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Taravat Ghafourian, Pharm.D., Ph.D., FHEA

Taravat Ghafourian

Title:

Associate Professor

Department:

Pharmaceutical Sciences

College/Division:

Barry and Judy Silverman College of Pharmacy

Campus:

Fort Lauderdale/Davie

Dr.  Taravat Ghafourian, PhD, PharmD, FHEA is an Associate Professor at the Department of Pharmaceutical Sciences. She specializes in drug discovery and drug delivery, with an established record in the prediction of pharmacological and toxicological properties of drug candidates and ADME/tox using molecular modeling, machine learning, and in-vitro assessments of mitochondrial respiration. 

She obtained her PhD in Pharmaceutical Sciences from Liverpool John Moores University in 1997 and was awarded a Postdoctoral Fellowship funded by the EU (Fifth framework programme) 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 since 2001, including the University of Kent (11 years), the University of Sussex (5 years), and the University of Bedfordshire (2 years). She has supervised many Ph.D. and Pharm.D. 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 discovery of biomarkers for Alzheimer’s disease, prediction of mental health-related drug effects and side effects, and modeling of mitochondrial function. 

Dr Ghafourian’s research is focused on estimating the properties and biological effects of compounds using cheminformatics and wet laboratory experimentations. Her cheminformatics and computer-based techniques include molecular modelling, docking, QSAR with various machine-learning methods to predict drug properties and mechanisms to aid drug discovery (virtual screening and rational drug design) and repurposing. Some of her current research focus includes:

  • Mechanistic studies on measuring the effects of compounds on various aspects of mitochondrial function, and its relationship to their chemical structures.
  • Prediction of drug toxicities such as drug-induced hepatotoxicity and phospholipidosis.
  • Modelling of ADME/Tox properties such as characterisation of distribution and elimination routes of drugs and prediction of drug pharmacokinetics.
  • Interaction of compounds with various ABC and SLC transporter proteins.
  • The effects of compounds/drugs on ageing.
  • Characterisation of pharmaceutical excipient effects on drug delivery endpoints, including the effect of formulation factors on transdermal and oral delivery of drugs.

  • Fellow of the UK Higher Education Academy

  • Postdoctoral research, University College London (UCL) and Liverpool John Moores University

  • Postgraduate Certificate in Higher Education, University of Kent

  • Ph.D. in Pharmaceutical Chemistry, Liverpool John Moores University

  • Pharm.D., Tabriz University of Medical Sciences

  • Rosell-Hidalgo, A., Young, L., Moore, A.L. Ghafourian, T. QSAR and molecular docking for the search of AOX inhibitors: a rational drug discovery approach (2020). J Comput Aided Mol Des. pp. 1-16.
  • Rajabnezhad, S., Ghafourian, T., Rajabi-Siahboomi, A., Missaghi, S., et al. A. Investigation of water vapour sorption mechanism of starch-based pharmaceutical excipients, (2020). Carbohydrate Polymers, 238:116208.
  • Barardo, D.G., Newby, D., Thornton, D., Ghafourian, T., de Magalhães, J.P., Freitas, A.A. Machine learning for predicting lifespan-extending chemical compounds (2017). Aging, 9 (7), pp. 1721-1737.
  • Sharifi, M., Ghafourian, T. Effect of OATP-binding on the prediction of biliary excretion (2017). Xenobiotica, 47 (7), pp. 614-631.
  • Aniceto, N., Freitas, A.A., Bender, A., Ghafourian, T. A novel applicability domain technique for mapping predictive reliability across the chemical space of a QSAR: Reliability-density neighbourhood (2016). Journal of Cheminformatics, 8 (1):69.
  • Newby, D., Freitas, A.A., Ghafourian, T. Decision trees to characterise the roles of permeability and solubility on the prediction of oral absorption (2015). European Journal of Medicinal Chemistry, 90, pp. 751-765.