Accuracy Study; Retrospective method comparison study
Clinical samples (banked extracted DNA from whole blood); Sample Size: 459
Bidirectional DNA sequencing
Genotype call accuracy
Indications for Use
The xTAG® CYP2D6 Kit v3 is a device used to simultaneously detect and identify a panel of nucleotide variants found within the highly polymorphic CYP2D6 gene located on chromosome 22 from genomic DNA extracted from an EDTA or citrate anticoagulated whole blood sample. This kit can also identify gene rearrangements associated with the deletion (*5) and duplication genotypes. The xTAG® CYP2D6 Kit v3 is a qualitative genotyping assay which can be used as an aid to clinicians in determining therapeutic strategy for therapeutics that are metabolized by the CYP2D6 gene product. This kit is not indicated for standalone diagnostic purposes. This test is not intended to be used to predict drug response or non-response.
Device Story
The xTAG® CYP2D6 Kit v3 is an in vitro diagnostic assay for genotyping the CYP2D6 gene. It processes genomic DNA extracted from EDTA or citrate anticoagulated whole blood. The workflow involves multiplex PCR amplification, followed by multiplex allele-specific primer extension (ASPE) using universally-tagged primers. The resulting products are hybridized to fluorescing microparticles and detected via flow cytometry on Luminex 100 or 200 IS instruments. The xTAG® Data Analysis Software (TDAS) processes the fluorescence signals to identify specific nucleotide variants, gene deletions (*5), and duplications. The output provides the patient's CYP2D6 genotype, which clinicians use to inform therapeutic strategies for drugs metabolized by the CYP2D6 enzyme. The device is intended for use in clinical laboratory settings by trained personnel.
Clinical Evidence
No clinical trials were performed. Substantial equivalence was established via analytical performance studies. Method comparison against bi-directional dideoxy sequencing using 459 clinical samples showed 98.4% overall agreement. Precision/reproducibility was assessed across multiple sites, operators, and lots, achieving 94.44% agreement. Limit of detection was established at 2 ng/μL. Analytical specificity was confirmed with common blood interferents (hemoglobin, bilirubin, albumin, triglycerides).
Technological Characteristics
Multiplex PCR and allele-specific primer extension (ASPE) assay. Detection via flow cytometry using Luminex 100/200 IS instruments. Analyzes genomic DNA from whole blood. Components include PCR mixes, ASPE mix, bead mix, and enzymes (Platinum TFI Exo(-) DNA Polymerase, LA Taq). Software-based analysis (TDAS).
Indications for Use
Indicated for qualitative genotyping of the CYP2D6 gene from genomic DNA extracted from EDTA or citrate anticoagulated whole blood. Used as an aid to clinicians in determining therapeutic strategy for drugs metabolized by CYP2D6. Not for standalone diagnostic use; not intended to predict drug response or non-response.
Regulatory Classification
Identification
A drug metabolizing enzyme genotyping system is a device intended for use in testing deoxyribonucleic acid (DNA) extracted from clinical samples to identify the presence or absence of human genotypic markers encoding a drug metabolizing enzyme. This device is used as an aid in determining treatment choice and individualizing treatment dose for therapeutics that are metabolized primarily by the specific enzyme about which the system provides genotypic information.
Special Controls
The special control is FDA's guidance document entitled "Class II Special Controls Guidance Document: Drug Metabolizing Enzyme Genotyping System."
*Classification.* Class II (special controls). The special control is FDA's guidance document entitled “Class II Special Controls Guidance Document: Drug Metabolizing Enzyme Genotyping Test System.” See § 862.1(d) for the availability of this guidance document.
Predicate Devices
Amplichip CYP450 Test (k042259)
Submission Summary (Full Text)
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1
# 510(k) SUBSTANTIAL EQUIVALENCE DETERMINATION DECISION SUMMARY ASSAY ONLY TEMPLATE
A. 510(k) Number:
k093420
B. Purpose for Submission:
New device
C. Measurand:
Genotype of Cytochrome P450 2D6 (CYP2D6)
D. Type of Test:
Multiplex PCR followed by multiplex allele specific primer extension for genotyping, hybridized to multiplexed fluorescing microparticles, detected by flow cytometry
E. Applicant:
Luminex Molecular Diagnostics, Inc.
F. Proprietary and Established Names:
xTAG® CYP2D6 Kit
G. Regulatory Information:
| Product Code | Classification | Regulation Section | Panel |
| --- | --- | --- | --- |
| NTI | II | 21 CFR §862.3360 - Drug Metabolizing Enzyme Genotyping System | Toxicology |
H. Intended Use:
1. Intended use(s):
The xTAG® CYP2D6 Kit v3 is a device used to simultaneously detect and identify a panel of nucleotide variants found within the highly polymorphic CYP2D6 gene located on chromosome 22 from genomic DNA extracted from an EDTA or citrate anticoagulated whole blood sample. This kit can also identify gene rearrangements associated with the deletion (*5) and duplication genotypes. The xTAG® CYP2D6 Kit v3 is a qualitative genotyping assay which can be used as an aid to clinicians in determining therapeutic strategy for therapeutics that are metabolized by the CYP2D6 gene product. This kit is not indicated for standalone diagnostic purposes. This test is not intended to be used to predict drug response or non-response.
2. Indication(s) for use:
See Intended use above.
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3. Special conditions for use statement(s):
For prescription use only
4. Special instrument requirements:
Luminex 100 or 200 IS instruments
I. Device Description:
The xTAG® CYP2D6 Kit v3 includes the following components:
- PCR Mix A (includes dNTPs)
- PCR Mix B (includes dNTPs)
- ASPE Mix (includes dNTPs)
- Bead Mix
- 10x Buffer
- Shrimp Alkaline Phosphatase
- Exonuclease I
- Streptavidin, R-Phycoerythrin conjugate
- Platinum TFI Exo(-) DNA Polymerase and associated MgCl₂ and PCR buffer
- xTAG® Data Analysis Software (TDAS) CYP2D6
- LA Taq DNA Polymerase
J. Substantial Equivalence Information:
1. Predicate device name(s):
Amplichip CYP450 Test
2. Predicate K number(s):
k042259
3. Comparison with predicate:
| Similarities | | |
| --- | --- | --- |
| Item | Predicate device (k042259) | Proposed device |
| Indications for use | Intended to identify a patient's CYP2D6 genotype from genomic DNA extracted from a whole blood sample. | Same |
| Limitation | This test is not intended to be used to predict drug response or non-response. | Same |
| CYP450 gene genotyped by the test | 2D6 | Same |
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| Differences | | |
| --- | --- | --- |
| Item | Predicate device (k042259) | Proposed device |
| Number of 2D6 star genotypes detected by the device | 20 | 16 |
| CYP450 2D6 star genotypes detected by the test | *1, *2, *3, *4, *5, *6, *7, *8, *9, *10, *11, *15, *17, *19, *20, *29, *35, *36, *40, *41 | *1, *2, *3, *4, *5, *6, *7, *8, *9, *10, *11, *15, *17, *29, *35, *41 |
| Sample types | EDTA anticoagulated whole blood | EDTA and citrate anticoagulated whole blood |
| Instrument | Affymetrix GeneChip Microarray Instrumentation System | Luminex 100 and 200 IS Instrument |
| Test principle | PCR amplification of purified DNA, fragmentation and labeling of amplified products, hybridization of amplified products to a microarray and staining of the bound products, and scanning of the microarray | Multiplex PCR followed by multiplex allele specific primer extension for genotyping, hybridized to multiplexed fluorescing microparticles, detected by flow cytometry |
K. Standard/Guidance Document Referenced (if applicable):
- Class II Special Controls Guidance Document: Instrumentation for Clinical Multiplex Test Systems - Guidance for Industry and FDA Staff
- Guidance for Industry and FDA Staff: Statistical Guidance on Reporting Results from Studies Evaluating Diagnostic Tests
- Guidance for Industry and FDA Staff: Pharmacogenetic Tests and Genetic Tests for Heritable Markers
- Guidance for Industry and FDA Staff: Content of Pre-Market Submission for Software Contained in Medical Devices
- CLSI Guideline MM01-A2: Molecular Diagnostic Methods for Genetic Diseases
- CLSI Guideline MM13-PE: Collection, Transport, Preparation, and Storage of Specimens for Molecular Methods
- CLSI Guideline EP05-A2: Evaluation of Precision Performance of Clinical Chemistry Devices
- CLSI Guideline EP07-A2E: Interference Testing in Clinical Chemistry
- CLSI Guideline EP12-A: User Protocol for Evaluation of Qualitative Test Performance
- CLSI Guideline EP17-A: Protocols for Determining Limits of Detection and Limits of Quantitation
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L. Test Principle:
For each genomic sample being tested, two separate PCR reactions are performed. PCR-A produces an alpha fragment used to detect the mutations in the table below, as well as a duplication amplimer which indicates the presence of the duplication genotype. PCR-B produces a beta fragment used to detect the mutations in the table below, as well as a deletion amplimer indicative of the deletion genotype.
Following PCR amplification, the two reactions (PCR-A and PCR-B) are pooled. To enable efficient incorporation of biotin-dCTP during the ASPE reaction, the pooled PCR product is treated with Shrimp Alkaline Phosphatase to dephosphorylate any remaining nucleotides (particularly dCTP), and with Exonuclease I to degrade any primers left over from the PCR reaction.
ASPE is then carried out using universally-tagged primers supplied in the ASPE primer mix. An aliquot of the ASPE reaction is hybridized with the universal array (Bead Mix) in the presence of the hybridization buffer and incubated with Streptavidin, R-Phycoerythrin conjugate (reporter solution).
Samples are read on the Luminex 100 IS or 200 IS instrument and a signal is generated for each of the loci as well as for the duplication and deletion amplimers, if present. These fluorescence values are then analyzed to determine whether the wild-type/mutant allele for each of the loci has been detected, or whether the samples carry an allele(s) with the deletion or duplication.
| Star Genotype | Mutations and Polymorphisms detected by xTAG® CYP2D6 | |
| --- | --- | --- |
| | PCR A | PCR B |
| *1 | None | None |
| *2 | -1584C>G, 1661G>C, | 2850C>T, 4180G>C |
| *3 | | 2549A>del |
| *4 | 100C>T, 1661G>C, 1846G>A, | 2850C>T, 4180G>C |
| *5 | | Deletion |
| *6 | 1707T>del, | 4180G>C |
| *7 | | 2935A>C |
| *8 | 1661G>C, 1758G>T, | 2850C>T, 4180G>C |
| *9 | | 2613delAGA |
| *10 | 100C>T, | 1661G>C, 4180G>C |
| *11 | 883G>C, 1661G>C, | 2850C>T, 4180G>C |
| *15 | 138insT | |
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| Star Genotype | Mutations and Polymorphisms detected by xTAG® CYP2D6 | |
| --- | --- | --- |
| | PCR A | PCR B |
| *17 | 1023C>T, 1661G>C, | 2850C>T, 4180G>C |
| *29 | 1659G>A, 1661G>C, | 2850C>T, 3183G>A, 4180G>C |
| *35 | -1584C>G, 31G>A, 1661G>C, | 2850C>T, 4180G>C |
| *41 | 1661G>C, | 2850C>T, 2988G>A, 4180G>C |
| DUP | Duplication | |
Nucleotide changes that define the star genotype (*) are shown in bold font.
## M. Performance Characteristics (if/when applicable):
### 1. Analytical performance:
#### a. Precision/Reproducibility:
Reproducibility of the xTAG® CYP2D6 Kit v3 was assessed using a multiple-center, multiple-operator, multiple-lot, blinded study design. A subset of samples was extracted at each site with a different extraction method, therefore three different DNA extraction methods were included in the study. Reproducibility of the complete assay, including the extraction step, was assessed on 8 EDTA anticoagulated whole blood samples (genotypes provided in table below). Evaluation of the reproducibility of the analytical (post-extraction) steps of the assay was further assessed with testing of 5 purified genomic DNA samples.
This study was designed to distinguish between Site-to-Site, Lot-to-Lot and Operator-to-Operator reproducibility in the CYP2D6 assay. There were 2 operators per site, each performing one run per day across 3 non-consecutive days (3 runs per operator or 6 runs per site). Thus the number of replicates per sample for this study was:
(3 sites) x (2 operators/site) x (3 runs/operator) x (2 replicates/run) = 36 replicates.
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Reproducibility of the xTAG® CYP2D6 Kit v3 was demonstrated with a panel of 13 samples representing the following star (*) alleles (also termed 'star genotypes'):
| Sample ID | Star Alleles Present | Sample Type |
| --- | --- | --- |
| 1 | *1/*5 | genomic DNA |
| 2 | *1/*41 | genomic DNA |
| 3 | *4/*35 | genomic DNA |
| 4 | *2/*4, DUP | genomic DNA |
| 5 | *35/*41 | genomic DNA |
| 6 | *1/*10 | whole blood |
| 7 | *10/*17 | whole blood |
| 8 | *1/*2 | whole blood |
| 9 | *1/*1 | whole blood |
| 10 | *2/*10 | whole blood |
| 11 | *2/*17 | whole blood |
| 12 | *1/*5 | whole blood |
| 13 | *2/*2 | whole blood |
| Sample | Genotype | Replicates per site | # Total replicates | # correct calls (Site 1) | # correct calls (Site 2) | # correct calls (Site 3) | Total correct calls^{1} | # No calls^{1} | # Incorrect calls^{1} | 95% LCB | % Agreement |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 1 | *1/*5 | 12 | 36 | 12 | 12 | 12 | 36 | 0 | 0 | 90.25 | 100.00 |
| 2 | *1/*41 | 12 | 36 | 12 | 12 | 12 | 36 | 0 | 0 | 90.25 | 100.00 |
| 3 | *4/*35 | 12 | 36 | 11 | 12 | 12 | 35 | 1 | 0 | 85.47 | 97.22 |
| 4 | *2/*4, DUP | 12 | 36 | 10 | 12 | 12 | 34 | 2 | 0 | 81.34 | 94.44 |
| 5 | *35/*41 | 12 | 36 | 12 | 12 | 11 | 35 | 1 | 0 | 85.47 | 97.22 |
| 6 | *1/*10 | 12 | 36 | 12 | 12 | 12 | 36 | 0 | 0 | 90.26 | 100.00 |
| 7 | *10/*17 | 12 | 36 | 11 | 12 | 7 | 30 | 6 | 0 | 67.19 | 83.33 |
| 8 | *1/*2 | 12 | 36 | 12 | 12 | 12 | 36 | 0 | 0 | 90.26 | 100.00 |
| 9 | *1/*1 | 12 | 36 | 9 | 12 | 11 | 32 | 4 | 0 | 73.94 | 88.89 |
| 10 | *2/*10 | 12 | 36 | 10 | 12 | 10 | 32 | 4 | 0 | 73.94 | 88.89 |
| 11 | *2/*17 | 12 | 36 | 10 | 12 | 11 | 33 | 3 | 0 | 77.53 | 91.26 |
| 12 | *1/*5 | 12 | 36 | 8 | 12 | 12 | 32 | 4 | 0 | 73.94 | 88.89 |
| 13 | *2/*2 | 12 | 36 | 11 | 12 | 12 | 35 | 1 | 0 | 85.47 | 97.22 |
1 across all three sites
Overall percent agreement after the first test was 94.44% with a 95% lower bound confidence interval of 91.97%.
At Site 1, one replicate of one sample required two re-tests to resolve the No Call. The root cause was identified to be extraction failure. All other No Calls were resolved after one re-test. All Site 3 No Calls were resolved after one re-test.
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Reproducibility results presented (# of correct calls per 12 replicates) by site, operator and lot:
| Sample | Genotype | Site 1 | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- |
| | | Operator 1 | | | Operator 2 | | |
| | | Day A Lot 1 | Day B Lot 2 | Day C Lot 3 | Day A Lot 1 | Day B Lot 2 | Day C Lot 3 |
| 1 | *1/*5 | 12 | 12 | 12 | 12 | 12 | 12 |
| 2 | *1/*41 | 12 | 12 | 12 | 12 | 12 | 12 |
| 3 | *4/*35 | 12 | 11 | 12 | 12 | 12 | 12 |
| 4 | *2/*4, DUP | 11 | 12 | 12 | 12 | 11 | 12 |
| 5 | *35/*41 | 12 | 12 | 12 | 12 | 12 | 12 |
| 6 | *1/*10 | 12 | 12 | 12 | 12 | 12 | 12 |
| 7 | *10/*17 | 12 | 12 | 12 | 12 | 12 | 11 |
| 8 | *1/*2 | 12 | 12 | 12 | 12 | 12 | 12 |
| 9 | *1/*1 | 11 | 10 | 12 | 12 | 12 | 12 |
| 10 | *2/*10 | 12 | 12 | 10 | 12 | 12 | 12 |
| 11 | *2/*17 | 11 | 11 | 12 | 12 | 12 | 12 |
| 12 | *1/*5 | 12 | 12 | 12 | 11 | 11 | 10 |
| 13 | *2/*2 | 11 | 12 | 12 | 12 | 12 | 12 |
| Sample | Genotype | Site 2 | | | | | |
| 1 | *1/*5 | 12 | 12 | 12 | 12 | 12 | 12 |
| 2 | *1/*41 | 12 | 12 | 12 | 12 | 12 | 12 |
| 3 | *4/*35 | 12 | 12 | 12 | 12 | 12 | 12 |
| 4 | *2/*4, DUP | 12 | 12 | 12 | 12 | 12 | 12 |
| 5 | *35/*41 | 12 | 12 | 12 | 12 | 12 | 12 |
| 6 | *1/*10 | 12 | 12 | 12 | 12 | 12 | 12 |
| 7 | *10/*17 | 12 | 12 | 12 | 12 | 12 | 12 |
| 8 | *1/*2 | 12 | 12 | 12 | 12 | 12 | 12 |
| 9 | *1/*1 | 12 | 12 | 12 | 12 | 12 | 12 |
| 10 | *2/*10 | 12 | 12 | 12 | 12 | 12 | 12 |
| 11 | *2/*17 | 12 | 12 | 12 | 12 | 12 | 12 |
| 12 | *1/*5 | 12 | 12 | 12 | 12 | 12 | 12 |
| 13 | *2/*2 | 12 | 12 | 12 | 12 | 12 | 12 |
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| Sample | Genotype | Site 3 | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- |
| | | Operator 1 | | | Operator 2 | | |
| | | Day A Lot 1 | Day B Lot 2 | Day C Lot 3 | Day A Lot 1 | Day B Lot 2 | Day C Lot 3 |
| 1 | *1/*5 | 12 | 12 | 12 | 12 | 12 | 12 |
| 2 | *1/*41 | 12 | 12 | 12 | 12 | 12 | 12 |
| 3 | *4/*35 | 12 | 12 | 12 | 12 | 12 | 12 |
| 4 | *2/*4, DUP | 12 | 12 | 12 | 12 | 12 | 12 |
| 5 | *35/*41 | 12 | 12 | 12 | 11 | 12 | 12 |
| 6 | *1/*10 | 12 | 12 | 12 | 12 | 12 | 12 |
| 7 | *10/*17 | 12 | 10 | 10 | 12 | 12 | 11 |
| 8 | *1/*2 | 12 | 12 | 12 | 12 | 12 | 12 |
| 9 | *1/*1 | 12 | 12 | 12 | 12 | 11 | 12 |
| 10 | *2/*10 | 12 | 12 | 11 | 11 | 12 | 12 |
| 11 | *2/*17 | 12 | 12 | 12 | 12 | 12 | 11 |
| 12 | *1/*5 | 12 | 12 | 12 | 12 | 12 | 12 |
| 13 | *2/*2 | 12 | 12 | 12 | 12 | 12 | 12 |
b. Linearity/assay reportable range:
Not applicable.
c. Traceability, Stability, Expected values (controls, calibrators, or methods):
Quality control materials are not provided with the kit. The package insert states that previously characterized clinical samples or commercially available 2D6 controls are recommended for quality control testing. All quality control requirements and testing should be performed in conformance with local, state and/or federal regulations or requirements.
Stability: The stability performance of the xTAG® CYP2D6 Kit v3 was evaluated using three kit lots. The stability protocol includes testing under real-time and accelerated conditions. Real-time data is based on kit components stored at the recommended temperature of $-20^{\circ}\mathrm{C}$ . Accelerated data is based on stressed conditions where components are stored at $25^{\circ}\mathrm{C}$ . The current claimed shelf-life for the xTAG® CYP2D6 Kit v3 is 12 months. Real-time stability testing is on-going. In addition, repeated freeze-thaw cycles (up to 6) of the xTAG CYP2D6 Kit v3 are acceptable; stability testing protocols, acceptance criteria and summary of the data was reviewed and found to be acceptable.
d. Detection limit:
The limit of detection (LoD) and range of the xTAG® CYP2D6 Kit v3 was assessed by analyzing serial dilutions of genomic DNA samples from cell lines and whole blood samples containing one or more mutations or polymorphisms analyzed by the assay. DNA concentrations tested ranged
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from 0.1 – 300 ng/uL. Determination of the DNA range of the assay was performed at 1 site, with 3 operators running a total of five samples across three consecutive days, 2 lots of reagents, and 3 different sets of equipment. Genotypes for all samples were established by bi-directional DNA sequencing.
Five samples used in the study had the following genotypes: *1/*41, *1/*5, *35/*41, *17/*29, and *2/*4 DUP.
Results for the Limit of Detection study:
| Input DNA (ng/μL) | Number of Samples | # Correct Calls | # Incorrect Calls | # No calls | % Agreement | Lower Bound of 95% Confidence Interval | Upper Bound of 95% Confidence Interval |
| --- | --- | --- | --- | --- | --- | --- | --- |
| 100 | 160 | 160 | 0 | 0 | 100.00 | 97.72 | 100.00 |
| 82.5^{1} | 40 | 40 | 0 | 0 | 100.00 | 91.19 | 100.00 |
| 50 | 200 | 199 | 0 | 1 | 99.50 | 97.25 | 99.99 |
| 10 | 200 | 200 | 0 | 0 | 100.00 | 98.17 | 100.00 |
| 5 | 200 | 196 | 0 | 4 | 98.00 | 94.96 | 99.45 |
| 2 | 200 | 200 | 0 | 0 | 100.00 | 98.17 | 100.00 |
| 1 | 200 | 200 | 0 | 0 | 100.00 | 98.17 | 100.00 |
| 0.1 | 200 | 105 | 1^{2} | 94 | 52.50 | 45.34 | 59.59 |
1 The highest DNA concentration extracted from the whole blood sample was 82.5 ng/μL.
2 For one replicate, the *1/*5 sample was incorrectly called as *1/*1.
The recommended DNA concentration for this assay is 10 ng/μL and the lowest level of DNA that should be used is 2 ng/μL.
e. Analytical specificity:
Six patient blood samples were tested in this study. The genotypes of the samples were: *1/*1, *1/*5, *2/*2, *1/*10, *2/*17 and *10/*17. A given blood sample was split into 6 equivalent parts. One part was bi-directionally sequenced. One part was left as a control. The other parts of the blood sample were mixed, respectively, with hemoglobin, albumin, bilirubin, or triglycerides. Each was then split into three, and underwent DNA extraction by one of three different DNA extraction methods. The following potential interferents and the levels tested were: 500 mg/dL hemoglobin, 20 mg/dL bilirubin, 3000 mg/dL albumin and 2000 mg/dL triglycerides. Genotypes for all samples were established by bi-directional DNA sequencing.
None of the substances tested interfered with the assay, since test results of all samples that had added compounds were 100% in agreement with the expected genotype determined by sequencing.
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f. Assay cut-off:
Not applicable.
## 2. Comparison studies:
a. Method comparison with predicate device:
Four hundred and fifty-nine clinical samples were analyzed in the method comparison study. Samples were either EDTA or citrate whole blood samples. Results using the xTAG® CYP2D6 Kit v3 was compared to bidirectional dideoxy sequencing. The percent agreement for genotype detection of the xTAG® CYP2D6 Kit v3 was calculated by determining the percentage of tested samples with the correct genotype assigned, compared to the total number of samples tested of that genotype.
| CYP2D6 Allele Star (*) Genotype | Number of Alleles Sequenced | xTAG® CYP2D6 Kit v3 First Run Results | | | |
| --- | --- | --- | --- | --- | --- |
| | | # Correct Calls | # Incorrect Calls | # No Calls | % Agreement |
| *1 | 199 | 198 | 0 | 1 | 99.45 |
| *2 | 143 | 139 | 1¹ | 3 | 97.20 |
| *3 | 15 | 15 | 0 | 0 | 100.00 |
| *4 | 124 | 123 | 0 | 1 | 99.14 |
| *5 | 59 | 57 | 0 | 2 | 96.61 |
| *6 | 16 | 16 | 0 | 0 | 100.00 |
| *7 | 2 | 2 | 0 | 0 | 100.00 |
| *9 | 28 | 28 | 0 | 0 | 100.00 |
| *10 | 82 | 79 | 0 | 3 | 95.12 |
| *11 | 2 | 2 | 0 | 0 | 100.00 |
| *15 | 1 | 1 | 0 | 0 | 100.00 |
| *17 | 73 | 72 | 0 | 1 | 98.61 |
| *29 | 23 | 22 | 0 | 1 | 95.64 |
| *35 | 45 | 45 | 0 | 0 | 100.00 |
| *41 | 106 | 106 | 0 | 0 | 100.00 |
| Total | 918 | 905 | 1 | 12 | 98.4 |
¹ Incorrect call for *2/*10; result given by xTAG® CYP2D6 Kit v3 was *10/*35 because of a HET call at the 31 G>A loci
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Results by sample genotype:
| CYP2D6 Genotype | Total Unique Samples | xTAG® CYP2D6 Kit v3 First Run Results | | | |
| --- | --- | --- | --- | --- | --- |
| | | # Correct Calls | # No Calls | # Incorrect Calls | % Agreement |
| *1 / *1 | 17 | 17 | 0 | 0 | 100 |
| *1 / *1, DUP | 1 | 1 | 0 | 0 | 100 |
| *1 / *10 | 16 | 16 | 0 | 0 | 100 |
| *1 / *17 | 21 | 21 | 0 | 0 | 100 |
| *1 / *2 | 29 | 28 | 1 | 0 | 96.55 |
| *1 / *2, DUP | 5 | 5 | 0 | 0 | 100 |
| *1 / *29 | 6 | 6 | 0 | 0 | 100 |
| *1 / *29, DUP | 1 | 1 | 0 | 0 | 100 |
| *1 / *3 | 6 | 6 | 0 | 0 | 100 |
| *1 / *3, DUP | 1 | 1 | 0 | 0 | 100 |
| *1 / *35 | 12 | 12 | 0 | 0 | 100 |
| *1 / *4 | 25 | 25 | 0 | 0 | 100 |
| *1 / *41 | 17 | 17 | 0 | 0 | 100 |
| *1 / *5 | 13 | 13 | 0 | 0 | 100 |
| *1 / *5, DUP | 1 | 1 | 0 | 0 | 100 |
| *1 / *6 | 5 | 5 | 0 | 0 | 100 |
| *1 / *9 | 5 | 5 | 0 | 0 | 100 |
| *10 / *10 | 18 | 18 | 0 | 0 | 100 |
| *10 / *17 | 8 | 7 | 1 | 0 | 87.50 |
| *10 / *35 | 1 | 1 | 0 | 0 | 100 |
| *10 / *41 | 5 | 5 | 0 | 0 | 100 |
| *15 / *41 | 1 | 1 | 0 | 0 | 100 |
| *17 / *17 | 4 | 4 | 0 | 0 | 100 |
| *17 / *29 | 4 | 4 | 0 | 0 | 100 |
| *17 / *29, DUP | 1 | 1 | 0 | 0 | 100 |
| *17 / *41 | 4 | 4 | 0 | 0 | 100 |
| *2 / *10 | 6 | 4 | 1 | 1¹ | 66.67 |
| *2 / *17 | 10 | 10 | 0 | 0 | 100 |
| *2 / *17, DUP | 1 | 1 | 0 | 0 | 100 |
| *2 / *2 | 7 | 7 | 0 | 0 | 100 |
| *2 / *2, DUP | 3 | 3 | 0 | 0 | 100 |
| *2 / *29 | 3 | 3 | 0 | 0 | 100 |
| *2 / *3 | 1 | 1 | 0 | 0 | 100 |
| *2 / *35 | 6 | 6 | 0 | 0 | 100 |
| *2 / *11 | 2 | 2 | 0 | 0 | 100 |
| *2 / *4 | 21 | 21 | 0 | 0 | 100 |
| *2 / *41 | 14 | 14 | 0 | 0 | 100 |
¹ The number of samples was calculated from the mean ± SD of the mean ± SD of the 2000 samples.
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| *2 / *41, DUP | 2 | 2 | 0 | 0 | 100 |
| --- | --- | --- | --- | --- | --- |
| *2 / *5 | 9 | 8 | 1 | 0 | 88.89 |
| *2 / *6 | 3 | 3 | 0 | 0 | 100 |
| *2 / *7 | 2 | 2 | 0 | 0 | 100 |
| *2 / *9 | 9 | 9 | 0 | 0 | 100 |
| *29 / *41 | 3 | 3 | 0 | 0 | 100 |
| *3 / *3 | 1 | 1 | 0 | 0 | 100 |
| *3 / *35 | 2 | 2 | 0 | 0 | 100 |
| *3 / *4 | 1 | 1 | 0 | 0 | 100 |
| *3 / *5 | 1 | 1 | 0 | 0 | 100 |
| *3 / *9 | 1 | 1 | 0 | 0 | 100 |
| *35 / *35 | 2 | 2 | 0 | 0 | 100 |
| *35 / *41 | 6 | 6 | 0 | 0 | 100 |
| *4 / *10 | 6 | 6 | 0 | 0 | 100 |
| *4 / *17 | 4 | 4 | 0 | 0 | 100 |
| *4 / *17, DUP | 2 | 2 | 0 | 0 | 100 |
| *4 / *29 | 1 | 1 | 0 | 0 | 100 |
| *4 / *29 DUP | 2 | 1 | 1 | 0 | 50.00 |
| *4 / *35 | 7 | 7 | 0 | 0 | 100 |
| *4 / *35, DUP | 1 | 1 | 0 | 0 | 100 |
| *4 / *4 | 12 | 12 | 0 | 0 | 100 |
| *4 / *4, DUP | 1 | 1 | 0 | 0 | 100 |
| *4 / *41 | 20 | 20 | 0 | 0 | 100 |
| *4 / *5 | 3 | 3 | 0 | 0 | 100 |
| *4 / *5, DUP | 2 | 2 | 0 | 0 | 100 |
| *4 / *6 | 1 | 1 | 0 | 0 | 100 |
| *4 / *9 | 2 | 2 | 0 | 0 | 100 |
| *41 / *41 | 13 | 13 | 0 | 0 | 100 |
| *5 / *10 | 4 | 3 | 1 | 0 | 75.00 |
| *5 / *17 | 10 | 10 | 0 | 0 | 100 |
| *5 / *29 | 2 | 2 | 0 | 0 | 100 |
| *5 / *35 | 3 | 3 | 0 | 0 | 100 |
| *5 / *41 | 2 | 2 | 0 | 0 | 100 |
| *5 / *5 | 3 | 3 | 0 | 0 | 100 |
| *5 / *6 | 3 | 3 | 0 | 0 | 100 |
| *6 / *35 | 2 | 2 | 0 | 0 | 100 |
| *6 / *6 | 1 | 1 | 0 | 0 | 100 |
| *9 / *35 | 1 | 1 | 0 | 0 | 100 |
| *9 / *41 | 6 | 6 | 0 | 0 | 100 |
| *9 / *9 | 2 | 2 | 0 | 0 | 100 |
| Total | 459 | 446 | 6 | 1 | 98.4² |
12
{12}
13
1 Incorrect call for *2/*10; result given by xTAG® CYP2D6 Kit v3 was *10/*35 because of a HET call at the 31 G>A loci
2 95% Lower Bound Confidence Interval = 96.89%
For rare alleles, the performance of the xTAG® CYP2D6 Kit v3 was evaluated by blending plasmids with mutations for rare alleles with homozygous genomic DNA. Plasmid clones for the *8 allele were created by blending plasmids harboring mutations for rare alleles with genomic DNA. A total of 5 blends of genomic DNA and plasmid were tested. Four replicates of each blend were amplified (total of 20 samples) and assayed by xTAG® CYP2D6 Kit v3. Genotype results using plasmid clone-genomic DNA blends are shown in the table below. The CYP2D6 genotype call rate was 100% for plasmid clone genomic DNA blends.
| Plasmid Genotype | Genomic DNA Genotype | Blended sample Expected genotype | Blended samples (n) | # Correct Calls | # Incorrect calls | # No calls | % Agreement |
| --- | --- | --- | --- | --- | --- | --- | --- |
| *8 | *1/*1 | *1/*8 | 4 | 4 | 0 | 0 | 100 |
| *8 | *35/*2 | *35/*8 | 4 | 4 | 0 | 0 | 100 |
| *8 | *17/*17 | *17/*8 | 4 | 4 | 0 | 0 | 100 |
| *8 | *2/*2 | *2/*8 | 4 | 4 | 0 | 0 | 100 |
| *8 | *4/*4 | *4/*8 | 4 | 4 | 0 | 0 | 100 |
| Total | | | 20 | 20 | 0 | 0 | 100 |
b. Matrix comparison:
Twenty five specimens were tested that had been collected both in EDTA and citrate, extracted and run on the Luminex 2D6 assay. There were no No Calls or Incorrect calls. In addition, citrate anticoagulated whole blood samples were tested in the method comparison study in 2a above.
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):
Not applicable.
4. Clinical cut-off:
Not applicable.
{13}
# 5. Expected values/Reference range:
| Final TDAS Output (*genotype or * allele) | SNPs that xTAG® CYP2D6 v3 kit detects | Frequency in the U.S. Caucasian population1 | Frequency in the African American population2 | Predicted Enzyme Activity | Reference where the effect of the genotype on drug metabolism is described |
| --- | --- | --- | --- | --- | --- |
| *1 | None | 37 – 40% | 29 – 35% | Normal | (Kimura, Umeno et al. 1989; Marez, Legrand et al. 1997; Sachse, Brockmoller et al. 1997) |
| *2 | -1584C>G, 1661G>C, 2850C>T, 4180G>C | 26 - 33% | 18 - 27% | Normal | (Johansson, Lundqvist et al. 1993; Panserat, Mura et al. 1994; Marez, Legrand et al. 1997; Raimundo, Fischer et al. 2000; Sakuyama, Sasaki et al. 2008) |
| *3 | 2549A>del | 1% | 0.2%-0.6% | None | (Kagimoto, Heim et al. 1990; Marez, Legrand et al. 1997) |
| *4 | 100C>T, 1661G>C, 1846G>A, 4180G>C, 2850C>T | 18 - 20% | 6 - 9% | None | (Gough, Miles et al. 1990; Hanioka, Kimura et al. 1990; Kagimoto, Heim et al. 1990; Sachse et al, 1997; Marez et al, 1997) |
| *5 | deletion | 2 - 4% | 6 - 7% | None | (Gaedigk, Blum et al. 1991; Steen, Molven et al. 1995) |
| *6 | 1707T>del, 4180G>C | 1% | 0.5% | None | (Evert, Griese et al. 1994; Saxena, Shaw et al. 1994; Daly, Leathart et al. 1995; Marez, Legrand et al. 1997) |
| *7 | 2935A>C 1661G>C, 1758G>T, 2850C>T, 4180G>C | Not known | Not known | None | (Evert, Griese et al. 1994) |
| *8 | 1661G>C, 1758G>T, 2850C>T, 4180G>C | Not known | Not known | None | (Broly, Marez et al. 1995) |
{14}
| *9 | 2613delAGA | 2 - 3% | 0.3% | Reduced | (Tyndale, Aoyama et al. 1991; Broly and Meyer 1993) |
| --- | --- | --- | --- | --- | --- |
| *10 | 100C>T, 1661G>C, 4180G>C | 2 - 8% | 0.3%-0.4% | Reduced | (Yokota, Tamura et al. 1993; Johansson, Oscarson et al. 1994; Ishiguro, Kubota et al. 2004; Sakuyama, Sasaki et al. 2008) |
| *11 | 883G>C, 1661G>C, 2850C>T, 4180G>C | Not known | Not known | None | (Marez, Sabbagh et al. 1995) |
| *15 | 138insT | Not known | Not known | None | (Sachse, Brockmoller et al. 1996) |
| *17 | 1023C>T, 1661G>C, 2850C>T, 4180G>C | 0.2-0.3% | 15 - 26% | Reduced | (Masimirembwa, Persson et al. 1996; Oscarson, Hidestrand et al. 1997) |
| *29 | 1659G>A, 1661G>C, 2850C>T, 3183G>A, 4180G>C | Not known3 | Not known3 | Reduced | (Marez, Legrand et al. 1997; Wennerholm, Johansson et al. 2001; Wennerholm, Dandara et al. 2002) |
| *35 | -1584C>G, 31G>A, 1661G>C, 2850C>T, 4180G>C | 7.4%4 | 1%4 | Normal | (Marez, Legrand et al. 1997; Gaedigk, Ryder et al. 2003) |
| *41 | 1661G>C, 2850C>T, 2988G>A, 4180G>C | 9%4 | 11%4 | Reduced | (Raimundo, Fischer et al. 2000; Raimundo, Toscano et al. 2004) |
$^{1,2}$ Bradford, L. D. (2002). "CYP2D6 allele frequency in European Caucasians, Asians, Africans and their descendants." Pharmacogenomics 3(2): 229-43.
3 The frequency of the *29 allele in the U.S. population is not known; however, it is very common in Tanzanian Africans, with an allele frequency of 20 percent (Wennerholm, A., I. Johansson, et al. (2001). "Characterization of the CYP2D6*29 allele commonly present in a black Tanzanian population causing reduced catalytic activity." Pharmacogenetics 11 (5): 417-27).
4 Gaedigk, A., D. L. Ryder, et al. (2003). "CYP2D6 poor metabolizer status can be ruled out by a single genotyping assay for the -1584G promoter polymorphism." Clinical Chemistry 49(6 Pt 1): 1008-11.
{15}
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.
16
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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.