ABSTRACT
Quantitative structure activity relationship (QSAR) study was carried out using B3LYP (6-31G) variant of density functional theory and genetic function approximation (GFA) techniques to correlate the chemical structures of coumarin and neolignams derivatives and their biological activities against resistant strains of Candida albicansand Epidermophyton floccosum.QSAR models generated were evaluated using internal as well as external test set predictions and applicability domain.The results showed that thermodynamic, dimensional, steric, geometrical, quantum-chemical, WHIM, electrotopological state, moment of inertia, lipophilicity and electronic descriptors were responsible for the biological activities of thecompounds.The proposed models provided a good understanding of the anti-Candida albicans andanti-Epidermophyton floccosum activity of Coumarin and Neolignans derivatives and could be used as guidance for proposition of more potent and safer chemopreventive agents,within the four series of chemical compounds.
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TABLE OF CONTENTS
Cover page i Fly Leaf ii Title page iii Declaration iv Certification v Dedication vi Acknowledgement vii Abstract viii Table of Content ix List of Tables xvi List of Figures xvii Abbreviation xx CHAPTER ONE 1.0 INTRODUCTION 1
1.1 Background of the Study 1
1.1.1Occurences of Candida albicans and Epidermophyton floccosum 4 1.1.2Toxicities of Candida albicans and Epidermophyton floccosum 6 1.1.3Epidemiology and Economic Impacts of Candida albicans and Epidermophyton floccosum 7 1.1.4Historical Efforts in Curing and Managing C.albicans and E. floccosum 7
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1.1.5 Risk Factors of Candida albicans and Epidermophyton floccosum 16 1.2Statement of the Research Problem 18 1.3Significance of the Study 20 1.4The Research Questions 20 1.5TheResearch Hypothesis 20 1.6Research Design 211.7Theoretical Framework of the Study 21 1.8Aim and Objectives of the Study 23 1.9Scope and Limitations of the Study 23 CHAPTER TWO 2.0 LITERATURE REVIEW 23 2.1Candida albicans and Epidermophyton floccosum 23 2.2 Treatment Protocols of C. albicans andE. floccosum 26 2.3 Antimycotic Drugs of Candida albicans and Epidermophyton floccosum 27 2.3.1 Pyrimidine Analogues 27 2.3.2 Azoles 27 2.2.3 Polyenes 28
2.3.4 Echinocaudins 28
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2.3.5 Allylamins 28 2.3.6 Griseofulvin 29 2.4 Evaluation of Drug Resistance by C. albicans and E. floccosum to Antimycotics 29 2.5 Exploiting Compounds Active Against New Target 29 2.5.1 Coumarins 29 2.5.2 Neolignans 33 2.6 Need for an Alternative Tools 33 2.7 QSAR Study in Computer Aided Drug Design 42 2.7.1Steps Involve in Building QSAR Models 43 2.8 Previous QSAR Works on Anti-Candida albicans and Epidermophyton floccosumMolecules 43 2.8.12D-QSAR Study on 1-Acetyl-3-Aryl-5- ( 4-Methoxyphenyl ) Pyrazole Analogues as an Antifungal Agents 43 2.8.2 QSAR and Docking Studies of Coumarin Derivatives as Potent HIV-1 integrase Inhibitors 45 2.8.3 2D and 3D QSAR Studies of Flavonoids , Biflavones and Chalcones : Antiviral, Antibacterial , Antifungal and Anti-mycobacterial Activities 46 2.8.4 Antifungal and Antibacterial Activities of Imidazolylpyrimidines Derivatives and their QSAR Studies 46 2.8.5 QSAR Studies , Design , Synthesis and Antimicrobial Evaluation of Azole Derivatives 46 2.8.6 QSAR Studies of some Pyrazolones as Antimicrobial Agent 47 2.8.7 3D-QSAR ofProtein Tyrosine Phosphatase 1b Inhibitors by Genetic Function Approximation 47
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2.8.8 Chemistry by Computer: an Overview of the Applications of Computers in Chemistry 48 2.8.9 Pharmacophore Modeling and Atom-based 3D-QSAR Studies of Antifungal Benzofurans 48 2.8.10 QSAR Modeling of Antifungal Activity of Some Heterocyclic Compounds 48 2.8.11 Medicinal Chemistry QSAR Analysis of n-myristoyl Transferase Inhibitors : Antifungal Activity of Benzofurans 49 2.8.12 Synthesis, Antifungal Activity Evaluation and QSAR Studies on Podophyllotoxin Derivatives. 49 2.7.13 2D QSAR Studies on a Series of Bifonazole Derivatives with Antifungal Activity 50 2.7.14 QSAR Study on 2, 5-disubstituted 1,3,4-oxadiazoles as Antifungal Agents 50 CHAPTER THREE 3.0 MATERIALS AND METHODS 52 3.1 Materials 52 3.2 Methods 52 3.2.1 Collection of Chemical Data 52 3.2.2 Optimization of the Geometry of the Molecular Structure 74 3.2.3 Descriptor Calculation 83 3.2.4 Normalization of Data 91 3.2.5 Training Set and Test Set 93 3.2.6 Learning Process 93 3.2.7 Evaluation of the Validity and Predictability of the Developed QSAR Models 103
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3.3 Multicollinearity Among Descriptors 116 3.4 Applicability Domain (AD) 121 3.4.1 Procedure for Computing Applicability Domain of William‘s plot 122 CHAPTER FOUR 4.0 RESULTS 132 4.1 Results of Molecular Optimization 132 4.2 Descriptor Calculation 132 4.3 GFA Derived Models for MIC of Anti-Candida albicans Compounds 132 4.3.1 Univariate Analysis of the pMIC Value of Model 1, 4, 7 and 10 136 4.3.2 Plot of Experimental Against Predicted pMIC of Model 1, 4, 7 and 10 136 4.3.3 Residual Plot of Model 1, 4, 7 and 10 136 4.3.4 Comparison of Observed and Predicted pMIC of Model 1, 4, 7 and 10 136 4.3.5 External Validation of Model 1, 4, 7 and 10 136 4.3.6 Variance Inflation Factor for the Descriptors in Model 1, 4, 7 and 10 136 4.3.7 Outlier Analysis of Model 1, 4, 7 and 10 136 4.3.8 William‘s Plot applicability domain of Model 1, 4, 7 and 10 137 CHAPTER FIVE 5.0 DISCUSSION OF RESULTS 175
5.1 Molecular Optimization and Descriptor Calculation 175
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5.2 GFA Derived QSAR Model for anti-Candida albicans Activity of model-1 compounds 175 5.2.1 Significance of the Descriptors in Model 1 177 5.3 GFA Derived QSAR Model for Anti-Candida albicans Activity of Model-4 Compounds 179 5.3.1 Significance of the Descriptors in the Model 4 181 5.4 GFA Derived QSAR Model for Anti-Candida albicans Activity of Model-7 Compounds 182 5.4.1 Significance of the Descriptors in the Model 7 184 5.5 GFA Derived QSARModel for anti-Epidermophyton floccosumActivity of Model-10 Compound 186 5.4.1 Significance of the Descriptors in the Model 10 188 CHAPTER SIX 6.0 SUMMARY, CONCLUSION AND RECOMMENDATIONS 190 6.1 Summary 190 6.2 Conclusion 190 6.3 Recommendation 191 6.4 References 192
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CHAPTER ONE
1.0 INTRODUCTION
1.1 Background of the Study
Candidiasis (Plate 1.1) is a diverse group of infections caused by members of the genus Candida, especially Candida albicans(Havlickova et al., 2008). These organisms typically infect the skin, nails, mucous membranes and gastrointestinal tract when natural resistance to its overgrowthis reduced. Cutaneous and mucosal manifestations of candidiasis can be divided into several distinct clinical syndromes. Thirty years ago, superficial fungal infections were common, but systemic fungal infections were not as frequent as today (Achkar and Fries, 2010). The incidence of superficial and systemic fungal infection has been increasing because of the increasing incidence of severe diseases (e.g. malignancies or HIV-infection) or immunosuppressant therapies (systemic steroids or chemotherapy) (Havlickova et al., 2008).
Epidermophyton floccosum (Plate 1.2) on the other hand contain a number of species from three different genera (Durdu et al., 2017) which invade the keratinized layers of skin, hair and nail causing infections known as dermatophytosis (Peter et al, 2010). Dermatophyte infections are frequently encountered and are responsible for 90% of the dermatophyte infections in Britain (Peter et al.,2010). In experiments designed to extend the original finding of (Longbottom and Pepys, 1964)with the dermatophyte, many skin diseases such as tinea and ringworm caused by dermatophytes exist in tropical and semitropical areas.
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Plate 1.1: Candida albicans infection (public health library)
3
Plate 1.2: Epidermophyton floccosum (CDC Public image Library)
4
1.1.1Occurences of Candida albicans and Epidermophyton floccosum
Candida albicans is the most common type of yeast infection found in the mouth, intestinal tract and vagina, and it may affect skin and other mucous membranes. If the immune system is functioning optimally, this type of yeast infection is rarely serious. However, if the immune system is not functioning properly, the candida infection can migrate to other areas of the body, including the blood and membranes around the heart or brain.The organism is part of the normal microbial flora in human beings and domestic animals, and is associated with the mucous surfaces of the oral cavity, gastrointestinal tract and vagina. Immune dysfunction can allow this organism to switch from a commensal to a pathogenic organism capable of infecting a variety of tissues and causing a possibly fatal systemic disease (Noverr et al., 2004; Noverr et al., 2001; Traynor and Huffnagle, 2001). The clinical spectrum of this organism infectionsranges from mucocutaneous to systemic life-threatening infections. The main risk factors that predispose to severe Candida infections are congenital or acquired defects of cell-mediated immunity (CMI), including quantitative and qualitative defects in neutrophils and dysregulated Th-cell reactivity (Blanco and Garcia, 2008).
The Epidermophyton floccosum infections are found commonly in scalp, non-hairy, glaborous region of the body, athletes foot, foot, nail, hands, barbers‘ itch‖; bearded region of face and neck, steroid modified (White et al., 2008) as shown in Plate 1.1. In general, these fungi live in the dead, top layer of skin cells in moist areas of the body, such as between the toes,the groin, and under the breasts. These fungal infections causeonly a minor irritation. Other types of fungal infections could be more serious. They can penetrate into the cells and cause itching, swelling, blistering and scaling.
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Fig 1.1: The schematic route of entry of dermatophytes into the host system. (Lakshmipathy and Kannabiran, 2010)
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1.1.2 Toxicities of Candida albicans and Epidermophyton floccosum
Fungal infections are notable for their chronicity (e.g.,dermatophyte infections) and nonprotective or injurious inflammatory responses (e.g., dermatophytoses, subcutaneous mycoses, and vaginal candidiasis). In mammals, prostaglandins and leukotrienes can play a dual role in the pathogenesis of inflammatory diseases, both promoting and counteracting inflammatory processes (Nicosia et al., 2001).
Dermatophytosis can affect all keratinized areas of the body (hair, skin and nails)(Degreef, 2008). Depending on the region that is affected, the symptoms may vary. If hair is infected (tinea capitis, tinea barbea), there may be hair loss (ectotrix) or breakage (endotrix). On the skin, lesions may look circular or annular and elevated, producing a ringworm infection form. Zoophilic dermatophyte infections are more inflammatory (vesicle, pustules and blisters) than those caused by antropophilic dermatophytes. Infection of human nails may be present as discoloration, dystrophy, hyperkeratosis and occasionally onycholysis. The disease is not fatal. The main effects are aesthetic and will persist until treated with the appropriate medication (Degreef, 2008).
1.1.3 Epidemiology and economic impacts of Candida albicans and
Epidermophyton floccosum
Since the early 1980s, fungi have emerged as major causes ofhuman disease, especially among the immuno-compromised and those hospitalized with serious underlying disease (Blumberg et al., 2001; Pfaller et al., 2007; Wilson et al., 2002). A recent study of the epidemiology of sepsis found that the annual number of cases of sepsis caused by fungal organisms in the United States increased by 207% between 1979 and 2000 (Moss, 2003). The morbidity and mortality associated with these infections are substantial (Golmohammadi and Dashtbozorgi, 2010; Gudlaugsson et al., 2003; Lin et al., 2001) and it is clear that fungal diseases have emerged as important public health
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problems (Mcneil et al., 2001). An analysis of trends in infectious disease mortality in the United States (Wu et al., 2009),found a dramatic increase in multiple-cause mortality due to mycoses, from 1,557 deaths in 1980 to 6,534 deaths in 1997; the majority of these mycoses-related deaths were associated with Candida, Aspergillus, and Cryptococcus sp. infections.
Candida species are the fourth leading cause of nosocomialbloodstream infection (BSI) in the United States, accounting for 8% to 10% of all BSIs acquired in the hospital (Wisplinghoff et al., 2004). The estimation of the number of cases of nosocomial candidemia in the United States (Ibrahim et al., 2000; Wisplinghoff et al., 2004) as shown in Table 1.1, by working backward from estimates that (i) 2.5% to 10% of all patients admitted to U.S. hospitals will develop a nosocomial infection, (ii) BSIs represent 10% of all nosocomial infections, and (iii) 8% of nosocomial BSIs are caused by Candidaspecies. Given these assumptions, the absolute number of cases of nosocomial candidemia ranges from 7,000 to 28,000 annually Table 1.1, If the crude mortality rate of Candida BSI is 40%, then 2,800 to 11,200 deaths each year may be associated with nosocomial candidemia. Given that approximately two-thirds of all Candida BSIs are nosocomial (Hajjeh et al., 2004), the total annual burden of candidemia in the United States is approximately 10,500 to 42,000 infections. These estimates are comparable to those obtained from National Hospital Discharge Survey (NHDS) statistics.
Fungal infections have become increasingly important in hospitalized patients and are a major cause of morbidity and mortality. The costs associated with treating an episode of Candidemia have been estimated at $34123– 44536, and the prolongation of hospital stay may average as much as 34 days(Rentz et al., 1998).
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Table 1.1 : Estimated annual number of cases and associated deaths due to nosocomial BSIs with Candida species in the United Statesa(Wisplinghoff et al., 2004)
.
Total nosocomial infection rate (%)
Total number of:
Nosocomial infectionsb
Nosocomial BSIsc
Nosocomial BSIs due to candidad
Deaths due to Candida BSIse
2.5
875,000
87,500
7,000
2,800
5
1750,000
175,000
14,0000
5600
10
3500,000
350,000
28,0000
11,200
bAssumes that 35 million patients are hospitalized in the United States each year in critical care settings. cAssumes that 10% of all nosocomial infections are BSIs.dAssumes that 8% of all nosocomial BSIs are due to Candida species. eAssumes a 40% mortality rate associated with Candida BSIs
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Candida spp. are the fourth most common cause of bloodstream infections in the US (Wisplinghoff et al., 2004) and Candida albicans remains the most commonly isolated species(Abi-said et al., 1997). This rising prevalence of Candida infections has been well characterized in immuno compromised patients(Fraser et al., 1992). In recent years, there has been a shift toward invasive Candidiasis and Candidemia due to non-albicans Candida spp (NAC) in hospitalized patients (Pappas et al., 2016). This is significant since many of the NAC species have variable susceptibilities to some commonly prescribed antifungal agents (Marco et al., 1998)
This pathogen occurs worldwide and infections are relatively frequent(Seebacher et al., 2008).Historically, Candida albicans accounted for 70 to 90% of the isolates recovered from infected patients while other Candida species rarely isolated from clinical specimens (Dhib et al., 2013). Table 1.2 represents epidemiological data of Candida spp. for the last 10 years. In the last few decades, there have been numerous reports of Candida infections in India (Basu et al, 2003), documented that out of sixty three isolates of yeasts (66.6%) were Candida albicans. In another study, Candida albicans is the major cause of serious fungal infections in the United States and Candida species are the fourth most commonly cultured microbe from blood(Morrell et al., 2005).
In addition to differences in the fungal ecology of the different continents, the large use of azoles antifungal agents may have contributed to this progressive shift of the epidemiology of candidemia(Eggimann et al., 2005). These infections are associated with a high mortality rate that ranges from 46 to 75%, reflecting the severity of this illness (Pfaller and Diekema, 2007).
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Table 1.2:Distribution of Candida species in epidemiological surveys of clinical isolates since the last 10 years.
Period of study
Region
Candida albicans (%)
Candida tropicalis (%)
Candida glabrata (%)
Candida parapsilosis (%)
Other Candida species (%)
1991-2000
India
14
38
3
2
26
2000-2003
Switzerland
Norway
64
70
9
7
15
13
1
6
2-9
1-3
2001
Us
55
9
21
11
2
2001-2002
Paris ICU
54
9
17
14
2-4
2001-2005
India
21.5
35.3
17.5
20
1-3.3
2003
India
45.8
24.7
1
10.5
10-38.4
2004
Japan
41
12
18
23
2
2004
France
55
5
19
13
4
2004-2008
North America
46
8
26
16
1-3
2004-2008
Denmark
57
5
21
4
9
2007-2010
Brazil
34
15
10
24
17
2007-2011
India
30
50
20
–
–
World
48
11
18
17
4
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Table 1.2:Distribution of Candida species in epidemiological surveys of clinical isolates since the last 10 years (continuation).
2008-2009
Europe
North America
55
43
75
11
6
24
14
17
3-4
2-4
Asia
57
12
14
14
2
2009
Portugal
90
3
5
2
2
2010
Turkey
36
1
–
12
2
2010-2011
Worldwide
43
7
31
20
7
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As a result of high mortality rates and prolonged length of hospital stays, Candida infections also have their impact on health care costs, which is estimated at one billion dollar per year in the US alone (Rentz et al., 1997).
Candida species uncommonly cause vertebral osteomyelitis. A case of lumbar vertebral osteomyelitis was caused by Candida albicans and review 59 cases of Candidal vertebral osteomyelitis reported in the literature. The mean age was 50 years, and the lower thoracic or lumbar spine was involved in 95% of patients. 83% of patients had back pain for 11 month, 32% presented with fever, and 19% had neurological deficits. The erythrocyte sedimentation rate was elevated in 87% of patients, and blood culture yielded Candida species for 51%. Candida albicans was responsible for 62% of cases, Candida tropicalis for 19%, and Candida glabrata for 14% (Miller and Mejicano, 2001).
Invasive infection due to Candida species is largely a condition associated with medical progress, and is widely recognized as a major cause of morbidity and mortality in the healthcare environment. There are at least 15 distinct Candida species that cause human disease, but >90% of invasive disease is caused by the 5 most common pathogens, Candida albicans, Candida glabrata, Candida tropicalis, Candida parapsilosis, and Candida krusei. Each of these organisms has unique virulence potential, antifungal susceptibility, and epidemiology (Pappas et al., 2016).
Recurrent vulvovaginal candidiasis (RVVC) can affect approximately 8% of women of reproductive age be defined as four or more attacks of symptomatic candidal vaginitis in a 12-month period (Sobel, 2003). The true incidence of RVVC remains unknown. Estimates over many years suggest that the inci coldence is approximately 5% of women during their reproductive age. A recent study by Foxman et al., which included interviewing 2000 women, determined that the incidence of RVVC in the US is
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approximately 8% of women of reproductive age (Sobel, 2003). This almost certainly represents an overestimation. The availability of over the counter (OTC) an timycotic agents precludes any prospective data collection determining the incidence of candidal viginitis in North America (Sobel, 2003).
Dermatophytes occurs worldwide and infections are relatively frequent (Clayton and Hospital,1992).The main pathogen in northern Europe and North America is Trichophyton rubrum, and Microsporum canis. Zoophilic dermatophytes are more common in southern Europe and Arabic countries. Farm worker are more susceptible to infection with tinea barbea (Guo et al., 1996). In the case of tinea cruris, men are more susceptible than women (Marchisio et al., 1996).
The distribution of dermatophytes (in Germany), isolated from skin and nail lesions has changed significantly within the last 100 years Epidermophyton floccosum and Microsporum audouiniidominated among human pathogenic dermatophytoses in the twenties, whereasTrichophyton rubrum was almost insignificant (Ghannoum and Rice, 1999) . A completely different spectrum of dermatophyte species has been reported from Islamic states like Isfahan, Iran, Crete and Greece among others. A total of 1,213 patients from the Tehran area, Iran,suspected to have dermatophytic lesions were examinedover a 3-year period (1999–2001), in this study, Epidermophyton floccosum was the most frequent dermatophyte (31.4%) (Falahati et al., 2003). In another study encompassing 2,203 patients with dermatomycoses in Isfahan, Iran, the prevalence of clinical forms and of the causative agents of dermatophytoses were determined, Epidermophyton floccosum was 17.6% (Dehghan, 1997).
1.1.4 Historical efforts in curing and managing Candida albicans and
Epidermophyton floccosum
The burden of Invasive Candidiasis (IC) is tremendous in terms of morbidity, mortality and cost, and it is clear that we must do more than seek better therapeutic agents if we
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are to impact this burden (Diekema and Pfaller, 2004; Fridkin, 2005b; Wisplinghoff et al., 2004). Fungal BSIs have been shown to have some of the highest rates of inappropriate initial therapy and hospital mortality among all etiologic agents of BSI examined (Ibrahim. et al, 2000; Morrell et al., 2005). The most common cause of inappropriate therapy for fungal BSI is the omission of initial empirical therapy (Morrell et al., 2005), such omission or delay in therapy has been linked directly to mortality (Blot et al, 2002; Morrell et al., 2005; Pfaller et al., 2007). Thus, despite an impressive array of new, potent, and nontoxic antifungal agents, we are failing in the management of these infections (Miller and Mejicano, 2001; Morrell et al., 2005; Puzyn et al., 2010). Lack of specific clinical findings and slow, insensitive diagnostic testing complicate the early recognition and treatment of IC (Pfaller et al., 2006).
Given the substantial excess mortality due to candidemia and the difficulties encountered in administering early and effective antifungal therapy (Puzyn et al., 2010), better methods of prevention will decrease candidemia-associated mortality more effectively than will advances in therapy (Diekema and Pfaller, 2004; Fridkin, 2005b). Prevention of nosocomial candidemia is similar to that of many other nosocomial infections and should involve three ―low-tech‖ strategies (Diekema and Pfaller, 2004), the first being the implementation of intensive programs to maximize compliance with current hand hygiene recommendations. Such programs are essential; although seemingly simple, compliance with hand washing recommendations among health care providers occurs only 40% of the time in settings where it is indicated (Laverdiere et al., 2002). Both alcohol and chlorhexidine are effective in killing Candida species on the hands of health care workers (Apic and Didier, 2002) and will decrease the risk of patients acquiring Candida colonization and subsequent infection in the health care setting. Second, strategies to improve adherence to current recommendations for
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placement and care of central venous catheters (Fridkin, 2005b) are also necessary. An educational program emphasizing essential components of these guidelines successfully reduced catheter-related BSIs due to both bacteria and Candida in an SICU (Coopersmith et al., 2002): catheter-related candidemia was reduced from 12% of all BSIs to 0% in the 18 months following the educational program. Finally, given the importance of antibiotic exposure as a risk factor for candidemia, control of antimicrobial use, especially those with antianaerobic activity (Blumberg et al., 2001), as well as piperacillintazobactam and vancomycin (Lin et al., 2005), is an important component of candidemia prevention. These three strategies improved hand hygiene, optimal catheter placement and care, and prudent antimicrobial use should be primary in the approach to prevention of morbidity and mortality resulting from nosocomial candidemia (Diekema and Pfaller, 2004; Fridkin, 2005b). These efforts may also reduce candidemia-related costs by reducing the need to treat central line-related candidemia (Fridkin, 2005a, 2005b). The remaining preventive strategy, antifungal prophylaxis, must always be considered secondary to the three low-tech approaches and should be applied only when the rate of candidemia remains elevated despite assiduous application of these measures, preferably in a subpopulation within the ICU with a cumulative incidence of IC approaching or exceeding 10% (Diekema and Pfaller, 2004).
Antifungal prophylaxis has proven to be effective in decreasing mucosal candidiasis and IC in neutropenic patients (Marr et al., 1997). Administration of fluconazole (400 mg/day) during neutropenia has proven effective in decreasing infections due to Candidaalbicans, C. tropicalis, and C. parapsilosis(Abi-said et al., 1997; Antoniadou et al., 2003). In contrast, the relative benefits, harms and the cost-effectiveness of antifungal prophylaxis in nonneutropenic critically ill patients remain incompletely defined (Calandra and Marchetti, 2002; Sobel and Rex, 2001), with resultant wide
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variations in clinical practice (Eggimann et al., 2005; Pappas et al., 2004). Nonetheless, the implementation of targeted antifungal prophylaxis has been shown to be effective in certain ICU settings (Blumberg et al., 2001; Calandra and Marchetti, 2002; Lipsett, 2004; Vincent et al., 1998), although the generalizability of these findings has been questioned (Diekema and Pfaller, 2004; Sobel and Rex, 2001).Management of patients with systemic candidosis might include measures to prevent cross infection and handwashing with disinfectants that are active against candida (Lee et al., 1985). The cutaneous mycoses are superficial fungal infections of the skin, hair or nails. Essentially no living tissue is invaded, however a variety of pathological changes occur in the host because of the presence of the infectious agent and its metabolic products. The usual approach to the management of cutaneous infections is to treat with topical agents if possible, but nail and hair infections, widespread dermatophytosis and chronic non-responsive yeast infections are best treated with oral antifungal agent (Ellis, et al 2006).
1.1.5 Risk factors of Candida albicans and Epidermophyton floccosum
Candidasis is an acute or chronic infection produced by Candida, generally limited to the skin and mucous membranes, but it could produce a serious systemic disease (Gamboa et al., 2006). The risk factors associated with candidemia and invasive candidiasis (IC) have been well established and have not changed substantially in the past 2 decades (Diekema and Pfaller, 2004; Mun et al., 2000; Pfaller et al., 2007; Wey et al., 2015). Those determined to be independent risk factors for IC on the basis of multivariate analysis include exposure to broad-spectrum antimicrobial agents, cancer chemotherapy, mucosal colonization by Candida spp., indwelling vascular catheter (especially central venous catheter), total parenteral nutrition (TPN), neutropenia, prior surgery (especially gastrointestinal), and renal failure or hemodialysis (Blumberg et al.,
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2001; Diekema and Pfaller, 2004; Morrell et al., 2005; Mun et al., 2000; Piarroux et al., 2004; Sobel and Rex, 2001; Wey et al., 2015)
Epidermophyton floccosum on the other hand, are fungi obtaining a mid-transmittable disease which are acquired from infected animals or birds and fomites. Detection of dermatophyte Texas is correlated to epidemiological apprehension. These are important to manage infection and public health issues associated with types of Dermatophytosis. Traditionally, the dermatophytosis is normally referred to as ―tinea‖ or ―ring-worm‖ infections (Lakshmipathy et al., 2010). In humans, pruritus is a widespread symptom. The skin lesion is usually characterized by inflammation with erythema, scaling and occasionally blister formation. The habitual signs of inflammatory reactions such as redness, swelling, heat and alopecia are distinguishing at the infection position (Laksmipathy et al., 2010). The identification of dermatophytes is based on methods that focus on morphological, physiological, ecological and genetic features. Anthropophilic and zoophilic dermatophytes has mostly been recognized through internal transcribed spacer (Sharma et al., 2015).
Despite the availabilty of the aforementioned drugs for combating Candida albicans and Epidermophyton floccosum, there have been increasing cases of infections attributed to these organisms. Even more worrisome is the pace of resistance to the existing antibiotics by these pathogenic organisms pushing researchers to ask if humanity have reached the hypothetical post anti-biotic era (Alanis, 2005). This has necesitated an urgent need for new anti-Candida albicans and Epidermophyton floccosum.
Certainly, rational prediction of the biological activities of potential drugs candidates will undoubtedly helps in quick , safe and economical discovery and development of Candida albicans and Epidermophyton floccosum drugs by relegating to the
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background the old try and error methods which is costly, time consuming and constituting serious problem to the environment due to large waste usually released.This can only be achieved via Quantitative Structural Activity Relationship (QSAR) modelling. This has the knock-on effect of reducing cost, a major consideration in all commercial companies. Structure activity relationship (SAR) studies are usually carried out by making minor changes to the structure of a lead to produce analogues and assessing the effect that these structural changes have on biological activity. The investigation of numerous lead compounds and their analogues has made it possible to make some broad generalizations about the biological effects of specific types of structural changes. The success of the SAR approach to drug design depends not only on the knowledge and experience of the design team but also a great deal of luck (Puzyn et al., 2010).
1.2 Statement of the Research Problem
The development of resistance to existing anti-biotics by Candida albicans and Epidermophyton floccosum is still unacceptably high adding a serious threat in modern healthcare and contributing heavily to the world disease burden in the form of higher treatment costs, increased morbidity and mortality and in some cases permanent loss of specific drug therapies. Skin diseases causing fungus are therefore confronted with some major problems:
a. Increase resistance in strains of Candida albicans and Epidermophyton floccosum to classical drugs, the treatment costs and the fact that most antifungal drugs have only fungistatic activity, justify the search for new strategies (Jose et al., 2006).
b. Globally significant increase in the prevalence of systemic fungal infections during the past decades which is due to greater use of broad-spectrum antibiotics (Graybill, 1992).
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c. The number of antifungal drugs available at present is very small, with much greater difficulty in production, with many side-effects, and with the possibility of the appearance of resistance (Blanco and Garcia, 2008)
d. Diagnosis of these diseases can be problematic because of the difficulty of interpreting the very different clinical pictures in individuals in the presence of colonization, infection and/or disease (Blanco and Garcia, 2008).
However, the increasing problem of multi-drug resistant strains is the major challenge for the investigation and designs of novel drug candidates which are not only active against stable drug resistant Candida albicans and Epidermophyton floccosum but also shorten the length of therapy. Also the intimidating cost of drug development both in terms of money and time has contributed to fungi drug development.
1.3 Significance of the Study
This study will provide information on the safety and efficacy of Coumarin and Neolignans derivatives with anti-Candida albicans and Epidermophyton floccosum activity. This studies identifies the activity of these compounds that can be used in drug discovery and could be sources of lead compounds for new anti-Candida albicans and Epidermophyton floccosumdrug development.
1.4 The Research Questions
What structural and electronic properties of Coumarin and Neolignans derivatives determine its activity and what can be altered to improve this activity?
1.5 Research Hypothesis
The alternatively hypothesis to this research includes :
The size of the observed anti-Candida albicans and Epidermophyton floccosum inhibitory abiliities of Coumarin and Neolignans and their activities are functions of the
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empirical poperties which makes the descriptor of the total chemical structure of the compounds under study.
The null hypothesis to this research includes:
The observed activity and inhibitory activities of Coumarin and Neolignan compounds against Candida albicans and Epidermophyton floccosum is independent of the descriptors of the total chemical structure of the compounds.
1.6 The Research Design
QSAR methodologies have the potential of decreasing substantially the time and effort required for the discovery of the new medicines (Tong et al., 2005). A major step in constructing the QSAR models is to find a set of molecular descriptors that represents variation of the structural properties of the molecules (He and Jurs, 2005). The QSAR analysis employs statistical methods to drive quantitative mathematical relationships between chemical structure and biological activity (Cronin, 2005). Thus, the use of the QSAR in the development of a theoretical model to predict the biological activity of a set of compounds is very important. The strategy used in the QSAR methodology includes the following steps:
a. selection of a data set;
b. generation of the molecular structures;
c. optimization of the geometry of the molecular structures by appropriate method;
d. generation of several structural descriptors;
e. application of variable selection or/and methods data reduction of the calculated descriptors;
f. regression analysis; and finally
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g. evaluation of the validity and predictability of the developed QSAR models (Kiralj and Ferreira, 2009).
1.7 Theoretical Framework of the Study
Computational chemistry represents molecular structures as numerical models and simulates their behavior with the equations of quantum and classical physics. Available programs enable scientists to easily generate and present molecular data including geometries, energies, and associated properties (electronic, spectroscopic, and bulk). The usual paradigm for displaying and manipulating these data is a table in which compounds are defined by individual rows and molecular properties (or descriptors) by the associated columns. A QSAR attempts to find consistent relationships between the variations in the values of molecular properties and the biological activity for a series of compounds so that these ‗‗rules‘‘ can be used to evaluate new chemical entities (Patel et al., 2014). A QSAR generally takes the form of a linear equation
1.1
where the parameters through are computed for each molecule in the series and the coefficients through are calculated by fitting variations in the parameters and the biological activity (Patel et al., 2014).
While searching, one finds numerous hits for lead candidates, and thus lead optimization is hindered. To get more target structural information, high-through put proteincrystallization can be explored (Khan et al., 2011). Cheminformatics methods must be applied while generating data using highthrough put techniques in order to assure that good absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties are achieved while making and screening compounds, this approach is called a multi-parametric optimization strategy (Khan et al., 2011). Several physicochemical properties of drug molecules such as aqueous solubility, partition coefficient (log P),
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distribution coefficient (log D), ionization constant (pKa), and topological polar surface area (tPSA) play an important role in majority of the processes.
Statistical or chemometric techniques form the mathematic foundation for building a QSAR model. Most easilyinterpretable method was found to be linear regressionanalysis among various statistical methods for QSAR . These regressions represent direct correlation of independentvariables (x) with a dependent variable (y). Thismodel can be considered for prediction of y from the dataof x variables. This can either belong to qualitative orquantitative set of system (Khan et al., 2011).
1.8 Aim and Objectives of the Study
The aim of this study is to correlate the molecular properties of Coumarin and Neolignans derivatives with experimental data from biological activity of anti-candida albicans and anti-Epidermophyton floccosum
The aim can be achieved through the following objectives:
a. Selection of a data set.
b. Generation of the molecular structure
c. Optimization of the geometry of the molecular structure by appropriate method
d. Generation of several structural descriptors
e. Application of variable selection or / and methods data reduction of the calculated descriptors
f. Regression analysis; and finally
g. Evaluation of the validity and predictability of the developed QSAR models.
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1.9 Scope and Limitation of the Study
Any QSAR method wouldn‘t be tried for a dataset unless the experimental expects that the study will provide useful three-dimensional structure–activity insights. Since scientists know that it is the properties of molecules that govern their biological properties, it is especially gratifying to see a summary of how changes in structure change biological properties. Methods that do not provide such a graphical result are often less attractive to the scientific community.
A major factor in the continuing enthusiasm for QSAR comes from the proven ability of several of the methods to forecast correctly the potency of compounds not used in their derivation (Eriksson et al., 2003).Validation by forecasting compounds not used in the derivation is usually included in QSAR reports, this ability to forecast affinity is gaining new respect as scientists realize that we are far away from the hoped-for fast and accurate forecast of affinity from the structure of a protein-ligand complex.Thus scientists whose primary focus is laboratory work can use the computer to gain insights into the structure–activity relationships of their compounds.
The following are the limitation of QSAR
a. Lack of sufficient number of training molecules.
b. Consideration of only two-dimensional structures.
c. Insufficient parameters for relating drug–receptor interactions such as
Hammett constant.
d. Unavailability of specific physiochemical parameters.
e. Unavailability of representation of stereochemistry.
f. No unique solution with high risk of failure and chance correlations.
g. Requirement of knowledge of substituent constants and chemistry utilized to
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design a molecule.
h. Lack of suggestion to synthesize a new compound through classical QSAR
equations with no graphical output (Puzyn et al., 2010).
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