DISGENET plus is provided by Medbioinformatics Solutions S.L.
C/ Almogàvers 165, 08018 Barcelona (Spain)
Contact us at

logo

Introduction

There is compelling evidence of the value of incorporating genetic information in the drug development process. A drug target supported by genetic evidence has a 2-fold higher probability of successful clinical development compared to those targets with no genetic support (Cook et al. 2014). A systematic analysis of FDA drug approvals showed that drugs with human genetics support were 2-5 fold more likely to lead to an improved therapy (Nelson et al. 2016).

What is the genetic support for the 50 drugs that were approved by the FDA the last year (Table 1)(Mullard 2022)? To answer this question requires accessing all current knowledge regarding the association of the drug target to the disease and data analytics capabilities that are provided in platforms like DISGENET plus. A recent publication (Ochoa et al. 2022) estimates that for two-thirds of the drug approvals (33 out of 50) the targets of the drugs, or a protein in the vicinity of the human interactome, have been associated with the indication, or a similar phenotype. But, is it possible that this number is higher? Let’s take a closer look at the details and use DISGENET plus to assess whether the targets of the drugs have been previously reported as involved in the disease mechanism.

Data

Preprocessing

We will remove from the analysis 4 drugs that target proteins from other organisms (Table 2)

Table 2: Drugs without human targets
Drug_brand_name Properties Indication
Cabotegravir; rilpivirine (Cabenuva Kit) INSTI and an NNRTI HIV-1 infection
Fexinidazole (Fexinidazole) Nitroimidazole antimicrobial Sleeping sickness
Ibrexafungerp (Brexafemme) Triterpenoid antifungal Vulvovaginal candidiasis
Maribavir (Livtencity) CMV pUL97 kinase inhibitor Post-transplant CMV infection

… and drugs that do not target a gene or protein (Table 3)

Table 3: Drugs without protein/gene targets
Drug_brand_name Properties Indication
Asparaginase erwinia chrysanthemi (Rylaze) Recombinant asparagine-specific enzyme ALL and LBL, in patients allergic to E. coli-derived products
Fosdenopterin (Nulibry) cPMP MoCD type A
Melphalan flufenamide (Pepaxto) Peptide-conjugated alkylating drug Multiple myeloma

Converting indications to UMLS identifiers

The remaining 43 drugs are indicated for 41 conditions. We mapped 34 indications to an exact Unified Medical Language System (UMLS) Concept Unique Identifiers (CUI), corresponding to 41 CUIs (Table 4). Using UMLS CUIs allowed more exact mappings of the indications for the drugs, due to the richer and more granular descriptions of phenotypes provided by the UMLS.

Indications with no direct mappings to UMLS were manually assign to similar terms (Table 5).

Table 5: Mappings to similar conditions
Indication diseaseid disease_name
1 Chemotherapy-induced myelosuppression C0149925; C0854467 Small cell carcinoma of lung; Myelosuppression
2 EGFR exon 20-mutated NSCLC C0007131 Non-Small Cell Lung Carcinoma
3 FGFR2-mutated bile duct cancer C0740277; C0005426 Bile duct carcinoma; Bile duct neoplasm
4 KRASG12C-mutated NSCLC C0007131 Non-Small Cell Lung Carcinoma
5 Pruritus in Alagille syndrome C0033774; C0085280; C1535964 Pruritus; Alagille Syndrome; Cholestatic pruritus
6 Pruritus in PFIC C0033774; C1535964; C4551898 Pruritus; Cholestatic pruritus; Cholestasis, progressive familial intrahepatic 1
7 Relapsing multiple sclerosis C0026769 Multiple Sclerosis

Table 6 shows the mappings of indications to codes in the Experimental Factor Ontology (Malone et al. 2010), as performed in (Ochoa et al. 2022). We show in red the indications that have been mapped to a similar phenotype (16)

Drug-target information

We used the indications from ChEMBL to find the targets for the FDA approvals (Table 7). All 43 drugs were found to be associated to at least one human target, and the total number of targets is 76

Results

We will use the DISGENET plus API to retrieve the genes associated to the indications. To perform this operation, you will need to register, to get an API key.

Exact mappings

Using DISGENET plus information, we find that 28 drugs have at least a gene associated to their indication in DISGENET plus, thus 65.1 percent of the drugs are supported by genetic info, with exact mappings (counting only the ones that have a human target, thus 43 ).

Figure 1 shows the top scoring gene associated to the pair drug-indication.

Similar mappings

12 drugs where found to have at least a gene associated to their indication in DISGENET plus using the similarity mapping, thus 93 percent of the drugs are supported by genetic info, with exact mappings or similar (counting only the ones that have a human target)

Drugs with genetic support for the indication, or a disease similar to the indication

Drug without genetic suppport

Drugs that do not have a genetic support are shown in the table below

Table 10: Drugs without genetic support
Drug_brand_name Indication Properties symbol disease_name
1 Pegcetacoplan (Empaveli) PNH Complement protein C3 inhibitor C3 Paroxysmal nocturnal hemoglobinuria
2 Ropeginterferon alfa-2b (Besremi) Polycythaemia vera PEGylated interferon α-2b IFNAR1; IFNAR2 Polycythemia Vera
3 Voclosporin (Lupkynis) Lupus nephritis Calcineurin inhibitor PPP3CA Lupus Erythematosus, Systemic; Lupus Nephritis

Genetic support for indications with missing drugs

Drugs without genetic support have a close protein that is similar to the one related to the mechanism.

PNH and Complement proteins

Polycythaemia vera and IFNAR1 or IFNAR2

PPP3CA and Lupus nephritis

Conclusion

In summary, this analysis shows that DISGENET plus provides genetic support for 90% of the drugs approved during 2021 by the FDA. This contrasts with previous reports using publicly available tools, which achieved 66% of genetic support for drug approvals (Ochoa et al. 2022). Thus, DISGENET plus is a key resource for drug R&D to provide actionable information on potential targets for a wide range of indications. In addition, it illustrates the potential of DISGENET plus as a resource for information on disease biomarkers, as it also contains the evidence that relates the association of the biomarkers with their indications for the two imaging agents approved by the FDA.

References

Cook, David, Dearg Brown, Robert Alexander, Ruth March, Paul Morgan, Gemma Satterthwaite, and Menelas N. Pangalos. 2014. “Lessons learned from the fate of AstraZeneca’s drug pipeline: a five-dimensional framework.” Nature Reviews Drug Discovery 2014 13:6 13 (6): 419–31. https://doi.org/10.1038/nrd4309.
Malone, James, Ele Holloway, Tomasz Adamusiak, Misha Kapushesky, Jie Zheng, Nikolay Kolesnikov, Anna Zhukova, Alvis Brazma, and Helen Parkinson. 2010. “Modeling sample variables with an Experimental Factor Ontology.” Bioinformatics 26 (8): 1112–18. https://doi.org/10.1093/BIOINFORMATICS/BTQ099.
Mullard, Asher. 2022. “2021 FDA approvals.” Nature Reviews. Drug Discovery 21 (2): 83–88. https://doi.org/10.1038/D41573-022-00001-9.
Nelson, Matthew R., Toby Johnson, Liling Warren, Arlene R. Hughes, Stephanie L. Chissoe, Chun Fang Xu, and Dawn M. Waterworth. 2016. “The genetics of drug efficacy: opportunities and challenges.” Nature Reviews Genetics 2016 17:4 17 (4): 197–206. https://doi.org/10.1038/nrg.2016.12.
Ochoa, David, Mohd Karim, ​​Maya Ghoussaini, David G. Hulcoop, Ellen M. McDonagh, and Ian Dunham. 2022. “Human genetics evidence supports two-thirds of the 2021 FDA-approved drugs.” Nature Reviews Drug Discovery, August. https://doi.org/10.1038/D41573-022-00120-3.