Article | November 21, 2023
How deep immune phenotyping via small molecule biomarkers could change the way we diagnose and treat autoimmune diseases
Autoimmune diseases have notoriously been difficult to properly diagnose and treat. While the rise of new bioanalytical technologies has driven greater understanding about the underlying biology of these disorders, the vast majority are still treated with broad-acting immunosuppressants and anti-inflammatories that are not disease-specific. These therapies might bring relief to some, but for many patients they have either low efficacy or no efficacy at all. For example, in the context of inflammatory bowel disease (IBD), up to 30% of patients treated with tumor necrosis factor-alpha (TNFα) antagonists do not respond to initial treatment, and up to 46% experience a loss of response over time.
A big factor in this lack of efficacy is the immense heterogeneity of autoimmune diseases. While researchers have been able to uncover some linkages to specific genetic signatures, data suggest that germline genetic variation accounts for less than 20% of autoimmune disease trait variability. Autoimmunity commonly presents in late childhood or beyond, indicating that dynamic factors over time – including epigenetic programming, autoantibody formation, and environmental and lifestyle exposures – likely contribute significantly to disease risk and development. As a result, a single disease, such as rheumatoid arthritis, can present with considerable variation in clinical symptoms and severity. So how can we effectively map this variability to develop more specific therapeutics for autoimmune diseases?
If we look to the cancer space, we can see how biomarkers can have a transformative impact on such efforts. There was a time when the majority of cancer patients were recommended either chemotherapy or radiation – both of which were indiscriminate in targeting cancerous vs. healthy cells. Today, genetic biomarkers have given us the ability to subtype a patient’s cancer to better inform treatment plans. Lung cancers, for example, can be subtyped by identifying EGFR, KRAS, and ALK gene mutations, among other biomarker signatures.
The challenge is that while many cancers are largely genetically driven, the opposite is typically true for autoimmune diseases. Biomarkers that can help subtype autoimmune diseases are needed, but genetic biomarkers alone are not enough. We must add biomarkers to the development arsenal that can read out the non-genetic factors that influence autoimmunity, so we can deeply phenotype the immune system and better target treatments to the underlying pathways of autoimmune diseases.
How small molecule biomarkers can enhance deep immune phenotyping
Small molecule biomarkers, inclusive of metabolites and lipids, have unique and complementary characteristics to DNA and protein biomarkers and can be integrated with these datasets to build a more comprehensive understanding of autoimmune disease variability – which also informs differences in patient response and drug safety. Here’s how:
They are dynamic – While genome sequencing does give us important information, it is static. The genome we are born with is the genome we have throughout our entire lives, barring any random mutations that might arise. Small molecules, on the other hand, are continually fluctuating in response to various conditions and exposures. This enables us to compare and contrast between different timepoints, whether that be different years, different months, or even different times of day. Further, small molecule biomarker levels are influenced by both genetic and non-genetic factors and can provide a means for understanding the underlying mechanisms of autoimmune disease.
They are easily measured via blood or other liquid media – Tissue biopsies are invasive and burdensome for patients. Liquid biopsy samples, particularly blood, are generally easier to collect and allow for efficient longitudinal sampling to monitor biomarker changes over time. Small molecules are also more readily released from tissue into blood compared to DNA or proteins, so a large breadth of small molecule chemistries can be captured via this matrix.
They enable deeper disease stratification – Much like stratifying lung cancer into EGFR+ or negative groups, with enough patient data we can classify those with a particular autoimmune disease into different subtypes based on certain small molecule signatures. This information could prove immensely useful for clinical trial planning as well as future disease treatment strategies.
Their biology is conserved across species – One of the biggest pain points in drug development is having an investigational therapy work well in a cell or animal model but not work in humans. Unlike DNA and proteins, small molecules are highly conserved across biological systems. Identifying small molecule biomarkers in early drug development could thus increase the likelihood of success as researchers move into preclinical research and clinical trials.
How next-generation mass spectrometry is amplifying our ability to map autoimmune disease biology
Small molecule biomarkers are by no means a new concept – doctors have been running blood panels that measure small molecule levels for decades. Rather, the innovation and potential lie in the power of emerging technology to scale our ability to capture and map the full breadth of small molecules in circulation.
Historically, the exploration of the metabolome has been hindered by the slow pace of traditional bioanalytical tools, leading to a limited understanding of this complex biochemical landscape. Discovery efforts have thus largely been focused on measuring abundant and well-established molecules. However, given the vast number of small molecules circulating in the blood, there are likely numerous biologically relevant biomarkers that have not yet been identified.
The rise of next-generation, high throughput mass spectrometry, including Sapient’s own rLC-MS technology, has revolutionized the speed of metabolomics analyses to assay thousands vs. dozens of small molecules at a time, enabling more comprehensive exploration across various chemistries. Researchers can now identify and examine a broader range of biologically significant markers, revealing deeper and more nuanced insights that can be applied across the entire autoimmune drug development spectrum.
In fact, Sapient has used our rLC-MS systems to rapidly build an expansive Human Biology Database, comprised of data generated from assaying over 100,000 human biosamples from diverse individuals across multiple timepoints. The spectral data is matched with genomics, microbiome, and phenotypic information for these individuals to enable multi-dimensional analyses. This provides a rich repository to mine for and validate biomarker discoveries and allows us to map the biology of autoimmune diseases with population-level insight.
The applications in autoimmune disease for these technologies and small molecule biomarker data span drug development phases, including:
Discovery and Preclinical Phases: Using the speed and scalability of high throughput, nontargeted mass spectrometry methods, we can widen the scope of molecules that are examined and more fully assess dynamic changes that occur across the metabolome with immune system activation, dysregulation, and response to drug exposure. This approach has great potential to identify entirely new and biologically relevant small molecule biomarkers.
Clinical Development: Moving into clinical trials, drug developers can use these small molecule biomarkers to stratify patients for different clinical trial treatment regimens, by disease subtype and/or likelihood of response. They can also be used to better discern pharmacodynamics as well as optimal therapy doses and dosing timing for patients receiving treatment for their autoimmune disease.
As part of this, small molecule biomarkers can monitor drug responses for therapies that could trigger immune-related adverse events (irAEs). Cancer immunotherapy is one such example that is becoming more prevalent. Even in patients who do not have an existing autoimmune condition, immunotherapies by nature stimulate the immune response and thus can induce events such as anti-drug antibody production and cytotoxic T cell responses. Measuring small molecule biomarkers as part of a deep immune phenotyping strategy gives us much more power to monitor and predict irAEs that could arise during treatment.
Healthcare: Ultimately, when integrated with other -omics data, small molecule biomarkers can help doctors recommend the best treatments for their patients that are most likely to work for their specific disease subtype.
With small molecule biomarkers, we gain a more holistic understanding of the potential causes and contributing factors of autoimmune disease, and we can thus detect more meaningful correlations to better inform hypotheses. Through the power of new bioanalytical technologies to enable deep immune phenotyping, we’re nearing an inflection point in the way we diagnose and treat these diseases. We believe that small molecule biomarker discovery is a key component needed to fully unlock this milestone, and its potential has largely been untapped until now. With our advanced rLC-MS platform and Human Biology Database we are helping sponsors to accelerate drug discovery and development across autoimmune diseases and immune-based therapeutic modalities.
Learn more about dynamic small molecule biomarkers and Sapient’s discovery approaches here.