By Floor den Hollander
We go through life, day to day, not thinking twice about how great it is to be able to breathe through our noses until the annual flu comes along, and suddenly, we are not able to do so anymore. Most of the time we take life, and our health in particular, for granted until we get sick. Luckily, many medical conditions from infectious diseases to chronic illnesses, and even life-threatening disorders can be treated. Effective drugs can save lives, cure diseases, improve the quality of life, and reduce health care costs. All the above emphasize the pivotal role of drug development in alleviating suffering and maintaining our health.
Drug development, however, comes with challenges. It is a very time-consuming process that can cost billions for one single medicine. This is due to the need for a tremendous amount of research and development to find new drug candidates, strict safety and efficacy testing, and complex regulatory requirements. New drugs deemed safe and effective during pre-clinical testing, which mostly consists of cell culture and animal testing, still fail in the clinical trials and do not make it to market. This happens in 90% of all clinical trials, of which 80% of the failures can be attributed to safety concerns and/or a lack of efficacy1,2. While looking at the high failure rates, the effort, time and money might seem wasted. A lot could be saved and put back into the development of safe and effective drugs if the process of drug testing was evaluated and improved.
Is conventional pre-clinical research good enough?
These high failure rates, despite the initial success of the drugs in pre-clinical testing, raise the question whether the pre-clinical research done today is sufficient. How similar are 2-dimensional (2D) cell culture and animal models to the human body’s characteristics and functions? Are the models used in pre-clinical research representative enough? It is becoming increasingly evident that we need models that more accurately resemble the human body to bridge the gap between conventional pre-clinical testing and clinical trials1. Three-dimensional (3D) models might have the potential to close this gap. In recent years, 3D models have made significant advancements, especially in disease modeling and drug testing. They are the addition that current pre-clinical testing needs.
Benefits of 3D models
3D organ and disease models better resemble human physiology in multiple ways. Unlike two-dimensional models, they can recreate the complex environment of human tissue and organs. Many different cell types can be combined in a 3D configuration to mimic physiological architecture and cell-to-cell interactions. Moreover, certain 3D models are even capable of replicating mechanical forces and fluid dynamics. For example, a 3D microfluidic liver model can replicate a vascular network by leveraging gravity to induce fluid flow throughout the liver model. This model has been used to screen 159 compounds and found similar effects to those found in actual human livers, demonstrating the potential of 3D microfluidic models to evaluate the effect of new compounds3.
Furthermore, 3D models enable scientists to recreate diseases allowing the study of disease progression and the testing of therapeutic interventions. A high-throughput kidney model resembling acute kidney damage has been developed. With this model, the researchers demonstrated the protective effect of adenosine during kidney damage4. Another study developed a pancreatic cancer model to study how the tumor microenvironment affects the recruitment of immune cells. The goal was to better understand the complex interactions between different cells in this type of tumor. With this model, new treatments can be discovered and tested in a more representative way, which is not achieved in a pancreatic cancer mouse model5,6.
Overall, 3D models can better mimic the human body’s characteristics and functions than 2D models and animals, which allows for more accurate predictions of drug behavior. Current studies highlight extensive research in this field, alongside a rising number of 3D organ and disease models integrated into drug development. Implementing 3D models into the pre-clinical testing phase can uncover issues earlier on and decrease the failure rate in clinical trials.
Challenges of 3D models
The implementation of 3D organ and disease models in drug development comes with many benefits. However, several challenges and limitations should be considered. While these models offer an improved resemblance to the human body, fully capturing human biology is still a big challenge. Additionally, due to the novelty of the technology it can be hard to achieve consistent and reproducible results. The lack of standardized protocols makes it difficult to compare results. Furthermore, some 3D models are not suited for high-throughput screening, which is necessary to be able to test large amounts of compounds. This could result in high costs and a time-consuming development process. Finally, regulatory agencies such as the FDA and EMA are still adapting to the use of these novel models, making it challenging to get approval for treatments developed using 3D models.
Revolutionizing drug development
Nonetheless, 3D models offer a level of resemblance to the human body that 2D models and animals do not provide. This allows for the detection of safety and efficacy concerns that might have been missed in conventional pre-clinical testing. As these models are improving, they hold the promise to enhance the efficiency of drug discovery, reduce costs, and improve the success rate of clinical trials and therefore accelerate the development of new drugs. Ultimately, while there are still challenges to overcome, 3D models hold the potential to revolutionize drug development and bridge the gap between conventional pre-clinical testing and clinical trials.
About the Author
Floor den Hollander is a second-year Biomedical master student at VU Amsterdam, studying the effect of adeno-associated viral vectors on a 3D liver model.
Further reading
- Sun D, Gao W, Hu H, Zhou S. Why 90% of clinical drug development fails and how to improve it? Acta Pharm Sin B. 2022 Jul;12(7):3049–62. ↩︎
- Harrison RK. Phase II and phase III failures: 2013–2015. Nat Rev Drug Discov. 2016 Dec 4;15(12):817–8. ↩︎
- Bircsak KM, DeBiasio R, Miedel M, Alsebahi A, Reddinger R, Saleh A, et al. A 3D microfluidic liver model for high throughput compound toxicity screening in the OrganoPlate®. Toxicology. 2021 Feb;450:152667. ↩︎
- Vormann MK, Tool LM, Ohbuchi M, Gijzen L, van Vught R, Hankemeier T, et al. Modelling and Prevention of Acute Kidney Injury through Ischemia and Reperfusion in a Combined Human Renal Proximal Tubule/Blood Vessel-on-a-Chip. Kidney360. 2022 Feb;3(2):217–31. ↩︎
- Geyer M, Gaul LM, D`Agosto SL, Corbo V, Queiroz K. The tumor stroma influences immune cell distribution and recruitment in a PDAC-on-a-chip model. Front Immunol. 2023 May 2;14. ↩︎
- Mallya K, Gautam SK, Aithal A, Batra SK, Jain M. Modeling pancreatic cancer in mice for experimental therapeutics. Biochim Biophys Acta Rev Cancer. 2021 Aug;1876(1):188554 ↩︎