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Iranian Journal of Mathematical Chemistry, Vol. 4, No. 1, March 2013, pp. 91 109
QSAR Modeling of Antimicrobial Activity with Some
Novel 1,2,4-
Triazole Derivatives, Comparison with
Experimental Study

AHRA ROSTAMI , ABBAS AMINI MANESH AND LEILA SAMIE
Department of Chemistry, Payame Noor University, I. R. of Iran (Received December 31, 2012; Accepted March 1, 2013) ABSTRACT
Our study performed upon an extended series of 28 compounds of 1,2,4-triazole derivatives
that demonstrate substantial in vitro antimicrobial activities by serial plate dilution method,
using quantitative structure-activity relationship (QSAR) methods that imply analysis of
correlations and multiple linear regression (MLR); a significant collection of molecular
descriptors was used e.g., Edge adjacency indices, GETAWAY , 3D-MoRSE , Burden
eigenvalues and Constitutional descriptors. The obtained multi-parametric models when a
different class of molecular descriptors was used led to three correlation coefficients closed to
0.900, 0.896 and 0.901 respectively. Results indicated this is no significant statistical
differences between calculated activities of these compounds with laboratory methods thus,
the obtained models allowed us to predict antimicrobial activity of substituted 1,2,4-triazole
derivatives .
Keywords: Quantitative structure-activity relationship, Multiple linear regression,
Antimicrobial activity, 1,2,4-triazole derivatives
1. INTRODUCTION
The steadily increasing bacterial resistance to existing drugs is a serious problem in antibacterial therapy and necessitates continuing research into new classes of antibacterials. Various 1,2,4-triazole derivatives have been reported to possess diverse types of biological properties such as antibacterial [1], antifungal [2], anti-inflammatory [3] antihypertensive [4], antiviral [5], antileishmanial [6] and antimigraine activities [7]. A thorough literature • Corresponding author. (Email: Rostami@pnu.ac.ir) 92 Z. ROSTAMI, A. AMINI MANESH AND L. SAMIE
survey reveals that presence of 4-substituted thio phenoxy and 4-methyl sulphonyl phenoxy
moieties is an important structural feature of wide variety of synthetic drugs [8]. It has been
established that introduction of 4-methyl mercapto phenyl and 4-methyl sulphonyl phenyl
groups to different heterocycles has yielded many biologically active compounds endowed
with wide spectrum of pharmacological and antimicrobial activities [9]. It is well known
that the N-bridged heterocycles derived from 1,2,4-triazoles find applications in the field of
medicine agriculture and industry. A large number of triazolothiadiazoles and
triazolothiadiazines have been reported to possess CNS depressant, antibacterial,
antifungal, antitumour, anti-inflammatory, herbicidal, pesticidal and insecticida properties
[10]. Therefore, it was envisaged that chemical entities with both 1,2,4-triazolo[3,4-b]-
1,3,4-thiadiazoles/1,2,4-triazolo[3,4-b]-1,3,4-thiadiazines and 4-sulphursubstituted phenyl
moieties, containing aryl ether linkage would result in compounds of interesting biological
activities. In continuation of Karabasanagouda et al research program on the synthesis of
novel heterocyclic compounds exhibiting biological activity, it was thought to be
interesting to synthesize compounds containing the features, namely, 1,2,4-triazole moiety
fused with the 1,3,4-thiadiazole/1,3,4-thiadiazine rings, in addition to have a sulphur
substituted phenoxy group and to study their antimicrobial activities. The present study
describes the synthesis of hither to unreported 6-aryl-3-{(4-thiosubstituted/methyl
sulphonyl phenoxy) methyl}-1,2,4-triazolo[3,4-b]-1,3,4-thiadiazoles (6a-s) and 6-aryl-3-
{(4-thiosubstituted/methyl sulphonyl phenoxy) methyl}-7H-1,2,4-triazolo[3,4-b]-1,3,4-
thiadiazines (7a-i) and evaluation of their anti bacterial and antifungal activities [11].
Quantitative structure-activity relationships (QSAR), as a major factor in drug design, are
mathematical equations relating chemical structure to their biological activity [12]. This
work studies the role of different structural parameters in the case of a series of pyrimidinic
congeners with antimicrobial activity: the objective was to assess electronic, transportation
and topological effects of ideal substitutions to express while binding to target receptors. In
the present study, we aimed to develop QSAR equations for the antimicrobial activity of a
series of 1,2,4-triazole drugs. We therefore used six types of molecular descriptors to derive
a quantitative relation between the antimicrobial activity and structural descriptors obtained
by multiple linear regression (MLR) for the modeling and prediction of antimicrobial
activities of 1,2,4-triazole derivatives.
2.
ABOUT 1,2,4-TRIAZOLE DERIVATIVES
2.1 Chemistry
The reaction sequences employed for synthesis of title compounds are shown in Fig. 1. The
key intermediate, ethyl [4-(thioalkyl) phenoxy] acetates (2a-b) was prepared by treating
ethyl chloroacetate with 4-(thioalkyl) phenols (1a- b) in boiling dry acetone in presence of
QSAR Modeling of Antimicrobial Activity 93 potassium carbonate. The compound, ethyl [4-(methyl sulphonyl phenoxy] acetate (2c) was
obtained by the oxidation of ethyl [4-(methyl thio) phenoxy] acetate (2a) with 30%
hydrogen peroxide in acetic acid. These esters (2a-c) were conveniently converted to 2-[4-
(thioalkyl/methyl sulphonyl phenoxy] acetohydrazides (3a-c) by refluxing it with hydrazine
hydrate in methanol. The compounds 3a-c on reaction with carbon disulphide in methanolic
potassium hydroxide yielded corresponding potassium dithiocarbazates (4a-c) in good
yield. The required 4-amino-5-{[thioalkyl/methyl sulphonyl phenoxy) methyl}-4H-1,2,4-
triazole-3-thiols (5a-c) were synthesized by refluxing 4a-c with aqueous hydrazine hydrate.
Condensation of 5a-c with various aromatic carboxylic acids in presence of boiling
phosphorous oxychloride yielded 6-aryl-3-{(4-thioalkyl/methyl sulphonyl phenoxy)
methyl}-1,2,4-triazolo[3,4-b]-1,3,4-thiadiazoles (6a-s) and with various phenacyl bromides
in refluxing ethanol gave 6-aryl-3-{(4-thioalkyl/methyl sulphonyl phenoxy) methyl}-7H-
1,2,4-triazolo[3,4-b]-1,3,4-thiadiazines (7a-i) in good yield. The structural assignments to
new compounds were based on their elemental analysis and spectral (IR, 1H NMR, 13C
NMR and mass) data. The characterization data of all the new compounds are summarized
in Table 1[11].
2.2
Biological Activities
2.2.1 Antibacterial Studies
The newly synthesized compounds were screened for their antibacterial activity against Escherichia coli, Staphylococcus aureus, Pseudomonas aeruginosa and Klebsiella pneumonia bacterial stains by serial plate dilution method [13]. Serial dilutions of the drug in Muller-Hinton broth were taken in tubes and their pH was adjusted to 5.0 using phosphate buffer. A standardized suspension of the test bacterium was inoculated and incubated for 16-18 h at 37ºC. The minimum inhibitory concentration (MIC) was noted by seeing the lowest concentration of the drug at which there was no visible growth. 94 Z. ROSTAMI, A. AMINI MANESH AND L. SAMIE
CS2/Methanolic KOH OCH2CONHNH2CS -S-K+ Figure 1. Preparation of 1,2,4-triazolothiadiazoles (6a-s) and 1,2,4-trazolothiadiazines (7a-i).
QSAR Modeling of Antimicrobial Activity 95 Table 1. Characterization data, antibacterial and antifungal activities of title compounds.
C17H12Cl2N4OS2 371 C18H14Cl2N4OS2 437 C17H13ClN4O3S2 420 C17H13ClN4O3S2 420 2,3-Dichloro-C6H4 SO 2-OH-benzamide SCH3 2,4-Dichloro-C6H3 C18H14Cl2N4OS2 437 C18H15Cl2N4OS2 402 C19H16Cl2N4OS2 451 C18H15ClN4O3S2 434 Α ‐The antibacterial activity against Escherichia coli, Staphylococcus aureus, Pseudomonas aeruginosa and Klebsiella pneumonia. Β ‐ The antifungal activity against Penicillium marneffei. C ‐ The antifungal activity against Aspergilus flavus, Aspergilusfumigatus, and Trichophyton mentagrophytes. 96 Z. ROSTAMI, A. AMINI MANESH AND L. SAMIE
A number of antimicrobial discs are placed on the agar for the sole purpose of producing zones of inhibition in the bacterial lawn. Twenty milliliters of agar media was
poured into each Petri dish. Excess of suspension was decanted and plates were dried by
placing in an incubator at 37ºC for an hour. Using a punch, wells were made on these
seeded agar plates and minimum inhibitory concentrations of the test compounds in
dimethylsulfoxide (DMSO) were added into each labeled well. A control was also prepared
for the plates in the same way using solvent DMSO. The Petri dishes were prepared in
triplicate and maintained at 37ºC for 3-4 days. Antibacterial activity was determined by
measuring the diameter of inhibition zone. Activity of each compound was compared with
ciprofloxacin as standard [14]. The minimum inhibitory concentration (MIC) was
determined for 6a-7i and the results are summarized in Table 1.
2.2.2 Antifungal Studies
Newly prepared compounds were screened for their antifungal activity against Aspergilus
flavus
, Aspergilus fumigatus, Penicillium marneffei and Trichophyton mentagrophytes in
DMSO by serial plate dilution method [14]. Sabourands agar media was prepared by
dissolving peptone (1 g), D-glucose (4 g) and agar (2 g) in distilled water (100 ml) and
adjusting the pH to 5.7. Normal saline was used to make a suspension of spore of fungal
strains for lawning. A loopful of particular fungal strain was transferred to 3 ml saline to
get a suspension of corresponding species. Twenty milliliters of agar media was poured into
each Petri dish. Excess of suspension was decanted and plates were dried by placing in
incubator at 37ºC for 1 h. Using a punch, wells were made on these seeded agar plates
minimum inhibitory concentrations of the test compounds in DMSO were added into each
labeled well. A control was also prepared for the plates in the same way using solvent
DMSO. The Petri dishes were prepared in triplicate and maintained at 37ºC for 3-4 days.
Antifungal activity was determined by measuring the diameter of inhibition zone. Activity
of each compound was compared with cyclopiroxolamine as standard [15]. The minimum
inhibitory concentration (MIC) was determined for 6a-7i and the results are summarized in
Table 1.
3. MODELING
QSAR Model
QSAR expresses a multivariate mathematical relationship between a set of physicochemical properties or descriptors, {x , and a experimental function or biological activity, {y . The QSAR relationship is expressed as a mathematical model, quantitative in the sense that it is QSAR Modeling of Antimicrobial Activity 97 used to account for the observed activity. For a compound i, the linear equation that relates molecular properties, x , x ,. to the desired activity, y, is: y = x b +x b + . + x b + e Expressing the previous equation in a compact form for the general case of n selected descriptors, the QSAR equation results into: y = ∑ x b +e where b are the linear slopes that express the correlation of the particular molecular property with the activity of the compound i; and is a constant. The slopes and the constant are often calculated using regression analysis. Biological activity is usually related to log (C), where C is the molar concentration which determines a constant biological response. In this work, only the models with a single dependent variable, or y observation will be considered, although some models can deal with several biological activities. The strength of a QSAR model depends on the quality of this variable. The independent variables, so-called descriptors, are usually physicochemical properties that describe some aspects of the chemical structure, which may be either experimentally or theoretically determined. The improper choice of independent variables can result in poor QSAR models. In a typical QSAR study, a large number of descriptors can be used; however, attention must be paid to overfitting, because with enough parameters any model can be successfully correlated. The final QSAR equation seeks to find the smallest number of descriptors that can adequately model the activity of the compounds in the study. The maximum recommended ratio is a single independent variable to compounds [16]. Actual drug design methods quantify biological activity depending on the molecular structure. Usually it is accomplished by modifying a reference structure through grafting X
substituent. This leads to a series of bioactive compounds called effectors (E), or more
recently ligands (L). L biological activities are determined and then correlated with the
structure using correlation analysis methods, multi-linear regression equations like
Equation 1 [17].
3.2 Computational Details
3.2.1 Moleculardescriptores
The considered structures to be analyzed starting from the general structure shown in Figure 1 presented in Table1. Upon these structures, molecular modeling was performed using the HyperChem 7.1 programme package (MM+ programme) [18] with 0.05 kcal/mol 98 Z. ROSTAMI, A. AMINI MANESH AND L. SAMIE
RMS gradient. Optimization process comprised the optimized potentials for liquid simulations (OPLS). Conformational analysis was performed with Conformational Search from HyperChem 7.1 package program [18]. Antimicrobial activities will be correlated with molecular descriptors from Table 2. To calculate these descriptors, DRAGON 5.4 programme [19] was used; it allows
importing 3D structures from the HyperChem package [18].
Table 2. Molecular descriptors for antimicrobial activity considered in this work.
No. Symbol Definition Eigenvalue 11 from edge adj. matrix eeig11r weighted by resonance integrals. Eigenvalue 12 from edge adj. matrix Edge adjacency indices weighted by resonance integrals. Eigenvalue 09 from edge adj. matrix eeig09x weighted by resonance integrals. Standardized information content on the leverage equality. R maximal index / weighted by atomic GETAWAY descriptors R maximal autocorrelation of lag 6 / weighted by atomic masses. R maximal autocorrelation of lag 3 / 3D-MoRSE – signal 20 / weighted by mor20e atomic Sanderson electronegatives. 3D-MoRSE – signal 18 / weighted by 3D-MoRSE descriptors atomic van der waals volumes. 3D-MoRSE – signal 20 / weighted by mor20p atomic polarizabilities. Lowest eigenvalue n. 2 of Burden matrix / weight by atomic masses. Burden eigenvalues Highest eigenvalue n. 5 of Burden matrix / weight by atomic polarizabilities. Mean atomic van der waals volume Constitutional descriptors (scaled on Carbon atom) QSAR Modeling of Antimicrobial Activity 99 3. 2.2 Stepwise Multiple Linear Regression
DRAGON provides 1664 molecular descriptors and in order to select the predominant parameters that significantly affect the antimicrobial activity of the compounds, we employed the statistic software SPSS, taking MIC as the dependent variable and every candidate descriptor calculated above as an independent variable to perform the stepwise multiple linear regression. Therefore we derived some molecular descriptors from the DRAGON programme, such as Edge adjacency indices, GETAWAY descriptors, 3D-MoRSE descriptors, Burden eigenvalues and Constitutional descriptors. The mentioned descriptors are presented in Table 2 and their numerical values are listed in Tables 3, 4 and 5 respectively. In the next step, QSAR equation was made through the multiple linear regression method utilizing the three MLR models by thirteen calculated descriptors. 3.2.3 QSAR Equation Analysis and Model Validation
The attempt to obtain multi-linear mathematical models using molecular descriptors led to
improvement of correlation coefficients and by investigation the biological activities it can
been classified them in three groups (class A: the antibacterial activity against Escherichia
coli
, Staphylococcus aureus, Pseudomonas aeruginosa and Klebsiella pneumonia, class B:
the antifungal activity against Penicillium marneffei and class C: the antifungal activity
against Aspergilus flavus, Aspergilus fumigatus, and Trichophyton mentagrophytes. Finally,
multiple linear regression was used to derive the QSAR equation and the three best QSAR
models obtained (for classes A, B and C) with the molecular descriptors (Tables 3, 4 and 5
respectively) are given below together with the statistical parameters of the regression:
Model A:
Log X = − 44.664 (± 5.864)+ 3 858 eeig r − .
1 415(± 0.137)mor20e + 23.672 (± 3.043)belm2 −1.502(± 0.293) eeig r −1.210(± 0 373 mor v − .
R = 0.
900 SE = 0.097, F = 28.
Log Y = − 38 305 )+2.424(± 0.307) eeig r −1.271(± 0 224 31 289(± 4.
+ 4.092(± 0.704)rtm + − 5.661(±1 068 )behp5−8 462 100 Z. ROSTAMI, A. AMINI MANESH AND L. SAMIE
Log Z = − )+ .4032(± 0 436 eeig r −0 755 )r3u ++ .
)belm2−1 600 SE = .
097 F = 28 where N is the number of compounds included in the model, R2 is the square correlation coefficient, SE is the standard error, F is the Fisher statistic ratio and Q2Loo is the leave one out cross-validation. As can be seen, the MLR models have good statistical quality with low prediction error and although the equations in models A, B and C do not reproduce the absolute values of the experimental data, they can predict the activity of the drug. The mentioned descriptors in Table 2 are usually used in QSAR analysis to judge how much the model is reliable. In order to check the reliability of the proposed equation, the observed versus predicted activities MIC values according to the QSAR equation using molecular descriptors in each class are plotted in Figures 2, 3 and 4 respectively. As can be seen, the experimental values are in good agreement with the predicted values and these figures show a linear regression between the predicted and observed values for log MIC using molecular descriptors, indicating the reliability of the equations. Tables 3, 4 and 5 show the experimentally determined activity. The values of the key features of the molecular descriptors on the basis of our calculation in models A, B and C are also listed in Tables 3, 4 and 5 respectively. In addition, with the number of the descriptors, various optimal combinations of the descriptors and some important statistics such as the correlation coefficient (R), R Square, Adjusted R Square and Standard Error of the Estimate are listed in Table 6. QSAR Modeling of Antimicrobial Activity 101
Table 3. Experimentally determined activity and molecular descriptors used for multi-
linear regressions used in model A. The descriptions of the codes are given in Table 2. 102 Z. ROSTAMI, A. AMINI MANESH AND L. SAMIE
Table 4. Experimentally determined activity and molecular descriptors used for multi-
linear regressions used in model B. The descriptions of the codes are given in Table 2. QSAR Modeling of Antimicrobial Activity 103 Table 5. Experimentally determined activity and molecular descriptors used for multi-
linear regressions used in model C. The descriptions of the codes are given in Table 2. 104 Z. ROSTAMI, A. AMINI MANESH AND L. SAMIE
RESULTS AND DISCUSSION
The investigation of antibacterial and antifungal screening data revealed that all the tested
compounds 6a-s and 7a-i showed moderate to good inhibition at 1.56-25 mg/ml in DMSO.
The compounds 6a, 6c, 6d, 6e, 6f, 6g, 6h, 6j, 6s, and 7b-d showed comparatively good
activity against all the bacterial strains. The good activity is attributed to the presence of
pharmacologically active ─ CH3 , ─ OCH3 , ─ NH2 and 2,3- dichloro groups attached to
phenyl group at position 6 of the thiadiazole ring. Introduction of aryl moiety carrying
phenyl, 2,3-dichloro, 4-chloro, and 2-hydoxy-4-amide groups at position 6 of thiadiazine
caused enhanced activity. The presence of ─ SCH3 and ─ SC2H5 groups at position 4 of
phenoxy group caused good antibacterial activity while methyl sulphonyl group caused
decrease in activity against most of the strains. The compounds 6o, 6p, 6q, 6r, and 7g-h,
exhibited moderate activity compared to that of standard against all the bacterial strains.
This may be due to presence of methyl sulphonyl group in position 4 of phenoxy moiety
[11].
The
6a, 6c, 6d, 6e, 6f, 6g, 6h, 6j, 6s, and 7b-d showed comparatively
good activity against all the fungal strains. The structure of these compounds contains
biologically active ─ CH3 , ─ OCH3 , ─ NH2, 2,3-dichloro groups attached to phenyl group
in position 6 of the thiadiazole ring and aryl moiety carrying phenyl, 2,4-dichloro, 4-chloro,
and 2-hydoxy- 4-amide groups, in position 6 of thiadiazine. The compounds 6o, 6p, 6q, 6r,
and 7g-h exhibited moderate activity compared to that of standard against T.
mentagrophytes
, A. flavus, and A. fumigatus. Results of antifungal screening showed that
the presence of S ─ CH3 and S ─ C2H5 groups at position 4 of phenoxy group caused
increased activity. It has been observed that the thiadiazole derivatives are found to be more
active than thiadiazines [11].
In addition, this work shows an extensive study performed by means of molecular modeling upon a series of 28 compounds of 1,2,4-triazole derivatives with antimicrobial activities. Molecular modeling methods (molecular mechanics and conformational analysis) and QSAR methods based on correlation analysis and multiple linear regression use a large number of molecular descriptors calculated with the HyperChem and DRAGON programme packages. The correlations performed for the whole set provided the optimal equations for different numbers of descriptors in the range of 1-6. Table 6 shows the values of R, R Square, Adjusted R Square and Standard Error of the Estimate corresponding to the number of variables in the regression model. It suggests that the best one-descriptor model with highest impact is eeig11r which is defined as Eigenvalue 11 from edge adj. matrix weighted by resonance integrals representing Edge adjacency indices. Subsequent addition of variables produces monotonously increasing QSAR Modeling of Antimicrobial Activity 105 values of R, R Square, Adjusted R Square and decreasing values Standard Error of the Estimate and the break point is not clearly defined. We decided to select the best models to be the one having the smallest number of parameters and satisfactory statistical parameters (the models A, B and C with 6 descriptors). They allowed obtaining essential data regarding the imposed structural requirements at molecular level in order to improve the antimicrobial potentiality of the studied compounds. icted log
red
P 0.9

Observed log X
Figure 2. The plot of calculated vs. observed antimicrobial activity of 1,2,4-triazole
derivatives in model A. icted l
red
P 0.9

Observed log Y
Figure 3. The plot of calculated vs. observed antimicrobial activity of 1,2,4-triazole
derivatives in model B. 106 Z. ROSTAMI, A. AMINI MANESH AND L. SAMIE
Observed log Z
Figure 4. The plot of calculated vs. observed antimicrobial activity of 1,2,4-triazole
derivatives in model C. 5. CONCLUSION
The research study reports the successful synthesis and antimicrobial activity of new 1,2,4-triazolothiadiazoles and 1,2,4-trazolothiadiazines carrying 4-methyl/ethyl thio and methyl sulphonyl phenoxy moieties at position 3. The antimicrobial activity study revealed that all the compounds tested showed moderate to good antimicrobial activity against pathogenic strains. Structure-biological activity relationship of title compounds showed that the presence 4-thioalkyl phenoxy groups at position 3 and biologically active groups like ─ CH3 , ─ OCH3 , ─ NH2, and 2,3-dichloro groups at aryl moiety attached to position 6 of title compounds are responsible for increased antimicrobial activity in newly synthesized title compounds. In this QSAR study, the proposed QSAR model, due to the high predictive ability, can therefore act as a useful aid to the costly and time consuming experiments for determining the maximal antimicrobial activities. We first tried to identify descriptors trends which lead to antimicrobial activity based on the proposed QSAR equation. We have obtained three mathematical models between descriptors and the antimicrobial activities with statistical analysis and the models (A, B and C) have shared six Edge adjacency indices, GETAWAY descriptors, 3D-MoRSE descriptors, Burden eigenvalues and Constitutional descriptors class descriptors (Table 2). As mentioned before, Eigenvalue 11 from edge adj. matrix weighted by resonance integrals (eeig11r) is the most important variable for predicting antimicrobial activity. The remaining five descriptors involve the QSAR Modeling of Antimicrobial Activity 107 summations of different functions corresponding to the different fragment lengths and with resonance integrals, atomic polarizabilities, atomic Sanderson electronegativities, atomic van der Waals volumes and atomic masses as the weighting parameter (Table 2). Since these molecular descriptors are the main factors which influence the antimicrobial activities of 1,2,4-triazole derivatives, it is necessary to explore such descriptors. Meanwhile, studying their applicability could lead to a vital improvement in QSAR studies. Therefore, obtained data by adequate designed QSAR studies allow observing aspects and essential molecular characteristics to have an increased biological activity, suggesting certain structural requirements for an increased antimicrobial potential. Our results open very interesting perspectives regarding 1,2,4-triazole derivatives. Finally the QSAR model could be helpful to predict the antimicrobial activities of compounds by calculating the descriptors involved in the QSAR equation. Table 6. The statistics of various combinations of the descriptors.
eeig11r, mor20e, belm2 eeig11r, mor20e, belm2, eeig12r eeig11r, mor20e, belm2, eeig12r, mor18v eeig11r, mor20e, belm2, eeig12r, mor18v, ish eeig11r, mor20p, belm2 eeig11r, mor20p, belm2, rtm+ eeig11r, mor20p, belm2, rtm+, behp5 eeig11r, mor20p, belm2, rtm+, behp5, Mv eeig11r, eeig09x eeig11r, eeig09x, r6m+ eeig11r, eeig09x, r6m+, r3u+ eeig11r, eeig09x, r6m+, r3u+, belm2 eeig11r, eeig09x, r6m+, r3u+, belm2, eeig12r ACKNOWLEDGEMENT The author is grateful to the payame Noor University,
Hamedan, for providing facilities to conduct this study.
108 Z. ROSTAMI, A. AMINI MANESH AND L. SAMIE
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PRACTICAL MANAGEMENT Plantar Fasciitis David D. Dyck, Jr., DO,* and Lori A. Boyajian-O'Neill, DO, FAOASM*† dial tubercle of the calcaneus. The plantar fascia extends from (Clin J Sport Med 2004;14:305–309) this tubercle to the metatarsal heads, forming the longitudinalarch that provides support for the foot. Excessive load or ten-sion on this aponeurosis can lead to the condition commonly

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Lewis-Manning Hospice One of the well-recognized reasons for deficiencies in the management of pain is inadequate pain assessment and lack of knowledge about the Analgesic Lewis-Manning Hospice. The Patient in Pain. Module 1. Lewis-Manning Hospice Aims of the presentation is to improve your knowledge about:  The Pain experience  Pain Assessment  The Analgesic Ladder