Growth-rate model predicts in vivo tumor response from in vitro data.

ABSTRACT: A major challenge in oncology drug development is to elucidate why drugs that show promising results in cancer cell lines in vitro fail in mouse studies or human trials. One of the fundamental steps toward solving this problem is to better predict how in vitro potency translates into in vivo efficacy. A common approach to infer whether a model will respond in vivo is based on in vitro half-maximal inhibitory concentration values (IC₅₀ ), but yields limited quantitative comparison between cell lines and drugs, potentially because cell division and death rates differ between cell lines and in vivo models. Other methods based either on mechanistic modeling or machine learning require molecular insights or extensive training data, limiting their use for early drug development. To ad

SUBMITTER: Diegmiller R 

PROVIDER: S-EPMC9469692 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature