Computational modelling of drug disposition is the integral part of computer aided drug design. different kinds of tools being used in the prediction of drug disposition in human body. This topic in the CADD explains the details about the drug disposition, active transporters and tools. 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 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 mid-1990s. To reduce the attrition rate at more expensive later stages, in vitroevaluation of ADMET properties in the early phase of drug discovery has been widely adopted.Many high-throughput in vitro ADMET property screening assays have been developed and applied successfully . For example, Caco-2 and MDCK cell monolayers are widely used to simulate membrane permeability as an in vitro estimation of in vivo absorption. These in vitro results have enabled the training of in silico models, which could be applied to predict the ADMET properties of compounds even before they are synthesized.

1. COMPUTATIONAL MODELING
OF
DRUG DISPOSITION
Presented By:Pooja Arya
M.Pharm(PHARMACEUTICS)
1

2. Contents
 1 Introduction
 2 Modeling Techniques
 3 Drug Absorption
3.1 Solubility
3.2 Intestinal Permeation
 4 Drug Distribution
 5 Drug Excretion
 6 Active Transport
6.1 P-gp
6.2 BCRP
6.3 Nucleoside Transporters
6.4 hPEPT1
6.5 ASBT
6.6 OCT
6.7 OATP
6.8 BBB-Choline Transporter
2

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 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 mid-1990s. To
reduce the attrition rate at more expensive later stages, in
vitroevaluation of ADMET properties in the early phase of drug
discovery has been widely adopted.
3

4. Cont…….
Many high-throughput in vitro ADMET property screening
assays have been developed and applied successfully .
For example, Caco-2 and MDCK cell monolayers are
widely used to simulate membrane permeability as an in
vitro estimation of in vivo absorption.
These in vitro results have enabled the training of in silico
models, which could be applied to predict the ADMET
properties of compounds even before they are synthesized.
4

5. MODELING TECHNIQUES
Two types of modeling approaches:
1.Quantitative approaches
2.Qualitative approaches
5

6. 1.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 a certain target.
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.
6

7. Cont…….
The availability of a protein’s three-dimensional structure,
from either X-ray crystallization or homology modeling,
would assist flexible docking of the active ligand to derive
important interactions between the protein and the ligand.
Three widely used automatedpharmacophore perception tools,
1. DISCO (DIStance COmparisons)
2. GASP (Genetic Algorithm Similarity Program)
3.and Catalyst/HIPHOP
 were critically evaluated and compared by Patel and
colleagues .
7

8. 2.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.
Some of these descriptors are closely related to a physical
property and are easy to comprehend (e.g., molecular weight),
whereas the majority of the descriptors are of quantum
mechanical concepts or interaction energies at dispersed space
points that are beyond simple physicochemical parameters.
8

9. Cont…..
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 (PLS),
and the nonlinear regression-type algorithms
Just like descriptor selection, 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, as illustrated in a recent solubility QSPR model.
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10. 10

11. 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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12. Solubility
By measuring a drug’s logP value (log of the partition
coefficient of the compound between water and n-octanol)
and its melting point, one could indirectly estimate
solubility using the “general solubility equation”.
To predict the solubility of the compound even before
synthesizing it, in silico modeling can be implemented.
There are mainly two approaches to modeling solubility.
1. based on the underlying physiological processes,
2. empirical approach.
12

13. Physiological Processes,
The dissolution process involves the breaking up of the solute from
its crystal lattice and the association of the solute with solvent
molecules.
For druglike molecules, solvent-solute interaction has been the major
determinant of solubility and its prediction attracts most efforts.
LogP is the simplest estimation of solvent-solute interaction and can
be readily predicted with commercial programs such as CLogP
(Daylight Chemical Information Systems, Aliso Viejo, CA),which
utilizes a fragment based approach.
 To recognize the contribution of solute crystal lattice energy in
determining solubility, other approaches amended LogP values with
additional terms for more accurate predictions
13

14. Empirical Approaches,
represented by QSPR, utilize multivariate analyses to identify
correlations between molecular descriptors and solubility.
 the molecular descriptor selection and model interpretation
requires understanding of the dissolution process.
 Selection of field descriptors that adequately describe the
physiological process and the appropriate multivariate analysis
is essential to successful modeling.
 The target property for most models is the logarithm of
solubility (logS), and many models are trained and verified
with the AQUASOL and PhysProp data bases.
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15. 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.
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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16. DRUG DISTRIBUTION
Distribution is an important aspect of a 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),
3. and blood-brain barrier (BBB) permeability
16

17. Volume of distribution (VD),
VD is a measure of relative partitioning of drug between plasma and
tissue, an important proportional constant that, when combined with
drug clearance, could be used to predict drug half-life.
computational models that are capable of predicting VD based solely
on computed descriptors are still under development.
However Lombardo and colleagues have proposed an approach to
predicting VD for neutral and basic compounds with two in vitro
physicochemical parameters . With additional data, this model was
further expanded and the robustness of the approach was tested and
validated. This represents a step in the right direction in accurately
predicting VD.
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18. Plasma-Protein Binding (PPB)
 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 model 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 proteins
 nonlinear regression analysis over 300 drugs with experimental human PPB percent
data.
 For neutral and basic drugs they found a sigmoidal correlation between logD
(distribution coefficient) and PPB,
 and for acidic drugs the same sigmoidal correlation between logP and PPB. The model
was validated with an external test set of 20 compounds. This work provides a useful
approximation of PPB
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19. Blood-Brain Barrier (BBB) Permeability
The evaluation of drug penetration through the BBB is an integral part of the
drug discovery and development process. For drugs that target the CNS, it is
imperative they cross the BBB to reach their targets.
Conversely, for drugs with peripheral targets, it is desirable to restrict their
passage through the BBB to avoid CNS side effects. 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.
19

20. Cont……
Pardridge suggests modeling of a more accurate parameter, log
BBB permeability surface area (logPS), which reflects the free drug
level in brain.
 This new concept was successfully adopted in two recent modeling
studies.
 A recent review discusses key considerations for development and
application of the BBB modeling .
In addition to forming complex tight junctions, the presence of
efflux transporters and metabolic enzymes is another mechanism
that the BBB employs to prevent xenobiotics from entering the
CNS.
20

21. 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 dosage regime. 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 dosage regime.
 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.
21

22. Cont…….
 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 the presence of active transporters.
 In a study performed by Sasaki and colleagues , the effect of active transport is
incorporated by measuring in vitro data from MDCK cells that express organic
anion transporting polypeptide (OATP) 4 and MRP2.
 However, to predict clearance for a given structure, knowledge of the structural
requirements for these transporters is required.
22

23. ACTIVE TRANSPORT
 Transporters should be an integral part of any ADMET modeling program
because of their ubiquitous 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.
23

24. 24

25. P-glycoprotein (P-gp)
P-glycoprotein (P-gp) is an ATP-dependent efflux transporter that
transports a broad range of substrates out of the cell. It affects drug
disposition by reducing absorption and enhancing renal and hepatic
excretion.
For example, P-gp is known to limit the intestinal absorption of the
anticancer drug paclitaxel and restricts the CNS penetration of human
immunodeficiency virus (HIV) protease inhibitors.
It is also responsible for multidrug resistance in cancer chemotherapy.
Because of its significance in drug disposition and effective cancer
treatment, P-gp attracted numerous efforts and has become the most
extensively studied transporter, with abundant experimental data.
25

26. Cont……
Ekins and colleagues generated five computational pharmacophore
models to predict the inhibition of P-gp from in vitro data on a diverse set
of inhibitors with several cell systems,
including inhibition of digoxin transport and verapamil binding in Caco-2
cells; vinblastine and calcein accumulation in P-gp-eexpressing LLC-
PK1 (L-MDR1) cells; and vinblastine binding in vesicles derived from
CEM/VLB100 cells.
By comparing and merging all P-gp pharmacophore models, common
areas of identical chemical features such as hydrophobes, hydrogen bond
acceptors, and ring aromatic features as well as their geometric
arrangement were identified to be the substrate requirements for P-gp.
26

27. 27

28. Breast cancer resistance protein(BCRP)
 BCRP is another ATP-dependent efflux transporter that confers resistance to a
variety of anticancer agents, including anthracyclines and mitoxantrone.
 In addition to a high level of expression in hematological malignancies and solid
tumors, BCRP is also expressed in intestine, liver, and brain, thus implicating its
intricate role in drug disposition behavior.
 Recentlyy, Zhang and colleagues generated a BCRP 3D-QSARb model by
analyzing structure and activity of 25 flavonoid analogs .
 The model emphasizes very specific structural feature requirements for BCRP
such as the presence of a 2,3-double bond in ring C and hydroxylation at position
5. Because the model is only based on a set of closely related structures instead of
a diverse set, it should be applied with caution.
28

29. Nucleoside Transporters
Nucleoside transporters transport both naturally occurring nucleosides
and synthetic nucleoside analogs that are used as anticancer drugs (e.g.,
cladribine) and antiviral drugs (e.g., zalcitabine).
There are different types of nucleoside transporters, including
concentrative nucleoside transporters (CNT1, CNT2, CNT3) and
equilibrative nucleoside transporters (ENT1, ENT2), each having
different substrate specificities.
The first 3D-QSAR model for nucleoside transporters was generated
back in 1990.
 It is an oversimplified general model limited by the scarce experimental
data at that time.
29

30. Cont……
A more comprehensive study generated distinctive models for CNT1,
CNT2, and ENT1 with both pharmacophore and 3D-QSAR modeling
techniques.
two hydrophobic features and one hydrogen bond acceptor on the
pentose ring.
The individual models also reveal the subtle characteristic
requirements for each specifi transporterr.
The modeling results also support the previous observation that CNT2
is the most selective transporter whereas ENT1 has the broadest
inhibitor specificity.
30

31. The human peptide transporter
(hPEPT1)
hPEPT1 is a low-affinity high-capacity oligopeptide transport system that
transports a diverse range of substrates including β-lactam antibiotics [56]
and angiotensin-converting enzyme (ACE) inhibitors .
It is mainly expressed in intestine and kidney, affecting drug absorption
and excretion.
 A pharmacophore model based on three high affinity substrates (Gly-Sar,
bestatin, and enalapril) recognized two hydrophobic features, one
hydrogen bond donor, one hydrogen bond acceptor, and one negative
ionizable feature to be hPEPT1 transport requirements.
31

32. Cont……
This pharmacophore model was subsequently applied to
screen the CMC database with over 8000 druglike
molecules.
The antidiabetic repaglinide and HMG-CoA reductase
inhibitor fluvastatin were suggested by the model and later
verified to inhibit hPEPT1 with submillimolar potency .
 This work demonstrated the potential of applying in silico
models in high-throughput database screening.
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33. Apical Sodium-Dependent Bile Acid
Transporter (ASBT)
 ASBT is a high-efficacy, high-capacity transporter expressed on the apical
membrane of intestinal epithelial cells and cholangiocytes.
 It assists absorption of bile acids and their analogs, thus providing an additional
intestinal target for improving drug absorption.
 Baringhaus and colleagues developed a pharmacophore model based on a training
set of 17 chemically diverse inhibitors of ASBT .
 The model revealed ASBT transport requirements as one hydrogen bond donor,
one hydrogen bond acceptor, one negative charge, and three hydrophobic centers.
 These requirements are in good agreement with a previous 3D-QSAR model
derived from the structure and activity of 30 ASBT inhibitors and substrates.
33

34. organic cation transporters (OCTs)
 OCT The organic cation transporters (OCTs) facilitate the uptake of many cationic drugs across
different barrier membranes from kidney, liver, and intestine epithelia.
 A broad range of drugs or their metabolites fall into the chemical class of organic cation (carrying a net
positive charge at physiological pH) including antiarrhythmics, β-adrenoreceptor blocking agents,
antihistamines, antiviral agents, and skeletal muscle-relaxing agents.
 Three OCTs have been cloned from different species,
 OCT1, OCT2, and OCT3.
 A human OCT1 pharmacophore model was developed by analyzing the extent of inhibition of TEA
uptake in HeLa cells of 22 diverse molecules.
 The model suggests the transport requirements of human OCT1 as three hydrophobic features and one
positive ionizabletransporter.
 Molecular determinants of substrate binding to human OCT2 and rabbit OCT2 were recently reported .
 Both 2D- and 3D-QSAR analyses were performed to identify and discriminate the binding
requirements of the two orthologs.
34

35. Organic anion transporting
polypeptides (OATPs)
OATP influence the plasma concentration of many drugs by actively
transporting them across a diverse range of tissue membranes such as
liver, intestine, lung, and brain .
Because of theirr broad substrate specificity, OATPs transport not only
organic anionic
Drugs, as originally thought, but also organic cationic drugs. Currently 11
human OATPs have been identified, and the substrate binding
requirements of the best-studied OATP1B1 were successfully modeled
with the metapharmacophore approach recently .
35

36. Cont……
Through assessing a training set of 18 diverse molecules, the
metapharmacophore model identified three hydrophobic features
flanked by two hydrogen bond acceptor features to be the
essential requirement for OATP1B1 transport.
 Similar requirements were derived from another 3D-QSAR study
based on rat Oatp1a5.
36

37. BBB-Choline Transporter
The BBB-choline transporter is a native nutrient transporter tha
transports choline, a charged cation, across the BBB into the CNS.
Its active transport assists the BBB penetration of cholinelike
compounds, and understaing its structural requirements should afford
a more accurate prediction of BBB permeation.
 Even though the BBB-choline transporter has not been cloned
Geldenhuys and colleagues applied a combination of empirical and
theoretical methodologies to study its binding requirements.
37

38. Cont……
The 3D-QSAR models were built with empirical Ki data obtained
from in situ rat brain perfusion experiments with a structurally
diverse set of compounds.
Three hydrophobic interactions and one hydrogen bonding
interaction surrounding the positively charged ammonium moiety
were identified to beimportant for BBB-choline transporter
recognition.
More accurate in silico models could be generated once higher-
quality data from the cloned BBB-choline transporter are available.
38

39. Thank you
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