ABX PENTRA AMYLASE CP; MULTICAL; N CONTROL; P CONTROL; CLEAN-CHEM CP; CLEAN-CHEM 99 CP
Applicant
Horiba Abx
Product Code
JFJ · Clinical Chemistry
Decision Date
Oct 2, 2006
Decision
SESE
Submission Type
Traditional
Regulation
21 CFR 862.1070
Device Class
Class 2
Indications for Use
The reagent in this submission is intended for use on the ABX PENTRA 400 for the quantitative in-vitro determination of alpha-amylase using human serum and plasma. The controls, calibrators and additional reagents are intended for use in association with the above reagent.
Device Story
Device is an enzymatic photometric assay for quantitative determination of amylase activity in human serum and plasma. Input: patient serum or plasma sample. Principle: substrate 4,6-ethylidene-(G7)-p-nitrophenyl-(G1)-α-D-maltoheptaoside (EPS-G7) is cleaved by amylase; fragments are hydrolyzed by α-glucosidase to produce glucose and p-nitrophenol; increase in absorbance measured photometrically. Used on ABX PENTRA 400 Clinical Chemistry Analyzer in clinical laboratory settings by trained personnel. Output: amylase activity concentration (U/L). Results assist clinicians in diagnosing and monitoring pancreatitis. Benefits include rapid, automated quantitative assessment of amylase levels.
Clinical Evidence
No clinical data. Performance established via bench testing, including precision (Total CV < 2.74%), linearity (reportable range 4-2000 U/L), and method comparison against predicate (n=131, r=0.9985). Interference testing confirmed no significant interference from hemoglobin, bilirubin, or triglycerides.
Technological Characteristics
Enzymatic photometric assay. Reagents: Good's buffer, NaCl, MgCl2, α-glucosidase, EPS-G7, sodium azide. Form factor: liquid bi-reagent cassette. Analyzers: ABX PENTRA 400. Calibration: traceable to IRMM/IFCC 456. Sterilization: not applicable (reagent).
Indications for Use
Indicated for quantitative in vitro diagnostic determination of amylase activity in human serum and plasma for the diagnosis and treatment of pancreatitis. For use on the ABX PENTRA 400 Clinical Chemistry Analyzer. Prescription use only.
Regulatory Classification
Identification
An amylase test system is a device intended to measure the activity of the enzyme amylase in serum and urine. Amylase measurements are used primarily for the diagnosis and treatment of pancreatitis (inflammation of the pancreas).
{0}
# 510(k) SUBSTANTIAL EQUIVALENCE DETERMINATION DECISION SUMMARY ASSAY ONLY TEMPLATE
A. 510(k) Number:
k062180
B. Purpose for Submission:
New Device
C. Measurand:
$\alpha$ - Amylase
D. Type of Test:
Quantitative, enzymatic
E. Applicant:
Horiba ABX
F. Proprietary and Established Names:
| Proprietary Name: | ABX Pentra Amylase CP |
| --- | --- |
| Common Name: | $\alpha$ – Amylase |
| Proprietary Name: | ABX Pentra N Control |
| Common Name: | Quality Control |
| Proprietary Name: | ABX Pentra P Control |
| Common Name: | Quality Control |
| Proprietary Name: | ABX Pentra MultiCal |
| Common Name: | Calibrator |
{1}
Page 2 of 9
G. Regulatory Information:
1. Regulation section:
21 CFR § 862.1070 - Amylase test system
21 CFR 862.1660 - Quality control material (assayed and unassayed)
21 CFR 862.1150 - Calibrator
2. Classification:
Class II – reagent and calibrator
Class I – controls
3. Product code:
JFJ – reagent
JIX – calibrator
JJY – controls
4. Panel:
Clinical Chemistry (75)
H. Intended Use:
1. Intended use(s):
Refer to Indications for Use.
2. Indication(s) for use:
Amylase reagent, with associated calibrators and controls, are intended for use on ABX PENTRA 400 Clinical Chemistry Analyzer to measure amylase analyte.
ABX PENTRA Amylase CP reagent with associated calibrators and controls are for quantitative in vitro diagnostic determination of the activity of the enzyme amylase in human serum and plasma based on an enzymatic photometric assay.
Amylase measurements are used primarily for the diagnosis and treatment of pancreatitis (inflammation of the pancreas).
The ABX PENTRA N Control is for use in quality control by monitoring accuracy and precision.
The ABX PENTRA P Control is for use in quality control by monitoring accuracy and precision.
The ABX PENTRA Multical is a calibrator for use in the calibration of quantitative Horiba ABX methods on Horiba ABX clinical chemistry analyzers.
{2}
Page 3 of 9
3. Special conditions for use statement(s):
Prescription use only
4. Special instrument requirements:
For use with the ABX PENTRA 400 analyzer only.
I. Device Description:
Reagent 1 consists of Good's buffer (0.1 mol/L), NaCl (62.5 mmol/L), MgCl₂ (12.5 mmol/L), α-glucosidase (≥ 2.5 kU/L), and sodium azide (< 1 g/L).
Reagent 2 consists of Good's buffer (0.1 mol/L), 4,6-ethylidene-(G7)-p-nitrophenyl-(G1)-α-D-maltoheptaoside (8.5 mmol/L) and sodium azide (< 1 g/L).
The ABX Pentra MultiCal is serum based and provided in lyophilized form. Users reconstitute the calibrator with 3 mL of deionized water. The MultiCal contains multiple analytes including amylase, which is obtained from porcine pancreas. The concentration or activities of the analytes are lot-specific.
The ABX Pentra N and P controls are serum based and provided in lyophilized form. Users reconstitute the controls with 5 mL of deionized water. The controls contain multiple analytes including amylase, which is obtained from porcine pancreas and human saliva. The concentration or activities of the analytes are lot-specific.
All human source materials were shown to be free from HBsAG and antibodies to HCV and HIV by FDA approved methods.
J. Substantial Equivalence Information:
1. Predicate device name(s):
COBAS Reagent for α-Amylase
ABX PENTRA N & P Control (amylase ranges added)
ABX PENTRA N Multical (amylase ranges added)
{3}
Page 4 of 9
2. Predicate 510(k) number(s):
k801295 (reagent)
k052007 (calibrator and control)
3. Comparison with predicate:
| Similarities - Reagent | | |
| --- | --- | --- |
| Item | Device | Predicate |
| Analyte | Alpha-amylase | Alpha-amylase |
| Method | Enzymatic photometric assay | Enzymatic photometric assay |
| Reagent components | Bi-reagent cassette, ready to use
REAGENT 1: Good’s buffer, NaCl, MgCl2, α-Glucosidase, Sodium azide
REAGENT 2: Good’s buffer, EPS-G7, Sodium azide | Single-reagent bottle, lyophilized
REAGENT: PNPG7, Sodium chloride, Calcium chloride, α-Glucosidase (microbial), buffers, stabilizers, fillers and preservative |
| Format | Liquid | Liquid |
| Sample volume | 4 μl/test | 5 μl/test |
| Upper linearity limit | 2000 U/l (6,000 U/L with automatic post-dilution) | 2000 U/L (10,000 U/L with automatic post-dilution) |
| Closed reagent stability | 24 months at 2-8°C | Until the expiration date when stored at 2-8°C |
| Open Reagent stability | on-board stability (refrigerated area): 42 days | after reconstitution:
30 days at 2-8°C
4 days at 15-25°C |
| Differences - Reagent | | |
| --- | --- | --- |
| Item | Device | Predicate |
| Specimen | Serum, Plasma | Serum, Urine |
| Precision | CV Total < 2.74% | CV Total < 8.2% |
| Lower limit | 4.5 U/l | 15 U/l |
| Calibration stability | 8 days | 30 days |
The controls and calibrators included in this submission are identical to the predicate devices.
{4}
Page 5 of 9
# K. Standard/Guidance Document Referenced (if applicable):
Valtec guideline (Vassault et al., Ann. Biol. Clin., 1986, (44), 686-745)
FDA Guidance Document: Guidance for Industry and FDA Staff: “Format for Traditional & Abbreviated 510(k)s”: August 12, 2005
FDA Guidance Document: In vitro diagnostics devices : Guidance for the preparation of 510(k) submissions Jan 1997
NCCLS (CLSI) EP-5A2: Evaluation of Precision Performance of Clinical Chemistry Devices; Approved Guideline
NCCLS (CLSI) EP-6A: Evaluation of the Linearity of Quantitative Measurement Procedures: A Statistical Approach; Approved Guideline
NCCLS (CLSI) EP-9A2: Method Comparison and Bias Estimation Using Patient Samples; Approved Guideline – Second Edition
NCCLS (CLSI) EP-21A: Estimation of Total Analytical Error for Clinical Laboratory Methods; Approved Guideline
# L. Test Principle:
The substrate 4,6-ethylidene-(G7)-p-nitrophenyl-(G1)-α-D-maltoheptaoside (EPS-G7) is cleaved by α-amylases into various fragments. These are further hydrolyzed in a second step by α-glucosidase producing glucose and p-nitrophenol. The increase in absorbance represents the total (pancreatic and salivary) amylase activity in the sample.
# M. Performance Characteristics:
1. Analytical performance:
a. Precision/Reproducibility:
To evaluate within run precision, the sponsor selected two controls and three serum samples. Each sample was run 20 times in a single run with the following results:
| | Control | | Sample | | |
| --- | --- | --- | --- | --- | --- |
| | Normal | Abnormal | Low | Medium | High |
| Mean (U/L) | 74.6 | 180.6 | 50.0 | 89.2 | 258.4 |
| SD | 0.51 | 1.28 | 0.89 | 1.05 | 1.56 |
| CV(%) | 0.69 | 0.71 | 1.78 | 1.18 | 0.60 |
{5}
To evaluate between run and total precision, the sponsor followed CLSI EP-5A. Two controls and two serum samples were tested in duplicate for 20 days, with two replicates per day, for a total of four results per day and 80 results total for each sample. The following results were observed:
| | Control | | Sample | |
| --- | --- | --- | --- | --- |
| | Normal | Abnormal | Low | High |
| Mean (U/L) | 76.7 | 184.1 | 71.5 | 415.0 |
| Within-Run SD | 0.75 | 1.32 | 0.84 | 2.79 |
| Within-Run CV | 0.98 | 0.72 | 1.18 | 0.67 |
| Total SD | 2.07 | 3.21 | 1.96 | 7.19 |
| Total CV | 2.70 | 1.74 | 2.74 | 1.73 |
| Between-Day SD | 1.61 | 1.86 | 1.60 | 4.87 |
| Between-Day CV | 2.09 | 1.01 | 2.23 | 1.17 |
| Between-Run SD | 1.07 | 2.26 | 0.77 | 4.50 |
| Between-Run CV | 1.39 | 1.23 | 1.07 | 1.08 |
# b. Linearity/assay reportable range:
To evaluate linearity and reportable range, the sponsor followed CLSI EP-6A. Two datasets were collected, one covering the lower range from approximately 7 - 150 U/L and another covering the upper range from approximately 100 - 2200 U/L. The CLSI guideline instructs users to examine whether a non-linear polynomial fits the data better than a linear one and then assess whether the difference between the two is less than the amount of allowable bias for the method. The sponsor did this, and determined that for both datasets the difference was less than their internal estimate of $8\%$ allowable bias.
In addition, post dilution studies were performed to validate the automated dilution function and range.
# c. Traceability, Stability, Expected values (controls, calibrators, or methods):
Calibrator. The sponsor states that the Multical calibrator is traceable to the Institute for Reference Materials and Measurements (IRMM)/ International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) reference material 456 for amylase.
Real-time stability data has been provided at the recommended storage temperature for both open and closed vials. The acceptance criterion was recovery $\pm 5\%$ of day 0 value.
Calibrator values are assigned by comparison to a master lot stored at $-80^{\circ}\mathrm{C}$ . Deviations of up to $4\%$ are allowed.
{6}
Page 7 of 9
Controls. Real-time stability data has been provided at the recommended storage temperature for both open and closed vials. The acceptance criterion was recovery ± 10% of day 0 value.
Control values are assigned from the ABX PENTRA calibrator, reagents and analyzers. The target value is determined by the median of results from 150 measurements. Six analyzers are used over 5 days, with one calibration per day. Confidence range is determined as the calculated range in percent which is based on the experimental results from the previous target value trials. The range declared in the target value sheet is equal to the assigned value +/- 3 standard deviations.
d. Detection limit:
Method: In accordance with the Valtec guideline (Vassault et al., Ann. Biol. Clin., 1986, (44), 686-745)
Minimum Detection Limit (MDL) is calculated from 30 measurements of saline water (0.9 g/l)
Formula: MDL = mean of measurements + 4.65 SD (mean of measurement = 0 when negative)
| n | Values | n | Values |
| --- | --- | --- | --- |
| 1 | -1.1 | 16 | -0.9 |
| 2 | -0.8 | 17 | -0.5 |
| 3 | -0.3 | 18 | -0.7 |
| 4 | -2.8 | 19 | -0.3 |
| 5 | -0.9 | 20 | -1.1 |
| 6 | -1.6 | 21 | 0.4 |
| 7 | -1.8 | 22 | -2.1 |
| 8 | -2 | 23 | -2.6 |
| 9 | -1.4 | 24 | -1 |
| 10 | -1 | 25 | -1.2 |
| 11 | -1.2 | 26 | -1.3 |
| 12 | -1.4 | 27 | -0.7 |
| 13 | -0.2 | 28 | -1.6 |
| 14 | -0.7 | 29 | -4 |
| 15 | -3.5 | 30 | -1.6 |
| Mean | -1.33 |
| --- | --- |
| SD | 0.95 |
| Minimum Detection Limit = | Mean + 4,65 SD : | |
| --- | --- | --- |
| | 4 | U/I |
{7}
Page 8 of 9
From the MDL value, the test range low assigned for this method and in the test instruction was calculated to be 4 U/L.
e. Analytical specificity:
Hemoglobin up to 278 μmol/l (479 mg/dl), total bilirubin up to 450 μmol/l (29 mg/dl), direct bilirubin up to 474 μmol/l (27.7 mg/dl) and triglycerides (as Intralipid ®, representative of lipemia) up to 7 mmol/l (612.5 mg/dl) were tested by the sponsor and found not to interfere with Amylase determination by this method. Potential interferents were tested at two amylase concentrations. The sponsor defined non-interference as the following: within ± 20 U/L for the low concentration (approximately 136 U/L) and within ± 50 U/L for the high concentration test sample (approximately 287 U/L).
f. Assay cut-off:
Not Applicable
2. Comparison studies:
a. Method comparison with predicate device:
Due to the difficulty of obtaining native samples covering the reportable range of the assay, the sponsor supplemented the native samples with both diluted and spiked samples, for a total of 131 samples. Each sample was analyzed in duplicate and the samples ranged in concentration from 4 to 1659 U/L by the predicate method. Linear regression was performed comparing the mean of predicate vs the mean of the new device, and the following line equation was calculated:
$$
\text{Horiba ABX} = (*1.231 \times \text{predicate method}) - 26 \, \text{U/L}
$$
$$
r = 0.9985
$$
*From the data provided, it appears that the positive bias increases as amylase values increase. However, this bias was judged to be clinically insignificant.
b. Matrix comparison:
To demonstrate comparable performance between serum and lithium-heparin plasma, the sponsor compared 70 paired serum and plasma samples on the Pentra 400 analyzer using the ABX Pentra Amylase CP reagent. The samples ranged in concentration from a low of 24 to a high of 255 U/L. Linear regression produced a slope of 1.00 with a y-intercept of -2.6 U/L. The correlation coefficient (r) was 0.998.
{8}
Page 9 of 9
3. Clinical studies:
a. Clinical Sensitivity:
Not Applicable
b. Clinical specificity:
Not Applicable
c. Other clinical supportive data (when a. and b. are not applicable):
4. Clinical cut-off:
Not Applicable
5. Expected values/Reference range:
< 100 U/L (both women and men)
Reference:
Roberts W.L., McMillin G.A., Burtis C.A., Bruns D.E., Reference Information for the Clinical Laboratory, TIETZ Textbook of Clinical Chemistry and Molecular Diagnostics, 4th ed.
N. Proposed Labeling:
The labeling is sufficient and it satisfies the requirements of 21 CFR Part 809.10.
O. 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.