Representative pictures showing wide variation in cellular uptake of radiopharmaceuticals.
A number of therapeutic cocktails have been developed to treat various diseases including metastatic cancers. However, patients vary widely in their beneficial responses to such therapies. It has been found that the uptake of targeted therapeutic agents by diseased cells varies dramatically even under ideal conditions. Accordingly, treatment with a single pharmaceutical is not likely to target all cells adequately to achieve the desired therapeutic outcome. To increase treatment effectiveness, formulations of therapeutic cocktails should be individually tailored to each patient to optimize therapeutic effect.
Rutgers scientists have developed a novel procedure to predict the response of an individual patient to therapeutic intervention with cocktails of radiopharmaceuticals, chemotherapeutics, or other agents. With a cell population derived from a patient, this technology can identify the optimal drug concentration of a given drug for use in a therapeutic cocktail of drugs. The optimal cocktail of therapeutic drugs can be identified by exposing the patient’s cells to a combination of drugs whereby the concentration of each drug is optimized.
A Monte Carlo simulation is then implemented to identify the combination of drugs that affords the highest degree of killing of the targeted cells. This method has been tested with a combination of a radiochemical and two anticancer drugs, and a cocktail of four antibodies. In addition, software is under development to implement this method.
- To design patient-specific cocktail formulations for personalized medicine.
- To identify drugs that can be added to a cocktail to facilitate targeting subpopulations of cells that would otherwise escape targeting.
- To develop new cocktail formulations with drug candidates that failed owing to efficacy issues, but might have synergistic effects with others.
Intellectual Property & Development Status:
Issued patent and pending application. Available for licensing or collaboration