Drug Design_L3 Flashcards
What steps are involved in the drug design?
(1) clinical need
(2) cell-virus molecular biology search
(3) relevant mechanism(X-ray, NMR and computation)
(4) library: natural products, compound collection, combinational chemistry
(5) mechanism-based/high-throughput screen
(6) hits
(7) lead compound
(8) 1. medicinal chemistry
2. analog synthesis(either for reduction of side effects or improvement in the affinity)
3. combinatorial chemistry
4. rerturn to hit step if the lead compound modification is not successful
(9) drug candidates
1. animal phamacology
2. toxicology
3. metabolism
4. pharmacokinetics
if the drug candidates do not fit in these criteria, last step will be repeated
(10) preclinical development
(11) clinical testing
How to identify new hits?
(1) High Throughput Screening (HTS):
1. Expensive: need robust cell-based assays
2. False positives
3. Assay variability or errors in data
4. based on wet-lab setup
5. Even the largest libraries of compounds in major pharmaceutical corporations (10 to the 7 power) are miniscule in comparison with the possible chemical diversity space, estimated to be over 1060 possible compounds for molecules based on up to 30 non-hydrogen atoms
(2) Virtual Screening (VS)
(3) Combination of HTS and VS
what are the techniques in seeding compounds exploratory unit for drug discovery platform?
(1) HTS dispenser
1. single line dispenser
2. microplate washer
3. multichannel dispenser
(2) biological activity meter
1. microchip electrophoresis(Caliper assay)
2. chemically amplified luminescence(alpha assay)
3. high content screening automatic microscopy
4. multi microplate reader
5. chemical bank unit for the drug dicovery platform
(3) interaction analysis
1. yeast genetic interaction analysis
2. isothermal titration calorimeter
3. SPR analyser
4. X ray crystallisation
5. docking simulation
What two classes can the computer-aided drug design be falling into?
CADD methods are classified into ligand-based methods (LBDD) and structure-based (SBDD):
(1) Structure-based methods require the 3D information of the target to be known. The conformation of protein and ligand is known.
(2) Ligand-based methods are used when the 3D structure of the target is not known. They use information about the molecules that bind to the target of interest. Hits are identified, filtered and optimized to obtain potential drug candidates that will be experimentally tested in vitro. The conformation about the inhibitor and substrate is known.
what methods can be used in the following situations:
(1) ligand and protein structures are both known
(2) ligand structure is unknown but the protein structure is known
(3) protein structure is unknown but the ligand structure is known
(4) neither of the structures are known
(1) structure-based drug desgin: docking&scoring, virtual screening
(2) de novo drug design: virtual screening
(3) library design: HTS, combichem, virtual screening
(4) ligand-based drug design: (dependent more on the hypothesis, therefore, greater possibility of false positives) pharmacophores, (2D/3D) similarity QSAR, virtual screening
What parameters can be applied for filtration?
(1) SBDD:
1. docking and ranking
(2) LBDD:
1. Lipinski’s filter
2. phamacophore’s filter
3. protein-receptor interaction analysis
4. not solely rely on the LBDD but in combination with the other methods
What is LBDD? How can the strategy be applied for drug design?
LBDD strategies used in drug design studies, taking into account different kinds of molecular information, such as 3D molecular shape, molecular and electronic properties and 3D pharmacophore. A ligand is uncovered for mimicing the behaviour of the drug or inhibitor by searching the library.
the cycle of optimising the drugs?
(1) buy or synthesize the hits
(2) test activity
(3) activity data
(4) general pharmacophore(fit the spheres with atoms to match the physical/chemical properties
(5) search compound library for actives: optimise/modify the structure/conformation of the ligand
(6) the cycle goes on from the first step
What three stages can the LBDD be divided into?
(1) Similarity searching
(2) Pharmacophore mapping
(3) Machine learning
What is the similarity searching?
(1) Structurally similar molecules tend to have similar properties
(2) Given an active reference structure (known molecule) rank order a database of compounds on similarity to the reference
(3) Select the top ranking compounds for biological testing
(4) Requires a way of measuring the similarity of a pair of compounds
(5) Requires a quantitative basis for ranking structures
(6) change other functional group to improve the binding
What is the similarity measure?
(1) Molecular descriptors:
1. Physicochemical properties, e.g., MW, logP, MR, etc: logP measures the hydrophobicity(how well the molecule integares into the lipid environment) of the molecule by mimicing the molecular environment(octane acts as the hydrophobic phospholipid bilayer)
2. 2D properties: fingerprints, topological indices, maximum common substructures
3. 3D properties: fingerprints, molecular fields
(2) Similarity coefficient: A quantitative measure of similarity between two sets of molecular descriptors
define the 2D fingerprinting
(1) Each bit in the bit string (binary vector) represents one molecular fragment
(2) The bit string for a molecule records the presence 1 or absence 0 of each fragment in the molecule: 1 or 0 represents the presence or absence of an atom, the searched ligand and the library ligand can be compared.
(3) Similarity is based on determining the number of bits that are common to two structures
(4) no information about the location of the functional group
define the hashed fingerprinting
(1) The chemical hashed fingerprint of a molecule is bit string (a sequence of “0” and “1” digits) that contains information on the structure (topology).
(2) Stereochemistry(cis- or trans-conformation) is not considered
(3) Each fragment is processed using several different hashing functions, each of which sets a single bit in the fingerprint
(4) Bit collision:
1. same bit is set by multiple patterns.
2. This phenomenon is called bit collision.
3. Few bit collisions in the fingerprint is ok, but too many may result in losing information in the fingerprint.
define similarity (Tanimoto) coefficient
(1) Tanimoto/Jaccard coefficient:
1. c bits set in common in the reference and database structure
2. a bits set in reference structure
3. b bits set in database structure
(2) T(a, b)=Nc/(Na+Nb-Nc)
(3) usually the filtering range for T should be above 70 or 80
What is the scaffold hoping?
Find a molecule that is as active as query one but with different core structure; usually requires 3D searches. Based on the structure of the ligands, a variety of the scaffolds with similar functions are discovered to overcome the limitation of single structure but still able to bind to the ligand.
