Login Register Cart 0
Generic placeholder image

Combinatorial Chemistry & High Throughput Screening

Editor-in-Chief

ISSN (Print): 1386-2073
ISSN (Online): 1875-5402

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

High Throughput Screening for Drug Discovery and Virus Detection

Author(s): Adetola Oke, Deniz Sahin, Xin Chen* and Ying Shang

Volume 25, Issue 9, 2022

Published on: 05 January, 2022

Page: [1518 - 1533] Pages: 16

DOI: 10.2174/1386207324666210811124856

Price: $65

Abstract

Background: High throughput screening systems are automated labs for the analysis of many biochemical substances in the drug discovery and virus detection process. This paper was motivated by the problem of automating testing for viruses and new drugs using high throughput screening systems. The emergence of severe acute respiratory syndrome coronavirus 2 (SARSCoV- 2) at the turn of 2019-2020 presented extraordinary challenges to public health. Existing approaches to test viruses and new drugs do not use optimal schedules and are not efficient.

Objectives: The scheduling of activities performed by various resources in a high throughput screening system affects its efficiency, throughput, operations cost, and quality of screening. This study aims to minimize the total screening (flow) time and ensure the consistency and quality of screening.

Methods: This paper develops innovative mixed-integer models that efficiently compute optimal schedules for screening many microplates to identify new drugs and determine whether samples contain viruses. The methods integrate job-shop and cyclic scheduling. Experiments are conducted for a drug discovery process of screening an enzymatic assay and a general process of detecting SARS-CoV-2.

Results: The method developed in this article can reduce screening time by as much as 91.67%.

Conclusion: The optimal schedules for high throughput screening systems greatly reduce the total flow time and can be computed efficiently to help discover new drugs and detect viruses.

Keywords: Automatic testing, mathematical programming, operations research, optimal control, optimal scheduling, high throughput screening, SARS-CoV-2, COVID-19.

« Previous Next »
Graphical Abstract

[1]
Brunsch, T.; Raisch, J.; Hardouin, L. Modeling and control of high-throughput screening systems. Control Eng. Pract., 2012, 20(1), 14-23.
[http://dx.doi.org/10.1016/j.conengprac.2010.12.006]
[2]
Macarrón, R.; Hertzberg, R.P. Design and implementation of high-throughput screening assays. Methods Mol. Biol., 2009, 565, 1-32.
[http://dx.doi.org/10.1007/978-1-60327-258-2_1] [PMID: 19551355]
[3]
Brunsch, T. Modeling and control of complex systems in a dioid; Framework, 2014.
[http://dx.doi.org/10.14279/DEPOSITONCE-3967]
[4]
Major, J. Challenges and opportunities in high throughput screening: implications for new technologies. J. Biomol. Screen., 1998, 3(1), 13-17.
[http://dx.doi.org/10.1177/108705719800300102]
[5]
Mayr, L.M.; Fuerst, P. The future of high-throughput screening. J. Biomol. Screen., 2008, 13(6), 443-448.
[http://dx.doi.org/10.1177/1087057108319644] [PMID: 18660458]
[6]
Mayer, E.; Haus, U-U.; Raisch, J.; Weismantel, R. Throughput-optimal sequences for cyclically operated plants. Discrete Event Dyn. Syst., 2008, 18(3), 355-383.
[http://dx.doi.org/10.1007/s10626-008-0038-3]
[7]
Noah, J. New developments and emerging trends in high-throughput screening methods for lead compound identification; IJHTS, 2010, p. 141.
[http://dx.doi.org/10.2147/IJHTS.S8683]
[8]
Pereira, D.A.; Williams, J.A. Origin and evolution of high throughput screening. Br. J. Pharmacol., 2007, 152(1), 53-61.
[http://dx.doi.org/10.1038/sj.bjp.0707373] [PMID: 17603542]
[9]
Mayer, E.; Raisch, J. Modeling and optimization for high-throughput-screening systems. IFAC Proceedings Volumes, 2004, 37(1), pp. 469-474.
[http://dx.doi.org/10.1016/S1474-6670(17)38776-1]
[10]
Mayer, E.; Raisch, J. Time-optimal scheduling for high throughput screening processes using cyclic discrete event models. Math. Comput. Simul., 2004, 66(2-3), 181-191.
[http://dx.doi.org/10.1016/j.matcom.2003.11.004]
[11]
Beckman Coulter Inc. SAMI automated method development interface., 2021. Available from: https://www.beckman.com/liquid-handlers/software/sami-ex
[12]
Rogers, M.V. High-throughput screening. Drug Discov. Today, 1997, 2(11), 503-504.
[http://dx.doi.org/10.1016/S1359-6446(97)01101-X]
[13]
Rardin, R. Discrete Optimization Models. In: Optimization in Operations Research; Pearson higher education, Inc: Hoboken, NJ, 2017, pp. 655-730.
[14]
Murray, C.; Anderson, C. Scheduling software for high throughput screening. Lab. Robot. Autom., 1996, 8(5), 295-305.
[http://dx.doi.org/10.1002/(SICI)1098-2728(1996)8:5<295:AID-LRA6>3.0.CO;2-W]
[15]
Draper, D.L.; Jónsson, A.K.; Clements, D.P.; Joslin, D. Cyclic scheduling. In: Sixteenth International Joint Conference on Artificial Intelligence; , 1996; pp. 1016-1021.
[16]
Brucker, P. Scheduling Algorithms, 5th ed; Springer: Berlin, New York, 2007.
[17]
Gafarov, E.R.; Lazarev, A.A.; Werner, F. A Note on a Single Machine Scheduling Problem with Generalized Total Tardiness Objective Function. Inf. Process. Lett., 2012, 112(3), 72-76.
[http://dx.doi.org/10.1016/j.ipl.2011.10.013]
[18]
Li, X.; Chen, L.; Xu, H.; Gupta, J.N.D. Trajectory scheduling methods for minimizing total tardiness in a flowshop. Operations Research Perspectives, 2015, 2, 13-23.
[http://dx.doi.org/10.1016/j.orp.2014.12.001]
[19]
Levner, E.; Kats, V.; Alcaide López de Pablo, D.; Cheng, T.C.E. Complexity of cyclic scheduling problems: A state-of-the-art survey. Comput. Ind. Eng., 2010, 59(2), 352-361.
[http://dx.doi.org/10.1016/j.cie.2010.03.013]
[20]
Xiao, S.Y.; Wu, Y.; Liu, H. Evolving status of the 2019 novel coronavirus infection: Proposal of conventional serologic assays for disease diagnosis and infection monitoring. J. Med. Virol., 2020, 92(5), 464-467.
[http://dx.doi.org/10.1002/jmv.25702] [PMID: 32031264]
[21]
Chansaenroj, J.; Yorsaeng, R.; Posuwan, N.; Puenpa, J.; Sudhinaraset, N.; Chirathaworn, C.; Poovorawan, Y. Detection of SARS-CoV-2-specific antibodies via rapid diagnostic immunoassays in COVID-19 patients. Virol. J., 2021, 18(1), 52.
[http://dx.doi.org/10.1186/s12985-021-01530-2] [PMID: 33750394]
[22]
Campos, D.M.O.; Fulco, U.L.; de Oliveira, C.B.S.; Oliveira, J.I.N. SARS-CoV-2 virus infection: Targets and antiviral pharmacological strategies. J. Evid. Based Med., 2020, 13(4), 255-260.
[http://dx.doi.org/10.1111/jebm.12414] [PMID: 33058394]
[23]
Zhou, T.; Liu, Q.; Yang, Z.; Liao, J.; Yang, K.; Bai, W.; Lu, X.; Zhang, W. Preliminary prediction of the basic reproduction number of the Wuhan novel coronavirus 2019-nCoV. J. Evid. Based Med., 2020, 13(1), 3-7.
[http://dx.doi.org/10.1111/jebm.12376] [PMID: 32048815]
[24]
Wang, Z.; Fu, Y.; Guo, Z.; Li, J.; Li, J.; Cheng, H.; Lu, B.; Sun, Q. Transmission and prevention of SARS-CoV-2. Biochem. Soc. Trans., 2020, 48(5), 2307-2316.
[http://dx.doi.org/10.1042/BST20200693] [PMID: 33084885]
[25]
Tang, Y-W.; Schmitz, J.E.; Persing, D.H.; Stratton, C.W. laboratory diagnosis of COVID-19: Current issues and challenges. J. Clin. Microbiol., 2020, 58(6), e00512-e00520.
[http://dx.doi.org/10.1128/JCM.00512-20] [PMID: 32245835]
[26]
Wang, H.; Li, X.; Li, T.; Zhang, S.; Wang, L.; Wu, X.; Liu, J. The genetic sequence, origin, and diagnosis of SARS-CoV-2. Eur. J. Clin. Microbiol. Infect. Dis., 2020, 39(9), 1629-1635.
[http://dx.doi.org/10.1007/s10096-020-03899-4] [PMID: 32333222]
[27]
Dan, J.M.; Mateus, J.; Kato, Y.; Hastie, K.M.; Yu, E.D.; Faliti, C.E.; Grifoni, A.; Ramirez, S.I.; Haupt, S.; Frazier, A.; Nakao, C.; Rayaprolu, V.; Rawlings, S.A.; Peters, B.; Krammer, F.; Simon, V.; Saphire, E.O.; Smith, D.M.; Weiskopf, D.; Sette, A.; Crotty, S. Immunological memory to SARS-CoV-2 assessed for up to 8 months after infection. Science, 2021, 371(6529), eabf4063.
[http://dx.doi.org/10.1126/science.abf4063] [PMID: 33408181]
[28]
Tian, X.; Liu, L.; Jiang, W.; Zhang, H.; Liu, W.; Li, J. Potent and persistent antibody response in COVID-19 recovered patients. Front. Immunol., 2021, 12, 659041.
[http://dx.doi.org/10.3389/fimmu.2021.659041] [PMID: 34122416]
[29]
Meyer, B.; Drosten, C.; Müller, M.A. Serological assays for emerging coronaviruses: challenges and pitfalls. Virus Res., 2014, 194, 175-183.
[http://dx.doi.org/10.1016/j.virusres.2014.03.018] [PMID: 24670324]
[30]
Rongqing, Z.; Li, M.; Song, H.; Chen, J.; Ren, W.; Feng, Y.; Gao, G.F.; Song, J.; Peng, Y.; Su, B.; Guo, X.; Wang, Y.; Chen, J.; Li, J.; Sun, H.; Bai, Z.; Cao, W.; Zhu, J.; Zhang, Q.; Sun, Y.; Sun, S.; Mao, X.; Su, J.; Chen, X.; He, A.; Gao, W.; Jin, R.; Jiang, Y.; Sun, L. Early detection of severe acute respiratory syndrome coronavirus 2 antibodies as a serologic marker of infection in patients with coronavirus disease 2019. Clin. Infect. Dis., 2020, 71(16), 2066-2072.
[http://dx.doi.org/10.1093/cid/ciaa523] [PMID: 32357209]
[31]
Manual for the Laboratory Diagnosis and Virological Surveillance of Influenza; World Health Organization: Geneva, 2011.
[32]
Lim, J.; Lee, J. Current laboratory diagnosis of coronavirus disease 2019. Korean J. Intern. Med. (Korean. Assoc. Intern. Med.), 2020, 35(4), 741-748.
[http://dx.doi.org/10.3904/kjim.2020.257] [PMID: 32668512]

Rights & Permissions Print Cite
Article Metrics
25
1
© 2024 Bentham Science Publishers | Privacy Policy