Computer-aided drug discovery strategies for novel therapeutics for prostate cancer leveraging next-generating sequencing data

ABSTRACT

Introduction

Prostate cancer (PC) is the most common malignancy and accounts for a significant proportion of cancer deaths among men. Although initial therapy success can often be observed in patients diagnosed with localized PC, many patients eventually develop disease recurrence and metastasis. Without effective treatments, patients with aggressive PC display very poor survival. To curb the current high mortality rate, many investigations have been carried out to identify efficacious therapeutics. Compared to de novo drug designs, computational methods have been widely employed to offer actionable drug predictions in a fast and cost-efficient way. Particularly, powered by an increasing availability of next-generation sequencing molecular profiles from PC patients, computer-aided approaches can be tailored to screen for candidate drugs.

Areas covered

Herein, the authors review the recent advances in computational methods for drug discovery utilizing molecular profiles from PC patients. Given the uniqueness in PC therapeutic needs, they discuss in detail the drug discovery goals of these studies, highlighting their translational values for clinically impactful drug nomination.

Expert opinion

Evolving molecular profiling techniques may enable new perspectives for computer-aided approaches to offer drug candidates for different tumor microenvironments. With ongoing efforts to incorporate new compounds into large-scale high-throughput screens, the authors envision continued expansion of drug candidate pools.

KEYWORDS:

• Computational drug discovery
• castration-resistant prostate cancer
• drug repurposing
• prostate cancer
• molecular profiling
• next-generation sequencing
• prostate cancer transcriptomics

Article highlights

• We reviewed recent advances in computer-aided drug discovery for prostate cancer (PC) utilizing next-generation sequencing data from PC patients and high-throughput drug screens.

• Based on drug discovery objectives, most studies focused on identifying new treatment strategies for aggressive, advanced PC subtypes.

• Experimental evaluations with appropriate control groups are crucial to benchmark computational rationales and translational values of drug candidates.

• Emerging profiling of PC tumors reveal previously untapped subtypes that require tailored treatment options.

• It is envisioned that there will be continued growth of versatile in silico frameworks to tackle origins of therapy resistance such as tumor heterogeneity in advanced PC.

• Evolving molecular profiling techniques such as single-cell RNA sequencing and spatial transcriptomics may enable new perspectives for computer-aided approaches to offer drug candidates for different tumor microenvironments.

Declaration of interest

The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/17460441.2024.2365370

Additional information

Funding

The authors were funded by the University of Minnesota through the Advanced pathway to research program from the Clinical and Translational Science Institute. They also were funded by the University of Minnesota through their graduate school for doctoral dissertation and interdisciplinary fellowships. They also received support from the University of Minnesota in the form of a College of Pharmacy SURRGE award and through a GIA award as well as via a Faculty Research Development grant. Finally, the authors received funding through the National Cancer Institute via grants R01CA204856 and R01CA229618.