Computational drug discovery for advanced prostate cancers

Abstract

Prostate cancer (PC) is one of the most diagnosed malignancies and a leading cause of cancer deaths in US men. Though primary and localized PC can be well-managed by current interventions, a fraction of PC will progress to castration-resistant prostate cancer (CRPC), a lethal subtype that displays universal resistance to standard-of-care (SOC) therapies. Given the current lack of efficacious treatments, survival among CRPC patients remains poor. Therefore, there is an urgent need to develop new treatment strategies to combat advanced PC. Traditional drug development pipelines, however, remain costly and time-consuming. Recently, there has been a rapid increase in available cancer genomic, phenotypic, and high-throughput drug screening data; this has enabled the invention of computational approaches to quickly repurpose existing compounds and consequently shorten the cycle of designing new therapeutics. Nonetheless, there is still a lack of integration of efficient computational methods for discovering new treatment opportunities for CRPC. Thus, it remains imperative to establish efficient in-silico drug repurposing pipelines for CRPC that leverage computational models. Such methods will enable the rapid discovery of treatment options to improve CRPC prognoses and patient outcomes and have the potential to be applied to other diseases to advance our knowledge towards better patient care. To this end, this dissertation leverages computational approaches to cast light on drug resistance mechanisms and facilitate rapid drug repositioning for advanced PC. Chapter 1 systematically reviews recent advances in computer-aided drug discovery strategies for PCs. Chapter 2 develops a computational pipeline to screen for new drugs against resistance to androgen-deprivation therapies (ADTs) in CRPC and nominates COL-3 which shows higher efficacy in ADT-resistant models in vitro. Chapter 3 designs an analytical pipeline to select effective treatments against neuroendocrine prostate cancer (NEPC)—a detrimental CRPC subtype with extremely limited treatment options—and proposed nicotinamide phosphoribosyltransferase (NAMPT) inhibitors as drug candidates. A novel biomarker discovery approach is also developed to select robust key genes strongly associated with response to NAMPT inhibitors. Chapter 4 and Chapter 5 tackle intratumoral heterogeneity which is often linked to therapy resistance in many cancers. Chapter 4 discusses recent approaches for predicting drug response at an individual-cell level to depict variations in therapeutic vulnerability within tumors. Finally, Chapter 5 develops a new computational algorithm to infer cellular drug response and showcases drug discovery applications for diverse heterogeneous tumors, including taxane resistant CRPC. Collectively, this dissertation develops and implements computational frameworks to quickly identify efficacious drug candidates for advanced PC patients. Proposed drugs are also validated experimentally using appropriate models in vitro and warrant further investigations. Once clinically validated, these drugs can be used to tailor PC patient care and help curb the current high mortality rate in these advanced diseases. Meanwhile, this work also presents methodological contributions toward single-cell drug sensitivity predictions and applications of drug discovery for heterogeneous tumors. In addition, the proposed computational methods may be adapted to enable efficient drug screens in many other diseases.