Choose a target and find hit/lead compounds I Flashcards
Choosing a target compound
- Requirement for a new drug
- Economic factor
- Understand the macromolecules involved in drug target
- Targeting specific species e.g antiviral
- Target specific to body or tissues
Target validation
- Confirm association with disease proton interaction and signalling pathways
In-vitro
- Specific to tissue cells and enzymes
- Use bacteria and yeast to produce enzymes IC50 wich are competitive or non competitive
- Receptor agonist or antagonist can be tested on isolectic tissue has target receptor on surface
- pK properties - metabolism of drug
In-vivo
- Introduce clinical condition in animals
- Trangenic animal has some human tissue in animal
- Slow and expensive with animal symptoms
- Could be caused due to physiology
- Invalid results sometimes
- Variable according to species
High-throughput screening
- Automated test of a large number of compounds to a large number of targets to HIT identification
- False positive HITS can occour
Screening NMR
- Detects whether the proton binds to the proton target the screen mixture tests 1000 molecules a day
- Detection of eeak binding so there is no false positive
Process of screening NMR
- NMR of the drug is taken
- Protien is added and spectrum is re-run
- If drug didn’t bind then NMR spectrum will be detected
- Drug binding then no NMR spectrum will be detected
Isothermal calorimetry
- Determine the thermodynamic between drug and its protein target
- Can see its binding affinity and enthalpy change
Finding HIT compound
Screening natural compounds
- Active on compound with low cytotoxicity
- Active principle metabolites are extracted fractionate and isolate
- Plant source - Morphine, Cocaine, Taxol
- Microorganism - Bacteria fungi
- Marine sources - coral, sponges and fishes
- Animal sources - Anti venom and toxin
Finding HIT compound
Screening synthetic compound libraries
- Compound or synthetic ingreedient that has been previously synthesised
Finding HIT compound
Existing drugs
- Use estabilished drugs from competitors as HIT compound to design compound to modify the structure
- Avoid patient restrictions, retains activity and has better theraputic effect
Selective optimisation of side effects activities
- Enhance the desired effect and eliminate major biological activity of existing drugs
Repurposing
- Screen existing compounds that are either in use in clinical or have reached late clinical stage againt a new target
Starting from natural ligand or modulator
Natural ligand
- Used as a HIT agonist (adrenaline) for the design of an antagonist (histamine)
Starting from natural ligand or modulator
Natural substrates for enzymes
- Used as HIT design inhibitors HIV protease enabled development of HIV protease inhibitor
Starting from natural ligand or modulator
Ezyme products as HIT compounds
- Use HIT to design inhibitors such as carboxypeptidase
Starting from natural ligand or modulator
Natural modulators as HIT compounds
- Receptor enzymes are under allosteric control
- Natural chemicalexert control on mudulators serving as HIT compounds
Finding HIT compound
Serendipity
- Found by chance
- Unexpected and benificial spin-offs - lipophilic can cross blood brsin barrier add hydrophobic amide - practonol fewer side effect
- Reaserch from different field
Finding HIT compund
Computer aided design
- Stuudy the 3D stucture on the computer
- X-ray crystallography
- Target analysis - rationally design a target to bind
- Easier if co-crystallised ligand where binding site is known
HIV protease inhibitor
- Active as a homodimer for viral replication forms the active version of viral non-structure protiens
What is target analysis?
- Residue from active binding site analysed to identify key interactions to design new ligand inhibtors
Intermolecular bonding forces
Strong to weak
-Ionic
- Hydrophobic
- Hydrogen - donor or acceptor
- Pi bonding
- Weak hydrogen bond
- Vderwaals and induced dipole
HIV potent inhibitors
- HAART drugs such as saquinavir and nelfinavir
- Nelfinavir non-peptidic inhibitor occupies active site smaller and compact improved fit to hydrophobic region
Pharmacophores modelling
- Extract information on essential functional groups and 3D spatial arrangement required for activity on a given target
- Shows binding groups for essential activity design new mordels or improve existting
Virtual screening
- Computer simulations which predicts if the compund is good or not for binding
- Faster, easier, cheaper and safter - forms virtual HITs
Structural based virtual screening
- Molecular docking virtual compound explore its possible conformations
- Identify best predicable binding
- Ranks generated pose scoring predict free energy change when binding
- interaction + association + conformation + Rotation + vibration + solvation = Free energy bind
Ligand based virtual screening
- Shape similarites with know active molecules - Shape functional group matching with query molecule
- Higher HIT rates
Mixed virtual screening
- Matching pharmacophoric query fitting to given model
Exclusion volumes
- Added to mixed screening to show actual volume occupied avoid selecting molecules that can clash to the target
Fragment base HIT discovery
- Small molecule fragments are screened against a given target binding weakly
- Once multiple fragments are identified the crystal structure is resolved linking fragments together optimised high affinity molecule
Linking fragments
- Bigger molecules with good affinity futher optimisation occours
What fragments should be used in screening
- Relative molecular mass <300 and >150
- H-bond donating groups 3 or less
- H-bond accepting groups 3 or less
- Log(P) 3 or less
- Number of rotatable bonds 3 or less
What fragments should be used in screening
- Relative molecular mass <300 and >150
- H-bond donating groups 3 or less
- H-bond accepting groups 3 or less
- Log(P) 3 or less
- Number of rotatable bonds 3 or less
SAR by NMR epitope mapping
- Screen small fragment then combing them to a potent HIT using NMR
- Shifts seen on protien amide signals motier if and where binding takes place
- Repeat to find small ligand binding sub-region then optimised