mrr iti phar_mu

Mariam Reyad (OST) - PharMu Portal - June 2011

PharMu PortalBugs vs Drugs Database

Graduation Project By:Mariam Reyad Rizkallah

Open Source Technologies DepartmentInformation Technology Institute

Intake 31


Presentation Outline

Introduction Scientific Problem and Technical Solution PharMu Aim Project Scope Methodologies Results: Delivered Features Demo Future Work Acknowledgement


Introduction: EgyBio Network


Introduction: HGP (2001)Human Genome Project:- To know how different we are!

- To assess how similar we are!

- To predict susceptibility to diseases (e.g., heart disease and cancer)

- To know if there is a certain population that is protected by its genes against certain diseases.

- To use this information for tailoring medicine for patients based on their genetic profiles. Source: Science, 16 FEBRUARY

2001VOL 291, ISSUE 5507, PAGES 1145-1434

Result: Humans are 99% alike!


Introduction: HMP (2005)

Human Microbiome Project:

- It's gotta be the microbes!!

Human cells 10^13 vs. microbial cells 10^14.

“Combined genomes of the human-associated microbes.“

Microbes freely live inside/on the surface of humans as their favorite place to live.

Source: http://commonfund.nih.gov/hmp/


Introduction: Bugs vs DrugsHuman-Microbiota Relationship:

It's complicated! Beneficial:

-Protective (against intruders)

-Productive (vitamins)

-Digestive (breakdown food) Harmful:

-Imbalance (diarrhea)

-Invasion

-Become virulent


Introduction: Bugs vs Drugs

Drug-Microbiome Relationship


PharMu Aim

Explore how resident microbes change the behavior of drugs.

Introduce bioinformatics and microbial genomics to pharmacy students while benefiting the research community.

Build a knowledge base that allows interested students and scholars, in the future, to predict the behavior of untested members of drug classes or unstudied microbial species, and to design laboratory experiments for testing these predictions.


Scientific Problem and Technical Solution


PharMu Phases

Step1: mining existing literature and extracting all known microbe-drug interactions.

Step2: manual curation of the extracted literature data(Pubmed) and their classification by drug classes (PubChem), microbial families (Taxonomy), and body systems (HMP).

Step3: creation of a relational database that includes the microbes at different body sites and their effects on drugs’ pharmacokinetic(fate) and pharmacodynamic(action) properties.


PharmacoMicrobiomics

Body site

Chemical ID

Drug name

Assay

Drug class

Effect (Increase/Decrease)

Microbial ID

Description

Pathway/genes responsiblePubmed ID

Classificationhierarchy

Scientific Problem and Technical Solution


Methodologies

Powered by:


Methodologies: Database Diagram


Demo

http://196.202.46.195/pharmacomicrobiomics/


Delivered Functionalities

User module Admin backend Search engine


Future Work

Deploment on Server (http://www.pharmacomicrobiomics.com)

Advanced search Use of BioPython libraries to retrieve data from NCBI

public database. Deployment on Ubuntu Enterprise Cloud. Integration with a content management system. Annual update of data by designing a Perl script to

parse MeSH tree of keywords binary files. Addition of Chemical and pharmacological classification

hierarchy (inclusion of >10 tables).


Acknowledgement

I thank

Eng. Sherine Bahader, Head of OST Dept

Eng. Moataz Ahmed, TA at OST Dept

Eng. Ahmed Abdel-Khaleeq, E-Business Dept

Eng. Hany Safwat, Head of SD Dept

I thank ITI for giving me all the tools and knowledge necessary to solve my scientific problems.


Thank you

mrr iti phar_mu

Technology

microbial cells

behavior of drugs

human cells

humanassociated microbes

drug classes pubchem

microbial genomics

drugs pharmacokineticfate

hgp2001 human genome