In the area of pharmacology, we have been working on the problem of drug repurposing, drug side effect prediction and drug target prediction.
Drug discovery and development present several challenges, including high attrition rates, long development times, and substantial costs. Drug repositioning involves the use of de-risked compounds in humans, which translates into lower costs and shorter development times. With the onset of the COVID-19 pandemic we began working on methods for drug repositioning against COVID-19 that target SARS-CoV-2 and its cellular processes in the host. We developed a matrix decomposition approach exploits drug developmental information to predict broad-spectrum antivirals. We also introduced a graph kernel-based approach, rooted in ideas from network medicine, that predicts which FDA-approved drugs are more likely to perturb the human subnetwork that is crucial for SARS-CoV-2 infection/replication. Recently, we have been expanding on these methods, combining ideas from network medicine with collaborative filtering to predict host-centric antivirals for different viruses.
We have also developed pipelines for repositioning drug combinations for neglected tropical diseases. Currently we are focusing on Chagas disease (among the top 10 target diseases in the Gates Foundation), which is caused by the protozoan parasite Trypanosoma cruzi (T. cruzi) and is endemic throughout Latin America. About 6 to 7 million people are infected with T. cruzi, around 40 million are at risk of infection. and no drug is effective against the parasite in the chronic phase of the disease. We developed a method that exploits concepts from comparative genomics for the prediction of FDA approved drugs which could be effective against T.cruzi. Our method selects FDA approved drugs which target enzymes in model organisms that are evolutionarily related to enzymes in T.cruzi pathways and could therefore be effective at disrupting its metabolic pathways. This work is a collaboration with the groups of Dr Celeste Vega at CEDIC, Asuncion, Paraguay and Prof Dario Pescini at the University of mIlan, Bicocca, Italy.
The frequency of drug side effects in the population is determined through placebo-controlled studies during drug clinical trials. However, it is well recognised that many drug side effects are not observed during such trials, thus remaining a leading cause of morbidity and mortality in health care. We developed a matrix decomposition model that can predict the frequency of drug side effects with high accuracy. Our biologically interpretable model goes beyond the standard machine learning black-box modelling and can shed new lights into population-level drug response. We are currently working on extending these results to drug compinations.
We have recently begun to develop a novel matrix completion approaches for tackling the problem of prediction of drug-target interactions, an important step in the drug discovery and repositioning process. Our final aim is to extend the druggable genome and preliminary results show that our models can be very effective at predicting drug-target associations involving previously unknown targets.