A network based approach to drug repositioning identifies candidates for breast cancer and prostate cancer

The high cost and the long time required to bring drugs into commerce is driving efforts to repurpose FDA approved drugs—to find new uses for which they weren’t intended, and to thereby reduce the overall cost of commercialization, and shorten the lag between drug discovery and availability. In comparison to traditional drug repositioning, which relies on serendipitous clinical discoveries, computational methods can systemize the drug search and facilitate the drug development timeline even further. In this dissertation, I report on the development, testing and application of a promising new approach to drug repositioning. This novel computational drug repositioning method is based on mining a human functional linkage network for inversely correlated modules of drug and disease gene targets. Functional linkage network is an evidence-weighted network that provides a quantitative measure of the degree of functional association among any set of human genes. The method takes account of multiple information sources, including gene mutation, gene expression, and functional connectivity and proximity of within module genes. The method was used to identify candidates for treating breast and prostate cancer. We found that (i) the recall rate for FDA approved drugs for breast and (prostate) cancer is 20/20 (10/11), while the rates for drugs in clinical trials were 131/154 and (82/106); (ii) the Area Under the ROC Curve performance substantially exceeds that of two comparable previously published methods; (iii) preliminary in vitro studies indicate that 5/5 identified breast cancer candidates have therapeutic indices superior to that of Doxorubicin in Luminal-A (MCF7) and Triple-Negative (SUM149) breast cancer cell lines. I briefly discuss the biological plausibility of the candidates at a molecular level in the context of the biological processes that they mediate. In conclusion, our method provides a unique way of prioritizing disease causal genes and identifying drug candidates for repositioning, based on innovative computational method. The method appears to offer promise for the identification of multi-targeted drug candidates that can correct aberrant cellular functions. In particular the computational performance exceeded that of existing computational methods. The approach has the potential to provide a more efficient drug discovery pipeline

Using Functional Signatures to Identify Repositioned Drugs for Breast, Myelogenous Leukemia and Prostate Cancer

The cost and time to develop a drug continues to be a major barrier to widespread distribution of medication. Although the genomic revolution appears to have had little impact on this problem, and might even have exacerbated it because of the flood of additional and usually ineffective leads, the emergence of high throughput resources promises the possibility of rapid, reliable and systematic identification of approved drugs for originally unintended uses. In this paper we develop and apply a method for identifying such repositioned drug candidates against breast cancer, myelogenous leukemia and prostate cancer by looking for inverse correlations between the most perturbed gene expression levels in human cancer tissue and the most perturbed expression levels induced by bioactive compounds. The method uses variable gene signatures to identify bioactive compounds that modulate a given disease. This is in contrast to previous methods that use small and fixed signatures. This strategy is based on the observation that diseases stem from failed/modified cellular functions, irrespective of the particular genes that contribute to the function, i.e., this strategy targets the functional signatures for a given cancer. This function-based strategy broadens the search space for the effective drugs with an impressive hit rate. Among the 79, 94 and 88 candidate drugs for breast cancer, myelogenous leukemia and prostate cancer, 32%, 13% and 17% respectively are either FDA-approved/in-clinical-trial drugs, or drugs with suggestive literature evidences, with an FDR of 0.01. These findings indicate that the method presented here could lead to a substantial increase in efficiency in drug discovery and development, and has potential application for the personalized medicine

Applications of Genome-Wide Screening and Systems Biology Approaches in Drug Repositioning

Simple Summary Drug repurposing is an accelerated route for drug development and a promising approach for finding medications for orphan and common diseases. Here, we compiled databases that comprise both computationally- or experimentally-derived data, and categorized them based on quiddity and origin of data, further focusing on those that present high throughput omic data or drug screens. These databases were then contextualized with genome-wide screening methods such as CRISPR/Cas9 and RNA interference, as well as state of art systems biology approaches that enable systematic characterizations of multi-omic data to find new indications for approved drugs or those that reached the latest phases of clinical trials. Modern drug discovery through de novo drug discovery entails high financial costs, low success rates, and lengthy trial periods. Drug repositioning presents a suitable approach for overcoming these issues by re-evaluating biological targets and modes of action of approved drugs. Coupling high-throughput technologies with genome-wide essentiality screens, network analysis, genome-scale metabolic modeling, and machine learning techniques enables the proposal of new drug-target signatures and uncovers unanticipated modes of action for available drugs. Here, we discuss the current issues associated with drug repositioning in light of curated high-throughput multi-omic databases, genome-wide screening technologies, and their application in systems biology/medicine approaches

Anthelmintics for drug repurposing : opportunities and challenges

Drug repositioning is defined as a process to identify a new application for drugs. This approach is critical as it takes advantage of well-known pharmacokinetics, pharmacodynamics, and toxicity profiles of the drugs; thus, the chance of their future failure decreases, and the cost of their development and the required time for their approval are reduced. Anthelmintics, which are antiparasitic drugs, have recently demonstrated promising anticancer effects in vitro and in vivo. This literature review focuses on the potential of anthelmintics for repositioning in the treatment of cancers. It also discusses their pharmacokinetics and pharmacodynamics as antiparasitic drugs, proposed anticancer mechanisms, present development conditions, challenges in cancer therapy, and strategies to overcome these challenges

Drug Repurposing (DR): An Emerging Approach in Drug Discovery

Drug repurposing (DR) (also known as drug repositioning) is a process of identifying new therapeutic use(s) for old/existing/available drugs. It is an effective strategy in discovering or developing drug molecules with new pharmacological/therapeutic indications. In recent years, many pharmaceutical companies are developing new drugs with the discovery of novel biological targets by applying the drug repositioning strategy in drug discovery and development program. This strategy is highly efficient, time saving, low-cost and minimum risk of failure. It maximizes the therapeutic value of a drug and consequently increases the success rate. Thus, drug repositioning is an effective alternative approach to traditional drug discovery process. Finding new molecular entities (NME) by traditional or de novo approach of drug discovery is a lengthy, time consuming and expensive venture. Drug repositioning utilizes the combined efforts of activity-based or experimental and in silico-based or computational approaches to develop/identify the new uses of drug molecules on a rational basis. It is, therefore, believed to be an emerging strategy where existing medicines, having already been tested safe in humans, are redirected based on a valid target molecule to combat particularly, rare, difficult-to-treat diseases and neglected diseases

Drug repurposing using biological networks

Drug repositioning is a strategy to identify new uses for existing, approved, or research drugs that are outside the scope of its original medical indication. Drug repurposing is based on the fact that one drug can act on multiple targets or that two diseases can have molecular similarities, among others. Currently, thanks to the rapid advancement of high-performance technologies, a massive amount of biological and biomedical data is being generated. This allows the use of computational methods and models based on biological networks to develop new possibilities for drug repurposing. Therefore, here, we provide an in-depth review of the main applications of drug repositioning that have been carried out using biological network models. The goal of this review is to show the usefulness of these computational methods to predict associations and to find candidate drugs for repositioning in new indications of certain diseases