Abstract

Background: Despite continued efforts to develop new treatments, there is an urgent need to discover new drug leads to treat tumors exhibiting primary or secondary resistance to existing drugs. Cell cultures derived from patient-derived orthotopic xenografts are promising pre-clinical models to better predict drug response in cancer recurrence.

Objective: The aim of the study was to investigate the relationship between the physiochemical properties of drugs and their in vitro potency as well as identifying chemical scaffolds biasedtowards selectivity or promiscuity of such drugs.

Methods: The bioactivities of 158 drugs screened against cell cultures derived from 30 cancer orthotopic patient-derived xenograft (O-PDX) models were considered. Drugs were represented by physicochemical descriptors and chemical structure fingerprints. Supervised learning was employed to model the relationship between features and in vitro potency.

Results: Drugs with in vitro potency for alveolar rhabdomyosarcoma and osteosarcoma tend to have a higher number of rings, two carbon-hetero bonds and halogens. Selective and promiscuous scaffolds for these phenotypic targets were identified. Highly-predictive models of in vitro potency were obtained across these 30 targets, which can be applied to unseen molecules via a webserver (https://rnewbie.shinyapps.io/Shobek-master).

Conclusion: It is possible to identify privileged chemical scaffolds and predict the in vitro potency of unseen molecules across these 30 targets This information and models should be helpful to select which molecules to screen against these primary cultures of pediatric solid tumors.

Keywords: Chemoinformatics, Data mining, pediatric cancers, patient-derived xenografts, machine learning, QSAR.