Novel approaches to treating and preventing malaria
Bibliographic record
Abstract
Malaria presents a severe economic and healthcare burden for the developing world. Recent efforts to reduce the incidents of malaria-associated deaths achieved some success, but drug resistance is increasing and the number of drugs available to treat the diseases is thinning. The current thesis seeks to advance two complementary strategies to develop platform solutions to treat and prevent malaria. First, we applied cheminformatics to assess the chemical space of anti-malarial drugs to identify promising scaffolds. Open-source tools were used to analyze the scaffolds of candidates and approved anti-malarial drugs. Our scaffold-centric analysis reveals that the anti-malarial chemical space is disjointed and segregated into few dominant structural groups with these structures being distributed according to Paretos’ principle. This structural convergence can potentially be exploited for future drug discovery by incorporating it into bioinformatics workflows. This could be used to predict new combination therapies and areas for the development of new molecules. Our second strategy seeks to develop a better tool for repellent discovery; repellent usage to prevent mosquito bites is a safe way to control these infections. The current methods for repellent screening are time consuming. The olfactory pathway involves odorant receptors that form a heterodimeric ion channel with an odorant receptor co-receptor (Orco) and a switching odorant receptor (OR). The heterologous expression of these proteins in Xenopus-oocytes and HEK293 cells, have suggested that the Orco-OR complex is functional. While these hosts have permitted significant discoveries, they have slow growth rates and extensive handling requirements, which make them unwieldy for high-throughput screens. We sought to develop high-throughput repellent screens by reconstructing this olfactory pathway into a simpler host (Pichia pastoris) with the Orco receptor. The Anopheles gambiae Orco protein was successfully expressed, being able to discriminate between compounds (VUAA1, citronella and oct-1-en-3-ol) and doses (0.125 mM to 2 mM for VUAA1) when coupled with a reporter signal. In the future this system could be used to screen chemical libraries. Moreover, the heterologous expression of Orco protein could lead to future structural and functional investigation of OR compounds as well as the development of newer repellents and behavior-modifying compounds.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".