This document discusses the role and methods of systems biology in drug discovery and development. It covers key topics such as: - The challenges of interpreting large omics data sets and how systems biology aims to integrate multi-omics data. - Examples of how systems biology approaches like computational modeling can be used in target discovery, understanding drug mechanisms of action, predicting drug combinations, and more. - How systems biology methods that combine experimental data with modeling are being applied across various stages of the drug development process from preclinical research to determining side effects.

1. MIKEL TXOPITEA ELORRIAGA

2. • 1.-Introduction: What is system biolgy
• The Omics revolution
• From Omics to systems biology
• 2.-System biology methods
• 2.1-Challenges and perspectives
• 2.2-System biology and drug discovery
• 3.-System biology in the pharmaceutical
industry
• 3.1-The role of systems biology in target discovery
• 3.2-Drug Mechanism of Action
• 3.3-Drug combinations, Computational modeling
• 3.4-Preclinical research
• 3.5- Examples of computational models relevant to human
disease biology
• 3.6- Uses for, and challenges of, each systems biology approach:
omics, complex cell systems and modeling
• 3.7-Development cycle of integrated in silico models using
component level and system response data.
INDEX
• 4.-Systems Biology Methods in Drug Discovery
• 4.1-New directions: System biology and system chemistry
• 4.2-Structural Systems Pharmacology
• 4.3-Drug Efficacy through Systems Biology
• 4.4-Drug-to-Target and Target Profiling Technologies
• 4.5-Therapeutic Perfomance Mapping System (TPMS)
• 5-Drug development
• 5.1-Drug repositioning
• 5.2-Determining side-effects
• 5.3-Conclusion

3. 1.-INTRODUZIONE
Nowadays we have large
amount of omics
molecular data at the
level of genome,
transcriptome, proteome,
and metabolome.
The field of 'omics'
currently polarizes the
community of
biologists
The omics driver:
DNA secuence data
are doubling every 5
months

4. Its not about number the complexity is increasing
1.1-The omics revolution
Metogenomics new discoveriese

5. 1.2-From Omics to system biology
System biology has emerged:
We need to know structure o
the biological systems, how
these interacting components
can produce complex system
behaviors
The current challenge of
interpreting the
overwhelming amount of
genome-scale data on a
systems level.
We use visualization of
omics data for systems
biology

6. Systems Biology approaches look to integrate
1-Across levels of structure and scale, building blocks
and functional modules to the large-scale of
organization of the system
2-Across phases of processes, inking the insights
from the many "omics" that have emerged from
technologtical advances
3.-A tight linking of experiment und modeling
processes, involving both data-driven and hypothesis-
driven phases
4.-A multi-disciplinary collaboration to provide
insights from the natural sciences, mathematics,
computer science, engineering and medicine
2.-SYSTEM BIOLOGY
METHODS

7. Some key challenges facing systems biology today are:
1. -Invention of new experimental methods (based on eg microfluidics, nanotechnology, femtochemistry) to provide
high-quality and comprehensive data needed for modeling and simulation,
2.-Development of modeling and computational approaches to handle large complex systems, which have highly
diversified components and encompass multiple scales (in space and time)
3.-Establishment of "systems engineering-oriented" ways of collaboration among experimentalists, among modelers
and between both groups, an important aspect of which would be to establish standards and platforms for data
schemes and software tools used
4.-Education of scientists -across all the disciplines-with the right balance of experimental and modeling
understanding and skills.
2.1.-Challenges and perspectives

8. Systems biology has the potential to impact the
entire drug discovery and development process,
as it looks to bridge the molecular and the
physiological
Industrial research labs plan to use system biology for
example for developing biomarkers for efficacy and
toxicology could lead to more efficient preclinical
developments approaches in screening for combination
drug targets elucidating side effects in drugs
find alternative indications for drugs already on the market
3.2-System biology and drug discovery

9. 3.- SYSTEMS BIOLOGY IN THE PHARMACEUTICAL INDUSTRY
Practical approach to systems biology at
the cell signaling network and cell-cell
interaction scales.
Generation of incorporate biological
complexity at multiple levels: multiple
interacting active pathways, multiple
intercommunicating cell types and
multiple different environments

10. 3.1-The role of systems biology in target discovery
Identify genes or
proteins whose
modulation will halt
or significantly alter
their progression

11. Network and system biology
strategies are more likely to have
an immediate contribution:
predictive toxicology and drug
repurposing
If successful, these models could help to iden
fy potential drug targets that are likely to trig
er severe adverse reactions at early stages of
the discovery process, and to rationally desig
the toxicity tests needed to check the safety
other drug targets under the area of influen
of a certain red node

12. 3.2-Drug Mechanism of Action
Drugs act by affecting the function of
different molecules of the human’s body, or
its invading pathogens in case of infectious
diseases. The pathway leading from the
molecules with which a drug directly
interacts (its ‘targets’) to the ones that cause
its effects is named its ‘mechanism of action
Gaining this knowledge is one of the
leading topics of the pharmaceutical
industry, because the mechanism of
action is fundamental to understand the
precise actions of a drug

13. System-biology research cycle with drug discovery and treatment cycles

14. 3.3-Drug combinations, Computational modeling
Integration of these systems approaches
will enable faster discovery and translation
of clinically relevant drug combinations
Mechanism of action discovery traditionally
required serendipitous or resource-extensive
approaches, but systems biology allows us to
compile all the scientific knowledge generated over
the years and draw conclusions from all of it at a
time, thanks to the use of powerful algorithms and
computers.
In this way, we find the most probable mechanism of
action from any suggested set of ‘stimuli’ and
‘responses

15. Future strategy for drug combination predictions with parallel
integration of computational modeling, preclinical testing, and
clinical trials
Future combinatorial drug
discovery approaches will benefit
from tighter integration of gene
signatures and phenotypic screen
data with computational models
For clinical application, patient
gene signatures can be clustered
with gene expression signatures
from previously modeled cell lines
All this is algorithmically comput
what allows us to provide seve
nice features

16. 3.4-Preclinical Research
The Systems Biology group’s role is to develop a
detailed understanding.This includes research to
identification of molecular targets or other
intervention points, model development, and
discovery of predictive biomarkers and outcome
measures

17. 3.5-Examples of computational models relevant to human disease biology
See http://www.cellml.org/examples/repository/ for more examples.Computer models: from pathways to disease physiolo

18. 3.6- Uses for, and challenges of, each systems biology approach: omics, complex ce
systems and modeling
aApproach cannot address issue, -; approach can address issue, +; approach can address issue under certain conditions, +/-.
Omics: large-scale data generation and mining

19. 3.7-Development cycle of integrated in silico models using component
level and system response data
Models are iteratively tested and improved by
comparison of predictions with systems
level responses measured experimentally through
traditional assays or from profiles generated from
complex, activated human cell mixtures under a set
of different environmental conditions. Component
level 'omics' data can provide a scaffold, limiting the
range of possible models at the molecular level

20. 4.-Systems Biology Methods in Drug Discovery
Leveraging complexity in cell systems biology for drug discovery
The combination of multiple
cell types and multiple
pathways activated elicits
complex network regulation
and emergent properties th
enhance the sensitivity and
ability of the systems to
discriminate unique drug an
gene effects.

21. Several complex human cell 'systems‘ are
interrogated with genes or drugs of interest
and the effects on the levels of selected
protein readouts are determined, generating
a profile that serves as a multisystem
signature of the function of the test agent
Statistical measures of
profile similarity can be used
to cluster genes or drugs by
function, and to generate
graphical representations of
their functional relationships
with each other
In the example shown, BioMAP
clustering defines two functional
activity classes among structurally
related p38 MAPK inhibitors.

22. 4.1-New directionsfor drug discovery
The CGBVS ligand-protein interaction discovery method
(center) is a change in exploring the interface between
chemistry and biology, not requiring the protein three-
dimensional structure required in traditional SBVS (right),
nor limited in scope to a single protein as is the case in
LBVS (left). In CGBVS, protein subsequences and chemical
descriptions of topology and other physicochemical
properties are combined for each known interaction and
non-interaction. The set of (non-)interactions is used to
build a predictive model that can rank novel ligand-protein
interactions for prioritization in bioassay experiments.

23. Complexity of Interaction Networks between
chemical and biological spaces
The need to shift the drug design strategy from “one-
ligand-one-protein” to “many-to-many.” The node color
indicates the classes that compounds and GPCRs belong
to (blue, amines; red, peptides; yellow, prostanoids;
green, nucleotides). The links colored from green to
yellow to red indicate increasing confidence in the GPCR-
ligand interaction, with a number of interclass GPCR-
ligand interactions exhibiting high predictive confidence.
Similar to the CGBVS method that can utilize protein and ligand
promiscuity, the incorporation of multiple interactions
Will provide constraints to guide future generations of
molecule design for producing medicines that are
more personal and contain fewer side effects

24. The structural characterization of cellular netwo
helps explaining drug mechanisms of action and
expanded the universe of therapeutic strategie
revealing novel classes of targetable entities bet
suited to fight complex diseases
Polypharmacology involves the modulation of
various proteins to target the network more
efficiently
Targeting PPI interfaces: a drug can alter PPIs
by inhibiting them through competitive binding
at the interface or by stabilizing them
Allostery: PPIs can be also modulated through
conformational changes induced in distal sites of
the protein
4.2-Structural Systems Pharmacology

25. Structural Systems Pharmacology: the role of 3D structures in next generation drug development
Mapping genetic variations
onto pharmacological targets
can rationalize interindividual
variability in drug response,
giving valuable hints to
advance towards personalized
medicine.
Genetic variations in
pharmacological targets can have
an important effect through the
direct or indirect perturbation of
the drug-binding cavity

26. 4.3-Drug Efficacy through Systems Biology
In vitro and in
vivo assays are
time-consuming
and expensive
TPMS technologyit is possible to assess the efficacy of either an experimental
or an already marketed compound by just knowing its targets. Through
artificial neural networks (ANNs), a type of Artificial Intelligence methods, we
are able to predict the efficacy of a drug in treating a disease by analysing the
effect of the modulation of its molecular targets over a molecularly defined
model of the disease.
We can predict the
target profile of a
drug based on their
structure and
biological and
clinical features

27. 4.4-Drug-to-Target and Target Profiling Technologies
State-of-the-art scientific knowledge
with various chemical modelling
approaches to identify potential targets
and off-targets of a compound.

28. 4.5-Therapeutic Perfomance Mapping System (TPMS)
TPMS integrates data from clinical
and preclinical studies about the
drug in mathematical models that
have been designed in agreement
with the currently available
biological knowledge. Then, the
system calculates the probability o
each of the predicted targets and
off-targets causing the clinical and
physiological manifestations
observed in studies

29. On one hand, it can simultaneously analys
thousands of compounds at a very low cos
compared to in vitroor in vivo experiments
On the other, it considers all the available
information about diseases and drugs and
puts it in the context of human physiology
achieving a holistic understanding of the
problem under study

30. 5-Drug development
Drug discovery and development is very checked road
Drug development costs broken down by
stage. Source: Paul S. (2010).

31. 5.1-Drug Repositioning
Too time- and cost-consuming, whereas the
second is inherently slow and can easily
overlook not-too-evident properties of the
compounds under study.

32. Drug candidates have frequently undergone exhaustive
testing before being approved for their original indication

33. 5.1-Side-effects
Torcetrapid disaster
The drug have to pass
a lot of exams
Only a few druug
candidates will pass

34. 3.5-Conclusion
During drug development, million-dollar decisions are (and
must be) routinely made using flawed criteria based on
incomplete biological knowledge: for example, targets are
prioritized because they are upregulated at the gene level in
disease; compounds are selected to be biochemically
specific; animal models are considered essential
Better biology, preferably more relevant to human disease
and capable of being integrated into the drug discovery
process, is sorely needed to inform decision-making.
Although the systems biology approaches outlined here are
in their infancy, they are already contributing to meaningful
drug development decisions by accelerating hypothesis-
driven biology, by modeling specific physiologic problems in
target validation or clinical physiology and by providing
rapid characterization and interpretation of disease-
relevant cell and cell system level responses.
Markup languages for gene expression data, emerging ontologies for
sharing and integrating different kinds of omic and conventional biolo
data4 and the introduction of standardi- zed high-throughput systems
biology and associated informatics approaches represent important fi
steps on this path.