The document discusses computational modeling of drug disposition, emphasizing the importance of drug absorption, distribution, excretion, and the role of active transporters in pharmacokinetics. It outlines various modeling techniques including quantitative and qualitative approaches for predicting drug properties and addresses current challenges in ADMET modeling, particularly the need for better incorporation of transporter effects. Overall, advancements in computational power and modeling algorithms have improved the predictive capabilities of in silico models in drug discovery.

1. COMPUTATIONAL
MODELLING OF DRUG
DISPOSITION
Presented by: - Joshi Lalita Sandeep
First year m. pharm
Guide: - Dr. Sudhir Pandya
Dr. D.Y. Patil college of pharmacy, Akurdi, pune-411044

2. Table of contents
Introduction
Modelling techniques
Drug absorption
Drug distribution
Drug excretion
Active transporters
1. P- gp
2. BCRP
3. Nucleoside transporters
4. hPEPT1
5. ASBT
6. OCT
7. OACP
8. BBB-choline transporters
Current challenges and future directions

3. INTRODUCTION
 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.
 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 vitro
evaluation 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.

4.  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.
 Fueled by the ever-increasing computational power and significant
advances of in silico modeling algorithms, numerous computational
programs that aim at modeling drugADMET properties have emerged. A
comprehensive list of available commercialADMET modeling software has
been provided previously by van de Waterbeemd and Gifford.

5. MODELLINGTECHNIQUES
There are two main approaches of the drug
modelling techniques.
I. Quantitative approach
II. Qualitative approach

6. Modelling technique: - Quantitative approach
It is 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.
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.

7. Three widely used automated pharmacophore perception tools,
DISCO (DIStance COmparisons)
GASP (Genetic Algorithm Similarity Program)
Catalyst/HIPHOP.
 The application of different flexible docking algorithms in drug discovery has
recently been reviewed.The essential interactions derived from either study
can be used as a screen in evaluating drugADMET properties.

8. Modelling techniques: - qualitative approaches
• It is 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.
• it is important to select the molecular descriptors that represent the type
of interactions contributing to the targeted biological property.

9. • It is essential to select the right mathematical tool for most effective
ADMET modelling .sometimes it is possible to apply multiple statistical
methods and compare the result to identify the best approach.
• 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 such as artificial
neural networks (ANN) and support vector machine (SVM).

10. 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.
• Drug bioavailability and absorption is the result of the interplay between
drug solubility and intestinal permeability.

11. 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 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.
 The dissolution process involves the breaking up of the solute from its
crystal lattice and the association of the solute with solvent molecules.
Empirical approaches, represented by QSPR, utilize multivariate analyses
to identify correlations between molecular descriptors and solubility.

12. Even though the calculation process ignores the underlying
physiological processes, the molecular descriptor selection and
model interpretation still 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 databases.

13. 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.
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.

14. C) Other considerations: -
The ionization state will affect both solubility and permeability and, as a
result, influence the absorption profile of a compound.
The charge of a molecule can be determined using the compound’s
ionization constant value (pKa), which indicates the strength of an acid or
a base.
Several commercially and publicly available programs provide pKa
estimation based on the input structure, including
SCSpKa (ChemSilico,Tewksbury, MA),
Pallas/pKalc (CompuDrug, Sedona, AZ),
ACD/pKa (ACD,Toronto, ON, Canada), and
SPARC online calculator.

15. To correctly predict overall oral absorption, drug metabolism in intestinal
epithelial cells by cytochrome P450 enzymes should also be considered.
Other than the different approaches mentioned above, commercial
packages such as
GastroPlus (Simulations Plus, Lancaster, CA) and
iDEA (LionBioscience, Inc. Cambridge, MA) are available to predict oral
absorption and other pharmacokinetic properties.
 They are both based on the advanced compartmental absorption and
transit (CAT) model, which incorporates the effects of drug moving
through the gastrointestinal tract and its absorption into each
compartment at the same time.

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 its distribution, which is mainly reflected by three parameters:
1. Volume of distribution (Vd)
2. Plasm protein binding (PPB)
3. Blood brain barrier (BBB)

17.  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.
 The half-life of a drug is a major determinant of how often the drug
should be administered.
However, because of the scarcity of in vivo data and the complexity of
the underlying processes, computational models that are capable of
predictingVD based solely on computed descriptors are still under
development.
A)Volume of distribution

18. B) Plasma protein binding
Drugs bind to a variety of plasma proteins such as serum albumin, as
unbound drug primarily contributes to pharmacological efficacy.
PPB is an important consideration when evaluating the effective
(unbound) drug plasma concentration.
The models proposed on PPB should not rely on the binding of the only
one protein while plasma protein binding because it is a composite
parameter reflecting interaction with multiple protein.

19. C) Blood brain barrier
The BBB maintains the restricted extracellular environment in the central
nerve system (CNS).
 The evaluation of drug penetration through the BBB is an integral part of
the drug discovery and development process.
Again, because of the few experimental data derived from the inconsistent
protocols, most BBB permeation prediction models are of limited practical
use despite invasive efforts.
 Most approaches model log blood/brain (logBB), which is a measurement
of the drug partitioning between blood and brain tissue.
 This measurement is an indirect implication of the BBB permeability,
which does not discriminate between free and plasma protein-bound
solute.

20. 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 withVd, 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.
 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.

21. ACTIVETRANSPORTER
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.
 Their incorporation into current modelling, programs would also result in
more accurate prediction of drug disposition behavior.

22. 1) 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 .
 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.
 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.

23. 2) BCRP(Breast cancer resistance protein)
Breast cancer resistance protein (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.
 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.
 Satisfying the transport model would render a compound susceptible to BCRP,
but not fitting into the model does not necessarily exclude the candidate from
BCRP transport.

24. 3) Nucleoside transporter
Nucleoside transporters transport both naturally occurring nucleosides and
synthetic nucleoside analogues that are used as anticancer drugs (e.g.,
cladribine) and antiviral drugs.
 There are different types of nucleoside transporters, including
1. concentrative nucleoside transporters (CNT1, CNT2, CNT3)
2. equilibrative nucleoside transporters (ENT1, ENT2) each having
different substrate specificities.
The broad-affinity, low-selective ENTs are ubiquitously located,
 The high-affinity, selective CNTs are mainly located in epithelia of intestine,
kidney, liver, and brain, indicating their involvement in drug absorption,
distribution, and excretion.
A more comprehensive study generated distinctive models forCNT1, CNT2,
and ENT1 with both pharmacophore and 3DQSAR modeling techniques .

25. 4) hPEPT1 (human peptide transporter )
 Low-affinity high-capacity oligopeptide transport system that transports
a diverse range of substrates including β-lactam antibiotics and
angiotensin-converting enzyme (ACE) inhibitors.
 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.

26. 5)ASBT(Apical sodium-dependent bile acid
transporter )
 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.
 The model revealed ASBT transport requirements as one hydrogen bond
donor, one hydrogen bond acceptor, one negative charge, and three
hydrophobic centers

27. 6)OCT(organic cation transporters)
 facilitate the uptake of many cationic drugs across different barrier
membranes from kidney, liver, and intestine epithelia.
 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
 The model suggests the transport requirements of human OCT1 as three
hydrophobic features and one positive ionizable feature.

28. 7)OATP(Organic anion transporting polypeptides)
 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 their 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 meta pharmacophore approach recently.

29. 8) BBB-CholineTransporter
 Native nutrient transporter that transports choline, a charged cation, across
the BBB into the CNS.
 Its active transport assists the BBB penetration of choline like compounds,
and understanding its structural requirements should afford a more accurate
prediction of BBB permeation.
Three hydrophobic interactions and one hydrogen bonding interaction
surrounding the positively charged ammonium moiety were identified to be
important for BBB-choline transporter recognition

30. CURRENT CHALLENGES AND FUTURE
DIRECTIONS
The major recent advancements in ADMET modelling is in elucidating a
role and successful modelling of various transporters.
Incorporation of the influence of these transporters in the current models
is an ongoing task in ADMET modelling.
Some commercial programs are already implemented the capability of
modelling active transport, such as recent version of GastroPlus
(simulation plus, Lancaster, CA), PK- slim (Bayer technology services,
Germany) and ADMET/Tox web(pharma algorithms,Toronto, Canada).

31. In the latter software, compounds are screen against the
pharmacophore models of different active transporters. the
compounds that fits these models is removed for further prediction,
which is based solely on physicochemical properties.
Not all pharmaceutical companies can afford to design their own in-
house modelling programs, so the commercially available in silico
suits have became an attractive option.
Some modelling programs such as algorithm builder(pharma
algorithms,Toronto, Canada) are offering flexibility for consumers
to generate their in- house models with their own training set and
statistical algorithm of their choice.
These trends will accelerate the shift of models building for
computational scientists to experimental scientists.

32. Reference
Ekins Sean, “computer application in pharmaceutical research and
development”, Wiley series in drug discovery and development, Binghe
wang, series editor, “JohnWiley and sons, INC., publication”, page no 495-
508