Drug repurposing improves disease targeting 11-fold and can be augmented by network module targeting, applied to COVID-19

Abstract

This analysis presents a systematic evaluation of the extent of therapeutic opportunities that can be obtained from drug repurposing by connecting drug targets with disease genes. When using FDA-approved indications as a reference level we found that drug repurposing can offer an average of an 11-fold increase in disease coverage, with the maximum number of diseases covered per drug being increased from 134 to 167 after extending the drug targets with their high confidence first neighbors. Additionally, by network analysis to connect drugs to disease modules we found that drugs on average target 4 disease modules, yet the similarity between disease modules targeted by the same drug is generally low and the maximum number of disease modules targeted per drug increases from 158 to 229 when drug targets are neighbor-extended. Moreover, our results highlight that drug repurposing is more dependent on target proteins being shared between diseases than on polypharmacological properties of drugs. We apply our drug repurposing and network module analysis to COVID-19 and show that Fostamatinib is the drug with the highest module coverage.

Figure 1

Schematic overview of study workflow. (a) The human interactome (FunCoup) was used to map drug target associations for FDA-approved drugs from DrugBank and disease-gene associations from DisGeNET. The sets of drug targets were also extended using first order neighbors in the human interactome. The overlap between drug targets and disease gene sets was analyzed to determine the potential disease leverage that could be offered by repurposing. (b) In order to find disease modules, the interactions between disease genes retrieved from the human interactome were partitioned using Infomap. The overlap between drug targets and disease genes was later analyzed to map drugs to the disease modules.

Figure 2

Drug repurposing may offer an average 11-fold increase of disease leverage. (a) Distribution of the number of genes targeted by FDA-approved drugs e.g., drug targets. (b) Distribution of the number of extended drug targets. (c) Distribution of the number of diseases covered by direct targets of FDA-approved drugs. (d) Distribution of the number of diseases covered by the extended targets of the drug set. (e) Distribution of the number of diseases targeted per drug for the “FDA indications” data set. (f) Statistical comparison of the distributions of disease coverage between the “FDA indications” data set and the direct drug target data set. (g) Quantification of the maximum number of diseases covered by 1, 2, 3, 4, 5 and all drugs for the “FDA indications”, the drug set with direct targets and a randomized version of the drug set. In the randomized drug set each drug has the same number of targets as in the original drug set, but the target genes are randomly chosen from the human genome. This random drug set represents the background levels of disease coverage. (h) Correlation between the number of targets and the number of diseases mapped to a drug. For the direct targets: rho = 0.636, p-value < 2.2 × 10^–16. For the extended targets: rho = 0.644, p-value < 2.2 × 10^–16.

Figure 3

Most drugs target few modules. (a) Distribution of the number of modules targeted per drug considering the direct targets. (b) Distribution of the number of modules targeted per drug considering their extended targets. (c) Correlation between the number of disease modules targeted by a drug and its number of direct gene targets (rho = 0.495, p-value < 2.2 × 10^–16). (d) Correlation between the number of disease modules targeted by a drug and its number of extended gene targets (rho = 0.503, p-value < 2.2 × 10^–16). (e) Correlation between the total number of modules and the number of drug-targeted modules in a disease (rho = 0.903, p-value < 2.2 × 10^–16). (f) Correlation between the size of a disease module (number of genes) and the number of drugs targeting it (rho = 0.488, p-value < 2.2 × 10^–16). (g) Number of modules targeted per drug in a disease targeted by that drug. (h) Density plot of the number of direct targets per disease for all drugs (average ratio = 0.52). (i) Density plot of the Szymkiewicz–Simpson similarity coefficients between disease modules targeted by single drugs (average Szymkiewicz–Simpson similarity coefficient = 0.211).

Figure 4

Drug targeting of the COVID-19 disease modules. Bipartite network linking drugs with their targets in the COVID-19 disease modules, where nsps are the SARS-CoV-2 non-structural proteins and orfs are its other open reading frames. Several polypharmacological drugs can be observed, highlighting Fostamatinib as the only one that is linked to two modules. The remaining polypharmacological drugs are linked to one module only.

Figure 5

Network examples of disease module targeting by drugs. (a) Disease modules for uterine cervical neoplasm accompanied by the drugs that target them. The biggest module is the one associated with most drugs. There are two polypharmacological drugs: Acetylsalicylic acid and Fostamatinib, both of them targeting two out of the three disease modules. (b) Disease modules for childhood acute lymphoblastic leukemia with the drugs that target them. In this case the smallest module is the one associated with most drugs. There are four polypharmacological drugs (Ponatinib, Fostamatinib, Bosutinib and Dasatinib), all of them associated only with the small module.