A computational drug repositioning method applied to rare diseases: Adrenocortical carcinoma

Abstract

Rare or orphan diseases affect only small populations, thereby limiting the economic incentive for the drug development process, often resulting in a lack of progress towards treatment. Drug repositioning is a promising approach in these cases, due to its low cost. In this approach, one attempts to identify new purposes for existing drugs that have already been developed and approved for use. By applying the process of drug repositioning to identify novel treatments for rare diseases, we can overcome the lack of economic incentives and make concrete progress towards new therapies. Adrenocortical Carcinoma (ACC) is a rare disease with no practical and definitive therapeutic approach. We apply Heter-LP, a new method of drug repositioning, to suggest novel therapeutic avenues for ACC. Our analysis identifies innovative putative drug-disease, drug-target, and disease-target relationships for ACC, which include Cosyntropin (drug) and DHCR7, IGF1R, MC1R, MAP3K3, TOP2A (protein targets). When results are analyzed using all available information, a number of novel predicted associations related to ACC appear to be valid according to current knowledge. We expect the predicted relations will be useful for drug repositioning in ACC since the resulting ranked lists of drugs and protein targets can be used to expedite the necessary clinical processes.

The use of the Heter-LP algorithm to reposition antibiotics for managing E. coli mastitis in dairy cattle

“…In the current research, the biological algorithm Heter-LP was used to reposition antibiotics for managing E. coli mastitis in dairy cattle. The utility of Heter-LP, to discover new drug repositioning to rare diseases in human have been explored previously [43]. Data that was available in the public repositories along with other specialize biological information for E. coli mastitis including crucial genes, antibiotic or drugs used for treatment of E. coli mastitis, and its association with other disease or cell processes were used as input data for the Heter-LP algorithm.…”

Systems Biology–Derived Genetic Signatures of Mastitis in Dairy Cattle: A New Avenue for Drug Repurposing

“…It has been observed that the use of network-based methods in the integration of biological data at different levels has yielded good results. The utility of Heter-LP to discover new drug repositioning options for rare diseases in humans has been explored previously [ 21 ]. Heter-LP is a semi-supervised learning method based on label propagation on a heterogeneous network consisting of three types of nodes (targets, drugs, and diseases) and six different kinds of edges (three kinds of similarities and three kinds of associations) [ 22 ].…”

“…Different essential data for each part were gathered and organized as a comprehensive dataset for a previous study (available through GitHub [ , accessed on 26 March 2021] and the DKR site [ , accessed on 28 March 2021]) [ 21 ]. The data resources are summarized in Table 1 .…”

“…For example, three different criteria used to construct the drug similarities sub-network are chemical substructure similarities, side effect similarities, and Anatomical Therapeutic Chemical (ATC) code similarities. In total, similarities among 5089 drugs are provided by the integration of these resources [ 21 ]. The latest versions of data resources used to generate the six matrices are provided according to a detailed description of that dataset presented on the above-mentioned GitHub and DKR sites.…”

“…Different computational tools for drug repositioning analysis and methods for the prediction of drug-target interactions have been presented in a recent review [ 20 ]. Among them, Heter-LP was selected because of advantages such as accuracy, lack of requirement for negative samples, ability to predict trivial and non-trivial relationships between drugs, diseases, and protein targets, and ability to use heterogeneous data [ 21 , 22 ].…”