pharm 202 computer aided drug design l.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Pharm 202 Computer Aided Drug Design PowerPoint Presentation
Download Presentation
Pharm 202 Computer Aided Drug Design

Loading in 2 Seconds...

play fullscreen
1 / 30

Pharm 202 Computer Aided Drug Design - PowerPoint PPT Presentation


  • 403 Views
  • Uploaded on

Pharm 202 Computer Aided Drug Design. Phil Bourne bourne@sdsc.edu http://www.sdsc.edu/pb -> Courses -> Pharm 202. Several slides are taken from UC Berkley Chem 195. Perspective. Principles of drug discovery (brief) Computer driven drug discovery Data driven drug discovery

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Pharm 202 Computer Aided Drug Design' - Olivia


Download Now An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
pharm 202 computer aided drug design
Pharm 202Computer Aided Drug Design

Phil Bourne

bourne@sdsc.edu

http://www.sdsc.edu/pb -> Courses -> Pharm 202

Several slides are taken from UC Berkley Chem 195

perspective
Perspective
  • Principles of drug discovery (brief)
  • Computer driven drug discovery
  • Data driven drug discovery
  • Modern target identification and selection
  • Modern lead identification

Overall strong structural bioinformatics emphasis

what is a drug
What is a drug?
  • Defined composition with a pharmacological effect
  • Regulated by the Food and Drug Administration (FDA)
  • What is the process of Drug Discovery and Development?
drugs and the discovery process
Drugs and the Discovery Process
  • Small Molecules
    • Natural products
      • fermentation broths
      • plant extracts
      • animal fluids (e.g., snake venoms)
    • Synthetic Medicinal Chemicals
      • Project medicinal chemistry derived
      • Combinatorial chemistry derived
  • Biologicals
    • Natural products (isolation)
    • Recombinant products
    • Chimeric or novel recombinant products
discovery vs development
Discovery vs. Development
  • Discovery includes: Concept, mechanism, assay, screening, hit identification, lead demonstration, lead optimization
  • Discovery also includes In Vivo proof of concept in animals and concomitant demonstration of a therapeutic index
  • Development begins when the decision is made to put a molecule into phase I clinical trials
discovery and development
Discovery and Development
  • The time from conception to approval of a new drug is typically 10-15 years
  • The vast majority of molecules fail along the way
  • The estimated cost to bring to market a successful drug is now $800 million!! (Dimasi, 2000)
drug discovery processes today
Drug Discovery Processes Today

Physiological

Hypothesis

Primary Assays

Biochemical

Cellular

Pharmacological

Physiological

Molecular

Biological

Hypothesis

(Genomics)

Initial Hit

Compounds

Screening

+

Sources of Molecules

Natural Products

Synthetic Chemicals

Combichem

Biologicals

Chemical

Hypothesis

drug discovery processes ii
Drug Discovery Processes - II

Hit to Lead

Chemistry

- physical

properties

-in vitro

metabolism

Secondary

Evaluation

- Mechanism

Of Action

- Dose Response

Initial Hit

Compounds

Initial Synthetic

Evaluation

- analytics

- first analogs

First In Vivo

Tests

- PK, efficacy,

toxicity

drug discovery processes iii
Drug Discovery Processes - III

Lead Optimization

Potency

Selectivity

Physical Properties

PK

Metabolism

Oral Bioavailability

Synthetic Ease

Scalability

Pharmacology

Multiple In Vivo Models

Chronic Dosing

Preliminary Tox

Development

Candidate

(and Backups)

drug discovery disciplines
Drug Discovery Disciplines
  • Medicine
  • Physiology/pathology
  • Pharmacology
  • Molecular/cellular biology
  • Automation/robotics
  • Medicinal, analytical,and combinatorial chemistry
  • Structural and computational chemistries
  • Bioinformatics
drug discovery program rationales
Drug Discovery Program Rationales
  • Unmet Medical Need
  • Me Too! - Market - ($$$s)
  • Drugs in search of indications
    • Side-effects often lead to new indications
  • Indications in search of drugs
    • Mechanism based, hypothesis driven, reductionism
serendipity and drug discovery
Serendipity and Drug Discovery
  • Often molecules are discovered/synthesized for one indication and then turn out to be useful for others
    • Tamoxifen (birth control and cancer)
    • Viagra (hypertension and erectile dysfunction)
    • Salvarsan (Sleeping sickness and syphilis)
    • Interferon-a (hairy cell leukemia and Hepatitis C)
issues in drug discovery
Issues in Drug Discovery
  • Hits and Leads - Is it a “Druggable” target?
  • Resistance
  • Pharmacodynamics
  • Delivery - oral and otherwise
  • Metabolism
  • Solubility, toxicity
  • Patentability
a little history of computer aided drug design
A Little History of Computer Aided Drug Design
  • 1960’s - Viz - review the target - drug interaction
  • 1980’s- Automation - high trhoughput target/drug selection
  • 1980’s- Databases (information technology) - combinatorial
  • libraries
  • 1980’s- Fast computers - docking
  • 1990’s- Fast computers - genome assembly - genomic based
  • target selection
  • 2000’s- Vast information handling - pharmacogenomics
progress
Progress

About the computer industry…

“If the automobile industry had made as much progress in the past fifty years, a car today would cost a hundredth of a cent and go faster than the speed of light.”

  • Ray Kurzweil, The Age of Spiritual Machines
growth of pixel fill rates
Growth of pixel fill rates

SGI

PC cards

* Not counting custom hardware or special configurations

  • Fill rates recently growing by x2 everyyear

Data source: Product literature

slide21

Bioinformatics - A Revolution

Biological Experiment Data Information KnowledgeDiscovery

Collect Characterize Compare Model Infer

Complexity

Technology

Data

Higher-life

1

10 100

1000

100000

Computing

Power

Organ

Brain

Mapping

Cardiac

Modeling

Cellular

Model Metaboloic

Pathway of E.coli

Sub-cellular

102

106

1

Neuronal

Modeling

# People/Web Site

Ribosome

Assembly

Virus

Structure

Genetic

Circuits

Structure

Human

Genome

Project

Yeast

Genome

E.Coli

Genome

C.Elegans

Genome

1 Small

Genome/Mo.

Sequencing

Technology

ESTs

Gene Chips

Human

Genome

Sequence

90

95

00

05

Year

(C) Copyright Phil Bourne 1998

the accumulation of knowledge
The Accumulation of Knowledge

This “molecular scene”

for cAMP dependant

protein kinase (PKA) depicts years of collective knowledge.

Traditionally structure determination has been functional driven

As we shall see it is becoming genomically driven

history

History

History
  • Strong sense of
  • community ownership
  • We are the current
  • custodians
  • The community
  • watches our every
  • move
  • The community
  • itself is changing
slide24

Status - Numbers and Complexity

(a) myoglobin (b) hemoglobin (c) lysozyme (d) transfer RNA

(e) antibodies (f) viruses (g) actin (h) the nucleosome

(i) myosin (j) ribosome

Courtesy of David Goodsell, TSRI

slide25

No?

  • Bioinformatics
  • Distant
  • homologs
  • Domain
  • recognition
  • Bioinformatics
  • Alignments
  • Protein-protein
  • interactions
  • Protein-ligand
  • interactions
  • Motif recognition

Automation

Better

sources

  • Automation
  • Bioinformatics
  • Empirical
  • rules

Software integration

Decision Support

MAD Phasing Automated

fitting

Anticipated Developments

The Structural Genomics Pipeline

(X-ray Crystallography)

Basic Steps

  • Crystallomics
  • Isolation,
  • Expression,
  • Purification,
  • Crystallization

Target

Selection

Data

Collection

Structure

Solution

Structure

Refinement

Functional

Annotation

Publish

slide26

Protein sequences

structure info

sequence info

NR, PFAM

SCOP, PDB

Prediction of :

signal peptides (SignalP, PSORT)

transmembrane (TMHMM, PSORT)

coiled coils (COILS)

low complexity regions (SEG)

Building FOLDLIB:

------------------------------------

PDB chains

SCOP domains

PDP domains

CE matches PDB vs. SCOP

-----------------------------------

90% sequence non-identical

minimum size 25 aa

coverage (90%, gaps <30, ends<30)

Structural assignment of domains by PSI-BLAST on FOLDLIB-PRF

Only sequences w/out A-prediction

Structural assignment of domains by 123D on FOLDLIB-PRF

Only sequences w/out A-prediction

Create PSI-BLAST profiles for FOLDLIB vs. NR

Functional assignment by PFAM, NR,

PSIPred assignments

FOLDLIB-PRF

Domain location prediction by sequence

Store assigned regions in the DB

The Genome Annotation Pipeline

combinatorial libraries
Combinatorial Libraries
  • Thousands of variations to a fixed template
  • Good libraries span large areas of chemical and
  • conformational space - molecular diversity
  • Diversity in - steric, electrostatic, hydrophobic interactions...
  • Desire to be as broad as “Merck” compounds from
  • random screening
  • Computer aided library design is in its infancy

Blaney and Martin - Curr. Op. In Chem. Biol. (1997) 1:54-59