This document summarizes computational models of active transporters involved in drug disposition. It discusses models developed for P-gp, BCRP, nucleoside transporters, hPEPT1, ASBT, OCT, OATP, and the BBB choline transporter. The models were generated using techniques like pharmacophore modeling and QSAR to identify structural features important for substrate recognition and transport by each protein, which can help predict drug absorption, distribution, and excretion. The document provides details on specific structural requirements identified for several important transporters from the models.

1. COMPUTATIONAL MODELING OF
DRUG DISPOSITION: ACTIVE
TRANSPORT
Mr. Omkar Tipugade
M-Pharm , Sem –II
( Department Of Pharmaceutics )
Shree Santkrupa College
Of Pharmacy , Ghogaon .

2. CONTENT :
 Introduction
 Active transport
 P-gp
 BCRP
 Nucleoside Transporters
 hPEPT1
 ASBT
 OCT
 OATP
 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 properties including 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.
 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.

4. ACTIVE TRANSPORT
 Transporters should be an integral part of any ADMET modeling program
because of their ubiquitous presence on barrier membranes and the
substantialoverlap 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.

5.  A comprehensive list of available commercial ADMET modeling software has
been provided till date.

6. P-GP :
 P-glycoprotein (P-gp) is an ATP-dependent effl ux 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.

7.  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.
 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.
 Identified transport requirements not only to help screen compounds with
potential effl ux related bioavailability problems, but also to assist the
identification of novel P-gp inhibitors, which when coadministered with target
drugs would optimize their pharmacokinetic profile by increasing bioavailability.

8. BCRP :
 Breast cancer resistance protein (BCRP) is another ATP-dependent effl ux
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.
 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, it should be applied with caution.

9. 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 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 [53], indicating their involvement in drug absorption, distribution,
and excretion. The
 first 3D-QSAR model for nucleoside transporters was generated back in 1990 .

10.  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.

11. HPEPT1 :
 The human peptide transporter (hPEPT1) is a low-affinity high-capacity
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. A pharmacophore model based on three 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 .
 The antidiabetic repaglinide and HMG-CoA reductase inhibitor fluvastatin were
suggested by the model and later verified to inhibit hPEPT1 with submillimolar
potency .

12. 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
centers.

13. 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 ionizable feature .

14. 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 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.
 The metapharmacophore model identified three hydrophobic features flanked
by two hydrogen bond acceptor features to be the essential requirement for
OATP1B1 transport.

15. 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
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 .
 Three hydrophobic interactions and one hydrogen bonding interaction
surrounding the positively charged ammonium moiety were identified to be
important for BBB-choline transporter recognition.

16. Thank you …..