In search of novel anti-inflammatory agents: Computational repositioning of approved drugs

Computer-Aided Drug Design of Anti-inflammatory Agents Targeting Microsomal Prostaglandin E2 Synthase-1 (mPGES-1)

Inflammation is a natural process in response to external stimuli associated with organism protection. However, this reaction could be exaggerated, leading to severe damages related to physiopathological processes, such as rheumatoid arthritis, cancer, diabetes, allergies, infections, among others. Inflammation is mainly characterized by pain, increased temperature, flushing, and edema, which can be controlled using anti-inflammatory drugs. In this context, prostaglandin E2 (PGE2) inhibition has been targeted for designing new compounds with anti-inflammatory properties. It is a bioactive lipid overproduced during an inflammatory process, in which its increased production is carried out mainly by COX-1, COX-2, and microsomal prostaglandin E2 synthase-1 (mPGES-1). Recently, studies have demonstrated that mPGES-1 inhibition is a safe strategy to develop anti-inflammatory agents, which could protect against pain, acute inflammation, arthritis, autoimmune diseases, and different types of cancers. To decrease production costs and increase the probability of discovering active substances, computer-aided drug design (CADD) approaches have been increasingly used for designing new inhibitors. Thus, this review will cover all aspects involving high-throughput virtual screening, molecular docking, dynamics, fragment-based drug design, quantitative structure-activity relationship in seeking new promising mPGES-1 inhibitors.

DDREL: From drug-drug relationships to drug repurposing

Analyzing the relationships among various drugs is an essential issue in the field of computational biology. Different kinds of informative knowledge, such as drug repurposing, can be extracted from drug-drug relationships. Scientific literature represents a rich source for the retrieval of knowledge about the relationships between biological concepts, mainly drug-drug, disease-disease, and drug-disease relationships. In this paper, we propose DDREL as a general-purpose method that applies deep learning on scientific literature to automatically extract the graph of syntactic and semantic relationships among drugs. DDREL remarkably outperforms the existing human drug network method and a random network respected to average similarities of drugs’ anatomical therapeutic chemical (ATC) codes. DDREL is able to shed light on the existing deficiency of the ATC codes in various drug groups. From the DDREL graph, the history of drug discovery became visible. In addition, drugs that had repurposing score 1 (diflunisal, pargyline, fenofibrate, guanfacine, chlorzoxazone, doxazosin, oxymetholone, azathioprine, drotaverine, demecarium, omifensine, yohimbine) were already used in additional indication. The proposed DDREL method justifies the predictive power of textual data in PubMed abstracts. DDREL shows that such data can be used to 1- Predict repurposing drugs with high accuracy, and 2- Reveal existing deficiencies of the ATC codes in various drug groups.

Computational functional genomics-based approaches in analgesic drug discovery and repurposing

Persistent pain is a major healthcare problem affecting a fifth of adults worldwide with still limited treatment options. The search for new analgesics increasingly includes the novel research area of functional genomics, which combines data derived from various processes related to DNA sequence, gene expression or protein function and uses advanced methods of data mining and knowledge discovery with the goal of understanding the relationship between the genome and the phenotype. Its use in drug discovery and repurposing for analgesic indications has so far been performed using knowledge discovery in gene function and drug target-related databases; next-generation sequencing; and functional proteomics-based approaches. Here, we discuss recent efforts in functional genomics-based approaches to analgesic drug discovery and repurposing and highlight the potential of computational functional genomics in this field including a demonstration of the workflow using a novel R library ‘dbtORA’.

Discovery of anti-Ebola drugs: a computational drug repositioning case study

Computational drug repositioning has complemented and guided the experimental drug repositioning assignments in the recent past.