Identification of 7 Proteins in Sera of RA Patients with Potential to Predict ETA/MTX Treatment Response

Towards Personalized Medicine in Rheumatoid Arthritis

Rheumatoid arthritis (RA) is a chronic, incurable, multisystem, inflammatory disease characterized by synovitis and extra-articular features. Although several advanced therapies targeting inflammatory mechanisms underlying the disease are available, no advanced therapy is universally effective. Therefore, a ceiling of treatment response is currently accepted where no advanced therapy is superior to another. The current challenge for medical research is the discovery and integration of predictive markers of drug response that can be used to personalize medicine so that the patient is started on "the right drug at the right time". This review article summarizes our current understanding of predicting response to anti-rheumatic drugs in RA, obstacles impeding the development of personalized medicine approaches and future research priorities to overcome these barriers.

A proteomics study of rheumatoid arthritis patients on etanercept identifies putative biomarkers associated with clinical outcome measures

OBJECTIVES

Biologic DMARDs (bDMARDs) are widely used in patients with RA, but response to bDMARDs is heterogeneous. The objective of this work was to identify pretreatment proteomic biomarkers associated with RA clinical outcome measures in patients starting bDMARDs.

METHODS

Sequential window acquisition of all theoretical fragment ion spectra mass spectrometry (SWATH-MS) was used to generate spectral maps of sera from patients with RA before and after 3 months of treatment with the bDMARD etanercept. Protein levels were regressed against RA clinical outcome measures, i.e. 28-joint DAS (DAS28) and its subcomponents and DAS28 <2.6 (i.e. remission). The proteins with the strongest evidence for association were analysed in an independent, replication dataset. Finally, subnetwork analysis was carried out using the Disease Module Detection algorithm and biological plausibility of identified proteins was assessed by enrichment analysis.

RESULTS

A total of 180 patients with RA were included in the discovery dataset and 58 in the validation dataset from a UK-based prospective multicentre study. Ten individual proteins were found to be significantly associated with RA clinical outcome measures. The association of T-complex protein 1 subunit η with DAS28 remission was replicated in an independent cohort. Subnetwork analysis of the 10 proteins from the regression analysis identified the ontological theme, with the strongest associations being with acute phase and acute inflammatory responses.

CONCLUSION

This longitudinal study of 180 patients with RA commencing etanercept has identified several putative protein biomarkers of treatment response to this drug, one of which was replicated in an independent cohort.

Serum proteomics analysis of biomarkers for evaluating clinical response to MTX/IGU therapy in early rheumatoid arthritis

Methotrexate (MTX) and iguratimod (IGU) are conventional synthetic disease modifying antirheumatic drugs widely used in the treatment of Rheumatoid arthritis (RA) in China. Although MTX combined with IGU can significantly inhibit the progression of RA, some patients do not respond to the treatment. The purpose of this study is to explore the difference of serum protein expression between RA patients with good and poor response to the combined therapy by label-free quantitative proteomic approach. From the proteomics data, a total of 782 proteins in the serum of RA patients were detected, and of which 9 were upregulated and 18 were downregulated in the good response group compared to poor response group. Among them, four significantly differentially expressed proteins (RELN, LDHA, MRC1 and TKT) were further validated by multiple reaction monitoring (MRM)-based quantification approach, and three of them (RELN, LDHA and MRC1) were confirmed to be correlated with the response to MTX/IGU therapy. Logistic regression and ROC analysis indicated that the combination of RELN, LDHA and MRC1 had good performance in evaluating the response. This result proved the different serum proteins signature fingerprint between response group and non-response group; and highlighted the potential of the label-free and mass spectrometry-based quantitative proteomic approach in screening biomarkers for evaluating clinical response to MTX/IGU therapy in RA.

Escherichia coli Biofilm Formation, Motion and Protein Patterns on Hyaluronic Acid and Polydimethylsiloxane Depend on Surface Stiffness

The surface stiffness of the microenvironment is a mechanical signal regulating biofilm growth without the risks associated with the use of bioactive agents. However, the mechanisms determining the expansion or prevention of biofilm growth on soft and stiff substrates are largely unknown. To answer this question, we used PDMS (polydimethylsiloxane, 9-574 kPa) and HA (hyaluronic acid gels, 44 Pa-2 kPa) differing in their hydration. We showed that the softest HA inhibited Escherichia coli biofilm growth, while the stiffest PDMS activated it. The bacterial mechanical environment significantly regulated the MscS mechanosensitive channel in higher abundance on the least colonized HA-44Pa, while Type-1 pili (FimA) showed regulation in higher abundance on the most colonized PDMS-9kPa. Type-1 pili regulated the free motion (the capacity of bacteria to move far from their initial position) necessary for biofilm growth independent of the substrate surface stiffness. In contrast, the total length travelled by the bacteria (diffusion coefficient) varied positively with the surface stiffness but not with the biofilm growth. The softest, hydrated HA, the least colonized surface, revealed the least diffusive and the least free-moving bacteria. Finally, this shows that customizing the surface elasticity and hydration, together, is an efficient means of affecting the bacteria's mobility and attachment to the surface and thus designing biomedical surfaces to prevent biofilm growth.

Rapid evolution of pollen and pistil traits as a response to sexual selection in the post-pollination phase of mating

Sexual selection is the basis of some of the most striking phenotypic variation in nature.^1,2 In animals, sexual selection in males can act on traits that improve access to mates prior to copulation,^3-8 but also on sperm traits filtered by sperm competition,^9-14 or female choice expressed simply by the morphology and physiology of genital tracts.^14-16 Although long overlooked as a mode of selection on plant traits, sexual selection should act on land plants too because they are anisogamous: males produce more, and smaller, gametes than females.^17-19 Numerical asymmetry in gamete production is thought to play a central role in selection on traits that affect pollen transfer to mates,^20,21 but very little is known about how pollen competition or cryptic female choice might affect the evolution of traits expressed after pollination.^22,23 Here, we report the divergence of pollen and pistil traits of the dioecious wind-pollinated annual herb Mercurialis annua during evolution over three generations between populations at low versus high plant density, corresponding to low versus higher levels of polyandry;²⁴ we expected selection under higher polyandry to strengthen competition among pollen donors for fertilizing ovules. We found that populations at high density evolved faster-growing pollen tubes (an equivalent of greater sperm velocity), greater expression of pollen proteins involved in pollen growth, and larger stigmas (a trait likely enhancing the number of pollen donors and thus competition for ovules). Our results identify the post-pollination phase of plant mating as an important arena for the action of sexual selection.

Gene Ontology Analysis Highlights Biological Processes Influencing Non-Response to Anti-TNF Therapy in Rheumatoid Arthritis

Anti-TNF therapy has significantly improved disease control in rheumatoid arthritis, but a fraction of rheumatoid arthritis patients do not respond to anti-TNF therapy or lose response over time. Moreover, the mechanisms underlying non-response to anti-TNF therapy remain largely unknown. To date, many single biomarkers of response to anti-TNF therapy have been published but they have not yet been analyzed as a system of interacting nodes. The aim of our study is to systematically elucidate the biological processes underlying non-response to anti-TNF therapy in rheumatoid arthritis using the gene ontologies of previously published predictive biomarkers. Gene networks were constructed based on published biomarkers and then enriched gene ontology terms were elucidated in subgroups using gene ontology software tools. Our results highlight the novel role of proteasome-mediated protein catabolic processes (p = 2.91 × 10⁻¹⁵) and plasma lipoproteins (p = 4.55 × 10⁻¹¹) in anti-TNF therapy response. The results of our gene ontology analysis help elucidate the biological processes underlying non-response to anti-TNF therapy in rheumatoid arthritis and encourage further study of the highlighted processes.

Validation in the ESPOIR cohort of vitamin K-dependent protein S (PROS) as a potential biomarker capable of predicting response to the methotrexate/etanercept combination

Background

To validate the ability of PROS (vitamin K-dependent protein S) and CO7 (complement component C7) to predict response to the methotrexate (MTX)/etanercept (ETA) combination in rheumatoid arthritis (RA) patients who received this therapeutic combination in a well-documented cohort.

Method

From the ESPOIR cohort, RA patients having received the MTX/ETA or MTX/adalimumab (ADA) combination as a first-line biologic treatment were included. Serum concentrations of PROS and CO7 were measured by ELISA prior to the initiation of ETA or ADA, at a time where the disease was active (DAS28 ESR > 3.2). The clinical efficacy (response/non-response) of both combinations has been evaluated after at least 6 months of treatment, according to the EULAR response criteria with some modifications.

Results

Thirty-two were treated by MTX/ETA; the numbers of responders and non-responders were 24 and 8, respectively. Thirty-three patients received the MTX/ADA combination; 27 and 5 patients were respectively responders and non-responders. While there were no differences for demographic, clinical, biological, and X-rays data, as well as for CO7, serum levels of PROS tended to be significantly higher in responders to the MTX/ETA combination (p = 0.08) while no difference was observed in the group receiving MTX/ADA. For PROS, the best concentration threshold to differentiate both groups was calculated at 40 μg/ml using ROC curve. The theranostic performances of PROS appeared better for the ETA/MTX combination. When considering the response to this combination, analysis of pooled data from ESPOIR and SATRAPE (initially used to validate PROS and CO7 as potential theranostic biomarkers) cohorts led to a higher theranostic value of PROS that became significant (p = 0.009).

Conclusion

PROS might be one candidate of a combination of biomarkers capable of predicting the response to MTX/ETA combination in RA patients refractory to MTX.

Trial registration

ClinicalTrials.gov identifiers: NCT03666091 and NCT00234234.

Anti-Carbamylated Fibrinogen Antibodies Might Be Associated With a Specific Rheumatoid Phenotype and Include a Subset Recognizing In Vivo Epitopes of Its γ Chain One of Which Is Not Cross Reactive With Anti-Citrullinated Protein Antibodies

To identify the targets recognized by anti-carbamylated protein antibodies (anti-CarP) in patients with early Rheumatoid Arthritis (RA), to study the cross-reactivity between anti-CarP and anti-citrullinated protein antibodies (ACPA) and to evaluate their prognostic value. 331 patients (184 RA and 147 other rheumatisms) from the Very Early Arthritis (VErA) French cohort were analyzed. We performed mass spectrometry analysis of RA sera displaying anti-CarP activity and epitope mapping of the carbamylated fibrinogen γ chain to identify immunodominant peptides. The specificity of these targets was studied using competition assays with the major antigens recognized by ACPA. The prognostic value of anti-carbamylated fibrinogen IgG antibodies (ACa-Fib IgG) was compared to that of anti-cyclic citrullinated peptide antibodies (anti-CCP) and anti-CarP using an in-house ELISA. Besides the α chain, the γ chain of fibrinogen, particularly one immunodominant epitope that has a specific reactivity, was identified as a circulating carbamylated target in sera. The prevalence of ACa-Fib was 37% at baseline and 10.9% for anti-CCP-negative RA. In anti-CCP-negative patients, ACa-Fib positivity was associated with a more inflammatory and erosive disease at baseline but not with rapid radiological progression, which remains strongly related to anti-CCP antibodies. Fibrinogen seems to be one of the antigens recognized in vivo by the anti-CarP response, particularly 2 epitopes of the γ chain, one of which is not cross reactive with ACPA. This specificity might be associated with a distinct clinical phenotype since ACa-Fib IgG were shown to be linked to systemic inflammation in very early RA but not to rapid radiological progression.

Protein array-based companion diagnostics in precision medicine

INTRODUCTION

The development of companion diagnostics (CDx) will increase efficacy and cost-benefit markedly, compared to the currently prevailing trial-and-error approach for treatment. Recent improvements in high-throughput protein technology have resulted in large amounts of predictive biomarkers that are potentially useful components of future CDx assays. Current high multiplex protein arrays are suitable for discovery-based approaches, while low-density and more simple arrays are suitable for use in point-of-care facilities.

AREA COVERED

This review discusses the technical platforms available for protein array focused CDx, explains the technical details of the platforms and provide examples of clinical use, ranging from multiplex arrays to low-density clinically applicable arrays. We thereafter highlight recent predictive biomarkers within different disease areas, such as oncology and autoimmune diseases. Lastly, we discuss some of the challenges connected to the implementation of CDx assays as point-of-care tests.

EXPERT OPINION

Recent advances in the field of protein arrays have enabled high-density arrays permitting large biomarker discovery studies, which are beneficial for future CDx assays. The density of protein arrays range from a single protein to proteome-wide arrays, allowing the discovery of protein signatures that may correlate with drug response. Protein arrays will undoubtedly play a key role in future CDx assays.

Contribution of Multiplex Immunoassays to Rheumatoid Arthritis Management: From Biomarker Discovery to Personalized Medicine

Rheumatoid arthritis (RA) is a multifactorial, inflammatory and progressive autoimmune disease that affects approximately 1% of the population worldwide. RA primarily involves the joints and causes local inflammation and cartilage destruction. Immediate and effective therapies are crucial to control inflammation and prevent deterioration, functional disability and unfavourable progression in RA patients. Thus, early diagnosis is critical to prevent joint damage and physical disability, increasing the chance of achieving remission. A large number of biomarkers have been investigated in RA, although only a few have made it through the discovery and validation phases and reached the clinic. The single biomarker approach mostly used in clinical laboratories is not sufficiently accurate due to its low sensitivity and specificity. Multiplex immunoassays could provide a more complete picture of the disease and the pathways involved. In this review, we discuss the latest proposed protein biomarkers and the advantages of using protein panels for the clinical management of RA. Simultaneous analysis of multiple proteins could yield biomarker signatures of RA subtypes to enable patients to benefit from personalized medicine.

Efficacy, safety and cost-effectiveness of a web-based platform delivering the results of a biomarker-based predictive model of biotherapy response for rheumatoid arthritis patients: a protocol for a randomized multicenter single-blind active controlled clinical trial (PREDIRA)

BACKGROUND

Rheumatoid arthritis (RA) is one of the leading chronic inflammatory rheumatism. First-line therapy with synthetic disease-modifying antirheumatic drugs (sDMARD) is insufficiently effective in 40% of cases and these patients are treated with biotherapies. The increased use of these drugs each year is becoming a public health issue with considerable economic burden. This cost is 20 times higher than that of sDMARD. However, among patients treated with biotherapies, clinical practice shows that about one third will not respond to the selected drug. In nonresponse cases, practitioners currently have no choice but to perform an empirical switching between different treatments, because no tool capable of predicting the response or nonresponse to these molecules is currently available.

METHODS

The study is a prospective, phase III, controlled, multicenter, and randomized, single-blind (patient) clinical trial, including RA patients with a previous failure to anti-TNF therapies. The main objective is the analysis of the clinical and pharmacoeconomic impact after 6 months of treatment. Intervention arm: prescription of biotherapy (rituximab, adalimumab, abatacept) using SinnoTest® software, a prediction software based on proteomic biomarkers. Control arm: prescription of biotherapy based on current practice, without the SinnoTest® software (any biotherapy). In addition, a substudy will be carried out within this trial to generate a biobank and further analyze the proteomic profile of the patients and their modification throughout the study.

DISCUSSION

This clinical trial study will be the first validation study of a biotherapy response prediction software, bringing personalized medicine into the management of RA. We expect that the findings from this study will bring several benefits for the patient and the Health Care System.

TRIAL REGISTRATION

ClincalTrials.gov NCT04147026 . Registered on 31 October, 2019.

Multiple biomarker approach for the diagnosis and therapy of rheumatoid arthritis

The lack of specific clinical symptoms for patients in the early stage of rheumatoid arthritis (RA) has created strong interest in the laboratory diagnosis of RA. The main laboratory markers of RA, rheumatoid factor (RF) and anti-citrullinated protein antibodies (ACPAs), can be found in patients with other pathologies and in healthy donors. Even today, there is no single laboratory test that can diagnosis RA with high sensitivity and specificity. To improve the diagnosis and treatment of RA, alternative biomarkers, including 14-3-3η protein, connective tissue growth factor (CTGF), antibodies against PAD4, antibodies against BRAF, and anti-acetylated and anti-carbamylated protein antibodies have been studied extensively. The use of a multiple biomarker approach, the simultaneous measurement of a set of biomarkers, is an alternative strategy for the diagnosis of RA and for predicting the therapeutic effect of biological disease-modifying antirheumatic drugs (DMARDs). However, despite the large number of studies, only a few biomarker combinations have been validated and can be applied in clinical practice. In this article, results of studies focused on the multiple biomarker approach (both multiplex and combined single-analyte assays) to diagnose RA and to predict response to biological drug therapy are reviewed. Additionally, general factors limiting the use of multiplex analysis in RA diagnostics and therapy are discussed.

Fetuin-A and thyroxin binding globulin predict rituximab response in rheumatoid arthritis patients with insufficient response to anti-TNFα

OBJECTIVES

Rheumatoid arthritis (RA) is a debilitating disease, but patient management and treatment have been revolutionized since the advent of bDMARDs. However, about one third of RA patients do not respond to specific bDMARD treatment without clear identified reasons. Different bDMARDs must be tried until the right drug is found. Here, we sought to identify a predictive protein signature to stratify patient responsiveness to rituximab (RTX) among patients with an insufficient response to a first anti-TNFα treatment.

METHODS

Serum samples were collected at baseline before RTX initiation. A proteomics study comparing responders and nonresponders was conducted to identify and select potential predictive biomarkers whose concentration was measured by quantitative assays. Logistic regression was performed to determine the best biomarker combination to predict good or nonresponse to RTX (EULAR criteria after 6 months' treatment).

RESULTS

Eleven biomarkers potentially discriminating between responders and nonresponders were selected following discovery proteomics. Quantitative immunoassays and univariate statistical analysis showed that fetuin-A and thyroxine binding globulin (TBG) presented a good capacity to discriminate between patient groups. A logistic regression analysis revealed that the combination of fetuin-A plus TBG could accurately predict a patient's responsiveness to RTX with an AUC of 0.86, sensitivity of 80%, and a specificity of 79%.

CONCLUSION

In RA patients for whom a first anti-TNFα treatment has failed, the serum abundance of fetuin-A and TBG before initiating RTX treatment is an indicator for their response status at 6 months. ClinicalTrials.gov identifier: NCT01000441. Key Points • Proteomic analysis revealed 11 putative predictive biomarkers to discriminate rituximab responder vs. nonresponder RA patients. • Fetuin-A and TBG are significantly differentially expressed at baseline in rituximab responder vs. nonresponder RA patients. • Algorithm combining fetuin-A and TBG accurately predicts response to rituximab in RA patients with insufficient response to TNFi.

ASIC1a induces synovial inflammation via the Ca2+/NFATc3/ RANTES pathway

Rationale: Synovial inflammation is one of the main pathological features of rheumatoid arthritis (RA) and is a key factor leading to the progression of RA. Understanding the regulatory mechanism of synovial inflammation is crucial for the treatment of RA. Acid-sensing ion channel 1a (ASIC1a) is an H+-gated cation channel that promotes the progression of RA, but the role of ASIC1a in synovial inflammation is unclear. This study aimed to investigate whether ASIC1a is involved in the synovial inflammation and explore the underlying mechanisms in vitro and in vivo. Methods: The expression of ASIC1a and nuclear factor of activated T cells (NFATs) were analyzed by Western blotting, immunofluorescence, and immunohistochemistry both in vitro and in vivo. The Ca2+ influx mediated by ASIC1a was detected by calcium imaging and flow cytometry. The role of ASIC1a in inflammation was studied in rats with adjuvant-induced arthritis (AA). Inflammatory cytokine profile was analyzed by protein chip in RA synovial fibroblasts (RASF) and verified by a magnetic multi-cytokine assay and ELISA. The NFATc3-regulated RANTES (Regulated upon activation, normal T cell expressed and secreted) gene transcription was investigated by ChIP-qPCR and dual-luciferase reporter assay. Results: The expression of ASIC1a was significantly increased in human RA synovial tissues and primary human RASF as well as in ankle synovium of AA rats. Activated ASIC1a mediated Ca2+ influx to increase [Ca2+]i in RASF. The activation/overexpression of ASIC1a in RASF up-regulated the expression of inflammatory cytokines RANTES, sTNF RI, MIP-1a, IL-8, sTNF RII, and ICAM-1 among which RANTES was increased most remarkably. In vivo, ASIC1a promoted inflammation, synovial hyperplasia, articular cartilage, and bone destruction, leading to the progression of AA. Furthermore, activation of ASIC1a upregulated the nuclear translocation of NFATc3, which bound to RANTES promoter and directly regulated gene transcription to enhance RANTES expression. Conclusion: ASIC1a induces synovial inflammation, which leads to the progression of RA. Our study reveals a novel RA inflammation regulatory mechanism and indicates that ASIC1a might be a potential therapeutic target for RA.

Using the Immunophenotype to Predict Response to Biologic Drugs in Rheumatoid Arthritis

Tumour necrosis factor (TNF)-α is a key mediator of inflammation in rheumatoid arthritis, and its discovery led to the development of highly successful anti-TNF therapy. Subsequently, other biologic drugs targeting immune pathways, namely interleukin-6 blockade, B cell depletion, and T cell co-stimulation blockade, have been developed. Not all patients respond to a biologic drug, leading to a knowledge gap between biologic therapies available and the confident prediction of response. So far, genetic studies have failed to uncover clinically informative biomarkers to predict response. Given that the targets of biologics are immune pathways, immunological study has become all the more pertinent. Furthermore, advances in single-cell technology have enabled the characterization of many leucocyte subsets. Studying the blood immunophenotype may therefore, define biomarker profiles relevant to each individual patient's disease and treatment outcome. This review summarises our current understanding of how immune biomarkers might be able to predict treatment response to biologic drugs.

Prealbumin, platelet factor 4 and S100A12 combination at baseline predicts good response to TNF alpha inhibitors in rheumatoid arthritis

OBJECTIVES

Tumour necrosis factor-alpha inhibitors (TNFi) are effective treatments for Rheumatoid Arthritis (RA). Responses to treatment are barely predictable. As these treatments are costly and may induce a number of side effects, we aimed at identifying a panel of protein biomarkers that could be used to predict clinical response to TNFi for RA patients.

METHODS

Baseline blood levels of C-reactive protein, platelet factor 4, apolipoprotein A1, prealbumin, α1-antitrypsin, haptoglobin, S100A8/A9 and S100A12 proteins in bDMARD naive patients at the time of TNFi treatment initiation were assessed in a multicentric prospective French cohort. Patients fulfilling good EULAR response at 6 months were considered as responders. Logistic regression was used to determine best biomarker set that could predict good clinical response to TNFi.

RESULTS

A combination of biomarkers (prealbumin, platelet factor 4 and S100A12) was identified and could predict response to TNFi in RA with sensitivity of 78%, specificity of 77%, positive predictive values (PPV) of 72%, negative predictive values (NPV) of 82%, positive likelihood ratio (LR+) of 3.35 and negative likelihood ratio (LR-) of 0.28. Lower levels of prealbumin and S100A12 and higher level of platelet factor 4 than the determined cutoff at baseline in RA patients are good predictors for response to TNFi treatment globally as well as to Infliximab, Etanercept and Adalimumab individually.

CONCLUSION

A multivariate model combining 3 biomarkers (prealbumin, platelet factor 4 and S100A12) accurately predicted response of RA patients to TNFi and has potential in a daily practice personalized treatment.

Right drug, right patient, right time: aspiration or future promise for biologics in rheumatoid arthritis?

Individualising biologic disease-modifying anti-rheumatic drugs (bDMARDs) to maximise outcomes and deliver safe and cost-effective care is a key goal in the management of rheumatoid arthritis (RA). Investigation to identify predictive tools of bDMARD response is a highly active and prolific area of research. In addition to clinical phenotyping, cellular and molecular characterisation of synovial tissue and blood in patients with RA, using different technologies, can facilitate predictive testing. This narrative review will summarise the literature for the available bDMARD classes and focus on where progress has been made. We will also look ahead and consider the increasing use of 'omics' technologies, the potential they hold as well as the challenges, and what is needed in the future to fully realise our ambition of personalised bDMARD treatment.

Defining the proteomic landscape of rheumatoid arthritis: progress and prospective clinical applications

INTRODUCTION

The heterogeneity of Rheumatoid Arthritis (RA) and the absence of clinical tests accurate enough to identify the early stages of this disease have hampered its management. Therefore, proteomics research is increasingly focused on the discovery of novel biological markers, which would not only be able make an early diagnosis, but also to gain insight into the different pathological mechanisms underlying the heterogeneity of RA and also to stratify patients, which is critical to enabling effective treatments. Areas covered: The proteomic approaches that have been utilised to provide knowledge about RA pathogenesis, and to identify biomarkers for RA diagnosis, prognosis, disease monitoring and prediction of response to therapy, are summarized. Expert commentary: Although each proteomic study is unique in its design, all of them have contributed to the understanding of RA pathogenesis and the discovery of promising biomarkers for patient stratification, which would improve clinical care of RA patients. Still, efforts need to be made to validate these findings and translate them into clinical practice.