Real-world evidence with a retrospective cohort of 15,968 COVID-19 hospitalized patients suggests 21 new effective treatments

drexml: A command line tool and Python package for drug repurposing

We introduce drexml, a command line tool and Python package for rational data-driven drug repurposing. The package employs machine learning and mechanistic signal transduction modeling to identify drug targets capable of regulating a particular disease. In addition, it employs explainability tools to contextualize potential drug targets within the functional landscape of the disease. The methodology is validated in Fanconi Anemia and Familial Melanoma, two distinct rare diseases where there is a pressing need for solutions. In the Fanconi Anemia case, the model successfully predicts previously validated repurposed drugs, while in the Familial Melanoma case, it identifies a promising set of drugs for further investigation.

Use of drugs for hypertension or heart failure and the risk of death in COVID-19: association with loop-diuretics

PURPOSE

To study the association between the use of drugs for hypertension or heart failure, particularly diuretics, and risk of death in COVID-19.

METHODS

We conducted a cohort study, based on record linked individual-based data from national registers, of all Swedish inhabitants 50 years and older (n = 3,909,321) at the start of the first SARS-CoV-2 wave in Sweden. The association between use of angiotensin-converting enzyme inhibitors (ACEI), angiotensin II receptor blockers (ARB), thiazides, loop diuretics, aldosterone antagonists, beta blocking agents and calcium channel blockers at the index date 6 March 2020, and death in COVID-19 during 7 March to 31 July 2020, was analysed using Cox-proportional hazards regression, adjusted for a wide range of possible confounders.

RESULTS

Use of loop diuretics was associated with higher risk [adjusted hazard ratio (HR) 1.26; 95% confidence interval (95% CI) 1.17-1.35] and thiazides with reduced risk (0.78; 0.69-0.88) of death in COVID-19. In addition, lower risk was observed for ACEI and higher risk for beta-blocking agents, although both associations were weak. For ARB, aldosterone antagonists and calcium channel blockers no significant associations were found.

CONCLUSION

In this nationwide cohort of nearly 4 million persons 50 years and older, the use of loop diuretics was associated with increased risk of death in COVID-19 during the first SARS-CoV-2 wave in Sweden. This contrasted to the decreased risk observed for thiazides. As treatment with loop diuretics is common, particularly in the elderly, the group most affected by severe COVID-19, this finding merit further investigation.

The mechanistic functional landscape of retinitis pigmentosa: a machine learning-driven approach to therapeutic target discovery

BACKGROUND

Retinitis pigmentosa is the prevailing genetic cause of blindness in developed nations with no effective treatments. In the pursuit of unraveling the intricate dynamics underlying this complex disease, mechanistic models emerge as a tool of proven efficiency rooted in systems biology, to elucidate the interplay between RP genes and their mechanisms. The integration of mechanistic models and drug-target interactions under the umbrella of machine learning methodologies provides a multifaceted approach that can boost the discovery of novel therapeutic targets, facilitating further drug repurposing in RP.

METHODS

By mapping Retinitis Pigmentosa-related genes (obtained from Orphanet, OMIM and HPO databases) onto KEGG signaling pathways, a collection of signaling functional circuits encompassing Retinitis Pigmentosa molecular mechanisms was defined. Next, a mechanistic model of the so-defined disease map, where the effects of interventions can be simulated, was built. Then, an explainable multi-output random forest regressor was trained using normal tissue transcriptomic data to learn causal connections between targets of approved drugs from DrugBank and the functional circuits of the mechanistic disease map. Selected target genes involvement were validated on rd10 mice, a murine model of Retinitis Pigmentosa.

RESULTS

A mechanistic functional map of Retinitis Pigmentosa was constructed resulting in 226 functional circuits belonging to 40 KEGG signaling pathways. The method predicted 109 targets of approved drugs in use with a potential effect over circuits corresponding to nine hallmarks identified. Five of those targets were selected and experimentally validated in rd10 mice: Gabre, Gabra1 (GABARα1 protein), Slc12a5 (KCC2 protein), Grin1 (NR1 protein) and Glr2a. As a result, we provide a resource to evaluate the potential impact of drug target genes in Retinitis Pigmentosa.

CONCLUSIONS

The possibility of building actionable disease models in combination with machine learning algorithms to learn causal drug-disease interactions opens new avenues for boosting drug discovery. Such mechanistically-based hypotheses can guide and accelerate the experimental validations prioritizing drug target candidates. In this work, a mechanistic model describing the functional disease map of Retinitis Pigmentosa was developed, identifying five promising therapeutic candidates targeted by approved drug. Further experimental validation will demonstrate the efficiency of this approach for a systematic application to other rare diseases.