The document discusses computational modeling of drug disposition. It covers modeling of drug absorption, distribution, excretion, and active transport. For drug absorption, it describes modeling of solubility, intestinal permeability, and transporters involved. It also discusses modeling approaches for distribution processes like volume of distribution, plasma protein binding, and blood-brain barrier permeability. Current challenges include better incorporating the effects of active transporters in models. The document emphasizes that while computational models are useful for predicting drug properties, fully accounting for complex biological factors remains difficult.

1. Computational Modeling in
Drug Disposition
HIMAL BARAKOTI
M.PHARM, 2ND SEM
FACULTY OF PHARMACEUTICAL SCIENCE
ASSAM DOWN TOWN UNIVERSITY

2. Contents
Introduction
Modeling Technique
Drug Absorption
(Solubility and Intestinal Permeation)
Drug Distribution
Drug Excretion
Active Transport
(P-gp, BCRP, Nucleoside Transporters, hPEPT1, ASBT, OCT,
OACP, BBB-Choline Transporter)
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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In silico modeling target of drug disposition

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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Fig: Intestinal drugs transporters

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&sectionid=124489864
(accessed in 13th May, 2018 )
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