Emerging Trends Shaping In Vivo Experimental Studies in 2025

In vivo studies are essential for drug discovery and development, bridging cellular assays and human applications^1,2. Significant progress is underway in creating new animal models that better reflect diseases, paving the way for promising new therapies in the near future^3,4. However, preclinical researchers and organizations can only use new developments if they remain engaged with the latest trends. In 2025, there will likely be an increased focus on integrating more ethical practices when using animals in research. Central to this shift is the 3Rs principle (Replacement, Reduction, Refinement)⁵. Another development making waves into 2025 is that the FDA Modernization Act no longer requires in vivo testing for drug approvals^6,7. This is a positive step for animal welfare and emphasizes opportunities for growth in areas like in silico approaches. In this blog, we’ll give you a quick rundown of the top trends to keep in mind as we move into the new year.

1. Integration of In Silico Approaches

In silico (computer-based) approaches are set for increased integration into drug discovery pipelines. While in vivo studies still remain a central pillar of translational biomedical research, in silico techniques are set to play a growing part in modeling and simulation and in enhancing in vivo work^1,8. 

In silico methods allow researchers to simulate complex biological processes and test hypotheses before conducting live experiments⁹. This could mean more efficient resource allocation and the removal of significant risks from in vivo workflows. Furthermore, these approaches could help researchers adhere more closely to one of the three Rs, reduction, which promotes the use of fewer animals in in vivo experiments⁵. This could be achieved by providing researchers with deeper insights for designing optimal experiments while ensuring adequate statistical power. Ultimately, this could lead to accelerated drug development timelines, improved animal welfare, and lower risk for researchers.

2. Emergence of Personalized Medicine Research

The promise of personalized medicine continues to be realized and is set for even more exciting advances in 2025¹⁰. While personalized medicine has been “on the horizon” for a while now, in vivo research methods are adapting to facilitate the expansion of precision approaches. Advances in patient-derived xenograft models allow researchers to gain greater insights into disease progression¹¹. Furthermore, there is a shift towards conducting animal studies using genetically diverse populations¹². This approach means in vivo models better represent human variability, significantly increasing the translational potential of in vivo work.

Rare disease research is an area that gained significant traction in 2024, supported by advances in gene therapies, which make rare genetic disorders more treatable^13–15. Researchers are developing animal models that more accurately represent rare diseases, which will enhance the efficiency of therapy development in 2025.

3. Advanced Imaging and Monitoring

Improved imaging and monitoring technologies allow researchers to generate more data from animal models and gain more insights into the pathophysiology and progression of different diseases¹⁶. Significant advances are being made in neuroimaging techniques for brain studies, enabling better modeling of conditions like Alzheimer’s disease and autism¹⁷. Wearable sensors for animals give researchers greater insights into the impact of disease on animal physiology during progression¹⁸. Monitoring systems are also valuable for tracking animal well-being and identifying underlying issues that may compromise animal welfare and affect the quality of experimental results. Advanced molecular imaging probes allow scientists to more precisely assess the effect of therapeutic interventions, track disease biomarkers, and visualize molecular pathways in real-time^19–21.

4. Ethical Considerations and Alternatives

Each year, there is an increased drive towards more ethical treatment of animals used in in vivo work⁵. This means that in 2025 the 3Rs principle will be in greater focus than ever. These principles have important implications across in vivo research. Not only do they support better animal welfare, but they can also lead to more reliable research outcomes. 

This sharpened focus drives the development of alternative methods for answering research questions and improving existing in vivo methods to enhance animal welfare⁸. Toxicology studies are one area where prediction modeling could significantly reduce the need for animal testing. Advanced simulation techniques allow more precise prediction of drug interactions, blood-brain barrier dynamics, and toxicological responses²².

5. AI and Big Data Integration

The AI revolution is in full swing, and 2025 holds exciting promise for furthering the application of AI and big data analytics for in vivo studies. This is particularly exciting for enabling researchers to conduct more advanced analyses of complex biological data, helping them interpret the growing volume and improved data quality generated by imaging advancements²³. This data integration will help to simplify the synthesis of multi-omics data, which is becoming increasingly high-resolution at the single-cell and single-organelle levels for in vivo work^24,25. 

Prediction modeling will help streamline and guide experimental design, which includes selecting the optimal models to use, determining the number of animals required, and reducing the need for pilot studies to determine biological responses. AI-driven literature searches already allow researchers to scan entire databases for optimal study design parameters, eliminating the need for protracted study design and dramatically shrinking early research and development timelines.

Future Proof Your In Vivo Experimental Design with ModernVivo 

ModernVivo is ahead of the curve in providing AI-driven literature reviews for in vivo study design. Many researchers from top institutions are using ModernVivo today to reap the benefits of higher-quality study design in 2025. Today, the only certainty is that advancements will accelerate, requiring researchers to stay actively informed about the latest trends and published research experimental designs and outcomes.

Step Confidently into 2025 with ModernVivo

ModernVivo is already providing researchers with the next-level tools they need to gain an advantage in 2025. Contact our team to schedule a demo today!

References

1. Chang MCJ, Grieder FB. The continued importance of animals in biomedical research. Lab Anim. 2024;53(11):295-297. 

2. Domínguez-Oliva A, Hernández-Ávalos I, Martínez-Burnes J, Olmos-Hernández A, Verduzco-Mendoza A, Mota-Rojas D. The Importance of Animal Models in Biomedical Research: Current Insights and Applications. Animals (Basel). 2023;13(7):1223. 

3. Sadler KE, Mogil JS, Stucky CL. Innovations and advances in modeling and measuring pain in animals. Nat Rev Neurosci. 2022;23(2):70-85. 

4. He S, Ru Q, Chen L, Xu G, Wu Y. Advances in animal models of Parkinson’s disease. Brain Research Bulletin. 2024;215:111024. 

5. The 3Rs | NC3Rs. Accessed December 5, 2024. 

6. Zushin PJH, Mukherjee S, Wu JC. FDA Modernization Act 2.0: transitioning beyond animal models with human cells, organoids, and AI/ML-based approaches. J Clin Invest. 2023;133(21):e175824. 

7. Animal research is not always king: researchers should explore the alternatives. Nature. 2024;631(8021):481-481. 

8. Madden JC, Enoch SJ, Paini A, Cronin MTD. A Review of In Silico Tools as Alternatives to Animal Testing: Principles, Resources and Applications. Altern Lab Anim. 2020;48(4):146-172.

9. Barh D, Yiannakopoulou ECh, Salawu EO, et al. In silico disease model: from simple networks to complex diseases. In: Animal Biotechnology. Elsevier; 2020:441-460. 

10. Roberts MC, Holt KE, Del Fiol G, Baccarelli AA, Allen CG. Precision public health in the era of genomics and big data. Nat Med. 2024;30(7):1865-1873. 

11. Liu Y, Wu W, Cai C, Zhang H, Shen H, Han Y. Patient-derived xenograft models in cancer therapy: technologies and applications. Sig Transduct Target Ther. 2023;8(1):160. 

12. Pera M, Greene A, Cardon L, et al. Improving the predictive power of mouse models. Nat Biotechnol. 2024;42(8):1175-1177.

13. author(s) G.  Shedding light on rare diseases: open data and model organisms. EMBL. February 28, 2023. Accessed December 5, 2024. https://www.embl.org/news/science/model-organism-data-rare-diseases/

14. Fox TA, Booth C. Improving access to gene therapy for rare diseases. Dis Model Mech. 2024;17(6):dmm050623. 

15. Rare Disease Day at NIH 2024: Showcasing Voices and New Opportunities | National Center for Advancing Translational Sciences. Accessed December 5, 2024. 

16. Boitet M, Achek A, Bouchenaki K, Grailhe R. BrightMice: a low-cost do-it-yourself instrument, designed for in vivo fluorescence mouse imaging.  Sci Rep. 2024;14(1):22685. 

17. Markicevic M, Savvateev I, Grimm C, Zerbi V. Emerging imaging methods to study whole-brain function in rodent models. Transl Psychiatry. 2021;11(1):457. 

18. Zhou J, Zhou S, Fan P, et al. Implantable Electrochemical Microsensors for In Vivo Monitoring of Animal Physiological Information. Nanomicro Lett. 2023;16(1):49. 

19. He H, Zhang X, Du L, et al. Molecular imaging nanoprobes for theranostic applications. Advanced Drug Delivery Reviews. 2022;186:114320.

20. Kim SJ, Lee HY. In vivo molecular imaging in preclinical research. Lab Anim Res. 2022;38(1):31. 

21. Geng Y, Wang Z, Zhou J, Zhu M, Liu J, James TD. Recent progress in the development of fluorescent probes for imaging pathological oxidative stress. Chem Soc Rev. 2023;52(11):3873-3926. 

22. Huang ETC, Yang JS, Liao KYK, et al. Predicting blood–brain barrier permeability of molecules with a large language model and machine learning.  Sci Rep. 2024;14(1):15844. 

23. Carey H, Pegios M, Martin L, et al. DeepSlice: rapid fully automatic registration of mouse brain imaging to a volumetric atlas. Nat Commun. 2023;14(1):5884. 

24. Dong Z, Jiang W, Wu C, et al. Spatial proteomics of single cells and organelles on tissue slides using filter-aided expansion proteomics. Nat Commun. 2024;15(1):9378. 

25. Ahmed Z, Wan S, Zhang F, Zhong W. Artificial intelligence for omics data analysis.BMC Methods. 2024;1(1):4, s44330-024-00004-00005.

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