Improving reproducibility in translational medicine

Published: 16-Jun-2017

In translational medicine, wherein bringing tools such as diagnostic biomarkers from bench to bedside is a main focus, a lack of reproducibility could have major repercussions

Academics have increasingly expressed concerns about the reproducibility of scientific studies, leading many to question the reliability of published data.1–4

The cost to develop and market a new drug is approximately $2.5 billion, largely because of the low success rates of early stage candidate drugs.5

It was recently revealed that only 10% of candidate drugs in Phase I are likely to transition to FDA approval (this is as low as 5.1% for oncology).6

However, the use of biomarkers for patients enrolling into clinical studies (as inclusion/exclusion criteria or selection biomarkers) improved the chance of transitioning from Phase I to approval to 25.9%.6

Biomarkers allow for more accurate patient selection and stratification, ensuring the right patient gets the right treatment.

To discover novel biomarkers that have accurate diagnostic and prognostic capabilities, scientists require sufficiently validated reagents. Improved biomarker discovery and use could mitigate some of the costs of drug development and improve patient outcomes.

The need for better reagents

Compared with polyclonal antibodies harvested from serum, or monoclonal antibodies generated using hybridoma-based methods, recombinant antibodies offer critical advantages: they can be genetically engineered for improved production yields and performance; they offer security of supply; but, most importantly, they provide batch-to-batch reproducibility.

Recombinant antibodies are generated from a known sequence and therefore provide consistent results between batches.

There are challenges with antibody target specificity and the transient nature of many disease biomarkers. For example, symptoms and markers of many autoimmune diseases frequently overlap, with patients experiencing periods of relapse and remission.

It is, therefore, essential to differentiate when a target biomarker protein is truly absent from an assay, from when the antibody has simply failed to recognise its target. False-positive and false-negative results can have serious consequences on a patient’s diagnosis and subsequent treatment.

Raising reproducibility standards

Strong data in the preclinical stages may reduce the chances of failure or inconsistencies as a project advances. Abcam uses a multi-application approach for the validation of its antibody products and offers more than 10,000 recombinant antibodies.

It also applies consistent upstream and downstream processes to ensure product consistency. Products are then tested for quality control and technical details are made available in online datasheets that list the applications in which an antibody has been validated.

In September 2015, Abcam introduced knockout validation (KO) as a standard quality control. To date, more than 1000 antibodies have been KO validated using CRISPR-edited haploid cell lines from Horizon Discovery to validate target specificity. For scientists working in translational medicine, increasing the number of validated reagents will help to improve reproducibility through assured specificity and consistency.


1. M. Baker, “1500 Scientists Lift the Lid on Reproducibility,” Nature 533, 452–454 (2016).

2. C.G. Begley and L.M. Ellis, “Drug Development: Raise Standards for Preclinical Cancer Research,” Nature 483, 531–533 (2012).

3. F. Prinz, et al., “Believe it or Not: How Much Can We Rely on Published Data on Potential Drug Targets?” Nat. Rev. Drug Discov. 10, 712 (2011).

4. S.N. Goodman, et al., “What Does Research Reproducibility Mean?” Sci. Transl. Med. 8, 1–6 (2016).

5. D.W. Thomas, et al., “Clinical Development Success Rates 2006-2015,” Biotechnol. Innov. Organ. 1–12 (2016): doi:10.1038/nrd.2016.85.

6. J.A. DiMasi, et al., “Innovation in the Pharmaceutical Industry: New Estimates of R&D Costs,” J. Health Econ. 47, 20–33 (2016).

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