ligand-based drug discovery - ron levy group drug discovery ... decisions faster in the area of drug...

| Eleonora Gianti – Structural bioinformatics II – Spring 2016

Ligand-Based Drug Discovery

Eleonora Gianti

Post-doctoral Fellow ICMS and ML Klein Group

Temple University, Philadelphia PA US [email protected]


Contents §  The Drug Discovery Pipeline §  Ligand-Based Drug Design

–  Methods –  Case studies

§ Sources of Chemical Structure & Information § Homework Assignment


!Figure 1. Computer-Aided Drug Design in the Drug Discovery Process.

Gianti, E. SBN: 9781303852534; 2013; Dissertation Abstracts International, Volume: 75-07(E), Section: B.; 342 p.; University of the Sciences in Philadelphia

Pipeline in Modern Drug Discovery


Cheminformatics and LBDD §  Chemoinformatics, FK Brown 1998

–  “Chemoinformatics is the mixing of those information resources to transform data into information and information into knowledge for the intended purpose of making better decisions faster in the area of drug lead identification and optimization.”

§  In drug discovery research, cheminformatics provides essential contributions to both ligand- and receptor-based drug design (LBDD and RBDD)

§  Examples: chemical library design, virtual screening, QSAR, etc. etc.


Useful Readings §  Cheminformatics in Drug Discovery

–  Wiley-VCH, Vol. 23, ISBN: 978-3-527-30753-1, 2005 –  Oprea TI (Editor), Mannhold R (Series Editor), Kubinyi H (Series Editor),

Folkers G (Series Editor) §  Remington: The Science and Practice of Pharmacy

–  Pharmaceutical Press & USciences, Allen LV (Editor) 2012 –  Chapter 8: Zauhar RZ and Gianti E. Structure-Activity Relationships and

Drug Design §  Handbook of molecular descriptors

–  Consonni V, Todeschini R, 2000, Wiley-VCH


Ligand-Based Drug Design §  Chemical Diversity and Similarity §  Structure-Activity Relationship (SAR) §  Pharmacophore Modeling

–  Pharmacophore features and activity prediction §  QSAR Modeling

–  Relate chemical-physical properties with bio-activity §  Ligand-based Virtual Screening §  Shape Recognition

–  Shape complementarity and molecular recognition


Assumptions in LBDD §  Structurally similar compounds are likely to have similar

physico-chemical properties and biological activities –  “On the analogy of arsenic and phosphoric acid with respect to

chemistry and toxicology” A Borodin, 1858 §  Molecules with similar shape and electrostatic properties

to known actives will likewise interact with the same target receptor –  “Structure-Activity Relationships and Drug Design” Zauhar RJ

and Gianti E. 2012 In: Remington: The Science and Practice of Pharmacy, 22nd Ed. Pharmaceutical Press & USciences, Allen LV (Editor)


Homologous Series §  Series of chemical analogs that differ by a simple change,

i.e. increment in the molecular formula (e.g. -CH2-) –  To infer the effects of chemical changes on bioactivity

R-(CH2)nCH3

Optimal activity reported for alkyl-chain of 6 carbon atoms (n=5)

Bio-target: Bacillus typhosus

Zauhar RJ and Gianti E. 2012 In: Remington: The Science and Practice of Pharmacy, 22nd Ed. https://en.wikipedia.org/wiki/Hexylresorcinol


Homologous Series §  Series of chemical analogs that differ by a simple change,

i.e. increment in the molecular formula (e.g. -CH2-) –  To infer the effects of chemical changes on bioactivity

R-(CH2)nCH3

Optimal activity reported for alkyl-chain of 6 carbon atoms (n=5)

Bio-target: Bacillus typhosus

Zauhar RJ and Gianti E. 2012 In: Remington: The Science and Practice of Pharmacy, 22nd Ed. https://en.wikipedia.org/wiki/Hexylresorcinol

Scaffold

R-groups Substituents


Androgen Receptor Binders

Zauhar RJ et al. 2013, J Comput Aided Mol Des. doi: 10.1007/s10822-013-9698-7

Non steroid ligands (NSL) - Behave as antagonists of the wild-type AR, and as agonists of AR mutants


(Dis)Similarity & SAR §  Structurally similar compounds are likely to have similar

physico-chemical properties and biological activities §  Still, different chemical moieties can result in the same

biological effect §  Structure-activity relationship (SAR)

–  Established between chemical structure and biological activity –  Monitors the effects (target modulation) of systematically

changing chemical groups responsible for bioactivity –  Widely used in medicinal chemistry to prioritize the synthesis of

more potent analogs or novel chemical classes


Two sides of the same coin? §  Similarity and Diversity

–  Structural similarity is expected to result in same bioactive responses

–  Compounds of diverse chemical classes can bind to the same molecular targets in different modes

§  Hit and Lead Identification –  Searching as much diverse chemical space as possible for

novel hits (actives) – “Diverse” chemical space §  Lead Optimization

–  Surveying a range of similar compounds for optimal potency and selectivity – “Target-focused” chemical space


§  Used to “quantify” molecular properties that are crucial for “druggability”

§  Broad categories of descriptors –  Physico-chemical

§ Measurable or predicted –  Theoretical

§ Derived from calculations

§  Other classification systems are used Todeschini, R and Consonni V. Handbook of Molecular Descriptors, 2008 Wiley-VCH

Molecular Descriptors


Physicochemical Descriptors §  Popular physicochemical descriptors

–  Molecular weight, atomic van der Waals volume, hydrophobicity, solubility, molar refractivity, etc. etc.

§  Log of the lipid-water partition coefficient (Lipophilicity) –  log P = log10 [Soct] / [Saq] –  where [Soct] and [Saq] are the eq. concentrations of the compound

in the octanol phase (low-polarity) and aqueous (polar) medium §  Hydrophobic drugs are mainly distributed to hydrophobic

areas (e.g. lipid bilayers). Hydrophilic drugs are found primarily in aqueous regions (e.g. blood serum)

§  Solubility in water is a measure of bioavailability


Theoretical Descriptors §  Constitutional descriptors

–  Count descriptors, chemical composition of molecules §  2D-Topological descriptors

–  Derived from molecular graphs –  Fingerprints (bit-strings) –  Connectivity (Weiner index; sums of bond distances)

§  High order descriptors (3D or higher) –  Pharmacophore modeling –  Quantum chemical calculations (e.g. HOMO and LUMO) –  Surface or volume representations –  Shape based-descriptors


“Druggability” Indices §  Lipinski Rule of 5 (Ro5) – ORAL active drugs in human

–  Compounds predicted to have poor oral absorption or permeation if: –  HBD count > 5; HBA count > 10; MW > 500 Da; MLogP > 4.15

Lipinski CA et al. 1997, Adv. Drug Del. Rev.

§  Rule of 3 (Ro3) – Fragment-like compounds –  HBD count ≤ 3; HBA count ≤ 3; MW ≤ 300 Da; RTB count ≤ 3

§  Drug-like (DLS) and lead-like (LLS) scores –  Ratio btw the number of satisfied conditions over the total of conditions –  Conditions are typically rules similar to those in Ro5 or Ro3

§  Ligand efficiency = (ΔG)/N (binding energy per heavy atom) –  ΔG = - RTlnKi (Gibbs free energy) –  N is the number of non-hydrogen atoms in the molecule

Abad-Zapatero A, Metz JT. 2005 Drug Disc Today


2D Similarity Measures §  Most popular are“fingerprints”

–  Binary vectors with 1 indicating the presence of the fragment (molecular feature) and 0 the absence

§  Other measures are structural keys, hashed fingerprints, or topological indexes –  Take into account molecular properties like size,

degree of branching, and overall shape


Jaccard/Tanimoto Coefficient §  Metric used to compare molecules §  Accounts for the intersecting and the union sets

SAB = Nc/(Na + Nb – Nc) Na = number of elements (bits set to 1) in A Nb = number of elements (bits set to 1) in B Nc = number of elements (bits set to 1) in common

(intersecting set) Range is 0 (no intersecting elements) to 1 (all elements

intersect). Value of 1 does not mean the molecules are identical


Similarity Coefficients

§  Euclidean Distance

§  Tanimoto Coefficient

§  Cosine Coefficient

a = Σ xjA number of bits “on” in A b = Σ xjb number of bits “on” in B c = Σ xjA xjB number of bits “on” in both A and B

Willett P et al. 1998, JCICS 38, 983

D(A,B) = distance between A and B S(A,B) = similarity between A and B


Fingerprints §  Useful source of information on fingerprints: §  http://pubs.acs.org/doi/abs/10.1021/ci100050t §  https://docs.chemaxon.com/pages/viewpage.action?

pageId=14483752 §  http://www.daylight.com/dayhtml/doc/theory/

theory.finger.html


DBs of Chemical Info §  ZINC database (Zinc Is Not Commercial)

–  http://zinc.docking.org/

§  DUD (Directory of Useful Decoys) –  http://dud.docking.org/

§  The NCI Data Catalogue –  http://www.cancer.gov/research/resources/data-catalog

§  PubChem –  https://pubchem.ncbi.nlm.nih.gov/ –  https://pubchem.ncbi.nlm.nih.gov/search/

ligand-based drug discovery - ron levy group drug discovery ... decisions faster in the area of drug...

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