Toward better drug repositioning: prioritizing and integrating existing methods into efficient pipelines

Abstract

Recycling old drugs, rescuing shelved drugs and extending patents' lives make drug repositioning an attractive form of drug discovery. Drug repositioning accounts for approximately 30% of the newly US Food and Drug Administration (FDA)-approved drugs and vaccines in recent years. The prevalence of drug-repositioning studies has resulted in a variety of innovative computational methods for the identification of new opportunities for the use of old drugs. Questions often arise from customizing or optimizing these methods into efficient drug-repositioning pipelines for alternative applications. It requires a comprehensive understanding of the available methods gained by evaluating both biological and pharmaceutical knowledge and the elucidated mechanism-of-action of drugs. Here, we provide guidance for prioritizing and integrating drug-repositioning methods for specific drug-repositioning pipelines.

Figure 1

Lotus leaves flowchart (LLF) for categorization of existing drug-repositioning methods. Drug repositioning takes advantage of different potential avenues to repurpose drugs for new indications, including drug, disease and treatment oriented. These avenues were developed according to the availability of biological and pharmaceutical knowledge and requirement of understanding the mechanisms of action of drugs. Traditional phenotype-based screening methods do not need prior knowledge, and the repositioned drugs are just serendipitously tested. Targeted-based methods need specific knowledge about the targets, such as 3D protein structures, whereas knowledge-based methods require the knowledge about the drugs or diseases, such as adverse effects, FDA approval labels, records of clinical trials and published disease biomarkers (potential targets) or disease pathways. Signature-based methods mainly make use of gene signatures defined by ‘-omics’ data (for diseases, drug treatments, or both). Pathway- or network-based methods generally use pathway analysis or network biology methods to discover essential pathways from genetic, genomic, proteomic and metabolic data of diseases to find new targets for repositioned drugs. More advanced drug-repositioning methods, such as targeted mechanism-based methods, aim to discover mechanisms of action of drugs by identification of off-targets or targeted pathways of treated drugs using drug omics data (before and after drug treatments). Details of these methods are in Table 1 (main text). Integrated knowledge and elucidated mechanisms of drug actions increases with the complexity of modeling methods.

Figure 2

Fishbone flowchart of drug-repositioning pipelines. Developed drug-repositioning pipelines comprise at least one of these methods (i.e., blinded, target based, knowledge based, signature based, pathway or network based, and targeted-mechanism based), preclinical studies (in vitro and/or in vivo validations) and clinical development (testing the new indications identified from the preclinical development). Development of a new drug-repositioning pipeline for a drug or a disease of interest should evaluate the priorities of these repositioning methods based on the available information of the drug or the disease. For example, an infectious disease that only has limited available signaling information related to cell wall and cytoplasmic membrane proteins would lead to high priority of target-based drug-repositioning studies focusing on these cell wall and cytoplasmic membrane proteins. Many drug-repositioning pipelines can reverse the order of the listed methods in the fishbone flowchart and make use of them flexibly. As an example, several existing drug-repositioning pipelines first consider pathway- or network-based drug-repositioning methods to reconstruct disease pathways and then use knowledge-based or targeted-based methods to identify candidate drugs. The fishbone provides all relevant components of general drug-repositioning pipelines, and one can customize specific drug-repositioning pipelines according to the available knowledge and information of targeted drugs or diseases. Abbreviations: FDA, Food and Drug Administration; GWAS, genome-wide association study; HTS/HCS, high-throughput and/or high-content screening; PK/PD, pharmacokinetics/pharmacodynamics.