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Pharmacogenomics - Biomatics.org

Pharmacogenomics

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Pharmacogenomics is the branch of pharmaceutics which deals with the influence of genetic variation on drug response in patients by correlating gene expression or single-nucleotide polymorphisms with a drug's efficacy or toxicity. By doing so, pharmacogenomics aims to develop rational means to optimise drug therapy, with respect to the patients' genotype, to ensure maximum efficacy with minimal adverse effects. Such approaches promise the advent of "personalized medicine", in which drugs and drug combinations are optimised for each individual's unique genetic makeup.

Pharmacogenomics is the whole genome application of pharmacogenetics, which examines the single gene interactions with drugs. 


Rational drug design

As it relates to Biomatics then perhaps Pharmacoepigenomics is a better term. It deals with the interaction of drugs with biomatically described systems.  

Epigenomic mechanisms play a critical role in controlling gene expression and thus drug responsiveness. The epigenome is composed of chromatin and its methylation, acetylation etc.modifications. Chromatin structure  dynamics  are involved in the pathology of cancer and in normal aging.  This field will address the epigenomic basis of individual differences in drug responsiveness, identification of side effects of drugs  and the discovery of novel drug targets. The control of epigenetic states presents therapeutic and prophylactic opportunities, which requires the development of drugs that target the epigenomic machinery. 

Biomatics describes the dynamics of gene clusters and the proteins they produce and coordinate.  The biomatic model provides a snapshot of the ideal or goal “state” as well as the actual state.  The opportunity for the pharmaceutical industry is to engineer therapies to restore the healthy state.  A good place to start is by studying the various types of Histone modifying reactions.

DNA Microarray technology can yield time series data of the entire genome, thus giving pictures of the different stages of diseases.  Clustering algorithms in many experiments have yielded precisely eight clusters of genes in  Asthma, various cancers and other areas of interest.  While clustering algorithms may not be entirely reliable, this may not be a mere coincidence and could be evidence of the models proposed elsewhere in this WIKI.  If this is true then these rich models may help to understand the underlying processes and aid development of treatment plans.  

Control Group acting on set of Gene Clusters


 The following multiplication table may manifest itself in DNA Microarray data if the "Group action on a set" model is correct.

*
0
A
B
C
AB
AC
BC
ABC
G0
G0
G1
G2
G3
G4
G5
G6
G7
G1
G1
G0
G4
G5
G2
G3
G7
G6
G2
G2
G4
G0
G6
G1
G7
G3
G5
G3
G3
G5
G6
G0
G7
G1
G2
G4
 G4
G4
G2
G1
G7
G0
G6
G5
G3
G5
G5
G3
G7
G1
G6
G0
G4
G2
G6
G6
G7
G3
G2
G5
G4
G0
G1
G7
G7
G6
G5
G4
G3
G2
G1
G0

 



The following  "cellular automaton" courtesy of wikipedia shows what a time series of DNA microarray data could look like.  Note that 32,000 genes could be represented by an array of approximate dimensions of  178 by 178 whereas the following cellular automaton has dimensions 199 by 175.

Image:Oscillator.gif

The following classes of chromatin modifying reactions represent seperate pathways that drugs may  influence in  the dynamics of various medically relevant situations.

     Methylation
          Methyltransferase inhibitors
     Acetylation
          Acetyltransferases
          Histone  deacetylase  inhibitors
     Phosphorylation 
          DNA topoisomerase I and II 
     Ubiquitination
     Sumoylation

Combinations of these agents could theoretically  be used to affect the Histone-DNA machinery to bring tissue back to healthier states. 

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