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National Accounting Review

2024, Volume 6, Issue 3: 407-448. doi: 10.3934/NAR.2024019
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A quantitative and qualitative macroeconomic and sociopolitical outlook of the MEDA transitional economies: development-paths, governance climate, and sociocultural factors

  • University of Perugia, Umbria, Italy

  • We present a quantitative and qualitative macroeconomic and sociopolitical outlook of the South-East Mediterranean transitional economies (MEDA), aiming to understand their development challenges and growth potential. We used various analytical techniques integrated in this study, including macroeconomic indicators, SWOT analysis, clustering, Markovian chains, investment development-path, and sociocultural factors. MEDA countries differ principally due to varying levels of oil dependence, underdeveloped manufacturing systems, and high unemployment rates. Despite various efforts, institutional deficiencies and a lack of governance persist, hindering countries' growth and attractiveness. Finally, the importance of international cooperation, particularly with the European Union, to support MEDA transitional economies in addressing governance issues, enhancing human development, and fostering internal stability is highlighted.

    Citation: Francesco Scalamonti. A quantitative and qualitative macroeconomic and sociopolitical outlook of the MEDA transitional economies: development-paths, governance climate, and sociocultural factors[J]. National Accounting Review, 2024, 6(3): 407-448. doi: 10.3934/NAR.2024019

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  • We present a quantitative and qualitative macroeconomic and sociopolitical outlook of the South-East Mediterranean transitional economies (MEDA), aiming to understand their development challenges and growth potential. We used various analytical techniques integrated in this study, including macroeconomic indicators, SWOT analysis, clustering, Markovian chains, investment development-path, and sociocultural factors. MEDA countries differ principally due to varying levels of oil dependence, underdeveloped manufacturing systems, and high unemployment rates. Despite various efforts, institutional deficiencies and a lack of governance persist, hindering countries' growth and attractiveness. Finally, the importance of international cooperation, particularly with the European Union, to support MEDA transitional economies in addressing governance issues, enhancing human development, and fostering internal stability is highlighted.



    1.   Introduction

    Mycobacterium tuberculosis is an acid-fast Gram-positive bacteria, the causative agent of tuberculosis in human beings [1]. TB is a disease of poverty, malnutrition and overcrowding, affecting people of all age groups [2]. It is a tough bacterium due to the presence of an inimitable cell wall which has a waxlike coating predominantly composed of mycolic acid [3],[4]. This allows the bacillus to lie in a covert situation for long periods, may be decades, centuries or even more [5]–[7]. The host's immune system may restrain the disease, but it does not destroy it [8]–[10]. According to a WHO factsheet from 2022, there were an estimated 10.6 million new TB cases in 2021, of which 6.7 % were people coinfected with human immunodeficiency virus (HIV) [11]. The treatment of TB has become a global public health program due to various factors like the requirement of long-term multidrug therapy, the emergence of multidrug resistance (MDR), extensively drug-resistant (XDR) strains, and its invasion in HIV patients [12]. The chemotherapeutic regime of a TB treatment includes administering of Isoniazid, Rifampin, Pyrazinamide and Ethambutol (EMB) for two months followed by Isoniazid and Rifampin for four months [13]. The latest WHO reports point out the emergence of TDR (totally drug resistant) strains of TB [14]

    Compounds with some antibacterial activity may be considered as a good source of new leads for TB drug development. Nitrofuranylamide, metronidazole, nitrofurantoin and nitroimidazole pyran, are some antibacterial agents (Figure 1) used in different microbial infections [15],[16]. Among these compounds, nitrofuranylamide has been reported to inhibit UDP-galactose mutase (Glf), an enzyme accountable for the biosynthesis of galactofuranose, an indispensable component in the bacterial cell wall [17]. Tangallapally et al. designed several nitrofuran derivatives as antitubercular agents [18] Previously, we have explored the QSARs of a few juglone derivatives [19], C-3 arylalkyl 2,3-dideoxy hex-2-enopyranosides and multi-functionalized heptenol and octenol derivatives for their antitubercular activity [20]. These studies have specified that for juglone derivatives, structures with compact molecular arrangement and the substituent groups with electropositive character are favorable for activity. For C-3 alkyl and arylalkyl 2,3-dideoxy hex-2-enopyranosides and highly functionalized heptenol and octenol derivatives, few degrees of symmetry, least quirkiness and squeezed geometric and electronegativity centers, few branches, and saturated structural templates favor antitubercular activity. Recently, there was a 2D-QSAR study performed on the O6-methylguanine-DNA methyltransferase (MGMT) inhibitors. The genetic algorithm multiple linear regression (GA-MLR) methods, Dragon descriptors and PaDEL software were combined together for the development of models. The study emphasized the importance of aliphatic primary amino groups, existence of O-S at topological distance, Al-O-Ar/Ar-O-Ar/R..O..R/R-O-C=X and hydrogen bond donors for the MGMT inhibition activity [21],[22]. The quantitative structure-activity relationship (QSAR) models between fused/non-fused polycyclic aromatic hydrocarbons (FNFPAHs) and toxicity were also explored [23].

    Figure 1.  Antibacterial agents.
    DownLoad: Full-Size Img PowerPoint

    In the medicinal chemistry paradigm, establishing a correlation between the structure and the associated activity helps in understanding the system under investigation. Additionally, rationales from different matrics provide mutually exclusive information. The COMFA and COMSIA analysis of nitrofuranylamide and related aromatic compounds suggested that lipophilic, steric and electronic features are important for the penetration of drug to the cell wall [24]. The pharmacophore mapping study of these compounds emphasized that the manifestation of negative potential regions above the oxygen atoms of the nitro group, covering laterally to the isoxazole ring/amide bond is indispensable for potent antitubercular activity [25]. In this context, we have contemplated a comprehensive quantitative structure-activity relationship (QSAR) study on the nitrofuran analogue with topological and structural fragment descriptors from Dragon [26] to offer rationales in terms of designated indices. Also, the quantitative structure–activity relationship models to determine the influences of physiochemical structures of nitroreductase inhibitors on antitubercular activities.

    2.   Materials and methods

    2.1.   Dataset

    The study has involved 126 diverse nitrofuran derivatives and related compounds along with their antitubercular activities reported in the literature [18],[27]–[30]. In these analogues, 102 compounds are with furan ring system, and 24 compounds are with different ring systems like, thiophene, thiazole, pyrrole, and imidazole. Broadly, the structural variation in the compounds may be represented as shown in (Figure 2). In 121 compounds, the furanyl/heterocyclic moiety is connected to the rest of the scaffold through the amide linkers. In 5 molecules an isoxazole linker is present in place of the amide linker.

    Figure 2.  General structure of nitrofuran derivatives.
    DownLoad: Full-Size Img PowerPoint

    The other variations in the scaffold are schematically represented as A, Q, S, and U regions connected to the amide/isoxazole with P, R, T linkers, respectively. The A region is satisfied with furan ring, imidazole ring, pyrrole ring, thiazole ring, etc. The Q region is satisfied with an aryl, substituted aryl, fused ring system, heteroaryl etc. and the P linkage consists of methyl, ethyl, isobutyl. The Q region is connected with the S region by a 1-4 or 1-3 system. The S region consists of piperazine, benzodiazepine, isoxazole, piperidine rings. The R linkage present in very few compounds. It consists of cyano, methoxy. The U region consists of an aromatic ring or open chain like methoxy, ethoxy, amide, etc. The T linkage consists of methyl, carbonyl group, etc.

    The antitubercular activity was taken as the logarithm of the inverse of minimum inhibitory concentration (−log MIC, where MIC is in moles per liter against M. tuberculosis, H37Rv). The common structure of all these compounds is given in (Figure 2), and their structural alternatives are given (Table 1 and Figure 3). For the QSAR study the structures of all compounds were drawn in ChemDraw 10 [31]. The 2D ChemDraw structures were changed into 3D structures using the default conversion procedure applied in the ChemDraw 10 3D Ultra. The 3D structures were energy minimized in the MM2 module using the minimum RMS gradient 0.100. All these energy structures were transported to Dragon software [26] for the computation of 0D, 1D and 2D molecular descriptors.

    Table 1.  Observed and calculated antitubercular activities of nitrofuran derivatives (Figure 2).
    Compd No X A B P Q R S T U
    		Activity (−log MIC)
    OBSDa
    		 Calcd
    Eq2 Eq3 Eq4 Eq5 Eq6 Eq7
    001 X1 A1 B1 P0 Q1 R0 S0 T0 U0 5.52 4.91 4.95 4.67 4.91 4.95 4.67
    002 X1 A1 B1 P0 Q2 R0 S0 T0 U0 5.29 4.91 4.95 4.67 4.91 4.95 4.67
    003 X1 A1 B1 P0 Q3 R0 S0 T0 U0 5.50 4.91 4.95 4.67 4.91 4.95 4.67
    004 X1 A1 B1 P0 Q4 R0 S0 T0 U0 5.52 4.91 4.95 4.67 4.91 4.95 4.67
    005 X1 A1 B1 P0 Q5 R0 S0 T0 U0 5.82 4.93 4.98 4.92 4.93 4.98 4.92
    006 X1 A1 B1 P0 Q6 R0 S0 T0 U0 4.89 4.69 4.71 4.42 4.69 4.71 4.42
    007 X1 A1 B1 P0 Q7 R0 S0 T0 U0 4.97 4.86 4.90 4.67 4.86 4.90 4.67
    008 X1 A1 B1 P0 Q8 R0 S0 T0 U0 5.46 4.69 4.71 4.42 4.69 4.71 4.42
    009 X1 A1 B1 P1 Q9 R0 S0 T0 U0 4.58 4.78 4.81 4.67 4.78 4.81 4.67
    010 X1 A1 B1 P0 Q10 R1 S0 T0 U0 5.51 4.93 4.98 4.92 4.93 4.98 4.92
    011 X1 A1 B1 P1 Q5 R0 S0 T0 U0 6.44 5.49 5.58 5.17 5.49 5.58 5.17
    012 X1 A1 B1 P1 Q11 R0 S0 T0 U0 5.24 5.19 5.26 5.42 5.19 5.26 5.42
    013 X1 A1 B1 P1 Q12 R0 S0 T0 U0 5.88 5.71 5.81 5.68 5.71 5.81 5.68
    014 X1 A1 B1 P1 Q13 R0 S0 T0 U0 6.19 5.62 5.71 5.68 5.62 5.71 5.68
    015 X1 A1 B1 P1 Q14 R0 S0 T0 U0 5.62 5.75 5.85 5.93 5.75 5.85 5.93
    016 X1 A1 B1 P0 Q15 R0 S0 T0 U0 4.97 5.24 5.30 5.42 5.24 5.30 5.42
    017 X1 A1 B1 P0 Q16 R0 S0 T0 U0 4.49 5.15 5.21 5.42 5.15 5.21 5.42
    018 X1 A1 B1 P2 Q8 R0 S0 T0 U0 5.21 4.69 4.72 4.67 4.69 4.72 4.67
    019 X1 A1 B1 P2 Q5 R0 S0 T0 U0 5.56 4.98 5.03 5.17 4.98 5.03 5.17
    020 X1 A1 B1 P3 Q8 R0 S0 T0 U0 5.21 4.98 5.03 5.17 4.98 5.03 5.17
    021 X1 A1 B1 P1 Q8 R0 S0 T0 U0 4.92 4.98 5.03 5.17 4.98 5.03 5.17
    022 X1 A1 B1 P2 Q13 R0 S0 T0 U0 5.90 5.49 5.58 5.68 5.49 5.58 5.68
    023 X1 A1 B1 P0 Q17 R0 S0 T0 U0 5.19 4.91 4.95 4.67 4.91 4.95 4.67
    024 X1 A1 B1 P0 Q18 R2 S0 T0 U0 4.43 5.28 5.35 4.92 5.28 5.35 4.92
    025 X1 A1 B1 P0 Q4 R0 S0 T0 U0 4.74 3.77 4.48 4.43 3.77 4.48 4.43
    026 X1 A1 B1 P0 Q4 R0 S0 T0 U0 4.76 4.12 4.85 4.70 4.12 4.85 4.70
    027 X1 A1 B1 P0 Q8 R0 S0 T0 U0 4.87 4.69 4.71 5.17 4.69 4.71 5.17
    028 X1 A1 B1 P0 Q5 R0 S0 T0 U0 4.62 4.93 4.98 5.68 4.93 4.98 5.68
    029 X1 A1 B1 P0 Q19 R0 S0 T0 U0 5.51 5.12 5.18 5.42 5.12 5.18 5.42
    030 X1 A1 B1 P0 Q20 R0 S0 T0 U0 4.87 4.69 4.71 4.42 4.69 4.71 4.42
    031 X1 A1 B1 P0 Q21 R0 S0 T0 U0 4.57 4.69 4.71 4.42 4.69 4.71 4.42
    032 X1 A1 B1 P0 Q22 R0 S0 T0 U0 4.87 4.69 4.71 4.42 4.69 4.71 4.42
    033 X1 A1 B1 P0 Q23 R0 S0 T0 U0 4.55 4.56 4.58 4.42 4.56 4.58 4.42
    034 X1 A1 B1 P0 Q24 R0 S0 T0 U0 4.57 4.69 4.71 4.42 4.69 4.71 4.42
    035 X1 A1 B1 P1 Q20 R0 S0 T0 U0 5.49 4.98 5.03 4.67 4.98 5.03 4.67
    036 X1 A1 B1 P0 Q25 R0 S0 T0 U0 5.30 4.96 4.14 5.42 4.96 4.14 5.42
    037 X1 A1 B1 P0 Q26 R0 S0 T0 U0 5.22 4.91 4.95 4.67 4.91 4.95 4.67
    038 X2 A1 B1 P1 Q27 R0 S0 T0 U0 5.24 5.19 5.26 5.42 5.19 5.26 5.42
    039 X3 A1 B1 P1 Q28 R0 S0 T0 U0 5.41 5.32 5.39 5.93 5.32 5.39 5.93
    040 X1 A1 B1 P0 Q10 R0 S1 T0 U0 5.01 5.50 5.59 5.42 5.50 5.59 5.42
    041 X1 A1 B1 P0 Q10 R0 S2 T0 U0 4.42 5.81 5.92 5.93 5.81 5.92 5.93
    042 X1 A1 B1 P0 Q10 R0 S2 T1 U1 5.71 6.16 6.28 5.93 6.16 6.28 5.93
    043 X1 A1 B1 P0 Q10 R0 S3 T1 U1 5.11 6.16 6.28 5.93 6.16 6.28 5.93
    044 X1 A1 B1 P0 Q10 R0 S2 T0 U2 5.99 5.68 5.77 6.43 5.68 5.77 6.43
    045 X1 A1 B1 P0 Q18 R0 S1 T0 U0 5.01 5.06 5.12 5.17 5.06 5.12 5.17
    046 X1 A1 B1 P0 Q18 R0 S2 T0 U0 4.42 5.32 5.39 5.68 5.32 5.39 5.68
    047 X1 A1 B1 P0 Q18 R0 S2 T1 U1 4.51 5.49 5.57 5.68 5.49 5.57 5.68
    048 X1 A1 B1 P0 Q18 R0 S3 T1 U1 4.51 5.49 5.57 5.68 5.49 5.57 5.68
    049 X1 A1 B1 P0 Q18 R0 S2 T1 U2 5.10 5.40 5.48 6.18 5.40 5.48 6.18
    050 X1 A1 B1 P1 Q10 R0 S1 T0 U0 6.22 5.55 5.64 5.68 5.55 5.64 5.68
    051 X1 A1 B1 P1 Q10 R0 S2 T0 U0 6.24 5.86 5.97 6.18 5.86 5.97 6.18
    052 X1 A1 B1 P1 Q10 R0 S2 T1 U1 7.53 6.21 6.34 6.18 6.21 6.34 6.18
    053 X1 A1 B1 P1 Q10 R0 S3 T1 U1 5.72 6.21 6.34 6.18 6.21 6.34 6.18
    054 X1 A1 B1 P1 Q18 R0 S2 T0 U0 5.04 5.99 6.11 5.93 5.99 6.11 5.93
    055 X1 A1 B1 P1 Q18 R0 S2 T1 U0 6.62 6.16 6.29 5.93 6.16 6.29 5.93
    056 X1 A1 B1 P1 Q18 R0 S3 T1 U1 5.43 6.16 6.29 5.93 6.16 6.29 5.93
    057 X1 A1 B1 P1 Q10 R0 S4 T0 U1 6.54 5.55 4.77 5.68 5.55 4.77 5.68
    058 X1 A1 B1 P1 Q10 R0 S5 T0 U0 5.06 5.86 5.11 5.44 5.86 5.11 5.44
    059 X1 A1 B1 P1 Q10 R0 S6 T0 U0 4.49 6.18 5.44 5.20 6.18 5.44 5.20
    060 X1 A1 B1 P1 Q10 R0 S2 T1 U3 6.89 6.04 6.17 6.24 6.04 6.17 6.24
    061 X1 A1 B1 P1 Q10 R3 S2 T0 U0 5.97 6.23 6.37 5.93 6.23 6.37 5.93
    062 X1 A1 B1 P1 Q29 R0 S2 T1 U1 7.24 6.68 6.84 6.43 6.68 6.84 6.43
    063 X1 A1 B1 P1 Q29 R0 S2 T0 U0 5.66 5.99 6.11 6.43 5.99 6.11 6.43
    064 X1 A1 B1 P1 Q29 R0 S4 T0 U0 5.66 5.68 4.90 5.93 5.68 4.90 5.93
    065 X1 A1 B1 P1 Q29 R0 S1 T0 U0 5.94 5.68 5.77 5.93 5.68 5.77 5.93
    066 X1 A1 B1 P1 Q10 R0 S3 T1 U1 6.34 6.68 6.84 6.43 6.68 6.84 6.43
    067 X1 A1 B1 P1 Q10 R0 S2 T1 U1 7.53 6.21 6.34 6.18 6.21 6.34 6.18
    068 X1 A1 B1 P1 Q10 R0 S2 T2 U4 7.24 6.73 6.89 6.45 6.73 6.89 6.45
    069 X1 A1 B1 P1 Q10 R0 S2 T2 U5 6.59 6.04 6.16 6.20 6.04 6.16 6.20
    070 X1 A1 B1 P1 Q10 R0 S2 T2 U6 7.81 6.04 6.16 6.20 6.04 6.16 6.20
    071 X1 A1 B1 P1 Q10 R0 S2 T2 U7 6.93 6.27 6.40 6.20 6.27 6.40 6.20
    072 X1 A1 B1 P1 Q10 R0 S2 T2 U8 6.93 6.32 6.46 6.45 6.32 6.46 6.45
    073 X1 A1 B1 P1 Q10 R0 S2 T2 U9 5.92 6.18 6.31 5.46 6.18 6.31 5.46
    074 X1 A1 B1 P1 Q10 R0 S2 T2 U10 6.32 6.18 6.31 6.20 6.18 6.31 6.20
    075 X1 A1 B1 P1 Q10 R0 S2 T2 U11 5.72 6.38 6.52 6.20 6.38 6.52 6.20
    076 X1 A1 B1 P1 Q10 R0 S7 T2 U4 6.65 6.71 6.88 6.45 6.71 6.88 6.45
    077 X1 A1 B1 P1 Q10 R0 S7 T1 U1 5.73 6.20 6.33 6.18 6.20 6.33 6.18
    078 X1 A1 B1 P1 Q10 R0 S7 T0 U12 6.62 6.02 6.14 6.20 6.02 6.14 6.20
    079 X1 A1 B1 P1 Q10 R0 S7 T0 U13 5.73 6.37 6.52 6.20 6.37 6.52 6.20
    080 X1 A1 B1 P1 Q30 R0 S2 T0 U14 6.94 6.73 6.89 6.45 6.73 6.89 6.45
    081 X1 A1 B1 P1 Q30 R0 S2 T1 U1 7.83 6.21 6.34 6.18 6.21 6.34 6.18
    082 X1 A1 B1 P1 Q30 R0 S2 T0 U15 6.91 6.04 6.16 6.20 6.04 6.16 6.20
    083 X1 A1 B1 P1 Q30 R0 S2 T0 U16 5.87 6.38 6.52 6.20 6.38 6.52 6.20
    084 X1 A1 B1 P0 Q31 R0 S2 T1 U1 7.86 6.69 6.86 6.68 6.69 6.86 6.68
    085 X1 A1 B1 P0 Q31 R0 S2 T0 U0 6.27 6.52 6.67 6.68 6.52 6.67 6.68
    086 X1 A1 B1 P0 Q31 R0 S4 T0 U0 5.97 6.03 5.28 6.18 6.03 5.28 6.18
    087 X4 A1 B1 P0 Q4 R0 S0 T0 U0 3.47 3.23 4.77 4.17 3.23 4.77 4.17
    088 X5 A1 B1 P0 Q4 R0 S0 T0 U0 3.17 3.74 4.45 3.69 3.74 4.45 3.69
    089 X6 A1 B1 P0 Q4 R0 S0 T0 U0 3.15 3.40 4.09 3.93 3.40 4.09 3.93
    090 X7 A1 B1 P0 Q4 R0 S0 T0 U0 3.16 3.40 4.95 4.18 3.40 4.95 4.18
    091 X8 A1 B1 P0 Q4 R0 S0 T0 U0 3.17 3.74 4.45 3.69 3.74 4.45 3.69
    092 X9 A1 B1 P0 Q4 R0 S0 T0 U0 4.11 3.60 4.30 4.17 3.60 4.30 4.17
    093 X1 A1 B1 P0 Q32 R0 S0 T0 U0 6.35 5.77 5.87 6.18 5.77 5.87 6.18
    094 X1 A1 B1 P1 Q33 R0 S0 T0 U0 6.29 5.45 5.53 5.42 5.45 5.53 5.42
    095 X1 A1 B1 P1 Q34 R0 S0 T0 U0 6.29 5.45 5.53 5.68 5.45 5.53 5.68
    096 X1 A1 B1 P1 Q35 R0 S0 T0 U0 6.46 5.67 5.76 5.68 5.67 5.76 5.68
    097 X1 A1 B1 P1 Q36 R0 S0 T0 U0 5.44 6.01 5.26 4.70 6.01 5.26 4.70
    098 X1 A2 B1 P1 Q12 R0 S0 T0 U0 3.20 5.97 6.08 5.93 5.97 6.08 5.93
    099 X1 A2 B1 P0 Q4 R0 S0 T0 U0 3.13 5.22 5.29 4.92 5.22 5.29 4.92
    100 X1 A2 B1 P0 Q5 R0 S0 T0 U0 3.13 5.24 5.31 5.17 5.24 5.31 5.17
    101 X1 A2 B1 P1 Q21 R0 S0 T0 U0 3.11 5.12 5.18 4.92 5.12 5.18 4.92
    102 X1 A2 B1 P0 Q8 R0 S0 T0 U0 3.08 4.83 4.87 4.67 4.83 4.87 4.67
    103 X1 A2 B1 P1 Q5 R0 S0 T0 U0 3.16 5.58 5.67 5.42 5.58 5.67 5.42
    104 X1 A2 B1 P1 Q13 R0 S0 T0 U0 3.20 5.88 5.99 5.93 5.88 5.99 5.93
    105 X1 A3 B1 P1 Q5 R0 S0 T0 U0 3.16 5.58 5.67 5.42 5.58 5.67 5.42
    106 X1 A3 B1 P1 Q13 R0 S0 T0 U0 3.20 5.88 5.99 5.19 5.88 5.99 5.19
    107 X1 A3 B1 P1 Q12 R0 S0 T0 U0 3.20 5.97 6.08 5.93 5.97 6.08 5.93
    108 X1 A3 B1 P2 Q5 R0 S0 T0 U0 3.18 5.32 5.39 5.42 5.32 5.39 5.42
    109 X1 A3 B1 P1 Q21 R0 S0 T0 U0 3.11 5.12 5.18 4.92 5.12 5.18 4.92
    110 X1 A3 B1 P0 Q32 R0 S0 T0 U0 3.23 6.02 6.14 5.69 6.02 6.14 5.69
    111 X1 A4 B1 P1 Q33 R0 S0 T0 U0 3.18 5.71 5.81 4.94 5.71 5.81 4.94
    112 X1 A4 B1 P0 Q33 R0 S0 T0 U0 4.07 4.89 4.07 4.43 4.89 4.07 4.43
    113 X1 A4 B1 P0 Q37 R0 S0 T0 U0 4.03 4.93 4.11 4.18 4.93 4.11 4.18
    114 X1 A4 B1 P0 Q5 R0 S0 T0 U0 4.05 4.93 4.11 4.18 4.93 4.11 4.18
    115 X1 A5 B1 P0 Q5 R0 S0 T0 U0 4.95 4.93 4.11 4.92 4.93 4.11 4.92
    116 X1 A5 B1 P1 Q11 R0 S0 T0 U0 4.07 5.19 4.39 5.42 5.19 4.39 5.42
    117 X1 A5 B1 P1 Q5 R0 S0 T0 U0 4.67 5.49 4.71 5.17 5.49 4.71 5.17
    118 X1 A5 B1 P1 Q13 R0 S0 T0 U0 4.71 5.62 4.85 5.68 5.62 4.85 5.68
    119 X1 A5 B1 P2 Q5 R0 S0 T0 U0 3.18 4.98 4.16 5.17 4.98 4.16 5.17
    120 X1 A5 B1 P1 Q10 R0 S1 T0 U0 4.73 5.55 4.77 5.68 5.55 4.77 5.68
    121 X1 A5 B1 P1 Q10 R0 S2 T1 U1 5.45 6.21 5.48 6.18 6.21 5.48 6.18
    122 X1 A1 B2 P0 Q10 R0 S2 T0 U14 9.65 9.71 9.75 9.46 9.71 9.75 9.46
    123 X1 A1 B2 P0 Q10 R0 S2 T1 U0 9.94 9.20 9.20 9.19 9.20 9.20 9.19
    124 X1 A1 B2 P0 Q10 R0 S2 T0 U15 8.62 9.02 9.02 9.21 9.02 9.02 9.21
    125 X1 A1 B2 P0 Q10 R0 S2 T0 U16 9.33 9.37 9.38 9.21 9.37 9.38 9.21
    126 X1 A1 B2 P0 Q10 R0 S2 T0 U0 8.34 8.60 8.56 8.69 8.60 8.56 8.69

    Note: Compd: Compound; a:Tangallapy et al., 2004, 2005, 2007a, 2007b, Sun et al., 2009.

     | Show Table
    DownLoad: CSV
    Figure 3.  Fragments of nitrofuran derivatives.
    DownLoad: Full-Size Img PowerPoint

    All active compounds were separated into training and test sets. For this, every fifth compound of active analogues has been positioned in the test set for the validation of the generated models. Table 2 shows the activity ranges in training and test set compounds. For all these active compounds, the plot of the activity Vs chosen descriptors indicated compound 119 as an outlier. The reason may be that it is less active (3.185).

    Table 2.  Distribution of antitubercular activities in training and test set compounds.
    Sets Compounds Activity spread
    Total 126 Max Min Avg SD
    Training set 89 9.94 3.15 5.61 1.2734
    Test set 22 9.33 3.16 5.78 1.2965
     | Show Table
    DownLoad: CSV

    2.2.   Descriptor

    In the Dragon software, the compounds have resulted in 529 0D-2D descriptors. All those descriptors which were intercorrelated beyond 0.95 (r ≥ 0.95) and correlated less than 0.1 with the biological end points (descriptor vs. activity, r ≤ 0.1) were omitted from the study. This has compacted the descriptors to 184 descriptors for investigating antitubercular activity. The QSAR model generation and validation have been done using the combinatorial protocol of multiple linear regression (CP-MLR) [32] and partial least squares (PLS) analysis. As the number of descriptors involved in this study is still very large, only those features recognized in the models have been focused on in the discussion.

    2.3.   Model development

    CP-MLR is one of the filter-based approaches for the importance of variables in the regression study at different stages of model development [32]. The four filters collectively regulate inter-parameter correlations, t-values of coefficients, multiple correlation coefficient and uniformity of the models through cross-validated R2 or Q2 with a leave-one-out (LOO) strategy.

    3.   Results

    3.1.   Indicator parameter study

    Biological response of a chemical entity may be viewed as a cumulative influence of individual components of the structure. The occurrence or nonappearance of individual structural components reflects in the activity of the compound. In view of this, for a quick structure activity assessment, the antitubercular activities of all compounds have been analyzed in terms of indicator parameters (Eq (1)). The definitions of these indicator parameters are given in Table 3.

    Table 3.  Definitions of indicator parameter.
    Parameter Indicator
    I1 If the compound has a furan ring, I1 takes a value of 1; otherwise, it is 0.
    I2 If the compound has a furan ring substituted by nitro group, I2 takes a value of 1; otherwise, it is 0.
    I3 If the compound has a piperazine and benzodiazepine rings, I3 takes a value of 1; otherwise, it is 0.
    I4 If the compound has an isoxazole ring, I4 takes a value of 1; otherwise, it is 0.
     | Show Table
    DownLoad: CSV

    In the statistic of regression equations, n is the number of compounds, r2 is the squared correlation coefficient of multiple linear regression, Q2 is cross-validated R2 from leave-one-out (LOO) procedure, Q2L3O is cross-validated R2 from leave 3 compounds out (randomly leave-three-out) procedure, s is the standard error of the estimate, F is the ratio between the variances of calculated and observed activities and r2t is the test set r2 value. The r2yrand (max) is the mean squared multiple correlation coefficients of the randomized activity (Y) from 100 regressions, with its maximum value in parentheses. This clearly shows the absence of chance correlation in the models. The values given in the parentheses immediately after the regression coefficients are their standard errors. The predicted activities of training compounds are in agreements with their experimental values. The predicted activities of test compounds using Eq (1) are statistically in acceptable limits. Furthermore, the compounds with uncertain activity were predicted to be less active. The positive regression coefficient of I1 in the regression equation indicates the favorable nature of the furan ring for antitubercular activity. Its replacement by other moieties like pyrrole and thiophene decreases the activity. The indicator I2 defined for the presence of a nitro group at the 5-positon of furan ring suggests its importance for activity of the compounds. The indicator I3 was introduced to account for the piperazine and benzodiazepine moieties in structure and represents their positive contribution to the activity. The I4 represents presence (or absence) of an isoxazole group in the structure. Its regression coefficient suggests in favor of this moiety for antitubercular activity.

    3.2.   Three parameter study

    The QSAR of the antitubercular activity of nitrofurans were also investigated in CP-MLR for three parameter equations using the 0D to 2D descriptors from the Dragon software [26]. The equations identified in CP-MLR shared 16 descriptors among themselves (Table 4). Eqs (2)–(5) are typical three parameter models from the identified ones. Also, the equations identified in the study have reasonably well predicted most of the highly active compounds in the training and test sets. However, in the training set some of the low active compounds (e.g., compounds 80, 84 and 86) were predicted about one to two orders more than their observed activity. It is very relevant to note that in congeneric series of compounds, certain modifications drastically alter the biological response of the altered analogue. Unlike the biological response, the physicochemical and molecular properties of congeners only show gradual variation in their values. For brevity, the agreement between the observed and predicted antitubercular activities of the compounds from Eq (2) is shown in Figure 4.

    Figure 4.  The plot of observed versus predicted antitubercular activity (−log MIC) of nitrofuran derivatives (Table 1) from Eq (2). The test and training set compounds are shown by (▲) and (o), respectively.
    DownLoad: Full-Size Img PowerPoint

    3.3.   Four parameter study

    The following are selected four parameter equations derived from the 16 identified descriptors listed in (Table 4). The parameters convey the same meaning as discussed as above. The plot of observed vs. predicted antitubercular activity (−log MIC) of nitrofuran derivatives from Eq (6) is shown in Figure 5.

    Figure 5.  The plot of observed versus predicted antitubercular activity (−log MIC) of nitrofuran derivatives (Table 1) from Eq (6). The test and training set compounds are shown by (▲) and (o), respectively.
    DownLoad: Full-Size Img PowerPoint
    Table 4.  Information content of the descriptors appearing in Eqs (2)–(8).
    S.No Descriptors Classes/Descriptors Descriptor Information
    Constitutional
    1 SS Sum of Kier-Hall electrotopological states.
    2 nDB No. of double bonds.
    3 nS No. of sulphur atoms.
    Topological Descriptors
    4 IC1 Information content index (neighborhood symmetry of 1-order).
    5 T(O…S) Sum of topological distance between O and S.
    Galvez Topological Charge Indices Descriptors
    6 GGI2 Topological charge index of order 2.
    7 GGI8 Topological charge index of order 8.
    8 GGI9 Topological charge index of order 9.
    2D-Autocorrelations Descriptors
    9 GATS4P Geary-autocorrelation-lag 4/weighted by atomic polarizabilities.
    Functional Group Descriptors
    10 nNO2Ph No. of nitro groups.
    11 nRSR No. of sulfides.
    12 nHAcc No. of acceptor atoms for H-bonds (NOF).
    Atom Centered Fragments
    13 C-025 R-CR-R.
    14 C-032 X-CX-X.
    15 H-051 H-attached to alpha carbon.
    16 N-076 Ar-NO2/R-N-(R)-O/RO-NO2.
     | Show Table
    DownLoad: CSV

    The indicator parameters identified in the study have displayed significance in some of the foregoing equations as well, and improved the overall significance of all the models. Inclusion of the indicator parameter I2 in Eqs (2) and (6) has improved the r2 value to 0.759 (three parameter equation) and to 0.765 (four parameter equation) in Eqs (8) and (9), respectively.

    3.4.   PLS analysis

    A PLS analysis has been applied to 16 descriptors acknowledged from the CP-MLR descriptors to enable the development of a single-window structure–activity model. It also gives a chance to assess relative importance to the descriptors. The descriptors were autoscaled (zero mean and unit SD) to give each one of them equal importance in the PLS analysis. In the PLS cross-validation two components were found to be best for describing 16 descriptors, and they explained 75.71 percent of the variance (r2 = 0.757, s = 0.634, F = 134.04) in the activity of the training set compounds and 76.8 percent variance in the activity of test set compounds (r2t = 0.768). The MLR-like PLS coefficients of these 16 descriptors are shown in Table 5. For the sake of evaluation, the plot display goodness of fit amongst observed and predicted activities (through PLS analysis) for the training and test-set compounds (Figure 6). The plot of fraction involvement of normalized regression coefficients of these descriptors to the activity is shown in Figure 7.

    Figure 6.  The plot of observed versus PLS predicted (Table 5) antitubercular activity (−log MIC) of nitrofuran derivatives. The test and training set compounds are shown by (▲) and (o), respectively.
    DownLoad: Full-Size Img PowerPoint
    Figure 7.  Plots of fraction contribution of MLR-like PLS coefficients (normalized) (Table 5) of 16 descriptors. The horizontal axis refers to the descriptors numbers as shown in Table 5.
    DownLoad: Full-Size Img PowerPoint
    Table 5.  MLR-like PLS model for the antitubercular activity of nitrofuran derivatives (Table 3) from the 16 descriptors of Eqs (2)–(7).
    S. No. MLR-like PLS equation
    		 −logMIC
    Descriptor MLR-like coeff (f.c)a
    1 Ss 0.009184 (0.051056)
    2 nDB −0.27353 (−0.09041)
    3 nS −0.24087 (−0.05315)
    4 IC1 −0.29546 (−0.03882)
    5 T(O…S) −0.00232 (−0.01709)
    6 GGI2 0.172361 (0.068364)
    7 GGI8 1.429797 (0.066809)
    8 GGI9 1.731782 (0.065156)
    9 GATS4p 0.457094 (0.023938)
    10 nNO2Ph 0.435595 (0.058818)
    11 nRSR −0.02468 (−0.00454)
    12 nHAcc 0.077507 (0.061746)
    13 C-025 0.185328 (0.07147)
    14 C-032 −0.15135 (−0.01471)
    15 H-051 0.746691 (0.166619)
    16 N-076 0.817919 (0.147313)
    Constant 2.749363
    Regression statistics
    n 89
    r2 0.757
    Q2 0.741
    Q2L3O 0.743
    s 0.634
    F 134.0418
    External test set
    r2t 0.768

    Note: a: coefficients of MLR-like PLS equation in terms of descriptors for their original values; fc is fraction contribution of regression coefficient, computed from the normalized regression coefficients obtained from the autoscaled (zero mean and unit s.d) data.

     | Show Table
    DownLoad: CSV

    3.5.   Docking of nitroreductase inhibitors

    The compound 123 binding energy found to be −8.84 kcal/mol and considered to be most effective at −8.84 kcal/mol. It binds effectively to the binding site Tyr50, Pro47, Leu25, Ala38, Ala37, Tyr97, Tyr91, Gly41 like the Ddn-PA-824 complex (PDB code 3R5R) as shown in Figure 8. The second-best compound, no. 122 was found to be effective at −7.79 kcal/mol (Figure 9).

    Figure 8.  (i): 2D interaction of compound 123 in the active site of Ddn-PA-824 (3R5R). (ii): 3D interaction of compound 123 in the active site of Ddn (3R5R), the compound represented in ball and stick format in green, with interacting residues labeled in black.
    DownLoad: Full-Size Img PowerPoint
    Figure 9.  (i): 2D interaction of compound 122 in the active site of Ddn-PA-824 (3R5R). (ii): 3D interaction of compound 122 in the active site of Ddn (3R5R), the compound represented in ball and stick format in pink, with interacting residues labeled in black.
    DownLoad: Full-Size Img PowerPoint

    4.   Discussion

    The 16 identified variables shown in Table 4 are from the Constitutional, Functional, Atom Centered Fragments, Topological, Galvez, and 2D autocorrelation classes of Dragon descriptors [26]. They are briefly described in Table 4. The 0D descriptors nDB (Eq (4)), nS (Eqs (3) and (6)), and SS are from the Constitutional class. The descriptor nDB (Eq (4)) signifies the number of sequestered double bonds in the molecule. In the compounds, this addressed the carbonyl moiety of the amide group located in the different parts. Its negative regression coefficient implies supports a minimum number of such functions in the different parts of the nitrofuran derivatives for better activity. The descriptor nS (Eqs (3) and (6)) denotes no. of sulphur atoms. Its negative regression coefficient advises that fewer sulphur atoms would be favorable for activity. The descriptor SS represents the sum of Kier-Hall electrotopological states. It has positively influenced the activity, indicating the preference for a higher SS for better inhibition.

    The counts of functional groups nNO2Ph (Eqs (2) and (7)), nRSR (Eqs (5) and (7)) and nHAcc are 2D descriptors. Descriptor nNO2Ph represents the number of aromatic nitro groups in the molecule. Its positive regression coefficient supports antitubercular activity. The descriptor nRSR represents the number of R-S-R groups in various regions of nitrofuran analogue, namely, thiazole, morpholine and thiophene etc. Its negative regression coefficient suggests the unfavorable nature of this fragment in the molecular structure for the activity. The positive regression coefficient of descriptor nHAcc recommends the use of acceptor atoms for hydrogen bonding for improving the activity.

    The descriptor T(O…S) and IC1 are from the topological class (TOPO). They are 2D graph theoretical descriptors from molecular topology and sensitive to changes in the molecules' size, shape, symmetry, branching, and cyclicity, etc. The descriptor T(O…S) represents the sum of topological distance between S and O atoms in the molecules. Its negative regression coefficient advocates that minimum distance between sulphur and oxygen will be advantageous for the activity. The descriptor IC1 measures the information content of 1st order neighborhood symmetry in the molecules. The negative coefficient of this descriptor suggests its unfavorable nature for activity. The other participating 2D descriptors GGI2 (Eq 4), GGI8 (Eqs (2), (3) and (5)–(7)) and GGI9 are Galvez topological charge indices (GALVEZ). They are from the first 10 eigenvalues of the multinomial of corrected adjacency matrix of the compounds. All the GALVEZ class descriptors consist of two classes. Of this, one class corresponds to the topological charge index of order n (GGIn), and the other corresponds to the mean topological charge index of order n (JGIn), where “n” denotes the order of eigenvalue. The positive influences of descriptors GGI2, GGI8 and GGI9 (topological charge indices of second, eighth and ninth order, respectively) proposed that a higher values of second, eighth, and ninth order charge indices would be beneficial for the activity.

    Conventionally, molar refractivity (MR), hydrophobicity and Hammett's sigma are a few elements for unfolding the drug receptor interactions. The non-obtainability of proper substituents is repeatedly considered as a restriction for these parameters. It is particularly true for Hammett's sigma constants. The Dragon software offers the estimations of hydrophobicity and molar refractivity of the compounds under descriptor names MLogP and MR, respectively. For the nitrofurans under analysis, the MLogP and MR have not inserted it to any model within the limits of set perimeters. Though, considering the significance of hydrophobicity and molar refractivity in modeling drug-receptor interactions, we have prolonged the study to re-examine the possibility of MLogP and MR along with the identified descriptors for describing the activity of nitrofuran derivatives. For the dataset under study, the relationship between MLogP and the activity is 0.502 (r = 0.502), while the connection between MR and the activity is 0.541 (r = 0.541). Also, MR and GGI8 are intercorrelated (r = 0.877). The GGI8 Galvez class descriptor is a measure of topological charge indices of eighth order. In the regression model (Eq (2)) the parameters GGI8 and MR appear to be exchangeable without causing any destruction (Eq (10)). Here, the positive coefficient of molar refractivity indicates the function of distribution or van der Waals forces in drug-receptor interactions.

    To identify the binding modality of nitroreductase, nitrofuran active derivatives were docked into the ligand-binding site of the deazaflavin-dependent nitroreductase (Ddn)(PDB code 3R5R). The binding energies were then studied and found to be proportional with the -log MIC values for maximum inhibitors. Compound no. 123 binded effectively to the binding site Tyr50, Pro47, Leu25, Ala38, Ala37, Tyr97, Tyr91, Gly41 like the Ddn-PA-824 complex, as shown in Figure 8. The presence of isoxazole linkage in place of amide linkage may be the reason for the highest dock score among the compounds. Notably, the residues Ser39, Lys40 Tyr26, Met48 are involved in the three conventional hydrogen bond formations with the isoxazole, furan and piperazine moieties, respectively. This information implies that the ligand has the ability to form conventional hydrogen bonds and lead towards the stability of protein-ligand complex with better binding affinity than the PA-824. The unique stability of the drug is due to the large number of pi-interactions, such as pi-pi interactions with Tyr 91 and Tyr94 other pi-interactions involved Tyr94 and Tyr49, pi-alkyl interactions with Tyr 91 and finally pi-sigma interactions with Tyr 26. The second-best binding energy of −7.79 kcal/mol was attained by compound no. 122. The hydroxyl group of residues Tyr26 and phenyl ring are involved in the non-covalent interactions between the π-bonds of aromatic rings (Figure 9). The moderately active compounds (compound no. 46 and compound no. 59) showed dock scores −5.92 and −5.95 kcal/mol, respectively. There were hydrophobic interactions with Tyr 97 and Lys 40. Only two hydrogen bond interactions were found between oxygen of nitro group and Lys 40 and Met 48 (compound no. 46). The oxygen of thiomorpholine 1,1-dioxide with Ser 39 and Tyr 94 involve in the hydrogen bond formation (compound no. 59). The least active compound (compound no. 89) docking score found to be −5.55 kcal/mol. The nitro group in the A region replaced by a methylsufinyl group and involvement of steric feature in the Q region leads to the least active compound. The hydrogen bond is involved between the carbonyl carbon of the B region and residue TRP 49. The hydrophobic interactions such as alkyl-alkyl and pi-alkyl were also found.

    Other reported nitrofuran derivatives 62, 70, 84, 102 and 125 were found to have necessary features required to enter the Ddn catalytic pocket and irreversibly reside in it. The development of the covalent complex implied that the enzyme would be permanently damaged, resulting in the release of lethal reactive nitrogen species (RNS) within the mycobacteria. Indeed, more work is required to confirm that the Ddn is a target for the nitrofuran derivatives.

    5.   Conclusions

    The quantitative structure-activity relationships (QSAR) of the antitubercular activities of 126 nitrofuran derivatives have been analyzed in terms of different indicator parameters and 0D-2D Dragon descriptors using CPMLR and partial least squares (PLS) procedures. For this study 89 compounds are in the training set, and 22 compounds are in the test set. The 16 descriptors identified in CP-MLR are from six different classes Constitutional, Functional, Atom Centered Fragments, Topological, Galvez, and 2D autocorrelation. The identified 3-parameter and 4-parameter models from CP-MLR have explained about 72% and 76 % variance, respectively, in the training set and equally well predicted the activity of test set compounds. The PLS analysis of the 16 descriptors has resulted in a 2-component model and explained 75.7 percent variance (r2 = 0.757, S = 0.634, F = 134.04) in the activity of the training set compounds and 76.8 per cent variance in the activity of test set compounds (r2t = 0.768).

    Indicator parameters and Dragon descriptors suggest the presence of a furan ring substituted by a nitro group is essential for antitubercular activity. Further descriptors from Constitutional, and Functional classes propose that the number of double bonds, number of sulphur atoms and number of fragments like thiazole, morpholine and thiophene should be minimum along with the positive influence of Kier-Hall electrotopological states (Ss) for improved activity. The ACF class descriptors N-076, H-051, C-025 and C-032 have also shown prevalence in the activity. The TOPO class descriptor T(O…S) suggests that minimum distance between sulphur and oxygen is favorable for activity. The GALVEZ class descriptors GGI2, GGI8 and GGI9 advocated that higher values of second, eighth and ninth order charge indices would be valuable for the activity. The 2D-AUTO descriptor GATS4p shown positive influence on the antitubercular activity. The PLS analysis has also confirmed the importance of information content of CP-MLR identified descriptors for modeling the antitubercular activity as compared to the leftover ones. In addition, exploration of mycobacterial cell enzymes with bioinformatic tools and different ligands of mycobacteria's protein co-crystals indicated nitroreductase as the most probable target of these compounds. Further optimization of highly active compounds may result in effective antitubercular agents.

    Use of AI tools declaration

    The authors declare they have not used Artificial Intelligence (AI) tools in the creation of this article.



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