Download Computational tools to predict and modulate biological activity

E F F I C ACY, TOX I C I T Y A N D P H A R M AC O K I N E T I C P R E D I C T I O N O F C O M P O U N D S A N D B I O LO G I C S AT T N O
COMPUTATIONAL TOOLS TO
PREDICT AND MODULATE
BIOLOGICAL ACTIVITY
Reducing attrition rates and
increasing efficiency are pivotal
in current drug discovery. Making
informed decisions on which
compound to progress early in the
pipeline, however, requires detailed
knowledge of its pharmacological,
toxicological and pharmacokinetic
properties on a molecular level.
Computational approaches offer
various techniques to gain this
insight and are indispensable in
modern, data-driven research.
At TNO, computational chemistry
is an integral part of our multi­
disciplinary research for predicting
and modulating the biological
effects of drugs.
Intelligent testing strategies to eliminate
drug candidates with safety liabilities or
efficacy issues go beyond a well-designed
experimental screening cascade by
including computational tools. In silico
approaches are particularly useful for
reducing cycle times and attrition rates
as they have the potential to predict
properties and activities of drugs prior
to synthesis or in vitro/in vivo screening.
This allows ranking and selection of
compounds based on potential adverse
effect or impaired activity during an early
stage of drug development. Leaving fewer
and more promising drug candidates to
be scrutinised experimentally. In the lead
optimisation stage, similar techniques
can be deployed to rationally optimise
drugs to clinical candidate status.
Computational support can be offered as
a stand-alone service or as an integrated
discipline. TNO has a strong track record
in multidisciplinary research and is
equipped with a large battery of in vitro
assays and in vivo models for efficacy,
toxicity and pharmacokinetics.
In addition, systems biology approaches
can be combined with computational
methods, for example in target discovery
and computational toxicology.
Depending on available knowledge and
the type of interaction involved (ligandprotein or protein-protein), various
computational strategies can be pursued.
TNO offers a full range of approaches for
which a concise description is provided in
this flyer.
E F F I C ACY, TOX I C I T Y A N D P H A R M AC O K I N E T I C P R E D I C T I O N O F C O M P O U N D S A N D B I O LO G I C S AT T N O
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Computational tools
Bioinformatics
Cheminformatics
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Target
discovery
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Pharmacophores
Docking
Protein Modeling
Lead
Optimization
Clinical
Candidate
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Hit finding
Hit-to-lead
Preclinical drug discovery stages and computational solutions
CHEMINFORMATICS
Cheminformatics encompasses a
collection of techniques and methods that:
generate accurate, high-throughput
prediction models for a specific
biological activity
use structural and physicochemical
features (including logP, polar surface
area and structural fingerprints).
correlate the most relevant descriptors
with a specific biological effect,
allowing prediction of this effect for
new entities.
Within a drug discovery setting, cheminformatics is highly valuable in compound
selection (hit finding), prioritisation (hit to
lead) and for scaffold hopping (novelty).
One of the applications within TNO is the
prediction of OATP1B1 transporter binding,
which plays a crucial role in the elimination of drugs by transporting them from
the blood to the liver.
PHARMACOPHORE MODELS
Approaches rely on the information
intrinsically encoded in the molecule and
are therefore mostly used when target
information is lacking.
A well-known ligand-based approach is
3D pharmacophore modelling where the
3D structures of compounds with known
activity are overlaid in space in such a
way that the functional (pharmacophoric)
groups governing the biological effect are
aligned. Pharmacophore models can be
used to derive QAR models or to make
compound screening selections.
STRUCTURE BASED APPROACHES
Structure based approaches utilise the
interaction between the small molecule
or biologic and its protein target to
predict or optimise its biological activity.
The required structural information of
the protein target can be obtained either
from an available crystal structure or
by homology modelling. In the latter
technique, a protein model is constructed
using the backbone structure of a closely
related protein. Ligand binding is
predicted by the ability of the compound
to dock favourably in the binding pocket.
Such a docking protocol can be run in
high-throughput mode, allowing large
databases of compounds to be screened.
A structure based approach within TNO
includes predicting the aggregation
propensity of biologics based on surface
properties. Protein aggregation is a major
driver of the immunogenic response of
biologics and therefore a predictor of
adverse effects.
BIOINFORMATICS
In addition to generating prediction
models, support with respect to target
discovery is also offered. In conjunction
with systems biology, biological pathways
can be dissected and targets relevant
for diseases or adverse effects can be
elucidated. By combining data mining and
molecular modelling techniques, drug­
ability assessment of novel targets can
be performed. This includes identification
of known ligands and homologues of the
protein, binding site detection and in
silico / in vitro screening options. TNO’s
preclinical disease models in diverse
therapeutic areas represent an excellent
starting point for such an approach.
TNO.NL
TNO HE ALTHY LIVING
TNO
Utrechtseweg 48
P.O. box 360
3700 AJ Zeist
The Netherlands
Dr. Simon Folkertsma
P +31 88 866 47 45
E [email protected]
North America (sales office)
Dr. Tineke Meijers
P +1 416 837 75 00
E [email protected]
Japan (sales office)
Kazuhiro Ariga
P +81 45 478 51 30
E [email protected]