Lecture Contents -- Unit 3 Drug Discovery –Basic objectives and problems –Screening approach vs. rational design –Phytopharmacology –Databases, QSAR, and.

Lecture Contents -- Unit 3

• Drug Discovery– Basic objectives and problems

– Screening approach vs. rational design

– Phytopharmacology

– Databases, QSAR, and CoMFA

– “Pharmacogenomics” and “proteomics”

– Case study: GV 150526A

Basic Facts About Drug Discovery

• Almost any metabolic pathway with all it’s adjuncts (receptors, enzymes, genes therefor, and regulatory elements) is a potential drug target

• During the past century, pharmacology has identified some 400 such targets; the human genome project confirms that thousands must exist

• Independent of this, the present rate of drug discovery is insufficient; new strategies are required

Some CompaniesSpecialize in Drug Discovery

Drug Discovery Strategies

• Screening-based:– Traditional medicine– Bioprospecting– Mass screening of microbial strains– Combinatorial chemistry

• Rational Drug Design– Target interaction analysis

and molecular modeling

Natural Product-BasedDrug Discovery

Natural Product Success Stories

• Microorganisms: Antibiotics

• Plants:– Taxoids for cancer– Artemisinin for malaria– Huperzine A and galanthamine for Alzheimer

• Animals: Conotoxins as ultra-high potency analgetics

Phytopharmacology: Decision Tree

„Microbial Pharmacology:“ Penicillin And Other ß-Lactames• Fleming (1928): Growth of bacterial cultures

inhibited by co-infection with Penicillium notatum “penicillin” postulated as a secreted molecule

• 1938: Penicillin isolated and characterized as part of British war preparations

• Beta-lactames became most important lead structure ever since then

Benzylpenicillin (Penicillin V)

Phytopharmacology: Taxoids

• Diterpene from Taxus brevifolia

• Most significant anticancer agent developed in the past two decades (“mitotic poison”)

Phytopharmacology: Artemisinin

• Unusual sesquiterpene endoperoxide from Artemisia annua (Quinghaosu in Chinese traditional medicine)

• Lead compound for new generation of malaria therapeutics (including chloroquine- resistant and cerebral malaria)

C15H22O5

MW = 282.3

Marine Pharmacology: Conotoxins

• Peptide neurotoxins (receptor channel blockers) from molluscs (snails and shells)

-conotoxin PnIa: nicotinic receptor blocker

-conotoxin MVIIc:P-type Ca-channel blocker

The Ideal Combinatorial Library

Made by forming all possible combinationsof a series of sets of precursor molecules,and applying the same sequence of reactionsto each combination

Combinatorial Chemistry:Basic Theoretical Approach

TEMPLATE

R1

R2

R3

Combinatorial Chemistry: Detection of Hits

Obstacles to Combinatorial Chemistry

• Restricted and specialized chemistry, needs training

• Not yet suitable for large molecules

• Automated synthesis needs to be installed and integrated with the laboratory workflow

• Equipment AND organization must be tightly integrated with a tailored data management infrastructure

A Well-Designed LibraryCan Mean BIG Money...

• 1995: Schering-Plough pays $3 million for access to certain parts of the Neurogen compound library

• Payment estimates for unrestricted access to targeted libraries run up to $15 million

• Construction of large (diverse or targeted) combinatorial libraries) has become a significant outsourcing business

Combinatorial Chemistry:SAR By NMR

New Frontiers in Receptor Ligand Screening

Databases In Drug Discovery

• Employ advanced search algorithms including artificial intelligence (AI) systems

• “Data Mining” -- knowledge discovery in databases:– Fuzzy logic -- “soft” search criteria– Structural similarity searches– Retrieve implicit information– Link structural information with bio-informatics

Tools for Rational Drug Design

• (Q)SAR: (Quantitative) Structure-Activity Relationships

• SAFIR: Structure-Affinity Relationships

• SPAS: Structure-Property/Affinity Studies

• CoMFA: Comparative Molecular Field Analysis

SARs, Easy and Obvious? Stimulants/Anorectics in Medicine

SARs, Easy and Obvious? Stimulant Drugs of Addiction

Can „Drug-Like“ StructuresBe Predicted?

• Only 32 basic templates describe half of all known drugs (Bemis et al. 1996)

• Medicinal chemists essentially use their intuition (“expert rules”) to gauge drug structures emulation by trainable (and self-entraining) neuronal networks working from relatively few molecular descriptors

• If “drug-likeness” can be quantified targeted design of combinatorial libraries

Comparative Molecular Field Analysis

• CoMFA: Method to analyze and predict structure-activity relationships (Cramer 1988)

• Based on superimposition techniques:– Steric overlap (“distance geometry”)– Crystallographic data– Pharmacophore theory– Steric and electrostatic alignment algorithms– „Automated field fit“

Further reading:http://www.netsci.org/Science/Compchem/feature11.html ; http://cmcind.far.ruu.nl/webcmc/camd/3dqsar.html

The Essence of CoMFA

• Superpose active and inactive analogues; calculate the “receptor excluded volume,” the occupancy of which would result in loss of activity

• Use ligand binding points and conformational restraints to decompose the distance matrix into differences and similarities

© Tripos Software

Somatostatin Receptor Ligand Modeling

Science 282, 737-9 (23 Oct 98)

New Buzzwords in Drug Discovery

A Case Study In Drug DiscoveryGV-150526A

(CAS: 153436-38-5)

3-[2-phenylaminocarbonyl)ethenyl]-4,6-dichloroindole-2-carboxylate,a glycine antagonist currently completing Phase III studies for stroke

Glutamate, Receptors, And Stroke

The NMDA Receptor Complex

Starting Point: Known Antagonists of Glycine Site at the NMDA

Receptor

Kynureic acid (R1 and R1 can be H or Cl)Nanomolar in vitro affinity but poor in vivoactivity due to insufficient CNS penetration

Improved CNS penetrationbut lack of receptor selectivity

4,6-dichloroindole-2-carboxylate:Good receptor selectivity and CNS penetration,but in vitro affinity for glycine site (pKi=5.7)needs to be improved; however:A NEW LEAD STRUCTURE IS IDENTIFIED!

!

Input From Theory

Comparison with receptor modelpredicts that a hydrogen bond acceptinggroup in the “northeast” of the template is required for optimal binding

C-3 unsaturated side chainsshould be able to considerably enhancethe affinity to the glycine binding site

Template Derivatization At C-3

PRIMARY SCREENING SYSTEM:

In vitro binding inhibition of [3H]-glycineto crude synaptic membrane preparationsfrom adult rat cerebral cortex

SARs From Primary Screening

R pKi

H 5.7CH2-CH2-COOH 7.4CH2-CH2-CONH-Ph 7.6CH=CH-COOH 7.7CH=CH-COO-tBu 6.3CH=CH-CONH-Ph 8.5CH=CH-CONH-C10H7 7.4CH=CH-CONH-CH2-Ph 6.9CH=CH-SO2NH-Ph 6.1

pKi = inverse logarithm of binding constant to the glycine site of the NMDA receptor

Can The in vitro Characteristics of the Refined Lead Be Improved Further?

Ro Rm Rp pKi

H H H 8.5H H NH2 8.9H NH2 H 8.3NH2 H H 8.5H H OH 8.7NO2 H H 7.6H OCH3 OCH3 8.1CH3 H OCH3 7.7NO2 H F 7.5H H COOH 7.2H H N(CH3)2 7.9H H O-CH2-CH3 8.3H NO2 Cl 6.9H H CF3 6.8

The Glycine Site of the NMDA Receptor