Javascript must be enabled for the correct page display

A computational approach to predicting drug resistance in kinase dependent lung cancer

Doornhof, Kevin (2019) A computational approach to predicting drug resistance in kinase dependent lung cancer. Research Project, Pharmacy.


Download (5MB) | Preview
[img] Text
Restricted to Registered users only

Download (119kB)


The use of kinase inhibitors in cancer treatment has shown much potential in improving how common variants of lung cancer are treated. The major issue all current inhibitors face is that after a period of treatment, resistance against the drugs tends to develop. A common mechanism for drug resistance in kinase driven lung cancer is the emergence of secondary mutations. In some cases, it is possible to switch to a different inhibitor after resistance develops. While this is often known for the most common resistance mutations, in cases with novel mutations there is often little to no information available to make informed clinical decisions. Computational methods such as homology modeling and molecular docking could provide a solution to this issue, but large scale implementation of such methods is difficult as it currently requires a large time investment by knowledgeable experts to interpret the relevant data. To help resolve this issue, molecular docking filters were designed that can identify docking poses that provide indicators of drug sensitivity in an automated manner. Validating these filters using known resistance mutations showed that such a method has the potential to drastically reduce the workload required to draw conclusions from docking data, potentially allowing for broader application of computational methods in predicting drug efficacy and informing clinical decision-making.

Item Type: Thesis (Research Project)
Supervisor nameSupervisor E mail
Vilacha Madeira R Santos,
Degree programme: Pharmacy
Thesis type: Research Project
Language: English
Date Deposited: 11 Jul 2019
Last Modified: 16 Jul 2019 08:43

Actions (login required)

View Item View Item