Precision medicine depends on the ability to understand biological differences with confidence. For Professor David Klug, Professor of Chemical Biophysics at Imperial College London, that confidence begins with quantification.
In biomarker analysis, simply detecting whether something is present is no longer enough. Researchers and clinicians increasingly need to know how much of a biomarker is present, how reliable that measurement is, and what level of uncertainty surrounds the result. Quantitative data helps reduce error, improve reproducibility, and support more meaningful clinical and translational decisions.
This shift is particularly important because biological data is rarely a simple yes-or-no answer. Useful biomarker analysis depends on understanding statistical confidence, including the risk of false positives and false negatives. Without that context, it becomes difficult to judge whether a result is reliable enough to guide drug development, patient stratification, or clinical decision-making.
This is especially important in areas such as organoid research and single-cell analysis. Organoids are powerful disease models, but they are complex and can vary depending on how they are grown and measured. Robust quantitative methods make it possible to distinguish between variation caused by the biology itself and variation introduced by the measurement process.
Single-cell analysis adds another layer of insight. While bulk analysis can show average biomarker changes, it may hide important differences between individual cells. For example, a biomarker may appear to increase overall, while a significant subset of cells shows no response at all. These differences can be critical when assessing drug response, patient stratification, or disease mechanisms.
Professor Klug also highlights the importance of sensitivity, dynamic range, and flexible instrument platforms. Biomarkers can exist at very low or very high levels, so technologies must be able to measure across that full range. A useful platform must also work across different sample types, including single cells, lysates, serum, urine, swabs, and saliva.
Another key factor is collaboration. Complex translational questions require strong partnerships between technology developers, clinicians, researchers, and industry teams. Transparent collaboration helps ensure that the right questions are being asked, the data is interpreted correctly, and the technology is applied where it can deliver the greatest value.
Looking ahead, quantitative biomarker analysis is expected to play an expanding role across therapeutic development, clinical trials, prognostics, and health maintenance. As precision medicine grows, the ability to generate robust, reproducible, and portable data will become increasingly important.
Ultimately, quantitative biomarker analysis is not just about better measurement. It is about building the reliable evidence needed to make precision medicine more predictive, practical, and effective. By improving confidence in biological data, it can help accelerate therapeutic development and support more personalised approaches to patient care.
