54 The Open Pharmacology Journal, 2008, 2, 54-62
1874-1436/08 2008 Bentham Open
Visualizing Pharmacological Activities of Antidepressants: A Novel
Hieronymus J. Derijks1,2, Eibert R. Heerdink1, Rob Janknegt2, Fred H.P. De Koning1,3,
Berend Olivier*,4, Anton J.M. Loonen5 and Antoine C.G. Egberts1,6
1Department of Pharmacoepidemiology and Pharmacotherapy, Utrecht Institute for Pharmaceutical Sciences (UIPS),
Utrecht University, P.O. Box 80082, 3508 TB Utrecht, The Netherlands
2Department of Clinical Pharmacy, Orbis Medical Center, P.O. Box 5500, 6130 MB, Sittard, The Netherlands
3Kring Apotheken Nederland, P.O. Box 210, 5201 AE Den Bosch, The Netherlands
4Division of Psychopharmacology, Utrecht Institute for Pharmaceutical Sciences (UIPS), Utrecht University, P.O. Box
80082, 3508 TB Utrecht, The Netherlands
5Department of Pharmacotherapy and Pharmaceutical Care, Pharmacy and Pharmaceutical Sciences - Faculty of
Mathematics and Natural Sciences, Groningen University, Antonius Deusinglaan 1, 9713 AV, Groningen, The
6Department of Clinical Pharmacy, University Medical Center Utrecht, P.O. Box 85500, 3508 GA Utrecht, The
Abstract: Antidepressants have different receptor binding profiles, which are related to therapeutic action and adverse
drug reactions. We constructed a model to classify antidepressants on the basis of their binding properties of most com-
mon transporter- and receptor sites. Receptor binding was quantified by calculating receptor occupancy for the 5-HT
(serotonin) reuptake transporter, norepinephrinic reuptake transporter, 5-HT2C-receptor, M3-recepto r, H1-receptor and 1-
receptor. To identify groups of antidepressants that show similar patterns of receptor occupancy for different receptors,
hierarchical cluster analysis (HCA) and principle component analysis (PCA) were used. In addition, to visualize
(a)symmetry between binding profiles of antidepressants, radar plots were constructed. On the basis of both analyses, four
clusters of antidepressants which exert similar pharmacological properties were identified. Potentially, this model could
be a helpful tool in medical practice and may be used as a prediction model for adverse effects of drugs entering the mar-
Since 1958, more than 20 antidepressants have reached
the market and they have proven to be effective in the treat-
ment of depression and other psychiatric disorders. It still
remains to be elucidated what the mechanism is behind these
therapeutic effects . All currently approved antidepres-
sants elevate central monoamines in the brain (particularly
serotonin and norepinephrine), although important pharma-
cological differences exists in the way antidepressants exert
these effects. Meta-analyses h ave revealed that modern anti-
depressants overall are not more efficacious and act not more
rapidly than the first generation agents such as imipramine
and clomipramine [2-5]. Besides, in treatment-resistant de-
pression, intraclass switching from one serotonergic reuptake
inhibitor (SSRI) to another has proven to be effective in 40-
70% of the patients  which is hard to explain from a
pharmacological point of view. In contrast, much more is
known about the relation between adverse drug reactions and
*Address correspondence to this author at the Division of Psychopharma-
cology, Utrecht Institute for Pharmaceutical Sciences, Faculty of Science,
Utrecht University, P.O. Box 80-082, 3508 TB, Utrecht, The Netherlands;
Tel: +31 302533529; Fax: +31 302531599; E-mail: B.Olivier @uu.nl
the pharmacological mechanisms of antidepressants . Two
major groups of adverse drug reactions can be recognized:
type A and B effects. Type A adverse drug reactions are ad-
verse effects related to the pharmacological actions of the
drug. Type B adverse drug reactions, refer to the phenome-
non that a medicine is well tolerated by the (vast) majority of
users, bu t occasionally elicits a patient specific reaction to
the drug not related to pharmacology . It has been shown
that important differences exist between antidepressants with
respect to the nature of adverse drug reactions and that these
tolerability and safety aspects are important for tailoring an
antidepressant to the individual patient as well as for adher-
ence of the patient to antidepressant therapy.
Tradition ally, antidepressants are put into the market and
classified on the basis of a) their molecular structure and/or
b) the way they interfere with the serotonergic and norepi-
nephrinic neurotransmitter systems. Five commonly defined
categories are: 1) tricyclic antidepressants (TCAs), 2) selec-
tive serotonin reuptake inhibitors (SSRIs), 3) dual serotonin
and norepinephrine reuptake inhibitors (SNRIs) 4) serotonin-
2 antagonist/reuptake inhibitors (SARIs) and 5) norepineph-
rinic and specific serotonergic antidepressants (NaSSAs).
From a pharmacological point of view this classification can
Binding Profiles of Antidepressants The Open Pharmacology Journal, 2008, Volume 2 55
be quite confusing. For example, clomipramine is classified
as a TCA but pharmacologically shows very much similarity
with SSRIs. A pharmacodynamic system of classification
can easily accommodate new agents as they become avail-
able. For example, it is known that it is difficult to translate
results about the safety of drugs from clinical trial data into
clinical practice, because trials are conducted in relatively
small and highly selected groups of patients. Furthermore,
most adverse drug reactions are discovered during extended
use after approval. A model which identifies antidepressants
based on their pharmacological binding properties may be
beneficial in the better assessment and understanding of the
adverse drug reaction profile of novel agents. Furthermore,
for many clinicians, it provides a rational basis for sequential
treatment selection, particularly in those cases when a patient
has experienced ADRs. Finally, a pharmacodynamic classi-
fication system also may be used in pharmacovigilance in
the search for high risk antidepressants for specific adverse
drugs reactions. This system may help us to unravel the
mechanism behind these adverse drug reactions.
Thus, for a better understanding of receptor-mediated
pharmacological action, we constructed a multivariate model
to classify antidepressants on the basis of their binding prop-
erties of most common transporter- and receptor sites.
MATERIALS AND METHODS
Receptor binding was quantified by calculating recep-
tor/transporter occupancy (hereafter: receptor occupancy) for
the 5-HT (5-hydroxytryptamine) reuptake transporter, nore-
pinephrine reuptake transporter, muscarine M3-receptor, his-
tamine H1-receptor, alpha 1-receptor and 5-HT2c-receptor.
The 5-HT reuptake transporter and norepinephrine reuptake
transporter are the primary transporters responsible for cen-
tral monoamines elevation and the muscarine M3-receptor,
histamine H1-receptor, alpha 1-receptor and 5-HT2c-receptor
are pharmacological related to common type A adverse drug
reactions of antidepressants. Receptor occupancy expresses
the magnitude of the binding of a drug to the receptor site at
mean steady state plasma concentration. To identify clusters
of antidepressants with a similar binding profile, hierarchical
cluster analysis and principle component analysis (PCA)
were used . Subsequently, to visualize (a)symmetry be-
tween binding profiles of antidepressants, radar plots were
Receptor Occupancy Model
The mean steady state plasma concentration (Css) of anti-
depressants was obtained by calculating the average value of
the lower limit (Cmin) and upper limit (Cmax) of the therapeu -
tic window using the following equation:
Css = (Cmax + Cmin) / 2 (Eq. 1)
The mean unbound plasma concentration (Cu) was calcu-
lated by multiplying Css by the plasma unbound fraction (fu):
Cu = Css * fu (Eq. 2)
max, Cmin and fu were obtained from reference lists used
in hospitals in The Netherlands for Therapeutic Drug Moni-
toring (TDM) [10, 11]. For bupropion, duloxetine and re-
boxetine a therapeutic window was not available. The mean
free steady state plasma concentrations for these compounds
were calculated by multiplying the plasma unbound fraction
(fu) by the bioavailability (F) and the dose of the drug (D0)
divided by the multiplication of volume of distribution (Vd),
elimination constant (k) and dosing interval ().
Cu = (fu * F * D0) / (Vd * k * ) (Eq. 3)
Inhibition Constants of Antidepressants
The inhibition constant (Ki) is a measure of the binding
affinity of a ligand (antidepressant) for its receptor. Ki is the
concentration of the ligand in which the receptor is occupied
for 50% by the ligand. Ki
’s for all antidepressants were ob-
tained from the Psychoactive Drug Screening Program
(PDSP) Ki database  and literature [7, 13-42]. The PDSP
Ki database serves as a data warehouse for published and
internally-derived Ki, or affinity, values for a large number
of drugs and drug candidates at an expanding number of G-
protein coupled receptors, ion channels, transporters and
enzymes. Most of the Ki-values were obtained from experi-
ments with cloned human receptor cell lines, but also human
receptors from brain tissue, (frontal) cortex, tissue, choroids
plexus tissue, striatum tissue, cortical membranes and plate-
lets were used. When we found more than one Ki-value for a
specific antidepressant-human receptor interaction we took
an average value of the Ki
’s. When no Ki-value for a specific
antidepressant-human receptor interaction was available, we
took a Ki-value for a sp ecific antid epressant-an imal receptor
interaction. If Ki-values exceeded 10,000 nM a value of
10,000 nM was assumed. Higher values will not contribute
substantially to receptor occupancy at mean steady state
plasma concentration of antidepressants.
Quantitative Prediction of Pharmacological Action Based
on Averag e Pharmacokinetic Parameters
The extent of pharmacological action by antidepressants
at steady-state concentrations was predicted by using the
following procedure. Receptor occupancy () for different
receptors, an index of the extent of different phar macological
actions, can be expressed in terms of unbound drug concen-
tration around the receptor (Cd) and the Ki of each antide-
pressant for all different receptors, according to the follow-
= (Cd / (Ki + Cd)) * 100% (Eq. 4)
(see appendix 1 for derivation)
The receptor occupancy values at steady state were calcu-
lated by assuming that Cd in equation 4 is equal to Cu in
equation 2 and substitu ting equation 2 in equation 4. This
assumption is true for well perfused peripheral tissue and
organs. Passage of the blood brain barrier is relatively easy
for lipophilic agents like antidepressants. However, not con-
cerned with hypothetical influence of p-glycoprotein, bind-
ing at solid tissue structures and dissolving in lipophilic tis-
sue, the free concentrations of antidepressants in the central
nervous system (CNS), and thus receptor occupancy, will be
lower because of a time lag of mass transport.
To identify clusters of antidepressants with a similar
binding profile, hierarchical cluster analysis was used. This
method classifies antidepressants and receptors in clusters in
accordance with their overall homology, based on receptor
56 The Open Pharmacology Journal, 2008, Volume 2 Derijks et a l.
occupancy, to yield a binary dendrogram (Fig. 1). Antide-
pressants were progressively fused into subclusters and clus-
ters until they comprised a single group. The length of the
bars between the pair of drugs reflect their dissimilarity that
is, the shorter the distance, the more closely related the pair
of drugs or receptors. Within the dendrogram a heatmap was
integrated. A heatmap is a graphical representation of data in
a two-dimensional map where the receptor occupancy values
are represented by a spectrum of colors ranging from yellow
(0% receptor occupancy) till black (100% receptor occu-
In addition, principle component analysis (PCA) was
used as a data reduction technique to find structure in a data
matrix of antidepressants versus receptor occupancy for dif-
ferent receptor types. PCA reduces the original set of vari-
ables into a smaller, orthogonal set of variables that is com-
posed of linear combinations of receptor occupancy data for
particular receptors, called principle components. The coor-
dinates of the orthogonal variable set are chosen such that
they captur e as much of the total variance as possible in the
original data. In this way, it is possible to identify groups of
antidepressants that show similar binding profiles. The score
plot displays the contribution of each receptor type as a func-
tion of the principle components. The loading plot displays
the projection of the receptor occupancy data of antidepres-
sants upon the principle components (Fig. 2). The correlation
matrix was used in the PCA and transformation w as
achieved by making use of eigen vectors.
Radar plots were used as a non-statistical method to visu-
alize symmetry or un-symmetry between pharmacological
profiles of antidepressants. A radar plot can be thought of as
a histogram for an individual antidepressant that has been
bent into a circle with each individual spoke representing
receptor occupancy for a particular receptor.
Hierarchical cluster analysis and PCA were performed
with SPSS® version 12.0. The heatmap was build with
Heatmap Builder® version 1.0. Radar plots were constructed
in Microsoft Excel® 2003.
Inhibitory constants and receptor occupancy of 20 anti-
depressants for 6 binding sites (5-HT reuptake transporter,
norepinephrine reuptake transporter, muscarine M3 receptor,
histamine H1-receptor, alpha 1-receptor and 5-HT2c-
receptor) were determined and summarized in respectively
Tables 1 and 2.
Fig. (1) shows the dendrogram from the hierarchical clus-
ter analysis with the heatmap integrated. A column within
the heat map can be viewed as a pharmacological barcode
for a single antidepressant. By comparing these barcodes
clusters of antidepressants with similar binding profiles can
be identified. Looking at the dendrogram, the most striking
differentiation between antidepressants is at the first two
nodes, which yields four clusters of antidepressants.
Application of PCA to the receptor occupancy data re-
veals that 83.4% can be accounted for by two axes: compo-
nent 1 and component 2. This means that a reduction of di-
mensionality from six receptors to two axes preserves almost
the entire variance of the data. The majority of the variance
(63.3%) can be attributed to principle component 1 which is
highly positive correlated to receptor binding to the norepi-
nephrine reuptake transporter, muscarine M3 receptor, hista-
mine H1-receptor, alpha 1-receptor and 5-HT2c-receptor.
Component 2 accounts for 20.1% of variance and is highly
positive correlated to receptor binding to the 5-HT reuptake
transporter. Fig. (2) shows the score plot and the loading
plot. The score plot involves the projection of the antidepres-
sants onto the two components. Antidepressants with similar
binding are located in the same area of the score plot. PCA
identifies the same four clusters as hierarchical cluster analy-
sis. The loading plot visualizes the contribution of each re-
ceptor to the two principle components by vectors.
Radar plots (Fig. 3) complement the dendrogram, heat-
map and score plot in visualizing symmetry or un-symmetry
between binding profiles in the four clusters of antidepres-
sants in a non-statistical way.
The first cluster comprises sertraline, fluvoxamine, escita-
lopram, paroxetine, venlafaxine, fluoxetine, citalopram, du-
loxetine and clomipramine, which all show high affinity for
the 5-HT reuptake transporter. Duloxetine and clomipramine
show high affinity for the 5-HT reuptake transporter but also
had little affinity for one or more other binding sites. The sec-
ond cluster comprises imipramine, amitriptyline and doxepin.
These antidepressants had in common that they show high
affinity for all six binding sites. The third cluster comprises
maprotiline, nortriptyline, mianserin and mirtazapine which all
show high affinity for the histamine H1-receptor and 5-HT2c-
receptor and less affinity for the 5-HT reuptake transporter.
Except mirtazapine, the other antidepressants also show high
affinity for the norepinephrine reuptake transporter and mod-
erate affinity for the alpha 1-receptor.
The fourth cluster comprised trazodone, nefazodone
(withdrawn from the market in 2003), reboxetine and
bupropion. These antidepressants were identified as a rest
group with no specific similarities within and outside the clus-
For a better understanding of receptor-mediated pharma-
cological action we constructed a model to classify antide-
pressants on the basis of their binding properties of most
common transporter- and receptor sites. W e used the recep-
tor occupancy model and analy zed it with hierarchical clus-
ter analysis and PCA. Both multivariate techniques were
complemented with radar plots to visualize sy mmetry or
nonsymmetry between binding profiles of antidepressants.
All methods showed three different clusters of antidepres-
sants with similar properties and a rest group with no spe-
This model deals with several assumptions and restric-
tions. First, we did not account for the degree of passage of
the blood brain barrier of antidepressants. Central nervous
system (CNS) con centrations will be lower than peripheral
plasma concentrations. Second, the ability of a drug to pro-
duce a physiological effect is dependent on receptor occu-
pancy and the propensity of the drug to activate the receptor
(intrinsic activity). Drugs bound to a receptor differ in their
ability to initiate a change in receptor conformation and
physiologic activity. In our model, we assumed that all anti-
Binding Profiles of Antidepressants The Open Pharmacology Journal, 2008, Volume 2 57
depressants are full agonists or antagonists for all receptor
types. Third, a certain number of receptors are "spare." Spare
receptors exist in excess of those required to produce a full
effect. The receptor occupancy model does not correct for
the existence of spare receptors. Fourth, prolonged treatment
with antidepressants results in downregulation of certain
receptor sites. This means that in time, the same receptor
occupancy may exert a different response because the num-
ber of receptor sites has changed. Fifth, many antidepres-
sants also have active metabolites with different pharmacol-
ogical binding profiles. Ki-data of the metabolites unfortu-
nately are less well documented than the parent compound.
Therefore, it was not possible to include the metabolites in
the PCA-model and visualize the binding profiles in radar
plots. We summarized the effects of antidepressants on cen-
tral monoamines in the brain based on a literature review in
table 3 to give further insights into the pharmacological
properties of the major active metabolites of antidepressants
[13, 14, 16-20, 22, 25, 26, 29, 31, 32, 43-45]. From these
data two metabolites are pharmacologically different from
the mother compound. These are N-desmethylclomipramine
(metabolite of clomipramine) and nortriptyline (metabolite
of amitriptyline). Both metabolites bind more specifically to
the NE reuptake transporter than the 5-HT reuptake trans-
porter. The metabolite nortriptyline, also available as a
mother compound included in the multivariate model, is a
cluster 3 antidepressant (with common affinity for norepi-
nephrine reuptake transporter, H1-receptor and 5-HT2c-
receptor) but its mother compound, amitriptyline, is catego-
rized in cluster 2 (with high affinity for all receptors investi-
gated). Sixth, our model was limited to the most common
transporters and receptors of antidepressants for simplifica-
tion. In addition to the 5-HT2C-receptor the 5-HT2A-receptor
is also associated with side effects of antidepressants. Be-
cause the 5-HT2C-receptor and the 5-HT2A-receptor are sub-
types of the same receptor we did not expect many differ-
ences in receptor occupancy of antidepressants for these re-
ceptor subtypes. To confirm this expectation we performed
analysis with the 5-HT2A-receptor in the model. The overall
classification in four clusters did not change. Furthermore,
Table 1. Pharmacokinetic Parameters, Inhibitory Constants of Antidepressants
Ki (in nM)
amitriptyline 450.61 10.00 45.06 22.71 46.46 4.301 25.90 14.20 0.81
bupropion 546.71 13.00 71.07 9550.00 10000.00 10000.002 10000.00 4200.00 10000.00
citalopram 385.33 20.00 77.07 5.40 7089.00 617.00 1430.004 5600.00 283.001
clomipramine 284.66 2.00 5.69 0.21 45.85 43.30 34.001,3 3.20 47.001
doxepin 554.15 25.00 138.54 68.00 29.50 8.801 52.00 23.50 0.27
duloxetine 39.53 4.00 1.58 1.23 8.72 916.00 3000.004 8300.00 2300.00
escitalopram 132.85 20.00 26.57 1.80 7177.00 2531.001 1242.004 3870.00 1973.00
fluoxetine 867.55 5.50 47.72 5.92 600.00 194.00 1000.00 2775.00 2683.00
fluvoxamine 345.30 23.00 79.42 6.22 2307.00 6245.001 10000.004 1288.00 10000.00
imipramine 347.72 15.00 52.16 8.37 83.00 94.001 60.00 32.00 26.50
maprotiline 766.04 10.00 76.60 5800.00 11.10 122.001 600.003 90.00 0.79
mianserin 149.60 10.00 14.96 4000.00 11.10 3.56 501.001 58.101 1.03
mirtazapine 226.07 15.00 33.91 10000.00 4600.00 39.00 800.004 676.301 1.60
nefazodone 1776.90 1.00 17.77 403.00 564.00 26.002 10000.004 26.75 10000.00
nortriptyline 375.25 8.00 30.02 129.40 7.39 41.002 50.00 55.00 7.35
paroxetine 129.02 5.00 6.45 0.29 130.80 10000.00 80.00 2779.00 10000.00
reboxetine 415.65 3.00 12.47 273.50 13.40 457.00 3900.001,4 10000.00 1400.00
sertraline 510.65 2.00 10.21 1.36 884.00 1649.001 1300.00 201.00 10000.00
trazodone 342.89 8.00 27.43 367.00 10000.00 208.402 10000.00 27.00 1100.00
venlafaxine 477.86 73.00 348.84 63.90 2448.00 2004.001 10000.00 10000.00 10000.00
Css : Mean steady state plasma concentration
fu : Plasma unbound fraction
Cu : Mean unbound plasma concentration
5-HT : 5-hydroxytryptamine
1 : No Ki- or Kd-data with human receptors available; Ki taken from binding study with animal recep tors
2 : No Ki- or Kd-data on 5-HT2C-receptor available; Ki taken from binding study with 5-HT-receptor
3 : No Ki- or Kd-data on M3-r eceptor available; Ki taken from binding study with M-receptor
4 : No Ki- or Kd-data on M3-r eceptor available; Ki taken from binding study with M1-receptor
58 The Open Pharmacology Journal, 2008, Volume 2 Derijks et a l.
Table 2. Receptor Occupancy of Antidepressants at Mean Steady State Plasma Concentration
Receptor Occupancy (%)
amitriptyline 66.49 49.24 91.29 63.50 76.04 98.23
bupropion 0.74 0.71 0.71 0.71 1.66 0.71
citalopram 93.45 1.08 11.10 5.11 1.36 21.40
clomipramine 96.44 11.05 11.62 14.34 64.02 10.80
doxepin 67.08 82.44 94.03 72.71 85.50 99.81
duloxetine 56.25 15.35 0.17 0.05 0.02 0.07
escitalopram 93.66 0.37 1.04 2.09 0.68 1.33
fluoxetine 88.96 7.37 19.74 4.55 1.69 1.75
fluvoxamine 92.74 3.33 1.35 0.79 5.81 0.79
imipramine 86.17 38.59 35.69 46.50 61.98 66.31
maprotiline 1.30 87.34 38.57 11.32 45.98 98.98
mianserin 0.37 57.41 80.78 2.90 20.48 93.56
mirtazapine 0.34 0.73 46.51 4.07 4.77 95.49
nefazodone 4.22 3.05 40.60 0.18 39.91 0.18
nortriptyline 18.83 80.25 42.27 37.52 35.31 80.33
paroxetine 95.70 4.70 0.06 7.46 0.23 0.06
reboxetine 4.36 48.20 2.66 0.32 0.12 0.88
sertraline 88.25 1.14 0.62 0.78 4.84 0.10
trazodone 6.95 0.27 11.63 0.27 50.40 2.43
venlafaxine 84.52 12.47 14.83 3.37 3.37 3.37
5-HT : 5-hydroxytryptamine.
Note: all antidepressants are agonists for the 5-HT-transporter and NE-transporter and antagonists for the 5-HT2C-, M3-, 1- and H1-receptor except fluoxetine, which is a agonist for
Fig. (1). Dendrogram of hierarchical cluster analysis and heatmap of 20 antidepressants for 2 transporters and 4 receptors. The length of the
bars between the pair of drugs in the dendrogram is inversely proportional to the overall homology of the antidepressants. That is, antidepres-
sants situated adjacently present very similar binding profiles, whereas those widely separated show substantially different binding profiles.
The heatmap represents the data in a two-dimensional map where the receptor occupancy values are represented by a spectrum of colors
ranging from yellow (0% receptor occupancy) till black (100% receptor occupancy). A column within the heat map can be viewed as a
pharmacological barcode for a single antidepressant. Antidepressants within the same clusters show practically the same pharmaceutical
Binding Profiles of Antidepressants The Open Pharmacology Journal, 2008, Volume 2 59
bupropion mainly acts by dopamine reuptake inhibition. We
performed additional analysis with the dopamine reuptake
transporter included in the model. This did not change the
overall classification in four clusters. Finally, it is important
to note that mianserin and mirtazapine both have alpha-2
receptor blocking actions and indirectly stimulate the reup-
take of norepinephrine. Unfortunately, Ki-data of the alpha-2
receptor for all antidepressants were not complete. There-
fore, it was not possible to perform additional analyses with
the alpha-2 receptor in the model.
We used multivariate techniques to identify groups of
antidepressants with similar binding profiles. This technique
permits hypothesis-free exploration of similarities and dif-
ferences as a function of overall binding profiles and has
been demonstrated its value earlier in identifying recepto r
binding profiles with antiparkinson agents . In the latter
study, however, modeling was based on Ki-data. Ideally,
receptor occupancy should be measured in vivo or ex vivo
using the same method. Pharmacodynamic modeling is often
based on Ki-data obtained from in vitro studies (which are
already available) and is widely recognized. However, com-
parison of Ki’s may not provide a proper evaluation of the
pharmacological properties of antidepressants in vivo. A
more than 100 fold range is not uncommon for the plasma
unbound fraction among drugs. To account for in vivo con-
centrations at the receptor site, we used the recep tor occu-
pancy model and calculated the occupancy-values of antide-
pressants at steady state conditions. It has proven to be a
appropriate measure to estimate the pharmacological effects
among the drugs with the same mechanism of action [47-49]
even if their receptor dissociation constants, clinical dosages,
or pharmacokinetic properties are different.
We combined the receptor occupancy model with multi-
variate statistical techniques like PCA and hierarchical clus-
Fig. (2). Score plot and loading plot of PCA analysis of 20 antidepressants for 2 transporters and 4 receptors. The horizontal axis is the first
principle component, which explains 63.3% of the variance in the data matrix, and the vertical axis is the second principle component, which
explains 20.1% of the total variance in the data matrix. The vectors within the score plot display the contribution of each receptor ty pe as a
function of the principle components. Drugs are shown in the loading plot as blue diamonds. The circles encompass the same 4 clusters
which were identified from hierarchical cluster analysis.
60 The Open Pharmacology Journal, 2008, Volume 2 Derijks et a l.
tering. This provides a framework for interpretation of con-
trasting functional profiles of antidepressants in vivo and
may aid in clinical decision making. For example, if an anti-
depressant from one cluster is not well tolerated by a patient
due to adverse drug reactions, continuation of therapy may
be more successful by switching to an antid epressant from
another cluster with different pharmacological properties.
This model may also be beneficial in the assessment of
safety of novel agents in addition to risk-benefit ratio as-
sessment in clinical trials and would be most appropriately
performed before their therapeutic evaluation and post mar-
keting surveillance. Finally, our model also may be used in
pharmacovigilance in the search for high risk antidepressants
for specific adverse drugs reactions. The pharmacological
profile may help us to unravel the mechanism behind these
adverse drug reactions. The model and the potential applica-
tions have to be validated by additional studies to prove its
benefit. Finally, this strategy could also be applied to other
groups of psychotropic drugs such as antipsychotics.
Fig. (3). Radar plots of 20 antidepressants for 2 transporters and 4 receptors. The radar plot is a histogram for an individual antidepressant
that has been bent into a circle with each individual spoke representing receptor occupancy for a particular receptor. The greater the distance
from the central node of the radar plot, the higher the receptor occupancy for a specific binding site. The radar plots are categorized in the
same 4 clusters which were identified from hierarchical cluster analysis. Antidepressants within the same cluster show very similar binding
Binding Profiles of Antidepressants The Open Pharmacology Journal, 2008, Volume 2 61
PowerPoint presentation at the 8th Congress of the Euro-
pean Association for Clinical Pharmacology and Therapeu-
tics, 22 August – 1 September 2007, Amsterdam. Visualiz-
ing pharmacological activities of antidepressants: a novel
Derivation of the receptor occupancy equation:
Cd = drug concentration around receptor
Cr= receptor concentration
Cdr= concentration drug-receptor complex
= receptor occupancy
The equilibrium reaction equation is:
Cr + Cd Cdr (5)
Equation 5 represents 2 reactions:
Cr + Cd Cdr (5a)
Cr + Cd Cdr (5b)
In steady state conditions the velocities of reactions 5a
and 5b are equal:
Cr * Cd * k+1 = Cdr * k-1 (6)
Rewriting equation 6:
Cr * Cd / Cdr = k-1 / k+1 = Ki (7)
Cdr = Cr * Cd / Ki (8)
Receptor occupancy can be expressed as:
= (Cdr / Cr + Cdr) * 100% (9)
Substitution (8) and (9):
= (Cr * Cd / Ki) / (Cr + (Cr * Cd / Ki)) * 100% (10)
Divide numerator and denominator by Cr:
= ((Cd / Ki) / (1 + (Cd / Ki))) * 100% (11)
Multiply numerator and denominator by Ki:
= (Cd / (Ki + Cd)) * 100% (12)
Table 3. Metabolite Activity of Antidepressants
Antidepressant t1/2 (in hr) Metabolite t1/2 (in hr) Activity
Amitriptyline 12-25 nortriptyline 22-88
Amitriptyline is a strong inhibitor of both the 5-HT and norepinephrine
transporter. Nortriptyline is preferentially a strong inhibitor of the norepi-
nephrine transporter. Nortriptyline has a longer half-life than amitriptyl ine
and will significantly contribute to the therapeutic effect o f amitriptyline.
Bupropion 15-22 hydroxybupropion 20
Bupropion is a weak inhibitor of the dopamine transporter and hydroxy-
bupropion is a weak inhibitor of the norepinephrine transporter. The
mechanisms of action responsible for the clinical effects of bupropion are
not fully understood but it has been suggested that both dopaminergic and
noradrenergic components play a role and based on animal models the
hydroxymetabolite contributes significantly to the antidepressant acitivity
Clomipramine 21 N-desmethylclomipramine 36
Clomipramine is a strong inhibitor of the 5-HT transporter and also the
most selective amon g the tricyclic antidepressants. Desmethylclomi-
pramine on the other hand is a more potent and selective norepinephrine
inhibitor. The half-life of desmethylclomipramine is longer than that of
clomipramine and plays an important role for the therapeutic effect of
Fluoxetine 1-3 days N-desmethylfluoxetine
Fluoxetine is a strong inhibitor of the 5-HT transporter but also has weak
affinity for the norepinephrine transporter. N-desmethylfluoxetine is also a
strong inhibitor of the 5-HT transporter and more selective than fluoxet-
ine. In addition, N-desmethylfluoxetine has a extremely long half life
compared to fluoxetine and plays an important role for the therapeutic
effect of fluoxetine.
Imipramine 24 N-desmethylimipramine
21 Imipramine and N-desmethylimipramine are both strong inhibitors of the
5-HT and norepinephrine transporter. Imipramine is more selective for the
5-HT transporter and N-desmethylimipramine more selective for the nore-
Sertraline 24 N-desmethylsertraline 64-104
Sertraline is a strong inhibitor of the 5-HT transporter. N-
desmethylimipramin e is a weaker and less selective inhibitor of the 5-HT
transporter but has a longer half-life and therefore might play a role in the
therapeutic effects of sertraline.
Venlafaxine 5 O-desmethylvenlafaxine 11
Venlafaxine and O-desmethylvenlafaxine are both inhibitors of the 5-HT
transporter and the norepinephrine transporter. O-desmethylvenlafaxine
has a longer half-life than venlafaxine and is consequenltly found at higher
plasma concentrations than the parent compound. It therefore is very
likely that O-desmethylvenlafaxine contributes significantly to the thera-
62 The Open Pharmacology Journal, 2008, Volume 2 Derijks et a l.
ACKKNOWL EDGEM ENTS
The authors are grateful to S.V. Belitser of the Utrecht
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Received: April 7, 2008 Revised: May 14, 2008 Accepted: May 19, 2008
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