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Visualizing Pharmacological Activities of Antidepressants: A Novel Approach

June 2008
The Open Pharmacology Journal 2(1)

DOI:10.2174/1874143600802010054

Project: Mechanism of depressive disorder

Authors:

H.J. Derijks

Jeroen Bosch Ziekenhuis

Eibert R Heerdink

Utrecht University

Rob Janknegt

Digitalis Mx Amsterdam

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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-receptor, 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- ket.

. Pharmacokinetic Parameters, Inhibitory Constants of Antidepressants

…

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 type 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.

…

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54 The Open Pharmacology Journal, 2008, 2, 54-62

1874-1436/08 2008 Bentham Open

Open Access

Visualizing Pharmacological Activities of Antidepressants: A Novel

Approach

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

Netherlands

6Department of Clinical Pharmacy, University Medical Center Utrecht, P.O. Box 85500, 3508 GA Utrecht, The

Netherlands

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-

ket.

INTRODUCTION

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 [1]. 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 [6] 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 [7]. 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 [8]. 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 [9]. Subsequently, to visualize (a)symmetry be-

tween binding profiles of antidepressants, radar plots were

constructed.

Receptor Occupancy Model

Pharmacokinetic Parameters

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 [12] 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-

ing equation:

 = (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.

Analysis

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-

pancy).

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.

RESULTS

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-

ter.

DISCUSSION

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-

cific similarities.

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

Pharmacokinetic Parameters

Ki (in nM)

Antidepressant

Css

(nM)

(%)

(nM)

5-HT-

Transporter

Norepinephrine

Transporter

5-HT2C-

Receptor

Muscarine M3

Receptor

Alpha 1

Receptor

Histamine H1

Receptor

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 (%)

Antidepressant 5-HT-

Transporter

Norepinephrine

Transporter

5-HT2C-

Receptor

Muscarine M3

Receptor

Alpha 1

Receptor

Histamine H1

Receptor

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

the 5-HT2C-receptor.

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

barcodes.

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 [46]. 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

patterns.

Binding Profiles of Antidepressants The Open Pharmacology Journal, 2008, Volume 2 61

SUPPLEMENTARY MATERIAL

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

approach.

APPENDIX

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:

k+1

Cr + Cd  Cdr (5a)

k-1

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

of bupropion.

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

clomipramine.

Fluoxetine 1-3 days N-desmethylfluoxetine

(=norfluoxetine)

7-15

days

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

(=desipramine)

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-

pinephrine transporter.

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-

peutic effect.

5-HT: 5-h

drox

tamine.

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

Institute for Pharmaceutical Sciences for her skillful assis-

tance with statistical analysis and Prof. Dr. C. N eef for his

valuable comments on the pharmacokinetic part of the

method section.

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Received: April 7, 2008 Revised: May 14, 2008 Accepted: May 19, 2008

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permits unrestrictive use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Jul 2002

Raymond G. Booth

Previously, (−)- trans -1-phenyl-3- N , N -dimethylamino-1,2,3,4-tetrahydronaphthalene ([−]- trans -H2-PAT) was shown to activate stereospecifically histamine H1 receptors coupled to modulation of tyrosine hydroxylase activity in guinea pig and rat forebrain in vitro and in vivo. Furthermore, the novel radioligand [3H](−)- trans -H2-PAT was shown to label selectively H1 receptors in guinea pig and rat brain with high affinity ( K D, ∼0.1 and 0.5 nM, respectively) and a B max about 50 and 15%, respectively, of that observed for the H1antagonist radioligand [3H]mepyramine. In the current study, [3H](−)- trans -H2-PAT-labeled cloned guinea pig and human H1 receptors in Chinese hamster ovary (CHO) cell membranes with high affinity ( K D, ∼0.08 and 0.23 nM, respectively) and a B max about 15% of that observed for [3H]mepyramine. The binding of H2-PAT to H1 receptors in both CHO-H1 cell lines was stereoselective with the (−)- trans -isomer having affinity ( K i, ∼1.5 nM) about 4-, 20-, and 50-times higher than the (−)- cis -, (+)- trans -, and (+)- cis -isomers, respectively; the affinity of (−)- trans -H2-PAT was unaffected by excess GTP. In functional assays, (−)- trans -H2-PAT was a full antagonist of histamine H1-mediated stimulation of phospholipase C (PLC) and [3H]inositol phosphates (IP) formation in CHO-H1 cells, a full inverse agonist of constitutively active H1 receptors in COS-7-H1cells, and a full competitive antagonist (p A 2 = 9.2) of histamine H1-mediated contraction of guinea pig ileum. It is concluded that (−)- trans -H2-PAT is an antagonist at H1 receptors coupled to PLC/IP formation and smooth muscle contraction. Meanwhile, the observation that [3H](−)- trans -H2-PAT labels only a subpopulation of H1 receptors and that (−)- trans -H2-PAT activates H1receptors coupled to modulation of tyrosine hydroxylase suggests that there may be post-translational H1 receptor heterogeneity.

Assessment of Systemic Adverse Reactions Induced by Ophthalmic Adrenergic Receptor Antagonists

Article

Jun 2001

To assess quantitatively the risks of ophthalmic beta-blocking agents for cardiovascular and respiratory adverse reactions, we analyzed the binding kinetics of beta-blocking agents to the beta-1 and beta-2 adrenoceptors. The relationship between the occupancies for beta-1 and beta-2 adrenoceptors and the effects on the exercise pulse rate or the forced expiratory volume in one second (FEV1) after topical administration of carteolol, befunolol, timolol and betaxolol was analyzed using a ternary complex model. The beta-1 and beta-2 receptor occupancies after ophthalmic administration were calculated to be quite high as well as those after oral administration. The maximum occupancies for beta-1 and beta-2 receptors after ordinary ophthalmic administration were 52% and 88% for carteolol, 52% and 61% for befunolol, 62% and 82% for timolol, and 44% and 3% for betaxolol, respectively. Concave relationships were obtained between a decrease in exercise pulse rate and the beta-1 receptor occupancy and between a decrease in FEV1 and beta-2 receptor occupancy, respectively. Nasolacrimal occlusion was estimated to decrease the exercise pulse rate and FEV1 by 65% and 50%, respectively. The beta-1 and beta-2 adrenoceptor occupancies were proved to be the most appropriate indicators for cardiac and pulmonary adverse reactions evoked by ophthalmic beta-blocking agents.

Histamine H1 receptors in human brain labelled with [3H]Doxepin

Article

Jul 1984
BRAIN RES

Doxepin, a tricyclic antidepressant, is one of the most potent histamine H1 antagonists. Therefore, the binding of [3H]doxepin to human brain membranes was examined. Scatchard analysis revealed two distinct binding sites. The high-affinity binding site with a dissociation constant (KD +/- S.E.M.) of 3.1 +/- 0.3 X 10(-10) M was pharmacologically identified as histamine H1 receptors. Dissociation curves at low concentrations of [3H]doxepin were biphasic, suggesting several possibilities about the interaction between [3H]doxepin and histamine H1 receptors. Tetracyclic antidepressants, mianserin and maprotiline, were very potent, with KDs of 3.6 +/- 0.7 X 10(-10) M and 7.9 +/- 0.5 X 10(-10) M, respectively. Mequitazine, a new antihistamine with a weak sedative effect, had a KD of 5.8 +/- 0.8 X 10(-9), making it ten times as potent as the classic antihistamine diphenhydramine. The highest binding of [3H]doxepin to histamine H1 receptors was found in cerebral neocortex and the limbic system. The distribution of histamine H1 receptors in human central nervous system did not correlate with the previously reported distributions in rat brain and guinea pig brain determined by [3H]doxepin binding.

Antagonism of the five cloned human muscarinic cholinergic receptors expressed in CHO-K1 cells by antidepressants and antihistaminics

Article

Jun 1993
BIOCHEM PHARMACOL

Based on molecular cloning studies, five different muscarinic receptor subtypes exist: m1, m2, m3, m4, and m5. We determined the affinity and selectivity of binding for sixteen antidepressants, two of their metabolites, and three antihistaminics (H1) at these subtypes. Using Chinese hamster ovary cells (CHO-K1) transfected with genes for the human muscarinic receptor subtypes, we obtained equilibrium dissociation constants (Kds) from competitive radioligand binding studies with [3H]-quinuclidinyl benzilate ([3H]QNB) and membranal preparations of these cells. QNB was the most potent compound studied (Kd 30-80 pM). Mequitazine (Kd 6-14 nM) and amitriptyline (Kd 7-16 nM) exhibited the highest affinity among the antihistaminics and antidepressants, respectively. Among the antidepressants examined were the serotonin-selective drugs sertraline and fluoxetine, both of which displayed Kd values > 1 microM. The remaining antidepressants were moderate to weak antagonists with some eliciting no radioligand competition at high concentrations. The compounds studied showed no significant selectivity among the five cloned subtypes.

Quantitative Evaluation of Pharmacological Effects and Adverse Effects Based on Receptor Occupancy Theory

Article

Mar 1997

For the quantitative assessment of therapeutic/pharmacologic effects and adverse/toxic effects of drugs which interact with several specific receptors, we developed an integrated pharmacokinetic analysis system by considering a receptor occupancy theory and a ternary complex model. The developed system has made it possible to predict the optimal dosage for the investigation of drugs using pre-clinical and clinical data (phase I study) under development, and to assess quantitatively the intensity of adverse/toxic effects of drugs which are not observed in the stage of clinical study. As for commercially available drugs, the developed system may be useful for planning the rational drug dosage regimen to obtain desired therapeutic effects with minimum adverse/toxic effects. Our new methodology for the rational dosage regimen of a new drug will be established based on the results of this study.

[3H]Rauwolscine: an antagonist radioligand for the cloned human 5-hydroxytryptamine2B (5-HT2B) receptor

Article

Feb 1998

In previous reports, [3H]5-HT has been used to characterize the pharmacology of the rat and human 5-HT2B receptors. 5-HT, the native agonist for the 5-HT2B receptor, has a limitation in its usefulness as a radioligand since it is difficult to study the agonist low-affinity state of a G protein-coupled receptor using an agonist radioligand. When using [3H]5-HT as a radioligand, rauwolscine was determined to have relatively high affinity for the human receptor (Ki human = 14.3+/-1.2 nM, compared to Ki rat = 35.8+/-3.8 nM). Since no known high affinity antagonist was available as a radioligand, these studies were performed to characterize [3H]rauwolscine as a radioligand for the cloned human 5-HT2B receptor expressed in AV12 cells. When [3H]rauwolscine was initially tested for its usefulness as a radioligand, complex competition curves were obtained. After testing several alpha2-adrenergic ligands, it was determined that there was a component of [3H]rauwolscine binding in the AV12 cell that was due to the presence of an endogenous alpha2-adrenergic receptor. The alpha2-adrenergic ligand efaroxan was found to block [3H]rauwolscine binding to the alpha2-adrenergic receptor without significantly affecting binding to the 5-HT2B receptor and was therefore included in all subsequent studies. In saturation studies at 37 degrees C, [3H]rauwolscine labeled a single population of binding sites, Kd = 3.75+/-0.23 nM. In simultaneous experiments using identical tissue samples, [3H]rauwolscine labeled 783+/-10 fmol of 5-HT2B receptors/mg of protein, as compared to 733+/-14 fmol of 5-HT2B receptors/mg of protein for [3H]5-HT binding. At 0 degrees C, where the conditions for [3H]5-HT binding should label mostly the agonist high affinity state of the human 5-HT2B receptor, [3H]rauwolscine (Bmax = 951+/-136 fmol/mg), again labeled significantly more receptors than [3H]5-HT (Bmax = 615+/-34 fmol/mg). The affinity of [3H]rauwolscine for the human 5-HT2B receptor at 0 degrees C did not change, Kd = 4.93+/-1.27 nM, while that for [3H]5-HT increased greatly (Kd at 37 degrees C = 7.76+/-1.06 nM; Kd at 0 degrees C = 0.0735+/-0.0081 nM). When using [3H]rauwolscine as the radioligand, competition curves for antagonist structures modeled to a single binding site, while agonist competition typically resulted in curves that best fit a two site binding model. In addition, many of the compounds with antagonist structures displayed higher affinity for the 5-HT2B receptor when [3H]rauwolscine was the radioligand. Typically, approximately 85% of [3H]rauwolscine binding was specific binding. These studies display the usefulness of [3H]rauwolscine as an antagonist radioligand for the cloned human 5-HT2B receptor. This should provide a good tool for the study of both the agonist high- and low-affinity states of the human cloned 5-HT2B receptor.

Comparison of the pharmacological properties of cloned rat, human, and bovine norepinephrine transporters

Article

Sep 1999

The aims of this study were to characterize the recently cloned rat norepinephrine transporter (NET) in more detail and in particular to study possible species differences in its pharmacological properties compared with the human and bovine NETs. The study was carried out by measuring the uptake of [(3)H]norepinephrine in COS-7 cells expressing the NET after transient transfection with rat, human, or bovine NET cDNA. There were small but significant differences between the rat NET and the human or bovine NETs with respect to the affinities of sodium ions (greater for rat than for bovine) of the substrates norepinephrine, epinephrine, and 1-methyl-4-phenylpyridinium (greater for human than for rat), and of the inhibitor cocaine (greater for human and bovine than for rat), whereas the affinities of dopamine and of most inhibitors, including tricyclic antidepressants, showed no species differences. The fact that the affinities for some substrates, cocaine and sodium ions exhibited small but significant interspecies differences among the rat, human, and bovine NETs suggests that ligand recognition, the translocation process, and sodium ion dependence are influenced differentially by just a few amino acid exchanges in the primary sequences of the transporters. On the other hand, the lack of any major differences in the pharmacological properties of the rat, human, and bovine NETs in this study suggests that data obtained in previous studies on rat tissues and bovine cells can be extrapolated, in all except the most quantitative analyses, to the properties of the human NET.

Management of nonresponse and intolerance: Switching strategies

Article

Feb 2000

Maurizio Fava

Approximately 29% to 46% of depressed patients show only partial or no response to treatment with antidepressants, with intolerance a frequent cause of treatment failure or discontinuation. Clinicians frequently switch to other antidepressants patients who have failed to tolerate or to respond to antidepressant treatment. The switching strategy involves substitution of another agent for the agent that has either caused intolerable side effects or has failed to induce a response. Fredman and colleagues have recently surveyed 402 psychiatrists from various parts of the country and asked them what steps they would take for patients who fail to respond to 8 weeks or more of an adequate dose of a selective serotonin reuptake inhibitor (SSRI). Interestingly, switching to a non-SSRI agent was the most popular choice indicated by psychiatrists (44% of respondents), with dual-acting agents and bupropion being the next most commonly chosen agents. Even though there are no controlled trials of switching strategies in the literature to date, clinicians often choose this course of action. This article will review some of the currently available studies on switching strategies.

An ABC of drug-related problems

Article

Jul 2000

The problems relating to the use of medicines are manifold. They may differ in pharmacological, pathological, epidemiological and legal respects, and may have different consequences, for example, as regards scientific study, regulation or rational use. Pharmacovigilance is concerned with all such problems: adverse effects and interactions as well as problems relating to ineffectiveness, inappropriate use, counterfeiting, dependence or poisoning. Practically all medicine-related problems can be classified in one basic system, taking into account their characteristics and distinctions. This system distinguishes between appropriate and inappropriate drug use; dose-related and dose-unrelated problems [corrected]; and types A ('drug actions'), B ('patient reactions') and C ('statistical') adverse effects. This classification may serve as an educational tool and may be useful in when choosing a study method and for the design of effective strategies in pharmacovigilance.