METHOD FOR DETERMINING TREATMENT RESPONSE AND REMISSION OF PROLIFERATIVE LUPUS NEPHRITIS USING THE SERPINA A3 BIOAMARKER

Abstract

In this study, we found that urinary Serpin A3 (uSerpA3) excretion was significantly elevated in patients with class III / IV lupus nephritis (LN), a type of proliferative glomerulonephritis associated with increased renal fibrosis. In contrast, uSerpA3 was much lower in patients with class V LN, which is less inflammatory and generally has a better prognosis. Based on these findings, we have developed a method to determine treatment response and remission in patients with proliferative lupus nephritis using urinary Serpin A3. The present invention also includes the use of uSerpA3 as a potential non-invasive biomarker to identify renal inflammation and the degree of treatment response in patients with lupus nephritis.

Description

METHOD FOR DETERMINING TREATMENT RESPONSE AND REMISSION OF PROLIFERATIVE LUPUS NEPHRITIS USING THE SERPINA A3 BIOAMARKER DESCRIPTION Technical Field The present invention falls within the field of clinical medicine and relates to a method for determining the response to treatment and remission in patients with proliferative lupus nephritis by using urinary serpin-A3 as a biomarker. INTRODUCTION Lupus nephritis (LN) is a common manifestation of systemic lupus erythematosus (SLE), the incidence of which varies according to demographic region and population ethnicity; however, it contributes significantly to morbidity and mortality in these patients (14). It is estimated that 10% of patients with LN will develop end-stage renal disease (ESRD) within 10–15 years of disease onset, which depends strongly on the histopathological phenotype. In particular, class IV LN represents a subtype of diffuse proliferative glomerulonephritis that increases the risk of ESRD by up to 44% (21). Furthermore, LN increases the standardized mortality rate six to 26 times, depending on the severity and phenotype of the disease, with infections being the leading cause of death in various cohorts (10, 25).Initiating appropriate immunosuppressive therapy is essential to improve the prognosis for these patients, as survival can improve from 46–95% if disease remission is achieved (2). This treatment is divided into an intensive induction phase lasting 3–6 months, followed by a less aggressive but longer maintenance phase. However, the arsenal of drugs used in these regimens significantly increases the risk of complications, and currently there are few clinical indicators to guide the ideal duration and dosage of therapy (4, 17). For this reason, it is necessary to develop methods to determine treatment response and identify early markers of response that facilitate medical decisions and offer prognostic advantages for these patients. Serpin A3 (also called alpha-1-antichymotrypsin) belongs to the family of serine protease inhibitors, which are attributed with various functions in different organs. The activity of this protein is associated with various cardiovascular, neurological, and oncological diseases (18). In particular, serpin A3 is expressed in response to various inflammatory cytokines and is capable of inhibiting the activity of several leukocyte-derived proteases, such as neutrophil cathepsin G (5, 9), which are known agents of inflammatory activity in LN (26). Recently, we identified urinary serpin A3 excretion as an early biomarker of renal fibrosis in rats undergoing a transition model from acute kidney injury (AKI) to chronic kidney disease (CKD). Serpin A3 is detectable in urine from the first month of follow-up, significantly preceding other typical parameters of renal dysfunction. Additionally, in the same study, we demonstrated that this biomarker is specifically elevated in urine samples from a cross-sectional cohort of patients with lupus nephritis (LN) and other etiologies of glomerulonephritis (19). Independently, Turnier et al. demonstrated that this protein is related to histological activity indices in a cross-sectional cohort of pediatric patients with LN (23).Based on the above, we believe that serpin A3 may participate in the course of renal inflammatory activity and therefore, its urinary levels may be associated with the degree of response to treatment and the remission of the disease. In the present study, we included a cohort of Mexican patients with an acute outbreak of proliferative NL with the aim of serially determining urinary levels of serpin-A3 (uSerpA3) and its relationship with inflammatory activity and the degree of response to immunosuppressive treatment during the first 12 months of follow-up. In humans, there are 36 genes that encode serpin proteins, which belong to a superfamily of serine protease inhibitors between 46 and 55 kDa with multiple biological functions, including the regulation of coagulation factors, extracellular matrix metalloproteinases, acute-phase reactants, inflammatory cytokines, and complement system proteins (18). Serpin-A3 is one of the 13 members of the serpin A group, which inhibits various proteases, including pancreatic chymotrypsin, cathepsin G and leukocyte elastase, mast cell chymase, and some kallikreins, among other enzymes (5). Serpin A3 expression has been reported in various mammalian organs, mainly found in the retina, liver, pancreas and kidneys (https: / / www.qenecards.org / cqibin / cardd¡sp.pl?qene=SERPINA3&keywords=serpinA3).Recently, a massive proteomic analysis identified serpin-A3 expression in the S2 segment and descending limb of the Loop of Henle in rat kidneys under basal conditions (11); however, its specific function in the renal parenchyma is unknown. It is worth mentioning that various mutations and polymorphisms in the serpin-A3 gene have been associated with chronic degenerative pulmonary and neurological disorders, among other conditions (8, 12, 20, 24). Currently, few cellular processes in which serpin-A3 may be involved have been explored; however, it has been reported to participate in the regulation of reactive oxygen species generation through the KEAP1 / Nrf2 signaling pathway, the reduction of oxidative stress associated with calcium overload necrosis, and an inhibitory role in the PI3K / mT0R pathway through PTEN activation (27, 30, 31). Furthermore, a regulatory function of VEGF and CTGF activity has also been attributed to serpin-A3 through Wnt / beta-catenin signaling in models of corneal neovascularization and diabetic retinopathy (28, 29). Several of these molecular pathways are known to be actively involved in the establishment and progression of CKD of various etiologies (6). Interestingly, Maicas et al.They demonstrated that intravenous administration of human serpin A1 significantly reduced tubular injury and leukocyte infiltration 24 h after inducing ischemia / reperfusion AKI in C57 / BI6 mice, suggesting a regulatory role in cell damage and the acute inflammatory process (13). This, combined with our previous findings in the AKI-CKD model (19) and considering the data obtained in this study, allows us to speculate that serpin A3 is a protein expressed by the tubular epithelium in response to a renal inflammatory microenvironment and is part of an intercellular communication network in various renal pathologies. Lupus nephritis (LN) is a condition with a complex pathophysiology, occurring primarily within the context of autoimmune activity, in which infiltrating leukocytes play a leading role (22). Recently, two independent studies involving adult patients with LN reported that urinary levels of the macrophage-specific protein CD163 function as a non-invasive biomarker reflecting glomerular inflammation and correlating significantly with proliferative classes, as well as with all indices of histological activity (3, 16). In fact, one of these studies longitudinally demonstrated that urinary CD163 levels are significantly associated with the degree of response to treatment, histological remission, and allow for early prediction of whether patients with persistent proteinuria will achieve remission (16).On the other hand, a similar study that included two independent cohorts of patients with lupus nephritis (LN) found that urinary levels of epidermal growth factor (EGF) are inversely associated with histological chronicity indices and with long-term loss of renal function (15). This protein is normally actively expressed in the glomerulus and distal segments of the nephron and can be found in the urine of healthy individuals; its decrease has been associated with the development of fibrosis in various kidney diseases (7). Taken together, these analyses, along with the present study, suggest that there are several biomarkers with potential utility in the course of LN, which possibly have different cellular origins, and that their study and integration could offer, in a non-invasive manner, information comparable to data obtained from biopsy, which is the current gold standard in the diagnosis and prognosis of LN. In conclusion, our study provides reasonable preliminary evidence that uSerpA3 is associated with renal inflammatory activity and follows a pattern consistent with the degree of response to immunosuppression. This, combined with the aforementioned previous studies, demonstrates that serpin-A3 is a useful biomarker in various renal disorders and that its study offers a valuable opportunity for developing appropriate diagnostic and therapeutic strategies for our patients. DETAILED DESCRIPTION OF THE INVENTION The present invention relates to a method for determining the response to a treatment for proliferative lupus nephritis by means of the Serpina A3 biomarker. The method involves determining the presence of Serpin A3 in urine samples from patients with proliferative lupus nephritis. These determinations are performed serially, beginning when the patient presents with an acute lupus flare requiring the initiation of aggressive immunosuppressive treatment, and subsequently at three, six, and twelve weeks. Comparing the biomarker levels allows for the identification of which patients will achieve disease remission and which will not. This information can then be used to adjust the dosage or type of medication used. For the present invention, serpin A3 can be determined by any qualitative and / or quantitative method. The treatment of lupus nephritis is with drugs that include high-dose corticosteroids, calcineurin inhibitors, and sometimes some biologics or any other drug useful for this pathology. In this study, we found a significant association between the degree of response to treatment and uSerpA3, demonstrated by a significant decrease in the biomarker from month six onward in the urine of patients who achieved a partial or complete response by month twelve, while non-responders maintained elevated uSerpA3 levels throughout the follow-up period. Additionally, we found that urinary excretion of the serpin A3 protein is associated with renal inflammatory activity, as it is found at higher levels in patients with diffuse proliferative nephritis (class IV) and correlates modestly with the histological activity index at the time of the acute lupus flare. This study provides reasonable evidence that uSerpA3 is associated with renal inflammatory activity and follows a pattern consistent with the degree of response to pharmacological treatment. This, combined with the aforementioned previous studies, demonstrates that serpin A3 is a useful biomarker in various renal disorders and that its study offers a valuable opportunity for the development of appropriate diagnostic and therapeutic strategies for patients with lupus nephritis. uSerpA3 is a potential non-invasive biomarker and is useful for developing devices that can identify renal inflammation and the degree of response to treatment in patients with lupus nephritis (LN). In conclusion, our study provides reasonable preliminary evidence that uSerpA3 is associated with renal inflammatory activity and follows a pattern consistent with the degree of response to immunosuppression. This, along with the aforementioned previous studies, demonstrates that uSerpA3 is a useful biomarker in various renal disorders and that its study offers a valuable opportunity for developing appropriate diagnostic and therapeutic strategies for our patients. EXAMPLES Example 1. Patients and biological samples This study included patients who presented to the National Institute of Medical Sciences and Nutrition “Salvador Zubirán” between March 2016 and April 2019 with an acute flare of proliferative class III or IV lupus nephritis (reference 2217). These patients were followed for the first year of immunosuppressive treatment, which included mycophenolate mofetil, azathioprine, intravenous cyclophosphamide, tacrolimus, and glucocorticoids. Patients were defined as having active lupus nephritis if they met at least four diagnostic criteria for systemic lupus erythematosus (SLE) as defined by the American College of Rheumatology (ACR), and presented with renal impairment and / or proteinuria >500 mg / mg with histopathological evidence of immune complex-mediated glomerulonephritis. Treatment response was evaluated at 12 months of follow-up.The degree of response was defined as follows: complete response, those patients who achieved stable renal function within 15% of baseline eGFR (stable renal function) and a protein-to-creatinine ratio <0.5 g / g; partial response, patients with stable renal function and a 50% reduction in baseline proteinuria levels to subnephrotic ranges; no response, patients who did not meet either of the aforementioned criteria. All clinical data were captured in the institutional Glomerulonephritis Clinic database. Urine samples were collected at the time of the acute NL flare and at three, six, and twelve months after the start of treatment. All follow-up was performed at the same center. Samples were stored in an ultra-low temperature freezer at -70°C immediately after collection. We performed a cross-sectional assessment to determine the association between uSerpA3 levels, histological class, and renal inflammatory activity. Subsequently, we performed a longitudinal assessment to determine the relationship between uSerpA3 excretion and treatment response during the first year of follow-up. A renal biopsy was performed at the time of the lupus flare, and the tissues were evaluated by a nephropathologist, who determined the histopathological class according to the criteria of the International Society of Nephrology and the Renal Pathology Society (ISN / RPS). In addition, each biopsy was assigned a semi-quantitative score according to the activity and chronicity indices of the US National Institutes of Health (NIH). Each of the components of these indices was evaluated as follows: 0 = present in <5% of glomeruli, 1+ = present in 6–25% of glomeruli, 2+ = present in 26–50% of glomeruli, or 3+ = present in >50% of glomeruli. Tubulointerstitial abnormalities were scored according to the Banff and Oxford classifications (scale 0-3) (1). Example 2. Determination of urinary levels of Serpin-A3 (uSerpA3) uSerpA3 levels, from samples taken from the patients in Example 1, were measured semi-quantitatively using Western blot. Briefly, 5 microliters of each sample were electrophoresis performed on 8.5% polyacrylamide gels under denaturing conditions. The proteins were transferred to polyvinyl difluoride (PVDF, Millipore) membranes with 1x transfer solution (190 mM glycine, 2 mM Tris base, 0.1% SDS, 20% methanol) using a semi-dry transfer system (BioRad) for 25 min at 25 volts. Next, the membranes were blocked for 90 min using blocking solution (TBS 1x, Biorad 5% blocking agent), then incubated with rabbit primed anti-serpinA3 / alpha1-antichemotrypsin antibody (1:1000, Proteintech, 55480-1AP) overnight at 4°C.Subsequently, the membranes were washed using TBS 1x and incubated with horseradish peroxidase (HRP)-coupled anti-rabbit secondary antibody for 90 min, followed by another wash with TBS 1x. Finally, the membranes were treated with a commercial chemiluminescence kit, and the presence of serpin-A3 was detected using photosensitive radiographic film (Juama, SA de CV) with 3-min exposures prior to development. The resulting films were digitized at 300 dpi, and densitometric analysis was performed using ImageJ software version 1.51. Arbitrary units were adjusted and normalized by urinary creatinine levels (Ucr, mg / dL). Statistical analysis Data distribution was assessed using the Kolmogorov-Smirnov test. Descriptive data are presented as frequencies, means ± SD, or medians with interquartile ranges according to their distribution. uSerpA3 levels were transformed by calculating log-10 to fit a Gaussian distribution and perform between-group comparisons and correlation tests. For the cross-sectional analysis of lupus flares, between-group comparisons were performed using the Kruskal-Wallis ANOVA or Fisher's exact test, as appropriate. Spearman correlations between uSerpA3 and histological activity / chronicity indices were calculated. For the longitudinal study, mixed-effects regression models were explored to assess the association between repeated uSerpA3 measurements and treatment response.The final model included the following: the logw value of uSerpA3 / Ucr as the dependent variable; the treatment response rate, follow-up in months, the interaction between the two, and histological class as fixed effects; and the intercept per subject as a random effect. Information criteria were considered for the selection of the final model. Subsequently, means and their 95% confidence intervals were estimated, and uSerpA3 values ​​were compared over time according to the treatment response rate using Bonferroni correction. The diagnostic capacity of uSerpA3 at the third and sixth months to identify patients without a response at the twelfth month was evaluated using the receiver operating characteristic (ROC) curve. For hypothesis testing, p-values ​​<0.05 were considered significant. Analyses were performed using SPSS version 24 software.0 (IBM, Armonk, NY) and GraphPad Prism 6 (GraphPad Software, San Diego, CA). Example 3. Results Cross-sectional study Urine samples were obtained from 60 patients at the time of their acute NL flare to examine the correlation between uSerpA3, pathological class, and renal histological activity. A summary of the descriptive clinical and histological data is presented in Table 1. bRQfrnn / zznz / B / YiAi Table 1. Clinical and histological characteristics of the patients included in the study (n=60) Characteristic Class III (n=21) Class IV (n=39) P-value Age, years 36 (30-43.5) 30 (24-39) 0.014 Women, n (%) 18 (85.7) 35 (89.7) 0.687 Serum creatinine, mg / dL 0.8 (0.59-1.23) 1.31 (0.99-2.81) 0.008 eGFR, mL / min / 1.73 m2 103 (55.8-124.3) 54.5 (21-5-84.2) 0.004 Proteinuria, 24 hg / g 3.6 (2.0-4.8) 4.0 (2.6-7.0) 0.163 Severe presentation (%) 4 (19.0) 16 (41.0) 0.158 Anti-dsDNA antibodies, 120.8 (26.2-515.3) 245.4 (27.0- 0.694 C3, mg / dL 77.0 (55.5-88.5) 54.0 (37.0-71.0) 0.009 C4, mg / dL 8 (8.0-12.5) 8 (8.0-9.0) 0.261 bRQfrnn / zznz / B / YiAi Histological index 1 (1.0-2.5) 7 (3-12) <0.001 Histological index 3 (3-4) 5 (3-7) 0.016 Treatment response at 12 months Complete (CR) 8 (38.1) 12 (30.8) 0.579 Partial (PR) 5 (23.8) 10 (25.6) 1.000 None (NR) 8 (38.1) 17 (43.6) 0.786 Baseline uSerpinA3 / Ucr (DPI / mg) 2.89 (0.5-4.9) 6.98 (2.9-12.7) 0.018 uSerpinA3 / Ucr at 3 months 1.62 (0.4-3.9) 5.17 (1.2-12.9) 0.013 uSerpA3 / Ucr at six months 1.89 (0.5-3.8) 2.40 (0.7-8.7) 0.300 uSerpA3 / Ucr at twelfth month 1.51 (0.5-4.1) 2.00 (0.3-6.2) 0.621 Median and Interquartile Range for quantitative variables. Number of patients and percentage for categorical variables. Of these patients, 35% presented with class III NL (n=21), and 65% with class IV (n=39). As expected, patients with class IV NL showed a more severe picture 5, indicated by a lower eGFR (54.5 vs. 103 mL / min / 1.73 m2SC, p=0.004), lower C3 complement levels (54 vs. 77 mg / dL, p=0.009), and greater histological abnormalities (activity and chronicity index 7 vs. 1 point, p<0.001, and 5 vs. 3 points, p=0.016, respectively). uSerpA3 was significantly higher in patients with class IV NL compared to patients with class III NL (6.98 vs 2.98 DPI / mg, p<0.05), which is consistent with the diffuse proliferative phenotype of the disease (Figure 1A). This difference persisted at the third month of follow-up; however, both groups of patients showed a significant decrease in biomarker levels from the sixth month onward (Table 1).Considering that serpin-A3 is associated with a tissue response to inflammatory processes, we decided to evaluate the relationship between uSerpA3 and histological activity indices. Interestingly, we found a modest but significant correlation between these two parameters (r = 0.29, p = 0.02, Figure 1B). Specifically, we found an association between this biomarker and three components of this index: the presence of cellular crescents, wire loops, and karyorrhexis. Additionally, we evaluated the correlation between uSerpA3 and chronicity indices; however, we found no significant association (Table 2). Figure 1C shows a representative photomicrograph of a renal biopsy from a patient with a low activity and chronicity index who consistently showed low uSerpA3, while Figure 1D shows the photomicrograph of a patient with high activity and chronicity and high uSerpA3. Surprisingly, uSerpA3 did not correlate with 24-hour proteinuria levels (r = 0.19, p = 0.07). Table 2. Correlation between uSerpA3 and histological parameters of activity and chronicity (see methodology in Example 1) bRQbnn / zznz / B / YiAi Histological Component Spearman r P value index Activity 0.29 0.023 Endocapillary hypercellularity 0.19 0.141 Leukostasis 0.10 0.438 Fibrinoid necrosis 0.20 0.119 Hyaline thrombi -0.08 0.515 Wire loops 0.27 0.036 Karyorrhexis 0.30 0.018 Cellular crescents 0.25 0.045 Interstitial inflammation 0.19 0.134 Chronicity index 0.13 0.298 Glomerular sclerosis 0.08 0.505 Fibrous crescents 0.01 0.903 Interstitial fibrosis 0.14 0.267 Tubular atrophy 0.20 0.111 bRQfrnn / zznz / Β / γΐΛ Longitudinal study We evaluated the time course of uSerpA3 levels during the first year of immunosuppressive therapy. Urine samples were obtained from 60 patients in our institutional cohort, each representing an independent acute flare of lupus nephritis (LN). Of these patients, 21 completed the collection of the four expected samples, while the remaining 39 completed at least three samples, for a total of 201 urine samples for subsequent analysis. After 12 months, 20 (33.3%) of the patients achieved a complete response to treatment, 15 (25%) a partial response, and 25 (41.7%) did not respond to treatment. To assess the association between uSerpA3 and treatment response, mixed-effects linear regression models were fitted (see Example 2). A significant relationship was found between logwuSerpA3 and the different treatment response groups (Tables 3 to 6).A significant association was found between the time course of the biomarker and the response group after adjusting for histopathological class and controlling for variance according to each subject (Figures 2 and 3). Table 3. Fixed Effects Estimates Estimator Coef. β 95% Confidence Intervals Sig. (p) Intercept 0.81 0.63 0.99 <0.001 Complete Response -0.88 -1.15 -0.61 0.002 Partial Response -0.46 -0.75 -0.17 <0.001 AIC=400; BIC=407; -2 Restricted Log _ikelihood=396; Marginal / Conditional R2=0.25 / 0.43 bRQfrnn / zznz / B / YiAi Table 4. Random Effects Estimates Parameter Coef. β Std Residual Error 0.33 0.03 Random Variance of Intercept 1 Patient 0.10 0.03 ICC=0.24 Dependent variable: LogiouSerpA3 201 observations Tables 3 and 4 present the details of the mixed-effects model for estimating 5 LogiouSerpA3 / Ucr. Description of the univariate model, showing a preliminary correlation between uSerpA3 and treatment response, considering non-responders as the reference (beta=zero). Table 5. Fixed Effects Estimates Estimator Coef. β 95% Confidence Intervals Sig. (p) Intercept -0.26 -1.17 0.64 0.567 Class 0.30 0.07 0.54 0.011 Month -0.02 -0.12 0.08 0.710 Full Response -0.16 -0.59 0.28 0.483 Partial Response -0.08 -0.56 0.39 0.729 Month * RC -0.29 -0.43 -0.14 <0.001 Month * PR -0.15 -0.31 0.01 0.059 AIC=400; BIC=379; -2 Restricted Log Likelihood=369; Marginal / Conditional R2=0.37 / 0.56 Table 6. Random Effects Estimates Parameter Coef. β Std Residual Error 0.26 0.03 Random Variance of Intercept I Patient 0.11 0.03 ICC=0.30 Tables 5 and 6 present the details of the mixed-effects model for estimating LogiouSerpA3 / Ucr. Description of the final model, adjusting for histological class and the interaction between Month and Response grade, the relationship between CR over time and uSerpA3 is maintained. Adjustments were explored based on sex, estimated glomerular filtration rate, proteinuria level, anti-dsDNA antibody levels, and C3 levels; however, these variables were excluded from the final model based on information criteria and model simplicity. Regardless of class, patients with a complete response showed lower baseline uSerpA3 at the acute flare compared to those who did not respond (2.41 vs. 6.67 dpi / mg). Furthermore, these patients had a significant decrease in the biomarker at six months after the start of treatment compared to baseline. In contrast, baseline uSerpA3 did not differ between patients who would achieve a partial response and those who did not respond. Interestingly, during follow-up, patients with a partial response reached a uSerpA3 concentration intermediate between complete responders and non-responders (Figure 2, Table 7).This analysis demonstrates that the time course of uSerpA3 during the first year of treatment is significantly related to the degree of response to treatment. Table 7. Temporal differences in the estimated means of uSerpA3 according to the degree of response to treatment. Month CR (95% CI) PR (95% CI) NR (95% CI) Outbreak (0) 2.41 (1.36-4.25)* 3.88 (2.01-7.50) 6.67 (4.01-11-07) 3 1.19 (0.75-1.89)** 2.62 (1.53-4.48)* 6.38 (4.23-9.64) 6 0.59 (0.37-0.94)*** + 1.78 (1.04-3.05)** $ 6.12 (3.99-9.35) 12 0.29 (0.16-0.52)*** +& 1.20 (0.62-2.35)** $ + 5.86 (3.42-10.05) Calculated means and 95% confidence interval of uSerpA3 DPI / mg based on the estimation of logw values ​​General Formula: Logw uSerpA3 / Ucra~ lb+ Class + Month + RtT + Month*RtT + (Sb| Patient) Differences between groups according to ANOVA and Bonferroni post hoc correction. *p<0.05 vs NR in the indicated month. $ p<0.01 vs CR in the indicated month.+p<0.001 vs uSerpA3 in the outbreak.&p<0.05 vs uSerpA3 at the third month. RC: Complete response, RP: Partial response, NR: No response. With these findings, we explored the ability of uSerpA3 in early follow-up stages to predict treatment response after 12 months using ROO curves. Interestingly, uSerpA3 showed an area under the curve of 0.76 (95% CI 0.62–0.91) at three months, and 0.86 (95% CI 0.76–0.96) at six months for identifying patients who did not respond to treatment (Figures 3 and 4). Finally, we evaluated the association between the temporal course of uSerpA3 and histological remission in 20 patients from the same cohort who underwent 10 subsequent biopsies for various clinical indications: suspected relapse (n=6), lack of response (n=8), or per protocol (n=6). Of these patients, only 30% (n=6) achieved histological remission (activity index <1). Interestingly, we found a significant association between the temporal course of uSerpA3 and histological remission (p=0.030 for the interaction of remission and time) (Tables 8 to 15). Table 8. Fixed Effects Estimates Estimator Coef. β 95% Confidence Intervals Sig. (p) Intercept 0.13 -0.28 0.53 0.538 Histological Remission=No* 0.49 0.00 0.98 0.048 AIC=128; BIC=133; -2 Restricted Log Likelihood=124; Marginal / Conditional R2=0.115 / 0.503 Table 9. Random Effects Estimates Parameter Coef. β Std Residual Error 0.24 0.05 Random Variance of Intercept 1 Patient 0.19 0.09 bRQfrnn / zznz / B / YiAi 100=0.44 Dependent variable: LogwuSerpAS 69 observations Table 10. Fixed Effects Estimates Estimator Coef. β 95% Confidence Intervals Sig. (p) Intercept 0.77 0.19 1.36 0.011 Histological Remission=No* -0.08 -0.78 0.63 0.828 Month -0.26 -0.42 -0.09 0.003 Month *No Remission 0.23 0.02 0.43 0.030 AIC=1 26; BIC=130; -2 Restricted Log Marginal / Conditional Likelihood=122; R2=0.174 / 0.572 Table 11. Random Effects Estimates Parameter Coef. β Std Residual Error 0.21 0.04 Random Variance of Intercept I Patient 0.19 0.09 100=0.48 Tables 8 to 11 present the association between the time course of uSerpA3 and histological remission, along with details of the models studied, which suggest a relationship between uSerpA3 and histological remission in 20 patients with repeat biopsies during follow-up. The presence of remission is considered the reference value (beta=zero). Tables 8 and 9 present the univariate model. 10 and 11 Model considering the effect of time. BRIEF DESCRIPTION OF THE FIGURES Figure 1 presents the evaluation of uSerpA3 and histological characteristics in 15 patients with acute lupus nephritis. A) Comparison of uSerpA3 between patients with class III and class IV lupus nephritis. B) Correlation between uSerpA3 and the activity index with the classification proposed by the US National Institutes of Health (NIH). C) Representative micrographs of patients with concordance in renal inflammatory activity and uSerpA3 (hematoxylin and eosin, scale bar = 100 micrometers). uSerpA3 values ​​are logarithmically transformed. Figure 2 presents the time course of uSerpA3 during the first year of treatment. A) Representative Western blot images showing different levels of uSerpA3 during follow-up according to the degree of treatment response (RespT) in nine patients. The bar indicates a molecular weight of 55 kDa. B) Estimation of uSerpA3 throughout the first year of follow-up according to the degree of RespT based on mixed-effects linear regression models. *p<0.05 Difference vs. responders. +p<0.001 Difference between baseline and follow-up levels in the corresponding group. Simplified regression formula: logw uSerpA3 / uCr ~ βο + Class + Month + RespT + Month*RespT + (1 | Patient). Figure 3 presents the graph of the diagnostic capacity of uSerpA3 at the third month for identifying patients who did not respond to treatment at month 12. uSerpA3 levels at the third month have significant diagnostic capacity for identifying patients who did not respond to treatment at the twelfth month. A cutoff point of 3.45 DPI / mg corresponds to a sensitivity of 68% and a specificity of 69%. Figure 4 presents the graph of the diagnostic capacity of uSerpA3 at six months for identifying patients who did not respond to treatment at month 12. uSerpA3 levels at six months have significant diagnostic capacity for identifying patients who did not respond to treatment at twelve months. A cutoff point of 2.17 DPI / mg corresponds to a sensitivity of 85% and a specificity of 70%. Finally, according to the initial biopsy, 35% presented with class III lupus nephritis (LN) and 65% with class IV LN. Higher levels of uSerpA3 were found in patients with class IV LN compared to class III (6.98 vs. 2.89 DPI / mg Cr, p<0.05). There was a positive correlation between baseline uSerpA3 and the histological activity index (r=0.29, p=0.02). A significant association exists between uSerpA3 and the degree of response to treatment; specifically, responding patients showed a significant decrease from the sixth month onward compared to baseline values ​​(p<0.001), while in non-responding patients, uSerpA3 remained elevated throughout the follow-up period. Additionally, uSerpA3 has great potential for identifying non-responders from the third month of follow-up (AUC=0.77). References [ PubMed ] Bajema IM, Wilhelmus S, Alpers CE, Bruijn JA, Colvin RB, Cook HT, D'Agati VD, Ferrado F, Haas M, Jennette JC, Joh K, Nast CC, Noel LH, Rijnink EC, Roberts ISD, Seshan SV, Sethi S, Fogo AB. 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Claims

1. A method for determining the response to treatment for proliferative lupus nephritis, characterized in that it comprises: a) Determining serpin A3 in a urine sample obtained from a subject diagnosed with said pathology and receiving drug treatment for the same; b) Determining the amount of urinary serpin A3 after a period of time from the same subject in step a); c) Comparing the amount of serpin A3 in the sample from step a) with that from step b) to determine whether the treatment has had an effect on altering the level of the biomarker, where a persistent amount of the biomarker indicates that there was no response to the drug treatment, nor remission, and it would be reasonable to modify the drug doses or change the type of drug used.

2. A method according to claim 1 wherein the drugs for the treatment of lupus nephritis are selected from immunosuppressants, corticosteroids, calcineurin inhibitors or a combination thereof or any other drug useful for the treatment of lupus nephritis.

3. A method according to claim 1 wherein the period in subparagraph b) is equal to or greater than 6 months.

4. A method according to the preceding claims wherein serpine A3 can be determined by any quantitative or qualitative method.

5. Use of serpin A3 as a biomarker to identify renal inflammation and the degree of response to treatment in patients with proliferative lupus nephritis.