Moving Forward in Translational Models

Translational medicine can be defined as ‘the process of applying ideas, insights, and discoveries generated through basic scientific inquiry to the treatment or prevention of human disease’. This is also known as taking research ‘from the bench to the bedside’.

The gap between basic science at the laboratory bench and clinical science – the human studies of therapeutic and diagnostic drugs and devices – is sometimes referred to as the valley of death, because in vitro and in vivo studies don’t always effectively reflect what will happen in clinical trials, meaning that only a small fraction of the drugs that succeed in preclinical studies and enter clinical trials actually make it all the way to approval and the market. [1-3]

In order to bridge this so-called ‘valley of death’, researchers are developing new translational models designed to better predict the efficacy and safety of potential therapeutics in healthy volunteers and patients in clinical trials.

In vitro translational models: Clinical responses in a test tube

An organ-on-chip (OoC), also known as a microphysiological system (MPS), combines natural or engineered human cells with microfluidic chips and sensors to create an in vitro model of a human organ, such as a heart, lung, kidney, lymph node, bone marrow or liver, which can be used in real-time drug testing. An OcC can predict how drugs may behave in clinical trials, as well as allowing researchers a better understanding of disease mechanisms and drug responses. [4-6]

Clinical trials in dish (CTiD) allow researchers to screen potential clinical candidates in human cells from the planned patient population, getting early indications of safety and efficacy and creating a bridge between preclinical testing and clinical trials. The clinical trial in dish is based on induced pluripotent stem cells (iPSCs), whole genome sequencing and organs-on-chips. The use of patient-derived or genome-edited iPSCs mean that the CTiD can be tailored to a specific disease or a particular individual. [5]

Organoids are three-dimensional miniature organs that self-assemble from single or multiple adult or pluripotent stem cells or are 3D bioprinted using ‘bioinks’ that combine cells and biocompatible materials to create complex models that more closely mimic the tissues being tested. Examples include 3D bioprinted tumour models that include the tumour microenvironment, or complex tissue models along with the associated vasculature. The US Food and Drug Administration has recognised organoids as an alternative to animal studies as the step before clinical trials. [5, 6]

In silico translational models: Virtual reflections of human responses

Computational modelling and simulations can predict how drugs will interact with tissues and cells. Examples of in silico models range from how drug molecules bind with receptors to how they interact with entire biological systems or pathways. By incorporating biomarker data, researchers can create patient-specific models to predict outcomes in personalised medicine. [6]

Artificial intelligence and machine learning can use large datasets, such as literature databases, clinical trials databases and electronic patient records (EHRs) to create models to predict how new drugs interact with targets or disease mechanisms, or simulate clinical trials. [6]

A digital twin, a term that has arisen from the aerospace industry and is used in many areas in engineering, is a virtual model of a physical entity in its environment, with the two connected and exchanging data in real time. Digital twins in healthcare are virtual versions of the organs of real patients based on actual patient data and play a role in medicine by supporting healthcare professionals in planning and optimising treatment strategies, including personalised care. Digital twins have potential in drug R&D to identify drug targets and predict treatment outcomes in animal studies potentially reducing the number of animals required. They can also mimic human responses, which could reduce the risk involved in taking a drug from preclinical to clinical development. [7-10]

Digital twins are based on large quantities of data, often from a variety of different sources including wearable sensors and health records, including: [8]

· Heart rate

· Temperature

· Blood pressure

· Oxygen saturation

· Medical imaging, such as CT and MRI scans

· Omics data

Digital twins have been used in oncology, cardiovascular, immunology and pulmonary research, and as models for pregnant women. [8, 10, 11]

In vivo translational models: Mice and fish as human representations

In vivo models, such as patient-derived xenografts, zebrafish and genetically engineered and humanized mice play an important role in translational research. [6]

Patient-derived xenografts, where tumour tissues are transplanted from patients into immunodeficient mice, allow researchers to understand more about tumour microenvironments and tumour evolution, and to evaluate efficacy of drugs in development. [6]

Genome-edited zebrafish can be used as models of metabolic disease, inflammation, infection and cancer. Their rapid development, small size and transparency allows them to be used as whole organism phenotype-based drug screens, and to understand drug pharmacokinetics and toxicology. [6, 12]

Researchers can delete or add genes in transgenic and knockout mice, creating models that mimic human disease. These can be used to identify drug targets and assess drug efficacy and safety. [6]

Humanized mice are engineered to carry human genes, cells, tissues and organs, and are used to gain a better understanding of disease mechanisms therapeutics for conditions such as cancer, infectious disease and autoimmune disorders, because they can mimic human physiological responses. [6] 

The role of advanced analytics and AI

In vitro, in vivo and digital models in translational research all generate vast amounts of information. The power of artificial intelligence, including machine learning, deep learning, pattern matching and network analysis, can validate and analyse these vast quantities of data from diverse sources to predict the efficacy and safety of drugs in clinical trials. This will support drug developers’ decisions on whether to proceed with development and how to best design clinical trials. [13]

Moving forward in translational medicine

The growth in translational models has potential to help drug development cross the gulf between the bench and the bedside, but this will require collaboration between academic researchers, clinicians and industry partners in small and large biopharma companies to make a real difference for patients with unmet needs. [13]

References

1. Fang, F.C. and A. Casadevall, Lost in translation--basic science in the era of translational research. Infect Immun, 2010. 78(2): p. 563-6.

2. Seyhan, A.A., Lost in translation: the valley of death across preclinical and clinical divide – identification of problems and overcoming obstacles. Translational Medicine Communications, 2019. 4: p. 18.

3. Nikanjam, M., S. Kato, and R. Kurzrock, Of Mice, Not Men: When the Bench-to-Bedside Bridge Is Broken. Journal of Immunotherapy and Precision Oncology, 2022. 5(4): p. 87-89.

4. Cao, U.M.N., et al., Microfluidic Organ-on-A-chip: A Guide to Biomaterial Choice and Fabrication. Int J Mol Sci, 2023. 24(4).

5. Mir, A., et al., Applications, Limitations, and Considerations of Clinical Trials in a Dish. Bioengineering (Basel), 2024. 11(11).

6. El-Tanani, M., et al., Bridging the gap: From petri dish to patient - Advancements in translational drug discovery. Heliyon, 2025. 11(1): p. e41317.

7. Singh, M., et al., Digital Twin: Origin to Future. Applied System Innovation, 2021. 4(2): p. 36.

8. Meijer, C., H.W. Uh, and S. El Bouhaddani, Digital Twins in Healthcare: Methodological Challenges and Opportunities. J Pers Med, 2023. 13(10).

9. Genet, M., A digital twin of the lungs: what benefits for medicine of the future? Polytechnique Insights, 1 February 2023. Available from: https://www.polytechnique-insights.com/en/columns/health-and-biotech/a-digital-twin-of-the-lungs-what-benefits-for-medicine-of-the-future/.

10. Geddes, L., How digital twins may enable personalised health treatment, in The Guardian. 2023.

11. Laubenbacher, R., et al., Building digital twins of the human immune system: toward a roadmap. NPJ Digit Med, 2022. 5(1): p. 64.

12. Patton, E.E. and D.M. Tobin, Spotlight on zebrafish: the next wave of translational research. Dis Model Mech, 2019. 12(3).

13. The translational gap: bridging basic research, clinical practices and society. Last accessed: 28 February 2025. Available from: https://www.nature.com/articles/d42473-023-00253-y.