Coronavirus (Covid-19): Latest updates and information
Skip to main content Skip to navigation

Biography:

Ms Linda Wanika,  

Research Student (PhD)


School of Engineering
University of Warwick
Coventry
CV4 7AL, UK

Room: D229
Phone:
Fax: +44 (0)24 76 524560


Current Research: Meta-analysis of Uncommon Adverse Events Supervisors: Prof Mike Chappell (School of Engineering, Warwick), Dr Neil Evans (School of Engineering, Warwick) and Dr Martin Johnson (AstraZeneca, Cambridge).

My research focuses on the application of modelling and data analysis of drug-induced interstitial lung disease. Some patients who are diagnosed with Non-small cell lung cancer, may recieve tyrosine kinase inhibitors (TKIs) as their treatment. Some of these patients may develop interstitial lung disease as an adverse event. These events can decrease the patient’s ability to breathe, cause a suspension from their treatment, and unfortunately in some cases, lead to death. The mechanisms behind TKI-induced interstitial lung disease is not fully understood.

My aim is to investigate possible factors and correlations as to why certain patients experience these adverse events using mechanistic modelling, meta-analysis, machine learning and data analytics. 

Conferences attended:

QSP Reading 2018

QSP AMR Warwick 2018

PKUK 2018: Presented a Poster, “Meta-Analysis of Uncommon Adverse Events”

PAGE 2019: Presented a poster, “Analysis of Lactate Dehydrogenase and Haemoglobin
                                                                           in drug induced Chronic Lower respiratory adverse events”

QSP 2019

PKUK 2019: Gave an oral presentation, “Tyrosine Kinase Inhibitors in Drug Induced
                                                                                             Interstitial Lung Disease”

Past Research

Creating a model that characterises parallel elimination Supervisor: Prof Mike Chappell
2017  As part of the MSc Interdisciplinary Biomedical Research

This mini-project involved using a computer tool to create and test a compartmental model that could characterise and simulate parallel elimination during the metabolism of drugs. This model was based around the use of nonlinear Michaelis Menten kinetics and was able to generate model parameter estimates for dissociation constants (KD) for potentially four enzymes. These KD estimates suggest that there were different pathways of elimination for the drug. This outcome indicates that the model was able to predict parallel elimination and that it could be built upon to provide other clinically significant mechanistic characterisations. 

Investigating the relationship between astrocytes and neuroplasticity Supervisor: Dr Yuriy Pankratov (School of Life Sciences, Warwick)
2017  As part of the Msc Interdisciplinary Biomedical Research

In this mini-project, I investigated the interactions between astrocytes and neuroplasticity using Long Term Potentiation (LTP). In the past, astrocytes have been known to function as the “supportive cells” in the nervous system. During recent times however, astrocytes have been shown to be able to release transmitters such as GABA and Glycine. LTP is thought to be the mechanism by which the brain is able to learn new things. The results from this study show that there was a decrease in LTP when there was a mutation in the astrocytes’ functionality. This suggests that astrocytes are important for learning and neuroplasticity






Source: Linda Wanika