How to optimize combination therapy for non-small cell lung cancer (Nsclc) and melanoma?

Researchers at Vanderbilt University have created the MuSyC algorithm (Multi-dimensional synergy of combinations) able to distinguish between the interactions between drugs that can give greater efficiency and speed in killing cancer cells from those that instead lead to fewer effects side. The work was published on Cell Systems; the algorithm was developed based on half a million tests on cancer cells with 12 thousand different drug combinations. The primary objective of the study was to calculate the synergy profile of 64 different anticancer drugs in combination with osimertinib, a standard of care for lung cancer Nsclc.

Potency and efficacy

MuSyC reveals that combining a mutant-EGFR inhibitor with inhibitors of other kinases may result only in synergistic potency, whereas synergistic efficacy can be achieved by co-targeting mutant-EGFR and epigenetic regulation or microtubule polymerization. In mutant-BRAF melanoma, MuSyC determines whether a molecular correlate of BRAFi insensitivity alters a BRAF inhibitor’s potency, efficacy, or both. These findings showcase MuSyC’s potential to transform the enterprise of drug-combination screens by precisely guiding translation of combinations toward dose reduction, improved efficacy, or both.

Action targets identification

The drugs tested by Vanderbilti University researchers were divided into four different categories, based on the action target:

  • mitotic checkpoints
  • epigenetic regulators
  • receptors and / or channels
  • kinases

The hope of the American research group is that now the MuSyC algorithm can help change the ways to discover and translate the most effective drug combinations into true clinical innovation. The next steps of the research see its expansion to the combination of three different medicines and the evaluation in more complex pre-clinical models, such as organoids that replicate different types of tumors.

 

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