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OHP-027 Defining an integration process of personalised genomic medicine in clinic
  1. JM Guiu Segura1,
  2. M March Pujol1,
  3. J Monterde Junyent2
  1. 1Faculty of Pharmacy, Pharmacy Practice Research Group, Barcelona, Spain
  2. 2Asserta Foundation Knowledge for Sustainable Healthcare, Asserta, Badalona, Spain


Background Personalised medicine is based on availability of diagnostic biomarkers, but its future is strongly based on genomics. Genome sequencing may offer all this information but contests with properly analysed data. Genomic analysis will allow associating patients to therapies from the very beginning, saving time and costs and increasing the success of treatments. In these last years genome sequences prices are in free fall, therefore the implementation of this technology in clinic is almost upcoming.

Purpose To prepare the scenario for the introduction of the genome in clinics, defining an integration process of personalised genomic medicine in clinic, based on management of knowledge and big data.

Materials and methods We report an strategic approach of how the introduction of genome in clinics will develop in the following years, that it has been represented in three steps. In phase 1, it would be imperative generating the knowledge database, coding genetic variants that are linked to therapies through the knowledge of their functional effects. In phase 2 the knowledge database would be applied (that would include genomic sequencing, database markers and therapy prediction). Clinicians would receive hints on possible prescriptions and therapeutic interventions. Lastly, in phase 3 data recorded in previous steps would be used to produce new knowledge along with novel diagnosis and therapeutic guidelines.

Results We have described a rationale scenario where design of therapies rely on Systems Biology concepts. Pathways are complex and must be understood with proper bioinformatic tools. New therapeutic guidelines are expected to be based on validated genomic knowledge on a continuous feed-back of data.

Conclusions Healthcare professionals, including clinicians and pharmacists, will have to deal with ready for clinical interpretation decision support techniques; algorithms that relate biomarkers to treatments and outcomes coming from genomic diagnosis.

No conflict of interest.

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