Image Citation: [1]
Written by Darl Jacob ‘26
Edited by Parsa Lajmiri ‘26
The biotechnology industry faces a critical roadblock: high failure rates when transitioning from discovery and preclinical testing to clinical trials. With 90% of drugs failing during clinical phases, biotech companies must rethink the models that guide this process [2]. Traditional animal testing, treated as the gold standard, often fails to accurately predict human responses to new drugs, resulting in setbacks, wasted resources, and ethical dilemmas. Addressing this challenge requires a fundamental shift toward innovative discovery and validation models that can effectively bridge the preclinical-to-clinical gap.
The process of delivering new therapies to patients is complex given the high costs and stringent regulatory hurdles. For every successful drug, countless others are abandoned in clinical phases due to unforeseen side effects or lack of efficacy, leaving patients and researchers frustrated. Finding better ways to bridge preclinical and clinical testing can dramatically change this landscape. Moreover, with ethical concerns surrounding animal testing and the need for more human-relevant models, developing alternative methods is both a professional and moral necessity.
Drug development unfolds in several stages, beginning with the discovery phase, where potential therapeutic compounds are identified. This is followed by preclinical testing, where these compounds are tested in animals or other models to assess safety and efficacy before moving to clinical trials. After the clinical trials, the drug is reviewed by the FDA, following submission of a New Drug Application (NDA) by the company. Once the FDA approves, biotech companies must conduct post-market monitoring to ensure ongoing drug safety [3]. While animal testing has long been the backbone of preclinical assessment, it has limitations. Animal models often fail to capture the complexities of human biology, which leads to a high degree of variability in trial outcomes. These discrepancies not only reduce the likelihood of clinical success but also lead to substantial financial losses and extended development timelines.
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Figure 1. Stages of Drug Development & the “Valley of Death” [4]. This diagram illustrates the stages in the drug development life cycle, including each phase’s duration, capitalized costs, and probability of failure. The “death funnel” highlights the high failure rate as compounds progress through preclinical and clinical phases. |
Emerging Alternatives to Traditional Models
In response to these challenges, researchers are developing innovative alternatives that promise to more accurately model human responses. For example, organoids are small 3D tissue cultures derived from human cells.They allow scientists to study drug effects in an environment that more closely mimics human tissue [5]. Similarly, organ-on-chip platforms simulate human organ functions through microfluidic systems, offering insights into potential drug effects without animal testing [6]. Additionally, the FDA Modernization Act 2.0 is making regulatory space for these models by supporting the development of non-animal testing methods, though challenges remain in establishing clear validation standards [7]. One such example is the lack of universally accepted benchmarks to compare their data against traditional models like mice.
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Figure 2. Applications of Organoids [5]. Patient-derived organoids (PDOs) replicate physiological characteristics and functions, offering a realistic and efficient technical platform. |
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Figure 3. Different Applications of Organ-on-Chip Technology [8]. These microfluidic devices facilitate drug metabolism studies, toxicological evaluation and disease modeling, offering insights into disease progression, cellular responses, and potential therapeutic targets in a controlled environment. |
The Role of AI in Modernizing Drug Development
Artificial intelligence (AI) is also transforming drug development, enhancing our ability to predict human responses through complex data analysis and modeling. AlphaFold, for instance, represents a leap forward in drug discovery by predicting protein structures with remarkable accuracy. This capability is critical, as understanding protein structures can significantly speed up the identification of potential drug targets [9]. Another AI-based biotech company, QuantHealth, provides predictive insights into clinical trial outcomes, using over a trillion data points to simulate complex clinical scenarios [10]. Such tools help drug developers optimize protocols and endpoints, saving time and resources by testing variations quickly. AI-driven predictive models, alongside alternative testing systems, can further reduce dependency on animal models and enhance the translational relevance of preclinical findings.
Broader Implications and Future Prospects
The adoption of alternative models and AI has far-reaching implications. For one, these methods could dramatically increase success rates in clinical trials, thereby reducing the time and cost associated with drug development. Ethical advantages are significant, as reducing reliance on animal models aligns with the public concern for animal welfare and promotes more humane scientific practices. Additionally, by leveraging patient-specific models, we open the door to personalized medicine, where therapies are not only developed faster but also tailored to meet individual patient needs. This transition could redefine how we view and conduct drug development, creating a more efficient, ethical, and effective system for bringing new therapies to market.
Conclusion
The path forward in drug development lies in innovation—specifically in advancing alternative models that better reflect human biology and integrating AI to improve predictive accuracy. Shifting away from traditional animal testing and adopting these new methods have the potential to bridge the longstanding preclinical-to-clinical gap, paving the way for more successful trials, faster drug development timelines, and ultimately, better patient outcomes. To fully realize this potential, it is essential that industry leaders and regulators collaborate to support and validate these models. The future of medicine lies in embracing these innovations, revolutionizing drug development for the benefit of patients, researchers, and society as a whole.
References
Lansdowne, Laura Elizabeth. “The Future of Drug Discovery: AI, Automation and Beyond.” Drug Discovery from Technology Networks, 18 Mar. 2022, www.technologynetworks.com/drug-discovery/articles/the-future-of-drug-discovery-ai-automation-and-beyond-336064.
Sun, Duxin, et al. “Why 90% of clinical drug development fails and how to improve it?” Acta Pharmaceutica Sinica B, vol. 12, no. 7, July 2022, pp. 3049–3062, https://doi.org/10.1016/j.apsb.2022.02.002.
Commissioner, Office of the. “The Drug Development Process.” U.S. Food and Drug Administration, FDA, www.fda.gov/patients/learn-about-drug-and-device-approvals/drug-development-process. Accessed 20 Nov. 2024.
Zurdo, Jesus. (2013). Developability assessment as an early de-risking tool for biopharmaceutical development. Pharmaceutical Bioprocessing. 1. 29-50. 10.4155/pbp.13.3.
Xiang, Dongxi, et al. “Building consensus on the application of organoid-based drug sensitivity testing in cancer precision medicine and drug development.” Theranostics, vol. 14, no. 8, 2024, pp. 3300–3316, https://doi.org/10.7150/thno.96027.
Chopra H, Chakraborty S, Akash S, Chakraborty C, Dhama K. Organ-on-chip: a new paradigm for clinical trials - correspondence. Int J Surg. 2023 Oct 1;109(10):3240-3241. doi: 10.1097/JS9.0000000000000578. PMID: 37352514; PMCID: PMC10583935.
"S.5002 - 117th Congress (2021-2022): FDA Modernization Act 2.0." Congress.gov, Library of Congress, 29 September 2022, https://www.congress.gov/bill/117th-congress/senate-bill/5002.
Yan, Jiasheng, et al. “Organ-on-a-chip: A new tool for in vitro research.” Biosensors and Bioelectronics, vol. 216, Nov. 2022, p. 114626, https://doi.org/10.1016/j.bios.2022.114626.
Jumper, J., Evans, R., Pritzel, A. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021). https://doi.org/10.1038/s41586-021-03819-2
Licholai, Greg. “Ai Simulations Help Drug Trials.” Forbes, Forbes Magazine, 16 Oct. 2024, www.forbes.com/sites/greglicholai/2024/10/10/ai-simulations-help-drug-trials/.
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