New Technology in Drug Develpoment

How Computer Technology is helping in
Drug Discovery

1. Genomics , Proteomics & Biopharmateuticals

Genomics
• Genomics is fast-forwarding our understanding of how DNA, genes, proteins and protein function are related, in both normal and disease conditions
• Human genome project has mapped the genes in human DNA
• Hope is that this understanding will provide many more potentialprotein targets
• Allows potential "personalization" of therapies

Gene Chips

•"Gene chips" allow us to look for changes in protein expression for different people with a variety of conditions, and to see if the presence of drugs changes that expression

•Makes possible the design of drugs to target different phenotypes

Biopharmaceuticals
• Drugs based on proteins, peptides or natural products instead of small molecules (chemistry)
• Pioneered by biotechnology companies
• Biopharmaceuticals can be quicker to discover than traditional small-molecule therapies
• Biotechsnow paring up with major pharmaceutical companies

2.High-Throughput Screening

Screening perhaps millions of compounds in a corporate collection to see if any show activity against a certain disease protein

Informatics Implications
• Need to be able to store chemical structure and biological data for millions of datapoints
–Computational representation of 2D structure
• Need to be able to organize thousands of active compounds into meaningful groups
–Group similar structures together and relate to activity
• Need to learn as much information as possible from the data (data mining)
– Apply statistical methods to the structures and related information

3. Virtual Screening
• Build a computational model of activity for a particular target
• Use model to score compounds from "virtual" or real libraries
• Use scores to decide which to make, or pass through a real screen

Computational models of Activity
• Machine Learning Methods
–E.g. Neural nets, Bayesian nets, SVMs, Kahonennets
–Train with compounds of known activity
–Predict activity of "unknown" compounds
• Scoring methods
–Profile compounds based on properties related to target
• Fast Docking
–Rapidly "dock" 3D representations of molecules into 3D representations of proteins, and core according to how well they bind

Present molecules to model
• We may want to virtual screen
–All of a company’s in-house compounds, to see which to screen first
–A compound collection that could be purchased
–A potential combinatorial chemistry library, to see if it is worth making, and if so which to make

• Model will come out with witheither prediction of how well each molecule will bind, or a score for each molecule

4. Combinatorial Chemistry

• By combining molecular "building blocks", we can create very large numbers of different molecules very quickly.
• Usually involves a "scaffold" molecule, and sets of compounds which can be reacted with the scaffold to place different structures on "attachment points".

Combinatorial Chemistry Issues
•Which R-groups to choose
•Which libraries to make
–"Fill out" existing compound collection?
–Targeted to a particular protein?
–As many compounds as possible?
•Computational profiling of libraries can help
–"Virtual libraries" can be assessed on computer

5. Molecular Modeling
• 3D Visualization of interactions between compounds and proteins

• "Docking" compounds into proteins computationally

3D Visualization

• X-ray crystallography and NMR Spectroscopy can reveal 3D structure of protein and bound compounds
• Visualization of these "complexes" of proteins and potential drugs can help scientists understand the mechanism of action of the drug and to improve the design of a drug
• Visualization uses computational "ball and stick" model of atoms and bonds, as well as surfaces
• Stereoscopic visualization available

Docking with a Genetic Algorithm

• Put a compound in the approximate area where binding occurs
• Genetic algorithm encodes orientation of compound and rotatablebonds
• Optimize binding to protein
–Minimize energy
–Hydrogen bonding
–Hydrophobic interactions
•Can be used for "virtual screening"

6. In Vitro & In Silico ADME models

• Traditionally, animals were used for pre-human testing. However, animal tests are expensive, time consuming and ethically undesirable
• ADME (Absorbtion, Distribution, Metabolism, Excretion) techniques help model how the drug will likely act in the body
• These methods can be experemental(in vitro) using cellular tissue, or in silico, using computational models

In Vitro ADME Models

• Based around real tissue samples, which have similar properties to those in the body
• Example: CACO-2 tissue closely resembles the lining of the stomach, so if a molecule passes through CACO-2 it likely will also pass through the stomach lining, and thus be a candidate for oral delivery
• Cuts down animal tests, by acting as a "pre-screen"
• Enables ADME data to be discovered on many more compounds

In Silico ADME Models

• Computational methods can predict compound properties important to ADME, e.g.
–LogP, a liphophilicitymeasure
–Solubility
–Permeability
–Cytochromep450 metabolism
•Means estimates can be made for millions of compouds, helping reduce "atrittion" –the failure rate of compounds in late stage

Some of the challenges...

• Huge increase in the volume of information
–Genomics & High-throughput screening
–How do we use it to make better decisions (earlier)
• Immature technology and informatics
–Experimental hardware is changing rapidly
–Computing needs to meet complex, changing analysis needs
• "Fuzzy" science
–Even our understanding of the underlying science is constantly changing