BIOPARTITIONING MICELLAR CHROMATOGRAPHY TO PREDICT BLOOD TO LIVER, BLOOD TO LUNG AND BLOOD TO FAT PARTITION COEFFICIENTS OF DRUGS Y. Martín Biosca, S

BIOPARTITIONING MICELLAR CHROMATOGRAPHY TO PREDICT BLOOD TO LIVER, BLOOD TO LUNG AND BLOOD

TO FAT PARTITION COEFFICIENTS OF DRUGS

E

S VNIVER ITAT D VALÈNCIA

Y. Martín Biosca, S. Torres-Cartas*, R.M. Villanueva-Camañas, M.J. Medina Hernández and S. Sagrado

Dpto. Química Analítica, Universitat de València, C/ Vicente Andrés Estellés s/n, E-46100 Burjassot (Valencia), Spain

* Dpto. Química, Escuela Politécnica Superior de Gandia, Ctra. Nazaret-Oliva, s/n, E- 46730 Gandia (Valencia) Spain

ESCOLA POLITÈCNICASUPERIOR DE GANDIA

BIOPARTITIONING MICELLAR CHROMATOGRAPHY TO PREDICT BLOOD TO LIVER, BLOOD TO LUNG AND BLOOD TO FAT PARTITION COEFFICIENTS OF

DRUGS

INTRODUCTIONINTRODUCTION

Distribution of organic compounds between blood and tissues is of crucial importance in the understanding of potential toxic effects and in pharmacokinetic analysis. Several procedures have been developed for measuring drug partition coefficients from blood to different tissues (liver, lung, brain, muscle…). Retention data obtained in biopartitioning micelar chromatography (BMC) is useful in constructing good models (attributed to the fact that the characteristics of the BMC system are similar to the characteristics of biological barriers).

The capability of BMC as an in-vitro technique to describe distribution of organic compounds between blood and tissues is evaluated. Values of in vitro blood to liver, blood to lung and blood to fat partition coefficients of a heterogeneous set of compounds have been collected from the literature. Adequate correlations between the BMC retention data of compounds, obtained using a solution of Brij35 as micellar mobile phase, and their partition coefficients in different tissues are achieved.


DRUGS

EXPERIMENTALEXPERIMENTAL

SOFTWARE, AND DATA SOFTWARE, AND DATA PROCESSINGPROCESSINGMicrosoft® Excel 2000 software were used to perform the statistical analysis of

the regressions.

The Unscrambler Version 7.6 by CAMO was used to perform multivariate analysis.

EPI SuiteTM (ACD LabsTM, Advanced Chemistry Development Inc. Demo version) was used for parameters estimation: octanol-water partition coefficient (logP), molar refractivity (MR), polarizability (Pol), molar volumen (MV), parachor (Pr) and water solubility.

BMC methodBMC method

Stationary phase:Stationary phase: Kromasil octadecyl-silane C18 column (5 m, 150 x 4.6 mm i.d.) (Scharlab, Barcelona, Spain).

Mobile phase:Mobile phase: Brij35 0.04 M at pH 7.4; 0.05 M phosphate buffer.

HPLC conditions:HPLC conditions: flow 1 mL·min-1; detection at 220 nm; loop 20 L; T 35.5 ºC.

Drugs included in this study were chosen in order to cover a broad range of physico-chemical properties. All retention factor values (k) were averages of a least triplicate determinations.

EXPERIMENTALEXPERIMENTAL


DRUGSTable 1. k and blood to lung, blood to fat and blood to liver logP of drugs.

Log P from blood to

k ± sd

Lung [1] Fat [2] Liver [3]

Alprazolam 29.53 ± 0.07 0.32 - 1.02 Atenolol 1.48 ± 0.05 0.28 - - Barbital 5.782 ± 0.014 0 -0.14 0.57 Bisoprolol 5.37 ± 0.06 1.62 1.360 Butethal 24.55 ± 0.03 0.18 0.25 0.47 Buthetal 17.82 ± 0.08 0.18 0.250 0.470 Chlorpromazine 340 ± 11 1.81 - - clomipramine 250 ± 11 2.16 - - Diazepan 44.63 ± 0.04 0.52 1.13 0.65 Fentanyl 67.064 ± 0.31 1.14 1.430 0.580 Fluoxetine 45.3 ± 0.5 1.24 - 0.770 Haloperidol 102.2 ± 0.7 1.73 - - Hexobarbital 23.158 ± 0.009 0.45 0.2 0.78 Hydroxyzine 60.9 ± 0.5 1.06 - 0.7 Imipramine 187 ± 9 2.11 1.02 1.71 Lidocaine 21 ± 0.8 0.58 - 1.06 Lorazepam 30.99 ± 0.04 0.44 - 1.08 Metoprolol 5.73 ± 0.13 1.06 0.01 1.63 Midazolam 54.98 ± 0.06 0.6 0.94 0.77 Oxprenolol 13.58 ± 0.17 1.27 -0.2 1.02 Pentazocine 25.30 ± 0.14 1.43 0.400 0.370 Pentotal 43.0 ± 0.3 0.08 0.95 0.360 Phenobarbital 19.416 ± 0.008 -0.08 -0.52 0.26 Phenytoin 34.49 ± 0.11 -0.1 0.26 0.36 Pindolol 5.92 ± 0.02 1.01 - - Pyrene 72.0 ± 0.7 0.35 - 0.370 Salicylic Acid 4.24 ± 0.05 -0.72 - -0.65 Theophylline 2.3275 ± 0.0012 -0.15 - - Timolol 3.39 ± 0.05 1.43 -0.190 0.950 Valproic acid 1.47 ± 0.04 -0.38 -0.820 0.260

RESULTS AND RESULTS AND DISCUSSIONDISCUSSION


DRUGS

In order to study the importance of some physico-chemical variables in the construction of a regression model for predicting blood to lung partition coefficients for drugs (Table 1), a partial least squares analysis (PLS) was performed. The loading plot corresponding to the first two latent variables is shown in Figure 1.

Figure 1. PLS loading plot corresponding to the first two latent variables (y-block in pink and X-block in blue).

PC2· Solubility

· kBMC

· Pr

· MV

MR ·

MW ·

· logP

PC1

· logPLung

· Pol

PC2· Solubility

· kBMC

· Pr

· MV

MR ·

MW ·

· logP

PC1

· logPLung

· Pol

Blood to lung partition Blood to lung partition coefficient-retention coefficient-retention relationship (an relationship (an example)example)

Blood to lung partition Blood to lung partition coefficient-retention coefficient-retention relationshiprelationship

Figure 2.- The PLS-model regression coefficients together their uncertainty limits for the two latent variables model

k logP MR MV Pr Pol MW Solubilityk logP MR MV Pr Pol MW Solubility

Non-significant variables were eliminated step by step (Figure 2), re-analyzing each time the PLS model. Finally a PLS model was obtained by selecting the variables: kBMC and molar volume (MV). This model accounts for 82 and 80% of variance in calibration and cross-validation, respectively.


DRUGS

logPLung = (-1.7 0.6) + (0.003 0.002) * k + (0.010 0.002) * MV N = 30; r2 = 0.81; S.E. = 0.33

As can be observed, in Figure 3 the ability of the proposed model to describe and predict logPLung was adequate.

Blood to lung partition Blood to lung partition coefficient-retention coefficient-retention relationshiprelationship

Plot of logP

-0,8 -0,3 0,2 0,7 1,2 1,7 2,2

predicted

-0,8

-0,3

0,2

0,7

1,2

1,7

2,2

obse

rved

in-vitro logPlung

est

imate

dlo

gP

lung

Plot of logP

-0,8 -0,3 0,2 0,7 1,2 1,7 2,2

predicted

-0,8

-0,3

0,2

0,7

1,2

1,7

2,2

obse

rved

Plot of logP

-0,8 -0,3 0,2 0,7 1,2 1,7 2,2

predicted

-0,8

-0,3

0,2

0,7

1,2

1,7

2,2

obse

rved

Plot of logP

-0,8 -0,3 0,2 0,7 1,2 1,7 2,2

predicted

-0,8

-0,3

0,2

0,7

1,2

1,7

2,2

obse

rved

in-vitro logPlung

est

imate

dlo

gP

lung

in-vitro logPlung

est

imate

dlo

gP

lung

Figure 3.- Validation plot of the QRAR model

MLR


DRUGS

ReferencesReferences[1] M. H. Abraham and a. Ibrahim, Eur. J. Med. Chem. 4 (2006) 1403-1438..[2] M. H. Abraham et. al. Eur. J. Med. Chem. (2007), doi:10.1016/j.ejmech.2006.12.01.[1] Michael H. Abraham et. al. Eur. J. Med. Chem. (2007),

doi:10.1016/j.ejmech.2006.12.011.MLR vs. PLS coef.

E


ENANTIOSELECTIVE BINDING OF ANTIHISTAMINES TO HUMAN SERUM ALBUMIN BY AFFINITY

ELECTROKINETIC CHROMATOGRAPHY–PARTIAL FILLIG TECHNIQUE

Mª Amparo Martínez Gómez, S. Sagrado, R.M. Villanueva Camañas and M.J. Medina Hernández

Dpto. Química Analítica, Universitat de València, C/ Vicente Andrés Estellés s/n,

E-46100 Burjassot (Valencia), Spain.

ENANTIOSELECTIVE BINDING OF ANTIHISTAMINES TO HUMAN SERUM ALBUMIN BY AFFINITY ELECTROKINETIC CHROMATOGRAPHY–PARTIAL FILLIG TECHNIQUE

INTRODUCTION

A new methodology to evaluate the enantioselective binding to HSA of highly protein-bound drugs was proposed. This methodology consists in ultrafiltrating samples containing HSA and racemic drug and analysing the bound drug fraction using AEKC-partial filling technique (PFT) and HSA as chiral selector. The protein binding values, the affinity constants to HSA and the binding sites of the enantiomers of four antihistamines, brompheniramine, chlorpheniramine, hydroxyzine and orphenadrine, on the HSA molecule were evaluated.

REFERENCES

[1] J.J.Martínez-Plà, M.A. Martínez-Gómez, Y. Martín-Biosca, S. Sagrado, R.M. Villanueva-Camañas, M.J. Medina-Hernández, Electrophoresis 25 (2004) 3176-3185.

[2] M.A. Martínez-Gómez, S. Sagrado, R.M. Villanueva-Camañas, M.J. Medina-Hernández, Analytica Chimica Acta 592 (2007) 202–9.


EXPERIMENTAL AND METHODOLOGY

CHIRAL ANALYSIS

Unbound drug fractionBound drug fraction

Incubation 30 min at 36.5ºC Ultrafiltration

Precipitation of HSA with MeOH (centrif.)

SAMPLE

•Drug enantiomer (65-270 μM)•HSA (475 μM) •(-)-Sulpiride (I.S.) (80 μM)•Phosphate buffer 67 mM pH 7.4

• HP 3D CE system, diode array detector and HP 3DCE Chemstation software

• Fused-silica capillary of 50 m i.d and 363 m o.d. with total and effective length of 65 and 56.5 cm, respectively.

•Temperature 30º C, voltage 15 kV and detection wavelength, 225 nm

Drug pH[HSA] (μM)

SPL (s) Rs

Brompheniramine

8.50 180 180 2.50

Chlorpheniramine

8.25 160 150 1.49

Hydroxyzine 7.00 180 150 1.41Orphenadrine 7.80 160 150 1.12

Optimum experimental conditions for enantioresolution of drugs and resolution values


SITE II

Diazepam

SITE III: Digitoxin

Warfarin

SITE I

The binding sites of antihistamines in the HSA molecule were identified using warfarin, diazepam and digitoxin as marker ligands representatives of sites I, II and III, respectively in the HSA molecule.


Table 1 shows the estimated affinity constants obtained for each drug enantiomer evaluated using the results obtained at five concentration levels. Both enantiomers of brompheniramine and chlorpheniramine bind to the site II in the HSA molecule so, enantiomers follow a competitive binding model. On the contrary, enantiomers of orphenadrine and trimeprazine bind to different binding sites, following an independent binding model.

Enantioselectivity (ES) values were in all cases higher than 1 indicating that a certain degree of enantioselective binding of antihistamines to HSA exists. The results obtained represent the first evidence of the enantioselective binding of antihistamines to HSA, the major plasmatic protein.

Compound Binding Model

Enantiomer I Enantiomer II Enantioselectivity

KE1 (M-1) Binding Site

KE2 (M-1) Binding site

ES

Brompheniramine

Competitive (9.39±0.10)·102

Site IIDiazepam

(2.60±0.17)·103

Site IIDiazepam

2.8 ± 0.2

Chlorpheniramine

Competitive (9.20±0.20)·102

Site IIDiazepam

(1.69±0.17)·103

Site IIDiazepam

1.8 ± 0.3

Hydroxyzine Independent

(5.3±0.5)·103 Non defined

(6.3±0.4)·103 Site I Warfarine

1.2 ± 0.6

Orphenadrine Independent

(1.26±0.13)·103

Site IIIDigitoxin

(1.67±0.11)·104

Non defined

13.3 ± 0.1

E


STEREOSELECTIVE PLASMA PROTEIN BINDING OF BASIC DRUGS BY CAPILLARY

ELECTROPHORESIS

Mª Amparo Martínez Gómez, S. Sagrado, R.M. Villanueva Camañas and M.J. Medina Hernández

Dpto. Química Analítica, Universitat de València, C/ Vicente Andrés Estellés s/n,

E-46100 Burjassot (Valencia), Spain.

STEREOSELECTIVE PLASMA PROTEIN BINDING OF BASIC DRUGS BY CAPILLARY ELECTROPHORESIS

INTRODUCTION

The stereoselective binding of antihistamines (brompheniramine, chlorpheniramine, hydroxyzine, orphenadrine and phenindamine), phenothiazines (promethazine and trimeprazine) and a local anaesthetic (bupivacaine) to human plasma proteins was evaluated. The results obtained represent the first evidence of the enantioselective binding of brompheniramine, hydroxyzine, orphenadrine, phenindamine, promethazine and trimeprazine to human plasma proteins.

REFERENCES

[1] J.J.Martínez-Plà, Y. Martín-Biosca, S. Sagrado, R.M. Villanueva-Camañas, M.J. Medina-Hernández, J.Chromatogr. A 1048 (2004) 111-118.




EXPERIMENTAL AND METHODOLOGY

CHIRAL ANALYSIS

Unbound drug fractionBound drug fraction

Incubation 30 min at 36.5ºC Ultrafiltration

Precipitation of HSA with ACN

SAMPLE

•Drug enantiomer (92-347 μM)•Plasma •(-)-Sulpiride (I.S.) (80 μM)•Phosphate buffer 67 mM pH 7.4

• HP 3D CE system, diode array detector and HP 3DCE Chemstation software

• Fused-silica capillary of 50 m i.d and 363 m o.d. with total and effective length of 65 and 56.5 cm, respectively.

•Temperature 30º C, voltage 15 kV and detection wavelength, 225 nm


Drug pH [HSA] (μM) SPL (s) Rs

Brompheniramine 8.50 180 180 2.50

Chlorpheniramine 8.25 160 150 1.49

Hydroxyzine 7.00 180 150 1.41

Orphenadrine 7.80 160 150 1.12

Phenindamine 6.80 140 150 1.75

Promethazine 7.60 170 170 2.00

Trimeprazine 7.50 170 190 1.53

Bupivacaine 8.00 140 180 1.52

Optimum experimental conditions for enantioresolution of drugs and resolution values


0

1

2

3

6 7 8 9

Time (min)

Abs

orba

nce

(mA

U)

0 //

0

1

2

3

11 12 13 14

Time (min)

Abs

orba

nce

(mA

U)

0 //

0

1

2

3

8 9 10 11 12

0

1

2

3

8 9 10 11 12Abs

orba

nce

(mA

U)

0

Time (min)

//

Abs

orba

nce

(mA

U)

0

1

2

3

8 10 12 14

Time (min)

// 0

Abs

orba

nce

(mA

U)

0

1

2

3

7 8 9 10

0

0,5

1

1,5

2

2,5

3

3,5

4

4,5

5

8 8,5 9 9,5 10 10,5 11

Time (min)

// 0

A B

C D

E

Figure 1

Figure 1 shows the experimental electropherograms corresponding to the analysis of the bound fractions of: (A) 242 μΜchlorpheniramine (B) 201μΜ orphenadrine(C) 180μΜ hydroxyzine (D) 100μΜ promethazine (E) 312 μΜ bupivacaine

RESULTS AND DISCUSSION


Compound C tot (μM) PB (%)

ESE1 E2

Chlorpheniramine 124 76±2 83±5 1.09±0.08

242 80±2 82±2 1.02±0.02

346 80±2 82±2 1.01±0.02

Hydroxyzine 92 99±5 100±3 1.01±0.02

180 92±3 98±2 1.06±0.03

261 93±3 96±2 1.03±0.02

Phenindamine 120 29±3 71±6 2.49±0.02

243 29±3 71±3 2.51±0.12

347 32±2 72±2 2.25±0.09

Bupivacaine 125 84±3 98±3 1.17±0.08

218 78±3 97±3 1.24±0.02

312 85±2 96±2 1.14±0.02

Brompheniramine 120 89±5 92±4 1.01±0.05

240 65±2 73±2 1.15±0.03

345 43±2 50±2 1.16±0.04

Promethazine 100 71±3 100±3 1.40±0.12

200 33±2 47±2 1.43±0.02

290 24±2 35±2 1.50±0.13

Orphenadrine 104 65±6 99±5 1.53±0.06

201 81±5 96±2 1.18±0.09

288 99±2 100±2 1.00±0.02

Trimeprazine 97 48±6 84±2 1.8±0. 2

194 51±4 87±3 1.7±0.2

277 61±5 89±2 1.5±0.2

Table 1 Table 1 shows the protein-binding (PB) values of each drug enantiomer and the enantioselectivity (ES) values obtained at 3 concentrations.

In general, the first eluted enantiomer (E1) presented lower affinity towards plasma proteins than the second enantiomer (E2) Saturation of binding sites of proteins was observed for brompheniramine and promethazine.

The different behaviour between the enantiomers of orphenadrine and trimeprazine indicated that the enantiomers follow an independent binding model.

Decreased order of ES was:Phenindamine>trimeprazine>promethazine≈orphenadrine>Bupivacaine>chlorpheniramine≈hydroxyzine≈brompheniramine

SCREENING OF SCREENING OF ACETYLCHOLINESTERASE INHIBITORS ACETYLCHOLINESTERASE INHIBITORS

BY CAPILLARY ELECTROPHORESIS BY CAPILLARY ELECTROPHORESIS AFTER ENZYMATIC REACTION AT AFTER ENZYMATIC REACTION AT

CAPILLARY INLETCAPILLARY INLETY. Martín-Biosca, L. Asensi-Bernardi, R.M. Villanueva-

Camañas, S. Sagrado and M.J. Medina-HernándezDpto. Química Analítica, Universitat de València, C/ Vicente Andrés

Estellés s/n, E-46100 Burjassot (Valencia), Spain

E


INTRODUCTIONINTRODUCTION

Alzheimer’s disease (AD) is an age-related neurodegenerative disorder that causes dementia characterized by a low level of the neurotransmitter acetylcholine in the brain. The current clinical treatment of AD is mainly based on acetylcholinesterase (AChE) inhibitors, such as tacrine, donepezil, rivastigmine, and galantamine, which pharmacological effect is to inhibit the activity of AChE, so as to keep a normal level of acetylcholine in the nerve system.

Capillary electrophoretic systems have been successfully applied for in-line enzymatic reactions by a methodology known as electrophoretically mediated microanalysis (EMMA) [1,3]. In this methodology, all the different steps (i.e. mixing, incubation, separation and in-line quantitation) are combined in the capillary, which is used as a microreactor for the enzymatic reaction. The aim of the present work is to develop a simple EMMA method for screening of AChE inhibitors in the early stage of drug discovery.

SCREENING OF ACETYLCHOLINESTERASE INHIBITORS BY SCREENING OF ACETYLCHOLINESTERASE INHIBITORS BY CAPILLARY ELECTROPHORESIS AFTER ENZYMATIC REACTION AT CAPILLARY ELECTROPHORESIS AFTER ENZYMATIC REACTION AT

CAPILLARY INLETCAPILLARY INLET

EXPERIMENTALEXPERIMENTALInstrumentationInstrumentation

A 50 m i.d. (363 m o.d.) fused-silica capillary with total and effective length of 56 and 47.5 cm respectively was employed (Agilent Technologies, Germany)

CE conditions: 15 kV; detection at 230 nm; T 37º C; hydrodynamical injection

Background electrolyte: 30 mM borate-phosphate buffer, pH 8.0

A Hewlett-Packard HP 3DCE capillary electrophoresis

system



EMMA procedureEMMA procedure

The enzyme activity was directly assayed by measuring the peak area of produced thiocholine (TCh) with UV detection at 230 nm

Figure 1.- AChE catalyzed reaction



SubstrateEnzyme

Product

+ Inhibitor

The enzyme solution and the substrate solution, with or without inhibitor, were introduced into the inlet part of the capillary by a sandwich injection mode: 1) Water, 20 mbar for 2 sec 2) AChE, 50 mbar for 2 sec 3) Substrate (with or without inhibitor), 50 mbar for 2 sec 4) AChE, 50 mbar for 2 sec 5) Water, 20 mbar for 2 sec 6) Waiting time (mixing and incubation time): 1 min 7) A voltage of 15 kV was applied to separate the product TCh from the unreacted substrate

+ -

Figure 2.- Schematic illustration of EMMA technique for AChE acitvity assay.



NOTE: I.S. in all solutions

Figure 3.- Typical electropherogram obtained after EMMA methodology applied with (red) and without (blue) the inhibitor edrophonium (100 µM) added to the substrate plug. Conditions: concentration of AChE, 0.4 mg/mL; AThCh, 10 mM; MgSO4 in the substrate solution, 20 mM.

min1 2 3 4 5

mAU

0

2

4

6

8

10

12

14

16

18

DAD1 A, Sig=230,16 Ref=off (ACHE\25050731.D) DAD1 A, Sig=230,16 Ref=off (ACHE\25050734.D)

Time (min)

Substrate (AThCh)

Product (TCh)

Alprenolol (IS)



x-area

Blank-area

A B

Figure 4.- (A) Michaelis-Menten and (B) corresponding Lineweaver-Burk plots obtained using the inhibitor edrophonium .

0 . 0 5 . 0 1 0 . 0 1 5 . 0 2 0 . 0 2 5 . 0[Substrate], m M

0

100

200

300

Peak a

rea

edrophonium 0 edrophonium 50 edrophonium 100 edrophonium 500

0 200 400 600

1/[AThCh] (M -1)

-0.02

0

0.02

0.04

0.06

0.08

1/V

edrophonium 0 edrophonium 50 edrophonium 100 edrophonium 500

RESULTS AND DISCUSSION RESULTS AND DISCUSSION (an example)(an example)



To estimate Ki

Characterize:mixed-mode inhibitor

The percentage of inhibition was determined according to the following equation: x: peak area of the product (TCh)

determined at a given concentration of inhibitor

blank: peak are of the product (TCh) whitout inhibitor being present

The measured IC50 for edrophonium (concentration of compound at which the reaction was inhibited by 50%) with the EMMA assay was aproximately 100 M at a concentration of substrate 10 mM. The proposed methodology is rapid, simple, automatic and can be very useful for screening of AChE inhibitors in the early stage of drug discovery.

0 100 200 300 400 500 600

[edrophonium ], M

0

20

40

60

80

100

Inh

ibit

ion

%

AThCh 2 mAThCh 6 mAThCh 10 mAThCh 16 mAThCh 20 m

ReferencesReferences

Figure 5.- Inhibition plot of edrophonium obtained using the proposed EMMA methodology for several substrate concentrations.



1.J. Zhang, J. Hoogmartens and A. Van Schepdael, Electrophoresis 2006, 27, 35-43.

2.M. Telnarová, S. Vytisková, R. Chaloupková and Z. Glatz, Electrophoresis 2004, 25, 290-296.

3.M. Telnarová, S. Vytisková, M. Monincová and Z. Glatz, Electrophoresis 2004, 25, 1028-1033.

100blank

x100%Inhibition

Documents

BIOPARTITIONING MICELLAR CHROMATOGRAPHY TO PREDICT BLOOD TO LIVER, BLOOD TO LUNG AND BLOOD TO FAT PARTITION COEFFICIENTS OF DRUGS Y. Martín Biosca, S