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Fosfomycin is a broad-spectrum antibiotic used as empirical treatment for uncomplicated urinary tract infections (UTIs), of which Escherichia coli is the most common cause. To rapidly detect fosfomycin-resistant E. coli isolates and consequently improve patients’ treatment and management, we have developed the Rapid Fosfomycin/E. coli NP test, a rapid, easy-to-perform, specific and sensitive diagnostic test.
by Dr Linda Mueller, Dr Laurent Poirel and Prof. Patrice Nordmann
Introduction
Fosfomycin, a phosphonic acid-derived bactericidal antibiotic discovered in 1969, is now of renewed interest, especially for the treatment of multidrug-resistant (MDR) Gram-negative bacterial infections. This antibiotic is water-soluble and has a low molecular weight, allowing high diffusion at the tissue level [1]. Its features such as broad-spectrum activity, safety and efficacy make fosfomycin as one of the first-line antibiotics used for uncomplicated urinary tract infections (UTIs) treatment [2]. More than 75% of UTIs are due to Escherichia coli [3].
Fosfomycin enters the bacterial cell by the transport proteins GlpT (glycerol-3-phosphate transporter) and UhpT (hexose-6-phosphat:phosphate antiporter); once in the cytosol it binds and inactivates MurA (UDP-N-acetylglucosamine enolpyruvyl transferase), the enzyme involved in the first step of peptidoglycan biosynthesis. Hence, it inhibits bacterial cell wall synthesis [4].
Because of its unique structure and mechanism of action, cross-resistance with fosfomycin and other bacterial agents has not been observed. Fosfomycin as a single agent works well for treating most of UTIs. Additionally, synergistic effects of fosfomycin with several unrelated molecules, such as gentamicin, carbapenems, aztreonam and aminoglycosides, have been observed when treating clinically-relevant MDR Gram-negative bacteria [5].
One of the main concerns with antibiotic resistance in E. coli corresponds to the acquisition of extended-spectrum β-lactamases (ESBL) leading to resistance to expanded-spectrum cephalosporins. ESBL-producing E. coli are mostly community-acquired and may represent 10 to 20% of E. coli isolates in several countries including in the US [6]. Those strains are often co-resistant to several aminoglycosides, to trimethoprim, cotrimoxazole and fluoroquinolones, leaving few therapeutic options available including fosfomycin [7].
Both wild-type susceptible E. coli and ESBL-producing E. coli show an overall high susceptibility rate to fosfomycin (>90%) [8]. However, a Spanish study monitoring fosfomycin resistance during 5 years, showed an increased use of fosfomycin [from 0.122 defined daily dose per 1000 inhabitants per day (DID) in 2004 to 0.191 DID in 2008] and an increased fosfomycin resistance rate in E.coli (from 1.6% to 3.8%) as well as in ESBL-producing E. coli (from 2.2% to 21.7%) [9].
The mechanisms of resistance to fosfomycin described in E. coli are either non-transferable or transferable. The non-transferable and chromosome-encoded resistance involve reduced permeability, resulting from mutations in glpT and uhpT genes, encoding for fosfomycin transporters, and amino acid mutations in the active site of the MurA target. Plasmid-mediated fosfomycin resistance mechanisms in E. coli correspond to production of fosfomycin-inactivating metallo-enzymes (encoded by the fosA genes) [10]. Among the plasmid-borne fosA variants described so far, fosA3 remains the most widespread resistance determinant among both human and animal isolates, those latter being either recovered from pets or livestock [11, 12]. Moreover, a study performed in Taiwan reported the transmission of FosA3-producing E. coli between companion animals and respective owners [13]. Importantly, the fosA3 gene is often identified onto conjugative plasmids along with CTX-M-type ESBL encoding genes, thus leading to acquired resistance to both fosfomycin and broad-spectrum cephalosporins [14, 15]. As fosfomycin is being used as an empiric treatment against UTIs, it was of great interest to develop a rapid test to evaluate the efficacy of this antibiotic.
Rapid Fosfomycin/E. coli NP test
Currently the reference technique recommended by the European Committee on Antimicrobial Susceptibility Testing (EUCAST) to evaluate fosfomycin susceptibility is agar dilution, a fastidious technique requiring 18±2 h to get the results [16]. According to EUCAST, an isolate of E. coli is categorized as susceptible or as resistant when minimum inhibitory concentrations (MICs) are ≤32 and >32 mg/L, respectively.
Alternatively, disk diffusion and gradient strips, although exhibiting some discrepancies with the reference agar dilution method, might be used [17]. To accelerate the process of fosfomycin resistance detection, we have developed the Rapid Fosfomycin/E. coli NP test that allows detection of resistance within 1 h 30 min of fosfomycin-resistant E. coli isolated from culture plates.
This user-friendly technique is based on carbohydrate hydrolysis, detecting bacterial growth of fosfomycin-resistant isolates in the presence of a defined concentration (40 mg/L) of fosfomycin. Of note, fosfomycin-resistant isolates are detected independently of the molecular mechanism of resistance.
Briefly, the technique includes the preparation of a bacterial suspension (109 CFU/mL; 3–3.5 McFarland) that is poured on a 96-well polystyrene microplate. This culture is made in the Rapid Fosfomycin NP solution supplemented with 25 mg/L glucose-6-phosphate with or without 40 mg/L fosfomycin. The plate is incubated for 1 h 30 min at 35±2 °C and colour changes are detected by visual inspected. Fosfomycin-resistant isolates grow in the presence and absence of fosfomycin, triggering a colour switch from orange to yellow in both wells, a test result which is, therefore, considered as positive (Fig. 1). When dealing with fosfomycin-susceptible isolates, the well supplemented with fosfomycin does not exhibit any bacterial growth and remains orange; the test is, therefore, considered as negative. This test was evaluated with 100 strains including 22 fosfomycin-resistant isolates. It showed a sensitivity and a specificity of 100% and 98.7% respectively.
Conclusion
The Rapid Fosfomycin/E. coli NP test is rapid (1 h 30 min), specific (98.7%) and sensitive (100%). It is easy to perform, cost-effective, and may be used worldwide, regardless of the technical capabilities of the lab. Ongoing work aims to evaluate its performances directly from urine samples, which would represent significant added-value in terms of diagnostic rapidity.
The speed of this test allows a saving of at least 16 h when compared to the traditional agar dilution method. It is a potentially useful clinical test for first-step screening of fosfomycin resistance in E. coli.
Even though a low level of resistance to fosfomycin is currently observed among E. coli, the fact that we usually observe an increased fosfomycin clinical use, meaning an increased selective pressure, argues for a likely increased occurrence of fosfomycin-resistant isolates in the future. Since the principle of this test is based on a rapid culture, it may be used to detect any fosfomycin resistance trait that may be either chromosomally or plasmid-encoded. Fosfomycin is an old antibiotic that is very useful for the treatment of uncomplicated UTIs. On the one hand, even after extensive use for such an indication, the prevalence of resistance remains low, likely due to the fitness cost of the chromosomal mutations needed for acquired resistance, and also as a consequence of a high urinary drug concentration. On the other hand, the worldwide spread of fosfomycin-modifying enzymes should be monitored, as the biological cost of this emerging mechanism of resistance is much lower than that induced by chromosomal mutations [18] and the co-occurrence of fosA-like genes on plasmids with other resistance genes is commonly observed, meaning that co-selection can occur quite frequently.
References
1. Dijkmans AC, Zacarias NVO, Burggraaf J, Mouton JW, Wilms EB, van Nieuwkoop C, et al. Fosfomycin: pharmacological, clinical and future perspectives. Antibiotics (Basel) 2017; 6(4): pii: E24.
2. Gupta K, Hooton TM, Naber KG, Wullt B, Colgan R, Miller LG, et al. International clinical practice guidelines for the treatment of acute uncomplicated cystitis and pyelonephritis in women: A 2010 update by the Infectious Diseases Society of America and the European Society for Microbiology and Infectious Diseases. Clin Infect Dis 2011; 52(5): e103–120.
3. Flores-Mireles AL, Walker JN, Caparon M, Hultgren SJ. Urinary tract infections: epidemiology, mechanisms of infection and treatment options. Nat Rev Microbiol 2015; 13(5): 269–284.
4. Castaneda-Garcia A, Blazquez J, Rodriguez-Rojas A. Molecular mechanisms and clinical impact of acquired and intrinsic fosfomycin resistance. Antibiotics (Basel) 2013; 2(2): 217–236.
5. Falagas ME, Vouloumanou EK, Samonis G, Vardakas KZ. Fosfomycin. Clin Microbiol Rev 2016; 29(2): 321–347.
6. Castanheira M, Farrell SE, Krause KM, Jones RN, Sader HS. Contemporary diversity of beta-lactamases among Enterobacteriaceae in the nine U.S. census regions and ceftazidime-avibactam activity tested against isolates producing the most prevalent beta-lactamase groups. Antimicrob Agents Chemother 2014; 58(2): 833–838.
7. Wiedemann B, Heisig A, Heisig P. Uncomplicated urinary tract infections and antibiotic resistance-epidemiological and mechanistic aspects. Antibiotics (Basel) 2014; 3(3): 341–352.
8. Falagas ME, Kastoris AC, Kapaskelis AM, Karageorgopoulos DE. Fosfomycin for the treatment of multidrug-resistant, including extended-spectrum β-lactamase producing, Enterobacteriaceae infections: a systematic review. Lancet Infect Dis 2010; 10: 4–-50.
9. Oteo J, Orden B, Bautista V, Cuevas O, Arroyo M, Martinez-Ruiz R, et al. CTX-M-15-producing urinary Escherichia coli O25b-ST131-phylogroup B2 has acquired resistance to fosfomycin. J Antimicrob Chemother 2009; 64(4): 712–717.
10. Silver LL. Fosfomycin: mechanism and resistance. Cold Spring Harb Perspect Med 2017; 7(2): pii: a025262.
11. Alrowais H, McElheny CL, Spychala CN, Sastry S, Guo Q, Butt AA, et al. Fosfomycin resistance in Escherichia coli, Pennsylvania, USA. Emerg Infect Dis 2015; 21(11): 2045–2047.
12. Xie M, Lin D, Chen K, Chan EW, Yao W, Chen S. Molecular characterization of Escherichia coli strains isolated from retail meat that harbor blaCTX-M and fosA3 genes. Antimicrob Agents Chemother 2016; 60(4): 2450–2455.
13. Yao H, Wu D, Lei L, Shen Z, Wang Y, Liao K. The detection of fosfomycin resistance genes in Enterobacteriaceae from pets and their owners. Vet Microbiol 2016; 193: 67–71.
14. Benzerara Y, Gallah S, Hommeril B, Genel N, Decre D, Rottman M, et al. Emergence of plasmid-mediated fosfomycin-resistance genes among Escherichia coli isolates, France. Emerg Infect Dis 2017; 23(9): 1564–1567.
15. Yang X, Liu W, Liu Y, Wang J, Lv L, Chen X, et al. F33: A-: B-, IncHI2/ST3, and IncI1/ST71 plasmids drive the dissemination of fosA3 and bla CTX-M-55/-14/-65 in Escherichia coli from chickens in China. Front Microbiol 2014; 5: 688.
16. Performance standards for antimicrobial susceptibility testing, 28th edn. Clinical and Laboratory Standards Institute (CLSI) document M100-S28 2018.
17. Hirsch EB, Raux BR, Zucchi PC, Kim Y, McCoy C, Kirby JE, et al. Activity of fosfomycin and comparison of several susceptibility testing methods against contemporary urine isolates. Int J Antimicrob Agents 2015; 46(6): 642–647.
18. Cattoir V, Guérin F. How is fosfomycin resistance developed in Escherichia coli? Future Microbiol 2018; 13(16): 1693–1696.
The authors
Linda Mueller*1,2 PhD; Laurent Poirel1,2,3 PhD; Patrice Nordmann1,2,3,4 MD, PhD
1Emerging Antibiotic Resistance Unit, Medical and Molecular Microbiology, Faculty of Science and Medicine, University of Fribourg, Fribourg, Switzerland
2Swiss National Reference Center for Emerging Antibiotic Resistance (NARA), University of Fribourg, Fribourg, Switzerland
3INSERM European Unit (IAME, France),University of Fribourg, Fribourg, Switzerland
4University Hospital Center and University of Lausanne, Lausanne, Switzerland
*Corresponding author
E-mail: Linda.mueller@unifr.ch
Antiepileptic drugs (AEDs) are widely used and their number is steadily increasing. Therapeutic drug monitoring of AEDs, when performed correctly, can be a valuable tool for the treating physician. This article describes the indications, limitations and pitfalls that must be observed when measuring and interpreting AED serum concentrations.
by Dr Arne Reimers and Prof. Eylert Brodtkorb
Why measure antiepileptic drug serum concentrations?
Antiepileptic drugs (AEDs) are widely used, not only for epilepsy, but also for a range of non-epilepsy conditions, such as bipolar (manic-depressive) disorder, migraine and neuropathic pain [1]. Thus, the total number of AED users substantially exceeds the number of people with epilepsy. Therapeutic drug monitoring (TDM) has for many years been used to support AED treatment, as many of these drugs have unfavourable pharmacokinetic properties, a potential to problematic drug interactions as well as narrow therapeutic windows. TDM is a means of assisting clinical decision-making and should always be done with a specific question in mind. The general indications for TDM of AEDs are listed in Table 1.
Non-linear and linear pharmacokinetics
TDM of AEDs has a long clinical tradition. When the concept of TDM was introduced in the early 1970s, phenytoin was one of the first drugs to which it was applied [2]. This was mainly because phenytoin, then one of the most frequently used AEDs, has so-called non-linear pharmacokinetics. Linear kinetics means that the serum concentration is linearly correlated with dose – a doubling of the dose will double the serum concentration. This applies to almost all medicinal drugs. However, some drugs exhibit non-linear or saturation kinetics; phenytoin is one of them. Doubling the phenytoin dose may result in an unpredictable increase of the serum concentration. Thus, monitoring the phenytoin serum concentration was desirable and soon became available in large parts of the world.
Most other AEDs, however, exhibit linear kinetics. Why then is it important to measure their serum concentrations? One reason is the nature of epilepsy itself and the issue of prophylactic treatment. The only clinical marker for successful management is the extent of seizure control. However, epileptic seizures may occur in random patterns. The intervals between seizures may be minutes or months, and if a seizure occurs, it may have dramatic consequences, not only for the patient, but even for others. Thus, it can be very demanding to evaluate the therapeutic effect of AED treatment by clinical observation alone.
Absorption, distribution, metabolism and excretion
In addition, the pharmacokinetics of AEDs may be affected by changes in absorption, distribution, metabolism and excretion (ADME). Co-morbidity, pregnancy, drug interactions, pharmacogenetic polymorphisms, etc, all may considerably affect the ADME of AEDs (Fig. 1). Pregnancy may induce pronounced pharmacokinetic alterations, including increased volume of distribution, elevated renal clearance, and induction of hepatic metabolism. Breakthrough seizures in previously seizure-free patients may occur [3–5].
The serum concentration of carbamazepine may rise threefold and produce toxic symptoms when the patient is prescribed certain antibiotics which inhibit its metabolism, such as erythromycin. On the other hand, carbamazepine and other inducers of hepatic metabolism, may reduce serum concentrations of several other drugs, among them valproate, lamotrigine and hormonal contraceptives. Valproate is also a potent inhibitor of drug-metabolizing liver enzymes and may double lamotrigine concentrations. The clinically important induction of the metabolism of lamotrigine by combined oral contraceptives was detected by routine use of TDM [6]. Gabapentin is excreted almost exclusively by the kidneys; hence reduced kidney function will give increased serum concentrations.
Adherence
Poor adherence to prescribed treatment is one of the most important obstacles to the management of epilepsy [7, 8]. It has been documented that roughly half of all patients take their medicine more or less irregularly [9]. A recent study in patients admitted to hospital with acute epileptic seizures found that almost 40 % had less than 75 % of their usual trough AED serum concentration, indicating one or more missed doses [8] (Fig. 2). In such situations, it is crucial that the treating clinician receives the lab result as soon as possible to be able to decide on how to proceed with the management of the patient. Should the daily AED dose be increased or not? In the event that the seizure occurred because of a missed intake, it would not be appropriate; dose increase could even be harmful to the patient. If the serum concentration was adequate (according to prescribed dose), the occurrence of a seizure would suggest that the daily dose was too low and should be increased. This decision must be made quickly as the patient usually will be dismissed from hospital the next morning. It is essential to identify pseudo-refractory epilepsy. Clinically unrecognized non-adherence is often mistaken as drug-resistant epilepsy [10].
How it is normally done
The common convention is that blood samples for measuring the concentration of AEDs be taken drug-fasting in the morning (i.e. from 12 h to a maximum of 24 h after the last dose intake, and before the morning dose). Also, the patient must be in pharmacological steady state. This means that the amount of drug administered per unit time is in equilibrium with the amount of drug eliminated from the body during the same time. For all drugs, this state is reached after five times the drug’s plasma half-life. These rules apply after every dose change (Fig. 2E). The difficulties in complying with these rules are an important obstacle to TDM and is one major reason its routine use is discredited in many parts of the world. If a blood sample is taken before steady state is reached, or when the patient is not drug-fasting, the interpretation of the measured blood concentration is tricky and requires profound clinical-pharmacological experience.
Most commonly, the analyses are performed in a central lab using serum or plasma, either with immunologic or chromatographic methods. Usually, the total AED concentration (protein-bound plus unbound drug) is measured. In certain situations, e.g. in the elderly with hypoalbuminemia or in pregnant women, it is desirable to measure the unbound (free) proportion of an AED. This applies mainly to valproate and phenytoin which are >90 % protein bound. Hypoalbuminemia may cause signs of overdose despite only modest total AED concentration. However, unbound concentrations are rarely requested and not offered by all labs.
Reference ranges for antiepileptic drugs
It must be noted that reference ranges (RRs) for AEDs apply to the treatment of epilepsy. RRs for bipolar disorder have been suggested [11] but are not broadly established, whereas in the treatment of chronic pain states, treatment is usually guided by the clinical response alone. Unfortunately, with few exceptions, most RRs are not well documented. The exceptions are those AEDs that have been around for decades, e.g. phenytoin, carbamazepine and valproate. For them, broadly accepted RRs are supported by long clinical experience.
For the newer AEDs (introduced after 1990), there is a considerable lack of data. One of the reasons for the poor documentation is that drug manufacturers rarely publish serum concentrations obtained in clinical phase III or IV studies. Another reason is a lack of studies specifically aimed at examining the correlation between serum concentrations and effect. Thus, RRs for AEDs are often based on extrapolation of pharmacokinetic data obtained in preclinical studies, or on data from large routine databases, i.e. by applying some sort of population kinetics. Such data often lack clinical correlates owing to incomplete information provided on the request forms.
One consequence of the above is that the RRs used by different labs, and reported in the literature, are often incoherent. Another weakness of these population-based RRs is the fact that many patients achieve a satisfactory therapeutic effect with serum concentrations below the RR, while others need concentrations above the RR, yet without suffering symptoms of overdose. This is also the reason why the term ‘therapeutic range’ should not be used; it wrongly implies that any concentration outside that range is ‘non-therapeutic’.
The concept of individual RRs where each patient serves as his/her own reference [12] is an alternative approach. An obvious prerequisite for this concept is the availability of several consecutive serum concentration measurements (within reasonable time intervals) in the individual patient as well as close clinical follow-up, to correlate various serum concentrations with their corresponding clinical effect. It would also be desirable to have non-sufficient concentrations as well as toxic concentrations. Most of these individual therapeutic ranges would fall within the population-derived RRs. However, as mentioned above, some patients respond well to concentrations outside the common RR. For the sake of clarity, it has been suggested that such individual RRs be called individual therapeutic ranges [13]. Despite its advantages, neither the concept itself nor the term individual therapeutic range can be regarded as generally established.
Concluding remarks
TDM of AEDs is controversial, as it has been repeatedly emphasized that ‘treating patients is more important than treating blood levels’ [14]. Clinical evaluation and follow-up will continue to be the leading element in the management of epilepsy.
Nevertheless, when correctly applied, appropriately sampled and analysed, as well as correctly interpreted, TDM stands out as an important and relatively inexpensive tool for optimizing the drug treatment of epilepsy. Obviously, blinding for the actual serum concentrations may have severe untoward consequences in specific patient populations, such as pregnant women and patients with poor medication-taking behaviour.
References
1. Johannessen Landmark C. Antiepileptic drugs in non-epilepsy disorders: relations between mechanisms of action and clinical efficacy. CNS Drugs 2008; 22(1): 27–47.
2. Richens A. Drug estimation in the treatment of epilepsy. Proc R Soc Med 1974; 67(12 Pt 1): 1227–1229.
3. Cappellari AM, Cattaneo D, Clementi E, Kustermann A. Increased levetiracetam clearance and breakthrough seizure in a pregnant patient successfully handled by intensive therapeutic drug monitoring. Ther Drug Monit 2015; 37(3): 285–287.
4. Reimers A, Helde G, Becser Andersen N, Aurlien D, Surlien Navjord E, Haggag K, Christensen J, Lillestølen KM, Nakken KO, Brodtkorb E. Zonisamide serum concentrations during pregnancy. Epilepsy Res 2018; 144: 25–29.
5. Voinescu PE, Park S, Chen LQ, Stowe ZN, Newport DJ, Ritchie JC, Pennell PB. Antiepileptic drug clearances during pregnancy and clinical implications for women with epilepsy. Neurology 2018; 91(13): e1228–1236.
6. Sabers A, Buchholt JM, Uldall P, Hansen EL. Lamotrigine plasma levels reduced by oral contraceptives. Epilepsy Res 2001; 47(1–2): 151–154.
7. Faught E. Adherence to antiepilepsy drug therapy. Epilepsy Behav 2012; 25(3): 297–302.
8. Samsonsen C, Reimers A, Bråthen G, Helde G, Brodtkorb E. Nonadherence to treatment causing acute hospitalizations in people with epilepsy: an observational, prospective study. Epilepsia 2014; 55(11): e125–128.
9. Adherence to long-term therapies: evidence for action World Health Organization 2003; http://www.who.int/chp/knowledge/publications/adherence_report/en/.
10. Brodtkorb E, Samsonsen C, Sund JK, Bråthen G, Helde G, Reimers A. Treatment non-adherence in pseudo-refractory epilepsy. Epilepsy Res 2016; 122: 1–6.
11. Hiemke C, Bergemann N, Clement HW, Conca A, Deckert J, Domschke K, Eckermann G, Egberts K, Gerlach M, et al. Consensus guidelines for therapeutic drug monitoring in neuropsychopharmacology: update 2017. Pharmacopsychiatry 2018; 51(1–02): 9–62.
12. Landmark CJ, Johannessen SI, Tomson T. Dosing strategies for antiepileptic drugs: from a standard dose for all to individualised treatment by implementation of therapeutic drug monitoring. Epileptic Disord 2016; 18(4): 367–83.
13. Patsalos PN, Berry DJ, Bourgeois BF, Cloyd JC, Glauser TA, Johannessen SI, Leppik IE, Tomson T, Perucca E. Antiepileptic drugs – best practice guidelines for therapeutic drug monitoring: a position paper by the subcommission on therapeutic drug monitoring, ILAE Commission on Therapeutic Strategies. Epilepsia 2008; 49(7): 1239–1276.
14. Chadwick DW. Overuse of monitoring of blood concentrations of antiepileptic drugs. Br Med J (Clin Res Ed) 1987; 294(6574): 723–724.
The authors
Arne Reimers*1,2 MD PhD and Eylert Brodtkorb3,4 MD PhD
1Dept. of Clinical Chemistry and Pharmacology, Division of Laboratory Medicine, Skåne University Hospital, Lund, Sweden
2Department of Clinical Chemistry and Pharmacology, Lund University, Lund, Sweden
3Dept. of Neuromedicine and Movement Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
4Dept. of Neurology and Clinical Neurophysiology, St. Olavs University Hospital, Trondheim, Norway
*Corresponding author
E-mail: arne.reimers@med.lu.se
Rheumatoid arthritis shows a prevalence of 0.5–1.6 % globally. The identification of biomarkers for early treatment response could aid in the fine tuning of therapy and therefore contribute to increased treatment efficacy and the timely use of biologicals when no response from disease-modifying anti-rheumatic drugs is observed. Our group has identified several biomarkers for early diagnosis and treatment response.
By F..L. Ochoa-González, J..C. Fernández-Ruiz, M..F. Romo-García and Dr J..E. Castañeda-Delgado
Introduction
Rheumatoid arthritis (RA) is a chronic disease of autoimmune etiology characterized by persistent inflammation of the synovial membrane, which lines the inner surface of capsules of synovial joints. The worldwide estimated prevalence is about 1 % of the adult population. It is more frequent in women with a ratio of 3:1. The cause of RA is unknown; however, genetic and environ-mental factors contribute to RA. Several genes have been associated with an increased risk of developing RA: mainly certain HLA class II antigens associated with the shared epitope that is responsible for antigen presentation to lymphocytes. Smoking and some causative microorganisms of oral diseases such as periodontitis and gingivitis (Porphyromonas gingivalis and Aggregati-bacter Actimomyctemcomitans) have also been associated with RA [1]. The relation-ship of genetic traits/environment and the link to inflammation and autoimmunity is being explored. In this regard post-translational modifications of proteins such as citrullination of arginine by peptidyl arginine deiminase (PAD; some of it mediated by PAD-like enzymes coming from oral pathogens) or carbamylation of lysine (mediated by cyanate from cigar smoke) contribute to breaking immunological tole-rance by creating neoepitopes of autologous proteins resulting in generation of auto-antibodies against modified peptides. An examples include anti-citrullinated protein antibodies (ACPAs), antibodies to the Fc part of IgG [rheumatoid factor (RF)], or autoantigens that cross-react with bacterial or viral antigens [2]. These autoantibodies contribute to the increased inflammatory response observed in RA patients.
The clinical manifestations of RA are mainly associated with symmetric inflammation of small and large joints, accompanied by morning stiffness. Patients with RA usually present multiple comorbidities as a result of chronic inflammation, the main ones being cardiovascular disease or pulmonary mani-festations. RA greatly affects the patient’s quality of life, as it interferes with physical function. In long-term disease without treatment, the accumulation of joint damage is irreversible and leads to disability at an early age without the possibility of recovering normal function. Therefore, it is of great importance to establish an early diagnosis; it has already been shown that beginning treatment prevents the progression of joint damage in up to 90 % of patients in the early stages of the disease.
There is no cure for RA, which is why the goal of treatment is to reach remission, defined as no disease activity and low disease activity with low risk to progression. Therefore, therapeutic approaches are based on drugs that interfere with signs and symptoms of RA, such as disease-modifying antirheumatic drugs (DMARDs). DMARDs are categorized into conventional synthetic (csDMARDs), targeted synthetic (tsDMARDs) and biologic (bDMARDs). The csDMARDs include sulfasalazine, leflunomide, hydroxychloroquine and methotrexate. The joint working group of the American College of Rheumatology and the European League Against Rheumatism (ACR-EULAR) recommends treating all new cases of RA as soon as possible using methotrexate combined with short-term glucocorticoids. It has been reported that a proportion of patients treated with either DMARDs or bDMARDs do not reach treatment target (reduction of DAS28 and disease activity). The presence of autoantibodies, joint damage and high disease activity are associated with rapid disease progression that can be slowed by adding bDMARDs [3]; therefore, better prognostic markers for treatment response are also needed.
According to current ACR-EULAR 2010 classification criteria (not diagnostic criteria), RA patients have joint pain and synovial inflammation, morning stiffness in the joints with duration of at least 30 minutes. There are several serological determinations that aid in the classification of these patients, such as the use of cyclic citrullinated peptides (CCPs) to detect ACPAs, as well as RF and erythrocyte sedimentation rate [4]. Nevertheless, the diagnostic tests, such as the CCP test, that have a high specificity (range: 90–96 %) have several caveats: (1) low sensitivity (range 67–83 %); and (2) when negative, RA cannot be discarded because of the possibility that the patient has seronegative RA [5]. This is also the case for RF, which shows a similar sensitivity but a lower specificity. Therefore, there is a need for diagnostic tools that could help to further clarify the diagnosis of RA. As stated above, early diagnosis and treatment remains one of the crucial points for the management of RA and thus prevention of loss of physical function; however, the auxiliary diagnostic tools remain insufficient to distinguish RA from other rheumatic diseases. It is for these reasons that our group has focused on the search for early biomarkers of disease and of treatment response.
MicroRNAs as diagnostic biomarkers
MicroRNAs (miRNAs) are small RNA molecules (approximately 21 bp long) that modulate transcription and translation. Without miRNAs all the genes that are transcribed into mRNA (messenger RNA) would be translated to proteins, but miRNAs regulate which mRNA will or will not be translated into proteins. One single miRNA can control the production of many proteins and therefore small changes in the abundance of an miRNA could result in bigger changes at the protein level. This is these molecules are very important and are indicators of what is happening inside the body before these changes can be observed as clinical symptoms.
Recently, using microarray technology, our group detected changes in the expression profile of several hundred mRNAs in patients with early RA whose symptoms at that moment were barely classifiable by a rheumatologist [6]. What was behind that drastic change? As miRNAs are the main regulators of mRNA, we hypothesized that miRNAs could be responsible. Therefore, we analysed this miRNA profile in patients with early RA (using the same technology) and we identified 97 miRNAs that were over-expressed in early RA [7]. It seemed (but more experiments are needed to confirm this) that only 97 miRNAs are responsible for regulating around 2000 mRNA and all this in the early phases of the disease and the start of arthritis, when symptoms can’t be clearly classified by the clinician. This discovery encouraged us to explore whether any of these miRNAs can serve as a biomarker for the detection of early RA. Thus we performed an analysis named receiver operator characteristic curve (ROC curve). This kind of graph shows how many patients a biomarker (in this case a miRNA) can classify correctly and how many incorrectly. From this analysis we found that miRNA mir-361-5p had a specificity of 82.61 and sensitivity of 81.25. The value for sensitivity is bigger than the one of CCP (as mentioned previously) meaning that probably mir-361-5p can help to correctly identify those with the disease (true positives). However, a wider study is needed to confirm such observations and take it to the clinical lab.
The interest in miRNAs as biomarkers is increasing not only because they are master regulators as we mentioned before, but also because they have much greater stability in several fluids types, such as saliva or serum [8], compared to mRNA and this facilitates detection. Currently, the detection of miRNAs is performed by PCR or massive sequencing, which are technologies that need special equipment and infrastructure. However, recently, research groups have started to work on novel ideas for miRNA detection, such as small electronic devices to quantify miRNAs from a drop of serum [9].
Circulating miRNAs as potential biomarkers of treatment response
miRNAs have been proposed by different authors as possible biomarkers of response to csDMARDs, bDMARDs and tsDMARDs in RA. The abnormal expression pattern of miRNAs reflects the underlying patho-physiological processes owing to its direct relationship with the inflammatory processes. Some miRNAs have been described as markers of response to anti-tumour necrosis factor alpha (TNFα) treatment in patients with RA, which is the case with hsa-miR-hsa-16-5p, hsa-miR-23-3p, hsa-miR125b-5p, hsa-miR-126-3p, hsa-miRN-146a-5p and hsa-miR-223-3p that are upregulated in patients who respond after therapy and show a reduction in inflammatory parameters [TNFα, interleukin-6 (IL-6), IL-17, RF and C-reactive protein (CRP)] [10, 11]. Even methotrexate treatment seemed to have an effect on the expression of hsa-miR-132-3p, hsa-miR-146a-5p and hsa-miR-155-5p, where good responders have a downregulation of these miRNAs [12] (Fig. 1).
Our research team evaluated the expression of serum miRNAs by flow cytometry (Firefly™ technology) in a cohort of patients receiving treatment with tofacitinib (TOFA, a tsDMARD) for 5 years as part of an open-label study. Treatment with TOFA does not affect miRNA expression directly; however, in patients experiencing RA flare-up we identified changes in two miRNAs: hsa-miR-432-5p was downregulated and hsa-miR-194-5p was upregulated. Our findings suggest that these miRNAs could be used as a biomarkers for relapse. By monitoring them, relapse could be predicted and prevented by allowing a return to a treatment scheme before the patient’s symptoms worsen. How-ever, more research is required, as it is the first time that these miRNAs have been found to be involved in the inflammatory response of patients with RA [13]. If these findings can be confirmed, these miRNAs could be useful biomarkers for prediction of therapy effectiveness as well as therapy monitoring and could, therefore, be a useful support tool for generating personalized treatment regimens for RA patients.
Perspectives
The miRNA biomarkers reported above await further validation in the clinical setting. The design of clinical studies for such validation should account for and analyse possible reallife situations, such as the similarity of symptoms of RA patients with other rheumatic diseases and confounding variables.
References
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6. Macías-Segura N, Castañeda-Delgado JE, Bastian Y, Santiago-Algarra D, Castillo-Ortiz JD, Alemán-Navarro AL, Jaime-Sánchez E, Gomez-Moreno M, Saucedo-Toral CA, et al. Transcriptional signature associated with early rheumatoid arthritis and healthy individuals at high risk to develop the disease. PLoS One 2018; 13: e0194205.
7. Romo-García MF, Bastian Y, Zapata-Zuñiga M, Macías-Segura N, Castillo-Ortiz JD, Lara-Ramírez EE, Fernández-Ruiz JC, Berlanga-Taylor AJ, González-Amaro R, et al. Identification of putative miRNA biomarkers in early rheumatoid arthritis by genome-wide microarray profiling: A pilot study. Gene 2019; 720: 144081.
8. Huang W. MicroRNAs: biomarkers, diagnostics, and therapeutics. Methods Mol Biol 2017; 1617: 57–67.
9. Labib M, Khan N, Ghobadloo SM, Cheng J, Pezacki JP, Berezovski MV. Three-mode electrochemical sensing of ultralow microRNA levels. J Am Chem Soc 2013; 135: 3027–3038.
10. Castro-Villegas C, Pérez-Sánchez C, Escudero A, Filipescu I, Verdu M, Ruiz-Limón P, Aguirre MA, Jiménez-Gomez Y, Font P, Rodriguez-Ariza A, et al. Circulating miRNAs as potential biomarkers of therapy effectiveness
in rheumatoid arthritis patients treated with anti-TNFα. Arthritis Res Ther 2015; 17: 49.
11. Filková M, Aradi B, Senolt L, Ospelt C, Vettori S, Mann H, Filer A, Raza K, Buckley CD, et al. Association of circulating miR-223 and miR-16 with disease activity in patients with early rheumatoid arthritis. Ann Rheum Dis 2014; 73(10): 1898–1904.
12. Singh A, Patro PS, Aggarwal A. MicroRNA-132, miR-146a, and miR-155 as potential biomarkers of methotrexate response in patients with rheumatoid arthritis. Clin Rheumatol 2019; 38: 877–884.
13. Fernández-Ruiz JC, Ramos-Remus C, Sánchez-Corona J, Castillo-Ortiz JD, Castañeda-Sánchez JJ, Bastian Y, Romo-García MF, Ochoa-González F, Monsivais-Urenda AE, et al. Analysis of miRNA expression in patients with rheumatoid arthritis during remission and relapse after a 5-year trial of tofacitinib treatment. Int Immunopharmacol 2018; 63: 35–42.
The authors
Fatima de Lourdes Ochoa-González1,2 MSc, Julio Cesar Fernández-Ruiz2,3 MSc, Maria Fernanda Romo García2,3 MSc, Julio Enrique Castañeda-Delgado*4 PhD
1 Unidad Académica de Biología, Universidad Autónoma de Zacatecas, Zacatecas, México
2 Unidad de Investigación Biomédica de Zacatecas, Instituto Mexicano del Seguro Social, Zacatecas, México
3 Centro de Investigación en Ciencias de la Salud y Biomedicina, Universidad Autónoma de San Luís Potosí, San Luís Potosí, México
4 Cátedras-CONACYT, Unidad de Investigación Biomédica de Zacatecas, Instituto Mexicano del Seguro Social, Zacatecas, México
*Corresponding author
E-mail: julioenrique_castaneda@yahoo.com.mx
Figure 1. Circulating microRNAs after treatment for rheumatoid arthritis (RA). Flow diagram showing the expression of some microRNAs after methotrexate (MTX), anti-TNFα and tofacitinib (TOFA) treatment. Red, upregulated microRNAs; blue, downregulated microRNAs; qPCR, quantitative PCR.
March 2024
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