RESUMO
BACKGROUND: Drug repurposing enables the discovery of potential cancer treatments using publically available data from over 4000 published Food and Drug Administration approved and experimental drugs. However, the ability to effectively evaluate the drug's efficacy remains a challenge. Impediments to broad applicability include inaccuracies in many of the computational drug-target algorithms and a lack of clinically relevant biologic modeling systems to validate the computational data for subsequent translation. METHODS: We have integrated our computational proteochemometric systems network pharmacology platform, DrugGenEx-Net, with primary, continuous cultures of conditionally reprogrammed (CR) normal and prostate cancer (PCa) cells derived from treatment-naive patients with primary PCa. RESULTS: Using the transcriptomic data from two matched pairs of benign and tumor-derived CR cells, we constructed drug networks to describe the biological perturbation associated with each prostate cell subtype at multiple levels of biological action. We prioritized the drugs by analyzing these networks for statistical coincidence with the drug action networks originating from known and predicted drug-protein targets. Prioritized drugs shared between the two patients' PCa cells included carfilzomib (CFZ), bortezomib (BTZ), sulforaphane, and phenethyl isothiocyanate. The effects of these compounds were then tested in the CR cells, in vitro. We observed that the IC50 values of the normal PCa CR cells for CFZ and BTZ were higher than their matched tumor CR cells. Transcriptomic analysis of CFZ-treated CR cells revealed that genes involved in cell proliferation, proteases, and downstream targets of serine proteases were inhibited while KLK7 and KLK8 were induced in the tumor-derived CR cells. CONCLUSIONS: Given that the drugs in the database are extremely well-characterized and that the patient-derived cells are easily scalable for high throughput drug screening, this combined in vitro and in silico approach may significantly advance personalized PCa treatment and for other cancer applications.
