Metabolon reveals a new ally for drug research and development programs: Picking program winners and demonstrating their value
Today’s biopharma companies are faced with extreme competition to secure support from investors, approval from regulators, and buy-in from payers. How can a company and its new molecule shine above the noise to overcome these challenges? Enter metabolomics, the study of metabolites, the small-molecule end products of metabolism collectively known as the metabolome.
Metabolon’s superior methodology and vast metabolite library allow for the capture of a complete story about your molecule at every stage, providing valuable input that builds a robust story about your data to shape a comprehensive Global Value Dossier, or what we call a Program Development Dossier.
Obstacles to clinical success
The barriers to program success for biopharma, whether success comes in the form of an asset sale or an approved therapy, are immense. The cost of securing approval for a new drug is now estimated at $2.6 billion, which has increased 145 percent over the last 10 years due in large part to an extremely high failure rate. But what is at the heart of the failure rate? When scientists and biopharma leaders reflect on the source of high drug attrition rates, they cite many factors including: failure of preclinical development models to predict treatment efficacy, safety issues or difficulty differentiating between responder and non-responder patients. A commonality to all these issues and their associated risks is that critical pieces of information are lacking or unclear, leading to a foggy decision-making landscape.
How can clarity emerge from the fog?
Are there better ways to clarify decisions along the R&D continuum to increase the probability that a molecule directed against a target will have efficacy in humans without compromising safety? Typically, this information is sought through two extremes:
- Profiling approaches (e.g. RNASeq, proteomics, etc.) which create overwhelming amounts of data.
- Assays that were selected based on the putative mechanism and target biology.
The former is difficult to wade-through to discern meaningful information and the latter relies on picking the right markers in advance, which frequently does not occur. Therefore, data that occupies a middle ground between these extremes is needed – something that is comprehensive, meaningful and readily interpreted. Metabolomics data provides a view of these properties and helps to enrich decision making from discovery through the clinic. Hence, small and large biopharmaceutical companies are constantly on the look-out for approaches that deliver this type of data.
Solutions, elixirs, promises
Biopharma companies are routinely bombarded by technologies that promise “the next great thing” that will “revolutionize drug discovery.” Clearly some of these can help incrementally, some will have little utility and some will provide true promise. The question is, how can you discern the best options for your program? One rubric for this is to reflect on what we described above, selecting the method that will deliver clarity through the fog – data that is comprehensive, meaningful and readily interpreted, and data that dynamically assesses a living system and links it to physiological changes induced by disease and drug response. These qualities describe metabolomics data.
Metabolomics provides comprehensive and meaningful insight into each stage of the drug development process, allowing drug developers to attain signals for both safety and efficacy (as well as their associated biomarkers). This capability informs decision-making across the drug development continuum. Importantly, successes driven by these insights accumulate along the way and across programs, suggesting strong potential for metabolomics to aid in reducing drug attrition. In isolated programs, these insights help build confidence, discharge risk and illuminate the value of the program. Regardless to the challenge at hand for biopharma, the Program Development Dossier approach presents the missing link to deliver winning drug programs.
Metabolomic insights empower effective decision-making
Metabolomics enriches R&D decision-making because of the fundamental properties of what it surveys – the metabolome – all the small biochemicals (metabolites) that circulate in the body or in a cell. Metabolomics is particularly important in creating clarity for drug research and development because nearly all the influences that effect physiological processes (disease, target biology, off target activity) affect the metabolome . Hence, measuring with metabolomics provides a consensus report for molecule action in the context of the model being used. Finally, because the metabolome and metabolic pathways are so extensively mapped, this consensus report is readily interpretable.
Image 1:Of course, the fundamental strengths of metabolomics are not new. What is new is that many biopharmaceutical companies have embraced the broadening view that metabolomics is a first-line tool for drug development. This video features leaders sharing their experience with metabolomics and Metabolon.
Enter the Program Development Dossier
Staples of drug research and development are to gain clarity on the effects of the target-molecule combination and obtain biomarkers that can accompany the program into clinical development. The importance of these insights and biomarkers is escalated when pursuing novel molecules and targets or entering a highly competitive space. As described above, metabolomics provides a powerful synergy to the standard data for gaining these important staples.
As companies build their Program Development Dossier – a comprehensive data package that includes detailed scientific insight into the molecule’s mechanism of action (MoA) and relevant clinical biomarkers – layering in informative data and biomarkers from metabolomics will help to build the most robust story to secure funding and move one step closer to approval. Ideally, studies begin in efficacy models, continue through preclinical development, and continue into first in human studies. In this way, the most translatable markers and understanding of the target/molecule combination will occur .
Image 2:Dossier data revealed through Metabolon’s proprietary untargeted metabolomics solution provides the framework to make a stronger, more confident and more valuable case for a molecule in a shorter timeframe and can travel through the drug development process. This dossier is equipped with translatable biomarkers, clarity on how the molecule/target combination is unique. With this type of Program Development Dossier, both large and small biopharma can have access to credible, highly relevant data that clearly demonstrates the potential value of their development program. In addition, this data can help organizations build an infrastructure in the form of translatable biomarkers that can be leveraged for future programs.
Some common questions that are addressed in a Program Development Dossier fueled by metabolomics are:
- Which model is most translatable/relevant?
- Is there a clear pharmacodynamic (PD) and efficacy signal from my animal model?
- Do I have blood biomarkers for monitoring PD and efficacy, including in non-rodent models?
- Will the biomarkers translate to humans?
- Are there any obvious liabilities with the molecule?
- Do I have the data to support a clear, compelling MoA that translates from animal models to humans?
- Is the molecule likely to achieve commercial success based on the competitor landscape?
- Do the biomarkers have potential for selecting sub-groups for trials or identifying responders?
- What is the clearest path for the next development milestone?
Insights from the dossier
Project teams are increasingly using metabolomics to enrich their drug research and development programs to solidify stakeholder support. Importantly, this use is agnostic to the target class (e.g. GPCR, enzyme, nuclear receptor) as it is recognized that nearly all areas of target biology on route to efficacy will impact metabolic pathways. Below are several abstractions from various organizations’ metabolomics-driven Program Development Dossier initiatives. .
Case studies of the Program Development Dossier in action
Markers of acute renal toxicity enable screening less toxic chemotypes
While in early development of a molecule directed at a novel target for lowering cholesterol, metabolomics screening revealed markers associated with acute renal failure in mice. Conventional markers did not clearly signify the issue or explain it. The metabolomic biomarkers provided confidence that the effects were clearly off-target and these markers were used to screen other chemotypes to show that the toxicity was isolated to a particular chemotype. This insight provided a potential way forward for the program. This example highlights the peril of relying solely on established markers as they were ineffective in reflecting and explaining the toxicity. In contrast, metabolomic fingerprinting provided clarity and a way forward .
Simple blood biomarkers for assessing response add to the development toolbox in NASH
Endpoints for nonalcoholic steatohepatitis (NASH) trials have many disadvantages including reliability. Simple blood biomarkers are highly desired. The need is amplified since there are so many novel targets in the NASH pipeline – quickly knowing if the program is heading in the right direction (i.e. hitting the target, signs of efficacy) is critical. Leveraging metabolomics, investigators at Gilead bolstered the confidence that they were on the right track in their successful phase II trial of GS-0976 by discovering several metabolite markers of efficacy. These biomarkers can accompany future development for reading out the pharmacodynamic activity or response of GS-0976 .
Biomarkers of acute inflammatory organ condition in early phase clinical trial delineated by metabolomics
Using metabolomics in clinical development can be beneficial to subtype individuals who respond but also may indicate who will experience adverse events. Metabolomics data showed a clear association with subjects that exhibited severe abdominal pain and inflammation of a specific organ. Over a dozen metabolite markers from two different classes – one related to the primary organ dysfunction and the rest to the activity associated with the inflammation clearly distinguished the group experiencing the adverse event. These metabolites can be used to monitor subjects in subsequent studies for early signs of the adverse event.
Distinguishing mechanism to differentiate molecule in crowded space
One route to finding molecules with novel MoAs is through phenotypic screening. One sponsor had discovered a potent angiogenesis inhibitor with similar potency to an approved drug. Despite it successfully moving through development, the MoA was unknown and Pharmacodynamic biomarkers were lacking. Metabolomics showed that the underlying MoA was clearly distinct from the approved drug and unique to any angiogenesis inhibitor described on the market. This provided a powerful leg-up in distinguishing their molecule from the competitors and identifying PD biomarkers that could accompany it into development.
Summary of what it can do
These examples highlight the importance of adding metabolomics to drug discovery and development programs. The clarifying enrichment and translatable biomarkers provided are clear assets embedded within the Program Development Dossier. The Program Development Dossier is a new companion to ultimately help pick the winners, determine how best to advance them and to demonstrate the value of the program. To learn more about how this approach can help your program read our whitepaper or contact us today to get started.
DiMasi, Joseph A., et al. “Innovation in the pharmaceutical industry: New estimates of R&D costs.” Journal of Health Economics Volume 47, (2016): 20-33. https://www.sciencedirect.com/science/article/abs/pii/S0167629616000291?via%3Dihub
Zgoda-Pols, Joanna R., et al. “Metabolomics analysis reveals elevation of 3-indoxyl sulfate in plasma and brain during chemically-induced acute kidney injury in mice: investigation of nicotinic acid receptor agonists.” Toxicology and applied pharmacology 255.1 (2011): 48-56.
Wang, Ganfeng, and Walter A. Korfmacher. “Development of a biomarker assay for 3‐indoxyl sulfate in mouse plasma and brain by liquid chromatography/tandem mass spectrometry.” Rapid Communications in Mass Spectrometry: An International Journal Devoted to the Rapid Dissemination of Up‐to‐the‐Minute Research in Mass Spectrometry 23.13 (2009): 2061-2069.
Loomba, Rohit, et al. “GS-0976 reduces hepatic steatosis and fibrosis markers in patients with nonalcoholic fatty liver disease.” Gastroenterology 155.5 (2018): 1463-1473.
Charlton, Michael, et al. ” Serum Acylcarnitines Are Biomarkers of Magnetic Resonance Imaging‒Proton Density Fat Fraction Response in NASH Patients Treated With the ACC Inhibitor Firsocostat (GS-0976).” Presented at EASL: The International Liver Congress™ 2019, April 10–14, 2019, Vienna, Austria
The field of lung cancer research has experienced many breakthroughs in recent years, particularly in precision medicine where oncologists can match genetic mutations to targeted therapies. However, many perplexing questions remain. Why does cancer impact some people and not others? Why do some people respond well to treatments and others don’t? For all the progress, we still have a long way to go.
Over the past two decades, numerous advances in lung cancer treatment have been achieved through genomics. And, while this science has made significant headway, some of the biggest current challenges in lung cancer may be well served by augmenting genomics with other approaches. Because metabolites both influence and are influenced by genetics, proteins and microbiomes, metabolomics can be used in conjunction with genomics to create a more complete understanding of health and treatment response. Specifically, metabolomics is poised to help drive solutions to questions such as: Which people will respond to immunotherapies like immune checkpoint blockade (ICB) – and why others don’t? How do we best understand treatment resistance (particularly in more rare types of lung cancer)? And, how can cancer be detected at earlier stages when the treatments are more tractable?
Notably, although many patients experience durable responses to ICB, the majority do not respond. While the precise factors and predictive biomarkers have remained elusive, some clues exist. In particular, the microbiome has been convincingly linked to response to immunotherapies such as immune checkpoint blockade including in certain types of lung cancers.
However, the precise mechanisms and even specific microbial strains driving differences in response to immunotherapies remain elusive. Given that metabolomics is increasingly being recognized as the pivotal approach for determining microbiome function, its use will likely be key to finding biomarkers in response/non-response immunotherapy.
In the context of lack of response to immune checkpoint blockade and determining the factors that may limit or drive response, another layer to consider is what’s happening beyond the tumor. Here is where the branches of genomics and metabolomics can really come together and work in harmony to uncover more insights. What’s happening in the person’s immune system overall is just as important as what’s happening in the tumor. Metabolomics can help us understand both of these activities. Metabolites in the circulation or the tumor microenvironment will likely be important when combined with gene signatures or profiles of the immune cell types that are present in the tumor.
Additionally, metabolomics provides implications for early detection of cancer by combining with other liquid biopsy approaches. In the future we may be able to study the microbiome of patients and see who may be a good candidate for certain therapies as well as who may be most at risk for developing cancer. Or, a circulating metabolite, combined with other diagnostic criteria may help to identify cancer at an earlier stage of the disease.
With cancer, early detection often leads to the best results for curing the disease, but small-cell and non-small-cell cancers are often detected late.
A recent study involving lymphangioleiomyomatosis (LAM) provides a powerful example of how metabolomics can advance lung disease research. LAM is a slow metastasizing neoplasm that is typically due to tuberous sclerosis complex 2 (TSC2) gene mutations resulting in mTORC1 activation in proliferative smooth muscle–like cells in the lung. Metabolon collaborated with researchers at Harvard Medical School to better understand these drivers and, through metabolomics, identified a signature that served as a biomarker. Researchers were able to target the disease based on that signature and identified a novel therapeutic target for lung disease – TSC2 as a negative regulator of COX-2 expression and prostaglandin biosynthesis. This approach, could be applied to lung cancers to better identify drivers of disease, especially in different subtypes such as non-responders.
There is still much to learn about lung cancer and related therapies. Metabolomics reveals biological insights otherwise unseen by other ‘omic technologies, and by combining this powerful technology with the great insights we’ve already seen through genomics, we can accelerate our understanding of lung cancer, and thus, its treatment.
Metabolon can help you take the research to even deeper levels and uncover more actionable insights. Contact us at [email protected] to get started.
Routy B, Chatelier EL, Derosa L, Duong CPM, Alou MT, Daillère R, et al. Gut microbiome influences efficacy of PD-1–based immunotherapy against epithelial tumors. Science. 2017Feb;359(6371):91–7.
Big data initiatives to understand individual health, disease, and therapeutic responses are underway using a wide range of ‘omics and clinical assessment tools across a variety of large cohorts in population health studies.
When it comes to understanding the drivers of health and disease, each person’s genetic profile is important in determining risks. However, factors independent of our genes – including the composition and activity of the microbiome, which is shaped predominantly by environmental factors – also have a profound impact on health and lifespan, influencing virtually every human disease, according to Metabolon president and CEO Dr. Rohan Hastie. Hastie spoke at the 2019 Big Data in Healthcare conference, hosted by the Weizmann Institute of Science in collaboration with Nature Medicine.
While genes illustrate risk of disease, metabolites illuminate the cause and manifestation of disease. This is where metabolomics – the study of chemical processes involving small molecules known as metabolites – comes into its own. The metabolome represents the integration of genetic information with the sum of all exogenous factors – including diet, xenobiotics, lifestyle and the exposome, a measure of all the exposures of an individual in a lifetime – and how these impact health.
As the keystone of systems biology, metabolomics provides a definitive molecular measurement of the phenotype, or characteristics that result from environmental and biologic interactions with the genotype. Changes in metabolites caused by disease processes can be used to identify clinically relevant areas of the genome and to functionally map genetic variants. This drives an increased understanding of disease outcomes, patient biology and all the ‘omics – and concrete progress towards precision medicine.For example, we’ve known for more than 80 years that rodents given a restricted-calorie diet live longer than those allowed to feed at will, showing the important role of metabolism in the early onset of disease. Metabolomics allows us to gain a deeper understanding of this process by integrating big data from genetics and transcriptomics, and mapping these to health outcomes.During its nearly 20-year history, Metabolon has built a unique repository of metabolomic biological insight, creating the world’s largest – and continuously growing – library of biologically relevant metabolites. This expansive knowledgebase has enabled Metabolon to leverage data to help address some of the most pressing needs in life sciences research, as outlined in the examples below.
Metabolomics Success Story 1:
New therapeutic targets identified in Sickle Cell Disease
Challenge: Sickle cell disease (SCD), an untreatable group of inherited red blood cell disorders, is caused by a single point mutation. Production of abnormal hemoglobin can cause red blood cells to become sickle shaped, blocking blood flow, and causing pain and damaging many other issues. In sub-Saharan Africa, some 300,000 babies are born with SCD each year; in the United States, about 100,000 people live with the disease. Mechanism-specific treatment options have remained elusive.
Insight from metabolomics: Metabolon has used metabolomics to advance understanding of the disease biology of SCD in preclinical models (mice overexpressing the mutant gene) and confirmed these results in humans.
Future promise: Several pathways and novel targets have been identified, including adenosine metabolism and lysophospholipids. These may be promising targets for new therapies.
Metabolomics Success Story 2:
Marrying metabolomics with genomics:
The role of hexadecanedioate in high blood pressure
Challenge: High blood pressure is a major contributor to the global burden of disease, affecting around 29% of U.S. adults. By 2020, over 1.5 billion people are forecast to have high blood pressure, and two-thirds will not achieve target control with current therapeutics. Discovering novel causal pathways of blood pressure regulation has been challenging.
Insight from metabolomics: Metabolon co-authored a paper revealing a novel functional association between hexadecanedioate, a dicarboxylic fatty acid, and increased blood pressure.
Future promise: The rising incidence of high blood pressure highlights the need for new treatments, potentially based on hexadecanedioate as a novel and important target.
An experienced partner can advance your projects using metabolomics. Metabolon is uniquely able to help clients by delivering four core capabilities essential for a truly comprehensive metabolomics offering:
- Coverage: The ability to see and compare thousands of molecules to reveal new insights and opportunities across diverse markets
- Competency: The ability to generate high-quality data, accompanied by the ability to derive biological insights to make actionable recommendations
- Comparability: The ability to compare the data of different researchers, in different geographies, over time, and among different patients, races and matrices
- Capacity: The ability to process hundreds of thousands of samples quickly and cost-efficiently to service rapidly growing demand
These elements are critical to effectively capture metabolomics big data and insights from large-scale population cohorts, and to combine these with genetic data, to provide actionable insights. Partner with Metabolon to benefit from our robust platform and visualization tools, expand knowledge of your molecule, and develop assay panels to zero in on the results you need.
If you’re ready to harness true value of metabolomics, contact us at [email protected]
McDonald RB, Ramsey JJ. Honoring Clive McCay and 75 years of calorie restriction research. J Nutr. 2010;140(7):1205–1210. doi:10.3945/jn.110.122804: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2884327/
Q&A: Approach to frailty population health study with Brent Richards, M.D., M.Sc., Faculty of Medicine at McGill University
We sat down with Brent Richards, M.D., M.Sc., from the Faculty of Medicine at McGill University, to discuss the Canadian population health collaborative study that is aimed at discovering key frailty biomarkers to shed light on why some people become frail, determine the severity of frailty and what can be done to help avoid the condition.
The Canadian Frailty Network, the Canadian Longitudinal Study of Aging (CLSA), the McMaster Institute for Research on Aging (MIRA) and Metabolon, Inc. are part of a collaborative partnership to develop a $4-million research program. Through the endeavor, Metabolon will leverage its proprietary metabolomics platform to analyze blood samples from Canada’s largest and most comprehensive study on aging.
Dr. Richards, what is your interest in this frailty study, and why do you think metabolomics is an integral part of the work?
I am interested in the biological causes of diseases related to frailty. Metabolomics provides an unprecedented opportunity to assess the circulating small molecules that could influence frailty and its associated diseases. This is because new technologies, such as those deployed by Metabolon, allow for the survey of hundreds of different biomarkers. Importantly, such studies do not presuppose that a specific biomarker is important for frailty, but it allows for a more comprehensive search for the metabolites that influence frailty and its associated diseases.
How will the identification of metabolites help to improve the early prediction of frailty, and how will healthcare providers be able to act upon these insights?
There are multiple examples in medicine where biomarkers can predict future disease risk. For example, high cholesterol levels allow for the identification of people at risk of heart attacks. This can be particularly helpful for diseases like frailty where there are interventions that healthcare providers can recommend. Further, such information can help for resource planning and identify people in the population that require enhanced care to deal with the morbidity and mortality associated with frailty. Last, associated biomarkers can sometimes act as therapeutic targets for drug development, which in turn, may help patients by forestalling the diseases associated with frailty.
You’ve worked with genetics a significant part of your career and metabolomics is gaining momentum as a connector that uncovers actionable insights. Can you explain your opinion on how these ‘omics are complementary?
These omics technologies are highly complementary. This is because levels of metabolites are often strongly related to genetic variation. When the genetic variation that influences the level of a metabolite can be reliably identified, this allows for the opportunity to test whether the same genetic variation influences levels of disease. Importantly, this can be done even in studies that have not measured the metabolite but have measured the genetic variation. Since the genetic variants that influence many diseases are now known, this study can be implemented rapidly and help to understand if metabolites are involved in the causal pathway for frailty and disease risk.
When did you first learn about metabolomics, and why has your interest continued to grow in this area?
We have been working in metabolomics for approximately five years by combining this information with genetics. It has been a fruitful area of study and my overlapping the genetic determinants of metabolites and diseases we, and others, have been able to identify compelling new biomarkers for several diseases.
Tell us about your role as co-lead of the CLSA Biomarker/Genetic/Epigenetic Working Group.
I have had the opportunity to co-lead a group of excellent scientists and clinicians from Canada interested in improving the data arising from CLSA. We are excited about opportunities to expand the richness of the data within CLSA and have done so by collaborating with companies like Metabolon. Data resultant from these efforts is available to qualified researchers and it is our hope that the community will embrace this rich data source to provide insights into aging and aging-related disease.
Professor, Departments of Medicine, Human Genetics, Epidemiology and Biostatistics, McGill University
Brent Richards is a professor, William Dawson Scholar and FRQS Clinician Scientist at McGill University and a senior lecturer at King’s College London. Trained in genetics, clinical medicine, endocrinology, epidemiology and biostatistics, Dr. Richards focuses on understanding the genetic determinants of common aging-related endocrine diseases, such as osteoporosis and diabetes. He co-chaired what was world’s largest whole-genome sequencing program for common disease (The UK10K Cohorts Program) and identified a novel and central protein critical to skeletal formation and fracture risk. His work has been recognized through a CIHR Clinician Scientist award, a CIHR Maud Menten award for research excellence in human genetics, and a CIHR/Canadian Society of Endocrinology and Metabolism Young Investigator Award. He is the co-lead of the Canadian Longitudinal Study on Aging Biomarker/Genetic/Epigenetic Working Group.
Metabolomics gives us valuable methods to compare preclinical model trials to human models of NASH. In this post, we share a case study with Gilead Sciences. More study is needed, but metabolomics may one day prove to outperform more invasive methods of assessing liver fibrosis, revealing opportunities to incorporate metabolomics into diagnostic tools.
Nonalcoholic fatty liver disease (NAFLD) is a condition in which fat builds up in the liver. Nonalcoholic steatohepatitis (NASH) is a type of NAFLD, and if you have NASH, you have inflammation and liver cell damage, along with fat in your liver. With an estimated 60 to 80 percent of the obese population suffering from fatty liver disease, NASH and NAFLD range across a spectrum of severity, with the worst cases frequently leading to the most common type of liver cancer: hepatocellular carcinoma.
Currently, there are no treatments approved by the U.S. Federal Drug Administration (FDA), leaving liver biopsy as the “gold standard” diagnostic and liver transplantation as the only remedy for treating these diseases.
“The use of human cohorts allows us to compare and contrast the metabolic signature to the preclinical models, which helps identify similarities,” said Kari Wong, PhD, a senior study director at Metabolon. “This can be helpful in the identification of biomarkers or even mechanism and natural history of disease.”
The role of metabolomics in the study of NAFLD and NASH is instrumental in the identification and assessment of metabolites. Metabolites have been used throughout history 1) as a method of assessing health, 2) aiding in the understanding of tissue function throughout the 20th century, and 3) are used in present-day clinical settings where metabolites, such as glucose and cholesterol, provide insight into an individual’s current health.
Dysfunctional metabolism lies at the center of NASH/NAFLD and includes lipid metabolism, cholesterol metabolism, inflammation, redox status and mitochondrial function, which can be assessed via metabolic screening.
As a global leader in metabolomics, Metabolon uses its proprietary Precision Metabolomics™ technology to provide a more holistic approach due to the ability to screen for thousands of metabolites in just one biological sample. The significance of this approach is apparent in the success experienced during Gilead’s utilization of metabolomics in its experiments using an acetyl CoA carboxylase inhibitor in a well-established animal model.
Hurdles to studying NASH
An unclear collection of risk factors, complicated by multiple molecular pathways, are involved in the progression of NASH. These risk factors include external inputs like diet, lifestyle, and environment, as well as others including genetic factors, family history and ethnicity. The molecular pathways involved include derangement in lipid metabolism, alteration in oxidative stress, changes in microbiome metabolism and inflammation.
Other hurdles include a lack of robust preclinical models generated by manipulating genes and diet either alone or in combination. The translatability of these models also remains in question.
The use of metabolomics in Gilead’s study of NASH/NAFLD in animal models
Gilead’s use of metabolomics provides a good example of how preclinical research can be applied in the study of NASH. Using two different mouse models Gilead has been able to investigate steatosis and fibrosis separately with the intention of understanding both the mechanism of disease as well as candidate treatments.
During a recent webinar on metabolomics, Jamie Bates, PhD, research scientist at Gilead Sciences, said, “Our strategy on the preclinical side, is to evaluate mechanisms in these two animal models and also understand the mechanism of action a little bit better with metabolomics and transcriptomics.”
The preclinical studies featured a model of mice treated with fast food diets and then treated for five to six months with liver-targeted acetyl CoA carboxylase inhibitor (ACCi) — an essential enzyme in de novo lipogenesis (DNL). The control group was age matched and fed a normal chow (lean) diet.
Overall findings showed that inhibition of ACCi allowed for hepatic steatosis, while improving the function of mitochondria within the cell and reducing redox stress. These findings showed that the fast food diet induced hepatic steatosis which was at least partially rescued by treatment with ACCi. In addition, levels of membrane phospholipids, including phosphatidylethanolamine (PE) and phosphatidylcholine (PC), were reduced in the NASH group (consistent with NAFLD and NASH patients ), and both PEs and PCs were reduced in the fast food diet model and increased upon ACCi treatment.
Metabolomics gives us valuable methods to compare preclinical model trials to human models of NASH. More study is needed, but metabolomics may one day prove to outperform more invasive methods of assessing liver fibrosis, revealing opportunities to incorporate metabolomics into diagnostic tools.
Puri P et al, Hepatology 2007 46:1081
You might think the title of this article is a little silly, but there is a good reason for excitement thanks to new insights from some recent studies that combined the power of genomics and metabolomics. What wows me is that when you couple information on genotype and molecular phenotype (aka metabolomics), you really get something special, not just for research but also for the clinic. While genomics on its own is widely used across biology, metabolomics is an incredibly powerful addition for extracting value out of that research. I believe that metabolomics puts some extra ‘Gee’ into genomics.
What genes should we focus on?
A big issue facing biologists and clinicians is the uncertainty of gene association to function in human health. What does a gene variant do or not do? Additionally, we know that the presence of a genetic mutation is not necessarily synonymous with the development of a disease. A range of factors, including epigenetics, environmental exposures, microbiome, and lifestyle choices such as diet and exercise, influence phenotype. Genotype ≠ phenotype.
Currently, genomic information can only suggest what diseases we might be predisposed to, but it’s an incomplete picture of health. The data may give either a false sense of security about implied good health or, at the other extreme, lead to some sleepless nights. That’s why 23andMe and companies that offer similar testing put several disclaimers in front of consumers before they can access their personal genetic results. Nature published an article in 2016, “A radical revision of human genetics: Why many ‘deadly’ gene mutations are turning out to be harmless,” that is particularly enlightening on the issue of understanding genomic information. For myself, at the moment, I believe that such personalized genetic testing is at an immature stage and am doubtful of the benefit without accompanying phenotypic information.
How do we identify variation in genes that could contribute to or cause disease?
One approach to identifying gene variants associated with disease is the genome-wide association study (GWAS). These studies have linked thousands of loci (regions on a chromosome) to human disease, but there are issues with exactly locating and identifying the specific gene and mutations linked to a trait. In addition, one needs to establish causality and not just correlation. On top of this, perhaps only a few thousand genes1 out of the 20,000 or so that we carry have so far been associated with human disease. So, how can we improve the process of identifying gene variants associated to disease and establishing causality? The answer, or at least part of the answer, to that question is quite straightforward – we need phenotypic data.
Metabolomics aims to identify and quantify all the metabolites in a sample such as a body fluid or tissue. Measurement of metabolites provides a molecular phenotype that can be used as a proxy or surrogate for a physical phenotype – a snapshot of current health status. Metabolomics can be used in combination with genetic sequencing information to improve medical interpretation of an individual’s disease risk.1
Similarly, metabolomics combined with genomic analysis has been used to identify significant associations of gene variants with metabolite concentrations in blood.2,3 In other words, the readily seen changes in metabolite levels help demonstrate what the gene, or mutation in the gene, is actually doing.
As genomics and metabolomics technology have matured, 246 gene variant associations to metabolism have been identified. The work published in 2017 by Long et al.3 used Metabolon’s Precision Metabolomics™ platform coupled with whole genome sequencing.
Deepening the search with whole genome sequencing combined with metabolomics
The concentration of metabolites in the blood can vary widely from individual to individual, with variation arising from both genetic and environmental factors. For this reason, genomics alone cannot tell the full biological story and phenotypic data is required to strongly identify significance. Researchers from Human Longevity, Health Nucleus, King’s College, Baylor College of Medicine and Metabolon demonstrated a significant level of heritability for a large number of metabolites using Precision Metabolomics to analyze blood samples, with the median heritability being quite high at 48%.3 Genetic sequence variations at 101 loci were associated with the levels of 246 metabolites, of which 90 associations of gene variant to metabolite level for 85 metabolites in plasma had not been seen before. Of the novel variants, five had previously been associated with diseases, but not with metabolite levels.
Rare variants revealed through metabolic outliers
Long, et al. focused on extreme outliers in the population in terms of metabolite levels to identify rare variants that associated to those extremely high or low levels. They identified 151 individuals who had one or more of 69 metabolites with levels consistently very different from the population mean. Additional individuals were identified using further methods to make a total of 175. They then looked for rare functional gene variants in these ‘outlier individuals’ that might explain the extreme metabolite levels. After excluding some variants that had also been identified in individuals with normal metabolite levels, they identified 14 rare variants in 10 genes. In addition, a further 14 rare variants from seven genes were identified by searching the genomes of the 1,960 study participants for rare functional variants that associated to statistically significant (abnormal but less extreme) differential levels of metabolites.
Overall, approximately one in 10 unrelated individuals had metabolite blood levels that associated with rare genetic variants. Many gene-metabolite pairs were associated with inherited metabolic disorders (IMDs), which should probably not come as a surprise. What might be surprising is that some of these were novel, and that some outlier metabolite levels in heterozygous individuals associated to autosomal recessive IMDs or other pediatric diseases. Since these associations were observed in heterozygous individuals, one would expect them to be clinically and phenotypically normal.
One individual, heterozygous for a rare variant in SLC6A3, suffers from adult-onset Parkinson’s disease. Variants in this gene have been shown to cause infantile parkinsonism dystonia when homozygous, with reduced dopamine reuptake observed. It is intriguing to think that elevated levels of dopamine sulfate detected in this individual by Precision Metabolomics may have resulted from this defective gene. We should consider the “possibility that the heterozygous variant may translate into adult-onset clinical symptoms.” Thus, there is the real possibility that late-onset phenotypes, previously thought to occur in childhood, could be present and result in adult disease.
Identifying unknown, unidentified metabolites using associations to genes of known function
A number of unidentified metabolites were associated with genes of known function. The authors attempted to identify these unknown metabolites using liquid chromatography-mass spectrometry (LC-MS) data from the metabolomics experiments in combination with the corresponding genetic information.
How do you identify these unknown and unnamed metabolites? Metabolon has a great deal of institutional expertise in, and developed proprietary methods for, interpreting mass spectral data for metabolite identification. In addition, knowledge derived from our already existing, extensive metabolite data aids in forming a putative identification of an unknown metabolite. For example, an unknown compound might have similar but not identical MS/MS data to a known metabolite. An example unknown metabolite designated as X-12511 was associated with N-acetyltransferase 8 (NAT8). Analysis of the LC-MS data, combined with this gene to metabolite association, gave the confident structural assignment of an acetylation product of 2-aminooctanoic acid, N-acetyl-2-aminooctanoic acid.
Many gene variants with large effects were identified in this study. There was a wide variance in metabolite levels from the extreme of IMDs through to abnormal outlier metabolite profiles in adults, supporting the role of rare gene variants in common diseases. More than one-third of unidentified metabolites were successfully mapped to genetic loci, with some of these unknowns being subsequently identified when analyzed using metabolomics LC-MS data. Lastly, and of incredible interest to me, the authors stated that, “Our data underscore the metabolic consequences of multiple rare variants and leaves open the possibility that they may translate into adult-onset clinical symptoms.”
We cannot rely on genomics to tell us about the state of our health. Genomics can only tell us about the potential risk of developing a disease sometime in the future. We need measurements that tell us about the state of our health now. We are all individuals; our metabolisms are different one from the other, and they can confer upon us disease just as easily as they can protect us from it. Metabolomics is a powerful tool to help us understand that. One of the most compelling conclusions to draw from genomics studies that have also collected metabolomics data is that we should consider using metabolic screening more routinely to understand chemical uniqueness and its impact on individual health.
If you would like to learn more about metabolomics coupled with genomics, you can download our free eBook Bringing the Genome to Life with Metabolomics: A “Sentinel” for the Genome-Phenotype Relationship.
1. Guo, L. et al. Plasma metabolomics profiles enhance precision medicine for volunteers of normal health. Proc. Natl. Acad. Sci USA 112, E4901-E4910 (2015)
2. Shin, S.Y et al. An atlas of genetic influences on human blood metabolites. Nat. Genet. 46, 543-550 (2014)
3. Long, T, et al Whole-genome sequencing identifies common-to-rare variants associated with human blood metabolites. Nat. Genet. 49, 568-578 (2017)
The metabolome describes all the small molecule metabolites in a single organism or biological sample and provides a snapshot of metabolic status at that particular moment. Combining the power of metabolomics with the genetic blueprint created by genome-wide association studies (GWAS) has allowed Metabolon and its collaborators to create an atlas of genetic influences on human blood metabolites.
This atlas will allow researchers to understand more about the impact of genetic polymorphisms on inherited metabolic disorders and metabolically-related genetic disorders, potentially leading to new diagnostics and treatments and new applications for existing drugs.
Metabolomics & Genomics: Making the Links
Metabolomics uses sensitive screening platforms, such as liquid-phase chromatography and gas chromatography-coupled mass spectrometry (LC/MS and GC/MS), to measure all the metabolites present in a biological sample. The resulting metabolic profile can provide a wide variety of information about the system under study, since metabolites change in response to environmental stimuli, lifestyle changes, diseases and drug treatments.
Metabolomics can also identify changes derived from genetic polymorphisms. In fact, there is a clear link between genomics and metabolomics, because genes code for proteins, many of which have metabolic roles in the cell. As such, the metabolic profile can be seen as a set of surrogate markers of an individual’s genotype and phenotype.
Focusing on changes in metabolites based on genetic polymorphisms can help researchers find biomarkers that are important in pharmaceutical drug discovery and development. These biomarkers are also important in diagnostics, as the changes in the metabolic profile caused by the polymorphisms may be detected before symptoms develop.
While genetic testing may just provide a yes or no answer, metabolic profiles yield a fuller picture, potentially giving more detail about prognosis and opening up a route to precision medicine. This could be particularly useful in population screening and patient categorization.
Integrating metabolomics with other ‘omics’ technologies, including genomics, transcriptomics and proteomics, can provide in-depth information where the whole is greater than the sum of its parts. This is the rationale behind the creation of the atlas of the genetic influences on human metabolism.
Creating the Atlas
The atlas was created to help to gain a better understanding of the role of inherited genetic changes in metabolism. These findings could help researchers learn more about metabolic disease and lead to the development of novel treatments.
A team of researchers from universities, hospitals and pharmaceutical companies combined Metabolon’s high-throughput, metabolic profiling platforms and data expertise from a genome-wide association study (GWAS) on thousands of healthy individuals. This project is believed to be the most comprehensive investigation of genetic influences on human metabolism to date.
Using LC/MS and GC/MS, the researchers characterized metabolic profiles from 7,824 European adults from the KORA (Kooperative Gesundheitsforschung in der Region Augsburg) and TwinsUK datasets. The profiles included 529 metabolites, with most representing eight key metabolic pathway groups (amino acids, carbohydrates, cofactors and vitamins, energy, lipids, nucleotides, peptides and xenobiotic metabolism).
The team then created genetic maps of the areas of the genome that had already been linked with a wide range of metabolic traits. By analyzing around 2.1 million SNPs (single nucleotide polymorphisms; changes of a single nucleotide of the genetic code) and combining genetic and metabolite information using mathematical modelling, the GWAS meta-analysis created a network view of genetic-metabolic interactions and linked 145 SNPs with metabolism, including 90 previously undescribed interactions.
This is the first comprehensive, high-resolution reference map of human metabolic relationships and their genetic influences. It will help scientists and researchers visualize the important genetic associations between metabolites and SNPs. The atlas is available for online viewing and download, along with extensive biochemical and biological annotations and a database of genetic associations and their biological, medical and pharmacological annotations.
Key Discoveries & Take-Aways
- The contribution of metabolic loci to variance in metabolite concentrations is high. Understanding the links between genetic loci and metabolite levels will help identify new biomarkers that could expedite clinical trials by helping pharmaceutical R&D scientists select better drug candidates and advance precision medicine.
- The atlas can be used to improve understanding of how complex metabolic systems and pathways and are linked with diseases, potentially identifying new targets for drug and gene therapy.
- The study suggests that blood metabolites might represent the activity of gene products expressed in the brain, as the enzymes associated with the metabolites were widely expressed, potentially allowing researchers to derive more information about patients’ health conditions using less-invasive methods such as blood draws.
Metabolon: Mapping the Metabolome
Metabolon is a pioneer and leader in metabolomics with more than 15 years’ experience in the field. Our comprehensive technology platform is backed by an extensive chemical reference library and has been used to rapidly identify biomarkers and elucidate biological pathways and processes in both complex and rare diseases.
Metabolomics unlocks the full potential of genomics data by connecting the dots between the genome and the phenotype. Download our free eBook Bringing the Genome to Life with Metabolomics to learn more.
There’s been a lot of interest recently in the microbiome and the “bugs” (bacteria) that live on and within us. Their breadth of influence on our health is astonishing – certainly in metabolic diseases such as diabetes, but also in disorders that aren’t so obvious, such as autism, chronic fatigue syndrome and depression.
by Kirk Beebe
There’s been a lot of interest recently in the microbiome and the “bugs” (bacteria) that live on and within us. Their breadth of influence on our health is astonishing – certainly in metabolic diseases such as diabetes, but also in disorders that aren’t so obvious, such as autism, chronic fatigue syndrome and depression.
Bacteria can dictate how drugs or metabolites interact with our brain, make us susceptible to infectious diseases, and affect a host of conditions including cardiovascular disease and cancer. Since they have co-habitated with humans throughout evolution, it’s not surprising that they play such an important role. For better or worse, we’re in this together.
While many recent publications associate different bacteria to certain diseases, they have often been criticized for failing to clarify how the microbiome functionally influences disease. In fact, the discussion sections of many of publications from the past few years reveal a need to move beyond “description” and into “function or mechanism.”
It’s all about functon
Function is where metabolites come in. The language and currency of microbial communities throughout nature are small molecules – metabolites. This is not lost on microbiome researchers. Many studies conduct some form of metabolite profiling, which often provides a key element to bringing more functional insight to the research. However, most microbiome studies have focused primarily on determining the type of microbes present and sometimes the collection of genes they contribute.
Now, there is a shift among many key opinion leaders who have started promoting metabolomics as a way forward in assigning function more directly to the microbiota. For a good example of this, see “Specialized Metabolites from the Microbiome in Health and Disease,” Cell Metabolism, 2014.
Small molecules reveal more about host-microbiota interaction
Metabolomics can take on many forms, each providing different levels of insight. The type of information you get from a technology that profiles 40 metabolites, such as NMR, is distinct from the information you get from a technology that measures 1,000 or more metabolites, such as LC/MS.
We believe that to effectively address the complexity of the microbiome’s influence on human health, you must employ a technology that is capable of surveying host metabolism, xenobiotics, dietary metabolites and novel metabolites produced by the microbiota. This is what Metabolon’s platform does. It offers a very precise, systematic surveillance of the metabolome, which may include novel, bacterially-produced compounds.
Combining metabolomics with traditional microbiome genetic research tools has resulted in some exciting findings in gut microbiome research.
- Sarkis Mazmanian’s group at Caltech discovered that 4-ethylphenylsulfate was altered with behavior in a mouse model of autism. They developed a probiotic strategy that led to the reduction of 4-EPS and a reduction in the behavioral phenotype.1
- Epidemiologic studies have revealed a number of environmental exposures associated with asthma that could account for its escalation over the past 30 years. Many of these exposures could be the result of microbiota changes. Recent experimental evidence suggests a “critical window” early in life where gut microbial changes (dysbiosis) may lead to asthma. To explore this hypothesis and determine if these changes precede the development of asthma, microbiota and metabolomics analysis was performed on subjects enrolled in the Canadian Healthy Infant Longitudinal Development (CHILD) Study. Infants at risk of asthma exhibited transient gut microbial dysbiosis and metabolome changes (e.g., urobilinogen) during the first three months of life. These metabolites or microbiome differences may aid in the design of pre- or probiotics or serve as prognostic biomarkers. 2
- Much of the body’s serotonin is produced by the gastrointestinal (GI) tract, but the mechanism for this production was unknown. Given the expanding connection of the gut microbiota to many aspects of physiology, investigators sought to determine if the microbiota played a role. Using an array of methods including metabolomics, they showed that the microbiota regulates GI-serotonin biosynthesis from colonic enterochromaffin cells (ECs), and that fecal metabolites likely provide the signals for production, since elevating their luminal concentrations increase colonic and blood serotonin in germ-free mice. The results are yet another example of the how microbiota are intimately connected to our physiology and that this connection is frequently modulated by metabolites. 3
- The ultimate prize for combining metabolomics and microbiome research is human health. We are excited to contribute to work that dovetails genomic, microbiome analysis with metabolomics data at Craig Venter’s Human Longevity, Inc. and Health Nucleus and Lee Hood’s Arivale.
Making sense out of skin microbiota
Metabolomics can be applied to another dimension of the human microbiome – the skin. Like the gut, our skin contains an entire ecosystem of microorganisms that are not just important in dermatological conditions such as acne, psoriasis and rosacea, but also in immune function. The skin is a major barrier to infection, and the microbes that live upon it are a front line of defense.
Microbiome research in the skin field is at an earlier stage compared to the gut. The first waves of basic characterization of the microbes that colonize the skin are completed, but studies probing associations with various conditions and diseases are few, relative to gut microbiome research.
By using metabolomic technology as a first-line tool, skin researchers can bypass the challenges that arose for gut microbiome researchers with assigning function to the myriad of associations they uncovered. Examples of the small molecule-metabolite connection are beginning to trickle out in dermatology research, such as that by D. Kang et al. showing how B vitamin metabolism is mechanistically linked to acne. 4
Realizing the importance of metabolomics in understanding skin biology, Metabolon has invested a tremendous effort in methods and tools for skin research. In addition to our metabolomics platform and accompanying methods for skin biopsies and tape strips, we have a stratum corneum panel directed at lipids, a metabolite class of high importance in skin biology.
We envision that these tools will not only promote better understanding of the basic principles governing skin biology, but also become a frontline approach for understanding the complex interaction between the host and microbiota that govern skin health.
Cracking the functional code
Much as the Rosetta Stone was vital to deciphering a previously untranslated ancient language, metabolomics may hold the key to unlocking the secrets of the microbiome. Microbiome research has outlined an important landscape for human health, and combining metabolomics with existing research tools will help populate this landscape with functional detail.
To learn more about metabolomics in microbiome research, download our free eBook.
- Hsiao, E.Y. et al. Microbiota Modulate Behavioral and Physiological Abnormalities Associated with Neurodevelopmental Disorders. Cell 155, 1451-1463 (2013).
- M. C. Arrieta et al., Science Translational Medicine 7, 307ra152 (2015).
- J. M. Yano et al., Cell 161, 264 (2015).
- D. Kang, B. Shi, M. C. Erfe, N. Craft, H. Li, Science Translational Medicine 7, 293ra103 (2015).