Towards a possible future solution against Multidrug Resistance: An in silico exploration of the Multidrug and Toxic compound Extrusion (MATE) transporter proteins as potential antimicrobial drug targets
- Authors: Damji, Amira Mahamood
- Date: 2024-04-04
- Subjects: Multidrug resistance , Multidrug and toxic compound extrusion family, eukaryotic , Docking , Molecular dynamics , Drug development , Transmembrane protein
- Language: English
- Type: Academic theses , Master's theses , text
- Identifier: http://hdl.handle.net/10962/435009 , vital:73123
- Description: The rise of multidrug resistance (MDR) has become a pressing global issue, hindering the treatment of cancers and infectious diseases, and imposing a burden on healthcare systems and the economy. The Multidrug and Toxic compound Extrusion (MATE) superfamily of membrane efflux transporters is one of the key players contributing to MDR due to their ability to export a wide range of cationic and hydrophilic xenobiotics, including treatment drugs, from cells, diminishing their efficacy. Targeting MATE transporters holds great promise in achieving some cellular control over MDR, but first, a deeper understanding of their structure-function-dynamics link is required. This study aimed to explore the MATE transporters as potential antimicrobial drug targets using a two-fold in silico approach. First, virtual screening of compounds from the South African Natural Compounds Database (SANCDB) was performed to identify prospective lead inhibitory compounds against the MATE transporters using molecular docking, and top hits were selected based on their binding energy and interaction with the active site on the N-lobe of the protein. Second, to investigate the molecular-level dynamics of their extrusion mechanism, the MATE transporter structures were embedded in a POPC membrane bilayer using the CHARMM-GUI online tool and then subjected to MD simulations for 100 ns with the CHARMM 36m force field using GROMACS. The resulting trajectories were evaluated using three standard metrics – RMSD, RMSF, and Rg; significant global structural changes were observed and key functional regions in both membrane- and non-membrane transmembrane (TM) segments were identified, containing more dynamic and flexible residues than other regions. Furthermore, the MATE transporters showed more of a loosely-packed structure, providing flexibility to allow for conformational switching during their substrate-transport cycle, which is typical for proteins whose secondary structures are composed of all α-helices. The scope of this study lied in the preliminary stages of the computer-aided drug design process, and provided insights that can be used to guide the development of strategies aimed at regulating or inhibiting the function of the MATE transporters, offering a possible future solution to the growing challenge of MDR. , Thesis (MSc) -- Faculty of Science, Biochemistry and Microbiology, 2024
- Full Text:
- Date Issued: 2024-04-04
- Authors: Damji, Amira Mahamood
- Date: 2024-04-04
- Subjects: Multidrug resistance , Multidrug and toxic compound extrusion family, eukaryotic , Docking , Molecular dynamics , Drug development , Transmembrane protein
- Language: English
- Type: Academic theses , Master's theses , text
- Identifier: http://hdl.handle.net/10962/435009 , vital:73123
- Description: The rise of multidrug resistance (MDR) has become a pressing global issue, hindering the treatment of cancers and infectious diseases, and imposing a burden on healthcare systems and the economy. The Multidrug and Toxic compound Extrusion (MATE) superfamily of membrane efflux transporters is one of the key players contributing to MDR due to their ability to export a wide range of cationic and hydrophilic xenobiotics, including treatment drugs, from cells, diminishing their efficacy. Targeting MATE transporters holds great promise in achieving some cellular control over MDR, but first, a deeper understanding of their structure-function-dynamics link is required. This study aimed to explore the MATE transporters as potential antimicrobial drug targets using a two-fold in silico approach. First, virtual screening of compounds from the South African Natural Compounds Database (SANCDB) was performed to identify prospective lead inhibitory compounds against the MATE transporters using molecular docking, and top hits were selected based on their binding energy and interaction with the active site on the N-lobe of the protein. Second, to investigate the molecular-level dynamics of their extrusion mechanism, the MATE transporter structures were embedded in a POPC membrane bilayer using the CHARMM-GUI online tool and then subjected to MD simulations for 100 ns with the CHARMM 36m force field using GROMACS. The resulting trajectories were evaluated using three standard metrics – RMSD, RMSF, and Rg; significant global structural changes were observed and key functional regions in both membrane- and non-membrane transmembrane (TM) segments were identified, containing more dynamic and flexible residues than other regions. Furthermore, the MATE transporters showed more of a loosely-packed structure, providing flexibility to allow for conformational switching during their substrate-transport cycle, which is typical for proteins whose secondary structures are composed of all α-helices. The scope of this study lied in the preliminary stages of the computer-aided drug design process, and provided insights that can be used to guide the development of strategies aimed at regulating or inhibiting the function of the MATE transporters, offering a possible future solution to the growing challenge of MDR. , Thesis (MSc) -- Faculty of Science, Biochemistry and Microbiology, 2024
- Full Text:
- Date Issued: 2024-04-04
Exploring socio-economic factors influencing incidences and outcome of multidrug resistance tuberculosis among patients and facility staffs in Makana Sub-District, Eastern Cape
- Cannon, Lesley-Ann https://orcid.org/0000-0002-7635-277X
- Authors: Cannon, Lesley-Ann https://orcid.org/0000-0002-7635-277X
- Date: 2022-02
- Subjects: Multidrug resistance , Tuberculosis
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10353/26706 , vital:65958
- Description: Background Drug-resistant Tuberculosis (DR-TB) is one of the main causes of global public health crisis, due to the morbidity and mortality rates associated with the disease. This DR TB is a complex illness having direct and indirect impact on finances, social functioning, and quality of life of infected individuals. Major research advances have been made in the diagnosis and treatment of DR-TB. However, minimal information exists on the socio-economic factors influencing the incidence and outcomes. This study aims to fill the gap by exploring the socio-economic factors from both the health care professional and patient perspective in particular settings to gain insights into developing context-specific strategies against the burden of DR-TB. Methodology The study applied a qualitative method to explore the socio-economic factors influencing MDR-TB through key-in-depth interviews (KIIs) and focus group discussions (FGDs). The study enrolled a total of thirty-two (32) consenting participants. The KIIs was conducted for ten (10) healthcare workers and nine (9) MDR-TB patients. Two focus group discussions were done involving seven (7) MDR TB patients and six (6) MDR-TB patients, respectively. The study targeted healthcare workers working in the MDR-TB field and TB patients with the following: GeneXpert Rifampicin resistance and patient confirmed as MDR TB. Eligible participants were selected using a purposive sampling technique from the hospitals` routine data electronic records (EDR-WEB database) and hardcopy registers (drug-resistant TB register) on MDR-TB patients enrolled in care at the study site. Informed consent was obtained from all study participants after thoroughly explaining the purpose. No personal information of participants was used. All responses from respondents were coded during analysis for autonomy and the respondents were not identifiable in any published or unpublished work following this research. The interviews were transcribed, some translated into English, where necessary, and analysed until saturation was reached. Data was coded and analysed using both thematic and content analysis technique. Results There were 3 main themes identified in the study: social factors, economic factors, and other contributing factors. 7 sub- themes were recorded under social factors and 2 subthemes under economic factors. Two independent factors that were also considered to impact MDR-TB were the attitude of healthcare workers, as well as the current COVID-19 pandemic. Conclusion MDR-TB is a major public health concern in the Makana Sub-district of the Eastern Cape. The findings of this study highlight the impact of socio- economic factors on the incidence, spread, defaulter rate and outcomes of MDR-TB. The social areas highlighted by the study participants as affecting the incidence and outcomes of MDR TB were housing and relocation, decreased immunity, stigma, patients’ attitude and lack of support, alcohol and other substance usage and prison/ incarceration. The economic factors identified by the participants were unemployment and job loss and health related expenses. Other factors are those factors contributing to the increased incidence and possible poor outcomes of MDR TB. Healthcare workers impact and attitude and the effects of the covid-19 pandemic were highlighted as additional factors influencing the incidence and outcomes of MDR TB. The management of MDR-TB requires rigorous efforts that should be directed at addressing the socio-economic factors. Therefore, future quantitative studies and important programmatic strategies should be considered to tackle the socio-economic challenges that contribute to the burden of MDR-TB infection in the Makana community. , Thesis (MPA) -- Faculty of Health Sciences, 2022
- Full Text:
- Date Issued: 2022-02
- Authors: Cannon, Lesley-Ann https://orcid.org/0000-0002-7635-277X
- Date: 2022-02
- Subjects: Multidrug resistance , Tuberculosis
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10353/26706 , vital:65958
- Description: Background Drug-resistant Tuberculosis (DR-TB) is one of the main causes of global public health crisis, due to the morbidity and mortality rates associated with the disease. This DR TB is a complex illness having direct and indirect impact on finances, social functioning, and quality of life of infected individuals. Major research advances have been made in the diagnosis and treatment of DR-TB. However, minimal information exists on the socio-economic factors influencing the incidence and outcomes. This study aims to fill the gap by exploring the socio-economic factors from both the health care professional and patient perspective in particular settings to gain insights into developing context-specific strategies against the burden of DR-TB. Methodology The study applied a qualitative method to explore the socio-economic factors influencing MDR-TB through key-in-depth interviews (KIIs) and focus group discussions (FGDs). The study enrolled a total of thirty-two (32) consenting participants. The KIIs was conducted for ten (10) healthcare workers and nine (9) MDR-TB patients. Two focus group discussions were done involving seven (7) MDR TB patients and six (6) MDR-TB patients, respectively. The study targeted healthcare workers working in the MDR-TB field and TB patients with the following: GeneXpert Rifampicin resistance and patient confirmed as MDR TB. Eligible participants were selected using a purposive sampling technique from the hospitals` routine data electronic records (EDR-WEB database) and hardcopy registers (drug-resistant TB register) on MDR-TB patients enrolled in care at the study site. Informed consent was obtained from all study participants after thoroughly explaining the purpose. No personal information of participants was used. All responses from respondents were coded during analysis for autonomy and the respondents were not identifiable in any published or unpublished work following this research. The interviews were transcribed, some translated into English, where necessary, and analysed until saturation was reached. Data was coded and analysed using both thematic and content analysis technique. Results There were 3 main themes identified in the study: social factors, economic factors, and other contributing factors. 7 sub- themes were recorded under social factors and 2 subthemes under economic factors. Two independent factors that were also considered to impact MDR-TB were the attitude of healthcare workers, as well as the current COVID-19 pandemic. Conclusion MDR-TB is a major public health concern in the Makana Sub-district of the Eastern Cape. The findings of this study highlight the impact of socio- economic factors on the incidence, spread, defaulter rate and outcomes of MDR-TB. The social areas highlighted by the study participants as affecting the incidence and outcomes of MDR TB were housing and relocation, decreased immunity, stigma, patients’ attitude and lack of support, alcohol and other substance usage and prison/ incarceration. The economic factors identified by the participants were unemployment and job loss and health related expenses. Other factors are those factors contributing to the increased incidence and possible poor outcomes of MDR TB. Healthcare workers impact and attitude and the effects of the covid-19 pandemic were highlighted as additional factors influencing the incidence and outcomes of MDR TB. The management of MDR-TB requires rigorous efforts that should be directed at addressing the socio-economic factors. Therefore, future quantitative studies and important programmatic strategies should be considered to tackle the socio-economic challenges that contribute to the burden of MDR-TB infection in the Makana community. , Thesis (MPA) -- Faculty of Health Sciences, 2022
- Full Text:
- Date Issued: 2022-02
Exploring socio-economic factors influencing incidences and outcome of multidrug resistance tuberculosis among patients and facility staffs in Makana Sub-District, Eastern Cape
- Authors: Cannon, Lesley-Ann Lynnath
- Date: 2022-02
- Subjects: Multidrug resistance , Multidrug-resistant -- tuberculosis
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10353/23471 , vital:57896
- Description: Drug-resistant Tuberculosis (DR-TB) is one of the main causes of global public health crisis, due to the morbidity and mortality rates associated with the disease. This DR-TB is a complex illness having direct and indirect impact on finances, social functioning, and quality of life of infected individuals. Major research advances have been made in the diagnosis and treatment of DR-TB. However, minimal information exists on the socio-economic factors influencing the incidence and outcomes. This study aims to fill the gap by exploring the socio-economic factors from both the health care professional and patient perspective in particular settings to gain insights into developing context-specific strategies against the burden of DR-TB. The study applied a qualitative method to explore the socio-economic factors influencing MDR-TB through key-in-depth interviews (KIIs) and focus group discussions (FGDs). The study enrolled a total of thirty-two (32) consenting participants. The KIIs was conducted for ten (10) healthcare workers and nine (9) MDR-TB patients. Two focus group discussions were done involving seven (7) MDR-TB patients and six (6) MDR-TB patients, respectively. The study targeted healthcare workers working in the MDR-TB field and TB patients with the following: GeneXpert Rifampicin resistance and patient confirmed as MDR TB. Eligible participants were selected using a purposive sampling technique from the hospitals` routine data electronic records (EDR-WEB database) and hardcopy registers (drug-resistant TB register) on MDR-TB patients enrolled in care at the study site. Informed consent was obtained from all study participants after thoroughly explaining the purpose. No personal information of participants was used. All responses from respondents were coded during analysis for autonomy and the respondents were not identifiable in any published or unpublished work following this research. The interviews were transcribed, some translated into English, where necessary, and analysed until saturation was reached. Data was coded and analysed using both thematic and content analysis technique. There were 3 main themes identified in the study: social factors, economic factors, and other contributing factors. 7 sub- themes were recorded under social factors and 2 subthemes under economic factors. Two independent factors that were also considered to impact MDR-TB were the attitude of healthcare workers, as well as the current COVID-19 pandemic. MDR-TB is a major public health concern in the Makana Sub-district of the Eastern Cape. The findings of this study highlight the impact of socio- economic factors on the incidence, spread, defaulter rate and outcomes of MDR-TB. The social areas highlighted by the study participants as affecting the incidence and outcomes of MDR TB were housing and relocation, decreased immunity, stigma, patients’ attitude and lack of support, alcohol and other substance usage and prison/ incarceration. The economic factors identified by the participants were unemployment and job loss and health related expenses. Other factors are those factors contributing to the increased incidence and possible poor outcomes of MDR TB. Healthcare workers impact and attitude and the effects of the covid-19 pandemic were highlighted as additional factors influencing the incidence and outcomes of MDR TB. The management of MDR-TB requires rigorous efforts that should be directed at addressing the socio-economic factors. Therefore, future quantitative studies and important programmatic strategies should be considered to tackle the socio-economic challenges that contribute to the burden of MDR-TB infection in the Makana community. , Thesis (MPA) -- Faculty of Health Sciences, 2022
- Full Text:
- Date Issued: 2022-02
- Authors: Cannon, Lesley-Ann Lynnath
- Date: 2022-02
- Subjects: Multidrug resistance , Multidrug-resistant -- tuberculosis
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10353/23471 , vital:57896
- Description: Drug-resistant Tuberculosis (DR-TB) is one of the main causes of global public health crisis, due to the morbidity and mortality rates associated with the disease. This DR-TB is a complex illness having direct and indirect impact on finances, social functioning, and quality of life of infected individuals. Major research advances have been made in the diagnosis and treatment of DR-TB. However, minimal information exists on the socio-economic factors influencing the incidence and outcomes. This study aims to fill the gap by exploring the socio-economic factors from both the health care professional and patient perspective in particular settings to gain insights into developing context-specific strategies against the burden of DR-TB. The study applied a qualitative method to explore the socio-economic factors influencing MDR-TB through key-in-depth interviews (KIIs) and focus group discussions (FGDs). The study enrolled a total of thirty-two (32) consenting participants. The KIIs was conducted for ten (10) healthcare workers and nine (9) MDR-TB patients. Two focus group discussions were done involving seven (7) MDR-TB patients and six (6) MDR-TB patients, respectively. The study targeted healthcare workers working in the MDR-TB field and TB patients with the following: GeneXpert Rifampicin resistance and patient confirmed as MDR TB. Eligible participants were selected using a purposive sampling technique from the hospitals` routine data electronic records (EDR-WEB database) and hardcopy registers (drug-resistant TB register) on MDR-TB patients enrolled in care at the study site. Informed consent was obtained from all study participants after thoroughly explaining the purpose. No personal information of participants was used. All responses from respondents were coded during analysis for autonomy and the respondents were not identifiable in any published or unpublished work following this research. The interviews were transcribed, some translated into English, where necessary, and analysed until saturation was reached. Data was coded and analysed using both thematic and content analysis technique. There were 3 main themes identified in the study: social factors, economic factors, and other contributing factors. 7 sub- themes were recorded under social factors and 2 subthemes under economic factors. Two independent factors that were also considered to impact MDR-TB were the attitude of healthcare workers, as well as the current COVID-19 pandemic. MDR-TB is a major public health concern in the Makana Sub-district of the Eastern Cape. The findings of this study highlight the impact of socio- economic factors on the incidence, spread, defaulter rate and outcomes of MDR-TB. The social areas highlighted by the study participants as affecting the incidence and outcomes of MDR TB were housing and relocation, decreased immunity, stigma, patients’ attitude and lack of support, alcohol and other substance usage and prison/ incarceration. The economic factors identified by the participants were unemployment and job loss and health related expenses. Other factors are those factors contributing to the increased incidence and possible poor outcomes of MDR TB. Healthcare workers impact and attitude and the effects of the covid-19 pandemic were highlighted as additional factors influencing the incidence and outcomes of MDR TB. The management of MDR-TB requires rigorous efforts that should be directed at addressing the socio-economic factors. Therefore, future quantitative studies and important programmatic strategies should be considered to tackle the socio-economic challenges that contribute to the burden of MDR-TB infection in the Makana community. , Thesis (MPA) -- Faculty of Health Sciences, 2022
- Full Text:
- Date Issued: 2022-02
Molecular characterization and antimicrobial resistance profiling of Salmonella species isolated from final effluent discharged from the Fort Hare Dairy Farm in Raymond Mhlaba Local Municipality
- Thinyane, Pindile https://orcid.org/0000-0001-8236-9407
- Authors: Thinyane, Pindile https://orcid.org/0000-0001-8236-9407
- Date: 2021-10
- Subjects: Salmonella typhimurium , Anti-infective agents , Multidrug resistance
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10353/22666 , vital:52618
- Description: The exposure of livestock to antimicrobials for treatment, prophylaxis, or development advancement can select for antimicrobial resistant organisms that can be transmitted to humans. Salmonella as a significant zoonotic microorganism can go about as a likely supply of antimicrobial resistant determinants. Salmonella is a zoonotic pathogen that causes food and waterborne infections. It affects wild and domestic animals, and humans, by causing a number of infections including Salmonellosis. Salmonella species infect humans through the consumption of contaminated meat, like beef, chicken, pork etc. This study aimed to determine the molecular characterization and antimicrobial resistance profile of Salmonella species isolated from effluent discharged from the Fort Hare Dairy Farm in Raymond Mhlaba Local Municipality. Polymerase chain reaction (PCR) was used for the molecular confirmation of the presumptive Salmonella isolates targeting both ompC gene and typh gene. Standard disc diffusion method was used for the antimicrobial susceptibility testing (AST) as recommended by the Clinical and Laboratory Standards Institute. The confirmed Salmonella isolates were tested against 12 test antimicrobial agents and were screened for antimicrobial resistance genes (ARGs) including blaTEM and amp for beta-lactams, and tetC for tetracycline. The research showed that the effluent discharge from this farm is contaminated with Salmonella. Presumptive Salmonella densities were ranging between 1,7 ×102 to 6,1×102 CFU/100ml, out of 83 presumptive isolates recovered, 61 were molecularly confirmed Salmonella typhimurium. The most prevalent Salmonella species found in this study was Salmonella typhimurium, which was more abundant in the final effluent discharges than in the water samples. This may be due to the contamination from farm animal faeces. The susceptibility against 12 different antibiotics by the recovered Salmonella typhimurium were examined, and Salmonella typhimurium isolates was notably resistant to azithromycin, ampicillin, amoxiclav, but less resistance were seen on doripenem , meropenem and ciprofloxacin but none of the isolates were resistant to norfloxacin. Antibiotic results obtained from this research suggest that Quinolones (Norfloxicin, Ciprofloxacin and Nalidixic acid), and Carbapenems (Meropenem and Doripenem.) were the most effective antibiotics against Salmonella. Forty-eight percent of isolates were found to be resistant to more than 3 antibiotics from different families thus considering them to be multidrug resistant. Resistant determinants ampC, blaTEM and tetC were detected on resistant isolates. Misuse and overuse of antibiotics on animal producing farms put human lives at risk as it promotes the emergency of multidrug resistant bacteria. Findings of this study revealed that animal producing farm pose a threat to the community as they harbour and promote the emergence of multidrug resistant Salmonella typhimurium. , Thesis (MSc) -- Faculty of Science and Agriculture, 2021
- Full Text:
- Date Issued: 2021-10
- Authors: Thinyane, Pindile https://orcid.org/0000-0001-8236-9407
- Date: 2021-10
- Subjects: Salmonella typhimurium , Anti-infective agents , Multidrug resistance
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10353/22666 , vital:52618
- Description: The exposure of livestock to antimicrobials for treatment, prophylaxis, or development advancement can select for antimicrobial resistant organisms that can be transmitted to humans. Salmonella as a significant zoonotic microorganism can go about as a likely supply of antimicrobial resistant determinants. Salmonella is a zoonotic pathogen that causes food and waterborne infections. It affects wild and domestic animals, and humans, by causing a number of infections including Salmonellosis. Salmonella species infect humans through the consumption of contaminated meat, like beef, chicken, pork etc. This study aimed to determine the molecular characterization and antimicrobial resistance profile of Salmonella species isolated from effluent discharged from the Fort Hare Dairy Farm in Raymond Mhlaba Local Municipality. Polymerase chain reaction (PCR) was used for the molecular confirmation of the presumptive Salmonella isolates targeting both ompC gene and typh gene. Standard disc diffusion method was used for the antimicrobial susceptibility testing (AST) as recommended by the Clinical and Laboratory Standards Institute. The confirmed Salmonella isolates were tested against 12 test antimicrobial agents and were screened for antimicrobial resistance genes (ARGs) including blaTEM and amp for beta-lactams, and tetC for tetracycline. The research showed that the effluent discharge from this farm is contaminated with Salmonella. Presumptive Salmonella densities were ranging between 1,7 ×102 to 6,1×102 CFU/100ml, out of 83 presumptive isolates recovered, 61 were molecularly confirmed Salmonella typhimurium. The most prevalent Salmonella species found in this study was Salmonella typhimurium, which was more abundant in the final effluent discharges than in the water samples. This may be due to the contamination from farm animal faeces. The susceptibility against 12 different antibiotics by the recovered Salmonella typhimurium were examined, and Salmonella typhimurium isolates was notably resistant to azithromycin, ampicillin, amoxiclav, but less resistance were seen on doripenem , meropenem and ciprofloxacin but none of the isolates were resistant to norfloxacin. Antibiotic results obtained from this research suggest that Quinolones (Norfloxicin, Ciprofloxacin and Nalidixic acid), and Carbapenems (Meropenem and Doripenem.) were the most effective antibiotics against Salmonella. Forty-eight percent of isolates were found to be resistant to more than 3 antibiotics from different families thus considering them to be multidrug resistant. Resistant determinants ampC, blaTEM and tetC were detected on resistant isolates. Misuse and overuse of antibiotics on animal producing farms put human lives at risk as it promotes the emergency of multidrug resistant bacteria. Findings of this study revealed that animal producing farm pose a threat to the community as they harbour and promote the emergence of multidrug resistant Salmonella typhimurium. , Thesis (MSc) -- Faculty of Science and Agriculture, 2021
- Full Text:
- Date Issued: 2021-10
In silico identification of selective novel hits against the active site of wild type mycobacterium tuberculosis pyrazinamidase and its mutants
- Authors: Gowo, Prudence
- Date: 2021-04
- Subjects: Mycobacterium tuberculosis , Pyrazinamide , Multidrug resistance , Antitubercular agents , Molecular dynamics , Hydrogen bonding , Ligand binding (Biochemistry) , Dynamic Residue Network
- Language: English
- Type: thesis , text , Masters , MSc
- Identifier: http://hdl.handle.net/10962/178007 , vital:42898
- Description: The World Health Organization declared Tuberculosis a global health emergency and has set a goal to eradicate it by 2035. However, effective treatment and control of the disease is being hindered by the emerging Multi-Drug Resistant and Extensively Drug Resistant strains on the most effective first line prodrug, Pyrazinamide (PZA). Studies have shown that the main cause of PZA resistance is due to mutations in the pncA gene that codes for the target protein Pyrazinamidase (PZase). Therefore, this study aimed to identify novel drug compounds that bind to the active site of wild type PZase and study the dynamics of these potential anti-TB drugs in the mutant systems of PZase. This approach will aid in identifying drugs that may be repurposed for TB therapy and/or designed to counteract PZA resistance. This was achieved by screening 2089 DrugBank compounds against the whole wild type (WT) PZase protein in molecular docking using AutoDOCK4.2. Compound screening based on docking binding energy, hydrogen bonds, molecular weight and active site proximity identified 47 compounds meeting all the set selection criteria. The stability of these compounds were analysed in Molecular Dynamic (MD) simulations and were further studied in PZase mutant systems of A3P, A134V, A146V, D8G, D49A, D49G, D63G, H51P, H137R, L85R, L116R, Q10P, R140S, T61P, V139M and Y103S. Generally, mutant-ligand systems displayed little deviation from the WT systems. The compound systems remained compact, with less fluctuations and more hydrogen bond interactions throughout the simulation (DB00255, DB00655, DB00672, DB00782, DB00977, DB01196, DB04573, DB06414, DB08981, DB11181, DB11760, DB13867, DB13952). From this research study, potential drugs that may be repurposed for TB therapy were identified. Majority of these drugs are currently used in the treatment of hypertension, menopause disorders and inflammation. To further understand the mutant-ligand dynamic systems, calculations such as Dynamic Residue Network (DRN) may be done. Also, the bioactivity of these drugs on Mycobacterium tuberculosis may be studied in wet laboratory, to understand their clinical impart in vivo experiments. , Thesis (MSc) -- Faculty of Science, Biochemistry and Microbiology, 2021
- Full Text:
- Date Issued: 2021-04
- Authors: Gowo, Prudence
- Date: 2021-04
- Subjects: Mycobacterium tuberculosis , Pyrazinamide , Multidrug resistance , Antitubercular agents , Molecular dynamics , Hydrogen bonding , Ligand binding (Biochemistry) , Dynamic Residue Network
- Language: English
- Type: thesis , text , Masters , MSc
- Identifier: http://hdl.handle.net/10962/178007 , vital:42898
- Description: The World Health Organization declared Tuberculosis a global health emergency and has set a goal to eradicate it by 2035. However, effective treatment and control of the disease is being hindered by the emerging Multi-Drug Resistant and Extensively Drug Resistant strains on the most effective first line prodrug, Pyrazinamide (PZA). Studies have shown that the main cause of PZA resistance is due to mutations in the pncA gene that codes for the target protein Pyrazinamidase (PZase). Therefore, this study aimed to identify novel drug compounds that bind to the active site of wild type PZase and study the dynamics of these potential anti-TB drugs in the mutant systems of PZase. This approach will aid in identifying drugs that may be repurposed for TB therapy and/or designed to counteract PZA resistance. This was achieved by screening 2089 DrugBank compounds against the whole wild type (WT) PZase protein in molecular docking using AutoDOCK4.2. Compound screening based on docking binding energy, hydrogen bonds, molecular weight and active site proximity identified 47 compounds meeting all the set selection criteria. The stability of these compounds were analysed in Molecular Dynamic (MD) simulations and were further studied in PZase mutant systems of A3P, A134V, A146V, D8G, D49A, D49G, D63G, H51P, H137R, L85R, L116R, Q10P, R140S, T61P, V139M and Y103S. Generally, mutant-ligand systems displayed little deviation from the WT systems. The compound systems remained compact, with less fluctuations and more hydrogen bond interactions throughout the simulation (DB00255, DB00655, DB00672, DB00782, DB00977, DB01196, DB04573, DB06414, DB08981, DB11181, DB11760, DB13867, DB13952). From this research study, potential drugs that may be repurposed for TB therapy were identified. Majority of these drugs are currently used in the treatment of hypertension, menopause disorders and inflammation. To further understand the mutant-ligand dynamic systems, calculations such as Dynamic Residue Network (DRN) may be done. Also, the bioactivity of these drugs on Mycobacterium tuberculosis may be studied in wet laboratory, to understand their clinical impart in vivo experiments. , Thesis (MSc) -- Faculty of Science, Biochemistry and Microbiology, 2021
- Full Text:
- Date Issued: 2021-04
Application of machine learning, molecular modelling and structural data mining against antiretroviral drug resistance in HIV-1
- Sheik Amamuddy, Olivier Serge André
- Authors: Sheik Amamuddy, Olivier Serge André
- Date: 2020
- Subjects: Machine learning , Molecules -- Models , Data mining , Neural networks (Computer science) , Antiretroviral agents , Protease inhibitors , Drug resistance , Multidrug resistance , Molecular dynamics , Renin-angiotensin system , HIV (Viruses) -- South Africa , HIV (Viruses) -- Social aspects -- South Africa , South African Natural Compounds Database
- Language: English
- Type: text , Thesis , Doctoral , PhD
- Identifier: http://hdl.handle.net/10962/115964 , vital:34282
- Description: Millions are affected with the Human Immunodeficiency Virus (HIV) world wide, even though the death toll is on the decline. Antiretrovirals (ARVs), more specifically protease inhibitors have shown tremendous success since their introduction into therapy since the mid 1990’s by slowing down progression to the Acquired Immune Deficiency Syndrome (AIDS). However, Drug Resistance Mutations (DRMs) are constantly selected for due to viral adaptation, making drugs less effective over time. The current challenge is to manage the infection optimally with a limited set of drugs, with differing associated levels of toxicities in the face of a virus that (1) exists as a quasispecies, (2) may transmit acquired DRMs to drug-naive individuals and (3) that can manifest class-wide resistance due to similarities in design. The presence of latent reservoirs, unawareness of infection status, education and various socio-economic factors make the problem even more complex. Adequate timing and choice of drug prescription together with treatment adherence are very important as drug toxicities, drug failure and sub-optimal treatment regimens leave room for further development of drug resistance. While CD4 cell count and the determination of viral load from patients in resource-limited settings are very helpful to track how well a patient’s immune system is able to keep the virus in check, they can be lengthy in determining whether an ARV is effective. Phenosense assay kits answer this problem using viruses engineered to contain the patient sequences and evaluating their growth in the presence of different ARVs, but this can be expensive and too involved for routine checks. As a cheaper and faster alternative, genotypic assays provide similar information from HIV pol sequences obtained from blood samples, inferring ARV efficacy on the basis of drug resistance mutation patterns. However, these are inherently complex and the various methods of in silico prediction, such as Geno2pheno, REGA and Stanford HIVdb do not always agree in every case, even though this gap decreases as the list of resistance mutations is updated. A major gap in HIV treatment is that the information used for predicting drug resistance is mainly computed from data containing an overwhelming majority of B subtype HIV, when these only comprise about 12% of the worldwide HIV infections. In addition to growing evidence that drug resistance is subtype-related, it is intuitive to hypothesize that as subtyping is a phylogenetic classification, the more divergent a subtype is from the strains used in training prediction models, the less their resistance profiles would correlate. For the aforementioned reasons, we used a multi-faceted approach to attack the virus in multiple ways. This research aimed to (1) improve resistance prediction methods by focusing solely on the available subtype, (2) mine structural information pertaining to resistance in order to find any exploitable weak points and increase knowledge of the mechanistic processes of drug resistance in HIV protease. Finally, (3) we screen for protease inhibitors amongst a database of natural compounds [the South African natural compound database (SANCDB)] to find molecules or molecular properties usable to come up with improved inhibition against the drug target. In this work, structural information was mined using the Anisotropic Network Model, Dynamics Cross-Correlation, Perturbation Response Scanning, residue contact network analysis and the radius of gyration. These methods failed to give any resistance-associated patterns in terms of natural movement, internal correlated motions, residue perturbation response, relational behaviour and global compaction respectively. Applications of drug docking, homology-modelling and energy minimization for generating features suitable for machine-learning were not very promising, and rather suggest that the value of binding energies by themselves from Vina may not be very reliable quantitatively. All these failures lead to a refinement that resulted in a highly sensitive statistically-guided network construction and analysis, which leads to key findings in the early dynamics associated with resistance across all PI drugs. The latter experiment unravelled a conserved lateral expansion motion occurring at the flap elbows, and an associated contraction that drives the base of the dimerization domain towards the catalytic site’s floor in the case of drug resistance. Interestingly, we found that despite the conserved movement, bond angles were degenerate. Alongside, 16 Artificial Neural Network models were optimised for HIV proteases and reverse transcriptase inhibitors, with performances on par with Stanford HIVdb. Finally, we prioritised 9 compounds with potential protease inhibitory activity using virtual screening and molecular dynamics (MD) to additionally suggest a promising modification to one of the compounds. This yielded another molecule inhibiting equally well both opened and closed receptor target conformations, whereby each of the compounds had been selected against an array of multi-drug-resistant receptor variants. While a main hurdle was a lack of non-B subtype data, our findings, especially from the statistically-guided network analysis, may extrapolate to a certain extent to them as the level of conservation was very high within subtype B, despite all the present variations. This network construction method lays down a sensitive approach for analysing a pair of alternate phenotypes for which complex patterns prevail, given a sufficient number of experimental units. During the course of research a weighted contact mapping tool was developed to compare renin-angiotensinogen variants and packaged as part of the MD-TASK tool suite. Finally the functionality, compatibility and performance of the MODE-TASK tool were evaluated and confirmed for both Python2.7.x and Python3.x, for the analysis of normals modes from single protein structures and essential modes from MD trajectories. These techniques and tools collectively add onto the conventional means of MD analysis.
- Full Text:
- Date Issued: 2020
- Authors: Sheik Amamuddy, Olivier Serge André
- Date: 2020
- Subjects: Machine learning , Molecules -- Models , Data mining , Neural networks (Computer science) , Antiretroviral agents , Protease inhibitors , Drug resistance , Multidrug resistance , Molecular dynamics , Renin-angiotensin system , HIV (Viruses) -- South Africa , HIV (Viruses) -- Social aspects -- South Africa , South African Natural Compounds Database
- Language: English
- Type: text , Thesis , Doctoral , PhD
- Identifier: http://hdl.handle.net/10962/115964 , vital:34282
- Description: Millions are affected with the Human Immunodeficiency Virus (HIV) world wide, even though the death toll is on the decline. Antiretrovirals (ARVs), more specifically protease inhibitors have shown tremendous success since their introduction into therapy since the mid 1990’s by slowing down progression to the Acquired Immune Deficiency Syndrome (AIDS). However, Drug Resistance Mutations (DRMs) are constantly selected for due to viral adaptation, making drugs less effective over time. The current challenge is to manage the infection optimally with a limited set of drugs, with differing associated levels of toxicities in the face of a virus that (1) exists as a quasispecies, (2) may transmit acquired DRMs to drug-naive individuals and (3) that can manifest class-wide resistance due to similarities in design. The presence of latent reservoirs, unawareness of infection status, education and various socio-economic factors make the problem even more complex. Adequate timing and choice of drug prescription together with treatment adherence are very important as drug toxicities, drug failure and sub-optimal treatment regimens leave room for further development of drug resistance. While CD4 cell count and the determination of viral load from patients in resource-limited settings are very helpful to track how well a patient’s immune system is able to keep the virus in check, they can be lengthy in determining whether an ARV is effective. Phenosense assay kits answer this problem using viruses engineered to contain the patient sequences and evaluating their growth in the presence of different ARVs, but this can be expensive and too involved for routine checks. As a cheaper and faster alternative, genotypic assays provide similar information from HIV pol sequences obtained from blood samples, inferring ARV efficacy on the basis of drug resistance mutation patterns. However, these are inherently complex and the various methods of in silico prediction, such as Geno2pheno, REGA and Stanford HIVdb do not always agree in every case, even though this gap decreases as the list of resistance mutations is updated. A major gap in HIV treatment is that the information used for predicting drug resistance is mainly computed from data containing an overwhelming majority of B subtype HIV, when these only comprise about 12% of the worldwide HIV infections. In addition to growing evidence that drug resistance is subtype-related, it is intuitive to hypothesize that as subtyping is a phylogenetic classification, the more divergent a subtype is from the strains used in training prediction models, the less their resistance profiles would correlate. For the aforementioned reasons, we used a multi-faceted approach to attack the virus in multiple ways. This research aimed to (1) improve resistance prediction methods by focusing solely on the available subtype, (2) mine structural information pertaining to resistance in order to find any exploitable weak points and increase knowledge of the mechanistic processes of drug resistance in HIV protease. Finally, (3) we screen for protease inhibitors amongst a database of natural compounds [the South African natural compound database (SANCDB)] to find molecules or molecular properties usable to come up with improved inhibition against the drug target. In this work, structural information was mined using the Anisotropic Network Model, Dynamics Cross-Correlation, Perturbation Response Scanning, residue contact network analysis and the radius of gyration. These methods failed to give any resistance-associated patterns in terms of natural movement, internal correlated motions, residue perturbation response, relational behaviour and global compaction respectively. Applications of drug docking, homology-modelling and energy minimization for generating features suitable for machine-learning were not very promising, and rather suggest that the value of binding energies by themselves from Vina may not be very reliable quantitatively. All these failures lead to a refinement that resulted in a highly sensitive statistically-guided network construction and analysis, which leads to key findings in the early dynamics associated with resistance across all PI drugs. The latter experiment unravelled a conserved lateral expansion motion occurring at the flap elbows, and an associated contraction that drives the base of the dimerization domain towards the catalytic site’s floor in the case of drug resistance. Interestingly, we found that despite the conserved movement, bond angles were degenerate. Alongside, 16 Artificial Neural Network models were optimised for HIV proteases and reverse transcriptase inhibitors, with performances on par with Stanford HIVdb. Finally, we prioritised 9 compounds with potential protease inhibitory activity using virtual screening and molecular dynamics (MD) to additionally suggest a promising modification to one of the compounds. This yielded another molecule inhibiting equally well both opened and closed receptor target conformations, whereby each of the compounds had been selected against an array of multi-drug-resistant receptor variants. While a main hurdle was a lack of non-B subtype data, our findings, especially from the statistically-guided network analysis, may extrapolate to a certain extent to them as the level of conservation was very high within subtype B, despite all the present variations. This network construction method lays down a sensitive approach for analysing a pair of alternate phenotypes for which complex patterns prevail, given a sufficient number of experimental units. During the course of research a weighted contact mapping tool was developed to compare renin-angiotensinogen variants and packaged as part of the MD-TASK tool suite. Finally the functionality, compatibility and performance of the MODE-TASK tool were evaluated and confirmed for both Python2.7.x and Python3.x, for the analysis of normals modes from single protein structures and essential modes from MD trajectories. These techniques and tools collectively add onto the conventional means of MD analysis.
- Full Text:
- Date Issued: 2020
Modelling the impact of risk factors affecting TB treatment
- Authors: Tsuro, Urgent
- Date: 2013
- Subjects: Diseases -- Risk factors , Tuberculosis -- Epidemiology , Multidrug resistance
- Language: English
- Type: Thesis , Masters , MSc (Biostatistics and Epidemiology)
- Identifier: vital:11787 , http://hdl.handle.net/10353/d1019782 , Diseases -- Risk factors , Tuberculosis -- Epidemiology , Multidrug resistance
- Description: The Tuberculosis infection rate has been generally escalating due to poor health conditions in the Gweru district of Zimbabwe. The study therefore seeks to identify the risk factors that affect TB treatment in the Gweru district. A cross sectional study was carried out in which a questionnaire was employed for data collection on 113 respondents. A binary logistic regression model was employed for data analysis. A total of 98 TB patients were interviewed: [50 respondents (44.0%) had Multi-drug resistant Tuberculosis and 63 respondents (56.0%) had general Tuberculosis). Before being enrolled into the study, an informed consent form was given to each of the participants. The data was then put into excel and later transferred to SPSS for analysis. Out of the 14 potential risk factors of TB treatment, only 6 variables (side effects, gender, alcohol use, HIV status, smoking during the treatment period and having been pre-exposed to TB drugs) were statistically significant in their association with treatment failure.
- Full Text:
- Date Issued: 2013
- Authors: Tsuro, Urgent
- Date: 2013
- Subjects: Diseases -- Risk factors , Tuberculosis -- Epidemiology , Multidrug resistance
- Language: English
- Type: Thesis , Masters , MSc (Biostatistics and Epidemiology)
- Identifier: vital:11787 , http://hdl.handle.net/10353/d1019782 , Diseases -- Risk factors , Tuberculosis -- Epidemiology , Multidrug resistance
- Description: The Tuberculosis infection rate has been generally escalating due to poor health conditions in the Gweru district of Zimbabwe. The study therefore seeks to identify the risk factors that affect TB treatment in the Gweru district. A cross sectional study was carried out in which a questionnaire was employed for data collection on 113 respondents. A binary logistic regression model was employed for data analysis. A total of 98 TB patients were interviewed: [50 respondents (44.0%) had Multi-drug resistant Tuberculosis and 63 respondents (56.0%) had general Tuberculosis). Before being enrolled into the study, an informed consent form was given to each of the participants. The data was then put into excel and later transferred to SPSS for analysis. Out of the 14 potential risk factors of TB treatment, only 6 variables (side effects, gender, alcohol use, HIV status, smoking during the treatment period and having been pre-exposed to TB drugs) were statistically significant in their association with treatment failure.
- Full Text:
- Date Issued: 2013
Investigation of the comparative cost-effectiveness of different strategies for the management of multidrug-resistant tuberculosis
- Authors: Rockcliffe, Nicole
- Date: 2003
- Subjects: Tuberculosis , Multidrug resistance , Tuberculosis -- Treatment
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:3788 , http://hdl.handle.net/10962/d1003266 , Tuberculosis , Multidrug resistance , Tuberculosis -- Treatment
- Description: The tuberculosis epidemic is escalating in South Africa as well as globally. This escalation is exacerbated by the increasing prevalence of multidrug-resistant tuberculosis (MDRTB), which is defined by the World Health Organisation (WHO) as resistance of Mycobacteria to at least isoniazid and rifampicin. Multi-drug resistant tuberculosis is estimated to occur in 1-2% of newly diagnosed tuberculosis (TB) patients and in 4-8% of previously treated patients. MDRTB is both difficult and expensive to treat, costing up to 126 times that of drug-sensitive TB. Resource constrained countries such as South Africa often lack both the money and the infrastructure to treat this disease. The aim of this project was to determine whether the performance of a systematic review with subsequent economic modelling could influence the decision making process for policy makers. Data was gathered and an economic evaluation of MDRTB treatment was performed from the perspective of the South African Department of Health. Three treatment alternatives were identified: a protocol regimen of second line anti-tuberculosis agents, as recommended in the South African guidelines for MDRTB, an appropriate regimen designed for each patient according to the results of culture and drug susceptibility tests, and non-drug management. A decision-analysis model using DATA 3.0 by Treeage® was developed to estimate the costs of each alternative. Outcomes were measured in terms of cost alone as well as the ‘number of cases cured’ and the number of ‘years of life saved’ for patients dying, being cured or failing treatment. Drug, hospital and laboratory costs incurred using each alternative were included in the analysis. A sensitivity analysis was performed on all variables in order to identify threshold values that would change the outcome of the evaluation. Results of the decision analysis indicate that the individualised regimen was both the cheaper and more cost-effective regimen of the two active treatment options, and was estimated to cost R50 661 per case cured and R2 070 per year of life saved. The protocol regimen was estimated to cost R73 609 per case cured and R2 741 per year of life saved. The outcome of the decision analysis was sensitive to changes in some of the variables used to model the disease, particularly the daily cost of drugs, the length of time spent in hospital and the length of treatment received by those patients dying or failing treatment. This modelling exercise highlighted significant deficiencies in the quality of evidence on MDRTB management available to policy makers. Pragmatic choices based on operational and other logistic concerns may need to be reviewed when further information becomes available. A case can be made for the establishment of a national database of costing and efficacy information to guide future policy revisions of the South African MDRTB treatment programme, which is resource intensive and of only moderate efficacy. However, due to the widely disparate range of studies on which this evaluation was based, the outcome of the study may not be credible. In this case, the use of a systematic review with subsequent economic modelling could not validly influence policy-makers to change the decision that they made on the basis of drug availability.
- Full Text:
- Date Issued: 2003
- Authors: Rockcliffe, Nicole
- Date: 2003
- Subjects: Tuberculosis , Multidrug resistance , Tuberculosis -- Treatment
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:3788 , http://hdl.handle.net/10962/d1003266 , Tuberculosis , Multidrug resistance , Tuberculosis -- Treatment
- Description: The tuberculosis epidemic is escalating in South Africa as well as globally. This escalation is exacerbated by the increasing prevalence of multidrug-resistant tuberculosis (MDRTB), which is defined by the World Health Organisation (WHO) as resistance of Mycobacteria to at least isoniazid and rifampicin. Multi-drug resistant tuberculosis is estimated to occur in 1-2% of newly diagnosed tuberculosis (TB) patients and in 4-8% of previously treated patients. MDRTB is both difficult and expensive to treat, costing up to 126 times that of drug-sensitive TB. Resource constrained countries such as South Africa often lack both the money and the infrastructure to treat this disease. The aim of this project was to determine whether the performance of a systematic review with subsequent economic modelling could influence the decision making process for policy makers. Data was gathered and an economic evaluation of MDRTB treatment was performed from the perspective of the South African Department of Health. Three treatment alternatives were identified: a protocol regimen of second line anti-tuberculosis agents, as recommended in the South African guidelines for MDRTB, an appropriate regimen designed for each patient according to the results of culture and drug susceptibility tests, and non-drug management. A decision-analysis model using DATA 3.0 by Treeage® was developed to estimate the costs of each alternative. Outcomes were measured in terms of cost alone as well as the ‘number of cases cured’ and the number of ‘years of life saved’ for patients dying, being cured or failing treatment. Drug, hospital and laboratory costs incurred using each alternative were included in the analysis. A sensitivity analysis was performed on all variables in order to identify threshold values that would change the outcome of the evaluation. Results of the decision analysis indicate that the individualised regimen was both the cheaper and more cost-effective regimen of the two active treatment options, and was estimated to cost R50 661 per case cured and R2 070 per year of life saved. The protocol regimen was estimated to cost R73 609 per case cured and R2 741 per year of life saved. The outcome of the decision analysis was sensitive to changes in some of the variables used to model the disease, particularly the daily cost of drugs, the length of time spent in hospital and the length of treatment received by those patients dying or failing treatment. This modelling exercise highlighted significant deficiencies in the quality of evidence on MDRTB management available to policy makers. Pragmatic choices based on operational and other logistic concerns may need to be reviewed when further information becomes available. A case can be made for the establishment of a national database of costing and efficacy information to guide future policy revisions of the South African MDRTB treatment programme, which is resource intensive and of only moderate efficacy. However, due to the widely disparate range of studies on which this evaluation was based, the outcome of the study may not be credible. In this case, the use of a systematic review with subsequent economic modelling could not validly influence policy-makers to change the decision that they made on the basis of drug availability.
- Full Text:
- Date Issued: 2003
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