This document discusses computational modeling techniques for predicting drug disposition properties. It covers modeling approaches for drug absorption, distribution, and excretion. For absorption, it describes models for predicting solubility, intestinal permeability, and transporters. For distribution, it discusses models for volume of distribution, plasma protein binding, and blood-brain barrier permeability. For excretion, it summarizes models for hepatic and renal clearance. Current challenges include incorporating active transporters and generating predictive models from physiological understanding rather than empirical correlations.

1. Computational Modeling in
Drug Disposition
PRESENTED BY
SUJITAH MARY
M PHARM
ST JOSEPH COLLEGE OF PHARMACY
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2. Contents
1. Introduction
2. Modeling Technique
3. Drug Absorption (Solubility and Intestinal Permeation)
4. Drug Distribution
5. Drug Excretion
6. Active Transport
[P-gp, BCRP, Nucleoside Transporters, hPEPT1, ASBT, OCT, OACP, BBB-
Choline Transporter)
7. Current Challenges and future Directions
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3. Introduction
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 the undesirable drug pharmacokinetics properties,
including absorption, distribution, metabolism, excretion and toxicity
(ADMET).
The pressure to control the escalating cost of new drug development has
changed the paradigm since the mod-1990s.
To reduce the attrition rate at more expensive later stages, in vitro
evaluation of ADMET properties in the early phase of drug discovery has
widely adopted
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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 modeling algorithms, numerous computational
programs that aim at modeling ADMET properties have emerged.
 A comprehensive list of available commercial ADMET modeling
software has been provided till date.
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5. Modeling technique: 2 Approaches
 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
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6.  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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8. Drug Absorption
 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
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9. a) Solubility
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.
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10. b) 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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11. Drug Distribution
Distribution is an important aspect of drug’s pharmacokinetic profile.
The structural and physiochemical properties of a drug determine the
extent of distribution, which is mainly reflected by three parameters:
1. volume of distribution (Vd),
2. plasma-protein binding (PPB) and
3. blood-brain barrier (BBB) permeability
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12. Volume of Distribution (Vd)
Vd is a measure of relative partitioning of drug between plasma and
tissue, an important proportional constant that, when combined a drug
is a major determinant of how often the drug should be administered.
However, because of the scarcity of in vivo data and complexity of the
underlying processes, computational models that are capable of
prediction Vd based solely on computed descriptors are still under
development.
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13. Plasma Protein Binding (PBP)
Drugs binding to a variety of plasma proteins such as serum albumin,
as unbound drug primarily contributes to pharmacological efficacy.
The effect of PPB is an important consideration when evaluating the
effective (unbound) drug plasma concentration.
The models proposed to predict PBB should not rely on the binding
data of only one protein when predicting plasma protein binding
because it is a composite parameter reflecting interactions with
multiple protein.
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14. Blood-Brain Barrier (BBB)
The BBB maintains the restricted extracellular environment in the central
nerve system.
The evaluation of drug penetration through the BBB is an integral part of
drug discovery and development process.
Again, because of the few experimental data derived from inconsistent
protocols, most BBB permeation prediction models are of limited practical
use despite intensive efforts.
Most approaches model log blood/brain (logBB), which is a measurement of
the drug partitioning between blood and brain tissue.
The measurement is an indirect implication of BBB permeability, which does
not discriminate between free and plasma protein-bound solute
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15. Drug Excretion
The excretion or clearance of a drug is quantified by plasma clearance, which is
defined as plasma volume that has been 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 and renal clearances are the two main components of plasma
clearance.
No model has been reported that is capable of predicting plasma clearance
solely from computed drug structures.
 Current modeling efforts are mainly focused on estimating in vivo clearance
from in vitro data.
 Just like other pharmacokinetic aspects, the hepatic and renal clearance
process is also complicated by presence of active transporters
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16. Active Transporters
 Transporters are an integral part of any ADMET modeling program
because of their presence on barrier membranes and the substantial
overlap between their substrates and many drugs.
 Unfortunately, because of our limited understanding of transporters,
most prediction programs do not have a mechanism to incorporate the
effect of active transport.
 However, interest in these transporters has resulted in a relatively
large amount of in vitro data, which in turn have enabled the generation
of pharmacophore and QSAR models for many of them.
 These models have assisted in the understanding of the complex
effects of transporters on drug disposition, including absorption,
distribution and excretion.
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18. Current Challenges and Future Direction
 The major recent advancement in ADMET modeling is in elucidating the
role and successful modeling of various transporters.
 Incorporation of the influence of these transporters in the current models
is an ongoing task in ADMET modeling.
 Some commercial programs have already implemented the capability of
modeling active transport, such as recent version of GastroPlus (Simulation
Plus, Lancaster,CA), PK-Slim (Bayer Technology Services, Germany) and
ADME/Tox WEB (Pharma Algorithms, Toronto, Canada).
 In the latter software, compounds are first screened against
pharmacophore models of different active transporters. The compound
that fits these models is removed for further predictions, which is based
solely on physiochemical properties
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19.  Not all pharmaceutical companies can afford the resources to generate
their own in-house modeling programs, so the commercially available in
silico modeling suites have become an attractive option.
 Some modeling programs such as Algorithm Builder (Pharma
Algorithms, Toronto, Canada) are offering flexibility for costumers to
generate their in-house models with their own training set and the
statistical algorithm of their choice.
 These trends will accelerate the shift of model building from
computational scientists to experimental scientists
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20. References:
 Ekins S, “Computer Applications in Pharmaceutical Research and
Development”, (2006) John Wiley and Sons Inc., chapter 20, pp495-508
 Ekins S, Nikolsky Y and Nikolskaya T. Techniques: Application of
systems biology to absorption, distribution, metabolism, excretion and
toxicity. Trends Pharmacol Sci 2005;26;202-9
https://hemonc.mhmedical.com/content.aspx?bookid=1810&sectioni
d=124489864
(accessed in 13th May, 2018 ]
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