Dr Bissan Al-Lazikani
Team: Computational Biology & Chemogenomics
Tel: 0208 722 4300
Location: Haddow Laboratories, Sutton
Cancer drug discovery efforts are faced with exponential growth of genomic, biochemical and screening data, fuelled by increasingly powerful and smarter technologies. These data present significant opportunities for drug discovery, but also immense challenges; particularly how we can extract the 'gold nuggets' out of the glut of large-scale data. An urgent need exists to integrate and use these diverse data effectively in the development of new drugs.
The team’s main aim is to empower translational research in cancer drug discovery by using computational techniques to bridge the gap between biological, chemical and clinical knowledge.
We develop and apply novel computational tools and approaches to integrate biological, chemical and clinical data at a large scale.
These tools are employed in the following areas:
- Using chemogenomics data to support decision-making in the drug discovery process, from target selection to lead identification. This should help identify opportunities and risks early on in the process, thus helping streamline novel drug discovery.
- Using clinical, RNAi screening and high-throughput biology data to identify targets for cancer.
- Analysing protein interaction networks for optimal therapy intervention points.
- Identifying patterns and rules in targets and networks which yield successful drugs, and applying these rules specifically in the complex field of cancer drug discovery.
- Leveraging large scale structural biology and Structure Activity Relationship (SAR) data in determining target tractability and in aiding drug design.
- Understanding rules governing ligand binding and target/ligand selectivity and using them predicatively in assessing target and compound development.
- Utilising published clinical outcome data to enhance the assessment and understanding of drug/target activities.
- Aiding experimental hypotheses by providing information on suitable cellular test system and chemical probes.
These aims will be achieved through four major areas of research and development within the group:
- Integrated chemogenomics tools: canSAR, canSAR-3D.
- Objective target assessment and prioritisation.
- 3-D structural analysis of cancer-associate protein families.
- Mapping 'druggable' protein networks.
Dr Bissan Al-Lazikani is formally trained in molecular biology and computing. She obtained a B.Sc. (Hons) in molecular biology from University College London, followed by a Masters degree in Computer Science from Imperial College.
Dr Al-Lazikani achieved her PhD in computational structural biology from Cambridge University and the MRC Laboratories of Molecular Biology under the supervision of Dr Cyrus Chothia, where she worked on understanding the structural basis for immune recognition. Subsequently, she became a Howard Hughes postdoctoral fellow at the laboratories of Professor Barry Honig in Columbia University, New York, where Dr Al-Lazikani focused on structure analysis, prediction and modeling for the purpose of understanding the basis of ligand-receptor interactions.
Upon completion of her postdoctoral, Dr Al-Lazikani joined a London-based Biotechnology company, Inpharmatica, where she led a team to development of Chemogenomics databases and tools to aid target prioritization and drug discovery.
These are now available to the community via a Wellcome strategic award through the ChEMBL resources at the European Bioinformatics Institute (EBI).
After helping with the transition of these resources to the EBI, Dr Al-Lazikani joined the Cancer Research UK Cancer Therapeutics Unit in 2009 to lead the computational biology and chemogenomics team in order to apply computational techniques to cancer drug discovery.
Empower translational research in cancer drug discovery by using computational techniques to bridge the gap between biological, chemical and clinical knowledge.
3-D Structural Analysis
Facilitates our understanding of drivers of conformational change within a family of related proteins and the drivers of selectivity in small molecule-protein interactions.