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Predicting new drug indications for prostate cancer: The integration of an in silico proteochemometric network pharmacology platform with patient‐derived primary prostate cells
Author(s) -
Naeem Aisha,
Dakshanamurthy Sivanesan,
Walthieu Henry,
Parasido Erika,
Avantaggiati Maria,
Tricoli Lucas,
Kumar Deepak,
Lee Richard J.,
Feldman Adam,
Noon Muhammad S.,
Byers Stephen,
Rodriguez Olga,
Albanese Chris
Publication year - 2020
Publication title -
the prostate
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.295
H-Index - 123
eISSN - 1097-0045
pISSN - 0270-4137
DOI - 10.1002/pros.24050
Subject(s) - prostate cancer , cabazitaxel , in silico , drug repositioning , drug discovery , drug , cancer research , proteases , cancer cell , pharmacology , drugbank , cancer , computational biology , medicine , biology , bioinformatics , androgen deprivation therapy , gene , biochemistry , enzyme
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 IC 50 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.