Login Register Cart 0
Generic placeholder image

Current Topics in Medicinal Chemistry

Editor-in-Chief

ISSN (Print): 1568-0266
ISSN (Online): 1873-4294

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

Pollutants in Organic Chemistry and Medicinal Chemistry Education Laboratory. Experimental and Machine Learning Studies

Author(s): Iker Montes-Bageneta, Urtzi Akesolo, Sara López, Maria Merino, Eneritz Anakabe and Sonia Arrasate*

Volume 20, Issue 9, 2020

Page: [720 - 730] Pages: 11

DOI: 10.2174/1568026620666200211110043

Price: $65

Abstract

Aims: Computational modelling may help us to detect the more important factors governing this process in order to optimize it.

Background: The generation of hazardous organic waste in teaching and research laboratories poses a big problem that universities have to manage.

Methods: In this work, we report on the experimental measurement of waste generation on the chemical education laboratories within our department. We measured the waste generated in the teaching laboratories of the Organic Chemistry Department II (UPV/EHU), in the second semester of the 2017/2018 academic year. Likewise, to know the anthropogenic and social factors related to the generation of waste, a questionnaire has been utilized. We focused on all students of Experimentation in Organic Chemistry (EOC) and Organic Chemistry II (OC2) subjects. It helped us to know their prior knowledge about waste, awareness of the problem of separate organic waste and the correct use of the containers. These results, together with the volumetric data, have been analyzed with statistical analysis software. We obtained two Perturbation-Theory Machine Learning (PTML) models including chemical, operational, and academic factors. The dataset analyzed included 6050 cases of laboratory practices vs. practices of reference.

Results: These models predict the values of acetone waste with R2 = 0.88 and non-halogenated waste with R2 = 0.91.

Conclusion: This work opens a new gate to the implementation of more sustainable techniques and a circular economy with the aim of improving the quality of university education processes.

Keywords: Chemical education research, Graduate Education / Research, Chemoinformatics, Environmental chemistry, Organic chemistry, Pollutant.

« Previous Next »
Graphical Abstract

[1]
Madariaga, J.M. Sustainability in chemistry. The sustainability from University: an interdisciplinary reflections (Text in Spanish/Basque); Lasagabaster Herrarte, I. ed., UPV/EHU Editorial Service: Vilnius, 2013, pp. 62-65.
[2]
Anastas, P.T.; Warner, J.C. Green Chemistry: Theory and Practice; Oxford University Press: New York, 2000, pp. 1-135.
[3]
Gerbase, A.E.; Coelho, F.S.; Machado, P.F.L.; Ferreira, V.F. Management of chemical waste in institutions of education and research. Quim. Nova, 2005, 28, 3.
[http://dx.doi.org/10.1590/S0100-40422005000100001]
[4]
Stuart, R.B.; McEwen, L.R. The safety “use case”: co-developing chemical information management and laboratory safety skills. J. Chem. Educ., 2016, 93, 516-526.
[http://dx.doi.org/10.1021/acs.jchemed.5b00511]
[5]
Directive 2008/98/EC of the European Parliament and of the Council of 19 November 2008 on waste
[6]
Herremans, I. Environmental management systems at North American Universities: what drives good performance? Int. J. Sust. Higher. Educ., 2000, 1(2), 168-181.
[7]
Alshuwaikhat, H.M.; Abubakar, I. An integrated approach to achieving campus sustainability: assessment of the current campus environmental management practices. J. Clean. Prod., 2007, 16, 1777-1785.
[http://dx.doi.org/10.1016/j.jclepro.2007.12.002]
[8]
Ramm, J.G. Dorscheid, G. L.; Passos, C. G.; Sirtori, C. Development of a Waste Management Program in Technical Chemistry Teaching. J. Chem. Educ., 2018, 95, 570-576.
[http://dx.doi.org/10.1021/acs.jchemed.7b00590]
[9]
Nisbet, R. The data mining process, In: handbook of statistical analysis and data mining applications. Academic Press: Cambridge, 2009; pp. 33-48.
[10]
Bediaga, H.; Arrasate, S.; González-Díaz, H. PTML combinatorial model of CHEMBL compounds assays for multiple types of cancer. ACS Comb. Sci., 2018, 20(11), 621-632.
[http://dx.doi.org/10.1021/acscombsci.8b00090] [http://dx.doi.org/30240186]
[11]
Blay, V.; Yokoi, T.; González-Díaz, H. Perturbation theory-machine learning study of zeolite materials desilication. J. Chem. Inf. Model., 2018, 58(12), 2414-2419.
[http://dx.doi.org/10.1021/acs.jcim.8b00383] [PMID: 30139249]
[12]
Simón-Vidal, L.; García-Calvo, O.; Oteo, U.; Arrasate, S.; Lete, E.; Sotomayor, N.; González-Díaz, H. Perturbation-theory and machine learning (ptml) model for high-throughput screening of parham reactions: experimental and theoretical studies. J. Chem. Inf. Model., 2018, 58(7), 1384-1396.
[http://dx.doi.org/10.1021/acs.jcim.8b00286] [PMID: 29898360]
[13]
Ferreira da Costa, J.; Silva, D.; Caamaño, O.; Brea, J.M.; Loza, M.I.; Munteanu, C.R.; Pazos, A.; García-Mera, X.; González-Díaz, H. Perturbation theory/machine learning model of chembl data for dopamine targets: docking, synthesis, and assay of new l-prolyl-l-leucyl-glycinamide peptidomimetics. ACS Chem. Neurosci., 2018, 9(11), 2572-2587.
[http://dx.doi.org/10.1021/acschemneuro.8b00083] [PMID: 29791132]
[14]
Concu, R.; Kleandrova, V.V.; Speck-Planche, A.; Cordeiro, M.N.D.S. Probing the toxicity of nanoparticles: a unified in silico machine learning model based on perturbation theory. Nanotoxicology, 2017, 11(7), 891-906. [PMID: ].
[http://dx.doi.org/10.1080/17435390.2017.1379567] [PMID: 28937298]
[15]
Kleandrova, V.V.; Luan, F.; González-Díaz, H.; Ruso, J.M.; Speck-Planche, A.; Cordeiro, M.N. 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. Environ. Sci. Technol., 2014, 48(24), 14686-14694.
[http://dx.doi.org/10.1021/es503861x] [PMID: 25384130]
[16]
Kleandrova, V.V.; Luan, F.; González-Díaz, H.; Ruso, J.M.; Melo, A.; Speck-Planche, A.; Cordeiro, M.N. Computational ecotoxicology: simultaneous prediction of ecotoxic effects of nanoparticles under different experimental conditions. Environ. Int., 2014, 73, 288-294.
[http://dx.doi.org/10.1016/j.envint.2014.08.009] [PMID: 25173945]
[17]
Kleandrova, V.V.; Ruso, J.M.; Speck-Planche, A.; Dias Soeiro Cordeiro, M.N. Enabling the discovery and virtual screening of potent and safe antimicrobial peptides. simultaneous prediction of antibacterial activity and cytotoxicity. ACS Comb. Sci., 2016, 18(8), 490-498.
[http://dx.doi.org/10.1021/acscombsci.6b00063] [PMID: 27280735]
[18]
González-Díaz, H.; Arrasate, S.; Gómez-SanJuan, A.; Sotomayor, N.; Lete, E.; Besada-Porto, L.; Ruso, J.M. General theory for multiple input-output perturbations in complex molecular systems. 1. Linear QSPR electronegativity models in physical, organic, and medicinal chemistry. Curr. Top. Med. Chem., 2013, 13(14), 1713-1741.
[http://dx.doi.org/10.2174/1568026611313140011] [PMID: 23889050]
[19]
Ivanković, A.; Dronjić, A.; Bevanda, A.M.; Talić, S. Review of 12 principles of green chemistry in practice. Int. J. Sustain. Green Energy, 2017, 6, 39-48.
[http://dx.doi.org/10.11648/j.ijrse.20170603.12]
[20]
Clark, J.; Farmer, T.; Herrero-Davila, L.; Sherwood, J. Circular economy design considerations for research and process development in the chemical sciences. Green Chem., 2016, 18, 3914-3934.
[http://dx.doi.org/10.1039/C6GC00501B]
[21]
Education and Training, ECTS key features. Available on: http://ec.europa.eu/education/ects/users-guide/key-features_en.htm (Accessed on 18/12/2018).
[22]
The bologna process and the european higher education area. Available on: https://ec.europa.eu/education/policies/higher-education/bologna-process-and-european-higher-education-area_es (Accessed on 18/12/2018).
[23]
Harris, D.C. Quantitative chemical analysis, 7th ed; W.H. Freeman: New York, 2003, pp. 65-85.
[24]
Broekaert, A.C.J.; Daniel, C. H. Quantitative chemical analysis. Anal. Bioanal. Chem., 2015, 407, 8943-8944.
[http://dx.doi.org/10.1007/s00216-015-9059-6]
[25]
Azadeh, M.; Gorovits, B.; Kamerud, J.; MacMannis, S.; Safavi, A.; Sailstad, J.; Sondag, P. Calibration curves in quantitative ligand binding assays: recommendations and best practices for preparation, design, and editing of calibration curves. AAPS J., 2017, 20(1), 22.
[http://dx.doi.org/10.1208/s12248-017-0159-4] [PMID: 29282611]
[26]
Danzel, K. Calibration in analytical chemistry, theoretical and metrological fundamentals. Anal. Chem., 2007, 123-175.
[http://dx.doi.org/10.1007/978-3-540-35990-6_6]
[27]
Vollhardt, K.P.C.; Schore, N.E. Organic Chemistry, 8th ed; W.H. Freeman: New York, 2018.
[28]
Carey, F.A.; Sundberg, R.J. Advanced Organic Chemistry, Parts A and B, 5th ed; Springer: Berlin, 2007.
[29]
Martinez, S.G. Tenorio-Borroto, E.; Barbabosa Pliego, A.; Díaz-Albiter, H. M.; Vázquez-Chagoyán, J. C. and González-Díaz, H. PTML model for proteome mining of b-cell epitopes and theoretic-experimental study of bm86 protein sequences from colima Mexico. J. Proteome Res., 2017, 16, 4093-4103.
[http://dx.doi.org/10.1021/acs.jproteome.7b00477]
[30]
Castillo, E.; Fernández-Canteli, A. A general regression model for lifetime evaluation and prediction. Int. J. Fract., 2001, 107, 117-137.
[http://dx.doi.org/10.1023/A:1007624803955]
[31]
Campbell, M.J. An introduction to generalized linear models. Biometrics, 1991, 47, 347.
[http://dx.doi.org/10.2307/2532526]
[32]
Hill, T.; Lewicki, P. Statistics: methods and applications. a comprehensive reference for science. industry and data mining. StatSoft: Tulsa, 2006, 1, 813.
[33]
Rodrigues, C.F.; Lima, F.J.; Barbosa, F.T. Importance of using basic statistics adequately in clinical research. Rev. Bras. Anestesiol., 2017, 67(6), 619-625.
[http://dx.doi.org/10.1016/j.bjane.2017.01.011]
[34]
Kupferschmid, L.L.; Perkins, J. Organic solvent recycling plant exposure levels. Applied Industrial Hygiene, 1986, 1, 122-131.
[http://dx.doi.org/10.1080/08828032.1986.10390494]
[35]
Weires, N.A.; Johnston, A.; Warner, D.L.; McCormick, M.M.; Hammond, K.; McDougal, O.M. Recycling of waste acetone by fractional distillation. J. Chem. Educ., 2011, 88, 1724-1726.
[http://dx.doi.org/10.1021/ed2001158]
[36]
Divisi, D.; Di Leonardo, G.; Zaccagna, G.; Crisci, R. Basic statistics with Microsoft Excel: a review. J. Thorac. Dis., 2017, 9(6), 1734-1740.
[http://dx.doi.org/10.21037/jtd.2017.05.81] [PMID: 28740690]
[37]
Box, G.E.P.; Jenkins, G.M. Time Series Analysis: Forecasting and Control, 1st ed; Holden-Day: San Francisco, 1970, p. 575.
[38]
García, I.; Fall, Y.; Gómez, G.; González-Díaz, H. First computational chemistry multi-target model for anti-Alzheimer, anti-parasitic, anti-fungi, and anti-bacterial activity of GSK-3 inhibitors in vitro, in vivo, and in different cellular lines. Mol. Divers., 2011, 15(2), 561-567.
[http://dx.doi.org/10.1007/s11030-010-9280-3] [PMID: 20931280]
[39]
Lide, R. CRC Handbook of Chemistry and Physics, 88th ed; CRC Press: Boca Raton, 2008.
[40]
Parker, J. Alan. Protic-dipolar aprotic solvent effects on rates of bimolecular reactions. Chem. Rev., 1969, 69, 1-32.
[http://dx.doi.org/10.1021/cr60257a001]
[41]
Wangdi, D.; Tshomo, S. Exploring higher secondary school students' knowledge, attitude and practices towards waste management. Available from https://www.researchgate.net/publication/325709231_Exploring_Higher_Secondary_School_Students'_Knowledge_Attitude_and_Practices_towards_Waste_Management (Accessed on 18/12/2018).
[42]
Tapilouw, M.; Firman, H.; Redjeki, S.T.; Chandra, D. Junior high school students’ perception about simple environmental problem as an impact of problem based learning. J. Phys., 2017, 895(1)012130
[http://dx.doi.org/10.1088/1742-6596/895/1/012130]

Rights & Permissions Print Cite
Article Metrics
36
1
World Chagas Disease
© 2024 Bentham Science Publishers | Privacy Policy