The application of metabolic profiling of human biofluids to the prediction of drug efficacy, pharmacokinetics, metabolism and/or toxicity forms a paradigm known as pharmacometabonomics. Pharmacometabonomics holds out promise for the improved delivery of personalized medicine in the future, as it takes into account both genetic and environmental factors, including diet, drug intake and most notably, the status of the gut microbiome, in deriving predictions. Pharmacometabonomics is thus complementary to pharmacogenomics and in some instances the two technologies can be synergistically used together. This article introduces pharmacometabonomics and covers current important application areas.
by Dr Dorsa Varshavi, Dorna Varshavi and Prof. Jeremy Everett
Introduction
In 21st century medicine, a major goal is to develop personalized medicine for selected groups of patients in order to reduce the likelihood of adverse drug reactions and to maximize the desired therapeutic effect [1]. Until recently, personalized drug therapy was delivered almost exclusively by pharmacogenomics (PG), where an individual’s genetic makeup is used to predict the outcome of drug treatment [2]. The best-recognized examples of PG involve drug effect predictions made by analysis of genetic polymorphisms in drug-metabolizing enzymes such as the cytochrome P450 isoenzymes [3].
Although genetic variation is an important determinant of individual variability in drug response, it is now well recognized that personalized drug therapy cannot always be attained using genetic knowledge alone. This is because inter-individual variation in drug response is a consequence of multiple factors, including genetic and epigenetic factors and in addition, environmental factors such as nutritional and health status, the condition of the microbiome, exposure to environmental toxins, and co- or pre-administration of other drugs, including alcohol. These environmental factors can strongly affect drug absorption, distribution, metabolism and excretion and thereby cause inter-individual variation in drug efficacy and safety.
Metabonomics is defined as: ‘The study of the metabolic response of organisms to disease, environmental change, or genetic modification’ [4]. In a metabonomics experiment, changes in the levels of biofluid or tissue metabolites, before and after an intervention, such as drug administration, are measured using analytical technologies such as nuclear magnetic resonance (NMR) spectroscopy or mass spectrometry (MS). The alternative term metabolomics is also used and although its definition is observational, rather than interventional, the two terms are now used interchangeably.
Pharmacometabonomics is a recent development from metabonomics and is defined as ‘the prediction of the outcome (for example, efficacy or toxicity) of a drug or xenobiotic intervention in an individual based on a mathematical model of pre-intervention metabolite signatures’ [5–7]. In contrast to a metabonomics experiment, where the effect of an intervention is assessed based on changes in metabolite profiles post-intervention, in a pharmacometabonomics experiment the effect of the intervention is predicted based on the pre-intervention metabolite profiles.
Although first demonstrated in animals [5], pharmacometabonomics was quickly also demonstrated in humans, in a study where an individual’s pre-dose urinary endogenous metabolite profile was used to predict the metabolism of the analgesic paracetamol [6]. NMR-based analyses showed that individuals excreting relatively high levels of the microbial co-metabolite para-cresol sulfate in their pre-dose urine, excreted less paracetamol sulfate and more paracetamol glucuronide post-dose than individuals excreting low pre-dose amounts of para-cresol sulfate (Figs 1 & 2). Para-cresol sulfate is a metabolite produced from the hepatic sulfation of para-cresol, which is itself generated by gut bacteria, particularly Clostridium species. Paracetamol and para-cresol have similar molecular structures and both compete for limited human sulfation capacity via the same sulfotransferase enzymes, particularly SULT1A1. Thus individuals with a microbiome producing large amounts of para-cresol use up a large degree of their sulfonation capacity in metabolizing this toxin to para-cresol sulfate, and a subsequent dose of paracetamol will be metabolized to a greater degree by glucuronidation. This study was important for two key reasons: (1) it was the first demonstration of pharmacometabonomics in humans and (2) it was the first demonstration of the influence of the gut microbiome on human drug metabolism; the fact that the key biomarker in this study of human drug metabolism was a bacterially-derived molecule was a shock. Furthermore, the findings of this study will have implications for other drugs for which sulfonation is important, as well as certain diseases such as autism where abnormal paracetamol metabolism has been observed.
Pharmacometabonomics has been also used to predict individual responses to therapy, including the prediction of patient responses to treatment with the statin simvastatin. Statins can reduce low-density lipoprotein cholesterol (LDL-C) and, therefore, are used for treatment of cardiovascular disease. Kaddurah-Daouk et al. demonstrated that pre-dose plasma levels of the phosphatidylcholine metabolite PC18:2n6, the cholesterol ester CE18:1n7 and the free fatty acid FA18:3n3, were positively correlated with the magnitude of simvastatin-induced reduction in LDL-C, in 36 good responders and 36 poor responders [8]. A targeted pre-dose plasma analysis by MS then demonstrated (amongst other results) a strong correlation between the degree of reduction in LDL-C and higher pre-dose concentrations of three secondary, bacteria-derived, bile acids: lithocholic acid (LCA), taurolithocholic acid (TLCA) and glycolithocholic acid (GLCA), as well as coprostanol (COPR) [9]. This study further supported the contribution of the microbiome in influencing drug responses.
Pharmacometabonomics studies can be pursued jointly with, or followed up by pharmacogenetics studies. A good exemplification of the so-called ‘pharmacometabonomics informed pharmacogenomics approach’ is an MS-based study which demonstrated that pre-dose plasma levels of glycine, a central nervous system inhibitory neurotransmitter, were associated with rates of response or remission during citalopram/escitalopram treatment in patients with major depressive disorder (MDD) in the Mayo Clinic–NIH Pharmacogenetics Research Network (PGRN) Citalopram/Escitalopram Pharmacogenomics (Mayo–PGRN SSRI) study [10]. Tag single-nucleotide polymorphism (SNP) genotyping of the genes encoding enzymes in the glycine pathway from 529 patients enrolled in the Mayo-PGRN SSRI study was then completed. A series of SNPs in the gene encoding glycine dehydrogenase (GLDC) were found to be significantly associated with disease remission, with rs10975641 SNP showing the strongest association. This study demonstrated that pharmacometabonomics data can inform and complement pharmacogenomics data and, when combined, they can provide improved insights into the mechanisms influencing variability in drug response.
There are now many examples of the use of pharmacometabonomics for the prediction of human drug efficacy, toxicity, metabolism and pharmacokinetics and recent reviews are available [7, 11].
Conclusion and future prospect
Since its initial discovery [5], pharmacometabonomics has been increasingly applied in both preclinical and clinical studies to predict drug safety, efficacy, metabolism and pharmacokinetics. Pharmacometabonomics has an important advantage over pharmacogenomics in that it takes into account both genetic and environmental influences on drug administration. Pharmacometabonomics is itself just one specific example of a broader class of approaches known as predictive metabonomics, where the analysis of pre-intervention metabolite profiles can be used to predict clinical responses to other types of intervention, including diet, exercise, or even just the passage of time. A good example of predictive metabonomics can be seen in the recent study by Wang-Sattler et al. [12], who demonstrated that low baseline levels of glycine and lysophosphatidylcholine were predictive of the development of impaired glucose tolerance and/or type-2 diabetes in hundreds of subjects from the population-based, Cooperative Health Research in the Region of Augsburg (KORA) cohort. Another emerging area for which pharmacometabonomics holds promise is in the monitoring of patients over time as they progress through therapies such as cancer chemotherapy or surgery, a paradigm called longitudinal pharmacometabonomics [13]. This approach involves the metabolic profiling of patients before, during and after clinical therapy, in order to predict responses to future treatments and thus choose the optimal treatment regime. PG is now over 50 years old and is still limited in its impact on the practice of medicine. Pharmacometabonomics is much younger and it will take time for it to impact in the clinical arena. We predict that in the near future, personalized medicine will be conducted with assistance from both PG and pharmacometabonomics.
References
1. Pokorska-Bocci A, Stewart A, Sagoo GS, Hall A, Kroese M, Burton H. ‘Personalized medicine’: what’s in a name? Personalized Medicine 2014; 11(2): 197–210.
2. Jorgensen JT. A challenging drug development process in the era of personalized medicine. Drug Discov Today 2011; 16(19–20): 891–897.
3. Pirmohamed M. Personalized pharmacogenomics: predicting efficacy and adverse drug reactions. Ann Rev Genomics Hum Genet. 2014; 15: 349–370.
4. Lindon J, Nicholson J, Holmes E, Everett J. Metabonomics: Metabolic processes studied by NMR spectroscopy of biofluids. Concepts Magn Reson. 2000; 12(5): 289–320.
5. Clayton T, Lindon J, Cloarec O, Antti H, Charuel C, Hanton G, Provost JP, Le Net JL, Baker D, Walley RJ, Everett JR, Nicholson JK. Pharmaco-metabonomic phenotyping and personalized drug treatment. Nature 2006; 440(7087): 1073–1077.
6. Clayton TA, Baker D, Lindon JC, Everett JR, Nicholson JK. Pharmacometabonomic identification of a significant host-microbiome metabolic interaction affecting human drug metabolism. Proc Natl Acad Sci U S A 2009; 106(34): 14728–14733.
7. Everett JR. Pharmacometabonomics in humans: a new tool for personalized medicine. Pharmacogenomics 2015; 16(7): 737–754.
8. Kaddurah-Daouk R, Baillie RA, Zhu HJ, Zeng ZB, Wiest MM, Nguyen UT, Watkins SM, Krauss RM. Lipidomic analysis of variation in response to simvastatin in the Cholesterol and Pharmacogenetics Study. Metabolomics 2010; 6(2): 191–201.
9. Kaddurah-Daouk R, Baillie RA, Zhu H, Zeng ZB, Wiest MM, Nguyen UT, Wojnoonski K, Watkins SM, Trupp M, Krauss RM. Enteric microbiome metabolites correlate with response to simvastatin treatment. PLoS One 2011; 6(10): e25482.
10. Ji Y, Hebbring S, Zhu H, Jenkins GD, Biernacka J, Snyder K, Drews M, Fiehn O, Zeng Z, Schaid D, Mrazek DA, Kaddurah-Daouk R, Weinshilboum RM. Glycine and a glycine dehydrogenase (GLDC) SNP as citalopram/escitalopram response biomarkers in depression: pharmacometabolomics-informed pharmacogenomics. Clin Pharmacol Ther. 2011; 89(1): 97–104.
11. Everett JR. NMR-based pharmacometabonomics: a new approach to personalized medicine. In: Everett JR, Harris RK, Lindon JC, Wilson ID. (eds) NMR in Pharmaceutical Sciences, pp 359–372. Wiley 2015.
12. Wang-Sattler R, Yu Z, Herder C, Messias AC, Floegel A, He Y, Heim K, Campillos M, Holzapfel C, Thorand B, Grallert H, Xu T, et al. Novel biomarkers for pre-diabetes identified by metabolomics. Mol Syst Biol. 2012; 8: 615.
13. Nicholson JK, Everett JR, Lindon JC. Longitudinal pharmacometabonomics for predicting patient responses to therapy: drug metabolism, toxicity and efficacy. Expert Opin Drug Metab Toxicol. 2012; 8(2): 135–139.
The authors
Dorsa Varshavi PhD, Dorna Varshavi MSc, Jeremy Everett* PhD
Medway Metabonomics Research Group, University of Greenwich, Chatham, Kent ME4 4TB, UK
*Corresponding author
E-mail: j.r.everett@greenwich.ac.uk
Fully automated system for bacterial culture, identification, AST, MIC and MDRO screening
, /in Featured Articles /by 3wmediaNOVA View – automated digital IFA microscope
, /in Featured Articles /by 3wmediaImmuno turbidimetric fecal CALIPROTECTIN Assay
, /in Featured Articles /by 3wmediaNew kit, calibrator and controls for clinical mass spectrometry
, /in Featured Articles /by 3wmediaConfidence at your point of care
, /in Featured Articles /by 3wmediaPharmacometabonomics: a new methodology for personalized medicine
, /in Featured Articles /by 3wmediaThe application of metabolic profiling of human biofluids to the prediction of drug efficacy, pharmacokinetics, metabolism and/or toxicity forms a paradigm known as pharmacometabonomics. Pharmacometabonomics holds out promise for the improved delivery of personalized medicine in the future, as it takes into account both genetic and environmental factors, including diet, drug intake and most notably, the status of the gut microbiome, in deriving predictions. Pharmacometabonomics is thus complementary to pharmacogenomics and in some instances the two technologies can be synergistically used together. This article introduces pharmacometabonomics and covers current important application areas.
by Dr Dorsa Varshavi, Dorna Varshavi and Prof. Jeremy Everett
Introduction
In 21st century medicine, a major goal is to develop personalized medicine for selected groups of patients in order to reduce the likelihood of adverse drug reactions and to maximize the desired therapeutic effect [1]. Until recently, personalized drug therapy was delivered almost exclusively by pharmacogenomics (PG), where an individual’s genetic makeup is used to predict the outcome of drug treatment [2]. The best-recognized examples of PG involve drug effect predictions made by analysis of genetic polymorphisms in drug-metabolizing enzymes such as the cytochrome P450 isoenzymes [3].
Although genetic variation is an important determinant of individual variability in drug response, it is now well recognized that personalized drug therapy cannot always be attained using genetic knowledge alone. This is because inter-individual variation in drug response is a consequence of multiple factors, including genetic and epigenetic factors and in addition, environmental factors such as nutritional and health status, the condition of the microbiome, exposure to environmental toxins, and co- or pre-administration of other drugs, including alcohol. These environmental factors can strongly affect drug absorption, distribution, metabolism and excretion and thereby cause inter-individual variation in drug efficacy and safety.
Metabonomics is defined as: ‘The study of the metabolic response of organisms to disease, environmental change, or genetic modification’ [4]. In a metabonomics experiment, changes in the levels of biofluid or tissue metabolites, before and after an intervention, such as drug administration, are measured using analytical technologies such as nuclear magnetic resonance (NMR) spectroscopy or mass spectrometry (MS). The alternative term metabolomics is also used and although its definition is observational, rather than interventional, the two terms are now used interchangeably.
Pharmacometabonomics is a recent development from metabonomics and is defined as ‘the prediction of the outcome (for example, efficacy or toxicity) of a drug or xenobiotic intervention in an individual based on a mathematical model of pre-intervention metabolite signatures’ [5–7]. In contrast to a metabonomics experiment, where the effect of an intervention is assessed based on changes in metabolite profiles post-intervention, in a pharmacometabonomics experiment the effect of the intervention is predicted based on the pre-intervention metabolite profiles.
Although first demonstrated in animals [5], pharmacometabonomics was quickly also demonstrated in humans, in a study where an individual’s pre-dose urinary endogenous metabolite profile was used to predict the metabolism of the analgesic paracetamol [6]. NMR-based analyses showed that individuals excreting relatively high levels of the microbial co-metabolite para-cresol sulfate in their pre-dose urine, excreted less paracetamol sulfate and more paracetamol glucuronide post-dose than individuals excreting low pre-dose amounts of para-cresol sulfate (Figs 1 & 2). Para-cresol sulfate is a metabolite produced from the hepatic sulfation of para-cresol, which is itself generated by gut bacteria, particularly Clostridium species. Paracetamol and para-cresol have similar molecular structures and both compete for limited human sulfation capacity via the same sulfotransferase enzymes, particularly SULT1A1. Thus individuals with a microbiome producing large amounts of para-cresol use up a large degree of their sulfonation capacity in metabolizing this toxin to para-cresol sulfate, and a subsequent dose of paracetamol will be metabolized to a greater degree by glucuronidation. This study was important for two key reasons: (1) it was the first demonstration of pharmacometabonomics in humans and (2) it was the first demonstration of the influence of the gut microbiome on human drug metabolism; the fact that the key biomarker in this study of human drug metabolism was a bacterially-derived molecule was a shock. Furthermore, the findings of this study will have implications for other drugs for which sulfonation is important, as well as certain diseases such as autism where abnormal paracetamol metabolism has been observed.
Pharmacometabonomics has been also used to predict individual responses to therapy, including the prediction of patient responses to treatment with the statin simvastatin. Statins can reduce low-density lipoprotein cholesterol (LDL-C) and, therefore, are used for treatment of cardiovascular disease. Kaddurah-Daouk et al. demonstrated that pre-dose plasma levels of the phosphatidylcholine metabolite PC18:2n6, the cholesterol ester CE18:1n7 and the free fatty acid FA18:3n3, were positively correlated with the magnitude of simvastatin-induced reduction in LDL-C, in 36 good responders and 36 poor responders [8]. A targeted pre-dose plasma analysis by MS then demonstrated (amongst other results) a strong correlation between the degree of reduction in LDL-C and higher pre-dose concentrations of three secondary, bacteria-derived, bile acids: lithocholic acid (LCA), taurolithocholic acid (TLCA) and glycolithocholic acid (GLCA), as well as coprostanol (COPR) [9]. This study further supported the contribution of the microbiome in influencing drug responses.
Pharmacometabonomics studies can be pursued jointly with, or followed up by pharmacogenetics studies. A good exemplification of the so-called ‘pharmacometabonomics informed pharmacogenomics approach’ is an MS-based study which demonstrated that pre-dose plasma levels of glycine, a central nervous system inhibitory neurotransmitter, were associated with rates of response or remission during citalopram/escitalopram treatment in patients with major depressive disorder (MDD) in the Mayo Clinic–NIH Pharmacogenetics Research Network (PGRN) Citalopram/Escitalopram Pharmacogenomics (Mayo–PGRN SSRI) study [10]. Tag single-nucleotide polymorphism (SNP) genotyping of the genes encoding enzymes in the glycine pathway from 529 patients enrolled in the Mayo-PGRN SSRI study was then completed. A series of SNPs in the gene encoding glycine dehydrogenase (GLDC) were found to be significantly associated with disease remission, with rs10975641 SNP showing the strongest association. This study demonstrated that pharmacometabonomics data can inform and complement pharmacogenomics data and, when combined, they can provide improved insights into the mechanisms influencing variability in drug response.
There are now many examples of the use of pharmacometabonomics for the prediction of human drug efficacy, toxicity, metabolism and pharmacokinetics and recent reviews are available [7, 11].
Conclusion and future prospect
Since its initial discovery [5], pharmacometabonomics has been increasingly applied in both preclinical and clinical studies to predict drug safety, efficacy, metabolism and pharmacokinetics. Pharmacometabonomics has an important advantage over pharmacogenomics in that it takes into account both genetic and environmental influences on drug administration. Pharmacometabonomics is itself just one specific example of a broader class of approaches known as predictive metabonomics, where the analysis of pre-intervention metabolite profiles can be used to predict clinical responses to other types of intervention, including diet, exercise, or even just the passage of time. A good example of predictive metabonomics can be seen in the recent study by Wang-Sattler et al. [12], who demonstrated that low baseline levels of glycine and lysophosphatidylcholine were predictive of the development of impaired glucose tolerance and/or type-2 diabetes in hundreds of subjects from the population-based, Cooperative Health Research in the Region of Augsburg (KORA) cohort. Another emerging area for which pharmacometabonomics holds promise is in the monitoring of patients over time as they progress through therapies such as cancer chemotherapy or surgery, a paradigm called longitudinal pharmacometabonomics [13]. This approach involves the metabolic profiling of patients before, during and after clinical therapy, in order to predict responses to future treatments and thus choose the optimal treatment regime. PG is now over 50 years old and is still limited in its impact on the practice of medicine. Pharmacometabonomics is much younger and it will take time for it to impact in the clinical arena. We predict that in the near future, personalized medicine will be conducted with assistance from both PG and pharmacometabonomics.
References
1. Pokorska-Bocci A, Stewart A, Sagoo GS, Hall A, Kroese M, Burton H. ‘Personalized medicine’: what’s in a name? Personalized Medicine 2014; 11(2): 197–210.
2. Jorgensen JT. A challenging drug development process in the era of personalized medicine. Drug Discov Today 2011; 16(19–20): 891–897.
3. Pirmohamed M. Personalized pharmacogenomics: predicting efficacy and adverse drug reactions. Ann Rev Genomics Hum Genet. 2014; 15: 349–370.
4. Lindon J, Nicholson J, Holmes E, Everett J. Metabonomics: Metabolic processes studied by NMR spectroscopy of biofluids. Concepts Magn Reson. 2000; 12(5): 289–320.
5. Clayton T, Lindon J, Cloarec O, Antti H, Charuel C, Hanton G, Provost JP, Le Net JL, Baker D, Walley RJ, Everett JR, Nicholson JK. Pharmaco-metabonomic phenotyping and personalized drug treatment. Nature 2006; 440(7087): 1073–1077.
6. Clayton TA, Baker D, Lindon JC, Everett JR, Nicholson JK. Pharmacometabonomic identification of a significant host-microbiome metabolic interaction affecting human drug metabolism. Proc Natl Acad Sci U S A 2009; 106(34): 14728–14733.
7. Everett JR. Pharmacometabonomics in humans: a new tool for personalized medicine. Pharmacogenomics 2015; 16(7): 737–754.
8. Kaddurah-Daouk R, Baillie RA, Zhu HJ, Zeng ZB, Wiest MM, Nguyen UT, Watkins SM, Krauss RM. Lipidomic analysis of variation in response to simvastatin in the Cholesterol and Pharmacogenetics Study. Metabolomics 2010; 6(2): 191–201.
9. Kaddurah-Daouk R, Baillie RA, Zhu H, Zeng ZB, Wiest MM, Nguyen UT, Wojnoonski K, Watkins SM, Trupp M, Krauss RM. Enteric microbiome metabolites correlate with response to simvastatin treatment. PLoS One 2011; 6(10): e25482.
10. Ji Y, Hebbring S, Zhu H, Jenkins GD, Biernacka J, Snyder K, Drews M, Fiehn O, Zeng Z, Schaid D, Mrazek DA, Kaddurah-Daouk R, Weinshilboum RM. Glycine and a glycine dehydrogenase (GLDC) SNP as citalopram/escitalopram response biomarkers in depression: pharmacometabolomics-informed pharmacogenomics. Clin Pharmacol Ther. 2011; 89(1): 97–104.
11. Everett JR. NMR-based pharmacometabonomics: a new approach to personalized medicine. In: Everett JR, Harris RK, Lindon JC, Wilson ID. (eds) NMR in Pharmaceutical Sciences, pp 359–372. Wiley 2015.
12. Wang-Sattler R, Yu Z, Herder C, Messias AC, Floegel A, He Y, Heim K, Campillos M, Holzapfel C, Thorand B, Grallert H, Xu T, et al. Novel biomarkers for pre-diabetes identified by metabolomics. Mol Syst Biol. 2012; 8: 615.
13. Nicholson JK, Everett JR, Lindon JC. Longitudinal pharmacometabonomics for predicting patient responses to therapy: drug metabolism, toxicity and efficacy. Expert Opin Drug Metab Toxicol. 2012; 8(2): 135–139.
The authors
Dorsa Varshavi PhD, Dorna Varshavi MSc, Jeremy Everett* PhD
Medway Metabonomics Research Group, University of Greenwich, Chatham, Kent ME4 4TB, UK
*Corresponding author
E-mail: j.r.everett@greenwich.ac.uk
Sepsis diagnosis in the laboratory
, /in Featured Articles /by 3wmediaSepsis is one of the major challenges in healthcare today, with some statistics predicting over 1 million cases per year in the United States, with mortality rates of about 10%. In a recently published study, between 36.9% and 55.9% of deaths among hospitalized individuals occurred in septic patients [1]. Two other findings of this study are critical to the laboratory. First, they observed that patients with initially less severe sepsis made up the majority of sepsis deaths. Secondly, most patients were already septic at the time they were admitted to the hospital. Thus laboratory testing during the initial emergency department (ED) encounter could be critical to improve sepsis-related mortality.
A systematic review of the literature [2] looked at nearly 200 proposed biomarkers for sepsis. Quoting the study’s final conclusions: “Our literature review indicates that there are many biomarkers that can be used in sepsis, but none has sufficient specificity or sensitivity to be routinely employed in clinical practice. PCT and CRP have been most widely used, but even these have limited abilities to distinguish sepsis from other inflammatory conditions or to predict outcome.” Here we take a look at the key issues the healthcare industry is facing and why physicians still do not have a reliable marker for sepsis to offer for their patients.
by Fernando Chaves
Key factors which can impact outcome in sepsis
In the last decade since sepsis awareness became more prevalent, many institutions have started implementing sepsis treatment protocols, which have been successful in decreasing mortality [3-4]. These protocols call for the collection of multiple blood cultures plus immediate start of intravenous fluids and antibiotics, and were implemented because studies have clearly demonstrated that the single most important factor in decreasing sepsis mortality was early intervention.
Later, a large prospective study comparing variations of these protocols, including invasive patient monitoring did not show any significant differences in mortality rates [5]. This indicates that there is minimal additional decreases in sepsis mortality that can be attained through improvements in treatment.
In contrast there are still options available to improve sepsis mortality through diagnostic testing. An optimal approach for early detection of sepsis still eludes us. Diagnosis today is still based primarily on the clinical recognition of systemic inflammation — increased heart and respiratory rates, fever, mental confusion, etc. followed by the documentation of a site of infection. When clinicians recognize these signs and symptoms, the window of opportunity to further decrease sepsis mortality by an earlier diagnosis has passed. Therefore, a laboratory test that could allow for earlier recognition of septic patients is a major unmet need for physicians.
What features should a laboratory test have to address this unmet need?
In order to allow for early recognition of septic patients, a laboratory test would need to meet both clinical performance and accessibility criteria. First, it must have sufficient diagnostic performance (measured by area under the curve “AUC” in the receiver operator curve “ROC curve”) to discriminate sepsis not only from healthy individuals, but also from other sick patients with conditions which mimic sepsis, such as systemic inflammatory response syndrome (SIRS). The traditional laboratory tests used during initial evaluation of patients, such as the complete blood count (CBC) fail to achieve this diagnostic performance.
Secondly, it must meet accessibility criteria, meaning it must be a test which can be widely used in all patients coming to the ED, without the need for the clinician to have an initial suspicion for sepsis. As discussed above, waiting for the physician to order the test will likely miss the window of opportunity to further improve mortality. Currently antibiotics and IV fluids are already being initiated before testing, as per the sepsis protocols now becoming increasingly prevalent in hospitals worldwide.
With so many proposed biomarkers for sepsis, which ones have met these criteria?
Unfortunately, although there have been many promises and exciting results in initial studies, results to date have been disappointing.
Early studies often yielded promising results because of their smaller size, and typically they were case-control studies comparing septic patients with healthy individuals [2]. The real challenge is discriminating sepsis from the plethora of mimicking conditions physicians encounter in the ED.
Once the biomarkers were evaluated in real life scenarios, their diagnostic performance, measured by AUC curve, did not match the results of earlier smaller studies. A perfect example was procalcitonin, PCT, the best known proposed biomarker for sepsis which initially showed very good discriminatory ability for sepsis. As PCT became more widely used and systematically studied, it became clear that it was far from the silver bullet it was optimistically thought to be. A systematic literature review and meta-analysis [6] showed an AUC of 0.78, with diagnostic performance upwardly biased in smaller studies, but moving towards a null effect in larger studies. Several years after PCT became available as a reportable test worldwide, its adoption among hospital laboratories is still sporadic, and when it is used, the most common clinical objective is the monitoring of antibiotic therapy for safe discontinuation, rather than initial diagnosis of the sepsis.
Even if the performance of these tests had been excellent, the accessibility challenge would still limit their ability to positively impact patient outcomes. As mentioned above, if the test for sepsis is ordered by a physician based on observation, the opportunity to start antibiotics sooner was missed, and the positive outcome reduced. Thus, real improvements in patient mortality will only be seen when tests are ordered routinely during initial patient care in the ED, such as the complete blood count with differential (CBC-diff).
Can we diagnose sepsis sooner using only data from a CBC-diff?
To date, there have been multiple attempts to improve the early detection of sepsis using CBC data, either via new parameters or via the creation of index values combining results from multiple traditional parameters. But so far no significant improvement in performance has been achieved —in great part due to the fact that cell counts are also elevated in inflammatory conditions mimicking sepsis. Thus, what is lacking is a parameter which is less sensitive to the inflammatory process, and more specific for the sepsis infection.
Cellular morphologic changes may be the critical tipping point in this quest. As key players in the fight against infection, white blood cells, such as monocytes and neutrophils, get activated and change their morphology. In fact, such changes have been used for years by pathologists and technologists when making their diagnostic decisions at the microscope.
Certain hematological analysers collect cellular morphologic data in their quest to recognize and count cells. Multiple studies have been published over the last decade discussing these parameters and their potential value for the early diagnosis of sepsis. However, as was true for multiple other proposed sepsis biomarkers, the small sample size and retrospective design of these studies limited their value to reliably assess their potential diagnostic performance for sepsis. This limitation has been addressed in a recent large prospective trial, probing the clinical value of morphologic parameters in the early diagnosis of sepsis in the general ED population, as well as in the discrimination between sepsis and its key mimic, SIRS.
The results of this trial will be presented at the Society for Critical Care Medicine (SCCM) annual meeting, and will not be in the public domain in time for publication in this article. But readers are invited to pay close attention to this data once it becomes available in the literature, as this abstract has been selected as one of the “Star Research Presentations” at the upcoming Critical Care Congress in February 2016.
References:
1. Liu V, Escobar GJ, et al. Hospital deaths in patients with sepsis from 2 independent cohorts. JAMA. 2014 Jul 2;312(1):90-2.
2. Pierrakos C, Vincent JL. Sepsis biomarkers: a review. Crit Care. 2010;14(1):R15.
3. Rivers E, Nguyen B, et al. Early Goal-Directed Therapy Collaborative Group. Early goal-directed therapy in the treatment of severe sepsis and septic shock. N Engl J Med. 2001 Nov 8;345(19):1368-77.
4. Jones AE, Shapiro NI, et al. Implementing early goal-directed therapy in the emergency setting: the challenges and experiences of translating research innovations into clinical reality in academic and community settings. Acad Emerg Med 2007;14:1072-8.
5. ProCESS Investigators, Yealy DM, et al. A randomized trial of protocol-based care for early septic shock. N Engl J Med. 2014 May 1;370(18):1683-93.
6. Tang BM, Eslick GD, et al. Accuracy of procalcitonin for sepsis diagnosis in critically ill patients: systematic review and meta-analysis. Lancet Infect Dis. 2007 Mar;7(3):210-7
The author
Fernando Chaves, Director of Global Scientific Affairs, Beckman Coulter Diagnostics.
Significant workflow improvement and cost savings for antinuclear antibody testing on HEp-2 substrate using an automated process
, /in Featured Articles /by 3wmediaThe antinuclear antibody (ANA) test is a standard screening assay for detecting multiple autoantibodies that may be produced by a patient with an ANA associated rheumatic disease (AARD). Patients with these AARD often present with vague symptoms posting challenges to make an early and accurate diagnosis. The presence of ANAs assists physicians in making a definitive diagnosis of AARD. During the past decade laboratories have tried to move ANA testing by IIF to solid-phase assays. However, solid phase technologies such as bead-based or enzyme-linked immune assay (ELISA) have their own limitations [1-4]. Although there are several methodologies available to screen ANA, in 2009 a task force of the American College of Rheumatology (ACR) issued a statement declaring HEp-2 indirect immunofluorescence (IIF) as the preferred method for ANA screening [5,6].
by Deborah S. Stimson CLS1, Claudia A. Ibarra CCS, MB (ASCP)
The ACR declaration was based on the findings of the task force which collected information from physicians to evaluate non-standardization of the various methodologies on the market for evaluating ANA. Using HEp-2 cells as the substrate, IIF allows detection of over 100 autoantibodies to different nuclear and cytoplasmic antigens [7].
There are 5 to 6 nuclear patterns that are commonly reported. These are: homogeneous, speckled, centromere, nucleolar, dense fine speckled, and nuclear dot. The pattern and titre aid the physician when deciding what further tests to order, if any.
Performing IIF is labour-intensive, subjective, and prone to transcription errors and reader bias. Technologists reading IIF must be well trained and experienced in the interpretation of the complex patterns [7-10].
After the ACR’s 2009 statement, the demand for IIF testing has outpaced the typical laboratory’s capability to perform this test manually. Implementing HEp-2 IIF testing to abide by the recommendations issued in the ACR statement presents a challenge to most laboratories. As newer test technologies emerged, the number of laboratories with knowledge and skill to perform ANA IIF declined. The cost of personnel can be prohibitive, considering the number of staff members who must have skills and expertise to run and interpret ANA IIF. There is a need for automation and standardization of ANA IIF. Since 2002 several studies of automated or digital IIF instruments for positive and negative discrimination have been performed. Some systems incorporate pattern recognition algorithms. All conclude that automated IIF analysis will improve inter- and intra-laboratory results [11-19].
To address the increased demand for ANA testing using HEp-2 IIF, and to overcome problems with manual performance of HEp-2 testing, Inova Diagnostics developed the Integrated Lab [11-19]. To automate IIF processing, the Integrated Lab uses QUANTA-Lyser®; to automate IIF interpretation, it uses NOVA View® with a digital IIF microscope (recently cleared through the FDA no. DEN140039); and to simultaneously confirm and report results directly to the LIS, it uses QUANTA Link® software. The new instrument configuration delivers positive patient identification for IIF samples, thereby eliminating the need for manual transcription, it provides a paperless laboratory environment, while reducing variability and hands-on time.
Materials and methods
Following the manual method currently in use by the lab, a single ANA run of 118 samples was performed and then positive samples were titrated. Subsequently, the same 118 samples were processed, read, and titres reported using the automated Integrated Lab. The Integrated Lab configuration implemented at Exagen consists of three primary instruments, QUANTA-Lyser EIA/IIF processor which processes, and reads and interprets NOVA Lite® bar coded slides to allow positive patient identification, NOVA View digital IIF microscope acquires, displays, and suggests interpretation of HEp-2 IIF images, and QUANTA Link a bi-directional software, as shown in Figure A. A single run of 118 samples sent to Exagen for ANA IIF were used for this study. The samples were processed both manually and using the QUANTA Lyser 240. IIF screens and endpoint titres were read manually on an Olympus BX41 halogen microscope and also with digital images captured by NOVA View. Manual results were reported by transcribing them onto a template in the darkroom, then transcribing them a second time into the LIS. Integrated Lab results were automatically reported to the LIS using QUANTA Link. The kit for both manual and automated runs was the NOVA Lite® HEp-2 IgG ANA kit with DAPI, containing barcoded slides. After screening, forty-two of the 118 samples (36%) produced positive results. In the manual method, the forty-two samples were serially diluted to determine the endpoint titre. By comparison, the Integrated Lab configuration utilizes a unique Single Well Titre (SWT) feature on NOVA View to predict an endpoint titre from the screening well result. The SWT function automatically predicts an endpoint titre using a series of standard curves programmed into the software. Each of the 5 recognized patterns is matched to a specific curve. The SWT feature on NOVA View can be used for up to eight of the most common IIF patterns and does not require additional dilution steps. This study was conducted to quantify hands-on time required to perform our ANA IIF testing, comparing tests run manually with tests run on the Integrated Lab. Each step was timed using a stop watch. Subsequently a 5-month retrospective study to quantify reagent cost savings due to using the Integrated Lab was performed.
Results
Both methods examined 118 screens and 42 endpoint titres; the manual method required 288 HEp-2 wells, while the Integrated Lab used 120 wells. Processing samples on QUANTA-Lyser requires two wells per run of slides designated for controls compared to running manually which requires a positive and negative control on each slide. The SWT feature on NOVA View reduced the number of IIF wells by 58% or 168 wells. Screening results: The Integrated Lab reduced the hands-on time from sample processing through confirmation and reporting results by 64%, from 205.2 minutes manual run to 74.5 minutes. Using the NOVA View to predict endpoint titre eliminated the need to make serial dilutions or process additional wells. Processing 42 positive ANAs, this automated feature reduced total hands-on time by 202 minutes compared to the manual method.
Using the Integrated Lab reduced hands-on time by 82% or 5.5 hours per day compared to the manual IIF process. (Table 1) The outcome was a total annual reduction of 1,442 staff hours. Complete details are compared in Figure B. In 5 months 19,321 ANA IIF sera had been run using the Integrated Lab. A breakdown of results is shown in Table 2. Using the manual method the positive screens would be titrated the following day using 5 wells per patient to ensure finding the endpoint and reporting results 24 hours after the screen. (Table 3) With the SWT application the results with pattern and titre were reported out the same day seconds after the ANA screen result was determined. This saved 24 hours per patient in TAT for reporting. It also saved the laboratory 50,665 HEp-2 wells in 5 months.
Discussion
Recent recommendations from the ACR to use ANA HEp-2 IIF as a screening test for ANA as an aid in the diagnoses of AARD have led to an increased number of ANA IIF tests being ordered. The Integrated Lab provided the solution for automating ANA IIF that helped meet these challenges. At Exagen, the time study we conducted demonstrated a reduced hands-on time of 82% from 407.2 to 74.5 minutes and allowed faster turn-around time by delivering same day results for endpoint titre. Endpoint titre results, using NOVA View’s SWT function reduced the number of additional IIF wells and time to process endpoint titre results allowing same day reporting along with cost savings. Using the NOVA View digital images has also provided standardization among IIF readers, who now enjoy the ability to read and consult using the same digital image at any time. This was an added benefit. We found that this sophisticated, automated technology led to workflow efficiencies and a cost effective alternative to the manual IIF procedure in our laboratory.
We redirected labour savings to developing areas and expanded the tests our lab offers, while satisfying the requests of our clients for ANA titre and pattern. This study was focused on the workflow optimization and cost savings not on analytical or clinical performance which have been addressed in previous studies with convincing outcome.
References
1. Agmon-Levin N, et al, Ann Rheum Dis. 2014 Jan:73(1):17-23 doi: 10.1136/annrheumdis-2013-203863. Epub 2014.
2. Fritzler Fritzler MJ, et al. J Rheumatol. 2003;30:2374-2381.
3. Peterson LK, et al. J Immunol Methods. 2009;349:1-2.
4. Tonuttia E, et al. Autoimmunity. 2004;37:171-176.
5. Tan EM, et al. Arthritis Rheum. 1999;42:455-464.
6. American College of Rheumatology. Current Practice Issues: ACR Tracking Concerns About ANA Testing Results. Atlanta, GA: American College of Rheumatology; 2009.
7. P. L. Meroni and P. H. Schur, Annals of the Rheumatic Diseases, vol. 69, no. 8, pp. 1420–1422, 2010.
8. R. W. Burlingame and C. Peebles, K.M. Pollard, Ed., pp. 159–188,Wiley-VCH, Weinheim, Germany, 2006.
9. S. S. Copple, et al. American Journal of Clinical Pathology, vol. 137, pp. 825–830, 2012.
10. B. M. Van, et al. Clinical Chemistry and Laboratory Medicine, vol. 47, no. 1, pp. 102–108, 2009.
11. X. Qin, et al. Nan Fang Yi Ke Da Xue Bao, vol. 29, no. 3, pp. 472–475, 2009.Pathology, vol. 137, pp. 825–830, 2012.
12. Edgner W. The use of laboratory tests in the diagnosis of SLE. J Clin Pathol, 2000:53:424-432.
13. Fenger M, et al. Clin Chem. 2004;50:2141-2147.
14. Swaak AJ. Ned Tijdschr, Geneeskd. 2000:144:585-589.
15. P. Perner, et al. Journal Artificial Intelligence in Medicine, vol. 26, no. 1, pp. 161–173, 2002.
16. K. Egerer, et al. Arthritis Research & Therapy, vol. 12, article R40, 2010.
17. R. Hiemann, et al. AutoimmunityReviews, vol. 9, no. 1, pp. 17–22, 2009.
18. A. Willitzki, et al. AutoimmunityReviews, vol. 9, no. 1, pp. 17–22, 2009.
19. J. Voigt, et al. Clinical and Developmental Immunology, vol. 2012, Article ID 651058, 7 pages, 2012.
20. D. Roggenbuck, et al. Clinical Chemistry and Laboratory Medicine, vol. 52, no. 2, pp. e9–e11, 2013.
21. C. Bonroy, et al. Clinical Chemistry and Laboratory Medicine, vol. 51, no. 9, pp.1771–1779, 2013.
22. X. Bossuyt, et al. Clinica Chimica Acta, vol. 415, pp. 101–106, 2013.
23. P. Foggia, et al. IEEE Transactions on Medical Imaging, vol. 32, no. 10, pp. 1878–1889, 2013.
24. D. Roggenbuck, et al. Clinica Chimica Acta, vol. 421, pp. 168–169, 2013.
The authors
Deborah S. Stimson CLS1, Claudia A. Ibarra CCS, MB (ASCP)1, Vice President, Laboratory Operations Exagen Diagnostics
1Exagen Diagnostics, Vista, CA. 92081, USA.
Corresponding author: Claudia Ibarra
1261 Liberty Way, Vista, CA 92081, USA.
Tel. 888-452-1522
E-mail: cibarra@exagen.com
StatStrip Glucose Hospital Wireless Meter System
, /in Featured Articles /by 3wmediaSTA R Max and STA Compact Max 2 coagulation analysers
, /in Featured Articles /by 3wmedia