It is well recognized that a drug may exert different therapeutic and/or toxic impacts in different individual patients due to distinct genome, proteome, metabolom profile of patients. In recent years, pharmacogenomics, proteomics, metabolomics studies have been concerned with the effects of several genes, proteins, metabolites on particular phenotype, especially when inter-individual variation exists in the response to a particular drug or when adverse drug reactions are known to occur. In order to develop optimal patient-specific therapy, we collected the biological samples (e. g. blood, serum, plasma, buffy coat, urine and DNA) from a total 33000 employees of the Ministry of Health (community health workers/Behvarz) selected from all rural areas of Iran through Behvarz health study (Cohort). These samples were collected, processed and stored in biobank based on the consensus protocols that guarantee the suitability of biological samples for omics analyses (e. g. genomics, epigenomics, proteomics and metabolomics). The behhvarz health study biobank was established for long-term storage (at least twenty years) of biological samples. In addition, a new laboratory information management system (LIMS) software was developed to manage, track, and organize the participant’s samples, data, and samples stored in the biobank. A whole experimental approach was performed to measure the blood parameters such as glucose, calcium, creatinine, blood urea nitrogen, Alanine aminotransferase, Alkaline phosphate, low and high density lipoprotein, cardiac c-reactive protein, aspartate aminotransferase, Cholesterol, Triglyceride, urea, phosphate and complete blood count. In addition, the urine parameters including color, appearance, bilirubin, ketone bodies, urobilinogen, ascorbic acid, protein, pH, nitrite, leukocyte and specific gravity were measured using urine test strip. The health and personal condition and lifestyle of all behvarzes were collected and will be tracked for more than 20 years. This approach will provide unique opportunities to determine the biomarkers and risk factors that associate with disease prevalence. The collected samples will be used for omics analyses and therefore, it is possible to develop patient-specific drugs.