Insilico Medicine has developed tools to analyze the molecular differences between patients who respond and fail to clinically approved immunotherapies based on checkpoints inhibitors.
Immunotherapy has been successfully used to fight several types of cancer, such as melanoma and non-small cell lung cancer, in many cases leading to complete cancer remissions. However, there are still a significant number of patients who fail cancer immunotherapy, including those who receive immunotherapies based on antibodies targeting immune checkpoints, such as CTLA-4 or programmed death-1 (PD-1).
These immune checkpoints are used by cancer cells to inhibit the activation of immune cells and prevent their action against tumors.
A limitation of current immunotherapies based on immune checkpoint inhibitors is that they specifically focus on a single mechanism to prevent inhibition of immune cells. However, other key molecular mechanisms are also responsible for immune dysfunction and may come into play as a compensatory mechanism.
Inhibiting multiple cross-talking pathways by combining different types of drugs is more likely to decrease the ability of cancer cells to develop therapeutic resistance, as is the case when using therapy based on a single drug.
The current challenge that scientists face is to find the exact combination of immunotherapies and conventional therapies that are effective for each tumor type.
“Immunotherapy is the most promising area in oncology resulting in cures, but we need to identify effective combinations of both established methods and new drugs developed specifically to boost response rates,” Artem Artemov, director of computational drug repurposing at Insilico Medicine, said in a press release.
“At Insilico Medicine we developed a new method for screening, scoring and personalizing small molecules that may boost response rates to PD-1, PD-L1, CTLA4 and other checkpoint inhibitors. We can identify effective combinations of both established methods and new drugs developed specifically to boost response rates to immunotherapy,” Artemov said.
Researchers performed a large in silico screening of compounds that can be administered in combination with anti-PD1 immunotherapy and collected data from patients’ tumors, analyzing their responses to treatment at a molecular level. This generated molecular signatures that could predict the success of immunotherapy in a particular tumor type.
Among the top-scoring drugs, scientists at Insilico Medicine detected several compounds that increase response rates in combination with cancer immunotherapy. One such compound is a naturally occurring substance marketed as a natural product.
Insilico Medicine is currently open for partnerships to perform additional tests and validate these findings in ex vivo cultures of tumor cells extracted from responsive and non-responsive patients to immunotherapy. The tests will proceed to in vivo mouse studies, with patient-derived tumor xenografts.
This approach promises to accelerate and reduce unnecessary costs of preclinical trials. These compounds, once validated in preclinical and clinical settings, offer the opportunity to significantly improve cancer treatments and increase the lifespan of cancer patients.