Thursday, July 27, 2006

Modern Drug Discovery

  • Target identification: usually through biological or genetic investigation.
  • An assay to look for modulators (either inhibitors, antagonists, or agonists) of the target activity.
  • High-throughput screen (HTS).
  • Elaboration of the initial small molecule hit through medicinal chemistry: combinatorial chemistry, quantitative structure activity relationships (QSAR), computer-aided drug design (CADD) and structure-based drug design.
  • Lead optimization into a candidate drug: multidimensional optimization problem searching within the relatively limited chemical space of analogs of the lead compound.
  • Large-scale production methods, preclinical animal safety studies, clinical trials.

Structural Bioinformatics on Drug Discovery

Target Assessment: target druggability: the energetically optimal protein would be spherical, with all its hydrophobic residues pointing inward. A quantitative approach is the rule of five (Lipinski):

A compound is likely to show poor absorption or permeation if:
  • It has more than five hydrogen bond donors
  • The molecular weight is over 500
  • The Clog P (calculated octanol/water partition coefficient) is over five
  • The sum of nitrogens and oxygens is over 10
  • Weak inhibition (< 100 nM) is observed

Another physicochemical complementary properties: surface area and volume of the pocket, hydrophobicity and hydrophilic character, curvature and shape of the pocket.

Target Triage: computer-aided target selection (CATS), based on the importance of a gene for the organism, the occurrence of the gene in multiple target species, specificity or inhibition by reference to sequence similarity, and easiness of assay. The group of residues lining a ligand-binding site are of more
importance than long-range interaction and conformational changes.

Target Validation: knock-out of the gene of interest or RNA antisense technology to inactivate the gene.

Lead Identification: structural bioinformatics can be used for function and ligand prediction. Using structural similarity to find chemical leads (usually, when the proteins share less than 30% sequence identity the active sites are nonidentical). Virtual screening: to derive a pharmacophore describing the functionally important sites in a ligand-binding site, and docking and scoring. Creating a chemical library.

Lead Optimization: repeated cycles of determining the structure of the target in complex with a number of lead compounds and their analogs. Structural bioinformatics should be used to design suitable constructs of the outset of the project. Alignment and secondary structure prediction on multiple alignments. Homology modeling and the use of a surrogate protein (an orthologous protein from another species or a similar member of the same gene family).

ADMET Modeling: additional parameters that can affect the biopharmaceutical and safety properties of the drug: in vivo absorption, distribution, metabolism, excretion, and toxicology. Sequence-structure relationship and protein homology modeling can be used in ADMET modeling.

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