Chapter 2: COMPUTATIONAL MODELLING OF DRUG DISPOSITION
1.What are the various computational techniques used in drug kinetics? (OR)
What are the various computational techniques used in Absorption, Distribution and
Excretion? (10marks)
Drug kinetics (pharmacokinetics) describes how the body handles a drug and accounts for the
processes of absorption, distribution, metabolism and elimination. (It mainly comprises of
understanding of ADMET)
In computational techniques two types of approaches are used:
✓ Quantitative approaches
✓ Qualitative approaches
Quantitative approaches
Represented by pharmacophore modelling and flexible docking studies to 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.
Three widely used automated pharmacophore perception tools are:
➢ DISCO (DIStance COmparisons)
➢ GASP (Genetic Algorithm Similarity Program) and
➢ Catalyst/HIPHOP
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 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.
A wide selection of statistical algorithms is available to researchers for correlating field
descriptors with ADMET properties which includes:
➢ simple multiple linear regression (MLR)
➢ multivariate partial least-squares (PLS)
➢ Nonlinear regression-type algorithms such as artificial neural networks (ANN) and
➢ Support vector machine (SVM)
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→The following are the details of various computational techniques used in drug kinetics:
DRUG ABSORPTION
Absorption is a process of movement of unchanged drug from dosage form to systemic
circulation.
In general, drug bioavailability and absorption is the result of the interplay between drug
solubility and intestinal permeability.
–Solubility:
There are mainly two approaches to modelling solubility. One is based on the underlying
physiological processes, and the other is an 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. Hence, weaker interactions within the
crystal lattice (lower melting point) and stronger interactions between solute and solvent
molecules will result in better solubility and vice versa. For drug like 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.
Empirical approaches, represented by QSPR (quantitative structure-property relationship),
utilize multivariate analyses to identify correlations between molecular descriptors and
solubility.
Selection of field descriptors that adequately describe the physiological process and the
appropriate multivariate analysis is essential to successful modelling. 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.
–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.
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.
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• Volume of distribution (VD),
• Plasma-protein binding (PPB),
• Blood-brain barrier (BBB) permeability.
–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. The half-life of a drug is a major determinant of how often the drug should be
administered. An approach was proposed to predict 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.
–Plasma-Protein Binding (PPB):
• Drugs bind 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.
• Several models have been proposed to predict PPB. 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 interaction with multiple proteins.
• A sigmoidal correlational was observed between log D and PPB for neutral and basic
drugs and for acidic drugs the same sigmoidal correlation between logP and PPB
–Blood-Brain Barrier (BBB) Permeability:
• 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.
• 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.
• 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,
• Also, three types of drug efflux transporters have been identified from brain which
have significant influence on drug disposition.
a) Multidrug resistance transporters
b) Monocarboxylic acid transporters and
c) Organic ion transporters
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.
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Hepatic and renal clearances are the two main components of plasma clearance. As such
model has been reported that is capable of predicting plasma clearance solely from computed
drug structures. Current modelling 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.
These are the computational modelling techniques used to describe the drug kinetics.
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).
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 successfully developed.
2. Write about various transporters for which computational modelling can be applied.
(OR)
Discuss the modelling techniques for various transporters used in drug disposition. (10M)
Both influx and efflux transporters are located in intestinal epithelial cells and can either
increase or decrease oral absorption.
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 behaviour.
1. P-gp(P-glycoprotein):
-It 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.
www.DuloMix.com
-Recent research derived a robust QSAR model that revealed two hydrophobic features, two
hydrogen bond acceptors, and the molecular dimension to be essential determinants of P-gp-
mediated transport.
2. BCRP (Breast cancer resistance protein):
-Breast cancer resistance protein 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 haematological malignancies and solid tumours,
BCRP is also expressed in intestine, liver, and brain, thus implicating its intricate role in drug
disposition behaviour.
-Specific structural feature requirements for BCRP are the presence of a 2,3-double bond in
ring C and hydroxylation at position 5.
3.Nucleoside Transporters:
-Nucleoside transporters transport both naturally occurring nucleosides and synthetic
nucleoside analogs that are used as anticancer drugs and antiviral drugs. 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, indicating
their involvement in drug absorption, distribution, and excretion.
-The 3D-QSAR model shows the common features required for nucleoside transporter-
mediated transport: two hydrophobic features and one hydrogen bond acceptor on the
pentose ring. The individual models also reveal the subtle characteristic requirements for
each specific transporter.
4. hPEPT1 (The human peptide transporter):
-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 high affinity substrates recognized two hydrophobic
features one hydrogen bond donor, one hydrogen bond acceptor, and one negative ionisable
feature to be hPEPT1 transport requirements.
5. ASBT (The human apical sodium-dependent bile acid transporter):
-The human apical sodium-dependent bile acid transporter (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.
www.DuloMix.com
-The present available 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.
6. OCT (organic cation transporters):
-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 analysing 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 ionisable feature.
-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. The models showed the same chemical features, highlighting their
similarities.
7. OATP (Organic anion transporting polypeptides):
-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 modelled with the meta-pharmacophore
approach recently.
-Through assessing the training set of 18 diverse molecules, the meta-pharmacophore model
identified three hydrophobic features flanked by two hydrogen bond acceptor features to be
the essential requirement for OATP1B1 transport.
8. BBB-Choline Transporter (blood brain barrier –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 choline like 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, a combination of empirical
and theoretical methodologies is applied to study its binding requirements.
www.DuloMix.com
-The 3DQSAR 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 be important for BBB-choline
transporter recognition.
5 Markers
4. What is modelling in drug discovery? Discuss about different modelling techniques.
• 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 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.
• 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 modelling algorithms, numerous computational programs that aim at modelling
drug ADMET properties have emerged.
The different Modelling Techniques used for determining drug disposition are:
A. Absorption
o Solubility → Log P and Log S
o Permeability → Caco-2 cells, MDCK, PAMPA
o Active transport → PEPT1, ASBT, NT, P-gp, BCRP, MRP
B. Distribution
o PPB
o VD
o BBB Passive → Log BB and Log PS
Active Transport → BBB choline transporter, Multidrug Resistance
Transporters, Monocarboxylic Acid Transporters, Organic Ion Transporters
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C. Excretion
o Hepatic Passive
Active Transport → OATP, NTCP
o Renal Passive
Active Transport → OAT, OCT
5. Discuss about computational modelling for drug absorption.
DRUG ABSORPTION
Absorption is a process of movement of unchanged drug from dosage form to systemic
circulation.
In general, drug bioavailability and absorption is the result of the interplay between drug
solubility and intestinal permeability.
–Solubility:
There are mainly two approaches to modelling solubility. One is based on the underlying
physiological processes, and the other is an 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. Hence, weaker interactions within the
crystal lattice (lower melting point) and stronger interactions between solute and solvent
molecules will result in better solubility and vice versa. For drug like 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.
Empirical approaches, represented by QSPR (quantitative structure-property relationship),
utilize multivariate analyses to identify correlations between molecular descriptors and
solubility.
Selection of field descriptors that adequately describe the physiological process and the
appropriate multivariate analysis is essential to successful modelling. 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.
–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.
www.DuloMix.com
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.
6. Write about important modelling techniques used for drug disposition.
In modelling/computational techniques two types of approaches are used:
✓ Quantitative approaches
✓ Qualitative approaches
Quantitative approaches
Represented by pharmacophore modelling and flexible docking studies to 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.
Three widely used automated pharmacophore perception tools are:
➢ DISCO (DIStance COmparisons)
➢ GASP (Genetic Algorithm Similarity Program) and
➢ Catalyst/HIPHOP
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 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.
A wide selection of statistical algorithms is available to researchers for correlating field
descriptors with ADMET properties which includes:
➢ simple multiple linear regression (MLR)
➢ multivariate partial least-squares (PLS)
➢ Nonlinear regression-type algorithms such as artificial neural networks (ANN) and
➢ Support vector machine (SVM)
The different Modelling Techniques used for determining drug disposition are:
D. Absorption
o Solubility → Log P and Log S
o Permeability → Caco-2 cells, MDCK, PAMPA
o Active transport → PEPT1, ASBT, NT, P-gp, BCRP, MRP
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E. Distribution
o PPB
o VD
o BBB Passive → Log BB and Log PS
Active Transport → BBB choline transporter, Multidrug Resistance
Transporters, Monocarboxylic Acid Transporters, Organic Ion Transporters
F. Excretion
o Hepatic Passive
Active Transport → OATP, NTCP
o Renal Passive
Active Transport → OAT, OCT
7. Write about important modelling techniques used for drug distribution and excretion.
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.
• Volume of distribution (VD),
• Plasma-protein binding (PPB),
• Blood-brain barrier (BBB) permeability.
–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. The half-life of a drug is a major determinant of how often the drug should be
administered. An approach was proposed to predict 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.
–Plasma-Protein Binding (PPB):
• Drugs bind 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.
• Several models have been proposed to predict PPB. 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 interaction with multiple proteins.
• A sigmoidal correlational was observed between log D and PPB for neutral and basic
drugs and for acidic drugs the same sigmoidal correlation between logP and PPB
www.DuloMix.com
–Blood-Brain Barrier (BBB) Permeability:
• 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.
• 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.
• 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,
• Also, three types of drug efflux transporters have been identified from brain which
have significant influence on drug disposition.
d) Multidrug resistance transporters
e) Monocarboxylic acid transporters and
f) Organic ion transporters
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.
Hepatic and renal clearances are the two main components of plasma clearance. As such
model has been reported that is capable of predicting plasma clearance solely from computed
drug structures. Current modelling 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.
8. Write about the transporter P-gp used in computational modelling.
P-gp(P-glycoprotein) 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.
-Recent research derived a robust QSAR model that revealed two hydrophobic features, two
hydrogen bond acceptors, and the molecular dimension to be essential determinants of P-gp-
mediated transport.
www.DuloMix.com
9. Write about the transporter BCRP used in computational modelling.
BCRP (Breast cancer resistance protein) 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 haematological malignancies and solid tumours,
BCRP is also expressed in intestine, liver, and brain, thus implicating its intricate role in drug
disposition behaviour.
-Specific structural feature requirements for BCRP are the presence of a 2,3-double bond in
ring C and hydroxylation at position 5.
10. Write about the transporter Nucleotide transporter used in computational modelling.
Nucleoside Transporters transport both naturally occurring nucleosides and synthetic
nucleoside analogs that are used as anticancer drugs and antiviral drugs. 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, indicating
their involvement in drug absorption, distribution, and excretion.
-The 3D-QSAR model shows the common features required for nucleoside transporter-
mediated transport: two hydrophobic features and one hydrogen bond acceptor on the
pentose ring. The individual models also reveal the subtle characteristic requirements for
each specific transporter.
11. Write about the transporter BBB-choline used in computational modelling.
BBB-Choline Transporter (blood brain barrier –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 choline like 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, a combination of empirical
and theoretical methodologies is applied to study its binding requirements.
-The 3DQSAR 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 be important for BBB-choline
transporter recognition.
www.DuloMix.com
12. Write about the transporter ASBT used in computational modelling.
ASBT (The human apical sodium-dependent bile acid transporter) 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.
-The present available 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.
13. Write about the transporter OCT used in computational modelling.
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 analysing 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 ionisable feature.
-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. The models showed the same chemical features, highlighting their
similarities.
14. Write about the transporter OATP used in computational modelling.
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 modelled with the meta-pharmacophore
approach recently.
-Through assessing the training set of 18 diverse molecules, the meta-pharmacophore model
identified three hydrophobic features flanked by two hydrogen bond acceptor features to be
the essential requirement for OATP1B1 transport.
www.DuloMix.com
15. Write about the transporter hPEPT1 used in computational modelling.
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 high affinity substrates recognized two hydrophobic
features one hydrogen bond donor, one hydrogen bond acceptor, and one negative ionisable
feature to be hPEPT1 transport requirements.