Rushikesh Shinde presented on computational modeling of drug disposition at Alard College of Pharmacy. The presentation discussed how modeling absorption, solubility, and intestinal permeation can help predict drug behavior in the body. Historically, drug candidates often failed in late-stage clinical trials due to issues related to metabolism, excretion, and toxicity, which computational modeling seeks to evaluate earlier in the drug development process. The presentation covered techniques like quantitative structure-activity relationship analysis and pharmacokinetic modeling to computationally simulate drug properties.

1. DEPARTMENTOFPHARMACEUTICS
TOPIC: Computational Modeling Of Drug Disposition
PRESENTED BY: RUSHIKESH SHINDE
(M.Pharm,First Year)
GUIDED BY: DR.NALANDA BORKAR MADAM
(Head Of Department Of Pharmaceutics)
Survey No. 50,Marunje,Near Rajiv Gandhi,
IT Park, Hinjawadi,Pune,Maharashtra,411028
ALARD COLLEGE OF PHARMACY
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2.  Contents
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Introduction
Modeling Technique
Drug Absorption
Solubility
Intestinal Permeation

3.  Introduction
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Historically, drug discovery has focused almost exclusively on efficacy and
selectivity against the biological target.
As a result, nearly half of drug candidates fail at phase II and phase III clinical
trials because of metabolism, excretion and toxicity (ADMET).
Thepressure to control the escalating cost of new drug development
has changed the paradigm since the mod-1990s.
Toreduce the attrition rate at more expensive later stages, in vitro evaluation
of ADMET properties in the early phase of drug discovery has widely adopted.

4. Many high-throughput in vitro ADMET property screening assays have
been developed and applied successfully.
Fueled by the ever-increasing computational power and
significant advances of in silico modelling alogrithms,numerous
compuational programs that aim at modeling ADMET properties have
emerged.
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A comprehensive list of available commercial ADMET modeling software
has been provided till date.

5. Computational simulated model of drug
discovery process:
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6. ADMET process and it’s relationship:
ADMET
Absorption
Solubility
Log p
Log g
Permiability
COCO-2
MDCK
PAMPA
Active
transport
PEPT-1
ASBT
NT
P-gp
BCRP
MRP
Distribution
PPB BBB
Passive
Log BB Log PS
Active
VD
Metabolism Excretion
Hepatic
Passive Active
OATP NTCP
Renal
Passive Active
OAT OCP
Toxicity
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7. Empirical classical compartmental model:
• In the classical compartment model, a drug is inputted into the gut
compartment, and absorption into the systemic circulation compartment is
governed by the absorption rate constant (ka). Elimination is described by the
elimination rate constant (ke).
Mathematical
model depend
on two
properties
Permeability Solubility
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8. • Classical compartmental pharmacokinetic models simply describe
absorption as a single first-order process.
• Typical empirical absorption models generally assume zero order or
first-order absorption kinetics, with or without a lag time, where the
absorption rate constants can be easily obtained by simple
compartmental modeling of the drug’s plasma concentration time
profiles.
• All assumption are based on a conceptual but not on a physiological
basis.
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9. Importance of model in drug disposition:
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10.  Absorption:
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11. 11

12. Calculation of
log p value
Molecular
simulation
ab initio
methods
Property-based
methods
Empirical
models
Linear solvation energy relationship;
molecular size and H-bond strength;
estimation of perturbed molecular
orbitals
Statistical-
based models
Developed-based on various descriptors,
such as topological indices, graph molecular
connectivity, estate descriptors, and
machine-learning methods
Substructure-
based methods
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13. Pharmaceutical parameters Lipophilicity:
• It contributes to the solubility, permeability, potency and selectivity of
a compound.
• lipophilicity of organic molecule is typically quantified as
log Po/w
• where P is the ratio of the concentrations of a compound in a mixture
of octanol and water phases at equilibrium.
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14. Modelingtechnique:2Approaches
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The 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 processes.
These are especially useful when there is an accumulation of knowledge against 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.
Three widely used automated pharmacophore perception tools are DISCO (DIStance
COmparisons), GASP (Genetic Algorithm Similarity Program) and Catalyst/HIPHOP.

15. The 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.
Some of these descriptors can be calculated based on drug structure.
It is essential to select the right mathematical tool for most effective ADMET modeling.
Sometimes it is necessary to apply multiple statistical methods and compare the results
to identify the best approach.
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16. In silico modeling target of drug
disposition
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17.  DrugAbsorption:
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Because of its convenience and good patient compliance, oral
administration is the most
preferred drug delivery form.
As a result, much of the attention of in silico approaches is focused
on modeling drug oral absorption, which mainly occurs in the human
intestine.
In general, drug bioavailability and absorption is the result of the
interplay between drug solubility and intestinal permeability.

18.  Solubility
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A drug generally must dissolve before it can be absorbed from the intestinal lumen.
By measuring a drug’s logP value (log of partition coefficient of compound between
water and n-octanol) and its melting point, one could indirectly estimate solubility using
“general solubility equation”.
To predict the solubility of compound even before synthesizing it, in silico modeling can
be implemented.
There are mainly two approaches to model solubility. One is based on the underlying
physiological processes, and the other is an empirical approach. The dissolution process
involves the breaking up of solute from its crystal lattice and the association of the solute
with solvent molecules.
Empirical approaches, represented by QSPR, utilize multivariate analysis to identify
correlations between molecular descriptors and solubility.

19. Solubilit
y
• Aqueous solubility is one of the most important factors affecting drug bioavailability. To
be absorbed, a drug must be soluble in water first and then have the opportunity to
permeate across biological membranes
• ΔG∗
sol= ΔG∗ + ΔG∗ −RT ln S V ,
sub solv 0 m
• ΔG∗
sol is the Gibbs free energy for solution,
• ΔG∗ is the Gibbs free energy for sublimation
sub
• ΔG∗
sol is the Gibbs free energy for solvation,
• R is the molar gas constant
• T is the temperature
• Thermodynamic cycle for the transfer from crystal to vapour and then to solution
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20. Models of
solubility
ab initio
Hartree–Fock
(HF) theory)
Depend on
molecular
orbital theory
Semi-empirical
molecular
orbital theory
AustinModel
1 (AM1)
It’s a
computational
chemistry
Hybrid density
functional
theory
Becke-3-Lee-
Yang-Parr
(B3LYP)
HF based
Recover
electrone
Through semi-emparical method
Loocv=leave oneoutcv (estimation
of error)
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21. Ionization
constant
• Drug distribution and diffusion rely heavily on the ionized state of the
drugs at a physiological pH because the neutral species of compounds are
more lipophilic, whereas ionized ones are polar and water soluble.
• Additionally, log D, which is an extension of log P by considering all forms
of the compound (i.e. ionized and un-ionized), was introduced to consider
the influences of ionization on the octanol-water partition coefficient 21

22. Models of ionization
constant
QM
ab initio
calculations
statistical and
machine-
learning
approaches
multi-linear
regression (MLR)
kernel-based
machine learning
ANNs
Semiempirical
approaches
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23.  Intestinal Permeation:
Intestinal permeation describes the ability of drugs to cross the intestinal mucosa
separating the gut lumen from the portal circulation.
It is an essential process for drugs to pass the intestinal membrane before entering
the
• systemic circulation to reach their target site of action.
The process involves both passive diffusion and active transport.
It is a complex process that is difficult to predict solely based on molecular mechanism.
As a result, most current models aim to simulate in vitro membrane permeation of
Caco-2,
• MDCK or PAMPA, which have been a useful indicator of in vivo drug absorption.
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24. ADME Prediction Model
(Human intestinal absorption)
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25. CLOE
HIA
• Predicted total absorption at several user-specified dose levels.
• Identification of factors (solubility and/or permeability)
limiting absorption at any poorly absorbed dose level(s).
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26. • Predicted dependence of the solubility on pH, over the range of the GI
tract contents (pH 2-7.5) and the peak concentration of compound
achieved in the different parts of the tract.
• Predicted absorption from the different segments of the GI tract at
each dose level.
• Predicted time course data representing absorption over time.
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27. References:
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Ekins S, “Computer Applications in Pharmaceutical Research and Development”,
(2006) John Wiley and Sons Inc., chapter 20, pp495-508.
(Accessed Date-03rd Of June 2021)
Ekins S, Nikolsky Y and Nikolskaya T. Techniques: Application of systems biology
to absorption,distribution,metabolism,excretion and toxicity.Trends Pharmacol
Sccccccci 2005;202-9.(Accessed Date-03rd Of June 2021)
 https://hemonc.mhmedical.com/content.aspx?bookid=1810&sectionid=1244898
64.(Accessed Date-03rd Of June 2021)
 (https://www.youtube.com/watch?v=xETq-LvRlrM)

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