Nicolas Moyer
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Nicolas Moyer has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Burns C. Blaxall
The Christ Hospital Health Network, Cincinnati, OH
1 shared publication - Chad Moretz
Invitae Corporation, San Francisco, CA (A.M., C.M., T.E.C., K.E.H., R.L.N., E.R., S. Rojahn, M.V., E.D.E.).
1 shared publication - Jennifer A. Wick
The Christ Hospital Health Network, 2139 Auburn Avenue, Cincinnati, OH 45219, USA
1 shared publication - Kendra Grande
Invitae Corporation, 1400 16th Street, San Francisco, CA 94103, USA
1 shared publication - Kristine Ashcraft
Invitae Corporation, 1400 16th Street, San Francisco, CA 94103, USA
1 shared publication - Sara L. Bristow
InVitae, San Francisco, CA
1 shared publication
Latest Publications
- Open Access
- 29 November 2022
- Published by MDPI AG in Journal of Personalized Medicine
- Vol. 12 (12) , 1972
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
Utilizing pharmacogenomic (PGx) testing and integrating evidence-based guidance in drug therapy enables an improved treatment response and decreases the occurrence of adverse drug events. We conducted a retrospective analysis to validate the YouScript® PGx interaction probability (PIP) algorithm, which predicts patients for whom PGx testing would identify one or more evidence-based, actionable drug–gene, drug–drug–gene, or drug–gene–gene interactions (EADGIs). PIP scores generated for 36,511 patients were assessed according to the results of PGx multigene panel testing. PIP scores versus the proportion of patients in whom at least one EADGI was found were 22.4% vs. 22.4% (p = 1.000), 23.5% vs. 23.4% (p = 0.6895), 30.9% vs. 29.4% (p = 0.0667), and 27.3% vs. 26.4% (p = 0.3583) for patients tested with a minimum of 3-, 5-, 14-, and 25-gene panels, respectively. These data suggest a striking concordance between the PIP scores and the EAGDIs found by gene panel testing. The ability to identify patients most likely to benefit from PGx testing has the potential to reduce health care costs, enable patient access to personalized medicine, and ultimately improve drug efficacy and safety.
- 29 November 2022
- Published by MDPI AG in Journal of Personalized Medicine
- Vol. 12 (12) , 1972
Open AccessValidation of Pharmacogenomic Interaction Probability (PIP) Scores in Predicting Drug–Gene, Drug–Drug–Gene, and Drug–Gene–Gene Interaction Risks in a Large Patient Population
Utilizing pharmacogenomic (PGx) testing and integrating evidence-based guidance in drug therapy enables an improved treatment response and decreases the occurrence of adverse drug events. We conducted a retrospective analysis to validate the YouScript® PGx interaction probability (PIP) algorithm, which predicts patients for whom PGx testing would identify one or more evidence-based, actionable drug–gene, drug–drug–gene, or drug–gene–gene interactions (EADGIs). PIP scores generated for 36,511 patients were assessed according to the results of PGx multigene panel testing. PIP scores versus the proportion of patients in whom at least one EADGI was found were 22.4% vs. 22.4% (p = 1.000), 23.5% vs. 23.4% (p = 0.6895), 30.9% vs. 29.4% (p = 0.0667), and 27.3% vs. 26.4% (p = 0.3583) for patients tested with a minimum of 3-, 5-, 14-, and 25-gene panels, respectively. These data suggest a striking concordance between the PIP scores and the EAGDIs found by gene panel testing. The ability to identify patients most likely to benefit from PGx testing has the potential to reduce health care costs, enable patient access to personalized medicine, and ultimately improve drug efficacy and safety.