Development and Application of Novel in Vitro Assays to Advance Drug Discovery for Arrhythmogenic Cardiomyopathy and Respiratory Syncytial Virus Infection
Bibliographic record
Abstract
The research described in this thesis is focused on the development of in vitro assays to better study human disease and advance drug discovery, specifically in the context of arrhythmogenic cardiomyopathy (ACM) and respiratory syncytial virus (RSV) infection. Despite the functional distinction between these diseases, they both share the issue of inadequate treatment options for patients. Dysfunction of gap junctions is thought to underlie the development of fatal ventricular arrhythmias early in ACM disease progression. Existing methods of studying gap junction function are low-throughput, technically challenging, and have poor reproducibility. Chapter II outlines the development of a robotic cell microinjection assay to quantify gap junction permeability. Human cardiomyocytes with knockdown of PKP2, a protein commonly mutated in ACM, were screened against a library of drugs using the robotic assay. Five compounds were found to enhance gap junction function in vitro, one of which reduced beating irregularity in a mouse model of ACM. Respiratory syncytial virus (RSV) is the leading cause of acute respiratory tract infections necessitating hospitalization in infants and young children. There are currently no vaccines for the virus and treatment options are limited to supportive care. Chapter III describes the development of a high content assay to rapidly quantify RSV infection rates. Application of this assay towards drug screening led to the identification of several viral inhibitors and host pathways targeted by the virus. Functional enrichment analyses revealed an interaction between the virus and lipid metabolic pathways, prompting further investigation into the antiviral mechanism of statins–a top screening hit. Chapter IV takes a closer look at the function of statins and mevalonate pathway metabolites during viral infection. Statins were shown to inhibit RSV through a combination of cholesterol and isoprenoid-mediated effects. Notably, statin treatment negated virus-induced increases to the prenylation and activation of Rho GTPases. A primary outcome of the conducted research was the identification therapeutic drugs and pathways for two unique diseases with limited treatment options. The obtained results also highlight the utility of each assay, and demonstrate the potential of applying this work towards future studies on other viral or gap junction-mediated diseases.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".