Could Biological Ageing Be the Next Big Shift in Drug Discovery?
“The pharma industry is beginning to look at biological age as a surrogate endpoint to improve clinical trials."
“The pharma industry is beginning to look at biological age as a surrogate endpoint to improve clinical trials."
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“The pharma industry is beginning to look at biological age as a surrogate endpoint to improve clinical trials."
Learn how optimised clinical trial data collection improves data quality, reduces protocol and patient burden, and supports smarter clinical development.
Explore new benchmarking research on pharmaceutical R&D success rates, revealing why some drug pipelines consistently achieve higher approval rates than others.
Can non-animal testing methods be used in preclinical drug discovery? Here is what the evidence is for their accuracy and limits assessing preclinical toxicity.
Learn how drug discovery scientists are evolving alongside AI, computational biology, and multidisciplinary research to accelerate therapeutic innovation.
Deep learning is changing how clinical trials are designed, run, and analysed in 2026. Examine the tools, outcomes, and strategic risks shaping adoption.
From gut hormones to global therapeutics, GLP-1 drug discovery transformed obesity treatment through peptide engineering and incretin biology translation.
Generative AI is transforming clinical trials, but a failure to distinguish between pseudonymised and anonymised data is creating costly regulatory gaps.
As precision medicine narrows patient cohorts to rare‑disease scales, deep learning models face a paradox of data scarcity and overfitting that threatens the future of clinical AI.
Discover why genomic equity is essential for reliable AI-driven drug discovery, precision medicine, and building more representative pharmaceutical innovation.
We've mapped the competitive, regulatory, and commercial stakes of the top 11 clinical trials that have defined pharma strategy so far in 2026.
Real-time clinical trials enable continuous data capture and faster decisions, improving how data is used in clinical trials. Here is what pharma leaders need to know.