Digital Potency Measurement: A New Approach Methodology for Antisera Effective Dose Assessments
Main Article Content
Abstract
Polyvalent antivenom potency tested for every batch and at four different stages of production is a regulatory requirement to be followed by the industry. Antivenom manufacturers have been following the gold standard test methodology for estimating median effective dose in mice for ages. Here, we report a non-animal New Approach Methodology that aligns with 3Rs in animal testing agenda, leveraging in vitro human stem cell technology for recreating microphysiological system complemented with process automation, Artificial Intelligence and Machine Learning digital workers’ effective utilization in the assay system to measure Naja naja snake antivenom potency. In vitro neutralization performed on specially configured human Microphysiological System acquired phenotype data sets at 20X magnification were analyzed against benchmark panels in the trained prediction model while the Artificial Intelligence predicted median effective dose value of venom-antivenom mixture was 3.9μL, showing a potency value of 2.04 mg recorded as the readout. This method adopted in the antivenom producer’s workflow will reduce reliance on mice-based testing and showcases potential for acceptance of robust alternative strategy to traditionally practiced protocols.
Keywords:
Artificial Intelligence, human stem cells, venom, antivenom, IC50, potency
Article Details
How to Cite
DASARI, Vasanthi et al.
Digital Potency Measurement: A New Approach Methodology for Antisera Effective Dose Assessments.
Medical Research Archives, [S.l.], v. 11, n. 12, dec. 2023.
ISSN 2375-1924.
Available at: <https://esmed.org/MRA/mra/article/view/4816>. Date accessed: 22 dec. 2024.
doi: https://doi.org/10.18103/mra.v11i12.4816.
Section
Research Articles
The Medical Research Archives grants authors the right to publish and reproduce the unrevised contribution in whole or in part at any time and in any form for any scholarly non-commercial purpose with the condition that all publications of the contribution include a full citation to the journal as published by the Medical Research Archives.
References
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33. Kusumoto D, Lachmann M, Kunihiro T, et al. Automated Deep Learning-Based System to Identify Endothelial Cells Derived from Induced Pluripotent Stem Cells. Stem Cell Reports. Jun 5 2018;10(6):1687-1695. doi:10.1016/j.stemcr.2018.04.007
34. Rudigkeit S, Reindl JB, Matejka N, et al. CeCILE - An Artificial Intelligence Based Cell-Detection for the Evaluation of Radiation Effects in Eucaryotic Cells. Front Oncol. 2021;11:688333. doi:10.3389/fonc.2021.688333
35. Kim D, Min Y, Oh JM, Cho YK. AI-powered transmitted light microscopy for functional analysis of live cells. Sci Rep. Dec 5 2019;9(1):18428. doi:10.1038/s41598-019-54961-x
36. Nys M, Zarkadas E, Brams M, et al. The molecular mechanism of snake short-chain alpha-neurotoxin binding to muscle-type nicotinic acetylcholine receptors. Nat Commun. Aug 4 2022;13(1):4543. doi:10.1038/s41467-022-32174-7
37. Tan CH, Bourges A, Tan KY. King Cobra and snakebite envenomation: on the natural history, human-snake relationship and medical importance of Ophiophagus hannah. J Venom Anim Toxins Incl Trop Dis. 2021;27:e20210051. doi:10.1590/1678-9199-JVATITD-2021-0051
38. Williams DJ, Gutierrez JM, Calvete JJ, et al. Ending the drought: new strategies for improving the flow of affordable, effective antivenoms in Asia and Africa. J Proteomics. Aug 24 2011;74(9):1735-67. doi:10.1016/j.jprot.2011.05.027
2. WHO. Guidelines for the production, control and regulation of snake antivenom immunoglobulins, Annex 5, TRS No 1004. WHO Press. 2013;
3. Gutierrez JM, Solano G, Pla D, et al. Preclinical Evaluation of the Efficacy of Antivenoms for Snakebite Envenoming: State-of-the-Art and Challenges Ahead. Toxins (Basel). May 13 2017;9(5)doi:10.3390/toxins9050163
4. Chippaux JP. [Guidelines for the production, control and regulation of snake antivenom immunoglobulins]. Biol Aujourdhui. 2010;204(1):87-91. Recommandations pour la production, le controle et l'enregistrement des immunoglobulines antivenimeuses. doi:10.1051/jbio/2009043
5. guidelines C. GUIDELINES ON THE REGULATION OF SCIENTIFIC EXPERIMENTS ON ANIMALS. Ministry of Environment & Forests. 2007;
6. Silva A, Hodgson WC, Tasoulis T, Isbister GK. Rodent Lethality Models Are Problematic for Evaluating Antivenoms for Human Envenoming. Front Pharmacol. 2022;13:830384. doi:10.3389/fphar.2022.830384
7. Hubrecht RC, Carter E. The 3Rs and Humane Experimental Technique: Implementing Change. Animals (Basel). Sep 30 2019;9(10) doi:10.3390/ani9100754
8. MacArthur Clark J. The 3Rs in research: a contemporary approach to replacement, reduction and refinement. Br J Nutr. Aug 2018;120(s1):S1-S7. doi:10.1017/S0007114517002227
9. Gutiérrez JM, Vargas M, Segura Á, et al. In Vitro Tests for Assessing the Neutralizing Ability of Snake Antivenoms: Toward the 3Rs Principles. Frontiers in Immunology. 2021;11doi:10.3389/fimmu.2020.617429
10. Sells PG. Animal experimentation in snake venom research and in vitro alternatives. Toxicon. 2003;42(2):115-133. doi:10.1016/s0041-0101(03)00125-9
11. Maria WS, Cambuy MO, Costa JO, Velarde DT, Chávez-Olórtegui C. Neutralizing potency of horse antibothropic antivenom. Correlation between in vivo and in vitro methods. Toxicon. 1998;36(10):1433-1439. doi:10.1016/s0041-0101(98)00077-4
12. Rial A, Morais V, Rossi S, Massaldi H. A new ELISA for determination of potency in snake antivenoms. Toxicon. 2006;48(4):462-466. doi:10.1016/j.toxicon.2006.07.004
13. Pla D, Rodriguez Y, Calvete JJ. Third Generation Antivenomics: Pushing the Limits of the In Vitro Preclinical Assessment of Antivenoms. Toxins (Basel). May 10 2017;9(5)doi:10.3390/toxins9050158
14. Rungsiwongse J, Ratanabanangkoon K. Development of an ELISA to assess the potency of horse therapeutic antivenom against Thai cobra venom. J Immunol Methods. Jan 24 1991;136(1):37-43. doi:10.1016/0022-1759(91)90247-d
15. Sanny CG. In vitro evaluation of total venom-antivenin immune complex formation and binding parameters relevant to antivenin protection against venom toxicity and lethality based on size-exclusion high-performance liquid chromatography. Toxicon. May 2011;57(6):871-81. doi:10.1016/j.toxicon.2011.03.003
16. Gomez-Betancur I, Gogineni V, Salazar-Ospina A, Leon F. Perspective on the Therapeutics of Anti-Snake Venom. Molecules. Sep 9 2019;24(18) doi:10.3390/molecules24183276
17. Senji Laxme RR, Khochare S, de Souza HF, et al. Beyond the 'big four': Venom profiling of the medically important yet neglected Indian snakes reveals disturbing antivenom deficiencies. PLoS Negl Trop Dis. Dec 2019;13(12):e0007899. doi:10.1371/journal.pntd.0007899
18. Senji Laxme RR, Attarde S, Khochare S, et al. Biogeographical venom variation in the Indian spectacled cobra (Naja naja) underscores the pressing need for pan-India efficacious snakebite therapy. PLoS Negl Trop Dis. Feb 2021;15(2):e0009150. doi:10.1371/journal.pntd.0009150
19. Lopes-de-Souza L, Costal-Oliveira F, Stransky S, et al. Development of a cell-based in vitro assay as a possible alternative for determining bothropic antivenom potency. Toxicon. 2019;170:68-76. doi:10.1016/j.toxicon.2019.09.010
20. Dasari V, Bolimera P, Krishna Gorthi L, Dravida S. In Vitro Profiling of Application Ready Human Surrogate Primary Progenitor Stromal Cell Fractions. Archives of Clinical and Biomedical Research. 2022;06(03) doi:10.26502/acbr.50170266
21. Randhawa MA. Calculation of LD50 values from the method of Miller and Tainter, 1944. J Ayub Med Coll Abbottabad. Jul-Sep 2009;21(3):184-5.
22. Senji Laxme RR, Khochare S, Attarde S, et al. Biogeographic venom variation in Russell's viper (Daboia russelii) and the preclinical inefficacy of antivenom therapy in snakebite hotspots. PLoS Negl Trop Dis. Mar 2021;15(3):e0009247. doi:10.1371/journal.pntd.0009247
23. Vasanthi Dasari, Papa Rao Bolimera, Sivarama Krishna Dokku, Leela Krishna Gorti, Dravida S. Neurotoxins Induced Toxicogenomic Patterns on Human Induced Pluripotent Stem Cell based Microphysiological System. Medical Research Archives. 2022;10(10)doi:10.18103/mra.v10i10.3202
24. He S, Minn KT, Solnica-Krezel L, Anastasio MA, Li H. Deeply-supervised density regression for automatic cell counting in microscopy images. Med Image Anal. Feb 2021;68:101892. doi:10.1016/j.media.2020.101892
25. Estevao-Costa MI, Gontijo SS, Correia BL, et al. Neutralization of toxicological activities of medically-relevant Bothrops snake venoms and relevant toxins by two polyvalent bothropic antivenoms produced in Peru and Brazil. Toxicon. Nov 2016;122:67-77. doi:10.1016/j.toxicon.2016.09.010
26. 26. Gutierrez JM, Calvete JJ, Habib AG, Harrison RA, Williams DJ, Warrell DA. Snakebite envenoming. Nat Rev Dis Primers. Sep 14 2017;3:17063. doi:10.1038/nrdp.2017.63
27. 27. Hulsart-Billstrom G, Dawson JI, Hofmann S, et al. A Surprisingly Poor Correlation between in Vitro and in Vivo Testing of Biomaterials for Bone Regeneration: Results of a Multicentre Analysis. Eur Cells Mater. Jan-Jun 2016;31:312-322. doi:10.22203/eCM.v031a20
28. Hoare SRJ, Hughes TE. Biosensor Assays for Measuring the Kinetics of G-Protein and Arrestin-Mediated Signaling in Live Cells. In: Markossian S, Grossman A, Brimacombe K, et al, eds. Assay Guidance Manual. 2004.
29. Riss T, Niles A, Moravec R, Karassina N, Vidugiriene J. Cytotoxicity Assays: In Vitro Methods to Measure Dead Cells. In: Markossian S, Grossman A, Brimacombe K, et al, eds. Assay Guidance Manual. 2004.
30. Hay EA, Parthasarathy R. Performance of convolutional neural networks for identification of bacteria in 3D microscopy datasets. PLoS Comput Biol. Dec 2018;14(12):e1006628. doi:10.1371/journal.pcbi.1006628
31. Eulenberg P, Kohler N, Blasi T, et al. Reconstructing cell cycle and disease progression using deep learning. Nat Commun. Sep 6 2017;8(1):463. doi:10.1038/s41467-017-00623-3
32. Campbell L, Kumar M, Turner S. Using CellProfiler to Analyze and Quantify Vascular Morphology. Methods Mol Biol. 2017;1544:179-189. doi:10.1007/978-1-4939-6722-3_13
33. Kusumoto D, Lachmann M, Kunihiro T, et al. Automated Deep Learning-Based System to Identify Endothelial Cells Derived from Induced Pluripotent Stem Cells. Stem Cell Reports. Jun 5 2018;10(6):1687-1695. doi:10.1016/j.stemcr.2018.04.007
34. Rudigkeit S, Reindl JB, Matejka N, et al. CeCILE - An Artificial Intelligence Based Cell-Detection for the Evaluation of Radiation Effects in Eucaryotic Cells. Front Oncol. 2021;11:688333. doi:10.3389/fonc.2021.688333
35. Kim D, Min Y, Oh JM, Cho YK. AI-powered transmitted light microscopy for functional analysis of live cells. Sci Rep. Dec 5 2019;9(1):18428. doi:10.1038/s41598-019-54961-x
36. Nys M, Zarkadas E, Brams M, et al. The molecular mechanism of snake short-chain alpha-neurotoxin binding to muscle-type nicotinic acetylcholine receptors. Nat Commun. Aug 4 2022;13(1):4543. doi:10.1038/s41467-022-32174-7
37. Tan CH, Bourges A, Tan KY. King Cobra and snakebite envenomation: on the natural history, human-snake relationship and medical importance of Ophiophagus hannah. J Venom Anim Toxins Incl Trop Dis. 2021;27:e20210051. doi:10.1590/1678-9199-JVATITD-2021-0051
38. Williams DJ, Gutierrez JM, Calvete JJ, et al. Ending the drought: new strategies for improving the flow of affordable, effective antivenoms in Asia and Africa. J Proteomics. Aug 24 2011;74(9):1735-67. doi:10.1016/j.jprot.2011.05.027