Computational (In Silico) Pharmacology
Ayanbode Emmanuel Oluwafemi –
CLI/2018/103
Azodo Chukwuemeka John –
CLI/2018/104
Owojuyigbe Moyosore Victoria –
CLI/2017/117
Presented by

Introduction to
Computational
Pharmacology

Outline
● Introduction to Computational pharmacology
● Molecular Modelling and Simulation
● Pharmacokinetics and Pharmacodynamics modelling
● Data Analytics and Machine learning
● Conclusion

Introduction
● Pharmacology is the study of how drugs interact within the human body.
● Early studies in pharmacology focused on the effects of natural
substances in the body as a means of therapeutic treatment.
● Modern pharmacology uses computational and modelling as research
tool on a cellular level

Introduction
● Computational pharmacology, also known as in silico pharmacology, is a
field of study that uses computer simulations and computational
techniques to predict the effects of drugs and other therapeutic agents on
biological systems.
● This approach allows researchers to study the interactions between drugs
and biological targets, as well as the pharmacokinetic and
pharmacodynamic properties of drugs, without the need for extensive
experimentation.

Introduction
● Computational pharmacology uses in silico techniques to better
understand and predict how drugs affect biological systems, which can,
in turn, improve clinical use, avoid unwanted side effects, and guide the
selection and development of better treatments
● The term 'in silico' is a modern word usually used to
mean experimentation performed by computer

Introduction
● Computational pharmacology can be observed under three subheadings
A. Molecular Modelling and Simulation
B. Pharmacokinetics and Pharmacodynamics modelling
C. Data Analytics and Machine learning

Molecular Modeling And Simulations

Molecular Modeling And Simulations
● Molecular modeling involves the use of computer programs to predict the
structure and properties of molecules.
● Molecular modeling and simulation are powerful tools used by scientists to
study the behavior of molecules and their interactions with each other. These
techniques are essential in the field of chemistry, biochemistry, materials
science, and drug discovery
● This technique uses a variety of mathematical and computational models to
simulate the behavior of atoms and molecules. These models can range from
simple static models to highly complex dynamic simulations.

Molecular Modeling And Simulations
● Molecular modeling and simulation have revolutionized the field of drug
discovery and development by enabling researchers to predict the
behavior of molecules and their interactions with target proteins.
● The use of computational tools and techniques in this field has
significantly reduced the time and cost required to develop new drugs.

Techniques used in Molecular Modeling And
Simulations
1. Molecular docking
2. Molecular Dynamic Simulations
3. Quantitative Structure-Activity Relationship (QSAR) modeling
4. Homology modeling
5. Pharmacophore modeling
6. Virtual screening
7. Quantum mechanics/molecular mechanics (QM/MM) simulations

Molecular docking
● It’s one of the most commonly used techniques in molecular modeling
● Molecular docking involves the prediction of the binding affinity between
a small molecule and a target protein. By simulating the interaction
between the two molecules, researchers can predict the binding strength
and identify potential drug candidate

Molecular Dynamic Stimulation
● This technique involves simulating the movement of atoms and molecules over
time, providing insights into the behavior of molecules in different environments.
● By studying the dynamics of molecules, researchers can identify potential drug
targets and optimize drug candidates for maximum efficacy.

Quantitative Structure-Activity Relationship
(QSAR) modeling
● QSAR involves the analysis of the relationship between the chemical structure of
a molecule and its biological activity.
● Quantitative structure-activity relationships (QSAR) is a computational method
used to predict the biological activity of a molecule based on its chemical
structure. It involves developing mathematical models that relate the
physicochemical properties of a molecule (such as its shape, size, polarity, and
electronic properties) to its biological activity.

Quantitative Structure-Activity Relationship
(QSAR) modeling
● QSAR is widely used in drug discovery, environmental toxicology, and other
areas of chemical and biological research. It can help researchers to identify
potential drug candidates and prioritize compounds for further experimental
testing, thus reducing the time and cost of drug development.

Homology modeling
● Homology modeling is one of the computational structure prediction methods
that are used to determine protein 3D structure from its amino acid sequence,
and the known structures of related proteins..
● This technique is particularly useful for proteins that are difficult to crystallize or
for which no experimental structure is available.
● Homology modeling allows researchers to identify potential drug targets and
predict the interactions between small molecules and the target protein.

Pharmacophore modeling
● This involves identifying the essential features of a molecule (ligand) that are
required for it to bind to a target protein. By analyzing the structure of a known
ligand-protein complex, researchers can identify the key structural features of
the ligand that are responsible for its binding to the protein. This information
can then be used to develop a pharmacophore model, which can be used to
predict the activity of new ligands against the same target protein.
● A pharmacophore model explains how structurally diverse ligands can bind to
a common receptor site

Pharmacophore modeling
● The process of pharmacophore modeling involves analyzing the structure of
a molecule and identifying its functional groups, such as hydrogen bond
donors, hydrogen bond acceptors, and hydrophobic regions.
● These features are then used to create a three-dimensional model of the
molecule, which can be used to screen large databases of compounds for
molecules that have similar pharmacophores and are likely to have similar
biological activity.

Virtual screening
● Virtual screening is a computational technique used in drug discovery to identify
potential drug candidate that could bind to a target protein or receptor of
interest.
● It involves using computer algorithms to search large databases of chemical
compounds for molecules that are predicted to have favorable interactions with
the target.

Virtual screening
● The goal of virtual screening is to identify compounds that have a high
probability of binding to a target protein or receptor, and therefore have potential
as a drug
● This method is useful because it can quickly screen large numbers of molecules,
allowing researchers to focus on a smaller number of compounds for further
study.

Quantum mechanics/molecular mechanics
(QM/MM) simulations
● QM/MM simulations combine the accuracy of quantum mechanics
calculations with the efficiency of molecular mechanics simulations to
provide insights into the behavior of large, complex systems. This
technique is particularly useful for studying the reactions of enzymes,
which are notoriously difficult to model using traditional molecular
mechanics simulations

Pharmacokinetic
and
Pharmacodynamic
models of
Computational
Pharmacology

Pharmacology and Pharmacodynamic models
● Pharmacokinetic and pharmacodynamic models are important
components of computational pharmacology.
● Computational pharmacology is the field of study that combines
computational methods and techniques with pharmacology, the science
of drugs and their effects on living organisms.

Model’s Components
● Basically, models are simplified descriptions of certain aspects of reality
by mathematical means, thereby allowing to concentrate on the factors
believed to be important.
● In case of PK/PD-modeling, the biological processes involved in the
elaboration of the observed drug effect are regarded with the overall
purpose to allow a quantitative description of the temporal pattern of
pharmacologic effects, and, even more important, a prediction beyond
the existing data.
● A PK/PD-model in general consists of a pharmacokinetic model section
and a pharmacodynamic model section

Pharmacokinetic models
● The pharmacokinetic model component provides the concentration-time
course in the sampled body fluid, normally plasma, serum or whole
blood (for which in the following plasma will be used as a synonym),
resulting from the administered dose. Compartmental pharmacokinetic
models are the most widely used for this purpose, as they provide a
continuous description of the concentration that can easily serve as
input function for the pharmacodynamic model portion. Since only the
free, unbound concentration at the effect site is pharmacologically
active, modeling of free concentrations should be preferred if any
nonlinearity in plasma or tissue binding is suspected and might obscure
the dose-concentration-effect relationship be characterized.

Pharmacokinetic models
● Pharmacokinetic models are used to describe the movement
of drugs through the body, including absorption, distribution,
metabolism, and excretion.
● These models can be used to predict how drugs will be
distributed and eliminated from the body, and can be used to
optimize drug dosing regimens.
● Pharmacokinetic models are also used to study drug-drug
interactions and drug toxicity.

Pharmacodynamic models
● The pharmacodynamic model component relates the concentration
provided by the kinetic model to the observed effect.
● Dependent on the mechanisms involved, it may consist of one or several
transduction and response elements that express the finally observed
effect directly or via multiple intermediary response steps.
● In its simplest form, the observed effect is directly related to the effect
site concentration and the concentrations at the effect site and in
plasma are in equilibrium.

Pharmacodynamic models
● The classic and most commonly used pharmacodynamic model under
these conditions is the sigmoid Emax•model, which is an empirical
function for describing non-linear concentration effect relationships. It
has the general form:
Emax • C"
E = EC'so + C"

Pharmacodynamic models
where the effect E is a function of Emax• the maximum effect, C, the
concentration of the drug, ECso, the concentration of the drug that produces
half of the maximal effect, and n, the so called shape factor.
The sigmoid Emax•model can be related to receptor theory. ECso is the
parameter characterizing the potency of the drug in the system, i.e., the
sensitivity of the organ or tissue to the drug, Em reflects its efficacy, i.e., the
maximum response. Although n can also be derived from receptor theory as
number of molecules interacting with one receptor, it is in practice merely
used to provide better data fits.

Pharmacodynamic models
● Pharmacodynamic models, on the other hand, are used to
describe the relationship between drug concentration and its
effects on the body.
● These models can be used to predict the efficacy of drugs and
to optimize dosing regimens. Pharmacodynamic models can
also be used to study the mechanisms of drug action and to
identify potential drug targets.

Computational Pharmacology -
Pharmacokinetic & Pharmacodynamic models
● Computational pharmacology combines both pharmacokinetic
and pharmacodynamic models to predict drug behavior and
optimize dosing regimens.
● These models can be used to simulate drug behavior in virtual
patients, allowing researchers to test different dosing regimens
and predict potential side effects.
● Computational pharmacology can also be used to design new
drugs and optimize drug properties.

Important tools in Computational Pharmacology
● Overall, pharmacokinetic and pharmacodynamic models are
important tools in computational pharmacology.
● They can be used to predict drug behavior, optimize dosing
regimens, and design new drugs.
● As computational methods and techniques continue to
advance, the role of pharmacokinetic and pharmacodynamic
models in drug discovery and development is likely to become
even more important.

CLASSIFICATION OF PK/PD-MODELS
Integrated PK/PD-models can be classified according to the manor in which
the measured pharmacokinetic and pharmacodynamic data are related to
each other. Four attributes have been proposed that might be used to
distinguish between different basic modeling concepts. These characterize
the link between the concentration and the response mechanism
accountable for the observed effect, the response mechanism by which the
effect is mediated, the information used to relate concentration to effect,
and the time-dependency of the parameters used in the pharmacodynamic
model component.

CLASSIFICATION OF PK/PD-MODELS
The resulting alternatives are:
• Direct link versus indirect link models
• Direct response versus indirect response models
• Soft link versus hard link models
• Time-variant versus time-invariant models

POPULATION PK/PD-MODELING
● The usefulness and validity of PK/PD-models for the evaluation of
dose-concentration-effect relationships is not limited to well-designed
clinical studies with relatively small groups of individuals and frequent
measurements of concentration and effect. It could also be proven for
observational data obtained from large trials with sparse and
imbalanced sampling schedules by applying population modeling
techniques.The PK/PD-concepts described in the previous paragraphs
are generally also applicable in population models, and are in most
cases merely expanded by inclusion of statistical models to account for
the different sources of variability. Population PK/ PD-modeling has
successfully been applied for numerous drugs.

Network Pharmacology
and Systems Biology

Introduction
● The field of pharmacology has seen significant advancements over the
years with the advent of new technologies and innovative methods.
● One of the most promising areas in the field of pharmacology is network
pharmacology and systems biology.
● This field involves the use of computational and mathematical methods
to understand the complex relationships between drugs, targets, and
diseases at a systems level.

Aim
● This section will provide a comprehensive overview of network
pharmacology and systems biology, including its applications, methods,
and future prospects.

Network Pharmacology
● Network pharmacology is an emerging field of study that focuses on
the identification of drug targets and the discovery of new drugs by
analyzing complex biological networks. It is based on the concept that
biological systems, including cells, tissues, and organs, are complex
networks of molecules that interact with each other to perform specific
functions.
● Network pharmacology aims to identify the key components of these
networks, including proteins, enzymes, and signaling pathways, and
understand how they are perturbed in disease states.

Network Pharmacology
● One of the key tools used in network pharmacology is the construction
of network maps, also known as interaction networks or interaction
maps.
● These maps represent the interactions between molecules in a
biological system, such as a cell or tissue.
● Network maps can be constructed using a variety of experimental and
computational methods, including protein-protein interaction assays,
gene expression profiling, and computational modeling.

42
Fig.1 A network map

Network Pharmacology
● Once a network map has been constructed, it can be analyzed using a
variety of computational tools, including graph theory, machine learning,
and statistical analysis.
● These tools can be used to identify the key components of the network,
including the hubs, bottlenecks, and clusters of highly interconnected
molecules.
● They can also be used to predict the effects of perturbations to the
network, such as the effects of drug treatments or genetic mutations.

Systems Biology
● In contrast, systems biology is a field of study that aims to understand
biological systems as a whole, rather than as individual components.
● It is based on the concept that biological systems are complex and
dynamic and that the interactions between molecules, cells, and tissues
are critical to understanding their behavior.
● Systems biology aims to identify the key components of biological
systems, including genes, proteins, and metabolites, and understand
how they interact to produce complex biological functions.

Systems Biology
● One of the key tools used in systems biology is mathematical modeling,
which involves using mathematical equations to describe the behavior
of biological systems.
● Mathematical models can be used to predict the behavior of biological
systems under different conditions, and to test hypotheses about how
these systems function.

Systems Biology-
● As aforementioned, understanding the complex behavior of biological
systems that emerges from separate cellular components and
interactions between them is the goal of systems biology.
● Thus, systems biology relies on the perfect blend of experimental
research that creates data about a system’s biological components
using computational tools that aid in the interpretation of multiple
datasets.
● In systems biology, the two main computational approaches involve
data-driven (top-down method) and hypothesis-driven (bottom-up
approach).
Computational approaches used in systems biology

Computational approaches used in systems
biology
1. Top-down modeling approaches
These are basically utilized statistical models for handling large-scale
datasets. The statistical approaches generally identify trends/patterns and
analyzed data to make predictions based on data analysis-derived system
organization. Network modeling provides knowledge of interactions among
diverse components of a biological system and commonly utilized
data-driven techniques.

Computational approaches used in systems
biology
The investigation of an interaction network for topological features is known
as network analysis. In random networks, partial least squares regression is
used, and the maximal nodes have nearly the same degree of distribution.
2. Bottom-up modeling approaches
It is used to investigate simpler systems with fewer interconnected
components. Equations are used in mechanistic or dynamical models to
describe how components interact.

Computational approaches used in systems
biology
Herein, simulations are used to develop predictions and make comparisons
between real-time and experimental time courses. For a mathematical
description of paths, ordinary differential equations and partial differential
equations are commonly utilized.
A deterministic or stochastic dynamic system can be built to study the
biological process.

Computational approaches used in systems
biology
To build a dynamical model, the following steps are generally required:
● Designing a connectivity diagram consisting of all the components of a
biochemical pathway and connectivity between them.
● Construction of mathematical equations from connectivity diagrams.
● Calibration of the model to estimate unknown kinetic parameter values
for a predefined set of parameters and initial concentration.
● Model validation by subjecting simulation results to experimental test.

Integration of Network Pharmacology and
Systems Biology
● The integration of network pharmacology and systems biology has led
to the development of new approaches for drug discovery and
development.
● It allows researchers to identify novel drug targets, repurpose existing
drugs, and optimize drug therapies.
● The methods used in network pharmacology and systems biology
include data integration, network analysis, machine learning, and
systems modeling.

Applications of Network Pharmacology &
Systems Biology
The applications of network pharmacology and systems biology are diverse
and include drug discovery, drug repurposing, drug target identification, and
drug toxicity prediction. These applications are discussed in detail below.
1. Drug Discovery
The traditional approach to drug discovery involves the identification of a
single target for a drug and the subsequent screening of compounds for
their activity against that target.

Applications of Network Pharmacology &
Systems Biology
However, this approach has several limitations, including the lack of efficacy
and the development of drug resistance. Network pharmacology and
systems biology provides an alternative approach to drug discovery by
identifying multiple targets for a drug.
Network pharmacology allows researchers to identify the targets of a drug
by analyzing the interactions between the drug and its targets in a network
context. This approach has been used to discover new drugs for diseases

Applications of Network Pharmacology &
Systems Biology
such as cancer, diabetes, and cardiovascular disease.
2. DRUG REPURPOSING
Drug repurposing is the process of discovering new uses for existing drugs.
This approach is attractive because it reduces the time and cost associated
with drug discovery and development. Network pharmacology can be used
to analyze the interactions between drugs and their targets, and identify
drugs that have the potential to target multiple pathways or interact with

Applications of Network Pharmacology &
Systems Biology
multiple targets.
This approach has been successful in identifying new uses for existing
drugs, such as the use of thalidomide in the treatment of multiple myeloma.
Another example is the drug metformin, which is commonly used to treat
diabetes.
It has been repurposed for the treatment of cancer. This was achieved by
analyzing the interactions between metformin and the proteins involved in
cancer signaling pathways.

56
Fig. 2 Drug repositioning via systems biology. Summary of components and methodologies used in drug
repositioning studies from the perspective of systems biology.

57
Fig.3 Network-based technique for the repositioning of drugs for severe acute respiratory syndrome-Coronavirus-2.

Applications of Network Pharmacology &
Systems Biology
3. Drug Target Identification
Drug target identification is the process of identifying the proteins that are
responsible for the therapeutic effects of a drug.
One of the key advantages of network pharmacology is its ability to identify
new drug targets that may not have been previously considered. By
analyzing the network of interactions between molecules in a disease state,
network pharmacology can identify key nodes in the network that are

Applications of Network Pharmacology &
Systems Biology
potential drug targets.
This approach has been used to identify the targets of drugs for diseases
such as Alzheimer's disease, Parkinson's disease, and cancer. The
identification of drug targets is an important step in drug discovery because
it allows researchers to develop drugs that are more effective and have
fewer side effects.

Applications of Network Pharmacology &
Systems Biology
4. Drug Toxicity Prediction
Drug toxicity prediction is the process of predicting the adverse effects of a
drug. This is an important step in drug development because it allows
researchers to identify drugs that are safe for human use.
By analyzing the interactions between drugs and their targets, network
pharmacology can identify drug combinations that target multiple pathways
or interact with multiple targets, leading to more effective and less toxic
treatments.

Applications of Network Pharmacology &
Systems Biology
This approach has been used to predict the toxicity of drugs for diseases
such as cancer, diabetes, and cardiovascular disease. The prediction of drug
toxicity is important in drug development because it allows researchers to
prioritize drugs that have a lower risk of causing adverse effects in humans.

Applications of Network Pharmacology &
Systems Biology
5. Personalized Medicine
Systems biology can be used in personalized medicine, which involves
tailoring medical treatments to individual patients based on their genetic
and molecular profiles.
Systems biology is used to analyze the interactions between drugs and their
targets in individual patients, and identify the most effective treatments for
each patient based on their unique molecular profile.

Methods Used in Network Pharmacology and
Systems Biology
The methods used in network pharmacology and systems biology are
diverse and include data integration, network analysis, machine learning,
and systems modeling.
A. Data Integration
Data integration is the process of combining data from multiple sources to
generate a comprehensive view of biological systems. This approach is
important in network pharmacology and systems biology because it allows
researchers to integrate information from different sources, such as
genomics, proteomics, and metabolomics, to gain a better understanding of
the relationships between drugs, targets, and diseases.

Methods Used in Network Pharmacology and
Systems Biology
B. Network Analysis
Network analysis is the process of analyzing the interactions between
drugs, targets, and diseases in a network context. This approach involves
the use of graph theory to identify the key components of a network and to
predict the effects of perturbations in the network.

65
Fig. 4 Depiction of Network Analysis

Methods Used in Network Pharmacology and
Systems Biology
C. Machine Learning
Machine learning is a subset of artificial intelligence that involves the
development of algorithms that can learn from data and make predictions.
This approach is important in network pharmacology and systems biology
because it allows researchers to identify patterns in complex datasets and
make predictions about the behavior of biological systems.

67
Fig.5 Quantitative systems pharmacology (QSP) is a quantitative and mechanistic platform describing the phenotypic interaction between
drugs, biological networks, and disease conditions to predict optimal therapeutic response.

Methods Used in Network Pharmacology and
Systems Biology
D. Systems Modeling
Systems modeling involves the development of mathematical models to
describe the behavior of biological systems. This approach is important in
network pharmacology and systems biology because it allows researchers
to simulate the behavior of biological systems and predict the effects of
perturbations in the system.

Future Prospects of Network Pharmacology and
Systems Biology
● The integration of network pharmacology and systems biology has the
potential to revolutionize drug discovery and development. However,
there are several challenges that must be addressed before this
potential can be fully realized.
● These challenges include the need for standardized data formats, the
development of more accurate predictive models, and the integration of
data from different sources.

Future Prospects of Network Pharmacology and
Systems Biology
Standardized Data Formats: The integration of data from different sources
is a key challenge in network pharmacology and systems biology. To
overcome this challenge, standardized data formats are needed to ensure
that data can be easily integrated and analyzed.
More Accurate Predictive Models: The development of more accurate
predictive models is another key challenge in network pharmacology and
systems biology. This requires the development of new algorithms and the
integration of more data sources to improve the accuracy of predictions.

Future Prospects of Network Pharmacology and
Systems Biology
Integration of Data from Different Sources: The integration of data from
different sources is a complex challenge in network pharmacology and
systems biology. This requires the development of new methods for data
integration and the integration of data from different domains, such as
genomics, proteomics, and metabolomics.

Conclusion
● Network pharmacology and systems biology are two branches of
computational pharmacology that have revolutionized the field of drug
discovery and development.
● These approaches utilize computational tools and techniques to analyze
complex biological systems and identify drug targets, drug
combinations, and drug repurposing opportunities.
● By combining the network-based analysis of network pharmacology

Conclusion
with the holistic analysis of systems biology, researchers can identify the
key components of biological systems, understand their interactions, and
predict their behavior under different conditions.
● The integration of network pharmacology and systems biology has the
potential to transform drug discovery and development, leading to more
effective and personalized treatments for a wide range of diseases.

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