This document discusses computational modeling of active transport mechanisms that influence drug disposition. It summarizes modeling efforts for several major drug transporters, including P-glycoprotein (P-gp), Breast Cancer Resistance Protein (BCRP), nucleoside transporters, peptide transporter 1 (hPEPT1), Apical Sodium-dependent Bile Acid Transporter (ASBT), Organic Cation Transporters (OCTs), Organic Anion Transporting Polypeptides (OATPs), and the Blood Brain Barrier choline transporter. While transporter modeling has advanced, fully incorporating active transport into predictive models remains an ongoing challenge.

1. COMPUTATIONAL MODELLING OF DRUG
DISPOSITION ACTIVE TRANSPORT
Presented By
Sujitha Mary
M Pharm
St Joseph College Of Pharmacy

2. CONTENTS
 INTRODUCTION
 ACTIVE TRANSPORT
1. P-GP
2. BCRP
3. NUCLEOSIDE TRANSPORTERS
4. hPEPT1
5. ASBT
6. OCT
7. OATP
8. BBB-CHOLINE 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 propertiesincluding 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.

4.  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

5. 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

6. 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 Eg: 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 .

7.  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.

8.  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 - expressing LLC-PK1 (L-MDR1) cells; and vinblastine binding in vesicles
derived from CEM/VLB100 cells

9.  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 aswell as their geometric arrangement
were identified to be the substrate requirements for P-gp.
 Identified transport requirements not only to help screen compounds with
potential efflux related bioavailability problems, but also to assist the
identification of novel P-gp inhibitors, which when co-administered with
target drugs would optimize their pharmacokinetic profileby increasing
bioavailability

10. BCRP
 Breast cancer resistance protein (BCRP) is another ATPdependent 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

11.  Recently, Zhang and colleagues generated a BCRP 3D-QSAR 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, itshould be applied with
caution.

12. 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.

13.  The broad-affinity, low-selective ENTs are ubiquitously located, whereas the
high-affinity, selective CNTs are mainly located in epithelia of intestine,
kidney, liver, and brain , indicating their involvement indrug absorption,
distribution, and excretion
 The first 3D-QSAR model for nucleoside transporters was generated back in
1990
 A more comprehensive study generated distinctive models for CNT1, CNT2,
and ENT1 with both pharmacophore and 3DQSAR modeling techniques.
 All models show the common features required for nucleoside transporter-
mediated transport: two hydrophobic features and one hydrogen bond
acceptor on the pentose ring.

14. HPEPT1
 The human peptide transporter (hPEPT1) is a low-affinity highcapacity
oligopeptide transport system that transports a diverse range of substrates
including β-lactam antibiotics and angiotensin-converting enzyme (ACE)
inhibitors .
 It is mainly expressed in intestine and kidney, affecting drug absorption and
excretion. Apharmacophore model based onthree highaffinity 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 .

15.  The antidiabetic repaglinide and HMG-CoAreductase inhibitor fluvastatin
were suggested by the model and later verified to inhibit hPEPT1 with
submillimolar potency .

16. ASBT
 The human apical sodium-dependent bile acid transporter (ASBT) is a
highefficacy, 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 center

17. OCT
 The organic cation transporters (OCTs) facilitate the uptake of many cationic
drugs across different barrier membranes from kidney,liver,and intestine
epithelia.
 Abroad 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 .

18.  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 TEAuptake in HeLa cells of 22 diverse molecules. The
model suggests the transport requirements of human OCT1 as three
hydrophobic features and one positive ionizable feature

19. OATP
 Organic anion transporting polypeptides (OATPs) 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
onlyorganic anionic drugs, asoriginally 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.
 The metapharmacophore model identified three hydrophobic features
flanked by two hydrogen bond acceptor features to be the essential
requirement for OATP1B1 transport

20. BBB-CHOLINE TRANSPORTER
 The BBB-choline transporter is a native nutrient transporter that transports
choline, a charged cation, across the BBB into the CNS.
 Its active transport assists the BBB penetration of cholinelike compounds,
and understanding its structural requirements should afford a more accurate
predictionof 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 .
 Three hydrophobic interactions and one hydrogen bonding interaction
surrounding the positively charged ammonium moiety were identified to be
important for BBB-choline transporter recognition.

21. CURRENT CHALLENGESAND FUTURE
DIRECTION
 The major recent advancement in ADMET modeling isin elucidating the
roleand 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.

22.  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 attractiveoption.
 Some modeling programs such as Algorithm Builder (Pharma Algorithms,
Toronto, Canada) are offering flexibility for costumers to generate their in-
house models with their owntraining set and the statistical algorithm of their
choice.
 These trends will accelerate the shift of model building from computational
scientists to experimental scientists.

23. CONCLUSION
 Computational Modelling reduces cost and complexity as faced in vivo
testing.
 We believe that data quality is the weakest link, thereby effectively limiting
the practical application of ADMET models.
 Although all these models are simplifications of the reality, some of the
models do provide valuable insight of the Drug Disposition phenomena

24. REFERENCES:
 Ekins S, “Computer Applications in Pharmaceutical Research and
Development”, (2006) John Wiley and Sons Inc., chapter 20,pp495-508
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