Skip to main content

Advertisement

SpringerLink
Log in

Alternatives of Animal Models for Biomedical Research: a Comprehensive Review of Modern Approaches

  • Published:
Stem Cell Reviews and Reports Aims and scope Submit manuscript

Abstract

Biomedical research has long relied on animal models to unravel the intricacies of human physiology and pathology. However, concerns surrounding ethics, expenses, and inherent species differences have catalyzed the exploration of alternative avenues. The contemporary alternatives to traditional animal models in biomedical research delve into three main categories of alternative approaches: in vitro models, in vertebrate models, and in silico models. This unique approach to artificial intelligence and machine learning has been a keen interest to be used in different biomedical research. The main goal of this review is to serve as a guide to researchers seeking novel avenues for their investigations and underscores the importance of considering alternative models in the pursuit of scientific knowledge and medical breakthroughs, including showcasing the broad spectrum of modern approaches that are revolutionizing biomedical research and leading the way toward a more ethical, efficient, and innovative future. Models can insight into cellular processes, developmental biology, drug interaction, assessing toxicology, and understanding molecular mechanisms.

Graphical Abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Data Availability

Data will be available upon request from reviewers or editors.

Code Availability

Not Applicable.

References

  1. Abdullah, K. A. L., Atazhanova, T., Chavez-Badiola, A., & Shivhare, S. B. (2023). Automation in ART: Paving the way for the future of infertility treatment. Reproductive Sciences, 30, 1006–1016.

    Article  PubMed  Google Scholar 

  2. Acarli, D., Semih, K., & Ali, K. (2019). New alternative fishing gear suggestions for trap fisheries from the waste recycle materials: Case study for Muricidae (Mollusca: Gastropoda). Marine Science and Technology Bulletin, 8, 92–97.

    Article  Google Scholar 

  3. Afshar, L., Aghayan, H. R., Sadighi, J., Arjmand, B., Hashemi, S. M., Basiri, M., Samani, R. O., Ashtiani, M. K., Azin, S. A., Hajizadeh-Saffar, E., et al. (2020). Ethics of research on stem cells and regenerative medicine: Ethical guidelines in the Islamic Republic of Iran. Stem Cell Research & Therapy, 11, 396. https://doi.org/10.1186/s13287-020-01916-z

    Article  Google Scholar 

  4. Agarrayua, D. A., Funguetto-Ribeiro, A. C., Trevisan, P., Haas, S. E., & Ávila, D. S. (2023). Safety assessment of different unloaded polymeric nanocapsules in Caenorhabditis elegans. Comparative Biochemistry and Physiology Part C: Toxicology & Pharmacology, 263, 109477. https://doi.org/10.1016/j.cbpc.2022.109477

    Article  CAS  Google Scholar 

  5. Andersen, M. L., & Winter, L. M. (2017). Animal models in biological and biomedical research-experimental and ethical concerns. Anais da Academia Brasileira de Ciências, 91, e20170238.

    Article  PubMed  Google Scholar 

  6. Ansorge, M., & Pompe, T. (2018). Systems for localized release to mimic paracrine cell communication in vitro. Journal of Controlled Release, 278, 24–36.

    Article  CAS  PubMed  Google Scholar 

  7. Ashammakhi, N., GhavamiNejad, A., Tutar, R., Fricker, A., Roy, I., Chatzistavrou, X., HoqueApu, E., Nguyen, K.-L., Ahsan, T., & Pountos, I. (2022). Highlights on advancing frontiers in tissue engineering. Tissue Engineering Part B: Reviews, 28, 633–664.

    Article  PubMed  Google Scholar 

  8. Athanasopoulou, K., Daneva, G. N., Adamopoulos, P. G., & Scorilas, A. (2022). Artificial Intelligence: The Milestone in Modern Biomedical Research. BioMedInformatics, 2, 727–744.

    Article  Google Scholar 

  9. Azeloglu, E. U., & Iyengar, R. (2015). Signaling networks: Information flow, computation, and decision making. Cold Spring Harbor Perspectives in Biology, 7, a005934.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Bailone, R. L., Fukushima, H. C. S., Ventura Fernandes, B. H., De Aguiar, L. K., Corrêa, T., Janke, H., GrejoSetti, P., Roça, R. D. O., & Borra, R. C. (2020). Zebrafish as an alternative animal model in human and animal vaccination research. Laboratory Animal Research, 36, 13. https://doi.org/10.1186/s42826-020-00042-4

    Article  PubMed  PubMed Central  Google Scholar 

  11. Banach, M., & Robert, J. (2017). Tumor immunology viewed from alternative animal models—the xenopus story. Current Pathobiology Reports, 5, 49–56.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Bédard, P., Gauvin, S., Ferland, K., Caneparo, C., Pellerin, È., Chabaud, S., & Bolduc, S. (2020). Innovative human three-dimensional tissue-engineered models as an alternative to animal testing. Bioengineering (Basel), 7. https://doi.org/10.3390/bioengineering7030115

  13. Benam, K. H., Gilchrist, S., Kleensang, A., Satz, A. B., Willett, C., & Zhang, Q. (2019). Exploring new technologies in biomedical research. Drug Discovery Today, 24, 1242–1247.

    Article  PubMed  Google Scholar 

  14. Borba, J. V., Alves, V. M., Braga, R. C., Korn, D. R., Overdahl, K., Silva, A. C., Hall, S. U., Overdahl, E., Kleinstreuer, N., & Strickland, J. (2022). STopTox: An in silico alternative to animal testing for acute systemic and topical toxicity. Environmental Health Perspectives, 130, 027012.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Borba, J. V. B., Braga, R. C., Alves, V. M., Muratov, E. N., Kleinstreuer, N., Tropsha, A., & Andrade, C. H. (2021). Pred-skin: A web portal for accurate prediction of human skin sensitizers. Chemical Research in Toxicology, 34, 258–267. https://doi.org/10.1021/acs.chemrestox.0c00186

    Article  CAS  PubMed  Google Scholar 

  16. Bovard, D., Sandoz, A., Luettich, K., Frentzel, S., Iskandar, A., Marescotti, D., Trivedi, K., Guedj, E., Dutertre, Q., & Peitsch, M. C. (2018). A lung/liver-on-a-chip platform for acute and chronic toxicity studies. Lab on a Chip, 18, 3814–3829.

    Article  CAS  PubMed  Google Scholar 

  17. Bray, A., Webb, J. B., Enquobahrie, A., Vicory, J., Heneghan, J., Hubal, R., TerMaath, S., Asare, P., & Clipp, R. B. (2019). Pulse physiology engine: An open-source software platform for computational modeling of human medical simulation. SN Comprehensive Clinical Medicine, 1, 362–377.

    Article  Google Scholar 

  18. Chen, T., Shu, X., Zhou, H., Beckford, F. A., & Misir, M. (2023). Algorithm selection for protein–ligand docking: Strategies and analysis on ACE. Scientific Reports, 13, 8219. https://doi.org/10.1038/s41598-023-35132-5

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  19. Chen, Y.-W., Huang, S. X., de Carvalho, A. L. R. T., Ho, S.-H., Islam, M. N., Volpi, S., Notarangelo, L. D., Ciancanelli, M., Casanova, J.-L., Bhattacharya, J., et al. (2017). A three-dimensional model of human lung development and disease from pluripotent stem cells. Nature Cell Biology, 19, 542–549. https://doi.org/10.1038/ncb3510

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Cho, Y. W., Min, D. W., Kim, H. P., An, Y., Kim, S., Youk, J., Chun, J., Im, J. P., Song, S. H., & Ju, Y. S. (2022). Patient-derived organoids as a preclinical platform for precision medicine in colorectal cancer. Molecular Oncology, 16, 2396–2412.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Chou, W.-C., Chen, Q., Yuan, L., Cheng, Y.-H., He, C., Monteiro-Riviere, N. A., Riviere, J. E., & Lin, Z. (2023). An artificial intelligence-assisted physiologically-based pharmacokinetic model to predict nanoparticle delivery to tumors in mice. Journal of Controlled Release, 361, 53–63. https://doi.org/10.1016/j.jconrel.2023.07.040

    Article  CAS  PubMed  Google Scholar 

  22. Choudhury, D. R., Chowdhury, S., Talukdar, P., & Talapatra, S. (2021). In-silico study of toxicity mechanisms for metabolites of phyto-compounds from musa sp. compared to synthetic medicine ranitidine. International Journal of Pharmaceutical Sciences and Research, 12, 1521–1528.

    CAS  Google Scholar 

  23. Chu, P.-Y., Koh, A.P.-F., Antony, J., & Huang, R.Y.-J. (2022). Applications of the chick chorioallantoic membrane as an alternative model for cancer studies. Cells, Tissues, Organs, 211, 222–237.

    Article  CAS  PubMed  Google Scholar 

  24. Ciallella, H. L., Russo, D. P., Aleksunes, L. M., Grimm, F. A., & Zhu, H. (2021). Revealing adverse outcome pathways from public high-throughput screening data to evaluate new toxicants by a knowledge-based deep neural network approach. Environmental Science & Technology, 55, 10875–10887.

    Article  ADS  CAS  Google Scholar 

  25. Davison, A., & Neiman, M. (2021). Mobilizing molluscan models and genomes in biology. Philosophical Transactions of the Royal Society B, 376, 20200163.

    Article  CAS  Google Scholar 

  26. de Cristo Soares Alves, A., RosaneDallemole, D., MedeiroCiocheta, T., Ferreira Weber, A., da Silva Gündel, S., Visioli, F., Figueiró, F., StanisçuaskiGuterres, S., & Raffin Pohlmann, A. (2023). Chicken embryo model for in vivo acute toxicological and antitumor efficacy evaluation of lipid nanocarrier containing doxorubicin. International Journal of Pharmaceutics: X, 6, 100193. https://doi.org/10.1016/j.ijpx.2023.100193

    Article  CAS  PubMed  Google Scholar 

  27. de Souza, A. M., Araujo-Silva, H., Costa, A. M., Rossi, A. L., Rossi, A. M., Granjeiro, J. M., Luchiari, A. C., & Batistuzzo de Medeiros, S. R. (2023). Embryotoxicity and visual-motor response of functionalized nanostructured hydroxyapatite-based biomaterials in zebrafish (Danio rerio). Chemosphere, 313, 137519. https://doi.org/10.1016/j.chemosphere.2022.137519

    Article  CAS  PubMed  Google Scholar 

  28. Dotti, I., & Salas, A. (2018). Potential use of human stem cell–derived intestinal organoids to study inflammatory bowel diseases. Inflammatory Bowel Diseases, 24, 2501–2509.

    PubMed  PubMed Central  Google Scholar 

  29. Dudley, J. T., Deshpande, T., & Butte, A. J. (2011). Exploiting drug–disease relationships for computational drug repositioning. Briefings in Bioinformatics, 12, 303–311.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Elemento, O., Leslie, C., Lundin, J., & Tourassi, G. (2021). Artificial intelligence in cancer research, diagnosis and therapy. Nature Reviews Cancer, 21, 747–752. https://doi.org/10.1038/s41568-021-00399-1

    Article  CAS  PubMed  Google Scholar 

  31. Esmaeilbeigi, M., BehzadiTayemeh, M., Johari, S. A., Ghorbani, F., Sourinejad, I., & Yu, I. J. (2022). In silico modeling of the antagonistic effect of mercuric chloride and silver nanoparticles on the mortality rate of zebrafish (Danio rerio) based on response surface methodology. Environmental Science and Pollution Research, 29, 54733–54744. https://doi.org/10.1007/s11356-022-19693-y

    Article  CAS  PubMed  Google Scholar 

  32. Fabre, K. M., Livingston, C., & Tagle, D. A. (2014). Organs-on-chips (microphysiological systems): Tools to expedite efficacy and toxicity testing in human tissue. Experimental Biology and Medicine, 239, 1073–1077.

    Article  CAS  PubMed  Google Scholar 

  33. Fentem, J. H. (2023). The 19th FRAME annual lecture, November 2022: Safer chemicals and sustainable innovation will be achieved by regulatory use of modern safety science, not by more animal testing. Alternatives to Laboratory Animals, 51, 90–101.

    Article  PubMed  Google Scholar 

  34. Fetah, K., Tebon, P., Goudie, M. J., Eichenbaum, J., Ren, L., Barros, N., Nasiri, R., Ahadian, S., Ashammakhi, N., & Dokmeci, M. R. (2019). The emergence of 3D bioprinting in organ-on-chip systems. Progress in Biomedical Engineering, 1, 012001.

    Article  Google Scholar 

  35. Gan, J., Bolon, B., Van Vleet, T., & Wood, C. (2022). Alternative models in biomedical research: In silico, in vitro, ex vivo, and nontraditional in vivo approaches. In Haschek and Rousseaux's handbook of toxicologic pathology (pp. 925–966). Elsevier.

  36. Gearty, A. J., Ignoffo, T. R., Slaughter, A. M., & Kimmerer, W. J. (2021). Growth and reproductive rates of the dominant copepod Pseudodiaptomus forbesi in response to environmental factors and habitat type in the northern San Francisco Estuary. Aquatic Ecology, 55, 825–848.

    Article  Google Scholar 

  37. Germany, E. M., Zahayko, N., & Khalimonchuk, O. (2019). Isolation of specific neuron populations from roundworm Caenorhabditis elegans. JoVE (Journal of Visualized Experiments), e60145. https://doi.org/10.3791/60145

  38. Gilbert-Sandoval, I., Wesseling, S., & Rietjens, I. M. (2020). Predicting the acute liver toxicity of aflatoxin B1 in rats and humans by an in vitro–in silico testing strategy. Molecular Nutrition & Food Research, 64, 2000063.

    Article  CAS  Google Scholar 

  39. Gimondi, S., Ferreira, H., Reis, R. L., & Neves, N. M. (2023). Microfluidic devices: A tool for nanoparticle synthesis and performance evaluation. ACS Nano, 17, 14205–14228.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Hasan, R., Herowati, R., & Widodo, G. P. (2023). Molecular docking and pharmacokinetic prediction of potential compounds from luffa acutangula as antidiabetic candidates. PHARMACY: Jurnal Farmasi Indonesia (Pharmaceutical Journal of Indonesia), 20, 71–76.

    Article  Google Scholar 

  41. Heikkinen, A. T., Baneyx, G., Caruso, A., & Parrott, N. (2012). Application of PBPK modeling to predict human intestinal metabolism of CYP3A substrates–an evaluation and case study using GastroPlus™. European Journal of Pharmaceutical Sciences, 47, 375–386.

    Article  CAS  PubMed  Google Scholar 

  42. Herrmann, K., Pistollato, F., & Stephens, M. L. (2019). Beyond the 3Rs: Expanding the use of human-relevant replacement methods in biomedical research. ALTEX-Alternatives to Animal Experimentation, 36, 343–352.

    Google Scholar 

  43. Hoffmann, P., Schnepel, N., Langeheine, M., Künnemann, K., Grassl, G. A., Brehm, R., Seeger, B., Mazzuoli-Weber, G., & Breves, G. (2021). Intestinal organoid-based 2D monolayers mimic physiological and pathophysiological properties of the pig intestine. PLoS ONE, 16, e0256143. https://doi.org/10.1371/journal.pone.0256143

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Huang, C., Sanaei, F., Verdurmen, W. P. R., Yang, F., Ji, W., & Walboomers, X. F. (2023). The application of organs-on-a-chip in dental, oral, and craniofacial research. Journal of Dental Research, 102, 364–375. https://doi.org/10.1177/00220345221145555

    Article  CAS  PubMed  Google Scholar 

  45. Huang, H.-J., Lee, Y.-H., Hsu, Y.-H., Liao, C.-T., Lin, Y.-F., & Chiu, H.-W. (2021). Current strategies in assessment of nanotoxicity: Alternatives to in vivo animal testing. International Journal of Molecular Sciences, 22, 4216.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Hwang, S.-H., Lee, S., Park, J. Y., Jeon, J. S., Cho, Y.-J., & Kim, S. (2021). Potential of drug efficacy evaluation in lung and kidney cancer models using organ-on-a-chip technology. Micromachines, 12, 215.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Ingber, D. E. (2022). Human organs-on-chips for disease modelling, drug development and personalized medicine. Nature Reviews Genetics, 23, 467–491. https://doi.org/10.1038/s41576-022-00466-9

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Ishwarya, R., Tamilmani, G., Jayakumar, R., Albeshr, M. F., Mahboob, S., Shahid, D., Riaz, M. N., Govindarajan, M., & Vaseeharan, B. (2023). Synthesis of zinc oxide nanoparticles using Vigna mungo seed husk extract: An enhanced antibacterial, anticancer activity and eco-friendly bio-toxicity assessment on algae and zooplankton. Journal of Drug Delivery Science and Technology, 79, 104002. https://doi.org/10.1016/j.jddst.2022.104002

    Article  CAS  Google Scholar 

  49. Johnson, K. B., Wei, W. Q., Weeraratne, D., Frisse, M. E., Misulis, K., Rhee, K., Zhao, J., & Snowdon, J. L. (2021). Precision medicine, AI, and the future of personalized health care. Clinical and Translational Science, 14, 86–93.

    Article  PubMed  Google Scholar 

  50. Kammala, A. K., Richardson, L. S., Radnaa, E., Han, A., & Menon, R. (2023). Microfluidic technology and simulation models in studying pharmacokinetics during pregnancy. Frontiers in Pharmacology, 14, 1241815. https://doi.org/10.3389/fphar.2023.1241815

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Khabib, M. N. H., Sivasanku, Y., Lee, H. B., Kumar, S., & Kue, C. S. (2022). Alternative animal models in predictive toxicology. Toxicology, 465, 153053.

    Article  CAS  PubMed  Google Scholar 

  52. Khan, P. M., & Roy, K. (2018). Current approaches for choosing feature selection and learning algorithms in quantitative structure–activity relationships (QSAR). Expert Opinion on Drug Discovery, 13, 1075–1089.

    Article  CAS  PubMed  Google Scholar 

  53. Koning, J. J., Rodrigues Neves, C. T., Schimek, K., Thon, M., Spiekstra, S. W., Waaijman, T., de Gruijl, T. D., & Gibbs, S. (2021). A multi-organ-on-chip approach to investigate how oral exposure to metals can cause systemic toxicity leading to langerhans cell activation in skin. Frontiers in Toxicology, 3, 824825. https://doi.org/10.3389/ftox.2021.824825

    Article  PubMed  Google Scholar 

  54. Krishna, S., Borrel, A., Huang, R., Zhao, J., Xia, M., & Kleinstreuer, N. (2022). High-throughput chemical screening and structure-based models to predict hERG inhibition. Biology, 11, 209.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Laksanasopin, T., Chin, C. D., Moore, H., Wang, J., Cheung, Y. K., & Sia, S. K. (2009). Microfluidic point-of-care diagnostics for resource-poor environments. IEEE, 1057–1059,. https://doi.org/10.1109/IEMBS.2009.5334942

  56. Lee, H. J., Mun, S. J., Jung, C. R., Kang, H. M., Kwon, J. E., Ryu, J. S., Ahn, H. S., Kwon, O. S., Ahn, J., & Moon, K. S. (2023). In vitro modeling of liver fibrosis with 3D co-culture system using a novel human hepatic stellate cell line. Biotechnology and Bioengineering, 120, 1241–1253.

    Article  CAS  PubMed  Google Scholar 

  57. Lee, J., Kim, J.-H., Hong, S.-H., & Yang, S.-R. (2021). Organoid model in idiopathic pulmonary fibrosis. International Journal of Stem Cells, 14, 1–8.

    Article  CAS  PubMed  Google Scholar 

  58. Lee, S. Y., Kang, J. H., Jeong, J. W., Kim, J. H., Kim, H. W., Oh, D. H., Kim, J.-M., Rhim, S.-J., Kim, G.-D., & Kim, H. S. (2022). Alternative experimental approaches to reduce animal use in biomedical studies. Journal of Drug Delivery Science and Technology, 68, 103131.

    Article  CAS  Google Scholar 

  59. Li, W., Zhou, Z., Zhou, X., Khoo, B. L., Gunawan, R., Chin, Y. R., Zhang, L., Yi, C., Guan, X., & Yang, M. (2023). 3D biomimetic models to reconstitute tumor microenvironment in vitro: Spheroids, organoids, and tumor-on-a-chip. Advanced Healthcare Materials. https://doi.org/10.1002/adhm.202202609

    Article  PubMed  PubMed Central  Google Scholar 

  60. Li, Z., Xu, H., Yu, L., Wang, J., Meng, Q., Mei, H., Cai, Z., Chen, W., & Huang, W. (2022). Patient-derived renal cell carcinoma organoids for personalized cancer therapy. Clinical and Translational Medicine, 12, e970. https://doi.org/10.1002/ctm2.970

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Liang, L., Li, W., Zhang, Z., Li, D., Pu, S., Xiang, R., & Zhai, F. (2023). Develop adult extrapolation to pediatrics and pediatric dose optimization based on the physiological pharmacokinetic model of azithromycin. Biopharmaceutics & Drug Disposition., 4, 245–258. https://doi.org/10.1002/bdd.2352

    Article  CAS  Google Scholar 

  62. Lipinski, C. F., Maltarollo, V. G., Oliveira, P. R., Da Silva, A. B., & Honorio, K. M. (2019). Advances and perspectives in applying deep learning for drug design and discovery. Frontiers in Robotics and AI, 6, 108.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Liu, Z., Cai, Y., Liao, Z., Xu, Y., Wang, Y., Jiang, X., Li, Y., Lu, Y., Nie, Y., Zhang, X., et al. (2019). Cloning of a gene edited macaque monkey by somatic cell nuclear transfer. National Science Review, 6, 101–108. https://doi.org/10.1093/nsr/nwz003

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Low, L. A., Mummery, C., Berridge, B. R., Austin, C. P., & Tagle, D. A. (2021). Organs-on-chips: Into the next decade. Nature Reviews Drug Discovery, 20, 345–361. https://doi.org/10.1038/s41573-020-0079-3

    Article  CAS  PubMed  Google Scholar 

  65. Ly, K. L., Rooholghodos, S. A., Rahimi, C., Rahimi, B., Bienek, D. R., Kaufman, G., Raub, C. B., & Luo, X. (2021). An Oral-mucosa-on-a-chip sensitively evaluates cell responses to dental monomers. Biomedical Microdevices, 23, 7. https://doi.org/10.1007/s10544-021-00543-6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Ma, C., Peng, Y., Li, H., & Chen, W. (2021). Organ-on-a-chip: A new paradigm for drug development. Trends in Pharmacological Sciences, 42, 119–133.

    Article  PubMed  Google Scholar 

  67. Madden, J. C., Enoch, S. J., Paini, A., & Cronin, M. T. D. (2020). A review of in silico tools as alternatives to animal testing: Principles, resources and applications. Alternatives to Laboratory Animals, 48, 146–172. https://doi.org/10.1177/0261192920965977

    Article  PubMed  Google Scholar 

  68. Mansouri, K., Cariello, N. F., Korotcov, A., Tkachenko, V., Grulke, C. M., Sprankle, C. S., Allen, D., Casey, W. M., Kleinstreuer, N. C., & Williams, A. J. (2019). Open-source QSAR models for pKa prediction using multiple machine learning approaches. Journal of Cheminformatics, 11, 60. https://doi.org/10.1186/s13321-019-0384-1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Mansouri, K., Grulke, C. M., Judson, R. S., & Williams, A. J. (2018). OPERA models for predicting physicochemical properties and environmental fate endpoints. Journal of Cheminformatics, 10, 1–19.

    Article  Google Scholar 

  70. Mansouri, K., Kleinstreuer, N., Abdelaziz, A. M., Alberga, D., Alves, V. M., Andersson, P. L., Andrade, C. H., Bai, F., Balabin, I., & Ballabio, D. (2020). CoMPARA: Collaborative modeling project for androgen receptor activity. Environmental Health Perspectives, 128, 027002.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Menegola, E., Battistoni, M., Metruccio, F., & Di Renzo, F. (2023). Advantages and disadvantages of the use of Xenopus laevis embryos and Zebra fish as alternative methods to assess teratogens. Current Opinion in Toxicology, 34, 100387. https://doi.org/10.1016/j.cotox.2023.100387

  72. Mukherjee, P., Roy, S., Ghosh, D., & Nandi, S. (2022). Role of animal models in biomedical research: A review. Laboratory Animal Research, 38, 18.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Noben, M., Verstockt, B., de Bruyn, M., Hendriks, N., Van Assche, G., Vermeire, S., Verfaillie, C., & Ferrante, M. (2017). Epithelial organoid cultures from patients with ulcerative colitis and Crohn’s disease: A truly long-term model to study the molecular basis for inflammatory bowel disease? Gut, 66, 2193–2195.

    Article  CAS  PubMed  Google Scholar 

  74. Ouchi, R., Togo, S., Kimura, M., Shinozawa, T., Koido, M., Koike, H., Thompson, W., Karns, R. A., Mayhew, C. N., & McGrath, P. S. (2019). Modeling steatohepatitis in humans with pluripotent stem cell-derived organoids. Cell Metabolism, 30, 374-384. e376.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Pagadala, N. S., Syed, K., & Tuszynski, J. (2017). Software for molecular docking: A review. Biophysical Reviews, 9, 91–102.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Paiola, M., Dimitrakopoulou, D., Pavelka, M. S., & Robert, J. (2023). Amphibians as a model to study the role of immune cell heterogeneity in host and mycobacterial interactions. Developmental & Comparative Immunology, 139, 104594. https://doi.org/10.1016/j.dci.2022.104594

    Article  CAS  Google Scholar 

  77. Panteleev, J., Gao, H., & Jia, L. (2018). Recent applications of machine learning in medicinal chemistry. Bioorganic & medicinal chemistry letters, 28, 2807–2815.

    Article  CAS  Google Scholar 

  78. Pappalardo, F., Russo, G., Tshinanu, F. M., & Viceconti, M. (2018). In silico clinical trials: Concepts and early adoptions. Briefings in Bioinformatics, 20, 1699–1708. https://doi.org/10.1093/bib/bby043

    Article  CAS  Google Scholar 

  79. Paramasivam, S., & Perumal, S. S. (2023). In silico rationalization for leads from Oldenlandia umbellata L. to inhibit multiple molecular targets regulating osteoporosis. Pharmacognosy Magazine, 09731296231196189. https://doi.org/10.1177/09731296231196189

  80. Park, C., Took, C. C., & Seong, J.-K. (2018). Machine learning in biomedical engineering. Biomedical Engineering Letters, 8, 1–3. https://doi.org/10.1007/s13534-018-0058-3

    Article  PubMed  PubMed Central  Google Scholar 

  81. Park, T.-E., Mustafaoglu, N., Herland, A., Hasselkus, R., Mannix, R., FitzGerald, E. A., Prantil-Baun, R., Watters, A., Henry, O., & Benz, M. (2019). Hypoxia-enhanced Blood-Brain Barrier Chip recapitulates human barrier function and shuttling of drugs and antibodies. Nature Communications, 10, 2621.

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  82. Pawar, G., Wu, F., Zhao, L., Fang, L., Burckart, G. J., Feng, K., Mousa, Y. M., Al Shoyaib, A., Jones, M.-C., & Batchelor, H. K. (2023). Integration of biorelevant pediatric dissolution methodology into PBPK Modeling to predict in vivo performance and bioequivalence of generic drugs in pediatric populations: A Carbamazepine case study. The AAPS Journal, 25, 67. https://doi.org/10.1208/s12248-023-00826-1

    Article  CAS  PubMed  Google Scholar 

  83. Priyadarshini, S., & Pati, S. (2023). Green synthesis of silver nanoparticles from Catharanthus roseus and its antibacterial properties. Applied Nanoscience, 13, 1–18. https://doi.org/10.1007/s13204-023-02900-8

    Article  CAS  Google Scholar 

  84. Rai, J., & Kaushik, K. (2018). Reduction of animal sacrifice in biomedical science & research through alternative design of animal experiments. Saudi Pharmaceutical Journal, 26, 896–902.

    Article  PubMed  PubMed Central  Google Scholar 

  85. Raies, A. B., & Bajic, V. B. (2016). In silico toxicology: Computational methods for the prediction of chemical toxicity. Wiley Interdisciplinary Reviews: Computational Molecular Science, 6, 147–172.

    CAS  PubMed  Google Scholar 

  86. Rani, N., Alam, M. M., Jamal, A., Bin Ghaffar, U., & Parvez, S. (2023). Caenorhabditis elegans: A transgenic model for studying age-associated neurodegenerative diseases. Ageing Research Reviews, 91, 102036. https://doi.org/10.1016/j.arr.2023.102036

    Article  CAS  PubMed  Google Scholar 

  87. Rao, M. S., Gupta, R., Liguori, M. J., Hu, M., Huang, X., Mantena, S. R., Mittelstadt, S. W., Blomme, E. A., & Van Vleet, T. R. (2019). Novel computational approach to predict off-target interactions for small molecules. Frontiers in Big Data, 2, 25.

    Article  PubMed  PubMed Central  Google Scholar 

  88. Rikhtegar, R., Pezeshkian, M., Dolati, S., Safaie, N., Afrasiabi Rad, A., Mahdipour, M., Nouri, M., Jodati, A. R., & Yousefi, M. (2019). Stem cells as therapy for heart disease: IPSCs, ESCs, CSCs, and skeletal myoblasts. Biomedicine & Pharmacotherapy, 109, 304–313. https://doi.org/10.1016/j.biopha.2018.10.065

    Article  CAS  Google Scholar 

  89. Rudrapal, M., Khairnar, S. J., & Jadhav, A. G. (2020). Drug repurposing (DR): An emerging approach in drug discovery. Drug repurposing-hypothesis, molecular aspects and therapeutic applications (10). https://doi.org/10.5772/intechopen.93193

  90. Saavedra, L. M., Martinez, J. C. G., & Duchowicz, P. R. (2024). Advances of the QSAR approach as an alternative strategy in the environmental risk assessment. In QSAR in safety evaluation and risk assessment (pp. 117–137). Elsevier.

  91. Saeidnia, S., Manayi, A., & Abdollahi, M. (2015). From in vitro experiments to in vivo and clinical studies; pros and cons. Current drug discovery technologies, 12, 218–224.

    Article  CAS  PubMed  Google Scholar 

  92. Sakai, Y., Matsumura, M., Yamada, H., Doi, A., Saito, I., Iwao, T., & Matsunaga, T. (2023). Development of a perfusing small intestine – liver microphysiological system device. Applied Sciences, 13, 10510.

    Article  CAS  Google Scholar 

  93. Sardanelli, F., Castiglioni, I., Colarieti, A., Schiaffino, S., & Di Leo, G. (2023). Artificial intelligence (AI) in biomedical research: Discussion on authors’ declaration of AI in their articles title. European Radiology Experimental, 7, 2. https://doi.org/10.1186/s41747-022-00316-7

    Article  PubMed  PubMed Central  Google Scholar 

  94. Seaman, K., Sun, Y., & You, L. (2023). Recent advances in cancer-on-a-chip tissue models to dissect the tumour microenvironment. Med-X, 1, 11.

    Article  Google Scholar 

  95. Sharmin, S., Islam, M. B., Saha, B. K., Ahmed, F., Maitra, B., Rasel, M. Z. U., Quaisaar, N., & Rabbi, M. A. (2023). Evaluation of antibacterial activity, in-vitro cytotoxicity and catalytic activity of biologically synthesized silver nanoparticles using leaf extracts of Leea macrophylla. Heliyon, 9, E20810. https://doi.org/10.1016/j.heliyon.2023.e20810

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Shrestha, J., Razavi Bazaz, S., AboulkheyrEs, H., Yaghobian Azari, D., Thierry, B., EbrahimiWarkiani, M., & Ghadiri, M. (2020). Lung-on-a-chip: The future of respiratory disease models and pharmacological studies. Critical Reviews in Biotechnology, 40, 213–230.

    Article  CAS  PubMed  Google Scholar 

  97. Singh, A. V., Ansari, M. H. D., Rosenkranz, D., Maharjan, R. S., Kriegel, F. L., Gandhi, K., Kanase, A., Singh, R., Laux, P., & Luch, A. (2020). Artificial intelligence and machine learning in computational nanotoxicology: Unlocking and empowering nanomedicine. Advanced Healthcare Materials, 9, 1901862.

    Article  CAS  Google Scholar 

  98. Singh, A. V., Rosenkranz, D., Ansari, M. H. D., Singh, R., Kanase, A., Singh, S. P., Johnston, B., Tentschert, J., Laux, P., & Luch, A. (2020). Artificial intelligence and machine learning empower advanced biomedical material design to toxicity prediction. Advanced Intelligent Systems, 2, 2000084.

    Article  Google Scholar 

  99. Smits, L. M., Reinhardt, L., Reinhardt, P., Glatza, M., Monzel, A. S., Stanslowsky, N., Rosato-Siri, M. D., Zanon, A., Antony, P. M., Bellmann, J., et al. (2019). Modeling Parkinson’s disease in midbrain-like organoids. npj Parkinson’s Disease, 5, 5. https://doi.org/10.1038/s41531-019-0078-4

    Article  PubMed  PubMed Central  Google Scholar 

  100. Soni, A. N., Varshney, M., & Tale, V. S. (2023). Molecular docking and pharmacodynamic study of potential inhibitors of streptococcus mutans biofilm. Biomedical and Biotechnology Research Journal (BBRJ), 7, 471–477. https://doi.org/10.4103/bbrj.bbrj_176_23

    Article  Google Scholar 

  101. Stern, N., Gacs, A., Tátrai, E., Flachner, B., Hajdú, I., Dobi, K., Bágyi, I., Dormán, G., Lőrincz, Z., Cseh, S., et al. (2022). Dual inhibitors of AChE and BACE-1 for reducing Aβ in Alzheimer’s disease: From in silico to in vivo. International Journal of Molecular Sciences, 23, 13098.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Sulimov, V. B., Kutov, D. C., & Sulimov, A. V. (2019). Advances in docking. Current Medicinal Chemistry, 26, 7555–7580.

    Article  CAS  PubMed  Google Scholar 

  103. Sundarakrishnan, A., Chen, Y., Black, L. D., Aldridge, B. B., & Kaplan, D. L. (2018). Engineered cell and tissue models of pulmonary fibrosis. Advanced Drug Delivery Reviews, 129, 78–94. https://doi.org/10.1016/j.addr.2017.12.013

    Article  CAS  PubMed  Google Scholar 

  104. Takla, T. N., Luo, J., Sudyk, R., Huang, J., Walker, J. C., Vora, N. L., Sexton, J. Z., Parent, J. M., & Tidball, A. M. (2023). A Shared pathogenic mechanism for valproic acid and SHROOM3 knockout in a brain organoid model of neural tube defects. Cells, 12, 1697.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. Testai, E., Bechaux, C., Buratti, F. M., Darney, K., Di Consiglio, E., Kasteel, E. E., Kramer, N. I., Lautz, L. S., Santori, N., & Skaperda, Z. V. (2021). Modelling human variability in toxicokinetic and toxicodynamic processes using Bayesian meta-analysis, physiologically-based modelling and in vitro systems. EFSA Supporting Publications, 18, 6504E.

    Article  CAS  Google Scholar 

  106. Thakur, A., Mishra, A. P., Panda, B., Rodríguez, D., Gaurav, I., & Majhi, B. (2020). Application of artificial intelligence in pharmaceutical and biomedical studies. Current Pharmaceutical Design, 26, 3569–3578.

    Article  CAS  PubMed  Google Scholar 

  107. Unagolla, J. M., & Jayasuriya, A. C. (2022). Recent advances in organoid engineering: A comprehensive review. Applied Materials Today, 29, 101582.

    Article  PubMed  PubMed Central  Google Scholar 

  108. Urzì, O., Gasparro, R., Costanzo, E., De Luca, A., Giavaresi, G., Fontana, S., & Alessandro, R. (2023). Three-dimensional cell cultures: The bridge between in vitro and in vivo models. International Journal of Molecular Sciences, 24, 12046.

    Article  PubMed  PubMed Central  Google Scholar 

  109. Van Norman, G. A. (2020). Limitations of animal studies for predicting toxicity in clinical trials: Part 2: Potential alternatives to the use of animals in preclinical trials. Basic to Translational Science, 5, 387–397.

    Article  PubMed  PubMed Central  Google Scholar 

  110. Villenave, R., Wales, S. Q., Hamkins-Indik, T., Papafragkou, E., Weaver, J. C., Ferrante, T. C., Bahinski, A., Elkins, C. A., Kulka, M., & Ingber, D. E. (2017). Human gut-on-a-chip supports polarized infection of coxsackie B1 virus in vitro. PLoS ONE, 12, e0169412.

    Article  PubMed  PubMed Central  Google Scholar 

  111. Wang, H., Brown, P. C., Chow, E. C., Ewart, L., Ferguson, S. S., Fitzpatrick, S., Freedman, B. S., Guo, G. L., Hedrich, W., & Heyward, S. (2021). 3D cell culture models: Drug pharmacokinetics, safety assessment, and regulatory consideration. Clinical and Translational Science, 14, 1659–1680.

    Article  PubMed  PubMed Central  Google Scholar 

  112. Wood, C. R., Wu, W.-T., Yang, Y.-S., Yang, J.-S., Xi, Y., & Yang, W.-J. (2023). From ecology to oncology: To understand cancer stem cell dormancy, ask a Brine shrimp (Artemia). Advances in Cancer Research, 158, 199–231.

    Article  CAS  PubMed  Google Scholar 

  113. Xie, Y., Wang, S., Wu, S., Gao, S., Meng, Q., Wang, C., Lan, J., Luo, L., Zhou, X., & Xu, J. (2022). Genome of the giant panda roundworm illuminates its host shift and parasitic adaptation. Genomics, Proteomics & Bioinformatics, 20, 366–381.

    Article  CAS  Google Scholar 

  114. Yadav, A. R., & Mohite, S. K. (2020). Toxicological evaluation of Psidium guajava leaf extracts using brine shrimp (Artemia salina L.) model. Research Journal of Pharmaceutical Dosage Forms and Technology, 12, 258–260.

    Article  Google Scholar 

  115. Yang, S., Kar, S., & Leszczynski, J. (2023). Tools and software for computer-aided drug design and discovery. In Cheminformatics, QSAR and machine learning applications for novel drug development (pp. 637–661). Elsevier.

  116. Yong, U., Kang, B., & Jang, J. (2021). 3D bioprinted and integrated platforms for cardiac tissue modeling and drug testing. Essays in Biochemistry, 65, 545–554.

    Article  CAS  PubMed  Google Scholar 

  117. Zeilinger, K., Auth, S., Unger, J., Grebe, A., Mao, L., Petrik, M., Holland, G., Appel, K., Nüssler, A., Neuhaus, P., & Gerlach, J. (2000). Liver cell culture in bioreactors for in vitro drug studies as an alternative to animal testing] [Article in German. ALTEX - Alternatives to Animal Experimentation, 17, 3–10.

    CAS  PubMed  Google Scholar 

  118. Zheng, F., Xiao, Y., Liu, H., Fan, Y., & Dao, M. (2021). Patient-specific organoid and organ-on-a-chip: 3D cell-culture meets 3D printing and numerical simulation. Advanced Biology, 5, e2000024.

    Article  PubMed  PubMed Central  Google Scholar 

  119. Jusop, A. S., Thanaskody, K., Tye, G. J., Dass, S. A., Wan Kamarul Zaman, W. S., & Nordin, F. (2023). Development of brain organoid technology derived from iPSC for the neurodegenerative disease modelling: a glance through. Frontiers in Molecular Neuroscience, 16,. https://doi.org/10.3389/fnmol.2023.1173433

Download references

Funding

BDK and PP are thankful to Indian Council of Medical Research (ICMR), New Delhi, India for providing financial assistances in the form of ICMR- Adhoc Research Project. (File No.:5/13/20/2022-NCD-III; RFCNumber NCD/Ad-hoc/202/2022-23;IRIS ID:2021-10799).

Author information

Authors and Affiliations

  1. Department of Pharmaceutics, ISF College of Pharmacy, GT Road, Moga, 142001, Punjab, India

    Abhinav Vashishat, Ghanshyam Das Gupta & Balak Das Kurmi

  2. Department of Pharmaceutical Chemistry, ISF College Pharmacy, GT Road, Moga, 142001, Punjab, India

    Preeti Patel

Authors
  1. Abhinav Vashishat
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Preeti Patel
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ghanshyam Das Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Balak Das Kurmi
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

A. Vashishat: Writing, Original draft preparation, P. Patel: Checking and evaluation, G.D. Gupta: Editing of manuscript, B.D. Kurmi: Conceptualization and Supervision.

Corresponding authors

Correspondence to Preeti Patel or Balak Das Kurmi.

Ethics declarations

Ethics Approval

Not applicable.

Consent to Participate

Not applicable.

Consent for Publication

Authors are agreeing to publish.

Conflicts of Interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Vashishat, A., Patel, P., Das Gupta, G. et al. Alternatives of Animal Models for Biomedical Research: a Comprehensive Review of Modern Approaches. Stem Cell Rev and Rep (2024). https://doi.org/10.1007/s12015-024-10701-x

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12015-024-10701-x

Keywords

Search

Navigation