Individual patients' efficacy of drug therapies in liver cancer can be predicted using mathematical modeling.
A team led by Johns Hopkins University's Mohammad Jafarnejad developed a mathematical model of cellular signaling in liver cells that can predict drug interactions affecting the growth pathways in liver cells that have been found to be responsible for tumor formation in liver cancer.
The model, which was calibrated using available data, predicted the efficacy of common therapies and predicted synergy for a combination of therapies. Overall, the mathematical modeling enabled us to investigate the differences in efficacy between different drugs as well as the efficacy of drug combinations.
The methods developed here can be used to benefit individual patients by determining which therapies are most likely to be effective as the availability of patient-specific data increases.
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