This new computational resource identifies and prioritizes treatment options based on a patient's profile of genetic alterations.
‘PanDrugs, a new computational methodology that prioritizes drug treatments based on patient genomic data has been developed. It is the first drug prescription tool that takes into account pathway context.’
A large majority of cancers carry a long list of genetic alterations whose biological and clinical relevance, and susceptibility to be pharmacologically-targeted isn't always clear. Several tools have been developed to identify clinically actionable genomic alterations and to suggest targeted therapies but they have some limitations and there's still a gap between raw genomic data and clinical usefulness.
To overcome this, a team led by Fátima Al-Shahrour, head of the Bioinformatics Unit at the CNIO, have implemented this novel method called PanDrugs. "The main novelty introduced in this methodology compared with current tools is the broadening of the search space to provide therapeutic options", explains Al-Shahrour.
In other words, PanDrugs suggests treatments for direct targets (e.g. genes that contribute to disease phenotype and can be directly targeted by a drug) and biomarkers (e.g. genes that have a genetic status associated with drug response but the protein product are not the drug target itself). But also, PanDrugs integrates a systems biology knowledge-based layer that automatically inspects biological circuits expanding cancer candidate therapies from beyond limited cancer-related gene lists to the whole druggable pathway.
"This novel strategy (called 'pathway member') extends the treatment opportunities of cancer patients by enriching the therapeutic arsenal against tumors and opens new avenues for personalized medicine", states Al-Shahrour. Thanks to pathway member strategy, the paper describes how PanDrugs is able to identify treatments used in clinical practice that might benefit prostate
and colorectal cancer
patients without druggable cancer driver altered genes.
Researchers emphasize that PanDrugs database represents, by itself, a remarkable contribution. "This database is the largest public repository of drug-target associations available from well-known targeted therapies to preclinical drugs. Current version of PanDrugsdb integrates data from 24 primary sources and supports >56000 drug-target associations".
PanDrugs can be fully integrated with custom pipelines through its programmatic API and its docker image facilitates PanDrugs in-house installation, enhancing reproducibility and improving performance.