Discovery and validation of biomarkers for multiple sclerosis

Multiple Sclerosis (MS) is an autoimmune disease leading to a chronic inflammation and degeneration of the central nervous system. It is one of the major neurological diseases with approximately 2.5 million suffering patients worldwide. Until now, the underlying mechanisms have not been fully elucidated, but the cause of the disease can be modulated to limit progression and severity. Currently, there are no validated biomarkers available to predict the progression of MS or response to a clinical intervention apart from MRI. In order to identify protein biomarkers for MS as well as other diseases, significant infrastructure is required and this is discussed.

The term ‘biomarker’ has been defined as a “characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention”^1,2. The measurement of normal and dysfunctional biological processes and their changes in response to therapeutic intervention forms the basis of biomarkers. The advances in genetics and molecular biology leading to the sequencing of the human genome has resulted in the identification of a variety of novel targets implicated in different disease states^3-5. Further technological developments including high throughput profiling of various samples using genomics, transcriptomics and proteomics^6,7 has led to the identification of gene and protein based markers that characterise disease states for a number of indications including breast cancer^8-10, colorectal cancer¹¹ and cardiovascular diseases¹². Additional initiatives that have led to the identification of biomarkers with minimal invasive methods such as proteomics technologies¹³ and systems biology¹⁴ have proven extremely effective for discovering potential biomarkers and drug targets. These technologies tend to provide large data sets that can be difficult to deconvolute for biomarker discovery. This bottleneck can be reduced by using several strategies. The first is to constrict the number of potential biomarkers and drug targets by dividing the proteome into smaller, more biologically significant segments. The second is to widen the bottleneck with higheroutput and higher-throughput screening technologies. The third is to incorporate more preliminary validation into the discovery process. New and emerging technologies provide promise for each of these strategies¹⁵.

Biomarkers for Multiple Sclerosis

MS is an autoimmune disease that preferentially manifests in young adults between 20 – 40 years of age and it affects women approximately twice as often as men¹⁶. Over 80 per cent of MS patients show functional disabilities and more than 70 per cent are limited in the quality of their everyday life within the first 15 years after the initial diagnosis¹⁷. Although MS is a heterogeneous disease it can be classified to some extent: approximately 85 per cent are of a relapse-remitting type (RR-MS) with recurrent attacks of neurologic dysfunction (relapses) followed by initial gradual improvements (remissions). More than 50 per cent of these RR-MS patients convert to a secondary progressive phenotype (SP-MS). Only approximately 15 per cent of individuals show primary progressive MS (PP-MS)¹⁸. MS is a complex genetic disorder of unknown etiology, and multiple genetic trait loci in combination with environmental factors, e.g. viral infections, can lead to disease manifestation in susceptible individuals¹⁹. Mechanisms explaining the interaction and co-contribution of genetic and environmental factors in disease induction are not elucidated yet. Due to the complexity of the disease process and heterogeneity in MS, it is unlikely that a single biomarker could be used as a tool for basic research or clinical practise but rather a combination of different biomarkers. These biomarkers are key for the generation of new drugs to treat MS since they could be used as a diagnostic tool for the staging or classification of the extent of disease, a predictor of the onset and disease course and a tool to monitor the clinical response to a clinical intervention²⁰.

Example of a Public-Private partnership in drug and biomarker discovery

In order to identify protein based biomarkers for MS, the European ScreeningPort (ESP) is working with various partners within academic institutions to create and deliver drug discovery programs addressing novel therapeutic targets. The key aim is to accelerate the translation of promising results generated in basic research disease biology into new therapeutic options in the clinic. To achieve this aim, the ESP provides facilities and expertise to the academic community that have, until recently, been available only to the pharmaceutical industry. The underlying causes of MS are relatively poorly understood and effective treatment options, which act via anti-inflammatory, neuroprotective or neuro-regenerative effects, are still lacking in all phases of MS. There is a clear need to identify biomarkers to support the development of superior therapeutics that can offer one or more of the differentiating factors such as the improvement of the efficacy and/or safety profile of a drug, determining the mechanism of drug action and a preferable route of administration over currently available therapies. These top-level objectives translate into the following experimental work packages: (1) generation of SOP’s defining the optimal use of valuable patient samples, (2) enabling high throughput biomarker analyses to allow patient stratification and thereby providing individualised medicine for patients, (3) initiating production screening of patient samples for already defined biomarkers and (4) identifying novel targets as being causative or contributory factors in MS.

Biomarker data can be gathered in conjunction with a clinical trial in order to achieve economies of scale. Biomarker discovery requires comparable phenotypic characterisations, logistics and statistical analyses. Therefore, sample repositories and data banks are needed in combination with validated assay systems. The Centre for Molecular Neurobiology Hamburg (Zentrum für Molekulare Neuro – biologie Hamburg, ZMNH) which is part of the University Medical Centre Hamburg-Eppendorf (Universitätsklinikum Eppendorf, UKE) has created a comprehensive Biobank containing in excess of >10,000 samples sourced from >1000 patients suffering from diverse stages of MS including serum/plasma, peripheral blood mononuclear cells (PBMCs), DNA, RNA, cerebrospinal fluid (CSF) and urine and is one of the largest resources of its type in Europe. The ZMNH is an internationally recognised research institute which works on the molecular genetics, anatomical, biochemical, physiological and patho-physiological aspects of neurobiology with a strong focus on MS. This facility is embedded within the Neu2 consortium (www.neu-quadrat.de/start-en.html), which is funded by the German Government and a consortium of pharmaceutical companies, including Merck Serono. The Neu2 consortium’s aim is to strengthen the development of innovative pharmaceuticals in Germany. The operating mode is to set up innovative models of networking to (1) form public-private partnerships in which academic partners are significantly involved in all stages of develop – ment, (2) constitute long-lasting technology transfer that can deliver the fruits of basic research to the patient and (3) create sustainable infrastructure and funding that enable high-risk, early-stage drug projects. The ESP provides the consortium partners access to industry standard instrumentation, chemical libraries and bio – informatics infrastructure. The collaborative programs involving the ZMNH/UKE and ESP focusing upon MS can be replicated and applied to additional broader areas of drug discovery in order to accelerate drug and biomarker discovery.

There is a clear need to identify biomarkers for MS and it is envisaged that the infrastructure described herein will facilitate this. The key components for this include clinical expertise, access to patient samples and facilities that enable the evaluation of the samples in a high-throughput manner across a variety of detection platforms. It is anticipated that the outcomes will be the discovery of novel biomarkers that characterise the different stages of MS for basic research. In general, this could also enable the optimisation of pre-clinical drug discovery in other indications where in-vivo models are a poor descriptor of the pathological disease state.

References

1. Floyd, E., & McShane, T. (2004). Development and Use of Biomarkers in Oncology Drug Development. Toxicologic Pathology, 32, 106-115
2. Wagner, J. a. (2009). Biomarkers: principles, policies, and practice. Clinical Pharmacology and Therapeutics, 86, 3-7
3. Colburn, W. A. (2003). Biomarkers in drug discovery and development: from target identification through drug marketing. J Clin Pharmacol, 43, 329-341
4. Overington, J. P., Al-Lazikani, B., & Hopkins, A. L. (2006). How many drug targets are there? Nat Rev Drug Discov, 5, 993-996
5. Terzic, A., & Waldman, S.A. (2010). Translational medicine: path to personalized and public health. Biomarkers Med, 4, 787-790
6. Smith, B., Selby, P., Southgate, J., Pittman, K., Bradley, C., & Blair, G. E. (1991) Detection of melanoma cells in peripheral blood by means of reverse transcriptase and polymerase chain reaction. Lancet, 338, 1227-1229
7. Frank, R., & Hargreaves, R. (2003). Clinical biomarkers in drug discovery and development. Nat Rev Drug Discov, 2, 566-580
8. Kaufmann, M., & Pusztai, L. (2011). Use of standard markers and incorporation of molecular markers into breast cancer therapy: Consensus recommendations from an International Expert Panel. Cancer, 117, 1575-1582
9. Weigel, M. T., & Dowsett, M. (2010). Current and emerging biomarkers in breast cancer: prognosis and prediction. Endocrine-Related Cancer, 17, R245-R262
10. Li, J., Zhang, Z., Rosenzweig J, Wang, Y. Y., & Chan, D. W. (2002). Proteomics and bioinformatics approaches for identification of serum biomarkers to detect breast cancer. Clin Chem, 48, 1296-1304
11. Bohanes, P., LaBonte, M. J., Winder, T., & Lenz, H.-J. (2011). Predictive molecular classifiers in colorectal cancer. Seminars in Oncology, 38, 576-87
12. Krishna, R., & Wagner, J. A. (2010). Applications of ‘decisionable’ biomarkers in cardiovascular drug development. Biomarkers Med, 4, 815-827
13. Blonder, J., Issaq, H. J., & Veenstra, T. D. (2011). Proteomic biomarker discovery: It’s more than just mass spectrometry. Electrophoresis, 1541-1548
14. Wang, K., Lee, I., Carlson, G., Hood, L., & Galas, D. (2010) Systems biology and the discovery of diagnostic biomarkers. Dis Markers, 28, 199-207
15. Ross, J. S., Symmans, W. F., Pusztai, L., & Hortobagyi, G. N. (2005). Pharmacogenomics and clinical biomarkers in drug discovery and development, American Journal of Clinical Patholology, 124, S29-S41
16. Sospedra, M., & Martin, R. (2005). Immunology of multiple sclerosis. Annual Review of Immunology, 23, 683-747
17. Hauser, S. L., & Oksenberg, J. R. (2006). The neurobiology of multiple sclerosis: genes, inflammation, and neuro – degeneration. Neuron, 52, 61-76
18. Compston, A., & Coles, A. (2008). Multiple sclerosis. Lancet, 372, 1502-1517. Elsevier Ltd
19. Kakalacheva, K., Münz, C., & Lünemann, J. D. (2011). Viral triggers of multiple sclerosis. Biochimica et Biophysica Acta, 1812, 132-140
20. Martin, R., Bielekova, B., Hohlfeld, R., & Utz, U. (2006). Biomarkers in multiple sclerosis. Annals of Neurology, 22, 183-185