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Dr Polina Prokopovich

(she/her)

Teams and roles for Polina Prokopovich

Overview

I lead a multidisciplinary research group conducting a range of methodologies in drug and formulation development and scale-up, medical device fabrication through preclinical and early phase clinical testing.

Another arm of my research is focused on real-world evidence (RWE) conducting large dataset analysis and employing machine learning algorithms to answer specific clinically relevant questions through predictive modelling. 

My portfolio of research covers infectious, autoimmune, musculoskeletal diseases and cancer. 

 

 

Publication

2025

2024

2023

2022

2021

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2007

2006

2005

2004

Articles

Book sections

Conferences

  • Omar, F., Elkaseer, A., Brousseau, E., Kolew, A. and Prokopovich, P. 2013. Demoulding forces in hot embossing: Model development and validation. Presented at: 8th International Conference on MicroManufacturing (ICOMM2013), Victoria, Canada, 25-28 March 2013Proceedings of the 8th International Conference on MicroManufacturing (ICOMM2013). pp. 642-649.
  • Prokopovich, P., Theodossiades, S., Rahnejat, H. and Hodson, D. 2010. Nano- and component level friction of rubber seals in dispensing devices. Presented at: ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, San Diego, CA, USA, 30 August-2 September 2009ASME Power Transmission and Gearing Conference; 3rd International Conference on Micro- and Nanosystems; 11th International Conference on Advanced Vehicle and Tire Technologies. ASME Conference Proceedings Vol. 6. New York: ASME pp. 339-344., (10.1115/DETC2009-86035)

Research

Current research projects:

-“Risk factors in prosthetic hip/knee joint replacement infections incidence and patient outcomes”

The aim is to explore the relationship between the risk of developing PJI and fixation types, intra-articular steroid injections, anticoagulants, smoking and other risk factors associated using  the Clinical Practice Research Datalink (CPRD) datasets.

-“The Risk Factors Associated with Treatment Failure among Rheumatoid Arthritis Patients and the Use of Machine Learning Models to Predict the Treatment Response Based on Routinely Collected Data”

The aim is using CPRD data from UK clinical practice to evaluate the sociodemographic and clinical characteristics associated with the initiation of different rheumatoid arthritis treatments, compare the safety and effectiveness of different types of treatments and predict the treatment response using machine learning techniques.

  • NHS
  • Wellcome Trust
  • Welsh Government
  • Innovate UK
  • EU funds
  • Industries

Teaching

Undergraduate

Presentation assessment lead of PH1121 (Molecule to Patient)

Personal Tutor

Contribution to MPharm modules:

    • PH1122  The role of the pharmacist in professional practice
    • PH1123  Structure and function of cells and microbes
    • PH1124  Human body systems
    • PH1000 Professional development
    • PH2107 Formulation Science I
    • PH2113 Diseases and Drugs 1
    • PH2000 Professional development
    • PH2110 Clinical and Professional Pharmacy (OSCEs)
    • PH3114  Design, formulation and quality assurance of medicinal products
    • PH3202  Research methodology
    • PH3000 Professional development
    • PH4116  Pharmacy research or scholarship project
    • EN3060 Biomaterials and Tissue Engineering

Postgraduate

  • MSc course - Cancer and Experimental Therapeutics
  • MSc and PhD student supervision

Contact Details

Specialisms

  • Big data
  • Biofabrication
  • Infectious diseases
  • Medical devices
  • Rheumatology and arthritis