C. Michael Foley
Head of Safety Pharmacology and Research Fellow
Also affiliated: Carnegie Institution for Science (2006); University of Arkansas System (2026); AbbVie (United States) (2018–2026); University of Colorado Health (2004); Abbott (United States) (2018); Carnegie Observatories (2006); Novartis (Ireland) (2025); Department of Terrestrial Magnetism (2006); Covance (United States) (2008–2016); University of Missouri (1983–2008); Mississippi State University (2026)
Research Areas
Biomedical Subjects
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Biography and Research Information
OverviewAI-generated summary
C. Michael Foley's research focuses on safety pharmacology, particularly investigating cardiovascular liabilities and safety margins in preclinical drug development. He has explored automated blood sampling techniques in canine telemetry models to enhance assessments of drug effects on cardiovascular function. His work also includes developing methods for monitoring the consistency of nonclinical electrocardiogram analysis and creating pharmaceutical databases to aid in the detection of drug-induced cardiac toxicity. Foley has also contributed to discussions on reevaluating safety pharmacology respiratory studies and the implementation of international guidance documents related to drug safety assessment. His scholarship metrics include an h-index of 18, with 86 total publications and over 1,000 citations.
Metrics
- h-index: 18
- Publications: 88
- Citations: 1,042
Positions
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Head of Safety Pharmacology and Research FellowAbbvie Safety Pharmacology ORCID
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Head of Safety Pharmacology and Research Fellow publications 1987–2026University of Arkansas at Fayetteville ORCID
Selected Publications
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Estimating colony strength and pollination efficiency in honey bees using a novel dataset and deep learning-based models (2026)
Collaboration Network
Top Collaborators
- Single-Laser, Single-Beam Pump/Probe Thermal Lens Spectroscopy
- Single-Laser, Single-Beam Pump/Probe Thermal Lens Spectroscopy
- Estimating colony strength and pollination efficiency in honey bees using a novel dataset and deep learning-based models
- Estimating colony strength and pollination efficiency in honey bees using a novel dataset and deep learning-based models
- Estimating colony strength and pollination efficiency in honey bees using a novel dataset and deep learning-based models
- Estimating colony strength and pollination efficiency in honey bees using a novel dataset and deep learning-based models
- Estimating colony strength and pollination efficiency in honey bees using a novel dataset and deep learning-based models
- Estimating colony strength and pollination efficiency in honey bees using a novel dataset and deep learning-based models
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