K040811 · Pharmacia Deutschland GmbH · LRM · May 13, 2004 · Immunology
Device Facts
Record ID
K040811
Device Name
VARELISA SSDNA ANTIBODIES, MODEL 14896
Applicant
Pharmacia Deutschland GmbH
Product Code
LRM · Immunology
Decision Date
May 13, 2004
Decision
SESE
Submission Type
Traditional
Regulation
21 CFR 866.5100
Device Class
Class 2
Indications for Use
The Varelisa ssDNA Antibodies EIA kit is designed for the semiquantitative and qualitative determination of anti-single stranded DNA (ssDNA) autoantibodies in serum or plasma. In conjunction with the Varelisa dsDNA Antibodies kit, it assists in the diagnosis of systemic lupus erythematosus (SLE) and certain other rheumatic diseases. This test is not definitive in isolation but has to be seen as one parameter in a multi-criterion process.
Device Story
Varelisa ssDNA Antibodies is an indirect noncompetitive enzyme immunoassay (EIA) for detecting anti-ssDNA autoantibodies in human serum or plasma. The device utilizes microtiter plate wells coated with synthetic ssDNA. Patient samples are added; specific antibodies bind to the immobilized antigen. An enzyme-labeled conjugate is added, binding to the antigen-antibody complex. A substrate is introduced, which the enzyme converts into a colored solution. The rate of color formation is proportional to the concentration of anti-ssDNA antibodies in the sample. The assay is intended for use in clinical laboratories by trained personnel. Results are interpreted by healthcare providers alongside clinical findings and Varelisa dsDNA Antibodies test results to aid in the diagnosis of systemic lupus erythematosus (SLE) and other rheumatic diseases.
Clinical Evidence
No clinical diagnostic sensitivity/specificity studies provided. Analytical performance established via bench testing: precision (within-run CV 5.8-8.1%, between-run 5.0-6.1%), linearity (1-200 U/mL), and recovery (89.6-104.4%). Method comparison with predicate (n=120) showed 99.2% overall agreement (95% CI 97.6-100.8%). Interference testing confirmed no significant impact from bilirubin, hemoglobin, chyle, or rheumatoid factor.
Indicated for the semiquantitative and qualitative determination of anti-ssDNA autoantibodies in human serum or plasma to assist in the diagnosis of systemic lupus erythematosus (SLE) and other rheumatic diseases. Not for use as a standalone diagnostic.
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).
Predicate Devices
INOVA Quanta Lite ssDNA (k954380)
Submission Summary (Full Text)
{0}
510(k) SUBSTANTIAL EQUIVALENCE DETERMINATION
DECISION SUMMARY
DEVICE ONLY TEMPLATE
A. 510(k) Number:
k040811
B. Purpose for Submission:
New device
C. Analyte:
Single stranded DNA (ssDNA) antibodies
D. Type of Test:
Qualitative and semi-quantitative enzyme immunoassay
E. Applicant:
Sweden Diagnostics (Germany) GMBH (Pharmacia Diagnostics)
F. Proprietary and Established Names:
Varelisa® ssDNA Antibodies
G. Regulatory Information:
1. Regulation section:
21 CFR 866.5100, Antinuclear Antibody Immunological Test System
2. Classification:
Class II
3. Product Code:
LRM, Anti-DNA Antibody (Enzyme-Labeled), Antigen, Control
4. Panel:
Immunology 82
H. Intended Use:
1. Intended use(s):
The Varelisa ssDNA Antibodies EIA kit is designed for the semiquantitative and qualitative determination of anti-single stranded DNA (ssDNA) autoantibodies in serum or plasma. In conjunction with the Varelisa dsDNA Antibodies kit, it assists in the diagnosis of systemic lupus erythematosus (SLE) and certain other rheumatic diseases. This test is not definitive in isolation but has to be seen as one parameter in a multi-criterion process.
2. Indication(s) for use:
The Varelisa ssDNA Antibodies EIA kit is designed for the semiquantitative and qualitative determination of anti-single stranded DNA (ssDNA) autoantibodies in serum or plasma. In conjunction with the Varelisa dsDNA Antibodies kit, it assists in the diagnosis of systemic lupus erythematosus (SLE) and certain other rheumatic diseases. This test is not definitive in isolation but has to be seen as one parameter in a multi-criterion process.
3. Special condition for use statement(s):
For prescription use only
4. Special instrument Requirements:
Microplate reader capable of measuring OD at 450 nm
I. Device Description:
{1}
The assay components include microplate strips coated with synthetic ssDNA, 6 levels of calibrator: 0, 12.5, 25, 50, 100 and $200\mathrm{U / mL}$ , positive and negative control materials, wash buffer concentrate, sample diluent concentrate, anti-human IgG horseradish peroxidase conjugate, $3,3^{\prime},5,5^{\prime}$ tetramethylbenzidine (TMB) substrate, and $0.5\mathrm{M}$ $\mathrm{H}_2\mathrm{S}0_4$ stop solution.
# J. Substantial Equivalence Information:
1. Predicate device name(s): INOVA Quanta Lite ssDNA
2. Predicate K number(s): k954380
3. Comparison with predicate:
| Similarities | | |
| --- | --- | --- |
| Item | Device | Predicate |
| Indications for Use | In conjunction with dsDNA antibodies kit it aids in the diagnosis of systemic lupus erythematosus (SLE) and certain other rheumatic diseases. The test is not definitive in isolation but has to be seen as one parameter in a multi-criterion diagnostic process. | Same |
| Result interpretation | In conjunction with anti-dsDNA assay result | Same |
| Conjugate | Anti-human IgG, HRP | Same |
| Substrate | TMB | Same |
| Assay principle | Indirect noncompetitive enzyme immunoassay | Same |
| Sample dilution | 1:101 | Same |
| Differences | | |
| Item | Device | Predicate |
| Antigen | Synthetic ssDNA | Purified heat denatured calf thymus ssDNA |
| Specimen matrix | Serum and plasma | Serum |
| Calibrators | Set of 6 prediluted calibrators: 0, 12.5, 25, 50, 100, and 200 U/mL | None provided |
| Controls | Positive and negative controls | High positive, low positive and negative controls |
| Result interpretation | ss/ds ≤ 1 = negative ss/ds > 1 positive AND ssDNA < 35 = negative ssDNA 35-55 = equivocal ssDNA >55 = positive | ss/ds ≤ 0.5 = negative ss/ds > 0.5 positive AND ssDNA < 68.6 = negative ssDNA 68.6-229 = moderate positive ssDNA >229 = positive |
{2}
Page 3 of 6
## K. Standard/Guidance Document Referenced (if applicable):
None
## L. Test Principle:
The wells of a microtiter plate are coated with synthetic ssDNA. Antibodies specific for ssDNA present in the patient samples bind to the antigen. In a second step, the enzyme labeled second antibody (conjugate) binds to the antigen-antibody complex which leads to the formation of an enzyme labeled conjugate-antibody-antigen complex. The enzyme labeled antigen-antibody complex converts the added substrate to form a colored solution. The rate of color formation from the chromogen is a function of the amount of conjugate complexed with the bound antibody and thus is proportional to the initial concentration of antibodies in the patient sample.
## M. Performance Characteristics (if/when applicable):
### 1. Analytical performance:
#### a. Precision/Reproducibility:
The purpose of the precision study was to investigate variation within and between runs. The samples (low, medium, and high) were used in a standard 1:101 dilution and were analyzed in 5 runs, with 4 replicates per run. Calibrator and controls were analyzed in triplicates. One operator carried out the analyses within one day. The target values set for the studies were: within and between run variance should be <10%. The within run % CV ranged from 5.8 to 8.1% and between run ranged from 5.0 to 6.1% thus meeting the target values.
#### b. Linearity/assay reportable range:
##### Dilution linearity:
The reportable range for the assay is 1 – 200 U/mL. The purpose of the dilution linearity study was to demonstrate linearity of the assay over the measuring range. Beginning with a standard dilution of 1:101, 4 samples were further diluted 1:1, 2:3, 1:2, 1:4 then doubling dilutions through 1:64. Calibrators, controls and each dilution step were analyzed in duplicate. Specifications set for the study were: the observed/expected values should be within ± 20% for at least 3 successive dilution steps of each tested sample. Measured values ranged from 18.4 to >200 U/mL. Percent recovery for all four samples through the 1:8 dilution, ranged from 97 to 102%, thus meeting the specifications. For dilutions up to 1:32 recovery ranged from 64 to 85%.
##### Recovery:
The purpose of this study was to demonstrate that the assay detects added amounts of ssDNA specific IgG antibodies. Two positive samples were used in a 1:101 dilution. The sample dilutions were spiked with 1:10 volume of calibrators S1 – S6. The spiked samples, calibrators and controls were analyzed in duplicate. The specifications for this study were: the percent recovery (observed/expected x 100) should be within ± 20% of the expected
{3}
Page 4 of 6
values. Recoveries ranged from 89.6 to 104.4% and met the study specifications.
c. Traceability (controls, calibrators, or method):
There is no recognized reference material for ssDNA antibodies. Results are reported in arbitrary units.
d. Detection limit:
The analytical sensitivity study was performed to verify the detection limit and to prove the ability of the assay to differentiate between the background and the first calibrator point. The sample diluent was diluted according to the directions for use and measured 56 times on one plate. Calibrators and controls were analyzed in 4 replicates. For curve control below calibrator point S2, 2 dilutions of Calibrator S2 were performed (1:2, 1:4) and also run in 4 replicates. The value for the analytical sensitivity (detection limit) was calculated as the mean of the optical densities (OD) of the Sample Diluent plus 3 times the standard deviation (SD), expressed in U/mL. The specifications for the study were: the mean plus 3 SD of the OD of the sample diluent should be lower than the Calibrator S2; the detection limit should be equal or below 1 U/mL; and the discrimination value should be >2.0. The mean plus 3SD of the sample diluent was 0.013 U/mL (lower than Calibrator S2 and lower than 1.0 U/mL). The discrimination value was above 2 U/mL and diluted Calibrator S2 could be discriminated from the background. Thus specifications for the study were met.
e. Analytical specificity:
The purpose of the study was to investigate whether high concentrations of potentially interfering substances in serum including bilirubin (F and C), hemoglobin, chyle, and rheumatoid factor (RF) adversely affect results of the assay. Three serum samples were diluted 1:101 in sample diluent and spiked with different amounts of interfering substances or their respective blank solutions and analyzed in triplicate. The calibrators and controls were run in duplicate. The specification to be met for this study was: the deviation of the values of the sample spiked with the interfering substance should be less than ± 20% of the value of the sample spiked with a buffer blank. The spiking of high concentrations of the potentially interfering substances showed no significant influence on the test results (deviations ranged from -15.8 to 16.2%). The specifications for this study were met.
f. Assay cut-off:
A study was performed to establish and confirm the defined cut-off by measuring 432 apparently healthy blood donor samples, equally distributed by gender and age. Specifications for the study were: 95% of the normal population should be negative. Therefore the 95th percentile should lie below the lower limit of the equivocal range. The cut-offs were set as <35 U/mL is negative, 35-55 U/mL is
{4}
Page 5 of 6
equivocal and >55 is considered positive. The 95 percentile was 36.7 U/mL, at the lower end of the equivocal range. The results were independent of gender and age.
2. Comparison studies:
a. Method comparison with predicate device:
The comparison was made by testing 120 sera (ssDNA antibody positive and negative). Analyses and calculations were performed according to assay procedures. Correlations and Four Field Analysis were performed. The comparison showed an overall agreement of 99.2% (119/120) (95% CI 97.6% to 100.8%). Positive and negative agreements were 100% (39/39) and 98.8% (80/81) (95% CI 96.4% to 101.2%) respectively. Comparison for the 40 positive sera was done by interpreting the ssDNA/dsDNA ratio. There was one discordant result (new device positive, predicate device negative).
b. Matrix comparison:
The predicate device uses serum only. The new device recommends use of both serum and plasma. A study was performed to demonstrate that the new assay gives the same results for serum, heparin plasma, citrate plasma and EDTA plasma collected from the same specimen. Eight ssDNA antibody negative samples, each available as serum, heparin plasma, citrate plasma and EDTA plasma variant; 8 ssDNA antibody spiked positive sera and 2 ssDNA antibody positive samples, each available as serum, heparin plasma, citrate plasma and EDTA plasma variant were assayed. The 8 ssDNA antibody negative samples were run in duplicate together with calibrators and controls. Then they were spiked with the 8 different ssDNA antibody positive sera. All positive sample preparations were run in four replicates together with calibrators and controls. The specifications are: percent deviation between serum and plasma results for positive samples should not be higher than ± 20% and negative samples should be negative as serum or plasma. The data showed no difference greater than ± 20% (deviations ranged from -19.8 to 5.7%) in the evaluation of a positive sample when testing serum, heparin, citrate or EDTA plasma and no negative sample changed from negative to positive. Thus the specifications were met.
3. Clinical studies:
a. Clinical sensitivity:
Not provided
b. Clinical specificity:
Not provided
c. Other clinical supportive data (when a and b are not applicable):
4. Clinical cut-off:
See assay cut-off.
5. Expected values/Reference range:
{5}
According to the literature about $6.7\%$ of the apparently healthy population may have anti-ssDNA antibodies. This proportion increases in the elderly population (up to $17.2\%$ ; mean age 81 years) with prevalence for females (Giardina et al. 1997; Manoussakis et al. 1987).
# N. Conclusion:
The submitted information in this premarket notification is complete and supports a substantial equivalence decision.
Predicate graph will load when search results are available.
Embedding visualization will load when search results are available.
PDF viewer will load when search results are available.
Loading panels...
Select an item from Submissions
Click any panel, subpart, regulation, product code, or device to see details here.
Section Matches
Results will appear here.
Product Code Matches
Results will appear here.
Special Control Matches
Results will appear here.
Loading collections...
Loading
My Alerts
You will receive email notifications based on the filters and frequency you set for each alert.
Sort by:
Create Alert
Search Filters
Agent Token
Create a read-only bearer token for Claude, ChatGPT, or other agents that can call HTTP APIs.
Copy this now. It will not be shown again.
Connected apps
Apps you authorized through browser sign-in. Disconnecting revokes their access immediately.
Learn the FDA Browser
Two short videos show you everything — or skip straight to the written tutorial if you'd rather read. You can reopen this any time from the Tutorial button in the top bar.
Part 1 — Search, results, and everyday workflows 16 min
Part 2 — Embeddings: the galaxy map 3 min
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.
Use the checkboxes above the results to narrow: SaMD keeps only software-only devices, AI / ML keeps only devices with AI.
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.
Where do you find a device's intended use without opening the PDF?
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.
4. Device detail page: chat and citations
Click a device name to open its detail page: device facts on the left, a chat window on the right. Ask something like "Describe the training data". The answer carries little citation bubbles — click one to jump to the highlighted passage in the source PDF, so you can verify every AI answer against the document. There's also a Download PDF button for sharing.
How do you verify an AI chat answer on the device detail page?
Click the citation bubbles to jump to the relevant highlight in the source document.
Reading rule for every project: how many summaries do you read in full?
At least the three most relevant 510(k) or De Novo summaries, in full. After that, use targeted chat questions to confirm your memory quickly. The tool supports this professional habit — it doesn't replace it.
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.
6. Collections
With rows selected, go to the Collections tab and create a labeled collection (e.g., "Cobb Angle Project"). Reload that selection any time — before a client call, pull up the collection and ask questions across all of its devices at once.
How do you save a set of selected devices for later use?
Select the rows, go to the Collections tab, and create a labeled collection (e.g., "Cobb Angle Project"). You can reload the selection anytime and carry it into the PDF viewer and other tabs that support selections.
7. Product codes and the regulations tree
Click a product code in the results to jump to it in the regulations tree — identification text, sibling product codes, and devices you can open in a PDF viewer on the right. Click a regulation number to see its identification, special controls, and related product codes. You can also search by product code or regulation number at the top of the tree. Always read the special controls if any exist for your device — it broadens your search and sharpens pre-kickoff research.
What can you do from the regulations tree view?
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.
8. Chart view
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.