Subhasis Banerjee (Asst. Professor)(Gupta College of Technological Sciences, West Bengal. India)

INTRODUCTION TO DRUG DESIGN

From serendipity to rationality

Areas to be touched

Introduction to Drug Discovery Process

Target Identification and Validation

Lead Finding and Optimization

Ligand-Based and Structure Based Drug design

Application of Cheminformatics in Drug Design

We don’t know how good a compound is until we make it. MedChem is a

voyage of discovery.

We can predict enough data to ensure we make better compounds and succeed sooner. We know so much already.

Two MedChem Worlds

Cheminformatics Bioinformatics Small molecule

drug Protein

Large databases Large databases

Not all can be drugs Not all can be drug targets

Opportunity for data mining techniques

Data Mining: The practice of examining large pre-existing databases in order to generate new information

Target Identification and Validation

~375 GPCRs375 GPCRs

168 GPCRs bind to 97 known ligands

207 orphan GPCRs207 orphan GPCRs 86 deorphanized121 remain to be characterised

Orphan GPCRsOrphan GPCRs

- Since the end of the 1980s by molecular cloning and genome sequencing projects were identified genes coding unknown GPCRs

- These receptors are termed “orphan” since their corresponding ligand(s) remains to be identified

- oGPCR could be new targets

Target identification

A drug target can be classified into two classes:

Established drug targets: Deals with good scientific understanding, supported by a

lengthy publication history regarding both how the target functions in normal physiology

and how it is involved in human pathology

Potential drug targets: Functions are not fully understood and which lack drugs

targeting them. Potential targets suggest directions for completely new drug research

Target Validation: The target

validation process is aimed at

gathering convincing proofs that

the target under study is a key

player in the development and/or

progression of a disease.

Biology of the target

Expression Profile

Expression in relevant areas

Expression in pathologic state

Expression consistent between human and

animal model

Functional role

Role in human disease

In vivo studies

In vitro studies

Scientific rationale definition

Predicted side effect

profile

Predicted additional indications

Target validation flow-chart

Lead finding and optimization

A lead compound (i.e. a "leading" compound, not lead metal) in drug discovery is a chemical compound that has pharmacological or biological activity likely to be therapeutically useful, but may still have suboptimal structure that requires modification to fit better to the target. 

Finding a lead compound

Screening of natural products

The plant kingdom: rich source of lead compounds (e.g. morphine, cocaine, digitalis, quinine, tubocurarine, nicotine and muscarine, paclitaxel

The microbial world: microorganisms such as bacteria and fungi are rich for lead compounds (e.g. Antimicrobial Drugs: pencillins, cephalosporines, tetracyclines, aminoglycosides, chloramphenicol, rifamycins)

The marine world: coral, sponges, fish and marine microorganisms have biological potent chemicals, with interesting, anti-inflammatory, antiviral, and anticancer activity. Eg: Curacin A (anti-tumour, from marine cyanobacterium)

Animal sources: antibiotic peptides were extracted from the skin of African clawed frog. Epibatidine (potent Analgasic) was also obtained from Ecuadorian frog.Teprotide (from venom of viper) was the lead compound for the development of antihypertensive agents, Cilazapril &

Captopril

Medical folklore

(Berries, leaves and roots used by local healer or shaman as medicines. Many are useless or dangerous

and if they work this may be due to Placebo Effect.

Some of these extracts indeed have a real effect. (e.g. quinine (cinchona), reserpine (Rauwolfia), atropine

(atropa beladona), morphine (opium poppy), digitalis (foxglove), emetine (ipeca), cocaine (coca).

Screening synthetic compound “ libraries”Me too drugs

Many companies use established drugs from their competitors as a lead compound in order to design a drug.

Modification done in such way that avoids the patent restrictions, retain the activity, and improved the

therapeutic properties.

Eg: Captopril (Anti-hypertension) used as lead compound by different companies to produce their own anti-

hypertension drugs.

Lead optimization: A Balancing Act

Rule of Five (Lipinski et al)Poor absorption/permeation and solubility are likely when: Number of H-bond donors (NH, OH) > 5; Number of H-bond acceptors > 10MW > 500; clogP > 590 % of oral drugs adhere to this rule

Refining the chemical structure of a confirmed hit to improve its drug characteristics

– Synthesis of analog series– Testing the series to correlate changes in chemical structure to biological and pharmacological data to establish structure-activity relationships (SAR)

•Potency•Bioavailability•Stability•Selectivity

– Optimization cycle is repeated until the candidate molecule is selected

Lead Optimization – Top 10 Tactics

1.Start with a good lead Low MW and logP, potent, selective, novel and functionally active!

2. Look before you leap ‘Why waste 2 hours in the library when you could spend 2 weeks in the lab’

3. Chemistry should allow rapid diversification 39 Multiple sites of variation and chemistry suitable for

parallel follow-up

4. Optimise Lipophilic Interactions LogP/Potency plots & Ligand Efficiency– spot outliers

5. Optimise Polar interactions Look for specific H-bonds and meaningful loss (or gains) in potency

Contd…

6. Hetero-atom Insertion Aryl/heterocycle switch or CH 2/O/N switch

7. Bioisosteres Amide reversal Isoelectronic and/or isosteric replacement

8. Optimise Dipole by F or CF3 su bstitution N/C-F switch

9. Conformational control If you see a ring break it. If you don’t then make it.

Preorganisation can be very beneficial to potency (If you get it right!)

10. Challenge your own hypotheses & invest in alternative templates/series

Get out of the box!

Ligand Based and Structure Based Drug design

Building Molecules at the Binding Site

Identify the binding regions Evaluate their disposition in space

Search for molecules in the library of ligands for similarity

Structure Based Ligand Design

O

NH

O

H

O

NH

?

O

O

O

H

O

NH

NSO

OH

O

NH

OH

O

NHS?

?

OH

O

NH

??

?O

O

H

O

NH

DockingBuilding

Linking

Homology modeling

Predicting the tertiary structure of an unknown protein using a known 3D structure of a homologous protein(s) (i.e. same family)

Assumption that structure is more conserved than sequence

Can be used in understanding function, activity, specificity, etc

•Alignment–Multiple possible alignments

•Build model•Refine loops

–Database methods–Random conformation–Score: best using a real force field

•Refine sidechains–Works best in core residues

Key step in Homology Modeling

Structure Prediction by Homology ModelingStructural Databases

Reference Proteins

Conserved Regions Protein Sequence

Predicted Conserved Regions

Initial Model

Structure Analysis

Refined Model

SeqFold,Profiles-3D, PSI-BLAST, BLAST & FASTA

C Matrix Matching

Sequence Alignment

Coordinate Assignment

Loop Searching/generation

WHAT IF, PROCHECK, PROSAII,..

Sidechain Rotamersand/or MM/MD

MODELER

Framework for just the target backbone is shown in yellow against the template structures

Fragments which have the right conformation to properly connect the stems without colliding with anything else in the structure

Generating a framework

Molecular Docking

The process of “docking” a ligand to a binding site mimics the natural course of interaction of the ligand and its receptor via a lowest energy pathway

Put a compound in the approximate area where binding occurs and evaluate the following: Do the molecules bind to each other? If yes, how strong is the binding? How does the molecule (or) the protein-ligand complex look like. (understand the intermolecular

interactions) Quantify the extent of binding

Contd…

Computationally predict the structures of protein-ligand complexes from their conformations and orientations.

The orientation that maximizes the interaction reveals the most accurate structure of the complex.

The first approximation is to allow the substrate to do a random walk in the space around the protein to find the lowest energy.

Algorithms used while docking

Fast shape matching (e.g., DOCK and Eudock) Incremental construction (e.g., FlexX, Hammerhead, and SLIDE) Tabu search (e.g., PRO_LEADS and SFDock) Genetic algorithms (e.g., GOLD, AutoDock, and Gambler) Monte Carlo simulations (e.g., MCDock and QXP)

Some Available Programs to Perform Docking

Affinity AutoDock BioMedCAChe CAChe for Medicinal

Chemists DOCK DockVision

FlexX Glide GOLD Hammerhead PRO_LEADS SLIDE VRDD

Docked structure

HIV protease inhibitors COX2 inhibitors

Application of “CHEMINFORMATICS”

Chemical information: Storage and retrieval of chemical structures and associated data

to manage the flood of data by the softwares are available for drawing and databases.

All fields of chemistry: Prediction of the physical, chemical, or biological properties of

Compounds, Analytical Chemistry, Chemical(s) of concern, Chemical Specific data,

Structural analogue, Property analogue, Biological or mechanistic analogue, Data bases

Data mining, Analysis of data from analytical chemistry to make predictions on the

quality, origin, and age of the investigated objects, Elucidation of the structure of a compound

based on spectroscopic data.

Contd…..

Organic Chemistry: Prediction of the course and products of organic

reactions, design of organic syntheses

Drug Design as well as for bioactive molecules: Identification of new lead

structures, Optimization of lead structures, Establishment of quantitative

structure-activity relationships, Comparison of chemical libraries

"The simple act of paying positive attention to people has agreat deal to do with productivity"--Tom Peters

Thank you