Application of machine learning, molecular modelling and structural data mining against antiretroviral drug resistance in HIV-1
- 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.
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- 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.
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- 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.
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- Date Issued: 2003