Computational modeling is the use of computers to simulate and study complex systems using mathematics, physics, and computer science. It is a powerful tool that can be used to understand and predict how systems behave, without having to conduct physical experiments. One way to think about computational modeling is to imagine a virtual world that you can create and control. You can use this virtual world to test different scenarios and see how the system behaves under different conditions. For example, you could create a computational model of a weather system to predict how a hurricane is going to develop or, you could create a computational model of a drug to predict how it will interact with the human body. Data Collection - Collecting experimental data on drug properties and interactions. Model Development - Building mathematical models that represent drug behavior in the body. Model Validation - Ensuring that models accurately predict real-world outcomes. Model Application - Using models for various purposes like drug design, dose optimization, and clinical trial simulations.

1. COMPUTATIONAL MODELLING OF
DRUG DISPOSITION
PRESENTED BY: MOHD AZHAR
M.PHARM (PHARMACEUTICS)
GUIDED BY: DR. MEENAKSHI BHARKATIYA

2. COMPUTATIONAL MODELING
● Computational modeling is the use of computers to simulate and study complex
systems using mathematics, physics, and computer science. It is a powerful tool that
can be used to understand and predict how systems behave, without having to
conduct physical experiments.
● One way to think about computational modeling is to imagine a virtual world that you
can create and control. You can use this virtual world to test different scenarios and
see how the system behaves under different conditions.
● For example, you could create a computational model of a weather system to predict
how a hurricane is going to develop or, you could create a computational model of a
drug to predict how it will interact with the human body.

3. WHY COMPUTATIONAL MODELING?
Reduces cost
Minimizes
animal testing
Tailors
treatments to
individuals
Speeds up drug
development
S R
M T

4. COMPONENTS OF COMPUTATIONAL MODELING
● Data Collection - Collecting experimental data on drug properties and interactions.
● Model Development - Building mathematical models that represent drug behavior in
the body.
● Model Validation - Ensuring that models accurately predict real-world outcomes.
● Model Application - Using models for various purposes like drug design, dose
optimization, and clinical trial simulations.

5. TYPES OF MODELS
1. Pharmacokinetic (PK) Models
- Describes drug concentration changes over time, including absorption and distribution.
- Examples: One-compartment model, physiologically-based PK (PBPK) model.
2. Pharmacodynamic (PD) Models
- Relates drug concentration to therapeutic effect.
- Example: Emax model.
3. Quantitative Structure-Activity Relationship (QSAR) Models
- Predicts biological activity based on chemical structure.
4. Systems Biology Models
- Simulates complex biological processes, including metabolism.
- Example: Cellular automaton models.

6. MODELLING TECHNIQUES
QUANTITATIVE APPROACH
01
QUALITATIVE APPROACH
02

7. QUANTITATIVE APPROACHES
● Represented by pharmacophore modeling and flexible docking studies investigate
the structural requirements for the interaction between drugs and the targets that
are involved in ADMET process.
● Useful for accumulation of knowledge against a certain target.
● For example, a set of drugs known to be transported by a transporter would enable a
pharmacophore study to elucidate the minimum required structural features for
transport.

8. QUALITATIVE APPROACHES
● Represented by quantitative structure-activity relationship (QSAR) and
quantitative structure-property relationship (QSPR) studies utilize multivariate
analysis to correlate molecular descriptors with ADMET related properties.
● A diverse range of molecular descriptors can be calculated based on the drug
structure.
● Important to select the molecular descriptors that represent the type of
interactions contributing to the targeted biological property.

9. ● Essential to select the right mathematical tool for most effective ADMET
modelling sometimes it is possible to apply multiple statistical methods and
compare the result to identify the best approach.
● A wide selection Of statistical algorithms is available to researchers for
correlating field descriptors with ADMET properties including simple multiple
linear regression (MLR), multivariate partial least-squares (PIS), and the
nonlinear regression-type algorithms such as artificial neural networks (ANN)
and support vector machine (SVM).

10. DRUG ABSORPTION
● Oral administration is the most preferred drug delivery form due to convenience
and patient compliance.
● Much attention in In-silico approaches focuses on modeling drug oral absorption,
primarily occurring in the human intestine.
● Drug bioavailability and absorption result from the interplay between drug
solubility and intestinal permeability.

11. SOLUBILITY
● In silico modeling predicts solubility even before synthesis.
● Solubility estimation: LogP value (partition coefficient) and melting point.
● Two modeling approaches: physiological processes-based and empirical (QSPR).
● Empirical models use multivariate analysis to correlate molecular descriptors
with solubility.
● Accurate selection of descriptors and understanding of the dissolution process
are crucial.

12. INTESTINAL PERMEATION
● Intestinal permeation allows drugs to cross the gut mucosa into the portal
circulation.
● Essential for drugs to reach systemic circulation and target sites.
● Involves passive diffusion and active transport.
● Complex process challenging to predict based solely on molecular mechanisms.
● Models simulate in vitro membrane permeation using Caco-2, MDCK, or PAMPA.

13. DISTRIBUTION
● Distribution is crucial in a drug's pharmacokinetic profile.
● Determined by structural and physiochemical properties.
● Reflects in VD (Volume of Distribution).
● VD predicts drug half-life and dosing frequency.
● Models for VD prediction based solely on computed descriptors are under
development.

14. DRUG EXCRETION
● Drug excretion is quantified by plasma clearance, clearing plasma volume free of
drug per unit time.
● Hepatic and renal clearances are the main components.
● No models predict plasma clearance solely from computed drug structures.
● Current efforts focus on estimating in vivo clearance from in vitro data.
● Complexities arise from active transporters in hepatic and renal clearance
processes.

15. CHALLENGES AND LIMITATIONS
Data Quality
Reliable ADMET data is
crucial for accurate
modeling.
Validation and
Verification
Ensuring models reflect
real-world ADMET
behavior.
Model Complexity
Complex ADMET processes
require sophisticated models.
Regulatory
Acceptance
Establishing regulatory
guidelines for model
use, especially in ADMET
predictions.

16. FUTURE DIRECTIONS
HOW WE DO IT
02
Combining models at
different biological
scales, including
ADMET, for a holistic
view.
Multi-scale
Modeling
03
Leveraging emerging
technologies for
better ADMET data.
Advancements in
Data Collection
04
Creating standardized
guidelines for ADMET
model acceptance.
Regulatory
Framework
Development
01
Integration of
AI and Machine
Learning
Enhancing ADMET
modeling capabilities
with advanced
algorithms.

17. CONCLUSION
● Computational modeling plays a pivotal role in understanding and predicting
drug absorption and disposition.
● In silico approaches aid in early-stage drug development, optimizing oral drug
delivery, and minimizing experimental work.
● It enhances our understanding of drug behavior in the body, including ADMET
properties.
● Despite challenges, ongoing advancements in computational modeling continue
to enhance our ability to design better drugs

18. THANK YOU FOR YOUR
ATTENTION.
● Any questions or comments?