[Summary + Blog Post] Lost in translation: the valley of death across preclinical and clinical divide – identification of problems and overcoming ob
Bridging the Gap: The Future of Translational Medicine and the Role of AI
Translational medicine, a rapidly evolving field, aims to bridge the gap between scientific discovery and clinical application. It is a multidisciplinary approach that combines expertise from various fields to accelerate the process of turning laboratory findings into treatments for patients. However, the path from bench to bedside is fraught with challenges, and the process is often described as trying to cross a chasm or ‘abyss.’
In a recent podcast episode, experts discussed an article titled “Lost in Translation: The Valley of Death Across Preclinical and Clinical Divide – Operational Challenges and Solutions,” published in Translational Medicine Communications. The discussion shed light on the complexities of the drug discovery and development process, the role of AI and machine learning, and the importance of collaboration and open innovation in overcoming these challenges.
The Challenges of Drug Development
The drug development process is a complex and resource-intensive endeavor. It involves multiple stages, each requiring expert feedback and decision-making. The article highlights that almost 95% of drugs entering human trials fail, and for every drug that gains FDA approval, over a thousand are developed but fail. The high failure rate and the escalating costs of drug development, estimated at around $2.6 billion for a newly approved drug, underscore the need for more efficient and effective strategies (Seyhan, 2019).
The Role of AI in Drug Development
This is where Artificial Intelligence (AI) and machine learning come into play. These technologies can analyze large datasets faster and more systematically than humans, making them invaluable tools in the drug discovery and development process. For instance, AI algorithms can identify new drug targets, predict potential toxicities, and even suggest the structure of a new therapeutic molecule from scratch. However, the effectiveness of AI is dependent on the quality of the data it is trained on, emphasizing the need for accurate and comprehensive datasets.
The Concept of Open Innovation
The article also discusses the concept of open innovation, a model that involves sharing the burden and risk of research and development costs with external partners. This approach can lead to the creation of new technologies and products for new markets while reducing R&D risk. However, open innovation also presents its own set of challenges, such as the potential loss of competitive advantage and the complexity of sharing financial and intellectual returns.
Despite these challenges, many pharmaceutical companies are beginning to embrace open innovation models to address R&D challenges. The article cites the Drugs for Neglected Diseases initiative (DNDi) as an example of a successful open innovation model. DNDi is a not-for-profit drug development consortium that outsources all its R&D activities, keeping development costs under control while providing high flexibility.
The Importance of Collaboration
The article concludes by emphasizing the importance of collaboration and multidisciplinary team science in overcoming the challenges in translational medicine. It calls for more inter-institutional collaborations and partnerships between academia and industry to utilize all parties’ strengths and expertise.
Looking Ahead
In conclusion, while the path from bench to bedside is fraught with challenges, the future of translational medicine looks promising. With the help of AI and machine learning, open innovation models, and increased collaboration, we can hope to bridge the abyss and bring life-saving treatments to patients more quickly.
Top 5 Takeaways from the Podcast
- The high failure rate and escalating costs of drug development highlight the need for more efficient and effective strategies.
- AI and machine learning can play a crucial role in the drug discovery and development process, but their effectiveness is dependent on the quality of the data they are trained on.
- Open innovation, while presenting its own set of challenges, can lead to the creation of new technologies and products while reducing R&D risk.
- Successful open innovation models, like the Drugs for Neglected Diseases initiative (DNDi), can serve as a blueprint for other pharmaceutical companies.
- Collaboration and multidisciplinary team science are key to overcoming the challenges in translational medicine.
Seyhan, A.A. Lost in translation: the valley of death across preclinical and clinical divide – identification of problems and overcoming obstacles. transl med commun 4, 18 (2019). https://doi.org/10.1186/s41231-019-0050-7
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Check out the Podcast Section for the Podcast Version of the article referred to in the summary above: Lost in translation: the valley of death across preclinical and clinical divide – identification of problems and overcoming obstacles | Translational Medicine Communications | Full Text (biomedcentral.com)
