Sara K. Quinney, PhD

Professor of Obstetrics & Gynecology

Professor of Medicine

Adjunct Professor, Health Informatics, School of Informatics and Computing

Director, Disease and Therapeutic Response Modeling Program

Email: squinney@iu.edu
Address: 950 W. Walnut St., R2 476
OBGY
Indianapolis, IN 46202
PubMed:

Bio

Dr. Sara Quinney received a PharmD (2000) and a PhD in Clinical Pharmacy (2004) from Purdue University. She then completed postdoctoral fellowships at Indiana University School of Medicine (IUSM) in Clinical Pharmacology under the mentorship of Dr. Stephen Hall and in Biostatistics and Bioinformatics under Dr. Lang Li. Dr. Quinney is currently an Assistant Professor in the Department of Obstetrics and Gynecology, where she is a member of PREGMED, the Indiana University Signature Center for Pharmacogenetics and Therapeutics Research in Maternal and Child Health. She is also a member of the Institute of Personalized Medicine and the Center for Computational Biology and Bioinformatics at IUSM and the Associate Director of the Indiana CTSI’s Disease and Therapeutic Response Modeling Program. Dr. Quinney’s work utilizes in vitro bench-based wet lab studies, in vivo clinical studies, and in silico pharmacokinetic models to better understand factors influencing drug pharmacokinetics and response. Her work focuses largely on the individualization of drug therapy in special populations, and especially in understanding of changes in drug metabolism and pharmacokinetics in pregnancy. She is involved in a number of studies examining drugs such as methadone, betamethasone, and misoprostol in pregnant women. Dr. Quinney has developed physiologically based pharmacokinetic models to describe changes in drug metabolism in pregnancy, and is investigating the role of fetal and placental metabolism in drug clearance. Dr. Quinney’s laboratory also studies complex drug-drug interactions, including multi-drug interactions with cytochrome P450 enzymes. In collaboration with Dr. Lang Li, a novel method for drug-drug interaction discovery was developed through integration of large electronic medical record data analysis with in vitro validation. Dr. Quinney has recently received R01 funding to investigate high-dimensional drug interactions using bioinformatics and in vitro approaches. Through her role in the Disease and Therapeutic Response Modeling Program, Dr. Quinney has established collaborations to develop pharmacokinetic models of herbal-drug interactions, evaluate the preclinical pharmacokinetics of drugs in Alzheimer’s Disease, and understand the effects of exercise on pharmacokinetics.

Key Publications

Haas DM, Daggy J, Flannery KM, Dorr ML, Bonsack C, Bhamidipalli SS, Pierson RC, Lathrop A, Towns R, Ngo N, Head A, Morgan S and Quinney SK. A comparison of vaginal versus buccal misoprostol for cervical ripening in women for labor induction at term (the IMPROVE trial): a triple-masked randomized controlled trial. Am J Obstet Gynecol (2019) 221(3): 259.e251-259.e216. DOI: 10.1016/j.ajog.2019.04.037 PMID: 31075246

McDowell ML, Tonismae TR, Slaven JE, Abernathy MP, Shanks AL, Benjamin TD and Quinney SK. The Impact of Hepatitis C Virus Infection on Buprenorphine Dose in Pregnancy. Am J Perinatol (2019). DOI: 10.1055/s-0039-1698838 PMID: 31655490

Quinney SK. Opportunities and Challenges of Using Big Data to Detect Drug-Drug Interaction Risk. Clin Pharmacol Ther (2019) 106(1): 72-74. DOI: 10.1002/cpt.1481 PMC6617974. PMID: 31184772

Quinney SK, Gullapelli R and Haas DM. Translational Systems Pharmacology Studies in Pregnant Women. CPT Pharmacometrics Syst Pharmacol (2018) 7(2): 69-81. DOI: 10.1002/psp4.12269 PMC5824114. PMID: 29239132

Quinney SK, Benjamin T, Zheng X and Patil AS. Characterization of Maternal and Fetal CYP3A-Mediated Progesterone Metabolism. Fetal Pediatr Pathol (2017) 36(5): 400-411. DOI: 10.1080/15513815.2017.1354411 PMC5704987. PMID: 28949811

Default table
Year	Degree	Institution
2004	PhD	Purdue University
2000	PharmD	Purdue University

Pharmacokinetics and Pharmacodynamics of Drugs in Pregnancy

During pregnancy, a woman’s body undergoes many changes that can affect drug disposition (pharmacokinetics, PK) and effect (pharmacodynamics, PD). This can then help us determine optimal therapeutic regimens for pregnant women. Pharmacokinetic modeling, including physiologically based PK modeling, allows us to explore factors that may affect drug disposition, including changes in maternal metabolism and the addition of placental and fetal drug metabolism. Based on in vitro and clinical data, we are examining the pharmacokinetics of a variety of drugs during pregnancy.

Drug-Drug Interaction Research

Adverse drug reactions (ADRs) contribute to 100,000 deaths annually, making it the 5^th leading cause of death. Drug-drug interactions (DDI’s) are a large contributor to ADRs. Working with Dr. Lang Li in the Center for Computational Biology, our lab integrates findings from electronic medical records with in vitro mechanistic experiments to help identify and understand the contribution of DDI’s to ADRs. Our studies focus primarily on pharmacokinetic DDI’s associated with inhibition and induction of Cytochrome P450 (CYP) enzymes. CYP enzymes are responsible for the majority of Phase 1 drug metabolism.

Integrated Bioinformatic and Pharmacokinetic Models of High-Dimensional Drug Interactions. Polypharmacy is associated with increased risk of adverse events. We hypothesize that individuals taking multiple medications are at an increased risk of drug-drug interactions, leading to clinically relevant adverse events. This NIGMS-funded R01 utilizes a combination of computational data mining algorithms, statistical inference, and mechanistic pharmacology models, to identify and evaluate clinically significant high dimensional drug interactions (HD-DDIs). In this study, we propose a novel frequent close itemset data mining algorithm to identify candidate HD-DDIs with adverse reactions from large health record data sets. These HD-DDIs identified by the computational algorithm will be subjected to an innovative empirical Bayes statistical inference to determine this false positive, hence its statistical significance in its potential relevance of each interaction. As a large number of drug interactions are potentiated through the cytochrome P450 (CYP450) system, the mechanistic potential of interactions among multidrug regimens will be evaluated using in vitro metabolism assays. This approach, combining graphical, statistical inference and mechanistic pharmacology models will provide insight into the role of polypharmacy in adverse drug events.

Pharmacometric Modeling

My research spans a large range of pharmacometric and big data applications, including physiologically based pharmacokinetic modeling, text mining, and machine learning approaches. I am involved in a number of collaborative research endeavors, including the MODEL-AD preclinical trial core and projects in oncology and opioid use disorder.

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Sara K. Quinney, PhD

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