Role of Drug Design in Medicinal Chemistry

Role of
CADD in Med. Chem.
Girinath G. Pillai, PhD
Zastra Innovations
www.zastrain.com
gpillai@zastrain.com

What to expect?
➤ Medicinal Chemistry & History
➤ Evolution of CADD
➤ Objectives of CADD
➤ Drug Design Approach
➤ Molecular Docking and Fragment Design
➤ QSAR & ADME
➤ Bioavailability & Lipinski Rule
➤ Workflow of CADD
➤ Chemical Representations
➤ SMILES Conversion
➤ Rethink/Thoughts
08/07/18 10:31 AM Girinath, Zastra Innovations 2017 Slide 2 of 58

Why I chose Med. Comp. Chem.
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Align your activity with your talents

A grand challenge for all of us is
how to best incorporate existing knowledge….
Martha S. Head, GSK, in 2009

Medicinal Chemistry
➤ Science that deals with the discovery or design of new therapeutic
chemicals and the development of these chemicals into useful
medicine
➤ Attempts to design and synthesize a medicine or a pharmaceutical
agent which will benefit humanity. Such a compound could be called a
'drug‘.
➤ Latin ars medicina, meaning the art of healing
➤ Involves :
➤ Synthesis
➤ Structure-Activity Relationships (SAR)
➤ Receptor interactions
➤ ADME/T

Medicinal Chemistry
Folklore/History

History of Medicinal Chemistry
2000 BC Materia Medica 250 vegetables drug and 120 mineral drugs
1500 BC Egyptian papyrus ebers 700 drugs originated from animal/plants/minerals
Emperor Frederick II issued the Magna Carta of pharmacy in 1240
Synthesis of Urea 1828 started Organic medicinal Chemistry
Ehrlich’s “Side chain theory” and chemotherapy and Fischer’s lock-and key theory
Birth of Modern Med Chem 1800
Medicinal chemistry received formal recognition in academic pharmacy in 1932

History (contd…)
➤ Earliest medicines ~ 5100 years ago
➤ Chinese emperor Shen Nung - book of herbs, Pen Ts’ao
➤ Ch’ang Shan - contains alkaloids; used today in the treatment of malaria
and for fevers
➤ Ma Huang - contains ephedrine; used as a heart stimulant and for asthma.
Now used by body builders and endurance athletes because it quickly
converts fat into energy and increases strength of muscle fibers.
➤ Modern Therapeutics:
Extract of foxglove plant, cited by Welsh physicians in 1250.
Used to treat dropsy (congestive heart failure) in 1785
Contains digitoxin and digoxin; today called digitalis

Before 3rd Century
➤ Based on plant parts & animal
products to prepare medicines.
➤ These drugs are only for medication
of viral, fungal, bacterial and other
related illness.

After 4th Century
➤ Charakan is the father of ancient medicinal
chemistry.
➤ He wrote a book called Charaka Samhitha.
➤ This is the first book of medication in 3rd & 4th
century.

5th & 6th Century
➤ BODHI DHARMA lived in 5th & 6th century.
➤ He known about all medicines, medication
also.

After 7th Century
➤ Susrutan is a father of modern
chemistry.
➤ He wrote a book called Susruta
Samhita.
➤ Charaka Samhita (~1500 BC)
➤ Ashtang Hridyam (~500 AD)
➤ Sushrut Samhita (~300 - 400 AD)

19th Century : Rise of Modern Medicine
➤ Old ideas of infectious disease epidemiology were gradually replaced by advances in
bacteriology and virology.
➤ Elizabeth Blackwell (1821–1910) became the first woman to
formally study and practice medicine in the US.
➤ Eminent French scientist Louis Pasteur confirmed
Schwann's fermentation experiments in 1857 and
afterwards supported the hypothesis that yeast
were microorganisms

“Big Pharma” Drug Discovery in the 21st Century
➤ The Problem: The pharmaceutical industry is short of new drugs. In the 2nd part of the 20th century,
about 50-60 New Chemical Entity (NCEs) were approved by the FDA every year. In contrast,
➤ in 2001, 24 NCEs,
➤ in 2002, 18 NCEs,
➤ in 2000, 27 NCEs,
➤ in 2003, 21 NCEs,
➤ 2011, 30 NCEs,
➤ 2012, 39 NCEs,
➤ 2013, 27 NCEs,
➤ 2014 , 41 NCEs + New Therapeutic Biological Product (NTBP)
➤ 2015, 15 NCEs + NTBP
➤ Conversely, research costs for a new drug are estimated to be in the $1-1.5 Bi. range.
➤ Considering all high-profile failures in recent drug discovery, this figure is unlikely to drop substantially

Beyond the Century in Medicines
➤ 2000 – The Human Genome Project draft was completed.
➤ 2001 - The first telesurgery was performed by Jacques Marescaux.
➤ 2003 – Carlo Urbani, of Doctors without Borders alerted the WHO to the threat
of the SARS virus, triggering the most effective response to an epidemic in
history. Urbani succumbs to the disease himself in less than a month.
➤ 2005 – Jean-Michel Dubernard performs the first partial face transplant.
➤ 2006 – First HPV vaccine approved.
➤ 2006 – The second rotavirus vaccine approved (first was withdrawn).
➤ 2007 – The visual prosthetic (bionic eye) Argus II.
➤ 2008 – Laurent Lantieri performs the first full face transplant.
➤ 2013 – The first kidney was grown in vitro in the U.S.
➤ 2013 – The first human liver was grown from stem cells in Japan.

Increase in the Cost of Medicine!

Past 25+ Years?
➤80’s:
“Classical MedChem”,
almost no predictive small molecules computing
➤90’s:
Advent of Rational DD,
early computing, docking (FlexX 1996), similarity (Daylight & Unity
Fingerprints )
➤2000+:
Outsourcing, merging, 300.000+ layoffs in pharma
Source:
Abou-Gharbia et al, JMC 2013, dx.doi.org/10.1021/jm401564r

Effect of Atorvastatin introduction to Lipitor Sales
Other Changes & Threats
➤ Generics / Patent cliffs
➤ FDA Drug Approvals

Today’s Challenges
➤We need fast ‘results’.
Competition, Stakeholders!
➤We need a strong visual focus.
Explaining to others, esp. MedChems.
➤We need it easy. (Do not confuse with blackboxing! )
No time for manuals, no backup from management for this.
In addition of course: Sound science,
things “of relevance”.

Natural Consequences
➤Higher pace (stakeholders, managers)
=> algorithmic challenges
➤Non-specialists more involved
=> communication challenges
➤Basic CADD skills are helpful throughout pharmaceutical R&D
=> UI / learning curve challenges

Drug Discovery
➤ One way to “discover” drugs

Representing Chemicals
➤ Trivial name, e.g. Baking Soda, Aspirin, Citric Acid, etc.
Identifies the compound, but gives no (or little) information about
what it consists of
➤ Chemical formula, e.g. C6H12O6.
Specifies the type and quantity of the atoms in the compound, but not
its structure (i.e. how the atoms are connected by bonds)
➤ Systematic name, e.g. 1,2-dibromo-3-chloropropane.
Identifies the atoms present and how they are connected by bonds.
Source:

Digital Representations
➤ How do we communicate structural information between humans and the
computer?
– Line notations, e.g. Wiswesser Line Notation (and later SMILES)
➤ How do we represent the atoms and bonds in a molecule internally in a
computer?
– Atom lookup and connection tables
Trivial name : proline
Systematic names : pyrrolidine-2-carboxylic acid
Source:

Chemical - File Formats
➤ .mol
➤ .mol2
➤ .sd / sdf
➤ .pdb
➤ .skc/.cdx/.mrv/…
➤ .smi CCCC

SMILES
➤ Simplified Molecular-Input Line-Entry System (SMILES)
is a specification in form of a line notation for describing the structure
of chemical species using short ASCII strings
➤ SMILES specification was initiated by David Weininger in the 1980s
➤ .smi is the file format for SMILES
➤ SMILES form depends on the choices:
➤ of the bonds chosen to break cycles,
➤ of the starting atom used for the depth-first traversal, and
➤ of the order in which branches are listed when encountered.
➤ There are different variants of SMILES
Source:
OC(=O)C1CCCN1

SMILES - Atoms
➤ Atoms represented by their chemical symbol (C, N, S, O, Br, etc) in square
brackets, such as [Au] for gold.
➤ Uppercase for aliphatic, lowercase for aromatic
➤ Brackets may be omitted in the common case of atoms which:
➤ are in the "organic subset" of B, C, N, O, P, S, F, Cl, Br, or I,
➤ have no formal charge,
➤are the normal isotopes, and
➤are not chiral centers.
➤ Hydrogens usually implicit
➤ SMILES for water may be written as either O or [OH2] or [H]O[H]
Source:

SMILES - Bonds
➤ Bond is represented using one of the symbols '.' '-' '=' '#' '$' ':' '/' or ''.
➤ Adjacent atoms implicitly or ‘-’ single bonded, or = for double bond, or # for
triple bond, or $ for quadruple bond
➤ "non-bond", indicated with ".", to indicate that
two parts are not bonded together.
➤ aromatic "one and a half" bond may be indicated with ':'
C-C-O or CCO - Ethanol
O=C=O – Carbon Dioxide
C#N – H Cyanide
[Ga-]$[As+] – Gallium Arsenide
[Na+].[Cl-] – Aq. Sodium Cholride
[H]C([H])([H])C([H])([H])C([H])([H])C([H])([H])[H]CCCC
Source:

SMILES – Rings & Branching
➤ Ring enclosures represented by using numbers to signify attachment
points
➤ Parentheses represent branching
C1CCC2CCCCC2C1
Decaline
C1COCCO1
Dioxane
C1CCCCC1
Cyclohexane
CCC(=O)O
Propionic acid
COC1=CC(=CC=C1)C#N or COc(c1)cccc1C#N
3-methoxybenzonitrile
Source:

SMILES – Other notations
➤ All Hydrogen atoms are implicit unless declared otherwise
➤ Non-organic (i.e. not C,N,S,O,Cl,Br), Hydrogens and modified atoms neet to
be placed in square brackets, e.g. [Br], [Xe]
➤ Charged species indicated by a + or – (and square brackets), e.g. [Na+],
[N+], [O-], [Ca++]
➤ Unknown atoms can be represented by a * (but watch out for confusion
with SMARTS!)
➤ Stereochemistry can be indicated using @@
➤“Canonical SMILES” can be created
Source:
J. Chem. Inf. Comput. Sci. 1989, 29, 97.

How to create SMILES
➤ Pick a starting atom
➤ Traverse the molecular graph in a depth-first manner
➤ Encode the atoms and bonds traversed as a text string
➤ Variation in 1st
and 3rd
step can generate different SMILES
➤ Ethanol can be C – C – O or O – C – C
Source:

Access or Interconvert
Chemical Information

Chemical Format Convertors
➤ OpenBabel
➤ Multiple chemical file formats (+ options) and utility
formats
➤ 2D coordinate generation and depiction (PNG and
SVG)
➤ 3D coordinate generation, forcefield minimisation,
conformer generation
➤ Binary fingerprints (path-based, substructure based)
and associated “fast search” database
➤ Bond perception, aromaticity detection and
atomtyping
➤ Canonical labelling, automorphisms, alignment
➤ Plugin architecture
➤ Several command-line applications, but also a
software library
Volunteer effort, an open source success story
– Originally a fork from OpenEye’s OELib in 2001
– Lead is Geoff Hutchison (Uni of Pittsburgh)
– 4 or 5 active developers
Source:
Open Babel: An open chemical toolbox, J. Cheminf., 2011, 3, 33.

Conversion Options
➤ Handle multimolecule files- join/m, sort, C
➤ Handle multicomponent molecules- r, separate
➤ Filter- filter, smallest/largest, s/v, f/l, unique
➤ Manipulate structure or atom order- addpolarh, align, b, c, canonical, d, h, gen2d/3d
➤ Forcefield- minimize, conformer, energy
➤ Conformers- readconformer, writeconformers
➤ Manipulate SDF properties and title- add, addfilename, addindex,
addoutindex, addtotitle, append, delete, property, title
➤ Particular file formats may have their own specific input or output options
➤ To provide or handle different flavours of the file format
➤ To specify additional information to include
➤ To provide additional functionality

OpenBabel - Interface
http://openbabel.org

SMILES Conversion

2D Chemical Searching
➤ Structure search
➤ Is this structure in the database?
➤ Substructure search
➤ Find all structures that contain the substructure?
➤ Similarity search
➤ Find structures that are similar to this one
Source:

A rational dialog between Medicinal Chemists
and Computational Chemists can be very productive
- Chris Lipinski, J. Med. Chem. 2004, 47, 4891

Apply data to Guide Decisions
SelectionPrioritiseData
In silico
In vitro
In vivo
Importance
Uncertainty
Quality
Diversity
‘Manual’

CADD Scenarios
Source:

CADD - Phases
Drug Discovery
Drug Development
Target
Identification
Lead
Discovery
Lead
Optimization
Preclinical
Tests
Clinical Trails
•Bioinformatics
•Reverse
Docking
•Pharmacophore
Mapping
•Structure
Prediction
•Druggability
•Probe Design
•Molecular Docking
•Pharmacophore Modeling
•Combinatorial Chemistry
•Library Design
•QSAR
•P450
•Multi Parameter Optimisation
•ADME
•Toxicity
•PBPK
Simulations
Computer Aided Design
Source:

Impact of CADD on Pharma (112+)
➤ Acetyl-CoA carboxylase Inhibitor Nimbus, US
➤ Captopril Capoten®, Bristol Myers-Squibb
➤ Dorzolamide Trusopt®, Merck
➤ Zanamivir Relenza®, Gilead Sciences
➤ Aliskiren Tekturna®, Novartis
➤ Boceprevir Schering-Plough
➤ Nolatrexed dihydrochloride Thymitaq®, Agouron
➤ LY-517717 Lilly/Protherics
➤ Rupintrivir AG7088, Agouron
➤ NVP-AUY922 Novartis
➤ Vemurafenib Plexxikon
➤ Venetoclax AbbVie, Genentech
➤ Erdafitinib Johnson & Johnson
➤ Verubecestat Merck Source:

Sample CADD Workflow
Target Protein Selection
Sequence Alignment
Protein Modelling
Validation & MD
Molecular Docking
Combinatorial/De novo
Pharmacophore/FBDD/SAR
QSAR/ADME/P450/Toxicity
Correlation with bioassay
Candidate Selection
Yes No

Molecular Docking
➤ Problem
➤Determine the optimal binding structure of a ligand (a drug candidate, a small
molecule) to a receptor (a drug target, a protein or DNA) and quantify the strength
of the ligand-receptor interaction.
➤ Where the ligand will bind?
➤ How will it bind?
➤ How strong?
➤ Why?
➤ What make a ligand binds to the
receptor better than the others?
➤ ????

“Docking is like a discotheque:
It’s all about posing and scoring.”
Roger Sayle in 1993

Approaches in Docking
➤ Complete conformation and configuration space are too large. Different approaches
were developed for effective sampling of the receptor-ligand configuration space.
Docking
• Use pattern-recognizing
methods to match ligand and
receptor site descriptors
• Ligand flexibility is limited
• Receptor is rigid
• Accuracy is not very good –
not discriminative
• Fast
Descriptor Matching
• Use simulation methods to sample the
local configuration space: MC-
Simulated Annealing, Genetic
Algorithm. Must run an ensemble of
starting orientations for accurate
statistics
• Ligand and protein flexibility can be
considered
• Free energy of binding is evaluated
• Accuracy is good
• Time consuming
• Grid map is often used to speed up
energy evaluations
Simulation-based

For a Problem like this?
➤ Instead of discarding all
knowledge set.
➤ Let us use them carefully and
cleverly

Fragments can form Drugs
N N
N
N
O
N
N
H+
N
Gleevec

QSAR - Quantitative Structure Activity Relationship
====================>
Experimental Assay
Activity/Property
Chemical, Physical, Biomedical
x
Molecular
Descriptors
y
Response
Variable
Activity/Property = f(physicochemical properties and/or structural properties) ± Error
Molecular Structure
Statistical/Machine Learning
Modelling
Validation
Prediction
y = f (x)
f (x) ??
Experimental Data
PredictedData
Source:
Molecular Structure
Optimization & Check
Descriptor Calculation
Classification
Feature Selection
Model Generation
Validation

Molecular Structure & Optimisation
➤ Structures are sketched in 2D and
can be converted to 3D
➤ Generation of low energy conformers
Molecular Structure
Ab initio
Best Accuracy, High Costs,
DFT/HF Methods
Semi Empi
Good Accuracy, High Costs,
AM1/RM1/PM7 Methods
Mol Mech
Medium Accuracy, Low
Costs, MM1/MMX Methods
Classification
Feature Selection
Model Generation
Validation

Molecular Descriptors
➤ Physico-chemical properties describe few aspects of chemical structure.
➤ Empirical Descriptors
➤ NMR Chemical Shift, MP, Log P, …
➤ Theoretical Descriptors
➤ 0D – Constitutional
➤ 1D – Hydrophobic parameters, MR
➤ 2D – Topological
➤ 3D – Geometrical, vdW, PSA
➤ Quantum Chemical – HOMO, LUMO, HoF, Partial
atomic charges, DM
➤ CoMFA – 3D field analysis including steric and elctostatic
Classification
Feature Selection
Model Generation
Validation
Molecular Structure

Descriptors? Caution!
➤ Dataset should have more than 5x of the descriptors
used in models.
➤ If not, the model generates false positives with high
correlations.
Classification
Feature Selection
Model Generation
Validation
Molecular Structure

Dataset & Classification
➤ Parameter Filter
➤ Property Filter
➤ Similarity Search
➤ Clustering
Classification
Feature Selection
Model Generation
Validation
Molecular Structure

Feature Selection
➤ (B)MLR – Best Multi Linear Regression
➤GA is implemented for BMLR, method checks the impact/significance or correlation
of each descriptors on the activity.
➤ PCA – Principal Component Analysis
➤ Superior than MLR in the case of number of
descriptors in the model. Tries to correlate all
descriptors to the number of compounds in a model
➤ PLS – Partial Least Squares
➤ Descriptors are extracted in to a new component
as to maximize the corrleation. This model has
multiple dependent parameters.
Classification
Feature Selection
Model Generation
Validation
Molecular Structure

Model Validation
➤ Careful selection of descriptors
➤ Significance of the descriptors with the activity
➤ Optimal number of compounds
Validation
Classification
Feature Selection
Model Generation
Molecular Structure

Custom QSAR Methodology
➤ Data Preparation
➤ Parameterization
➤ Significant Descriptor Generation
➤ Statistical or
Machine Learning Models
➤ Applicability Domain
➤ Reverse QSAR

Merits/Demerits of QSAR
➤ Significance or role of the chemical composition on activity.
➤ Models can be used to predict activity for novel compounds
➤ ??
➤ False correlation - experimental errors.
➤ GIGO
➤ ??

Not Better or Worse – Complementing!
➤ 2 descriptors which are orthogonal can give a good view on things:

Bioavailability
➤ Classification
Bioavailability
Liver Metabolism
Permeability Gutt Wall Metabolism
Absorption
Transporters
Lipophilicity Solubility Flexibility
Hydrogen Bonding Molecular Size/Shape

What is ADME?
Volume of
Distribution
Clearance Absorption
Half Life
Oral
Bioavailability
Dosage
Amount
Dosage
Frequency
ADMET in silico modeling towards Prediction Paradise?, Nature reviews

ADME/T
➤ ADME/T :
➤ Adsorption
➤ Distribution
➤ Metabolism
➤ Excretion/Elimination
➤ Toxicity
Factors such as poor compound solubility, gastric emptying time, intestinal transit
time, chemical instability in the stomach, and inability to permeate the intestinal wall
can all reduce the extent to which a drug is absorbed after oral administration.
Factors affecting drug distribution include regional blood flow rates, molecular size,
polarity and binding to serum proteins, forming a complex
Metabolism occurs, the initial (parent) compound is converted to new compounds
called metabolites. When metabolites are pharmacologically inert, metabolism
deactivates the administered dose of parent drug and this usually reduces the effects
on the body. Metabolites may also be pharmacologically active, sometimes more so
than the parent drug
Compounds and their metabolites need to be removed from the body via excretion,
usually through the kidneys (urine) or in the feces
Potential or real toxicity of the compound is taken into account. Route of
administration critically influences ADME

ADME Properties
➤ Requirements for a drug:
➤ Must bind tightly to biological
target invivo
➤ Must pass through physiological
barriers like cell membrane or blood-
brain barrier
➤ Must remain long enough to take
effect
➤ Must be removed from the body by
metabolism, excretion, or other
means
➤ Attrition in drug development
➤ Early Decision

Factors impact Bioavailability
➤ Hydrophobicity, pKa, Solubility
➤ Drug Formulation
➤ Gastric emptying rate
➤ Interactions with other drugs/food
➤ Transporters
➤ Enzyme induction/inhibition
➤ Metabolic rate difference
➤ Gastrointestinal tract health
➤ Functional insufficiency or poor renal function

Factors affecting LogP
LogP Bind to
Enzyme/
Receptor
Aqueous
Solubility
Bind to P450
Metabolising
Enzy
Absorption
through
membrane
Bind to
blood/tissue
protein
Bind to hERG
heart ion
channel
Cardiotoxicity
Risk

Conclusion
Drug Discovery Cell Therapy
Key
Advantages
• Focus on human disease relevance already at
screening stage
• Replacement of rodent cell & in vivo models
• Novel strategy for patient stratification
• In vitro clinical trials / precision medicine
• Novel translational biomarkers
• Excellent basis for more complex in vitro models
• Co-cultures / organoids
• Autologous cell therapy can circumvent
immune rejection
• In combination with gene editing
technology provides potential cures
Key
Challenges
• Standardization
• Industrialization
• Functional integration in tissues
• Regulatory challenges
➤ CADD helps for decision making and candidate selection
➤ Being using predictive models, consider the uncertainity/errors

― Prof. Alan R. Katritzky
(Aug 1928 – Feb 2014)
Slide 71 of 58

Zastra Innovations
➤ Scientific software providers/training
➤ Computational Biology
➤ Computational Chemistry
➤ Materials Science
➤ Nanotechnology
➤ BioStatistics
➤ Dosage Tolerance/Curve Fitting
➤ Medicinal Chemistry / Cheminformatics
➤ Collaborative research projects
➤ Computing Consultants
➤ 2 Interns per year

Agree / Disagree or Confused?
➤ Email : gpillai@zastrain.com
➤ Phone: 94483 67493
➤ Web : www.giribio.ga
➤ Inviting Programmers who can
code QSAR models and web portal

Role of Drug Design in Medicinal Chemistry

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