METHODS: From bioinformatics data, mismatched mouse amino acids in variable light and heavy chain amphipathic regions were identified and substituted with those common to human antibody framework. Appropriate synthetic DNA sequences inserted into vectors were transfected into HEK293 cells to produce the humanized antibody.
RESULTS: Humanized antibodies showed specific binding to CD20 and greater cytotoxicity to cancer WIL2-NS cell proliferation than rituximab in vitro.
CONCLUSION: A humanized version of rituximab with potential to be developed into a biobetter for treatment of B-cell disorders has been successfully generated using a logical and bioinformatics approach.
METHODS: Genomic DNA obtained from a 55 years old, self-declared healthy, anonymous male of Malay descent was sequenced. The subject's mother died of lung cancer and the father had a history of schizophrenia and deceased at the age of 65 years old. A systematic, intuitive computational workflow/pipeline integrating custom algorithm in tandem with large datasets of variant annotations and gene functions for genetic variations with pharmacogenomics impact was developed. A comprehensive pathway map of drug transport, metabolism and action was used as a template to map non-synonymous variations with potential functional consequences.
PRINCIPAL FINDINGS: Over 3 million known variations and 100,898 novel variations in the Malay genome were identified. Further in-depth pharmacogenetics analysis revealed a total of 607 unique variants in 563 proteins, with the eventual identification of 4 drug transport genes, 2 drug metabolizing enzyme genes and 33 target genes harboring deleterious SNVs involved in pharmacological pathways, which could have a potential role in clinical settings.
CONCLUSIONS: The current study successfully unravels the potential of personal genome sequencing in understanding the functionally relevant variations with potential influence on drug transport, metabolism and differential therapeutic outcomes. These will be essential for realizing personalized medicine through the use of comprehensive computational pipeline for systematic data mining and analysis.