Clinically defined patient samples (medical records/clinical diagnosis); Healthy subject samples
Retrospective clinical samples were used to establish clinical sensitivity, clinical specificity, and to perform a method comparison against the predicate device.
80 healthy subjects; Sample Size: 80; Number of Sites: 2
Not applicable for this study
Clinical specificity
Indications for Use
AESKULISA® ENA 6S is a solid phase enzyme immunoassay for the combined qualitative detection of IgG antibodies against six cellular and nuclear antigens in human serum. Each well is coated with recombinant SS-B, SS-A 52 kDa, Scl-70, Jo-1 and purified native human snRNP/Sm, Sm and SS-A 60 kDa. The assay is a tool in the diagnosis of certain systemic rheumatic diseases and should be used in conjunction with other serological tests and clinical findings.
Device Story
AESKULISA® ENA 6S is a solid-phase enzyme-linked immunosorbent assay (ELISA) for qualitative detection of IgG autoantibodies in human serum. Device utilizes microplate wells coated with a pool of recombinant (SS-B, SS-A 52 kDa, Scl-70, Jo-1) and native (snRNP/Sm, Sm, SS-A 60 kDa) antigens. Patient serum is added; specific antibodies bind to immobilized antigens. After washing, horseradish peroxidase-conjugated anti-human IgG is added, followed by TMB substrate. Color intensity, measured via microplate reader at 450 nm, is proportional to antibody concentration. Used in clinical laboratories by trained personnel. Results assist clinicians in diagnosing systemic rheumatic diseases when combined with other serological and clinical data.
Clinical Evidence
No clinical data provided in the document; substantial equivalence is based on bench-level performance characteristics of the immunoassay.
Technological Characteristics
Solid-phase enzyme immunoassay (ELISA). Microtiter wells coated with recombinant and purified native human antigens (SS-B, SS-A 52 kDa, Scl 70, Jo-1, snRNP/Sm, Sm, SS-A 60 kDa). Detects IgG antibodies in human serum.
Indications for Use
Indicated for the qualitative detection of IgG antibodies against six cellular and nuclear antigens (SS-A, SS-B, Sm, RNP/Sm, Scl-70, Jo-1) in human serum to aid in the diagnosis of systemic rheumatic diseases (e.g., SLE, Sjögren’s syndrome, scleroderma, polymyositis). For prescription use only.
Regulatory Classification
Identification
An antinuclear antibody immunological test system is a device that consists of the reagents used to measure by immunochemical techniques the autoimmune antibodies in serum, other body fluids, and tissues that react with cellular nuclear constituents (molecules present in the nucleus of a cell, such as ribonucleic acid, deoxyribonucleic acid, or nuclear proteins). The measurements aid in the diagnosis of systemic lupus erythematosus (a multisystem autoimmune disease in which antibodies attack the victim's own tissues), hepatitis (a liver disease), rheumatoid arthritis, Sjögren's syndrome (arthritis with inflammation of the eye, eyelid, and salivary glands), and systemic sclerosis (chronic hardening and shrinking of many body tissues).
Submission Summary (Full Text)
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# 510(k) SUBSTANTIAL EQUIVALENCE DETERMINATION DECISION SUMMARY
A. 510(k) Number:
k041753
B. Purpose for Submission:
New device
C. Analyte:
Anti-ENA antibodies (SS-B, SS-A (52 and 60 kDa), Scl-70, Jo-1, snRNP/Sm, Sm)
D. Type of Test:
Qualitative, EIA
E. Applicant:
AESKU, Inc.
F. Proprietary and Established Names:
AESKULISA® ENA 6S
G. Regulatory Information:
1. Regulation section:
21 CFR §866.5100, Antinuclear Antibody Immunological Test System
2. Classification:
Class II
3. Product Code:
LLL, Extractable antinuclear antibody, antigen and control
4. Panel:
Immunology (82)
H. Intended Use:
AESKULISA® ENA 6S is a solid phase enzyme immunoassay for the combined qualitative detection of IgG antibodies against six cellular and nuclear antigens in human serum. Each well is coated with recombinant SS-B, SS-A 52 kDa, Scl-70, Jo-1 and purified native human snRNP/Sm, Sm and SS-A 60 kDa. The assay is a tool in the diagnosis of certain systemic rheumatic diseases and should be used in conjunction with other serological tests and clinical findings.
1. Indication(s) for use:
Same as Intended Use.
2. Special condition for use statement(s):
The devices are for prescription use only.
3. Special instrument Requirements:
Microplate plate reader 450 nm reading filter and optional 620 nm reference filter.
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I. Device Description:
The device consists of 1) 12x8 antigen-coated well strips, 2) horseradish peroxidase conjugated anti-human IgG, 3) TMB substrate, 4) cut-off control, 5) positive control 6) negative control, 7) washing buffer concentrate (50x), 8) sample buffer concentrate (5x) and 9) stop solution. The positive, negative and cut-off controls are diluted human sera.
J. Substantial Equivalence Information:
1. Predicate device name(s):
ORGENTEC ENAScreen ELISA Assay
2. Predicate K number(s):
k955134
3. Comparison with predicate:
| DEVICE | PREDICATE |
| --- | --- |
| A. Similarities | |
| Intended Use. For the combined qualitative detection of IgG antibodies against six cellular and nuclear antigens in human serum. Each well is coated with recombinant SS-B, SS-A 52 kDa, Scl-70, Jo-1 and highly purified native human snRNP/Sm, Sm and SS-A 60 kDa. The assay is a tool in the diagnosis of certain systemic rheumatic diseases and should be used in conjunction with other serological tests and clinical findings.
Assay type – ELISA
Analytes – anti- SS-A, SS-B, Sm, RNP/Sm, Scl-70 and Jo-1 antibodies
Assay Format – Qualitative
Reporter conjugate - Horseradish peroxidase
Substrate – TMB | For the in vitro qualitative screening of IgG class autoantibodies against the extractable nuclear antigens, SS-A(Ro), SS-B(La), Sm, RNP/Sm, Scl-70 and Jo-1 in human serum or plasma. The assay is intended for the in vitro diagnostic use as an aid in the diagnosis of rheumatic diseases such as systemic lupus erythematosus (SLE), Sjögren’s syndrome, scleroderma and mixed connective tissue disease.
Same
Same
Same
Same |
| B. Differences | |
| Source of Antigens
Recombinant - SS-B, SS-A 52, Scl-70 and Jo-1
Human cell line - snRNP/Sm, Sm and SS-A 60
Sample Type – Serum
Cut-off Values – Negative = Index Ratio <1.0
Positive = Index Ratio >1.0 | Unknown
Serum and plasma
Negative = <OD of Cut-off Control
Positive = >OD of Cut-off Control |
K. Standard/Guidance Document Referenced (if applicable):
None referenced.
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Page 3 of 5
L. Test Principle:
The AESKULISA® ENA 6S Assay is an enzyme-linked immunoassay. The antigens are pooled prior to coating the microplate wells. Diluted patient serum is added to the microtiter well and if specific antibodies are present, they will bind to the immobilized antigens to form antigen/antibody complexes. Unbound material is washed away and an enzyme labeled anti-human IgG antibody (conjugate) is added to each well. The enzyme conjugate binds to the antigen/antibody complex. After washing away any unbound enzyme conjugates, the chromogenic substrate is added. The color intensity in the wells is proportional to the amount of autoantibodies in the sample.
M. Performance Characteristics (if/when applicable):
1. Analytical performance:
a. Precision/Reproducibility:
To determine intra-assay reproducibility 3 serum samples with high, medium and low antibody reactivity were assayed 24 times on one plate. For inter-assay reproducibility, three different sera were tested for 18 times on three plates on different days. The mean concentrations and %CV of the intra-assay and inter-assay results are summarized.
| Intra-Assay | | | Inter-Assay | | |
| --- | --- | --- | --- | --- | --- |
| Sample | Mean OD Ratio | CV (%) | Sample | Mean OD Ratio | CV (%) |
| 1 | 3.1 | 0.8 | 1 | 3.1 | 0.7 |
| 2 | 2.3 | 1.0 | 2 | 2.2 | 0.5 |
| 3 | 1.5 | 1.1 | 3 | 1.4 | 1.4 |
b. Linearity/assay reportable range:
A high and a low positive serum were serially diluted to 1:100, 1:200, 1:400 and 1:800. Percent recovery for the high concentration sample ranged from 91.4% to 107.9% and for the low concentration sample, 93.8% to 100%.
c. Traceability (controls, calibrators, or method):
Controls are traceable to CDC reference ANA sera. A negative, a positive and a cut-off control are included in each device.
d. Detection limit (functional sensitivity):
No applicable
e. Analytical specificity:
To test for cross-reactivity, 11 sera positive for other autoantibodies (tissue transglutaminase, thyroglobulin, thyroid peroxidase, gliadin, proteinase 3 and RF) were assayed. All results were negative.
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Page 4 of 5
Interference testing with hemolyzed, lipemic or icteric samples were not performed but the package insert specified that these types of samples should not be used.
f. Assay cut-off:
To determine the cut-off value, three fold serial dilutions of an ENA positive patient serum are tested in triplicates. The OD₄₅₀ value for each dilution is plotted (linear-log with a 4 parameter fitting) against the dilution factor to determine the linear range. The dilution in the linear range with an OD value of 2.0 is defined as Calibrator F and assigned an arbitrary unit of 300 U/mL. Calibrator F is diluted and calibrated to the respective CDC reference sera. The selected cut-off is equivalent to an OD of 0.5 to 0.6 of that of the reference sera. The cut-off values were validated by testing 80 healthy subjects from two hospitals. All subjects had OD values < 0.5.
Result interpretation:
OD Patient < OD cut-off Negative
OD Patient > OD cut-off Positive
Or
Index Value (OD Patient/OD cut-off)
Index Value < 1.0 Negative
Index Value > 1.0 Positive
2. Comparison studies:
a. Method comparison with predicate device:
Sixty-five clinically defined patient samples were analyzed on the new device and the predicate device. These samples consisted of 48 SLE, 7 Sjöegren’s syndrome, 7 polymyositis and 3 scleroderma.
Fifty-six of the 65 sera were from female patients and 9 from male patients. Twenty-eight percent of the patients were < 26y, 32% were 26y to 45y and 35% were ≥46y. Results are summarized in the following tables.
| | ORGENTEC ENAScreen | | | |
| --- | --- | --- | --- | --- |
| | | + | - | Total |
| AESKULISA ENA 6S | + | 28 | 12* | 40 |
| | - | 0 | 25 | 25 |
| | Total | 28 | 37 | 65 |
*10 SLE and 2 polymyositis
% positive agreement = 100%
% negative agreement = 67.6% (95% CI 52.5% to 82.7%)
% total agreement = 81.5% (95% CI 76.7% to 86.3%)
Of the 10 SLE discrepant samples, 3 samples had a homogeneous pattern and 7 had a mixed homogeneous/speckle pattern by IFA.
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The two polymyositis samples were anti-Jo-1 antibody positive by IFA.
# b. Matrix comparison:
Not applicable since both the predicate and the new device use serum samples.
# 3. Clinical studies:
# a. Clinical sensitivity:
Patient samples used in the method comparison study were used for determination of the clinical sensitivity of the AESKULISA ENA 6S assay. The clinical sensitivity was $61.5\%$ (40/65). The following table shows distribution in the 4 disease groups tested.
| Disease Group | N | AESKULISA ENA 6S | |
| --- | --- | --- | --- |
| | | Positive | % Total |
| SLE | 48 | 29 | 60.4 |
| Sjögrens Syndrome | 7 | 4 | 57 |
| Polymyositis | 7 | 5 | 71.4 |
| Scleroderma | 3 | 2 | 33 |
# b. Clinical specificity:
The clinical specificity of the AESKULISA ENA 6S assay is $100\%$ based on results of the 80 normal subjects.
# 4. Clinical cut-off:
Same as assay cut-off.
# 5. Expected values/Reference range:
The expected value for the normal population is negative. The frequency distribution of ENA autoantibodies in the various autoimmune disease cohorts according to published literature is depicted below:
| Disease | % Positive | | | | | |
| --- | --- | --- | --- | --- | --- | --- |
| | SS-A | SS-B | Sm | RNP/Sm | Scl-70 | Jo-1 |
| SLE | 10-30 | 30-50 | 10-30 | 10-30 | | |
| Connective Tissue Disease | | | | >90 | | |
| Sjogren syndrome | >90 | >90 | | | | |
| Scleroderma | 10-30 | | | | >90 | |
| Polymyositis/Dermatomyositis | | | | | | 50-90 |
# N. Conclusion:
The submitted information in this premarket notification is complete and supports a substantial equivalence decision
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1. Search: exact and fuzzy
Type a phrase like "coronary artery calcification" into the search box. You get two kinds of results. Exact results match the literal phrase — prefix searches work ("coronary artery calcificati") but suffix searches do not. Fuzzy results match on the meaning and intent of your phrase rather than the exact words, and are sorted by relevance score. Hover over the Exact or Fuzzy badge on any row to see exactly why it matched.
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Exact vs. fuzzy search: what's the difference?
Exact matches on the literal phrase (prefix search works, suffix does not). Fuzzy matches on the meaning and intent of the phrase rather than the exact words. Hover over the badge on any row to see why it matched.
You search "coronary artery calcification" and want only software devices with AI. What two filters do you apply?
Narrow by SaMD (software-only devices), then narrow by AI/ML (devices with AI).
2. The results table
Scroll right in the results table. The intended use is extracted for you — no need to open the PDF. The device story gives a high-level snapshot of what the device does and how it's used. The AI Performance sub-table shows each output name, acceptance criteria, observed values, and development/test dataset descriptions — the same format Innolitics uses for regulatory strategy outputs, and the fastest high-level fingerprint of an AI device. It is AI-generated but has been very reliable in practice.
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Scroll right in the search results table. The intended use column is extracted for you; no need to dig into the 510(k) summary PDF.
What does the AI Performance sub-table show, and why is it useful?
Output name, acceptance criteria, observed values, development dataset description, and test dataset description. It's the same format we use for regulatory strategy output and Fast 510(k) input, and the fastest high-level fingerprint of an AI device. AI-generated but reliable in practice.
3. Judging fuzzy relevance
Fuzzy results trail off in relevance as you scroll. Use three signals to decide how far down to go: the fuzzy badge explanations, the intended use column, and whether your target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, you're past the relevant zone. A top hit with a low score (~0.4) and a stretched explanation is a hint the closest predicates are far away — the project may be headed for De Novo. Note the fuzzy search is a pattern match: it doesn't handle negation ("not") well, and hardware devices can appear — filter by SaMD/AI ML to cut them.
How do you judge how far down fuzzy search results to go?
Use the relevancy signals: the fuzzy badge explanations, the intended use column, and whether the target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, results are trailing off in relevancy.
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Reading rule for every project: how many summaries do you read in full?
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5. Side-by-side comparison
Select multiple rows in the results table (aim for under ~10), then open the PDF Viewer tab. Ask one question — it goes to all selected devices in parallel, each with citations. This is the fastest way to compare and contrast devices: training data, PCCP scope, how they handled adding new scanners, and so on.
What does the side-by-side PDF viewer mode do?
Select multiple devices, open the PDF viewer tab, and ask one question (e.g., "Describe the training data"). It queries all selected devices simultaneously with citations, so you can compare and contrast quickly.
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Browse product codes and regulation numbers, read the identification text and special controls, browse sibling product codes, open device PDFs on the right, and search by product code or regulation number at the top of the tree.
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Click Show Chart and segment by regulation number (or product code) to see which regulations dominate your result set. Clicking a regulation takes you into the regulations tree. Great for spotting that most matches are, say, hardware laparoscopic devices — a cue to go back and filter.
How do you see which regulations dominate a search result set?
Click "Show Chart" and segment by Regulation Number. Clicking a regulation takes you to the regulations tree.
9. The predicate graph
Open the Predicates tab for a family-tree view of predicate relationships. Click a node to trace its parents and children; selections from search carry over pre-selected. Commonly predicated devices are worth reading — a lot of people predicated them for a reason. The visual lineage is also handy on client calls, e.g. to show how a predicate family evolved and justify why your predicate still holds.
In the predicate graph, why are commonly predicated devices worth reading?
A lot of people predicated them for a reason. Clicking a node traces parents and children, and selections from search carry over pre-selected.
10. Embeddings: the galaxy map
The Embeddings tab plots every matching document in a 2-D "galaxy map" where semantically similar devices cluster together. Hover or click clusters to explore, and let AI label the clusters for you. Embeddings beat product codes for grouping: two devices can carry different product codes (LLZ vs. QIH) yet do the same thing — the embedding captures the meaning of the intended use and device story. This is also exactly how retrieval-augmented generation (RAG) works under the hood, and it makes a great visual on client calls.
Try it yourself
Head to the search page and work through a few of these AI/ML fuzzy searches to build intuition: perivascular fat on CT · aortic valve calcification opportunistic screening on noncontrast CT · breast cancer prediction on digital pathology slides · autism detection · gestational age prediction · a hearing aid that can also detect a pulse · foundation model based analysis of ECG · large language models · penetration test. Watch how the relevance scores, intended use, and AI Performance tables tell you when results stop being meaningful.