27. https://doi.org/10.1080/1062936X.2014.977819

Article  PubMed  Google Scholar 

Dimitrov SD, Diderich R, Sobanski T et al (2016) QSAR Toolbox—workflow and major functionalities. SAR QSAR Environ Res 27:203–219. https://doi.org/10.1080/1062936X.2015.1136680

Article  PubMed  Google Scholar 

Drwal MN, Banerjee P, Dunkel M et al (2014) ProTox: a web server for the in silico prediction of rodent oral toxicity. Nucleic Acids Res 42:W53–W58. https://doi.org/10.1093/nar/gku401

Article  PubMed  PubMed Central  Google Scholar 

Dworkin J, Prescott M, Jamal R et al (2008) The long-term psychosocial impact of a surprise chemical weapons attack on civilians in Halabja, Iraqi Kurdistan. J Nerv Ment Dis 196:772–775. https://doi.org/10.1097/NMD.0b013e3181878b69

Article  PubMed  Google Scholar 

Ellison DH (2007) Handbook of chemical and biological warfare agents, 2nd edn. CRC Press, Boca Raton

Google Scholar 

Faria EC, Bercu JP, Dolan DG et al (2016) Using default methodologies to derive an acceptable daily exposure (ADE). Regul Toxicol Pharmacol 79:S28–S38. https://doi.org/10.1016/j.yrtph.2016.05.026

Article  PubMed  Google Scholar 

Gatnik MF, Worth AP (2010) Review of software tools for toxicity prediction. Publications Office of the European Union, Luxembourg. https://doi.org/10.2788/60101

Book  Google Scholar 

Gu Y, Lou C, Tang Y (2023) Chapter 14 - admetSAR—A valuable tool for assisting safety evaluation. In: Hong H (ed) QSAR in safety evaluation and risk assessment. Academic press, pp 187–201

Chapter  Google Scholar 

Hartung T (2009) Toxicology for the twenty-first century. Nature 460:208–212. https://doi.org/10.1038/460208a

Article  ADS  PubMed  Google Scholar 

Hartung T (2021) The state of the scientific revolution in toxicology. ALTEX. https://doi.org/10.14573/altex.2106101

Hiltermann JR (2007) A poisonous affair: America, Iraq, and the gassing of Halabja. Cambridge University Press, New York, NY

Google Scholar 

Kaiser KLE, Dearden JC, Klein W, Schultz TW (1999) Short communication: a note of caution to users of ECOSAR. Water Qual Res J 34:179–182. https://doi.org/10.2166/wqrj.1999.006

Article  Google Scholar 

Kirchmair J (ed) (2014) Drug metabolism prediction, 1st edn. Wiley, Hoboken

Google Scholar 

Kloske M, Witkiewicz Z (2019) Novichoks—The A group of organophosphorus chemical warfare agents. Chemosphere 221:672–682. https://doi.org/10.1016/j.chemosphere.2019.01.054

Article  ADS  PubMed  Google Scholar 

Kutsarova S, Mehmed A, Cherkezova D et al (2021a) Automated read-across workflow for predicting acute oral toxicity: i. the decision scheme in the QSAR toolbox. Regul Toxicol Pharmacol. https://doi.org/10.1016/j.yrtph.2021.105015

Article  PubMed  Google Scholar 

Kutsarova S, Schultz TW, Chapkanov A et al (2021b) The QSAR Toolbox automated read-across workflow for predicting acute oral toxicity: II. Verif Valid Computational Toxicol 20:100194. https://doi.org/10.1016/j.comtox.2021.100194

Article  Google Scholar 

Lapenna S, Fuart GM, Worth A (2010) Review of QSAR models and software tools for predicting acute and chronic systemic toxicity. JRC Pub Repos. https://doi.org/10.2788/60766

Article  Google Scholar 

Leist M, Hartung T, Nicotera P (2008) The dawning of a new age of toxicology. Altex 25:103–114. https://doi.org/10.14573/altex.2008.2.103

Article  PubMed  Google Scholar 

Lunghini F, Marcou G, Azam P et al (2019) Consensus models to predict oral rat acute toxicity and validation on a dataset coming from the industrial context. SAR QSAR Environ Res 30:879–897. https://doi.org/10.1080/1062936X.2019.1672089

Article  PubMed  Google Scholar 

Mansouri K, Grulke CM, Judson RS, Williams AJ (2018) OPERA models for predicting physicochemical properties and environmental fate endpoints. J Cheminform 10:10. https://doi.org/10.1186/s13321-018-0263-1

Article  PubMed  PubMed Central  Google Scholar 

Mansouri K, Karmaus AL, Fitzpatrick J et al (2021) CATMoS: collaborative acute toxicity modeling suite. Environ Health Perspect 129:047013. https://doi.org/10.1289/EHP8495

Article  PubMed  PubMed Central  Google Scholar 

Martin TM, Harten P, Venkatapathy R et al (2008) A hierarchical clustering methodology for the estimation of toxicity. Toxicol Mech Methods 18:251–266. https://doi.org/10.1080/15376510701857353

Article  PubMed  Google Scholar 

Martin T (2019) Prediction of toxicity using WebTEST (Web-services Toxicity Estimation Software Tool). ACS National Meeting & Expo Conference Location Orlando, FL Conference Dates March 31-April 4, https://doi.org/10.13140/RG.2.2.15742.08009

Melnikov F, Kostal J, Voutchkova-Kostal A et al (2016) Assessment of predictive models for estimating the acute aquatic toxicity of organic chemicals. Green Chem 18:4432–4445. https://doi.org/10.1039/C6GC00720A

Article  Google Scholar 

Misik J, Pavlikova R, Cabal J, Kuca K (2015) Acute toxicity of some nerve agents and pesticides in rats. Drug Chem Toxicol 38:32–36. https://doi.org/10.3109/01480545.2014.900070

Article  PubMed  Google Scholar 

Mombelli E, Pandard P (2021) Evaluation of the OECD QSAR toolbox automatic workflow for the prediction of the acute toxicity of organic chemicals to fathead minnow. Regul Toxicol Pharmacol 122:104893. https://doi.org/10.1016/j.yrtph.2021.104893

Article  PubMed  Google Scholar 

Moniz Bandeira LA (2019) Chemical Weapons Attack in Ghouta as a Pretext for US Intervention. In: Moniz Bandeira LA (ed) The World Disorder: US hegemony, proxy wars, terrorism and humanitarian catastrophes. Springer International Publishing, Cham, pp 127–136. https://doi.org/10.1007/978-3-030-03204-3_11

Chapter  Google Scholar 

Moon A, Khan D, Gajbhiye P, Jariya M (2017) Insilico prediction of toxicity of ligands utilizing admetsar. Int J Pharm Bio Sci. https://doi.org/10.22376/ijpbs.2017.8.3.b674-677

Article  Google Scholar 

Morita H, Yanagisawa N, Nakajima T et al (1995) Sarin poisoning in Matsumoto, Japan. Lancet 346:290–293. https://doi.org/10.1016/s0140-6736(95)92170-2

Article  PubMed  Google Scholar 

Moyer RA, Sidell FR, Salem H (2014) Nerve Agents. In: Wexler P (ed) Encyclopedia of toxicology, 3rd edn. Academic Press, Oxford, pp 483–488. https://doi.org/10.1016/B978-0-12-386454-3.00635-7

Chapter  Google Scholar