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Synthesis, antimicrobial evaluation and QSAR studies of monochloroacetic acid derivatives
⁎Corresponding author. Mobile: +91 9416649342. naru2000us@yahoo.com (Balasubramanian Narasimhan)
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Received: ,
Accepted: ,
This article was originally published by Elsevier and was migrated to Scientific Scholar after the change of Publisher.
Abstract
In the present study, a series of monochloroacetic acid derivatives were synthesized and characterized by physicochemical and spectral means. Antimicrobial evaluation was performed against the Gram-positive bacteria: Staphylococcus aureus MTCC 2901, Bacillus subtilis MTCC 2063, Gram-negative bacterium: Escherichia coli MTCC 1652 and the fungal strains: Candida albicans MTCC 227 and Aspergillus niger MTCC 8189 using the tube dilution method. Results of antimicrobial screening indicated that compounds 10 and 17 were found to be most potent against C. albicans, having antifungal activity comparable to the standard drug fluconazole. mt-QSAR models indicated the importance of the steric parameter, molar refractivity (MR) in describing antibacterial and antimicrobial activities and the electronic parameter, total energy (Te) in describing antifungal activity of synthesized monochloroacetic acid derivatives.
Keywords
Monochloroacetic acid
Antimicrobial activity
QSAR
1 Introduction
It has been observed that during the last 10 years many major pathogenic bacteria and parasites have acquired resistance toward chemotherapeutic agents in market. This has raised fears that infectious diseases may once again become a major cause of death in developing/developed countries. Now, there is a need to give serious consideration toward the development of novel chemotherapeutic agents to combat multi-drug resistant (MDR) strains (Kulandaivelu et al., 2011).
In previous papers, we described the preparation and antimicrobial properties of derivatives of simple organic acids viz. sorbic acid (Narasimhan et al., 2003), cinnamic acid (Narasimhan et al., 2004), anacardic acid (Narasimhan and Dhake, 2006a), veratric acid (Narasimhan et al., 2009), myristic acid (Narasimhan et al., 2006b), caprylic acid (Chaudhary et al., 2008), anthranilic acid (Mahiwal et al., 2012) and dodecanoic acid (Sarova et al., 2011). The antibacterial potential of monochloroacetic acid was reported by Poth and Slattery (1947). Monochloroacetic acid was also used for the treatment of warts (Steele et al., 1988).
Quantitative structure–activity relationship (QSAR) studies are indubitably of great importance in modern chemistry and biochemistry. To obtain a significant correlation, it is essential that appropriate descriptors are employed, whether they are theoretical, empirical or derived from readily available experimental characteristics of structures. Many descriptors reflect simple molecular properties and can thus provide insight into the physicochemical nature of the activity under consideration (Thakur et al., 2004).
Prompted from the above findings and in continuation of efforts in exploring the antimicrobial potential of organic acids (Kumar et al., 2010, 2012; Judge et al., 2012a,b; Narang et al., 2012a,b), in the present study we hereby describe the synthesis, antimicrobial evaluation and QSAR studies of a series of monochloroacetic acid derivatives for the first time.
2 Materials and methods
2.1 Instrumentation
All reagents and solvents used in study were of analytical grade and procured locally. The progress of the reaction was monitored by TLC and the products were purified through recrystallization and the purity of the compounds was checked by thin layer chromatography (TLC) performed on a silica gel G coated plate. The spectral studies, IR and NMR were determined by standard methods. Infra red (IR) spectra were recorded in KBr on a Perkin–Elmer spectrometer RX-I instrument and were recorded in cm−1. The 1H and 13C NMR spectra were recorded in DMSO-d6 on a Brucker DRX-300 FTNMR instrument. Elemental analysis was performed on a Perkin–Elmer 2400 C, H, N analyzer. Mass spectra were taken on a Waters Micromass Q-ToF Micro instrument.
2.1.1 General procedure for the synthesis of Schiff bases of monochloroacetic acid (1–17)
Monochloroacetic acid (0.01 M) and propanol (0.01 M) were refluxed in the presence of sulfuric acid (catalyst) for 3–4 h to yield propyl 2-chloroacetate. Propyl 2-chloroacetate (0.01 M) was refluxed with hydrazine hydrate (0.01 M) and ethanol was taken as the solvent to obtain 2-chloroacetohydrazide. Reaction mixture was cooled in icecold water and kept for evaporation. 2-Chloroacetohydrazide was further treated with different aromatic aldehydes to yield the title compounds (1–17). Schiff bases of monochloroacetic acid were filtered, dried and recrystallized from ethanol.
2.1.2 General procedure for the synthesis of esters of monochloroacetic acid (18–29)
Monochloroacetic acid (0.01 M) was refluxed with an appropriate (different) alcohol (0.01 M) in the presence of few drops of sulfuric acid for 3–4 h. The reaction mixture was then cooled in icecold water and neutralized with sodium bicarbonate solution (0.5 M) followed by the extraction of ester with ether (50 ml). The ether layer was separated which on evaporation yielded the ester derivatives (18–29) of monochloroacetic acid.
Compound 1: IR (KBr pellets) cm−1: 714 (C–Cl str., aromatic), 2975 (C–H Str., CH2), 1609 (C⚌O str., sec. amide), 960 (N–N str., NH–N), 1718 (C⚌N str., N⚌CH), 3075 (C–H str., phenyl); 1H NMR (DMSO): 7.136–7.842 (m, 4H, ArH), 4.378 (s, 2H, CH2Cl), 8.472 (s, 1H, N⚌CH).
Compound 2: IR (KBr pellets) cm−1: 718 (C–Cl str., aromatic), 2988 (C–H Str., CH2), 1622 (C⚌O str., sec. amide), 976 (N–N str., NH–N), 1718 (C⚌N str., N⚌CH), 3083 (C–H str., phenyl), 1334 (C–N str., aryl tertiary amine); 1H NMR (DMSO): 6.613–8.811 (m, 4H, ArH), 2.808 (s, 6H, N–CH3), 9.678 (s, 1H, N⚌CH).
Compound 8: IR (KBr pellets) cm−1: 724 (C–Cl str., acyclic), 2970 (C–H Str., CH2), 957 (N–N str., NH–N), 1630 (C⚌O str., Sec. amide), 1246 (C–O–C str., Ar–O–CH3), 1654 (C⚌N str., N⚌CH), 1171 (C⚌O str., phenol), 1H NMR (DMSO): 6.632–7.347 (m, 4H, ArH), 4.328 (s, 2H, CH2Cl), 8.415 (s, 1H, N⚌CH), 1.42 (t, 3H, CH3 of OC2H5), 4.157 (m, 2H, CH3 of OC2H5).
Compound 9: IR (KBr pellets) cm−1: 520 (C–Br str., phenyl), 697 (C–Cl str., acyclic), 1625 (C⚌N str., N⚌CH), 3448 (N–H str., amide), 955 (N–N str., NH–N), 2955 (C–H Str., CH2), 3100 (C–H str., phenyl); 1H NMR (DMSO): 7.443–8.355 (m, 4H, ArH), 8.669 (s, 1H, N⚌CH).
Compound 16: IR (KBr pellets) cm−1: 3432 (N–H str., amide), 1603 (C⚌N str., N⚌CH), 746 (C–Cl str.), 976 (N–N str., NH–N), 2970 (C–H asym str., CH⚌CH); 1H NMR (DMSO): 7.138–7.319 (m, 5H, ArH), 6.551 (t, CH⚌CH), 7.034 (s, 1H, NH), 7.500 (d, 1H, N⚌CH).
Compound 17: IR (KBr pellets) cm−1: 718 (C–Cl str., aromatic), 2982 (C–H Str., CH2), 1623 (C⚌O str., sec. amide), 984 (N–N str., NH–N), 1702 (C⚌N str., N⚌CH), 3064 (C–H str., phenyl), 1531 (NO2 str.); 1H NMR (DMSO): 7.893–8.546 (m, 4H, ArH), 8.688 (s, 1H, N⚌CH).
2.2 In vitro antimicrobial activity
The antimicrobial activity of the synthesized compounds was tested against Gram-positive bacteria: Staphylococcus aureus MTCC 2901, B. subtilis MTCC 2063, Gram-negative bacterium: Escherichia coli MTCC 1652 and fungal strains: Candida albicans MTCC 227 and Aspergillus niger MTCC 8189 using the tube dilution method (Cappucino and Sherman, 1999). Dilutions of test and standard compounds were prepared in double strength nutrient broth—I.P. (bacteria) or Sabouraud dextrose broth I.P. (fungi) (Pharmacopoeia of India, 2007). The samples were incubated at 37°C for 24 h (bacteria), at 25°C for 7 d (A. niger), and at 37°C for 48 h (C. albicans), and the results were recorded in terms of minimum inhibitory concentration (MIC).
2.3 QSAR studies
The structures of monochloroacetic acid derivatives (1–29) were first pre-optimized with the Molecular Mechanics Force Field (MM+) procedure included in Hyperchem 6.03 (Hyperchem 6.0, 1993) and the resulting geometries were further refined by means of the semiempirical method PM3 (Parametric Method-3). We chose a gradient norm limit of 0.01 kcal/A° for the geometry optimization. The lowest energy structure was used for each molecule to calculate physicochemical properties using TSAR 3.3 software for Windows (TSAR 3D Version 3.3, 2000). Further, the regression analysis was performed using the SPSS software package (SPSS for Windows, 1999).
3 Results and discussion
3.1 Chemistry
Monochloroacetic acid derivatives (1–29) were synthesized as outlined in Scheme 1. Initially, propyl 2-chloroacetate was synthesized using the reaction between propanol and monochloroacetic acid in the presence of sulfuric acid. The ester was then reacted with hydrazine hydrate in the presence of ethanol and yielded 2-chloroacetohydrazide. The Schiff bases (1–17) were then synthesized by the reaction of 2-chloroacetohydrazide and the corresponding aromatic aldehydes in the presence of small amount of glacial acetic acid. Ester derivatives (18–29) were synthesized by the reaction of monochloroacetic acid with different alcohols in the presence of sulfuric acid. The physicochemical properties of the synthesized compounds are presented in Table 1. The structures of the synthesized compounds were also confirmed by the IR and 1H and 13C NMR spectral analysis which were in full agreement with their structures. Further, the elemental analysis and mass spectra also supported the formation of title compounds. 13C NMR, Mass and elemental analysis data of synthesized compounds are given in Table 2.
Scheme for the synthesis of monochloroacetic acid derivatives.
| Comp. | M. Formula | M. Wt. | M.P./B.P.a (°C) | Rf Value⁎ | % Yield |
|---|---|---|---|---|---|
| 1 | C9H8Cl2N2O | 231.0 | 110–115 | 0.27 | 86.58 |
| 2 | C11H14ClN3O | 239.7 | 80–85 | 0.28 | 79.40 |
| 3 | C10H11ClN2O2 | 226.6 | 120–125 | 0.54 | 37.14 |
| 4 | C9H8Cl2N2O | 231.0 | 110–115 | 0.27 | 86.58 |
| 5 | C9H9ClN2O | 196.6 | 60–65 | 0.61 | 43.87 |
| 6 | C11H13ClN2O3 | 256.6 | 135–140 | 0.65 | 32.81 |
| 7 | C10H11ClN2O2 | 226.6 | 150–155 | 0.54 | 34.95 |
| 8 | C11H13ClN2O3 | 256.6 | 160–165 | 0.30 | 29.29 |
| 9 | C9H8BrClN2O | 275.5 | 135–140 | 0.78 | 50.18 |
| 10 | C10H11ClN2O3 | 242.6 | 130–135 | 0.77 | 53.97 |
| 11 | C12H15ClN2O4 | 286.7 | 135–140 | 0.88 | 69.23 |
| 12 | C10H9ClN2O2 | 224.6 | 205–215 | 0.31 | 44.64 |
| 13 | C9H8BrClN2O | 273.5 | 200–205 | 0.31 | 15.01 |
| 14 | C9H8Cl2N2O | 231 | 125–130 | 0.43 | 73.59 |
| 15 | C10H11ClN2O | 210.6 | 115–120 | 0.36 | 47.14 |
| 16 | C11H11ClN2O | 222.6 | 90–95 | 0.34 | 50.45 |
| 17 | C9H8ClN3O3 | 241.6 | 50–55 | 0.65 | 16.38 |
| 18 | C4H7ClO2 | 122.5 | 45–50a | 0.49 | 7.81 |
| 19 | C5H9ClO2 | 136.5 | 55–60a | 0.30 | 33.88 |
| 20 | C5H9ClO2 | 136.5 | 60–65a | 0.56 | 37.54 |
| 21 | C6H11ClO2 | 150.6 | 65–70a | 0.33 | 95.19 |
| 22 | C6H11ClO2 | 150.6 | 70–75a | 0.80 | 94.06 |
| 23 | C6H11ClO2 | 150.6 | 72–77a | 0.46 | 65.33 |
| 24 | C7H13ClO2 | 164.6 | 75–80a | 0.50 | 82.82 |
| 25 | C7H13ClO2 | 164.6 | 77–82a | 0.83 | 86.9 |
| 26 | C9H17ClO2 | 192.6 | 85–90a | 0.53 | 76.70 |
| 27 | C10H19ClO2 | 206.7 | 88–93a | 0.41 | 94.30 |
| 28 | C8H13ClO2 | 176.6 | 90–95a | 0.73 | 93.30 |
| 29 | C9H9ClO2 | 184.6 | 100–105a | 0.46 | 71.51 |
| Comp. | 13C NMR (DMSO) | Mass | Elemental analysis (%) | ||
|---|---|---|---|---|---|
| C (Found) | H (Found) | N (Found) | |||
| 1 | 135.5, 127.1, 130.7, 131.0, 134.5, 129.2, 45.5, 168.4, 143.2 | MS ES+(ToF): m/z 232 [M++1] | 46.78 (46.72) | 3.49 (3.44) | 12.12 (12.17) |
| 2 | 123.0, 130.3, 114.7, 151.5, 114.1, 130.0, 40.5, 40.5, 45.3, 168.2, 143.1 | MS ES+(ToF): m/z 241 [M++1] | 55.12 (55.16) | 5.89 (5.92) | 17.53 (17.47) |
| 3 | 116.5, 130.3, 121.0, 132.4, 114.2, 160.8, 55.6, 45.3, 168.1, 143.4 | MS ES+(ToF): m/z 228 [M++1] | 52.99 (52.95) | 4.89 (4.91) | 12.36 (12.33) |
| 4 | 133.6, 130.5, 127.3, 129.8, 132.2, 134.1, 45.4, 168.0, 143.1 | MS ES+(ToF): m/z 232 [M++1] | 46.78 (46.81) | 3.49 (3.53) | 12.12 (12.16) |
| 5 | 133.9, 129.5, 128.7, 131.3, 128.6, 129.0, 45.4, 168.1, 143.0 | MS ES+(ToF): m/z 198 [M++1] | 54.97 (54.99) | 4.61 (4.66) | 14.25 (14.30) |
| 6 | 127.3, 122.8, 115.2, 152.5, 149.7, 114.8, 45.0, 168.1, 143.3, 56.5, 56.5 | MS ES+(ToF): m/z 258 [M++1] | 51.47 (51.53) | 5.10 (5.14) | 10.91 (10.88) |
| 7 | 126.5, 130.3, 114.0, 163.1, 114.7, 130.3, 45.5, 168.1, 143.2, 55.6 | MS ES+(ToF): m/z 228 [M++1] | 52.99 (52.95) | 4.89 (4.84) | 12.36 (12.33) |
| 8 | 127.2, 122.5, 116.9, 148.4, 148.7, 114.5, 45.6, 168.3, 143.2, 65.3, 14.4 | MS ES+(ToF): m/z 258 [M++1] | 51.47 (51.53) | 5.10 (5.06) | 10.91 (10.88) |
| 9 | 136.2, 128.5, 131.3, 134.0, 123.4, 132.5, 45.1, 168.3, 143.4 | MS ES+(ToF): m/z 277 [M++1] | 39.23 (39.27) | 2.93 (2.98) | 10.17 (10.22) |
| 10 | 126.0, 127.8, 115.6, 158.7, 126.6, 130.5, 45.1, 168.1, 143.0, 14.3 | MS ES+(ToF): m/z 244 [M++1] | 49.50 (49.47) | 4.57 (4.60) | 11.54 (11.51) |
| 11 | 130.9, 126.2, 136.7, 138.5, 136.6, 126.8, 45.5, 168.1, 143.2, 18.0, 11.4, 18.3 | MS ES+(ToF): m/z 288 [M++1] | 50.27 (50.23) | 5.27 (5.25) | 9.77 (9.82) |
| 12 | 139.9, 129.5, 130.3, 139.4, 130.1, 129.9, 45.0, 168.2, 143.1, 191.3 | MS ES+(ToF): m/z 226 [M++1] | 53.47 (53.45) | 4.04 (4.08) | 12.47 (12.52) |
| 13 | 132.9, 131.6, 131.4, 125.2, 131.5, 131.7, 45.0, 168.0, 143.3 | MS ES+(ToF): m/z 275 [M++1] | 39.23 (39.28) | 2.93 (2.99) | 10.17 (10.13) |
| 14 | 131.6, 130.5, 129.3, 136.8, 129.2, 130.4, 45.5, 168.1, 143.00 | MS ES+(ToF): m/z 232 [M++1] | 46.78 (46.77) | 3.49 (3.44) | 12.12 (12.26) |
| 15 | 130.5, 129.3, 129.0, 140.9, 129.1, 129.5, 45.6, 168.1, 143.3, 24.5 | MS ES+(ToF): m/z 212 [M++1] | 57.01 (57.03) | 5.26 (5.29) | 13.30 (13.25) |
| 16 | 137.7, 126.2, 139.3, 135.0, 126.5, 128.4, 128.2, 128.4, 126.7, 45.1, 168.0 | MS ES+(ToF): m/z 224 [M++1] | 59.33 (59.35) | 4.98 (5.03) | 12.58 (11.54) |
| 17 | 134,.9 135.5, 129.4, 123.3, 148.7, 124.0, 145.3, 168.1, 143.4 | MS ES+(ToF): m/z 243 [M++1] | 44.74 (44.77) | 3.34 (3.38) | 17.39 (17.42) |
| 18 | 14.3, 60.4, 40.7, 166.5 | MS ES+(ToF): m/z 124 [M++1] | 39.20 (39.22) | 5.76 (5.72) | – |
| 19 | 10.1, 22.2, 66.5, 40.2, 166.6 | MS ES+(ToF): m/z 138 [M++1] | 43.97 (43.99) | 6.64 (6.69) | – |
| 20 | 68.5, 23.8, 23.8, 40.6, 166.5 | MS ES+(ToF): m/z 138 [M++1] | 43.97 (43.99) | 6.64 (6.61) | – |
| 21 | 64.5, 31.1, 19.2, 13.9, 40.1, 166.8 | MS ES+(ToF): m/z 152 [M++1] | 47.85 (47.82) | 7.36 (7.39) | – |
| 22 | 74.3, 27.9, 19.5, 19.5, 40.3, 166.7 | MS ES+(ToF): m/z 152 [M++1] | 47.85 (47.89) | 7.36 (7.33) | – |
| 23 | 73.8, 29.3, 7.9, 21.1, 40.6, 166.7 | MS ES+(ToF): m/z 152 [M++1] | 47.85 (47.88) | 7.36 (7.32) | – |
| 24 | 64.3, 28.9, 28.0, 22.6, 14.3, 40.4, 166.7 | MS ES+(ToF): m/z 166 [M++1] | 51.07 (51.11) | 7.96 (7.90) | – |
| 25 | 61.9, 38.5, 24.7, 22.6, 22.6, 40.2, 166.9 | MS ES+(ToF): m/z 166 [M++1] | 51.07 (51.10) | 7.96 (7.93) | – |
| 26 | 64.3, 29.1, 25.7, 29.2, 31.6, 22.5, 40.6, 167.0 | MS ES+(ToF): m/z 194 [M++1] | 56.10 (56.13) | 8.89 (8.53) | – |
| 27 | 64.0, 29.5, 25.9, 29.6, 31.2, 22.1, 14.3, 40.8, 167.3 | MS ES+(ToF): m/z 208 [M++1] | 58.10 (58.13) | 9.26 (9.23) | – |
| 28 | 151.7, 121.5, 129.3, 125.9, 129.3, 121.5, 40.5, 163.1 | MS ES+(ToF): m/z 178 [M++1] | 54.40 (54.44) | 7.42 (7.47) | – |
| 29 | 67.4, 141.3, 127.5, 129.3, 127.9, 129.3, 127.5, 40, 0.1 167.2 | MS ES+(ToF): m/z 186 [M++1] | 58.55 (58.52) | 4.91 (4.93) | – |
3.2 In-vitro antimicrobial activity
The synthesized monochloroacetic acid derivatives were evaluated for their in vitro antibacterial activity against S. aureus, B. subtilis, E. coli and antifungal activity against C. albicans and A. niger by the tube dilution method and the results of antimicrobial activity are given in Table 3. From the recorded pMIC values, it was observed that compound 8 was found to be most active against B. subtilis and E. coli, having pMICbs and pMICec values 1.61 and 1.91 respectively. The compounds 9 and 13 were found to be most active against S. aureus having a pMIC value of 1.64. Compound 11 was found to be most active against A. niger having a pMIC value of 1.66. The compounds 10 and 17 were found to be most potent against C. albicans, whose pMIC value (2.19) was comparable to the standard drug fluconazole (pMICaf = 2.64) and may be taken as lead compounds for the development of novel antifungal agents.
| Comp. | pMICbs | pMICsa | pMICec | pMICca | pMICan | pMICab | pMICaf | pMICam |
|---|---|---|---|---|---|---|---|---|
| 1 | 1.57 | 1.57 | 1.57 | 2.17 | 1.27 | 1.57 | 1.72 | 1.63 |
| 2 | 1.28 | 1.58 | 1.58 | 1.88 | 1.28 | 1.48 | 1.58 | 1.52 |
| 3 | 1.26 | 1.56 | 1.56 | 1.56 | 1.26 | 1.46 | 1.41 | 1.44 |
| 4 | 1.57 | 1.57 | 1.87 | 1.87 | 1.27 | 1.67 | 1.57 | 1.63 |
| 5 | 1.20 | 1.50 | 1.50 | 1.80 | 1.20 | 1.40 | 1.50 | 1.44 |
| 6 | 1.31 | 1.61 | 1.61 | 1.61 | 1.31 | 1.51 | 1.46 | 1.49 |
| 7 | 1.26 | 1.26 | 1.56 | 1.86 | 1.26 | 1.36 | 1.56 | 1.44 |
| 8 | 1.61 | 1.61 | 1.91 | 1.91 | 1.31 | 1.71 | 1.61 | 1.67 |
| 9 | 1.34 | 1.64 | 1.64 | 1.95 | 1.34 | 1.54 | 1.64 | 1.58 |
| 10 | 1.29 | 1.59 | 1.59 | 2.19 | 1.29 | 1.49 | 1.74 | 1.59 |
| 11 | 1.36 | 1.36 | 1.66 | 1.96 | 1.66 | 1.46 | 1.81 | 1.60 |
| 12 | 1.56 | 1.25 | 1.56 | 1.86 | 1.25 | 1.46 | 1.56 | 1.50 |
| 13 | 1.34 | 1.64 | 1.64 | 1.95 | 1.34 | 1.54 | 1.64 | 1.58 |
| 14 | 1.27 | 1.57 | 1.57 | 1.57 | 1.27 | 1.47 | 1.42 | 1.45 |
| 15 | 1.53 | 1.53 | 1.53 | 1.83 | 1.23 | 1.53 | 1.53 | 1.53 |
| 16 | 1.25 | 1.55 | 1.55 | 1.55 | 1.25 | 1.45 | 1.40 | 1.43 |
| 17 | 1.29 | 1.29 | 1.59 | 2.19 | 1.29 | 1.39 | 1.74 | 1.53 |
| 18 | 0.99 | 1.29 | 0.99 | 1.29 | 0.99 | 1.09 | 1.14 | 1.11 |
| 19 | 1.04 | 1.34 | 1.34 | 1.64 | 1.04 | 1.24 | 1.34 | 1.28 |
| 20 | 1.04 | 1.04 | 1.34 | 1.94 | 1.04 | 1.14 | 1.49 | 1.28 |
| 21 | 1.38 | 1.38 | 1.38 | 1.68 | 1.08 | 1.38 | 1.38 | 1.38 |
| 22 | 1.08 | 1.38 | 1.08 | 1.68 | 1.08 | 1.18 | 1.38 | 1.26 |
| 23 | 1.38 | 1.08 | 1.38 | 1.98 | 0.78 | 1.28 | 1.38 | 1.32 |
| 24 | 1.12 | 1.12 | 1.42 | 2.02 | 1.12 | 1.22 | 1.57 | 1.36 |
| 25 | 1.12 | 1.42 | 1.42 | 1.42 | 1.12 | 1.32 | 1.27 | 1.30 |
| 26 | 1.19 | 1.49 | 1.49 | 1.79 | 1.19 | 1.39 | 1.49 | 1.43 |
| 27 | 1.22 | 1.52 | 1.52 | 1.82 | 1.22 | 1.42 | 1.52 | 1.46 |
| 28 | 1.18 | 1.48 | 1.48 | 1.79 | 1.18 | 1.38 | 1.48 | 1.42 |
| 29 | 1.17 | 1.47 | 1.47 | 1.77 | 1.47 | 1.37 | 1.62 | 1.47 |
| SD | 0.17 | 0.17 | 0.18 | 0.22 | 0.16 | 0.15 | 0.15 | 0.13 |
| Std. | 2.61a | 2.61a | 2.61a | 2.64b | 2.64b | – | – | – |
Comp. = compound number, SD = standard deviation, Std. = standard drugs. i.e. Staphylococcus aureus, B. subtilis, Escherichia coli, Candida albicans and Aspergillus niger, respectively.
3.3 SAR (structure–activity relationship) studies
From the antimicrobial screening results of synthesized monochloroacetic acid derivatives, the following structure–activity relationship (SAR) can be derived:
-
The esters of monochloroacetic acid are less active as compared to Schiff bases of monochloroacetic acid.
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The compounds with electron releasing groups (OH, OC2H5, OCH3) are more active against B. subtilis, Escherichia coli and Aspergillus niger as evidenced by the high activity of compounds 8 and 11.
-
The compounds with electron withdrawing groups (Br) are more active against Staphylococcus aureus as evidenced by the high activity of compounds 9 and 13.
-
Increase in conjugation between nitrogen and phenyl nucleus results in less antimicrobial activity as in the case of compound 16.
-
The compounds with no substitution on phenyl ring are having less antimicrobial activity.
The aforementioned findings are summarized in Figure 1.
Structural requirements for the antimicrobial activity of synthesized monochloroacetic acid derivatives.
3.4 QSAR study for antimicrobial activity
In order to identify the substituent effect on the antimicrobial activity, quantitative structure–activity relationship (QSAR) study was undertaken using a linear free energy relationship model (LFER) described by Hansch and Fujita (Hansch and Fujita, 1964). Biological activity data determined as MIC values were first transformed into pMIC values (i.e. −log MIC) and used as dependent variables in the QSAR study (Table 3). The different molecular descriptors selected for the present study are listed in Table 4. The values of selected molecular descriptors used in the QSAR study are presented in Table 5.
| S.No. | QSAR descriptor | Type |
|---|---|---|
| 1 | log P | Lipophilic |
| 2 | Zero order molecular connectivity index (0χ) | Topological |
| 3 | First order molecular connectivity index (1χ) | Topological |
| 4 | Second order molecular connectivity index (2χ) | Topological |
| 5 | Valence zero order molecular connectivity index (0χv) | Topological |
| 6 | Valence first order molecular connectivity index (1χv) | Topological |
| 7 | Valence second order molecular connectivity index (2χv) | Topological |
| 8 | Kier’s alpha first order shape index (κα1) | Topological |
| 9 | Kier’s alpha second order shape index (κα2) | Topological |
| 10 | Kier’s first order shape index (κ1) | Topological |
| 11 | Randic topological index | Topological |
| 12 | Balaban topological index | Topological |
| 13 | Wiener’s topological index | Topological |
| 14 | Kier’s second order shape index (κ2) | Topological |
| 15 | Ionization potential | Electronic |
| 16 | Dipole moment (μ) | Electronic |
| 17 | Energy of the highest occupied molecular orbital (HOMO) | Electronic |
| 18 | Energy of the lowest unoccupied molecular orbital (LUMO) | Electronic |
| 19 | Total energy (Te) | Electronic |
| 20 | Nuclear Energy (Nu. E) | Electronic |
| 21 | Molar refractivity (MR) | Steric |
| Comp. | log P | MR | 0χ | 0χv | κ1 | κα1 | J | Te | LUMO | HOMO | μ |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 1.53 | 56.19 | 10.27 | 8.81 | 13.00 | 12.76 | 3.31 | −2641.87 | −0.45 | −9.12 | 3.90 |
| 2 | 1.17 | 64.98 | 11.68 | 9.85 | 15.00 | 14.43 | 3.29 | −2813.52 | −0.04 | −9.06 | 3.63 |
| 3 | 1.85 | 58.79 | 11.10 | 9.05 | 13.07 | 12.24 | 2.06 | −2858.73 | −0.18 | −8.71 | 2.16 |
| 4 | 2.62 | 57.13 | 10.39 | 8.84 | 12.07 | 11.57 | 1.98 | −2743.10 | −0.35 | −9.22 | 1.79 |
| 5 | 2.10 | 52.33 | 9.52 | 7.72 | 11.08 | 10.29 | 1.86 | −2383.08 | −0.29 | −9.01 | 1.22 |
| 6 | 1.60 | 65.25 | 12.67 | 10.38 | 15.06 | 14.19 | 2.07 | −3334.67 | −0.33 | −8.55 | 1.73 |
| 7 | 0.15 | 57.36 | 10.81 | 8.81 | 14.00 | 13.43 | 3.06 | −2757.59 | 0.33 | −9.14 | 4.37 |
| 8 | 1.91 | 65.23 | 12.67 | 10.13 | 15.06 | 14.19 | 2.04 | −3335.22 | −0.34 | −8.73 | 3.36 |
| 9 | 2.90 | 59.95 | 10.39 | 9.64 | 12.07 | 11.76 | 1.91 | −2722.45 | −0.52 | −9.19 | 2.70 |
| 10 | 1.57 | 60.48 | 11.97 | 9.42 | 14.06 | 13.20 | 2.02 | −3179.20 | −0.37 | −8.74 | 2.40 |
| 11 | 1.35 | 71.71 | 14.25 | 11.71 | 17.05 | 16.15 | 2.23 | −3810.27 | −0.39 | −8.81 | 1.58 |
| 12 | 1.78 | 58.92 | 11.10 | 8.63 | 13.07 | 11.95 | 1.92 | −2830.93 | −0.89 | −9.28 | 4.47 |
| 13 | 1.20 | 58.52 | 10.10 | 9.40 | 13.00 | 12.95 | 2.99 | −2621.36 | 0.16 | −9.63 | 5.54 |
| 14 | 2.62 | 57.13 | 10.39 | 8.84 | 12.07 | 11.57 | 1.90 | −2742.96 | −0.55 | −9.05 | 3.86 |
| 15 | 2.57 | 57.37 | 10.39 | 8.64 | 12.07 | 11.29 | 1.90 | −2538.72 | −0.30 | −8.85 | 3.21 |
| 16 | 2.51 | 62.57 | 10.93 | 8.88 | 13.07 | 12.02 | 1.79 | −2666.24 | −0.52 | −8.72 | 1.08 |
| 17 | 2.06 | 59.65 | 11.97 | 8.91 | 14.06 | 12.84 | 1.99 | −3213.92 | −1.14 | −9.59 | 7.21 |
| 18 | 0.80 | 26.91 | 5.70 | 4.93 | 7.00 | 6.92 | 2.83 | −1623.90 | 0.73 | −11.30 | 2.33 |
| 19 | 1.27 | 31.44 | 6.41 | 5.63 | 8.00 | 7.92 | 2.86 | −1779.77 | 0.30 | −11.24 | 1.68 |
| 20 | 1.21 | 31.33 | 6.57 | 5.80 | 8.00 | 7.92 | 3.10 | −1779.57 | 0.78 | −10.97 | 2.19 |
| 21 | 1.66 | 36.04 | 7.11 | 6.34 | 9.00 | 8.92 | 2.88 | −1935.57 | 0.74 | −11.25 | 2.30 |
| 22 | 1.67 | 35.91 | 7.28 | 6.50 | 9.00 | 8.92 | 3.06 | −1935.46 | 0.75 | −11.34 | 2.76 |
| 23 | 1.68 | 35.86 | 7.28 | 6.50 | 9.00 | 8.92 | 3.22 | −1935.29 | 0.39 | −10.90 | 1.68 |
| 24 | 2.06 | 40.64 | 7.82 | 7.05 | 10.00 | 9.92 | 2.89 | −2091.39 | 0.74 | −11.34 | 2.76 |
| 25 | 1.99 | 40.59 | 7.98 | 7.21 | 10.00 | 9.92 | 3.03 | −2091.32 | 0.31 | −11.22 | 1.57 |
| 26 | 2.85 | 49.84 | 9.23 | 8.46 | 12.00 | 11.92 | 2.90 | −2403.06 | 0.74 | −11.26 | 2.77 |
| 27 | 3.25 | 54.44 | 9.94 | 9.17 | 13.00 | 12.92 | 2.91 | −2558.90 | 0.74 | −11.22 | 2.84 |
| 28 | 2.04 | 43.18 | 8.10 | 7.33 | 9.09 | 9.01 | 1.95 | −2219.51 | 0.35 | −10.92 | 2.00 |
| 29 | 2.38 | 46.77 | 8.81 | 7.31 | 10.08 | 9.23 | 1.95 | −2290.99 | 0.10 | −9.76 | 2.48 |
Our earlier studies (Kumar et al., 2012; Judge et al., 2012a,b; Narang et al., 2012a,b) indicated that the multi-target QSAR (mt-QSAR) models are better than one-target QSAR (ot-QSAR) models in describing the antimicrobial activity. So, in the present study we have developed multi-target QSAR models to describe the antimicrobial activity of synthesized monochloroacetic acid derivatives.
According to the ot-QSAR models one should use five different equations with different errors to predict the activity of a new compound against the five microbial species. The ot-QSAR models, which are almost in the whole literature, become impractical to use when we have to predict each compound’s results for more than one target. In these cases we have to develop one ot-QSAR for each target. However, very recently the interest has increased in the development of multi-target QSAR (mt-QSAR) models. In opposition to ot-QSAR, the mt-QSAR model is a single equation that considers the nature of molecular descriptors which are common and essential for describing the antibacterial and antifungal activities (Gonzalez-Diaz et al., 2007, 2008; Cruz-Monteagudo et al., 2007; Gonzalez-Diaz and Prado-Prado, 2008).
In light of the above, we have attempted to develop three different mt-QSAR models viz. mt-QSAR model for describing the antibacterial activity of the synthesized compounds against S. aureus, B. subtilis and E. coli, mt-QSAR model for describing the antifungal activity of the synthesized compounds against C. albicans and A. niger as well as a common mt-QSAR model for describing the antimicrobial (overall antibacterial and antifungal) activity of the synthesized compounds against all the above mentioned microorganisms.
In order to develop mt-QSAR models, initially we calculated the average antibacterial, antifungal and antimicrobial activities of monochloroacetic acid derivatives which are presented in Table 3.
During the regression analysis studies it was observed that the response values of compounds 1, 3, 6 and 18 were outside the limits of response values of other synthesized monochloroacetic acid derivatives. Thus, these compounds were designated as outliers and were not involved in the data set for QSAR model generation. In multivariate statistics, it is common to define three types of outliers (Furusjo et al., 2006).
-
.
X/Y relation outliers are substances for which the relationship between the descriptors (X variables) and the dependent variables (Y variables) is not the same as in the (rest of the) training data.
-
.
X outliers are substances whose molecular descriptors do not lie in the same range as the (rest of the) training data.
-
.
Y outliers are only defined for training or test samples. They are substances for which the reference value of response is invalid.
As there was no difference in the activity (Table 3) as well as the molecular descriptor range (Table 5) of these outliers (1, 3, 6 and 18) when compared to the other monochloroacetic acid derivatives, these outliers belong to the category of Y outliers (Substances for which the reference value of response is invalid) (Furusjo et al., 2006).
Preliminary analysis was carried out in terms of correlation analysis. A correlation matrix constructed for antibacterial activity of the synthesized compounds is presented in Table 6. In general, high colinearity (r > 0.5) was observed between different parameters. A high interrelationship was observed between the topological parameter, Wiener index (W) and the electronic parameter, total energy (Te) (r = −0.981) and a low interrelationship was observed between the topological parameter, Balaban index (J) and the electronic parameter, dipole moment (μ) (r = 0.005). Correlation of calculated molecular descriptors with antibacterial, antifungal and antimicrobial activities is presented in Table 7.
| pMICab | log P | MR | κ1 | J | W | Te | LUMO | HOMO | μ | |
|---|---|---|---|---|---|---|---|---|---|---|
| pMICab | 1.000 | |||||||||
| log P | 0.292 | 1.000 | ||||||||
| MR | 0.790 | 0.116 | 1.000 | |||||||
| κ1 | 0.675 | −0.046 | 0.953 | 1.000 | ||||||
| J | −0.551 | −0.464 | −0.504 | −0.298 | 1.000 | |||||
| W | 0.688 | −0.011 | 0.954 | 0.970 | −0.469 | 1.000 | ||||
| Te | −0.704 | −0.011 | −0.933 | −0.951 | 0.492 | −0.981 | 1.000 | |||
| LUMO | −0.604 | −0.148 | −0.712 | −0.590 | 0.765 | −0.701 | 0.709 | 1.000 | ||
| HOMO | 0.722 | −0.040 | 0.852 | 0.741 | −0.644 | 0.808 | −0.775 | −0.815 | 1.000 | |
| μ | 0.187 | −0.173 | 0.334 | 0.383 | 0.005 | 0.302 | −0.353 | −0.341 | 0.209 | 1.000 |
| Descriptors | pMICab | pMICaf | pMICam |
|---|---|---|---|
| log P | 0.292 | −0.077 | 0.172 |
| MR | 0.790 | 0.688 | 0.872 |
| 0χ | 0.727 | 0.743 | 0.852 |
| 0χv | 0.759 | 0.713 | 0.861 |
| 1χ | 0.753 | 0.731 | 0.865 |
| 1χv | 0.771 | 0.633 | 0.833 |
| 2χ | 0.749 | 0.728 | 0.861 |
| 2χv | 0.746 | 0.568 | 0.786 |
| 3χ | 0.426 | 0.532 | 0.544 |
| 3χv | 0.294 | 0.184 | 0.292 |
| κ1 | 0.675 | 0.710 | 0.800 |
| κ2 | 0.475 | 0.464 | 0.547 |
| κα1 | 0.663 | 0.687 | 0.781 |
| κα2 | 0.404 | 0.378 | 0.458 |
| κα3 | −0.044 | −0.056 | −0.056 |
| R | 0.753 | 0.731 | 0.865 |
| J | −0.551 | −0.366 | −0.556 |
| W | 0.688 | 0.725 | 0.817 |
| Te | −0.704 | −0.781 | −0.853 |
| Ee | −0.672 | −0.753 | −0.818 |
| Ne | 0.664 | 0.746 | 0.809 |
| SA | 0.677 | 0.639 | 0.770 |
| IP | −0.722 | −0.575 | −0.772 |
| LUMO | −0.604 | −0.480 | −0.646 |
| HOMO | 0.722 | 0.575 | 0.772 |
The structural effects on variations in antibacterial activity of the monochloroacetic acid derivatives in terms of pMICab were examined by regression analysis with molecular parameters shown in Table 5. For 25 monochloroacetic acid derivatives, Eq. (1) was derived as that of the best quality using the steric parameter, molar refractivity (MR, r = 0.790, Table 6).
3.4.1 LR mt-QSAR model for antibacterial activity
Here and thereafter, n – number of data points, r – correlation coefficient, q2 – cross validated r2 obtained by leave one out method, s – standard error of the estimate and F – Fischer statistics.
Coefficient of MR in Eq. (1) is positive which indicates that the antibacterial activity of the synthesized compounds is positively correlated to molar refractivity (MR) i.e. antibacterial activity of synthesized compounds will increase with an increase in the value of MR. This is evidenced by the antibacterial activity data of the synthesized compounds (Table 3) and their MR values (Table 5). Compound 8 having a high MR value of 65.23 (Table 5) has maximum antibacterial activity (pMICab = 1.71, Table 3) and compound 18 having a minimum MR value of 26.91 (Table 5) has minimum antibacterial activity (pMICab = 1.09, Table 3).
In search of a better QSAR model, we coupled molar refractivity (MR) with log P, which yielded a better QSAR model for antibacterial activity (Eq. (2)) having improved r and q2 values.
3.4.2 MLR mt-QSAR model for antibacterial activity
log P is one of the key determinants of pharmacokinetic properties. Knowing the exact values for log P, it is possible to predict the inhibitory activity of the drugs (Podunavac-Kuzmanovic et al., 2008). log P is the logarithm of the ratio of the concentrations of the un-ionized solute in two solvents, which is calculated according to the following equation, where o is octanol and w is un-ionized water.
The hydrophobic effect is the major driving force for the binding of drugs to their receptor targets in pharmacodynamics, and is based on the log P contribution of each atom. Each atom in a molecule contributes to the log P by the amount of its atomic parameter multiplied by the degree of exposure to the surrounding solvent (Park et al., 2008).
The QSAR model expressed by Eq. (2) was cross validated by its high q2 value (q2 = 0.572) obtained by the leave one out (LOO) method. The value of q2 greater than 0.5 is the basic requirement for qualifying a QSAR model to be a valid one (Golbraikh and Tropsha, 2002). As the observed and predicted antibacterial activity values are close to each other (Table 8), the mt-QSAR model for antibacterial activity (Eq. (2)) is a valid one. The plot of predicted pMICab against observed pMICab (Figure 2) also favors the developed model expressed by Eq. (2). Further, the plot of observed pMICab vs residual pMICab (Figure 3) indicated that there was no systemic error in model development as the propagation of error was observed on both sides of zero (Kumar et al., 2007).
| Comp. | pMICab | pMICaf | pMICam | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Obs. | Pre. | Res. | Obs. | Pre. | Res. | Obs. | Pre. | Res. | |
| 1 | 1.57 | 1.44 | 0.13 | 1.72 | 1.55 | 0.17 | 1.63 | 1.50 | 0.13 |
| 2 | 1.48 | 1.50 | −0.02 | 1.58 | 1.59 | −0.01 | 1.52 | 1.57 | −0.05 |
| 3 | 1.46 | 1.47 | −0.01 | 1.41 | 1.59 | −0.18 | 1.44 | 1.52 | −0.08 |
| 4 | 1.67 | 1.49 | 0.18 | 1.57 | 1.57 | 0.00 | 1.63 | 1.50 | 0.13 |
| 5 | 1.40 | 1.42 | −0.02 | 1.50 | 1.50 | 0.00 | 1.44 | 1.46 | −0.02 |
| 6 | 1.51 | 1.52 | −0.01 | 1.46 | 1.69 | −0.23 | 1.49 | 1.58 | −0.09 |
| 7 | 1.36 | 1.39 | −0.03 | 1.56 | 1.57 | −0.01 | 1.44 | 1.51 | −0.07 |
| 8 | 1.71 | 1.53 | 0.18 | 1.61 | 1.69 | −0.08 | 1.67 | 1.58 | 0.09 |
| 9 | 1.54 | 1.53 | 0.01 | 1.64 | 1.57 | 0.07 | 1.58 | 1.53 | 0.05 |
| 10 | 1.49 | 1.48 | 0.01 | 1.74 | 1.66 | 0.08 | 1.59 | 1.53 | 0.06 |
| 11 | 1.46 | 1.57 | −0.11 | 1.81 | 1.79 | 0.02 | 1.60 | 1.63 | −0.03 |
| 12 | 1.46 | 1.47 | −0.01 | 1.56 | 1.59 | −0.03 | 1.50 | 1.52 | −0.02 |
| 13 | 1.54 | 1.44 | 0.10 | 1.64 | 1.55 | 0.09 | 1.58 | 1.52 | 0.06 |
| 14 | 1.47 | 1.49 | −0.02 | 1.42 | 1.57 | −0.15 | 1.45 | 1.50 | −0.05 |
| 15 | 1.53 | 1.49 | 0.04 | 1.53 | 1.53 | 0.00 | 1.53 | 1.51 | 0.02 |
| 16 | 1.45 | 1.53 | −0.08 | 1.40 | 1.56 | −0.16 | 1.43 | 1.55 | −0.12 |
| 17 | 1.39 | 1.49 | −0.10 | 1.74 | 1.67 | 0.07 | 1.53 | 1.53 | 0.00 |
| 18 | 1.09 | 1.14 | −0.05 | 1.14 | 1.35 | −0.21 | 1.11 | 1.24 | −0.13 |
| 19 | 1.24 | 1.20 | 0.04 | 1.34 | 1.38 | −0.04 | 1.28 | 1.28 | 0.00 |
| 20 | 1.14 | 1.20 | −0.06 | 1.49 | 1.38 | 0.11 | 1.28 | 1.28 | 0.00 |
| 21 | 1.38 | 1.26 | 0.12 | 1.38 | 1.41 | −0.03 | 1.38 | 1.32 | 0.06 |
| 22 | 1.18 | 1.26 | −0.08 | 1.38 | 1.41 | −0.03 | 1.26 | 1.32 | −0.06 |
| 23 | 1.28 | 1.26 | 0.02 | 1.38 | 1.41 | −0.03 | 1.32 | 1.32 | 0.00 |
| 24 | 1.22 | 1.32 | −0.10 | 1.57 | 1.44 | 0.13 | 1.36 | 1.36 | 0.00 |
| 25 | 1.32 | 1.31 | 0.01 | 1.27 | 1.44 | −0.17 | 1.30 | 1.36 | −0.06 |
| 26 | 1.39 | 1.43 | −0.04 | 1.49 | 1.50 | −0.01 | 1.43 | 1.44 | −0.01 |
| 27 | 1.42 | 1.49 | −0.07 | 1.52 | 1.53 | −0.01 | 1.46 | 1.48 | −0.02 |
| 28 | 1.38 | 1.34 | 0.04 | 1.48 | 1.47 | 0.01 | 1.42 | 1.38 | 0.04 |
| 29 | 1.37 | 1.39 | −0.02 | 1.62 | 1.48 | 0.14 | 1.47 | 1.41 | 0.06 |
- Plot of observed pMICab against predicted pMICab by Eq. (2).
- Plot of observed pMICab against predicted pMICab by Eq. (2).
Results of the correlation of calculated molecular descriptors with antifungal activity of synthesized compounds (Table 7) indicated that the electronic parameter, total energy (Te) was the most dominating descriptor for the antifungal activity of synthesized compounds. So, the QSAR model for the antifungal activity of monochloroacetic acid derivatives was developed by using the electronic parameter, total energy (Te) (r = 0.781, Table 7, Eq. (3)).
3.4.3 LR mt-QSAR model for antifungal activity
The coefficient of Te is negative (Eq. (3)), which indicates a negative correlation between the antifungal activity value of the synthesized compounds and their Te values i.e. the antifungal activity of synthesized compounds will decrease with increase in their Te values and vice versa (Tables 3 and 5).
The mt-QSAR model of antimicrobial activity (Eq. (4)) depicted the importance of the steric parameter, molar refractivity (MR) in describing the antimicrobial activity of the synthesized compounds.
3.4.4 LR mt-QSAR model for antimicrobial activity
As in the case of antibacterial activity, coefficient of MR in Eq. (4) is positive which indicates that the antimicrobial activity of the synthesized compounds is positively correlated to molar refractivity (MR) i.e. antimicrobial activity of synthesized compounds will increase with an increase in the value of MR. This is evidenced by the antimicrobial activity data of the synthesized compounds (Table 3) and their MR values (Table 5).
The QSAR models expressed by Eqs. (3) and (4) were cross validated by their high q2 values (q2 = 0.547 and 0.720, respectively) obtained by leave one out (LOO) method. The value of q2 greater than 0.5 is the basic requirement for qualifying a QSAR model to be valid one (Golbraikh and Tropsha, 2002). As the observed and predicted antifungal and antimicrobial activity values are close to each other (Table 8), the mt-QSAR models for antifungal and antimicrobial activities (Eqs. (3) and (4), respectively) are valid ones. The plot of the predicted pMICam against observed pMICam (Figure 4) also favors the developed model expressed by Eq. (4). Further, the plot of observed pMICam vs residual pMICam (Figure 5) indicated that there was no systemic error in model development as the propagation of error was observed on both sides of zero (Kumar et al., 2007). Further, high residual values observed in case of compounds 1, 3, 6 and 18 (Table 8) justify their removal as outliers.
Plot of observed pMICam against predicted pMICam by Eq. (4).
Plot of observed pMICam against residual pMICam by Eq. (4).
Generally for QSAR studies, the biological activities of compounds should span 2–3 orders of magnitude. But in the present study the range of antimicrobial activities of the synthesized compounds is within one order of magnitude. This is in accordance with results suggested by Bajaj et al. (2005) who stated that the reliability of the QSAR model lies in its predictive ability even though the activity data are in the narrow range (Bajaj et al., 2005). When biological activity data lie in the narrow range, the presence of minimum standard deviation of the biological activity justifies its use in QSAR studies (Narasimhan et al., 2007). The minimum standard deviation (Table 3) observed in the antimicrobial activity data justifies its use in QSAR studies.
4 Conclusion
In the present study, a series of monochloroacetic acid derivatives (1–29) were synthesized in appreciable yield and characterized by physicochemical as well as spectral means. The spectral data were found in agreement with the assigned molecular structures. Results of antimicrobial studies indicated that in general, Schiff bases were found to be more active as compared to esters. The compounds with electron releasing groups were found to be more active against B. subtilis, E. coli and A. niger and the compounds with electron withdrawing groups were found to be more active against S. aureus. mt-QSAR models [Eqs. 1–4] indicated the importance of the steric parameter, molar refractivity (MR) in describing antibacterial activity and antimicrobial activity and the electronic parameter, total energy (Te) in describing the antifungal activity of the synthesized monochloroacetic acid derivatives.
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