Cancer based pharmacogenomics network supported with scientific evidences: from the view of drug repurposing

The landscape of the methodology in drug repurposing using human genomic data: a systematic review

The process of drug development is expensive and time-consuming. In contrast, drug repurposing can be introduced to clinical practice more quickly and at a reduced cost. Over the last decade, there has been a significant expansion of large biobanks that link genomic data to electronic health record data, public availability of various databases containing biological and clinical information and rapid development of novel methodologies and algorithms in integrating different sources of data. This review aims to provide a thorough summary of different strategies that utilize genomic data to seek drug-repositioning opportunities. We searched MEDLINE and EMBASE databases to identify eligible studies up until 1 May 2023, with a total of 102 studies finally included after two-step parallel screening. We summarized commonly used strategies for drug repurposing, including Mendelian randomization, multi-omic-based and network-based studies and illustrated each strategy with examples, as well as the data sources implemented. By leveraging existing knowledge and infrastructure to expedite the drug discovery process and reduce costs, drug repurposing potentially identifies new therapeutic uses for approved drugs in a more efficient and targeted manner. However, technical challenges when integrating different types of data and biased or incomplete understanding of drug interactions are important hindrances that cannot be disregarded in the pursuit of identifying novel therapeutic applications. This review offers an overview of drug repurposing methodologies, providing valuable insights and guiding future directions for advancing drug repurposing studies.

Network-based drug repurposing for HPV-associated cervical cancer

In women, cervical cancer (CC) is the fourth most common cancer around the world with average cases of 604,000 and 342,000 deaths per year. Approximately 50% of high-grade CC are attributed to human papillomavirus (HPV) types 16 and 18. Chances of CC in HPV-positive patients are 6 times more than HPV-negative patients which demands timely and effective treatment. Repurposing of drugs is considered a viable approach to drug discovery which makes use of existing drugs, thus potentially reducing the time and costs associated with de-novo drug discovery. In this study, we present an integrative drug repurposing framework based on a systems biology-enabled network medicine platform. First, we built an HPV-induced CC protein interaction network named HPV2C following the CC signatures defined by the omics dataset, obtained from GEO database. Second, the drug target interaction (DTI) data obtained from DrugBank, and related databases was used to model the DTI network followed by drug target network proximity analysis of HPV-host associated key targets and DTIs in the human protein interactome. This analysis identified 142 potential anti-HPV repurposable drugs to target HPV induced CC pathways. Third, as per the literature survey 51 of the predicted drugs are already used for CC and 33 of the remaining drugs have anti-viral activity. Gene set enrichment analysis of potential drugs in drug-gene signatures and in HPV-induced CC-specific transcriptomic data in human cell lines additionally validated the predictions. Finally, 13 drug combinations were found using a network based on overlapping exposure. To summarize, the study provides effective network-based technique to quickly identify suitable repurposable drugs and drug combinations that target HPV-associated CC.

The impact of pharmacokinetic gene profiles across human cancers

BACKGROUND

The right drug to the right patient at the right time is one of the ideals of Individualized Medicine (IM) and remains one of the most compelling promises of the post-genomic age. The addition of genomic information is expected to increase the precision of an individual patient's treatment, resulting in improved outcomes. While pilot studies have been encouraging, key aspects of interpreting tumor genomics information, such as somatic activation of drug transport or metabolism, have not been systematically evaluated.

METHODS

In this work, we developed a simple rule-based approach to classify the therapies administered to each patient from The Cancer Genome Atlas PanCancer dataset (n = 2858) as effective or ineffective. Our Therapy Efficacy model used each patient's drug target and pharmacokinetic (PK) gene expression profile; the specific genes considered for each patient depended on the therapies they received. Patients who received predictably ineffective therapies were considered at high-risk of cancer-related mortality and those who did not receive ineffective therapies were considered at low-risk. The utility of our Therapy Efficacy model was assessed using per-cancer and pan-cancer differential survival.

RESULTS

Our simple rule-based Therapy Efficacy model classified 143 (5%) patients as high-risk. High-risk patients had age ranges comparable to low-risk patients of the same cancer type and tended to be later stage and higher grade (odds ratios of 1.6 and 1.4, respectively). A significant pan-cancer association was identified between predictions of our Therapy Efficacy model and poorer overall survival (hazard ratio, HR = 1.47, p = 6.3 × 10^- 3). Individually, drug export (HR = 1.49, p = 4.70 × 10^- 3) and drug metabolism (HR = 1.73, p = 9.30 × 10^- 5) genes demonstrated significant survival associations. Survival associations for target gene expression are mechanism-dependent. Similar results were observed for event-free survival.

CONCLUSIONS

While the resolution of clinical information within the dataset is not ideal, and modeling the relative contribution of each gene to the activity of each therapy remains a challenge, our approach demonstrates that somatic PK alterations should be integrated into the interpretation of somatic transcriptomic profiles as they likely have a significant impact on the survival of specific patients. We believe that this approach will aid the prospective design of personalized therapeutic strategies.

Practical aspects of NGS-based pathways analysis for personalized cancer science and medicine

Nowadays, the personalized approach to health care and cancer care in particular is becoming more and more popular and is taking an important place in the translational medicine paradigm. In some cases, detection of the patient-specific individual mutations that point to a targeted therapy has already become a routine practice for clinical oncologists. Wider panels of genetic markers are also on the market which cover a greater number of possible oncogenes including those with lower reliability of resulting medical conclusions. In light of the large availability of high-throughput technologies, it is very tempting to use complete patient-specific New Generation Sequencing (NGS) or other "omics" data for cancer treatment guidance. However, there are still no gold standard methods and protocols to evaluate them. Here we will discuss the clinical utility of each of the data types and describe a systems biology approach adapted for single patient measurements. We will try to summarize the current state of the field focusing on the clinically relevant case-studies and practical aspects of data processing.

An integrative system biology approach to unravel potential drug candidates for multiple age related disorders

Aging, though an inevitable part of life, is becoming a worldwide social and economic problem. Healthy aging is usually marked by low probability of age related disorders. Good therapeutic approaches are still in need to cure age related disorders. Occurrence of more than one ARD in an individual, expresses the need of discovery of such target proteins, which can affect multiple ARDs. Advanced scientific and medical research technologies throughout last three decades have arrived to the point where lots of key molecular determinants affect human disorders can be examined thoroughly. In this study, we designed and executed an approach to prioritize drugs that may target multiple age related disorders. Our methodology, focused on the analysis of biological pathways and protein protein interaction networks that may contribute to the pharmacology of age related disorders, included various steps such as retrieval and analysis of data, protein-protein interaction network analysis, and statistical and comparative analysis of topological coefficients, pathway, and functional enrichment analysis, and identification of drug-target proteins. We assume that the identified molecular determinants may be prioritized for further screening as novel drug targets to cure multiple ARDs. Based on the analysis, an online tool named as 'ARDnet' has been developed to construct and demonstrate ARD interactions at the level of PPI, ARDs and ARDs protein interaction, ARDs pathway interaction and drug-target interaction. The tool is freely made available at http://genomeinformatics.dtu.ac.in/ARDNet/Index.html.

Quality by design (QbD) approach of pharmacogenomics in drug designing and formulation development for optimization of drug delivery systems

Conventional approaches of drug discovery are very complex, costly and time consuming. But after the completion of human genome project, applications of pharmacogenomics in this area completely revolutionize the drug discovery and development process to produce a quality by design (QbD) approach based products. The applications of two areas of pharmacogenomics i.e. structural and functional pharmacogenomics excel the drug discovery process by employing genomic data in drug target identification and evaluation, lead optimization via high throughput screening, evaluation of drug metabolizing enzymes, drug transporters and drug receptors using computer aided technique and bioinformatics library data base. Pharmacogenomics also provides an important and reliable basis for evaluation and optimization of the dosage forms as well as repositioning of failed drugs for the treatment of new disease. Various dosage forms of category of drugs such as anticancer drugs, vaccines, gene and DNA delivery systems and immunological agents can be easily evaluated based on the genetic markers of the related disease. The effect of different formulation polymers on pharmacokinetic and pharmacodynamic properties of drugs can be assessed easily and therefore it plays an important role in formulation optimization. However, current applications of pharmacogenomics in drug discovery and formulation optimization are very limited because of costly and non accessible techniques for everyone, but in future, with the advancement in the technology; the application of genomic data in drug discovery will provide us with innovative, safer and more efficacious medicines.

Conceptual Knowledge Discovery in Databases for Drug Combinations Predictions in Malignant Melanoma

The worldwide incidence of melanoma is rising faster than any other cancer, and prognosis for patients with metastatic disease is poor. Current targeted therapies are limited in their durability and/or effect size in certain patient populations due to acquired mechanisms of resistance. Thus, the development of synergistic combinatorial treatment regimens holds great promise to improve patient outcomes. We have previously shown that a model for in-silico knowledge discovery, Translational Ontology-anchored Knowledge Discovery Engine (TOKEn), is able to generate valid relationships between bimolecular and clinical phenotypes. In this study, we have aggregated observational and canonical knowledge consisting of melanoma-related biomolecular entities and targeted therapeutics in a computationally tractable model. We demonstrate here that the explicit linkage of therapeutic modalities with biomolecular underpinnings of melanoma utilizing the TOKEn pipeline yield a set of informed relationships that have the potential to generate combination therapy strategies.