RP TLC data in correlation studies with in silico pharmacokinetic properties of benzimidazole and benztriazole derivatives

RP TLC data in correlation studies with in silico pharmacokinetic properties of benzimidazole and benztriazole derivatives

European Journal of Pharmaceutical Sciences 49 (2013) 10–17 Contents lists available at SciVerse ScienceDirect European Journal of Pharmaceutical Sc...

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European Journal of Pharmaceutical Sciences 49 (2013) 10–17

Contents lists available at SciVerse ScienceDirect

European Journal of Pharmaceutical Sciences journal homepage: www.elsevier.com/locate/ejps

RP TLC data in correlation studies with in silico pharmacokinetic properties of benzimidazole and benztriazole derivatives Nataša P. Miloševic´ a,⇑, Vesna B. Dimova b, Nada U. Perišic´-Janjic´ c a

Department of Pharmacy, Faculty of Medicine, University of Novi Sad, Hajduk Veljkova 3, 21000 Novi Sad, Serbia Faculty of Technology and Metallurgy, St. Cyril and Methodius University, Ruger Boskovic 16, P.O. Box 580, MK-1001 Skopje, Macedonia c Academy of Sciences and Arts of Vojvodina, University of Novi Sad, Vojvode Putnika 1, 21000 Novi Sad, Serbia b

a r t i c l e

i n f o

Article history: Received 8 October 2012 Received in revised form 20 December 2012 Accepted 30 January 2013 Available online 9 February 2013 Keywords: RP TLC Benzimidazole/benztriazole derivatives Lipophilicity Multiple regression analyses ADMET

a b s t r a c t Reversed-phase thin-layer chromatographic (RP TLC) retention constants for a newly designed series benzimidazole/benztriazole with expected biological activity were determined as parameters of their lipophilicity and this series was recognized as congeneric. Pharmacokinetic descriptors of the compounds investigated were calculated in silico with the use of the established drug design software. The bioactivity descriptors, which are assumed to predicted drug absorption, distribution, metabolism, elimination and toxicity (ADMETox) in humans, were correlated with retention constants and good statistical parameters were obtained. Multiple regression analysis which was introduced suggested that the absorption through different epithelial membranes (intestinal, blood–brain or erythrocyte membrane) and distribution process depend on retention constants (as measure of lipophilicty) and total polar surface area and molar weight/volume of the analyte. Finally, the compounds with halogen substituent (compounds A4/A7 and A5/A8 in Table 1), were suggested as the best drug candidates, because of their predicted proper pharmacokinetics and have been proposed for further biological tests. Ó 2013 Elsevier B.V. All rights reserved.

1. Introduction Among heterocyclic compound benzimidazole and benztriazole derivatives are well known as physiologically active compounds (Alper et al., 2003; Garuti et al., 2000; Kazimierczuk et al., 2005; Perišic´-Janjic´ et al., 2000; Tomic et al., 2004). Benzimidazole drugs (e.g. the anthelmintics albendazole, fenbendazole, oxfenbendazole, thiabendazole, and mebendazole and the proton-pump inhibitors omeprazole, lansoprasole, and pantoprasole) are used in both human and veterinary medicine (Velik et al., 2004). The toxicokinetics of benztriazoles toward benzimidazole is not well known. Presently, development of new drugs involves research on structure–activity relationships (SARs) and structure–property relationship (SPR). Having the pharmacophore system defined can allow one to concentrate on physicochemical properties of the agents which would provide their proper pharmacokinetics, which is necessary for in vivo activity. Knowledge of physicochemical properties of drug candidates at an early phase of drug development is crucial to reduce attrition rates due to poor biopharmaceutical properties. The development of additional active benzimidazole and benztriazole compounds should be based on QSAR and/or QSPR meth-

⇑ Corresponding author. Tel.: +381 637390105; fax: +381 21422760. E-mail address: [email protected] (N.P. Miloševic´). 0928-0987/$ - see front matter Ó 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.ejps.2013.01.018

ods (Evans et al., 1997; Khalafi-Nezhad et al., 2005; LópezRodríguez et al., 2004; Mor et al., 2004; Terzioglu et al., 2004), because drug design is an iterative process trying to optimize both the activity profile for the molecule and its chemical synthesis. Lipophilicity is an important physicochemical property for prediction and correlation of the biological activity and chemical behavior of a compound in, for example, pharmaceutical and environmental chemistry and, as a consequence, plays a central role in QSAR and QSPR studies. It influence a drug’s permeation ability (amount and rate of intestinal absorption and/or blood–brain barrier crossing), as well as its distribution properties (e.g., degree of plasma protein binding) (Di and Kerns, 2003; Henchoz et al., 2009; Kaliszan, 2007; Kerns and Di, 2003). Lipophilicity is usually quantified as the logarithm of the 1-octanol–water partition coefficient for partitioning of the agent between the two immiscible solvent phases. The 1-octanol–water system is a widely accepted reference system for the determination of lipophilicity expressed as log P (Poupaert et al., 1984). The key aspect of the biological activity of a majority of compounds is their ability to get through biological membranes by passive diffusion. This process involves a series of partitioning steps, in combination with diffusion through several regions, i.e., partitioning between aqueous intra- and extra-cellular media and phospholipid membranes. Lipophilicity plays an important role in the transport of compounds through a biological system, and it may also influence the interaction between a drug compound and a

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Table 1 The chemical structures of the benzimidazole and benztriazole derivatives investigated.

A

B N N

N

X

N

R

X R

NH - C -

CH2 - NH -

O Comp.

X

R

Comp.

X

R

A1 A2 A3 A4 A5 A6 A7 A8

C C C C C N N N

H 4-CH3 4-NO2 2-Cl 2-F H 4-Cl 4-F

B1 B2 B3 B4 B5 B6 B7 B8

C C C C N N N N

H 4-CH3 4-NO2 4-Br H 4-CH3 4-NO2 4-Br

pharmacological receptor and affect ADME drug properties (absorption, distribution, metabolism, elimination and toxicity). There is no doubt that ADME/Tox drug properties are properties crucial to the final clinical success of a drug candidate. It has been estimated that nearly 50% of drugs fail because of unacceptable efficacy, which includes poor bioavailability as a result of ineffective intestinal absorption and undesirable metabolic stability. It has also been estimated that up to 40% of drug candidates have failed in the past because of safety issues (Li, 2001). In addition to the development of experimental assays with greater throughput, there is an urgent need for effective computational methods for predicting ADME/Tox-related properties. Compared to experimental approaches, these in silico methods have advantages: they do not initially require the compounds to be synthesized and experimentally tested; compound databases can be virtually screened rapidly in a high-throughput fashion when the calculations are computationally efficient (Houa et al., 2006). Processes of drug absorption, distribution and excretion in the pharmacokinetic phase of drug action are dynamic in nature, as are the chromatographic separation processes. Therefore, chromatographic data are often used to model pharmacokinetics of drugs and other xenobiotics (Kaliszan, 2007), since quantitative structure–retention relationships (QSRRs) relate different physicochemical properties to chromatographic retention (Chiriac et al., 1996; Lyman et al., 1982). Using simple and inexpensive reversed-phase thin-layer chromatography (TLC) appears especially attractive for lipophilicity determination and characterization of pharmacologically active molecules, that may aid the drug design and discovery process (Nasal et al., 2003; Perisic-Janjic et al., 2005). The purpose of the work discussed in this paper was to investigate the correlation between the retention constants as lipohilicity measures of benzimidazole and benztriazole derivatives (Table 1) and selected pharmacokinetic descriptors in order to select those congeners which should be subjected to biological tests. 2. Experimental The investigated benzimidazole and benztriazole derivatives were synthesized by procedure described earlier (Lazarevic and Klisareva, 1983). Chromatography was performed on 20 cm  20 cm C18 silica gel plates (Merck). The compounds investigated (Table 1) were dissolved in methanol (1 mg/mL) and 3 lL of each solution were spotted on the plates. Plates were developed by the ascending technique at room temperature without previous saturation. Methanol was used as the organic modifier of the aque-

ous mobile phase in five different volume ratios in the range 45–60% (v/v). After development the plates were dried and examined under ultraviolet light at k = 254 nm. Each experiment was run in triplicate. The RM value (related to the molecular lipophilicity), obtained from different types of reversed-phase thin-layer chromatography, is calculated as:

RM ¼ logð1=RF  1Þ

ð1Þ

which usually depends linearly on the volume fraction of organic modifier in the mobile phase:

RM ¼ R0M þ bu

ð2Þ

where u is the volume fraction of organic modifier, R0M (intercept) is the extrapolated values corresponding to u = 0%, and b and is the slopes of the linear plot. The coefficients of the linear relationships, R0M and b between retention and the volume fraction of organic modifier in the mobile phase (Table 2) and statistical evaluation (R2 and SD) are previously reported (Djakovic´-Sekulic´ et al., 2005). Different log P values were calculated using several softwares (http://146.107.217.178/lab/alogps/start.html). The lipophilicity programs, such as C log P, log Pkowwin, X log P, A log Ps, A log P, V log P, and many others were developed using compounds either or in

Table 2 Regression data for the benzimidazole and benztriazole derivatives on C18 plates (R0M and b) (Djakovic´-Sekulic´ et al., 2005). Comp.

R0M

b

A1 A2 A3 A4 A5 A6 A7 A8 B1 B2 B3 B4 B5 B6 B7 B8

2.71 3.15 2.82 2.58 2.74 2.36 2.15 2.36 2.50 2.56 1.23 1.71 0.95 1.93 1.15 1.63

4.15 4.77 4.21 4.09 4.32 4.00 3.73 4.00 3.98 3.53 2.41 2.75 2.19 3.14 2.15 2.58

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Table 3 Different log P values calculated for investigated compounds.

A1 A2 A3 A4 A5 A6 A7 A8 B1 B2 B3 B4 B5 B6 B7 B8 a b c

A log Psa

mi log Pa

A log Pa

X log P3a

C log Pb

log PChemc

2.05 2.32 1.78 2.58 2.10 1.75 2.28 1.71 2.77 2.99 2.66 3.94 2.47 2.67 2.18 3.17

2.30 2.75 2.26 2.93 2.42 2.16 2.79 2.28 2.97 3.41 2.93 3.78 2.83 3.27 2.78 3.63

2.39 2.88 2.29 3.06 2.60 2.48 3.14 2.68 2.99 3.48 2.88 3.74 3.08 3.57 2.97 3.83

2.44 2.81 2.27 3.07 2.54 2.28 2.75 2.22 3.15 3.51 2.98 3.84 2.99 3.35 2.82 3.68

2.40 2.89 2.14 3.11 2.54 2.81 3.52 2.95 2.63 3.18 3.07 3.84 2.49 2.99 2.88 3.66

2.25 2.74 2.29 2.81 2.41 2.61 3.10 2.64 3.44 2.66 3.22 2.82 3.02 3.50 3.05 3.85

http://146.107.217.178/lab/alogps/start.html. Chemoffice software package. ChemDraw Pro software package.

combination from the same public set of databases (Table 3) (Mannhold et al., 2009). Calculations and graphics presented in this paper were done by use of Origin 6.1 software package and MATLAB software package. 3. Results and discussion 3.1. Correlations between chromatographic lipophilicity parameters and log P values Chromatographic retention depends on the net effect of intermolecular interactions between the analyte, the stationary phase and the mobile phase. Highly significant linear relationship was obtained between R0M (intercept) and b (slope) of the TLC equation:

R0M ¼ 0:498  0:759  b ðr ¼ 0:971; SD ¼ 0:161; n ¼ 16; p < 0:0001Þ

ð3Þ

which indicates that this set of compounds can be observed as congeneric (Cserhati, 1993).

Linear relationships between the retention factors, R0M and b, and the standard lipophilicity parameter (log P), can be expected because retention of compounds in reversed-phase liquid chromatography is governed by hydrophobic interactions (Guillot et al., 2009). In this study R0M and b determined on C18 plates were correlated against log P values, calculated by the use of several softwares (Table 3). The comparison of retention constants with different computational values of log P indicated that correlation coefficients are not always satisfactory (r < 0.6). Having in mind discrepancies between calculated values of log P in this paper, other descriptors like total polar surface area (TPSA) and molecular volume (V) of the analyte were introduced. The coefficients obtained from multiple linear regression analyses defined retention constants R0M and b of the solutes as a function of lipophilicity, total polar surface area and characteristic molecular volume of the analyte (Tables 4 and 5). The magnitude of the coefficients measure the relative strength of different types of solute – stationary and solute – mobile phase interactions affecting retention for a given pair of mobile – stationary phase condition. According to the equations given in Tables 4 and 5, the interactions solute – mobile phase and solute – stationary phase depend of the lipophilicity of the solute (since the chromatographic conditions maintain unchanged), its ability to interact with the electrons of the mobile phase and cavity effect of the stationary phase. These results are consistent with capacity factor of different compounds in reverse-phase liquid chromatography (Kim et al., 2001). 3.2. Correlations between retention constants, R0M and b with pharmacokinetic predictors The preferred and most common route of drug administration is oral. Therefore, there is a great interest in an a priori prediction of intestinal absorption and tissue distribution of predesigned pharmaceuticals. After analyzing physicochemical properties of the set of compounds here studied (Table 6), one can assume a good oral absorption of them according to the known Lipinski’s ‘‘rule of 5’’ (Lipinski et al., 2001). In order to analyze the influence of lipophilicity on biological activity both, lipophilic constants, R0M and slope b, were correlated with in silico pharmacokinetic properties (Table 7): human effective permeability in jejunum (Peff), logarithm of the blood–brain

Table 4 Multiple linear regression models of retention constants R0M , as function of log P, TPSA and V of the analyte and their statistical parameters. Model

r

F

p

R0M = 0.782  A log Ps  0.034  TPSA + 0.045  V  4.502 R0M = 0.870  mi log P  0.031  TPSA + 0.043  V  3.825 R0M = 0.928  A log P  0.031  TPSA + 0.041  V  2.916 R0M = 0.883  X log P3  0.032  TPSA + 0.043  V  3.279 R0M = 0.800  C log P  0.028  TPSA + 0.051  V  5.701 R0M = 0.565  log Pchempro  0.019  TPSA + 0.034  V  3.082

0.904

17.821

0.0001

0.902

17.396

0.0001

0.901

17.253

0.0001

0.896

16.252

0.0002

0.882

14.057

0.791

6.676

0.0003 0.002

The correlations with best statistical parameters should be given bold.

Table 5 Multiple linear regression models of retention constants b, as function of log P, TPSA and V of the analyte and their statistical parameters. Model

r

F

p

b = 1.212  A log Ps + 0.043  TPSA  0.053  V + 3.385 b = 1.379  mi log P + 0.039  TPSA  0.051  V + 2.224 b = 1.409  A log P + 0.038  TPSA  0.047  V + 1.061 b = 1.416  X log P3 + 0.042  TPSA  0.048  V + 1.289 b = 1.079  C log P + 0.0321  TPSA  0.061  V + 5.521 b = 0.787  log Pchempro + 0.020  TPSA  0.038  V + 1.816

0.929 0.937 0.915 0.934 0.843 0.698

25.147 28.597 20.578 27.142 10.132 4.873

1.8  05 9.4  106 5.1  105 1.2  105 0.001 0.019

The correlations with best statistical parameters should be given bold.

N.P. Miloševic´ et al. / European Journal of Pharmaceutical Sciences 49 (2013) 10–17 Table 6 Descriptors computed for the benzimidazole and benztriazole derivatives with fulfilled Lipinski’s rule of five.a

A1 A2 A3 A4 A5 A6 A7 A8 B1 B2 B3 B4 B5 B6 B7 B8

TPSA

Natoms

MW

nON

nrotb

V

46.924 46.924 92.748 46.924 46.924 59.816 59.816 59.816 29.853 29.853 75.677 29.853 42.745 42.745 88.569 42.745

19 20 22 20 20 19 20 20 17 18 20 18 17 18 20 18

251.924 265.316 296.286 286.734 269.279 252.277 286.722 270.267 223.279 237.306 268.276 302.175 224.267 238.294 269.264 303.163

4 4 7 4 4 5 5 5 3 3 6 3 4 4 7 4

3 3 4 3 3 3 3 3 3 3 4 3 3 3 4 3

228.842 245.403 252.176 242.378 233.774 224.686 238.222 229.617 209.859 226.42 233.193 227.744 205.702 222.263 229.037 223.588

TPSA – total polar surface area, natoms – number of atoms, MW – molecular weight, nON – number of hydrogen bond acceptors, nrotb – number of rotatable bonds, V – molecular volume. a Lipinski’s rule of 5: is set of simple molecular descriptors used by Lipinski et al., (2001). The rule states, that most ‘‘drug-like’’ molecules have: log P 6 5, MW 6 500, number of hydrogen bond acceptors 6 10, and number of hydrogen bond donors 6 5 (rule name because border values are 5, 500, 2 * 5, and 5). Molecules violating more than one of these rules may have problems with bioavailability.

Table 7 Pharmacokinetic predictors for investigated set of compounds calculated by simulation plus.

A1 A2 A3 A4 A5 A6 A7 A8 B1 B2 B3 B4 B5 B6 B7 B8

Peff

log BBB

Unbprot

Vd

RPB

8.23 7.16 8.58 8.15 7.33 8.47 9.73 11.53 3.79 3.89 4.04 4.56 4.51 4.64 4.89 5.37

0.32 0.25 0.35 0.06 0.08 0.47 0.18 0.34 0.47 0.57 0.54 0.44 0.60 0.70 0.70 0.57

12.05 11.16 21.80 8.52 12.70 17.77 12.14 18.27 11.87 10.72 22.72 9.42 17.58 15.96 31.00 13.59

1.07 1.21 0.74 1.15 1.29 0.99 0.95 1.02 1.52 1.50 0.83 1.61 1.30 1.25 0.75 1.32

0.92 0.80 0.87 0.82 0.95 1.01 0.78 0.85 0.98 0.85 0.97 0.89 1.05 0.89 1.04 0.96

Peff – human effective permeability in jejunum, logarithm of the blood–brain barrier partition coefficient (log BBB), Unbprot – human plasma protein binding as percent unbound, Vd – human volume of distribution (l/kg) and RPB – blood-toplasma concentration ratio.

barrier partition coefficient (log BBB), human plasma protein binding as percent unbound (UnbProt), human volume of distribution (Vd(l/kg)) and blood-to-plasma concentration ratio (RPB) calculated by simulation plus software package. Passive transcellular permeation is diffusion across a lipid bilayer and therefore, the permeability depends on the lipophilicity of the permeant. Paracellular permeation is diffusion through the negatively charged tight junction between the intestinal epithelial cells, and was modeled by size restricted diffusion within a negative electrostatic field of-force. Transcelluar permeation dominantly contributes drug absorption when diealing with hydrophilic neutral drugs (no acid groups with pKa < 7.5, nor a base group with pKb > 7) (Obata et al., 2005) as compounds investigated. Compounds of group A (more lipophilic) compared to compounds of group B with same substituent have been expected to

13

have better permeability (greater Peff). Other factors, like polar surface area, certainly affect complex process drug permeation, but lipophilicity is without doubt determinative for transcelluar diffusion through phospholipid bilayer and retention constants reflect its role. Combing models considering different factors are recommended (Houa et al., 2006). In order to determine the influence of molecular weight (MW) or volume (V) and polar surface area on process of drug perfusion through phospholipid bilayer multiple regression analysis was performed. The best statistical quality was determined when permeability in jejunum was correlated with slope b, molecular weight and total polar surface area as additional descriptor:

Peff ¼ 1:836 b þ 0:038  TPSA þ 0:023  MW  8:209 ðr ¼ 0:770; F ¼ 5:830; p ¼ 0:011Þ

ð4Þ

These mathematical models confirm the influence of lipophilicity (and retention constants being recognized as its measure) in the absorption process, but also explain the influence of other factors regarding this complex biological process. After being absorbed, a compound entrance circulatory system. Knowledge of the partitioning behavior of the therapeutic compound in the red blood cells is important to the interpretation and understanding of the compound pharmacokinetic profile. A high partitioning may lead to accumulation of the compound in red blood cells and therefore this parameter serves as an indicator of potential hematotoxicity. All compounds investigated have ratio blood to plasma (RPB) predicted to between 0.8 and 1, which indicates no possible hematotoxicity (Paixãoa et al., 2009). Since, another epithelial barrier should be passed during drug absorption in erythrocytes; besides lipophilicity other factors (molecular weight/volume and TPSA) were considered. So, RPB was established as function of retention constants, R0M and b but this time modified by considering V and TPSA:

RPB ¼ 0:002  R0M þ 0:003  TPSA  0:007  V þ 2:330 ðr ¼ 0:846; F ¼ 10:082; p ¼ 0:001Þ

ð5Þ

RPB ¼ 0:005b þ 0:003 TPSA  0:007V þ 2:305 ðr ¼ 0:847; F ¼ 10:159; p ¼ 0:001Þ

ð6Þ

Introducing another factor in the drug partition process between blood and plasma, suggested that RPB is governed by lipophilicity but it is also affected polar surface area and volume of molecule. The binding of therapeutic agents to plasma proteins is a reverse process which occurs through a mix of electrostatic, van der Waals and hydrogen interactions and it is determined by the physicochemical properties of the compound observed. The knowledge of the unbound fraction is essential to the correlation of total plasma concentration with activity, to the potential side effects, influence how much plasma protein binding can still be tolerated and how precisely this parameter has to be tuned for a new drug entity (Kratochwil et al., 2002). This set of compound have been predicted to have small to moderate unbound percent to proteins (Unbprot), from 8.5% to 31%. Less lipophilic compounds are predicted to attach less to plasma proteins and to have higher free fraction in plasma, while compounds which are more lipophilic are expected to attach easier to plasma proteins which is consistent with results described in the literature (Láznícˇek and Láznícˇková, 1995). In order to determine which factor influence the distribution process, Unbprot was tested as functions depending of retention constants (as measures of lipophilicity) and MW or V or TPSA. The best multiple linear regression models for Unbprot and their statistical parameters are given in Table 8 and two tridimensional models are presented in Fig. 1.

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Table 8 Multiple linear regression models of Unbprot as function of different molecular descriptors and statistical parameters (r, F, p). Model R0M

Unbprot = 3.536  + 0.235  TPSA + 10.741 Unbprot = 2.765  b + 0.242  TPSA + 12.418 Unbprot = 3.280 

R0M R0M

+ 0.267  TPSA  0.067  MW + 26.388

Unbprot = 0.923  + 0.322  TPSA  0.235  V + 54.432 Unbprot = 2.679  b + 0.275  TPSA  0.07  MW + 29.508 Unbprot = 1.032  b + 0.319  TPSA  0.222  V + 53.349

r

F

p

0.913

32.502

8.7  106

0.914 0.954

33.16 40.192

7.8  106 1.5  106

0.957

43.920

9.6  107

0.961 0.961

48.777 48.651

5.4  107 5.5  107

(a) 35 30

Unbprot [%]

25 20 15 10 4 5 100

2 80

60

40

20

0 TPSA

RM0

(b) 35

Unbprot [%]

30 25 20 15 10 5 -2

20 40 -2.5

60 -3

-3.5

-4 b, slope

80 -4.5

-5

100

TPSA

Fig. 1. Multiple linear regression models of Unbprot with TPSA and (a) R0M and (b) slope b.

Volume of distribution has no direct physical or anatomical meaning, but it represents a measure of relative partitioning of a drug between plasma and the tissues. High Vd means that drug is very lipophilic and can be accumulated in fat tissues which can prolong its excretion (Ghafourian et al., 2006; Stepensky, 2011). All the compounds considered are hydrophilic neutral and hence they have small or medium vales for Vd, usually less than 2 L/kg, which is desirable pharmacokinetic property. More lipo-

philic compounds have greater values for Vd. Again compounds with greater lipophilicity (greater retention constants) are expected to have greater Vd. Compounds observed in this paper which have higher measured retention constants R0M (lower measured values for slope b) are expected to bound easier to plasma proteins and are not prone to accumulate in cells (free drug fraction, Unbprot and RPB decreases with increasing lipophilicity) which results consequently with higher volume of distribution.

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Table 9 Multiple linear regression models of Vd as function of different molecular descriptors and statistical parameters (r, F, p). Model

r

F

p

R0M

0.951

62.210

2.2  107

R0M

0.954 0.954

66.255 40.559

1.5  107 1.5  106

0.957

43.138

1.1  106

Vd = 0.025   0.013  TPSA + 1.913 Vd = 0.030  b  0.013  TPSA + 1.964 Vd = 0.028   0.014  TPSA  0.0007  MW + 1.734 Vd = 0.031  b  0.014  TPSA  0.0007  MW + 1.789

(a) 2 1.8 1.6

Vd [l/kg]

1.4 1.2 1 0.8 0.6

4

0.4 100

2 80

60

40

20

0 RM0

TPSA

(b) 2 1.8 1.6

Vd [l/kg]

1.4 1.2 1 0.8 0.6 0.4 100

-2 80

-3 60

-4

40 20

-5 b, slope

TPSA Fig. 2. Multiple linear regression models of Vd with TPSA and (a)

Multiple linear regression models which define Vd as function of retention constants, TPSA and/or MW of the analyte and their statistical parameters are shown in Table 9, while Fig. 2 describes two of these. In the case of effective central nervous system (CNS) acting drugs, the knowledge of the penetration of drugs through BBB is critical to screen potential therapeutic agents and to improve the side effect profile of drugs with peripheral activity (Hou and Xu, 2003).

R0M

and (b) slope b.

Clark proposes ‘rules of thumb’ as simple guidance concerning the molecular properties that favor brain permeation (Clark, 2003):  Rule 1: if the sum of nitrogen and oxygen (N + O) atoms in a molecule is five or less, then the molecule has a high chance of entering the brain.  Rule 2: if C log P  (N + O) > 0, then log BBB is likely to be positive. In this rule, C log P denotes the logarithm of the octanol– water partition coefficient (P) of a compound.

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0.2

0

logBBB

-0.2

-0.4

-0.6

-0.8 200 250 300 350

-2

-2.5

-3.5

-3

-4

-4.5

-5

b, slope

MW

Fig. 3. Multiple linear regression models of log BBB with slope b and TPSA.

 Rule 3: for good brain permeation, the polar surface area (PSA) of the compound should be below a certain limit. The PSA is a measure of a molecule’s hydrogen-bonding capacity. Two differing limits have been proposed: 90 Å2, versus 60–70 Å2.  Rule 4: molecular weight (MW) should be kept below 450 to facilitate brain permeation.  Rule 5: a log D value in the range 1–3 is recommended. For neutral compounds, log D is equal to log P. Most of the compounds observed do not violate none of this rule and have been predicted to have log BBB around 0 (compounds A4, A5 and A7) for which good permeation is predicted, while some do not follow rule 2 and poor or none permeation through blood– brain barrier is expected (B6, B7). More hydrophobic compounds are prone to permeate in CNS easily as it is reported in literature (Hou and Xu, 2003). In order to determine other factors which affect the blood–brain permeation proces, multiple regression models were tested and the best statistical parameters were found when log BBB was presented as function of slope b and MW (Fig. 3) and as as function of slope b, TPSA and MW:

log BBB ¼ 0:181  b þ 0:003  MW  1:882 ðr ¼ 0:801; F ¼ 11:630; p ¼ 0:001Þ

ð7Þ

log BBB ¼ 0:178  b þ 0:001  TPSA þ 0:004  MW  1:891 ðr ¼ 0:809; F ¼ 7:600; p ¼ 0:004Þ

bilayer (intestinal, erythrocyte or blood–brain barrier) is governed by physico-chemical properties among which lipophilicity (measured with retention constants) is the leading although other factors are involved. Multiple regression models introduced total polar surface area and molecular weight/volume as additional descriptors of molecular bulkiness in the drug absorption process for this set of compounds. Drug distribution (volume of distribution, drug free fraction in blood) is also affected by hydrophobic behavior (and retention constants consequently) of these compounds since more lipophilic compounds are prone to bound strongly to proteins and to have higher volume of distribution. By introducing total polar surface area and molar weight/volume as additional descriptors, drug distribution models were refined. Having in mind the biological importance of benzimidazole and benztriazole derivatives, it is important to eliminate as early possible as possible those compounds which pharmacokinetic properties are not satisfactory. The best pharmacokinetic behavior was recognized in compounds A4, A5 and A7 (Table 1) which are recommended for further biological tests. Acknowledgements This work was performed within the frameworks of the research projects: No. 114-451-2048/2011, supported by the Provintial Secretariat for Science and Technological Development of Vojvodina. The authors thank Simulation plus for a free trial.

ð8Þ

These results are consisted with the results of Kaliszan and Markuszewski (1996) who reestablished the correlation of log BBB with log P and refined it, employing the molecular weight as an additional descriptor of molecular bulkiness. 4. Conclusion Benzimidazole/benztriazole derivatives subjected to retention behavior characterization were observed as homologous series. Retention constants, R0M (intercept) and b (slope) of the TLC equation, were recognized as function of lipophilicity, total polar surface area and volume of the analyte under same chromatographic conditions. Absorption processes calculated by simulation plus software package indicated that passage through phospholipid

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