The Future of Biomarker Discovery

The Future of Biomarker Discovery

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The Future of Biomarkers

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Author: Joy Kavanagh, Diagnostic Programme Manager | Reading time: 13 Minutes

In this article, we will explore:

The global biomarker market today
Opportunities and challenges for the future
What does the future for biomarkers hold?

Markers of disease have been used for centuries, but it wasn’t until the 1980s that the term, biomarker, became widely used. Biomarkers can range from simple physiologic measurements of heart rate and blood pressure measurements to imaging variables to complex molecular signatures.

 

Since the early 1980s, biomarker research across all fields of medicine has increased dramatically. This expansion shows no sign of slowing down as new technology facilitates the discovery of new biomarkers and the assessment of their place in modern medicine.

 

In 1990 the Human Genome Project collaboration embarked on a highly ambitious exploration project to create the first complete human genome sequence. Finishing in 2003, at a total cost of $3 billion, the landmark project helped to spark innovation that would lead to considerable advances in tools and ethics in genetics research.

 

Today, tech giants such as Illumina and Qiagen are making RNA and DNA sequencing increasingly accessible. Illumina, for example, is getting ever closer to its goal of a ‘$100 genome’. For biomarker research, advances in the performance and accessibility of such technologies have stoked the fires of exploration and innovation. In some fields, for example, oncology, biomarkers have played a key role in defining distinct subgroups of disease that respond to specific treatments, bringing about the birth of precision medicine.

 

With the increasing availability of data from large patient datasets and improved technology to analyse and identify potential biomarkers, huge advances will be seen in this field in the future.

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The Global Biomarker Market

The market for biomarkers is growing. The current value of the global biomarkers market is estimated to be USD 43.1 billion, with an expected compound annual growth rate (CAGR) of 12.6% from 2021 to 2026 (Figure 1).  The key factors boosting market growth are:

 

  1. An ageing population and increasing prevalence of chronic diseases.
  2. Advancements in the tools and technology used to discover and develop biomarker-based diagnostics.

Figure 1. Global biomarker market size and growth forecast. (Taken from Biomarkers Market, available at marketsandmarkets.com)

 

3. COVID-19 pandemic legacy

 

The escalation of COVID-19 infection rates to the global pandemic scale created an urgent need for efficient, reliable diagnostic testing. In parallel, socioeconomic pressures to return to and maintain ‘normality’ throughout seasonal infection spikes fuelled demand for low-cost, large-scale test availability. 

 

The scale of testing and penetration across societal levels globally has helped to normalise biomarker testing among the general public. Coupled with the increasing public interest in health and wellness, increased public awareness of biomarker testing through the pandemic has helped boost the direct-to-consumer (DTC) biomarker testing market (predicted to grow to $2.6 billion by 2025). 

 

The pandemic also created an environment ripe for medical research innovation. One example output of this, the Tiger Tech COVID Plus Monitor, was approved by the U.S. FDA in March 2021. This device was the first biomarker-based, artificial intelligence-powered device for COVID-19 screening.

 

Which biomarkers are seeing the fastest growth?  The diagnostics segment of the global biomarker market is estimated to register the fastest CAGR over the forecast period. The predicted growth is owed to advances in early diagnosis and improvement in the rate of diagnosis.

 

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What therapeutic areas contribute to the market growth?

The oncology segment is, as expected, the largest area for biomarker growth. Biomarkers can be used for early detection, estimate prognosis, guide selection of targeted therapies (companion diagnostics), and monitor response to treatment. The increased demand for rapid and accurate diagnostic tools, an increase in the global cancer burden, and an unmet need for more specific, personalised, therapeutic targets for cancer patients will continue to drive growth in the market. 

 

There are many clinical trials ongoing in different oncology biomarker discovery areas. There are a few key areas that novel research is focused on in addition to identifying new targets and their clinical significance:

 

  • Immunotherapy, where biomarkers can predict immune response to determine which patients benefit from which type of immunotherapy. The successes of anti-PDL1 therapies, such as nivolumab and pembrolizumab, are well-documented.
  • Liquid biopsies that can assess DNA shed from the tumour, and circulating tumour DNA can be used to identify individual mutations and establish personalised treatment choices.
  • Minimal residual disease measured by a liquid biopsy to assess if there is any detectable disease present after treatment. For example, Foresight Diagnostics was recently issued a patent for its minimal residual disease detection technology. 
  • Pharmacodynamic markers, use a biopsy before and during treatment to observe dynamic molecular changes within the tumour and determine how the drug is acting.

 

Recently, an important shift has been to improve access and increase adoption rates of advanced biomarker testing. Programs such as Amgen’s Biomarker Assist program or Eli Lilly’s Comprehensive Genomic Testing program aim to ensure more people have access to personalised treatment. 

 

The use of biomarkers in infectious disease diagnosis is tipped to become increasingly common in the coming years. A number of miRNA-based biomarkers have been identified for the diagnosis of influenza infections, HIV, Ebola, and malaria, to name a few. These miRNA-based biomarkers are designed to detect the early onset of infectious diseases. Additionally, after the onset of the COVID-19 pandemic, we saw a large global increase in biomarker testing not least with the expansion of PCR testing, rapid antigen tests and sequencing-based diagnostics.

 

Other therapeutic areas that will also reap the benefits of biomarker development include immune disorders, neurological disorders, and cardiovascular disorders.

 

Overall, the growing significance of companion diagnostics, increasing cancer incidence in an ageing global population, increasing investments in biomarker research, and continuous innovation are driving the growth of the biomarker market.

Opportunities and Challenges

Advances in biomarker research tools, methodologies and legislation have created great opportunities, but there are a fair share of challenges to overcome too. 

 

Opportunities

Precision medicine era: The move towards a biomarker-informed patient-centric approach is improving patient diagnosis, treatment and monitoring.  While oncology has dominated this area so far, particularly in the number of  FDA-approved companion diagnostics. Other disease areas are also benefiting from an increasingly biomarker-driven approach, including rheumatology, cardiovascular, and infectious diseases

 

Improve costs and failure rates in drug development: Recent estimates of the mean cost associated with taking a drug through development to approval is $1.3 billion. However, in oncology total research and development costs of >$4 billion have been reported. Including a biomarker-driven patient selection, strategy or biomarker monitoring strategy may reduce the risk of trial failure due to lack of efficacy or facilitate early triage of underperforming programs.

 

Inform clinical trial design and patient stratification: The inclusion of an appropriately validated biomarker in trial enrollment criteria can enable the selection of patients expected to have the greatest benefit from an experimental treatment. A biomarker-driven study design can increase the potential to meet endpoints and demonstrate increased efficacy relative to a competing drug or current standard of care. Alternatively, a biomarker strategy can enable study approval for a novel therapeutic for which side effects are a concern. In this case, screening with a biomarker for those who will gain the greatest benefit justifies the risk of side effects but spares those who will not benefit. >42000 trials currently registered on clinicaltrials.gov have biomarker involvement, and during 2015-2019 ~65% of drug approvals by EMA and FDA included use of a biomarker during development.

 

Reduce healthcare costs: One rationale for biomarker use is the efficient use of the healthcare budget. This is particularly true where patients are not treated unnecessarily with an expensive therapeutic when not expected to benefit. 

Overall, the appropriate use of biomarkers has the potential to: 

  • Improve the sustainability of drug development

  • Improve the quality and safety of drug trials

  • Reduce development cost

  • Accelerate the approval process significantly, and 

  • Advance the era of personalised medicine

 

However, there are challenges

Technology limitations: The most common techniques used for analysing biomarkers include liquid chromatography-mass spectrometry (LC-MS), ligand binding assays (is) and next-generation sequencing technologies. However, matrix effects, sensitivity, cost, throughput, selectivity and reagent availability have all been reported as the main challenges when using current technologies in biomarker discovery. 

 

Workforce limitations: As the field develops, technical limitations may become more apparent and hinder progression. The capacity for analysis (or lack thereof), the need for more sophisticated analytical tools due to the increasing complexity of biomarkers, and the need for skilled researchers are key potential issues in the field. 

 

Validation: Validation is critical for establishing biomarkers as reliable tools and securing marketing approval. The selection of appropriate validation methods is essential for a proper understanding of performance. Although failing a validation study is less than ideal in the short term, it should be seen as an opportunity for improvement leading to long-term gains. 

 

Inclusivity in clinical research: Inequality of representation by ethnicity, gender, age in clinical research can lead to significant safety and efficacy issues when therapeutics are released to market. Socioeconomic and geographical factors must also be considered and efforts made to eliminate barriers to inclusion in important clinical research and access to advanced diagnostics and therapeutics.

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What does the future for biomarkers hold?

The future of biomarker discovery looks exciting and one of constant evolution. The future may look like: 

 

  • Personalised/precision medicine with targeted treatments for individual patients
  • Accurate prediction and diagnosis of conditions and complications
  • The ability to monitor doses and treatment durations, and 
  • Predict short-term and long-term outcomes. 

 

Let’s take a closer look at some of these ideas.

 

Artificial intelligence in biomarker discovery, development, and deployment

Biomarker discovery relies on identifying previously unrecognised correlations between thousands of measurements and phenotypes. Omics technologies have enabled the high-throughput measurement of thousands of genes and proteins, and of millions of genomic and epigenomic aberrations. However, it is almost impossible for researchers to manually analyse and interpret the vast amount of data gathered from omics approaches. 

 

Machine-learning methods can identify the molecular patterns associated with disease status and disease subtypes. It can also account for the high-level interactions among the measurements and derive omics signatures to predict disease phenotypes. 

 

Gene expression, protein abundance levels and DNA methylation profiles can predict the status of a number of diseases, including cancers, infectious diseases and the risk of Down’s syndrome. Many of the biomarker panels derived from machine learning have outperformed those selected by experts or by conventional statistical methods. A number of them have been approved by the FDA and can be routinely used to guide treatment selection. 

 

It was recently reported that 90% of all healthcare data is imaging-based. This explains why many of the recent advances in AI and healthcare continue to be with medical imaging modalities. In terms of biomarkers, manual workflows involving the detection and quantification of histopathology stains such as Programmed Death-Ligand 1 immunohistochemistry are now being automated. The use of AI will improve turnaround times and reduce variability in subjective scoring techniques. 

 

A larger relatively untapped data modality in the medical imaging sector is the humble Haematoxylin and Eosin Stain (H&E). H&Es are routinely generated in pathology workflows. Now that these slides are being scanned and digitised the data that lies within these gigapixel images has huge potential diagnostic and predictive value. 

 

For example, we know that immune cell and stromal compartments are associated with poor prognosis and response to therapies. However, quantification and detection have only recently become tractable with the use of deep learning algorithms for fast and reproducible H&E image-based workflows.

 

The successful deployment of data-driven biomarkers has the potential to positively impact both routine patient management and clinical trial design. With the establishment of nationwide biobanks, standardised high-throughput profiling methods and advanced machine-learning methods, we expect to see an increasing number of AL/ML-developed biomarkers in the future.

 

Digital twins of patients

Initially developed for engineering applications, the Digital twins concept, can be applied to complicated systems such as airports or even cities. It enables complex systems to be modelled computationally. This allows for testing scenarios with a huge number of variables that would not be feasible in the real world, becoming efficient and economical.

 

From Digital twins to personalize medicineThe digital twin concept for personalized medicine. a) An individual patient has a local sign of disease (red). b) A digital twin of this patient is constructed in unlimited copies, based on computational network models of thousands of disease-relevant variables. c) Each twin is computationally treated with one or more of the thousands of drugs. This results in a digital cure of one patient (green). d) The drug that has the best effect on the digital twin is selected for treatment of the patient’.

 

Digital twins of patients could act as a solution for modelling the wide range of data relevant to human diseases. Data such as clinical and para-clinical outcomes, multi-omics, biomarkers, and patient metadata could improve prediction, prevention, and treatment. 

 

With successful application, digital twins create the potential for a future in which clinicians can simulate the treatment of an individual patient with a range of therapeutics and rapidly predict their disease outcome with each. Thus enabling the creation of a tailored clinical action plan. This is real personalised healthcare and it could cut healthcare costs and patient suffering remarkably. 

 

Researchers are investigating the potential application of the digital twins concept in various diseases including cancer, cardiology, multiple sclerosis and sepsis.

 

Non-invasive biomarkers

Innovation in non-invasive sampling techniques is expected to be a key future market driver. With 390 related publications in the last 12 months alone non-invasive (and minimally invasive) biomarkers are a hot topic and for very good reason. Non-invasive or minimally invasive sample methods (such as blood samples) reduce the patient risk associated with testing and can provide a more cost-effective solution to biomarker analysis. Advancement of analytical methods for liquid biopsy samples (including blood, saliva, sweat, and urine) will open up utility for point-of-care diagnosis and direct-to-consumer screening for early disease markers. 

 

Blood tests are minimally invasive, readily accessible and provide a cost‐effective and time-efficient solution in comparison to surgery. Serum, plasma and whole blood samples contain a plethora of biomarkers that can be used for the detection and monitoring of disease including angiogenesis, hormonal, tumour, apoptosis, oxidative stress and inflammatory markers. Such noninvasive analysis of biomarkers has the potential to impact future assessment and detection of disease.

 

As an example, advances have been made in the use of non-invasive blood tests for the detection of Alzheimer's disease in recent years. Two recent clinical studies measured variants of tau protein in the blood. They found that a blood test measuring one of two phosphorylated forms of tau could diagnose AD as accurately as other invasive methods of diagnosis.

 

Saliva is known to contain 360 different kinds of exogenous and endogenous VOCs. Recent research suggests salivary fluid can be a potential biofluid for the non-invasive, continuous, and instantaneous monitoring of different diseases. 

 

Several research groups investigated the integration of electrochemical sensors into mouthguards for monitoring biomarkers. In contrast with other biofluids, the salivary biofluid provides a significant amount of fluid for a greater functioning space for the placement of the sensing unit. With an abundance of VOCs in saliva, this is one of the promising biofluids for continuous and real-time monitoring of different diseases. 

 

Breath analysis is a budding domain for non-invasive testing which is being investigated for lung cancer detection and diabetes detection and monitoring. It is safe, painless, and allows repetitive sampling. 

 

Next-generation wearable health technology is being developed that enables in situ analysis of metabolites and electrolytes providing real-time non-invasive personal diagnostics in perspiration.

 

Conclusion

The biomarker market looks set to continue to grow fuelled by the prospects of precision medicine, reduction in healthcare costs, and minimisation of human suffering. The advancements in AI and continued investment in biomarker discovery will ultimately result in more robust biomarkers, a future of non-invasive diagnostics, digital twin-guided healthcare and much, much more. 

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