Biomarkers are widely used as a diagnostic tool to predict clinical and pathological outcome. These molecules enable to assess the progression of the disease, predict the prognosis and survival after clinical intervention. Biomarkers are extensively used to evaluate the process of drug development, and, optimally, to improve the efficacy and safety of drug candidates.
With drug development facing challenges from ever increasing cost of developing drugs and rise in the failure rates for drugs in clinical trials, biomarkers are being employed on large scale by pharmaceutical companies to overcome these challenges. Also the field of personalized medicine and companion diagnostics is witnessing a strong presence of use of biomarkers and holds to be a big opportunity for the overall biomarker market.
An ideal biomarker candidate is the one which can be related with high accuracy to a physiological state- normal or diseased. Hence significant effort goes into the discovery of novel biomarkers. Researchers rely on number of high-throughput molecular profiling technologies from whole genome sequencing to high resolution mass spectrometry- Genomics to Proteomics to Metabolomics. Though novel technologies can guide one to the most ideal marker, data volume and complexity associated with it makes the road towards ‘novel’ biomarkers tedious.
To discover new biomarkers that predict treatment effects and disease progression, it is necessary to analyze a broad spectrum of data from human studies. The challenge lies in assimilating and validating large amount of heterogeneous data generated through these assays. The successful translation of large volume of information into new biomarkers requires the coming together of heterogeneous patient data, ontologies, statistical packages and OLAP enabled platforms.
Biomarkers are quantifiable changes happening at the molecular or physiological state of an organism, which helps in assessing the risk or progression of a disease or susceptibility of a disease to a given treatment. Biomarkers can take different forms including genes, proteins, antibodies, lipids, hormones, enzyme levels, physiological states such as blood pressure or imaging studies of particular organs or it can be even a substance introduced into a patient to assess how internal organ systems are functioning.
Several studies are currently underway to identify novel reliable biomarkers across disease spectrum that are both sensitive and specific and can be easily reproducible among clinical laboratories. A single biomarker is often inconclusive or ambiguous because diseases with very different outcomes can affect the same molecule or marker. The sum of changes of a metabolic pathway or a suite of genes would be a much better indicator of the underlying pathology.
The driving force behind these studies is the revolution taking place in the ‘omics’ field. Instead of looking at single biomarker at a time, novel technologies in genomics, transcriptomics, proteomics and metabolomics have allowed investigators to identify patterns in the changes of tens, hundreds and even thousands of genes, proteins and small molecules that correlate with disease state. Currently nearly a third of the drugs in clinical development are associated with genomic or proteomic marker. Success story of personalized cancer drugs Zelboraf and Xalkori have enhanced pharmas interest in driving biomarker centric drug discovery.
In 2010, the total global market for biomarkers was an estimated $13.5 billion and is expected to grow to nearly $33.3 billion by the end of 2015 at a 5 year compound annual growth rate (CAGR) of 19.8%. The global market for diagnostic biomarker is expected to reach about $30.6 billion in 2020, growing at CAGR of 16% from 2013 to 2020. Disease diagnostic application remains the key area of application that is expected to reach 6.1 $billion in 2020. However, application of diagnostic biomarkers in forensics and molecular diagnostics is catching up quite significantly. Molecular diagnostics is expected to grow at a CAGR of 17.6% from 2013 to 2020. Of all segments, genomics will continue as the largest, at 2010 revenue of $5.1 billion, growing to $16.9 billion in 2015, a compound annual growth rate (CAGR) of 26.9% (Markets and Markets report, bcc research report).
Oncology biomarkers account for the lions share in the global biomarkers market and as the forecast says are to grow at the highest rate in the next five years. Global Cancer Biomarkers market is expected to grow at a CAGR of 18.22 percent over the period 2012-2016. The key factors driving the growth of overall oncology biomarkers market sector include prevalence of cancer across the globe, the early diagnosis and enhanced testing capabilities biomarkers facilitate. Oncology biomarkers market involves two segments mainly, Oncology biomarkers discovery market and Oncology biomarkers diagnostics market. Frost & Sullivan sized the U.S. cancer biomarker testing market alone to reach $11.46 billion by 2017. Moreover there is staggering amount of information available to be analyzed and evaluated on different cancers and its subtypes from literature and public dataset repositories.
The transfer of biomarkers from discovery to clinical practice is still a process filled with lots of pitfalls and limitations. Data collection and analysis are cited as the critical challenges in biomarker discovery. The rapid proliferation of high-throughput molecular profiling technologies has resulted in data accumulation in tera bytes. ‘Big Data’ is the biggest challenge faced by biomarker research groups. Another challenge stems from the difficulty of integrating data originating from different technologies and performing functional annotation. Several tools, both free and commercial have been employed to tackle this giant task, but then most of these tools are either statistical tools, data visualization supporting tool or literature centric databases, which makes drawing a conclusion based on either of the ways inconclusive.
The platform proposed by Sun Bio team for Biomarker intends to bring together information spread across heterogeneous sources onto one space in a seamless fashion. The carefully integrated relationship network and presence of quantitative and qualitative data points overlaid with predictive analytics would help the researchers to validate biomarker candidates, identify novel candidates and propose new biomarker panels. This platform would allow the researchers to derive new molecular relationships and patterns from literature information and publicly available gene/protein repositories in combination with exploratory analysis.