The translation of findings from animal models to clinical settings has always stood as a formidable challenge, particularly in the realm of oncology. This blog delves into the historical significance, current challenges, and promising innovations shaping translational science research.
The Legacy of Animal Models
The journey of medical research has been long and storied, with animal models playing a pivotal role for over three millennia. From the ancient insights of Alcmaeon of Croton to the groundbreaking vivisections by the Greek physician Galen of Pergamon, animal studies have laid the foundational physiological knowledge upon which modern science builds. Despite their crude beginnings, these early experiments paved the way for sophisticated animal models like the GFP mouse, illustrating the evolutionary leap in our methodological approach.
The Oncology Challenge
The field of oncology, with its myriad treatment modalities from immuno to chemotherapies, faces a daunting obstacle: the abysmal success rate of drug candidates. The financial and temporal costs are staggering, with only 1 in 10,000 preclinical candidates making it through to approval. The primary culprits for this attrition are lack of efficacy and toxicity, underscoring the need for more predictive and reliable preclinical models.
Current Mitigation Strategies
Our current strategies to mitigate these challenges include leveraging predictive biomarkers like Tumour Mutational Burden (TMB) and exploring phase zero trials. However, these approaches have limitations, such as TMB’s potential to overclassify non-Europeans, leading to ineffective treatments. This highlights the imperative for a precision medicine approach, necessitating more nuanced and predictive preclinical models.
The Rise of 3D Modelling
The limitations of traditional 2D cell cultures and animal models have catalysed the development of 3D modelling techniques. These innovative models, including spheroids and organoids, offer a more physiologically relevant environment that better mimics the complex tumour microenvironment and the interactions within. From spheroids’ scalability to organoids’ physiological accuracy, these models represent significant strides toward replicating human biology in a preclinical setting.
The Synexa Approach: Translational Solutions
At Synexa, we embrace the potential of these advanced models while recognising the value of integrating patient-derived samples into our research through Translational Solutions. This approach allows for a more tailored understanding of drug-target engagement, biomarkers, and the mechanism of action within the specific biological context of intended patient populations. Utilising techniques such as flow cytometry assays, we can explore the efficacy of immunomodulatory drugs like trastuzumab in a manner that closely mirrors clinical realities.
Looking Ahead: The Future of Oncology Drug Development
While the journey from animal models to clinical application is fraught with challenges, the innovations in 3D modelling and patient-derived Translational Solutions illuminate a path forward. These advancements, coupled with legislative support through initiatives like the FDA Modernisation Act 2.0, signal a paradigm shift towards more ethical, efficient, and effective drug development processes. As we continue to refine these models and methodologies, the goal of bridging the translational gap in oncology research inches closer to reality.
For more info, watch our latest webinar: The Human Touch; Patient Derived Data from Synexa Translational Solutions
About Synexa and Scientific Strategies
Synexa Life Sciences is a global provider of biomarker and bioanalytical services, specialising in the development, validation and delivery of a wide range of complex and custom-designed assays. With a team of 150 across five global laboratory locations; Cape Town, London, Berlin, Turku (Finland) and Rockville (Maryland USA), we provide innovative solutions to support our customers in achieving their clinical milestones.
Synexa’s Scientific Strategies team specialises in navigating the complexities and mitigating the risks associated with advancing compounds into clinical development. Our expertise is centred on critical biomarker and bioanalytical considerations, ensuring a streamlined path toward clinical trials. By offering bespoke consulting support, we deliver clear, actionable insights that address your unique biomarker and bioanalytical challenges, becoming a true strategic partner in your development journey.
Learn more about Synexa’s biomarker consulting service.
FAQ
Why do oncology candidates often fail to translate from animal models to humans?
Animal models cannot fully replicate human immune interactions, tumour complexity, or genetic variability. Synexa’s human‑derived translational platforms, including ProtoTrials®, allow early testing of therapeutic candidates in fresh human samples. This reveals mechanistic activity and pathway modulation in a clinically relevant environment. By identifying issues early, developers reduce reliance on non‑predictive animal outcomes and improve clinical readiness.
How do 3D tumour models strengthen translational oncology workflows?
3D spheroids and organoids mimic human tumour microenvironments more accurately than 2D cultures. When integrated with Synexa’s mass spectrometry, immune phenotyping, and proteomic platforms, they generate multi‑layered insights into drug penetration and pathway activation. This combination highlights resistance mechanisms or microenvironment‑driven effects that are not apparent in simpler models. It ultimately supports better therapeutic decision‑making before clinical entry.
How do Translational Solutions guide oncology programs transitioning from preclinical to clinical phases?
Translational Solutions characterises drug‑target engagement, immune activation, and pharmacodynamic responses within human donor samples. These outputs provide mechanistic evidence that can strengthen early‑phase study design and dose justification. Developers gain clarity on whether a therapy is likely to succeed under true human physiologic conditions. This reduces uncertainty at the transition point and supports more efficient clinical development planning.
