Computational Bioethics relates to the appropriateness of use, management of access and discovery of biological insights applied to patient health. A simple search in Google shows that this concept has only been used in the past at a workshop session in the Rocky Bioinformatics conference in 2008. In the resulting publication of this meeting, Computational Bioethics is said to encompass ethical issues that are unique to computational scientists compared with bench and clinical scientists. In addition, it is argued that as medical informatics, computational biology and bioinformatics become more widely used in medical research, these issues will become even more relevant in the future.
And the future is now become a present reality. The rise of next generation sequencing technology, together with the adoption of microarray techniques in normal clinical practice is revolutionizing the way patient’s data is handled. Similarly, the amount of data created is surpassing most software tools available. Projects like the 1000 Genomes are generating an enormous pool of variability of genome data and derived sources of information. This makes necessary the exchange of data between research centers to be able to establish what constitutes normal and pathogenic variation in human beings.
Personalized medicine, a widely used term since the near completion of the Human Genome Project, implies the application and use of personal genome data for the determining genomic changes leading to disease, risk factors and patient’s susceptibilities. The problem with this sort of data is that it may yield information that could severely change the life style of the person, affect relatives and, in many cases, with little ability to do anything to mitigate the effects of findings. As our current knowledge is so fragmented and rudimentary, it is expected that collected information with no current value may be relevant in the future, influencing analyses and results.
Some ethical practices currently implemented in Computational Bioethics include the creation of consent forms that allow putting anonymized patient information in genome databases. This information may be accessed by the scientific community and compared with other previously consented patient data for the characterization of new syndromes, the effect of knockout genes in the general population and the understanding of genome variation in normal individuals.