Professional Profile
Taravat Ghafourian, Pharm.D., Ph.D., FHEA
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Associate Professor Dept: Pharmaceutical Sciences Phone: 954-262-1876 Email: tghafour@nova.edu Campus: Fort Lauderdale |
| Biosketch: | |
| Dr Taravat Ghafourian, PhD, PharmD, FHEA is an Associate Professor at the Department of Pharmaceutical Sciences. She specializes in drug discovery and drug delivery, including the prediction of pharmacological and toxicological properties and ADME/tox using molecular modeling, machine learning, and in-vitro assessments.
She obtained her PhD in Pharmaceutical Sciences from Liverpool John Moores University in 1997. She was awarded a Postdoctoral Fellowship funded by the EU, where she developed models to estimate the endocrine disruption potential of environmental pollutant compounds. Before joining NSU in 2022, she held academic positions at various prestigious universities in the United Kingdom, including the University College London, the University of Kent (11 years), the University of Sussex (5 years), and the University of Bedfordshire. She has supervised many PhD and PharmD students and led various research projects. Dr Ghafourian is the Associate Editor of Springer’s Molecular Diversity, and an editorial board member of several other peer-reviewed journals. Most recently, her research has focused on the application of machine learning to the prediction of drug side effects, depression and antidepressants effects, the discovery of biomarkers for Alzheimer’s disease, and modeling of mitochondrial function. |
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| Research Interests: | |
| Dr Ghafourian’s research is on estimating the properties and biological effects of compounds using cheminformatics and wet laboratory experimentations to aid drug discovery and repurposing. Her cheminformatics and computer-based techniques include machine learning, molecular modelling, docking, and QSAR. Some of her current research includes: • Prediction of drug toxicities, including the effects of compounds on mitochondrial function, and their relationship to their chemical structures. • Prediction of drug side effects • Discovery of biomarkers for Alzheimer’s disease • Modelling of ADME/Tox properties and roles of ABC and SLC transporters • Characterization of formulation effects on drug delivery with nanoparticles, inhalation, and topical delivery systems. | |
| Research Interests keywords: | |
| Machine learning, Drug discovery, toxicity, QSAR, Alzheimer's disease, drug side effects, depression, antidepressants, mitochondria, ADME |
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