Predicting optimal co-encapsulation of drug combination therapy for cancer

Using drug combinations is the standard of care in many cancer treatments, due to the additive or synergistic effect of attacking different cellular targets. The use of combination therapy is also effective in delaying the evolution of the cancer cells, thus impeding drug resistance. However, the treatment with a combination of drugs introduces additional problems with the safety of the drugs, as these combinations are often toxic.

To deal with the increased toxicity and reduce side effects of the combination treatment we use nanomedicine. Nanoparticles (NPs) have been shown to reduce side effects by their tendency to accumulate in the tumor site in comparison to free drugs. However, since the field of nanomedicine is still largely based on trial and error in the formulation process, we apply nanoinformatics to develop a prediction model for dual drug combinations in the nanoprecipitation method.

Our preliminary results have shown that co-encapsulation of two drugs using nanoprecipitation is highly feasible and can be predicted from the chemical structure of the two drugs using nanoinformatic tools. We show that classifying molecules into 4 distinct chemical groups facilitates the prediction model and can enable optimal dual drug formulation for the treatment of cancer.