Myself Omkar Tipugade , M - Pharm sem II , department of Pharmaceutics , today will upload presentation on Computational modeling in drug disposition .

1. COMPUTATIONAL MODELING IN DRUG
DISPOSITION
Mr. Omkar Tipugade
M-pharm , Sem –II ( Pharmaceutics )
Shree Santkrupa College Of Pharmacy ,Ghogaon.

2. CONTENT:
 Introduction
 Modelling technique
 Quantitative approach
 Qualitative approach
 Drug absorption
 Solubility
 Intestinal permeation
 Drug distribution
 Volume of distribution
 Plasma protein binding
 BBB
 Drug excretion
 Active transporter

3. INTRODUCTION:
 Historically ,drug discovery has focused almost exclusively on efficacy and
selectivity against the biological target.
 Drug candidates fail at phase II & III clinical trial because of undesirable drug PK
properties including ADME & toxicity.
 To reduce the attrition rate at more expensive later stage , in-vitro evaluation of
ADME properties in the early phase of drug discovery has widely adopted.
 Many high throughput in-vitro ADMET property screening assay have developed
& applied successfully .
 Fueled by ever increasing computational power & significant advance of in silico
modeling algorithms , numerous computational program that aim at modeling
ADMET properties have emerged.
 A comprehensive list of available commercial ADMET modeling software has
been provided till date.

4. In silico modeling targets of drug disposition.

5. MODELLING TECHNIQUE
2 Approache
a) Quantitative approach :
 Represented by pharmacophore modeling & flexible docking studies investigate
the strutural requirement for the interaction between drug & target that are
involved in ADMET process.
 These are especially useful when these is an accumulation of knowledge against
certain target E.g., a set of drug known to be transported by transporter would
enable a pharmacophore study to elucidate the minimum required structure
feature for transport.
 3 widely used automated pharmacophore perception tool are DISCO ( Distance
Comparion ) , GASP ( Genetic Algorithm Similarity Program ) & Catalyst.
b) Qualitative approaches :
 Presented by QSAR & QSPR studies utilize multivarible analysis to corelate
molecular description with ADMET related proprties
 It is essential to select the right mathematical tool for most effective ADMET
modeling. Sometime it is necessary to apply multiple stastical method & compare
the result to identify the best approaches.

6. DRUG ABSORPTION :
 Because of its convenience & good patient compliance oral administration is the
most prefered drug delivery form.
 As a result , much of the attention of in silico approaches is focused on modeling
drug oral absorption which occuar in the human intestine.
 In general , drug bioavailability & drug absorption is the result of the interplay
between drug solubility & intestinal permeability .
a) Solubility :
 A drug generally must dissolve before it can be absorbed from intestinal lumen .
 By measuring a drug log P value ( log of partition coefficient of compound
between water & n octanol ) its MP , the would indirectly solubility using general
solubility equation.

7.  There are two approaches to model solubility , one is based on the underlying
physiological processes & the other is an empirical approach . The dissolution
process involve the breaking up of solute from its crystal lattice & association of
the solute or solvent molecule.
 To predict the solubility of compound even before synthesizing its , in silico
modeling can be implemented .
 Empirical approaches represented by QSAR , utilize multivariable analysis to
identify correlation between molecular descriptor & solubility.
b) Intestinal permeation :
 Describe the ability of drug to across the intestinal mucosa separating the gut
lumen from the portal circulation .
 It is an essential process for drug to pass the intestinal membrane before entering
the systematic circulation to reach their target site of action
 Process involve both passive & active transport
 Is a complex process that is difficult to predict solely based on molecular
mechanism .

8. DRUG DISTRIBUTION :
 is an important aspect of drug PK profile .
 The structure & physiochemical properties of drug determine the extent of
distribution which is mainly reflected by three parameter.
1. Volume of distribution :
 Is a measure of relative partitioning of drug between plasma & tissue an imp
proportional constant that when combined drug is a major determination of how
often the drug should administration .
 However , because of the scaricity of in-vivo data model that are capble of
prediction Vd based safety on computed descriptors an still under development .
2. Plasma – protein binding :
 Drug binding to variety of plasma protein such as albumin , as unbound drug
primarily contribute its pharmacological efficacy .
 The effect of PPB is an imp consideration whwn evaluting the effective drug
plasma conc.

9.  The model proposed to predict PPB should not on the binding data of only one
protein when predicting plasma protein binding because it is a composite
parameter reflecting interaction with multiple protein .
3. BBB:
 Maintain the restricted extracellular environment in the central nerve system.
 The evalution of drug penetration through BBB is an integral part of drug
discovery & development processs.
 Again , because of few experimental data derived from inconsistant protocol ,
most BBB permeation prediction model an are of limited practical use despite
intensive effort
 Most approaches model log blood brain which is a measurement of drug
partitioning between blood & brain tissue
 Measurement is an indirect implication of BBB permeability which does not
discriminate between free & plasma protein bound solute.

10. DRUG EXCRETION :
 The excretion or clearance of drug is quantified by plasma clearance , which is
defined as plasma volume that has cleared completely free of drug per unit of
time .
 Together with Vd, it can assist in the calculation of drug half-life thus determining
the dosage regimen .
 Hepatic & renal clearance are the two main component of plasma clearance.
 No model has been reported that is capable of predicting plasma clearance
solely from computed drug structure .
 Current modeling effort are mainly focused on estimating in-vivo clearance from
in-vivo data .
 Just like other PK aspect the hepatic & renal clearance process is also
complicate by presence of active transporter.

11. ACTIVE TRANSPORTER :
 Transporter are an integral part of any ADMET modeling program because of
their presence on barrier membrane & then substantial overlap between their
substrate & many drug.
 Unfortunately because of limited understanding of transporters , must prediction
program do not have a mechanism to incorporate the effect of action transport .

12. Thank you