metabolite likeness for selection of pharmaceutical drug libraries
DESCRIPTION
Metabolite likeness can be one of the criteria for Virtual screening along with Lipinski filter and Rule of three filterTRANSCRIPT
‘Metabolite-likeness’ for design and selection of Pharmaceutical
drug libraries
Ankit,
M.S.(Pharm.), 3rd Semester,
Dept. of Pharmacoinformatics,
NIPER, S.A.S. Nagar, Punjab.1
Outline
• Introduction
• Comparing drugs and library compounds to metabolites
• Metabolite-likeness curves
Compound set sources
Overview of protocol
Processing structures
Molecular Descriptors
Clustering and representative sets
• Dissimilar drugs
• Conclusion
• References2
Introduction
• Drug : A drug is, in the broadest of terms,
a chemical substance that has known biological effects
on humans or other animals.
• Hit molecule : A hit is usually defined as a molecule which
binds to the target, which has been identified to be important
in the disease of interest.
• Lead molecule : A molecule showing expected efficacy in
relevant in vitro pharmacological models, often also passed
the preliminary in vitro toxicological and ADME screening3
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• The search for pharmaceutically active drugs with desirable
properties and negligible side effects can be considered as a
multi objective optimisation problem over an enormous search
space of ‘possible’ drugs.
• According to Opera et al., ‘lead structures exhibit, on the
average, less molecular complexity (less MW, less number of
rings and rotatable bonds), are less hydrophobic (lower cLogP
and LogD), and less druglike’ than actual drugs.
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• To narrow the search space of chemically diverse candidate
compounds, cheminformatic methods are used to constrain
the compounds screened such that they tend to display ‘lead-
likeness’ or ‘drug-likeness’ (and even ‘CNS likeness’)
• Same concept used for promiscous drugs having multiple
intended targets or poly-pharmacology
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• Various filters used for screening purposes :
1. Lipinki’s Rule of Five (Ro5)
2. Rule of Three (Ro3)
3. Veber rule
4. MDDR like rules
5. Ghose filter
6. BBB filter
7. QED (Quantitative estimation of drug likeness)
8. CMC-50 like rules
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• Lipinski rules do not normally cover drugs derived from
natural products.
• These rules are biophysical rather than structural in nature.
• General descriptors are not entirely effective when drugs are
mainly transported by carriers.
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• Metabolite or Endogenite : Metabolites are small
molecule components of primary metabolism and not the
products of the reaction of drugs with drug metabolising
enzymes
• Present drugs are more metabolite-like than the typical
contents of pharmaceutical screening libraries ???
• Endogenite-likeness might be useful filter in design and
analysis of pharmaceutical libraries for drug discovery
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Comparing drugs and library
compounds to metabolites
• To assess the relationship between drugs and metabolites,
compared against a background of compounds of the kinds
that typically make up screening collections for hit discovery,
which we refer to as library compounds.
• Represent pre-drugs and can be considered as starting points
for drug discovery and development.
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Class Source Compounds Total
Metabolite HMDB 2835 5333
Metabolite Palsson 806
Metabolite BioCyc 772
Metabolite BIGG 698
Metabolite Edinburgh 2048
Drug DrugBank 4152 7330
Drug KEGG Drug 4435
Pre-drug ZINC 62,390
Table 1 : Sources of drug, metabolite and pre-drug structures
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Fig.1. Histograms for different simple molecular properties
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Metabolite-likeness curves
• The similarity of drug and library compounds to metabolites
is assessed by calculating the Tanimoto distance to the closest
metabolite
• A variety of molecular descriptors were computed, and
similarities calculated using the Tanimoto coefficient
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Molecular descriptors
• Extended connectivity fingerprints : operate by
identifying the substructural environment of each atom upto a
diameter of 4. Thus descriptor is set of substructures in a
molecule.
Similar molecules share more substructures than dissimilar
molecules.
• Path fingerprints : describe a compound by all paths
through the molecular graph upto length of 4.14
Using Pipeline Pilot implementation of a Daylight-like path
fingerprint.
Paths are not branched therefore represent the molecule
differently.
• E-state indices : captures the electronic and topological
prpperties of atoms via electronic interactions with
neighbouring atoms and by distance on the molecular
graph.
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• MDL keys : are substructural features observed to be of
utility in retrieval tasks like database searching.
Of the full set of 960 useful substructures , the definitions of
166 were released as the MDL Public keys.
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Clustering and representative sets
Descriptor space Drug Library
Connectivity fingerprint 5723 44,275
Path fingerprint 5835 44,318
E-state 6028 44,419
MDL Public Keys 5813 44,325
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Table 2 : Sizes of representative sets in each of the descriptor spaces
Overview of protocol
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Processing structures
• All compounds were ‘washed’ in Pipeline Pilot.
• Wahing involved :
isolation of the largest fragment in the structure
removal of salts
removal of hydrogens
Standardisation of stereochemical and charge information
Only compound with more than 3 atoms were considered
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Fig.2. A comparison of drug and library distances to closest metabolites in
various molecular descriptor spaces
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Dissimilar drugs
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Conclusion
• It is desirable to develop filters that include transporters
effect also.
• With advanced system biology, there is need to move
towards more mechanistic approaches.
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References
1. ‘Metabolite-likeness’ as a criterion in the design and selection of
pharmaceutical drug libraries. Paul D. Dobson, Yogendra Patel and
Douglas B. Kell. Drug Discovery Today Volume 14, Numbers 1/2
January 2009.
2. Navigating chemical space for biology and medicine. Lipinski, C.
and Hopkins, A .Nature 432, 855–861 2004.
3. Global mapping of pharmacological space. Paolini, G.V. et al. Nat. Biotechnol. 24, 805–815 2006.
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