Validation of Pharmacogenomic Interaction Probability (PIP) Scores in Predicting Drug–Gene, Drug–Drug–Gene, and Drug–Gene–Gene Interaction Risks in a Large Patient Population
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Population
2.2. YouScript Algorithm: Pharmacogenomic Interaction Probability
2.3. Analysis
3. Results
3.1. Characterization of the Study Population
3.2. Comparison of PIP Scores to PGx Test Results
3.3. Characterization of All Interactions
4. Discussion
Limitations
5. Conclusions and Future Work
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Minimum No. of Genes in Panel | 3 (All Study Patients) | 5 | 14 | 25 |
---|---|---|---|---|
Minimum Genes Included in Panel | CYP2C19, CYP2C9, CYP2D6 | CYP2C19, CYP2C9, CYP2D6, CYP3A4, CYP3A5 | CYP2C19, CYP2C9, CYP2D6, CYP3A4, CYP3A5, CYP2B6, CYP4F2, SLCO1B1, TPMT, DPYD, HLA-B*57:01, IFNL3, UGT1A1, VKORC1 | CYP2C19, CYP2C9, CYP2D6, CYP3A4, CYP3A5, ADRA2A, COMT, CYP1A2, CYP2B6, CYP4F2, DPYD, F2, F5, GRIK4, HLA-B*57:01, HTR2A, HTR2C, IFNL3, MTHFR, NAT2, OPRM1, SLCO1B1, TPMT, UGT1A1, VKORC1 |
No. of Patients | 36,511 | 28,613 | 3192 | 2068 |
Mean Age, years * | 61 | 61 | 60 | 53 |
Age Range, years *,** | 0–110 | 0–110 | 0–101 | 0–95 |
No. of Patients ≥ 65 years (%) | 18,124 (49.6) | 14,603 (51.0) | 1721 (53.9) | 758 (36.7) |
Sex *, No. (%) | ||||
Female | 20,554 (56.3) | 16,298 (57.0) | 1674 (52.4) | 1075 (52.0) |
Male | 14,360 (39.3) | 10,995 (38.4) | 1215 (38.1) | 691 (33.4) |
Unknown | 1597 (4.4) | 1320 (4.6) | 303 (9.5) | 302 (14.6) |
Race *, No. (%) | ||||
Black | 2968 (8.1) | 2478 (8.7) | 44 (1.4) | 44 (2.1) |
Asian | 387 (1.1) | 346 (1.2) | 14 (.4) | 14 (0.7) |
White | 17,215 (47.2) | 13,339 (46.6) | 614 (19.2) | 612 (29.6) |
Hispanic | 3413 (9.3) | 2520 (8.8) | 74 (2.3) | 72 (3.5) |
Jewish (Ashkenazi) | 145 (0.4) | 120 (0.4) | 4 (0.1) | 4 (0.2) |
Unknown | 12,383 (33.9) | 9810 (34.3) | 2442 (76.5) | 1322 (63.9) |
Mean No. of Medications per Patient (Range) | 9.4 (1–62) | 9.8 (1–62) | 9.5 (1–62) | 9.0 (1–62) |
Mean No. of Variants or Variant Phenotypes per Patient | 4.0 | 3.6 | 10.4 | 10.8 |
≥1 Variant or Variant Phenotype, % | 96.9 | 97.7 | 100 | 100 |
Minimum No. of Genes Tested | No. of Patients | Mean PIP Score (25 Genes) | Mean Adjusted PIP Score * (to Minimum Genes Tested per Patient) | EADGI Rate ** (No. of Patients with at Least One EADGI) | NNT | p-Value (Mean Adjusted PIP Score vs. % EADGIs Detected) |
---|---|---|---|---|---|---|
3 | 36,511 | 26.4% | 22.4% | 22.4% (8174) | 4.5 | 1.0000 |
5 | 28,613 | 27.5% | 23.5% | 23.4% (6707) | 4.3 | 0.6895 |
14 | 3192 | 31.0% | 30.9% | 29.4% (937) | 3.4 | 0.0667 |
25 | 2068 | 27.4% | 27.3% | 26.4% (545) | 3.8 | 0.3583 |
Clinical Area | No. (%) of Moderate EADGIs (n = 7360) | No. (%) of Major or Contraindicated EADGIs (n = 2444) | No. (%) of All EADGIs (Moderate, Major, Contraindicated) (n = 9804) |
---|---|---|---|
Behavioral Health | 765 (10.4) | 1106 (45.3) | 1871 (19.1) |
Cardiology | 3230 (43.9) | 877 (35.9) | 4107 (41.9) |
Pain Management | 470 (6.4) | 456 (18.7) | 926 (9.4) |
Hematology and Oncology | 37 (0.5) | 3 (0.1) | 40 (0.4) |
Infectious Disease | 0 | 2 (0.1) | 2 (0.0) |
Gastroenterology | 2634 (35.8) | 0 | 2634 (26.9) |
Urology | 108 (1.5) | 0 | 108 (1.1) |
Transplant | 8 (0.1) | 0 | 8 (0.1) |
Reproductive and Sexual Health | 16 (0.2) | 0 | 16 (0.2) |
Neurology | 42 (0.6) | 0 | 42 (0.4) |
Rheumatology | 0 | 0 | 0 |
Endocrinology | 0 | 0 | 0 |
Miscellaneous | 50 (0.7) | 0 | 50 (0.5) |
Affected Drug | Gene | Drug 2 | AUC Change: Affected Drug + − Gene | AUC Change: Affected Drug + − Drug 2 | Estimated AUC Change: Affected Drug + Gene + Drug 2 |
---|---|---|---|---|---|
clopidogrel (metabolite) | CYP2C19 Intermediate Metabolizer | tramadol | −31–50% | −31–50% | −51–80% |
citalopram | CYP2C19 Rapid Metabolizer | esomeprazole | −31–50% | 26–75% | −0–30% |
clopidogrel (metabolite) | CYP2C19 Intermediate Metabolizer | oxycodone | −31–50% | −31–50% | −51–80% |
amitriptyline | CYP2D6 Intermediate Metabolizer | bupropion | 26–75% | 26–75% | 76–200% |
clopidogrel (metabolite) | CYP2C19 Intermediate Metabolizer | morphine | −31–50% | −31–50% | −51–80% |
metoprolol | CYP2D6 Intermediate Metabolizer | dronedarone | 76–200% | 26–75% | >200% |
clopidogrel (metabolite) | CYP2C19 Intermediate Metabolizer | hydrocodone | −31–50% | −31–50% | −51–80% |
clopidogrel (metabolite) | CYP2C19 Poor Metabolizer | tramadol | −51–80% | −31–50% | −81–100% |
amitriptyline | CYP2D6 Poor Metabolizer | bupropion | 76–200% | 26–75% | >200% |
citalopram | CYP2C19 Rapid Metabolizer | fluvoxamine | −31–50% | 26–75% | −0–30% |
Drug (Clinical Area) | No. of Moderate EADGIs | Proportion of All EADGIs | Medication PIP Score | NNT | Rank among Top 200 Most Commonly Prescribed * |
---|---|---|---|---|---|
metoprolol (Cardiology) | 2852 | 29.1% | 48% | 2.1 | 5 |
omeprazole (Gastroenterology) | 1673 | 17.1% | 29% | 3.4 | 8 |
es(citalopram) (Behavioral Health) | 1038 | 10.6% | 32% | 3.1 | 19, 30 (10 combined) |
clopidogrel (Cardiology) | 872 | 8.9% | 29% | 3.4 | 36 |
pantoprazole (Gastroenterology) | 635 | 6.5% | 29% | 3.4 | 16 |
Drug (Clinical Area) | No. of EADGIs | Proportion of Major or Contraindicated EADGIs | Medication PIP Score | NNT | Rank among Top 200 Most Commonly Prescribed * |
---|---|---|---|---|---|
es (citalopram) (Behavioral Health) | 966 | 39.5% | 32% | 3.1 | 19, 30 (10 combined) |
clopidogrel (Cardiology) | 873 | 35.7% | 29% | 3.4 | 36 |
Tramadol (Pain Management) | 305 | 12.5% | 9% | 11.1 | 35 |
codeine (Pain Management) | 98 | 4.0% | 9% | 11.1 | 173 |
amitriptyline (Behavioral Health) | 76 | 3.1% | 50% | 2.0 | 94 |
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Ashcraft, K.; Grande, K.; Bristow, S.L.; Moyer, N.; Schmidlen, T.; Moretz, C.; Wick, J.A.; Blaxall, B.C. Validation of Pharmacogenomic Interaction Probability (PIP) Scores in Predicting Drug–Gene, Drug–Drug–Gene, and Drug–Gene–Gene Interaction Risks in a Large Patient Population. J. Pers. Med. 2022, 12, 1972. https://doi.org/10.3390/jpm12121972
Ashcraft K, Grande K, Bristow SL, Moyer N, Schmidlen T, Moretz C, Wick JA, Blaxall BC. Validation of Pharmacogenomic Interaction Probability (PIP) Scores in Predicting Drug–Gene, Drug–Drug–Gene, and Drug–Gene–Gene Interaction Risks in a Large Patient Population. Journal of Personalized Medicine. 2022; 12(12):1972. https://doi.org/10.3390/jpm12121972
Chicago/Turabian StyleAshcraft, Kristine, Kendra Grande, Sara L. Bristow, Nicolas Moyer, Tara Schmidlen, Chad Moretz, Jennifer A. Wick, and Burns C. Blaxall. 2022. "Validation of Pharmacogenomic Interaction Probability (PIP) Scores in Predicting Drug–Gene, Drug–Drug–Gene, and Drug–Gene–Gene Interaction Risks in a Large Patient Population" Journal of Personalized Medicine 12, no. 12: 1972. https://doi.org/10.3390/jpm12121972
APA StyleAshcraft, K., Grande, K., Bristow, S. L., Moyer, N., Schmidlen, T., Moretz, C., Wick, J. A., & Blaxall, B. C. (2022). Validation of Pharmacogenomic Interaction Probability (PIP) Scores in Predicting Drug–Gene, Drug–Drug–Gene, and Drug–Gene–Gene Interaction Risks in a Large Patient Population. Journal of Personalized Medicine, 12(12), 1972. https://doi.org/10.3390/jpm12121972