Lecture Contents -- Unit 3 Drug Discovery –Basic objectives and problems –Screening approach vs....
-
date post
19-Dec-2015 -
Category
Documents
-
view
214 -
download
0
Transcript of Lecture Contents -- Unit 3 Drug Discovery –Basic objectives and problems –Screening approach vs....
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
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 Success Stories
• Microorganisms: Antibiotics
• Plants:– Taxoids for cancer– Artemisinin for malaria– Huperzine A and galanthamine for Alzheimer
• Animals: Conotoxins as ultra-high potency analgetics
„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
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
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
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
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
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