Definition of biomarkers and efficacy end-points
2. How Biomarkers Are Used In Medicines Development
Many drug candidates fail at each development phase and will not be taken further (also known as ‘attrition’). But biomarkers have the potential to increase the efficiency of medicines development, by:
- Speeding up clinical trials: they can be used to detect an effect (or lack of effect) earlier and more frequently than if only a clinical endpoint is used. For example:
- A panel of biomarkers has been used in the early phases of a clinical trial for a psoriasis treatment. The biomarkers included ‘epidermal thickness’ (i.e. thickness of the outer layer of skin) and the activity (expression) levels of several genes. These were both measured in skin biopsies. (1)
- Haemoglobin levels have been used in phase III trials to support development of therapies for ‘type 1 Gaucher disease’. This is a rare disease that affects multiple organ systems and shortens life expectancy, but it can take years to progress. (2)
- Streamlining clinical trials: they can help prediction (forecast), early detection and monitoring adverse reactions.
- One of the most common serious side effects of medicines is damage to the liver. Biomarkers that give an early indication of liver health have long been used during medicines development, and new ones are still being discovered. (3)
- The importance of safety biomarkers was highlighted in 2011, when new guidelines were proposed for their validation. (4)
- Improving clinical trials through better patient selection: this reduces ‘heterogeneity’ (diversity) and is the most common goal for genomic biomarkers in medicines development. (5) Genomic biomarker(s) can be used to:
- Identify patients with a particular disease sub-type or severity – for example, most but not all patients with ‘chronic myeloid leukaemia’ have the ‘Philadelphia’ chromosome which is a particular genetic abnormality.
- Exclude patients at increased risk of serious adverse reactions – for example, melanoma patients are at risk of getting worse if treated with kinase inhibitors and their tumours do not have a certain mutation in the ‘BRAF’ gene.
- Identify patients with a high chance of benefitting from a particular medicine - for example, kinase inhibitors and patients with BRAF-mutated melanomas. In the same way, genomic biomarkers can help avoid that patients are exposed to a new drug if they are unlikely to benefit.
- Improving our understanding of the way new medicines work, and leading to new approaches to medicines development in both non-clinical and clinical phases.
- Showing an added ethical benefit.
- A trial can be stopped sooner if no benefit is to be gained by the patients in the trial.
- A medicine with a positive effect might be authorised sooner and hence be prescribed earlier for patients who will benefit.
(1) Papp et al. Anti-IL-17 Receptor Antibody AMG827 Leads to Rapid Clinical Response in Subjects with Moderate to Severe Psoriasis: Results from a Phase I, Randomized, Placebo-Controlled Trial. J Inv Derm 2012 132, 2466–2469.
(2) Bai JPF, Barrett JS, Burckart GJ, Meibohm B, Sachs HC, Yao L. Strategic biomarkers for drug development in treating rare diseases and diseases in neonates and infants. The AAPS Journal, 2013; 15(2):447-454.
(3) Schomaker S, Warner R, Bock J, Johnson K, Potter D, Van Winkle J, Aubrecht J. Assessment of emerging biomarkers of liver injury in human subjects. Toxicological Sciences 2013 132(2):276-83.
(4) Matheis K et al. A generic operational strategy to qualify translational safety biomarkers. Drug Discovery Today 2011: 16; 600-608.
(5) EMA: Good pharmacogenomic practice: Current effective version. Available at: https://www.ema.europa.eu/en/documents/scientific-guideline/guideline-good-pharmacogenomic-practice-first-version_en.pdf - This document describes requirements related to the choice of appropriate genomic methodologies during the development and the life-cycle of a drug. It discusses the principles for a robust clinical genomic dataset. It also highlights the key scientific and technological aspects for the determination and interpretation of the genomic biomarker data and their translation into clinical practice.