2016 bioinformatics i_bio_cheminformatics_wimvancriekinge

FBW
13-12-2016
Wim Van Criekinge

Examen
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• The keywords can be
– genome structure
– gene-organisation
– known promoter regions
– known critical amino acid residues.
• Combination of functional
modelorganism knowledge
• Structure-function
• Identify similar areas of biology
• Identify orthologous pathways (might
have different endpoints)
Comparative Genomics: The biological Rosetta

Example: Agro
Known “lethal” genes
from worm, drosphila
Sequence Genome
Filter for drugability”,
tractibility & novelty

Example: Extremophiles
Known lipases
Filter for
“workable”lipases
at 90º C
Look for species
with interesting
phenotypes
Clone and produce in large quantities
Washing Powder additives
Sequence Genome
Functional Foods
Convert Highly Energetic Monosaccharides to Dextrane

Drug Discovery: Design new drugs by computer ?
Problem: pipeline cost rise linear, NCE steady
Money: bypassing difficult, work on attrition
Every step requires specific computational tools

• Drugs are generally defined as molecules which
affect biological processes.
• In order to be effective, the molecule must be
present in the body at an adequate concentration
for it to act at the specific site in the body where
it can exert its effect.
• Additionally, the molecule must be safe -- that
is, metabolized and eliminated from the body
without causing injury.
• Assumption: next 50 years still a big market in
small chemical entities which can be
administered orally in form of a pill (in contrast
to antibodies) or gene therapy …
Drug Discovery: What is a drug ?

• Taxol a drug which is an unmodified natural
compound, is the exception
• Most drugs require “work” -> need for target
driven pipeline
• Humane genome is available so all target are
identified
• How to validate (within a given disease area) ?

• target - a molecule (often a protein) that is instrumental
to a disease process (though not necessarily directly
involved), which may be targeted with a potential
therapeutic.
• target identification - identifying a molecule (often a
protein) that is instrumental to a disease process (though
not necessarily directly involved), with the intention of
finding a way to regulate that molecule's activity for
therapeutic purposes.
• target validation - a crucial step in the drug
development process. Following the identification of a
potential disease target, target validation verifies that a
drug that specifically acts on the target can have a
significant therapeutic benefit in the treatment of a given
disease.
Drug Discovery: What is a target ?

Phenotypic Gap
# genes with
known function
Total # genes
Number of genes
1980 1990 2000 2010
Functional Genomics ?
More than running chip experiments !
Proposal to prioritize
hypothetical protein
without annotation, nice
for bioinformatics and
biologist

“Optimal” drug target
Predict side effect
Where is optimal drug target ?
How to correct disease state
Side effects ?

Genome-wide RNAi
RNAI vector
bacteria producing ds RNA for
each of the 20.000 genes
proprietary nematode
responding to RNAi
20.000 responses
20.000 genes insert
library

Normal insulin signaling
Reduced insulin signaling
fat storage LOW
fat storage HIGH
Type-II Diabetes

20,000 bacteria
each containing
selected
C. elegans gene
select genes with desired phenotypes
proprietary C.elegans strains
• sensitized to silencing
• sensitized to relevant pathway
Industrialized knock-downs

Molecular functions of 26 383 human genes
Structural Genomics

Lipinsky for the target ?
Database of all “drugable” human genes

Drug Discovery: Design new drugs by computer ?

screening - the automated examination and
testing of libraries of synthetic and/or organic
compounds and extracts to identify potential drug
leads, based on the compound's binding affinity
for a target molecule.
screening library - a large collection of
compounds with different chemical properties or
shapes, generated either by combinatorial
chemistry or some other process or by collecting
samples with interesting biological properties.
High Throughput Screening: Quick and Dirty…
from 5000 compounds per day
Drug Discovery: Screening definitions

• At the beginning of the 1990s, when the
term "high-throughput screening" was
coined, a department of 20 would
typically be able to screen around 1.5
million samples in a year, each
researcher handling around 75,000
samples. Today, four researchers using
fully automated robotic technology can
screen 50,000 samples a day, or around
2.5 million samples each year.
Drug Discovery: Screening Throughput

Robotic arm
Read-out
Fluorescence /
luminescence
Distribution
96 / 384 wells
Optical Bank
for stability
Drug Discovery: HTS – The Wet Lab

• Available molecules collections from pharma,
chemical and agro industry, also from
academics (Eastern Europe)
• Natural products from fungi, algae, exotic
plants, Chinese and ethnobotanic medicines
• Combinatorial chemistry: it is the generation
of large numbers of diverse chemical
compounds (a library) for use in screening
assays against disease target molecules.
• Computer drug design (from model
substrates or X-ray structure)
Drug Discovery: Chemistry Sources

• initial screen established
• Compounds screened
• IC50s established
• Structures verified
• Minimum of three independent
chemical series to evaluate
• Positive in silico PK data
Drug Discovery: HIT

• When the structure of the target is unknown,
the activity data can be used to construct a
pharmacophore model for the positioning of
key features like hydrogen-bonding and
hydrophobic groups.
• Such a model can be used as a template to
select the most promising candidates from the
library.
Drug Discovery: Hit/lead computational approaches

• lead compound - a potential drug candidate emerging from a
screening process of a large library of compounds.
• It basically affects specifically a biological process.
Mechanism of activity (reversible/ irreversible, kinetics)
established
• Its is effective at a low concentration: usually nanomolar
activity
• It is not toxic to live cells
• It has been shown to have some in vivo activity
• It is chemically feasible. Specificity of key compound(s) from
each lead series against selected number of receptors/enzymes
• Preliminary PK in vivo (rodent) to establish benchmark for in
vitro SAR
• In vitro PK data good predictor for in vivo activity
• Its is of course New and Original.
Drug Discovery: Lead ?

Christopher A. Lipinski, Franco Lombardo, Beryl W. Dominy, Paul J. Feeney
"Experimental and computational approaches to estimate solubility and
permeability in drug discovery and development settings":
"In the USAN set we found that the sum of Ns and Os in the molecular formula was
greater than 10 in 12% of the compounds. Eleven percent of compounds had
a MWT of over 500. Ten percent of compounds had a CLogP larger than 5 (or
an MLogP larger than 4.15) and in 8% of compounds the sum of OHs and NHs
in the chemical structure was larger than 5. The "rule of 5" states that: poor
absorption or permeation is more likely when:
A. There are less than 5 H-bond donors (expressed as the sum of OHs and
NHs);
B. The MWT is less than 500;
C. The LogP is less than 5 (or MLogP is < 4.15);
D. There are less than 10 H-bond acceptors (expressed as the sum of Ns and
Os).
Compound classes that are substrates for biological transporters are exceptions to
the rule."
Lipinski: « rule of 5 »

• A quick sketch with ChemDraw, conversion to a
3D structure with Chem3D, and processing by
QuikProp, reveals that the problem appears to be
poor cell permeability for this relatively polar
molecule, with predicted PCaco and PMDCK
values near 10 nm/s.
• Free alternative (Chemsketch / PreADME)

(Celebrex)
Methyl in this position makes it a weaker cox-2 inhibitor,
but site of metabolic oxidation and ensures an acceptable clearance
Drug-like-ness

To assist combinatorial chemistry, buy specific compunds

Structural Descriptors: (15 descriptors)
Molecular Formula, Molecular Weight, Formal Charge, The Number of Rotatable Bonds, The Number of Rigid
Bonds, The Number of Rings, The Number of Aromatic Rings, The Number of H Bond Acceptors, The
Number of H Bond Donors, The Number of (+) Charged Groups, The Number of (-) Charged Groups, No.
single, double, triple, aromatic bonds
Topological Descriptors:(350 descriptors)
• Topological descriptors on the adjustancy and distance matrix
• Count descriptors
• Kier & Hall molecular connectivity Indices
• Kier Shape Indices
• Galvez topological charge Indices
• Narumi topological index
• Autocorrelation descriptor of atomic masses, atomic polarizability, Pauling electronegativity and van der
Waals radius
• Information content descriptors
• Electrotopological state index (E-state)
• Atomic-Level-Based AI topological descriptors
Physicochemical Descriptor:(10 descriptors)
AlogP98 (calculated logP), SKlogP (calculated logP), SKlogS in pure water (calculated water solubility), SKlogS in
buffer system (calculated water solubility),SK vap (calculated vapor pressure), SK bp (calculated boiling
point), SK mp (calculated meling point), AMR (calculated molecular refractivity), APOL(calculated
polarizability), Water Solvation Free Energy
Geometrical Descriptor:(9 descriptors)
Topological Polar Surface Area, 2D van der Waals Volume, 2D van der Waals Surface Area, 2D van der Waals
Hydrophobic Surface Area, 2D van der Waals Polar Surface Area, 2D van der Waals H-bond Acceptor Surface
Area, 2D van der Waals H-bond Donor Surface Area, 2D van der Waals (+) Charged Groups Surface Area, 2D
van der Waals (-) Charged Groups Surface Area

• What can you do with these descriptors ?
• Cluster entire chemical library
– Diversity set
– Focused set
Drug Discovery: Hit/lead computational approaches

• Structure is known, virtual screening -> docking
• Many different approaches
– DOCK
– FlexX
– Glide
– GOLD
• Including conformational sampling of the ligand
• Problem:
– host flexibility
– solvatation
• Example: Bissantz et al.
– Hit rate of 10% for single scoring function
– Up to 70% with triple scoring (bagging)
Drug Discovery: Docking

• Given the target site:
• Docking + structure generator
• Specialized approach: growing
substituent on a core
– LUDI
– SPROUT
– BOMB (biochemical and organic model
builder)
– SYNOPSIS
• Problem is the scoring function
which is different for every protein
class
Drug Discovery: De novo design / rational drug design

Drug Discovery: Novel strategies using bio/cheminformatics
- HTS ? Chemical space is big (1041)
- Biased sets/focussed libraries -> bioinformatics !!!
- How ? Use phylogenetics and known structures to define
accesible (conserved) functional implicated residues to
define small molecule pharmacophores (minimal
requirements)
- Desciptor search (cheminformatics) to construct/select
biased compound set
- ensure serendipity by iterative screening of these
predesigned sets

Drug Discovery
Toxigenomics
Metabogenomics

• Preclinical - An early phase of development
including initial safety assessment
Phase I - Evaluation of clinical pharmacology,
usually conducted in volunteers
Phase II - Determination of dose and initial
evaluation of efficacy, conducted in a small
number of patients
Phase III - Large comparative study
(compound versus placebo and/or established
treatment) in patients to establish clinical
benefit and safety
Phase IV - Post marketing study
Drug Discovery: Clinical studies

Drug Discovery & Development: IND filing

Pharmacogenomics
Predictive/preventive – systems biology

Lab for Bioinformatics and computational genomics

The Technical Feasibility Argument
The Quality Argument
The Price Argument
The Logistics Argument

Recreational genomics

• Experimental designs are outdated by technological advances
• Genetic background (reference genome) as a concept will need to be
updated
• Traits dependent on multiple loci are “complicated”: educate and
provide tools to deal with it

• Eye color … why not the ear wax/asparagus or unibrown example
• … metabolize nutrients (newborns ?)
• … metabolize drugs in case you need it urgently ?

“several 23andMe users have reported taking the FDA’s
advice of reviewing their genetic results with their
physicians, only to find the doctors unprepared, unwilling,
or downright hostile to helping interpret the data”

my genome is too important (for me)
to leave it (only) to doctors

NXTGNT biohackerspace …

PGMv2: Personal Genomics Manifesto

Everyone should have the power and legitimacy to
be able to discover, develop and find new things
about their own genome data.
Intelligent exploration, experimentation and trial to
push the boundaries of knowledge are a basic
human right.

Personal genome data access should be
affordable to all irrespective of nationality, gender,
social background or any other circumstance.
Not having access to a personal genetic test is in
itself a new kind of discrimination.

Whether one wants to share genome data or keep it
private should be a matter of personal choice.
Whatever attitude a person has towards personal
genome privacy, it should be utterly respected.
Corporate interest can never compromise any human
right. Laws must fully protect individual human rights of
equality for every person, irrespective of predicted risks
from genetic data.

Stating that genetic tests merely provide non-
clinical information misses the point of what
personal genomics is all about.
Most genomic information is uninterpretable and
may well be meaningless. But those are not
reasons to deny it to people.
Genetic test results are not unrelated to
someone’s health, one’s ability to respond to
certain drugs and one’s ethnic ancestry.

Education in risks and opportunities for personal
genetic testing should be the primary aim of
policy makers.
Restricting access to interested people makes
no sense and it is virtually impossible to ensure.
Access to personal genomics data and tools for
its interpretation should become accessible to
everyone.

Sneak preview
Bioinformatics (re)loaded

Sneak preview
Bioinformatics (re)loaded
• Relational datamodels
– BioSQL (MySQL)
• Data Visualisation
– Interface
• Apache
• PHP
• Large Scale Statistics
– Using R
• mHealth, iGem, …

2016 bioinformatics i_bio_cheminformatics_wimvancriekinge

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