De-TarDis: Off-Targetlar, Promisküöz Hedefler, Advers/Yan Etkiler ve Güvenlik Panellerini Reciprocal Rank Fusion ile Bütünleştiren Güvenlik Odaklı Hedef Veritabanı

Öz

İlaç keşfi sürecinde hesaplamalı taramalar genellikle yalnızca “istenen” hedef protein üzerine odaklanır; oysa klinik aşamaya yaklaşıldıkça beklenmeyen hedef-dışı bağlanmaları, çoklu bağlanım gösteren proteinler, advers ve yan etkiyle ilişkilendirilmiş ve güvenlik panellerinde (örn. Eurofins SafetyScreen™ tier 1–3) olan hedefler başarısızlık nedeni hâline gelebilmektedir. Bu çalışma, erken aşamada bu riskli hedefleri topluca kontrol etmeyi kolaylaştıracak bütüncül bir kaynak eksikliğini gidermeyi amaçlamaktadır. Bunun için literatür ve kamuya açık veri tabanları tarandıktan sonra, beş ana grupta (1) hedef-dışı, (2) çoklu bağlanım, (3) advers etki, (4) yan etki ve (5) güvenlik kontrolü ilaç hedefler olmak üzere 49 ayrı liste derlendi, hedef tanımlayıcıları (UniProtKB) standartlaştırıldı ve her liste kendi içinde hacim ve çalışma-özgül skorlarına göre sıralandı. Ardından, Reciprocal Rank Fusion (RRF), bir hedefin farklı kaynaklardaki yüksek sıralarını ödüllendirerek çoklu çalışmada tutarlı görünen hedefleri en üste taşıyan basit ama sağlam bir sıralama tekniği uygulandı. Sonuçta “De-TarDis” (Decoy-Target Discovery Database) adı verilen ve hesaplamalı taramalarda “kaçınılacak hedef” olarak kullanılmaya uygun birleşik bir liste elde edilmiştir. Bu liste, öncü ilaç adayı bileşik belirleme aşaması ve öncü ilaç adayı bileşik optimizasyonu aşamalarında, aday bileşiğin istenmeyen güvenlik ilişkili proteinlere bağlanma olasılığını taramak için doğrudan kullanılabilir; böylece ileri aşamadaki ADMET kaynaklı sürprizlerin azaltılmasına katkı sağlar.

Anahtar Kelimeler

• Hedef güvenliği
• Hedef-dışı proteinler
• Promisküöz hedefler
• Yan etki / advers etki ilişkisi
• Karşılıklı Sıra Birleştirme (Reciprocal Rank Fusion)
• İlaç keşfi için birleşik veri tabanı

De-TarDis: Decoy-Target Discovery Database Integrating Off-Targets, Promiscuity, Adverse/Side-Effects, and Screening Panels via Reciprocal Rank Fusion for Safety Assessment

Öz

Computational screening in drug discovery typically concentrates on a single “intended” target; yet as projects approach the clinic, unexpected liabilities—off-target binding, promiscuous (multi-target) proteins, targets implicated in adverse and side effects, and those monitored in safety panels (e.g., Eurofins SafetyScreen™ tiers 1–3)—often drive failure. In order to address such a challenge, this study addresses the lack of a unified resource that enables early, collective checks against such risky targets. We surveyed the literature and public databases, compiling 49 lists organized into five groups: (1) off-target, (2) promiscuous target, (3) adverse-effect target, (4) side-effect target, and (5) safety-check target. Target identifiers were standardized to UniProtKB, and each list was internally ranked using volume and study-specific scores. Reciprocal Rank Fusion (RRF) has been applied to merge these heterogeneous rankings into a single, robust ordering—RRF rewards targets that rank highly across multiple sources, elevating consistently implicated proteins to the top. The resulting resource, “De-TarDis” (Decoy-Target Discovery Database), yields a consolidated “avoid-these-targets” list for computational campaigns. It can be used directly during hit-to-lead and lead-optimization to flag compounds likely to bind safety-relevant proteins, thereby reducing late-stage, ADMET-driven surprises.

Anahtar Kelimeler

• Target safety
• Off-target proteins
• Promiscuous targets
• Adverse/side-effect–target associations
• Reciprocal Rank Fusion (RRF)
• Integrated database for drug discovery

Kaynakça

1. Prajapati, A. (2025). COMPUTER-AIDED DRUG DISCOVERY: TRANSFORMING THE LANDSCAPE OF PHARMACEUTICAL RESEARCH. Frontline Chemistry Nexus, 1(1), 57-91.
2. Khurshid, B., Khurshid, S., Rafique, A. M., & Javaid, M. (2025). AI for Drug Discovery: From Algorithms to Medicines. Journal of Pharma and Biomedics, 3(2), 171-184.
3. dos Santos Nascimento, I. J., & de Moura, R. O. (2023). Ligand and structure-based drug design (LBDD and SBDD): Promising approaches to discover new drugs. In Applied Computer-Aided Drug Design: Models and Methods (pp. 1-32). Bentham Science Publishers.
4. Saini, M., Mehra, N., Kumar, G., Paul, R., & Kovács, B. (2025). Molecular and structure-based drug design: From theory to practice. In Advances in Pharmacology (Vol. 103, pp. 121-138). Academic Press.
5. Lionta, E., Spyrou, G., K Vassilatis, D., & Cournia, Z. (2014). Structure-based virtual screening for drug discovery: principles, applications and recent advances. Current topics in medicinal chemistry, 14(16), 1923-1938.
6. Horvath, D. (2010). Pharmacophore-based virtual screening. Chemoinformatics and computational chemical biology, 261-298.
7. Taft, C. A. (2014). Current state-of-the-art for virtual screening and docking methods. In New Developments in Medicinal Chemistry: Volume 2 (pp. 3-169). Bentham Science Publishers.
8. Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Tunyasuvunakool, K., ... & Hassabis, D. (2020). AlphaFold 2. Fourteenth Critical Assessment of Techniques for Protein Structure Prediction, 13.

9. Ruff, K. M., & Pappu, R. V. (2021). AlphaFold and implications for intrinsically disordered proteins. Journal of molecular biology, 433(20), 167208.
10. Sanchez Marin, M. (2025). Large-scale protein structure prediction methods for enhanced annotation (Master's thesis).
11. Wuyun, Q., Chen, Y., Shen, Y., Cao, Y., Hu, G., Cui, W., ... & Zheng, W. (2024). Recent progress of protein tertiary structure prediction. Molecules, 29(4), 832.
12. Abdin, O. (2024). Novel deep learning methods for the modelling and design of peptides and small proteins (Doctoral dissertation, University of Toronto (Canada)).
13. Chen, X., Ji, Z. L., & Chen, Y. Z. (2002). TTD: therapeutic target database. Nucleic acids research, 30(1), 412-415.
14. Wishart, D. S., Knox, C., Guo, A. C., Cheng, D., Shrivastava, S., Tzur, D., ... & Hassanali, M. (2008). DrugBank: a knowledgebase for drugs, drug actions and drug targets. Nucleic acids research, 36(suppl_1), D901-D906.
15. Wishart, David S., et al. "DrugBank 5.0: a major update to the DrugBank database for 2018." Nucleic acids research 46.D1 (2018): D1074-D1082.
16. Knox, Craig, et al. "DrugBank 6.0: the DrugBank knowledgebase for 2024." Nucleic acids research 52.D1 (2024): D1265-D1275.
17. Koscielny, G., An, P., Carvalho-Silva, D., Cham, J. A., Fumis, L., Gasparyan, R., ... & Dunham, I. (2017). Open Targets: a platform for therapeutic target identification and validation. Nucleic acids research, 45(D1), D985-D994.
18. Gao, Z., Li, H., Zhang, H., Liu, X., Kang, L., Luo, X., ... & Jiang, H. (2008). PDTD: a web-accessible protein database for drug target identification. BMC bioinformatics, 9(1), 104.
19. Liu, X., Ouyang, S., Yu, B., Liu, Y., Huang, K., Gong, J., ... & Jiang, H. (2010). PharmMapper server: a web server for potential drug target identification using pharmacophore mapping approach. Nucleic acids research, 38(suppl_2), W609-W614.
20. Tanoli, Z., Alam, Z., Vähä-Koskela, M., Ravikumar, B., Malyutina, A., Jaiswal, A., ... & Aittokallio, T. (2018). Drug Target Commons 2.0: a community platform for systematic analysis of drug–target interaction profiles. Database, 2018, bay083.
21. Girotra, K., Terwisch, C., & Ulrich, K. T. (2005). Managing the risk of development failures: A study of late-stage failures in the pharmaceutical industry. The Wharton School, University of Pennsylvania, January.
22. Sun, A., & Benet, L. Z. (2020). Late-stage failures of monoclonal antibody drugs: a retrospective case study analysis. Pharmacology, 105(3-4), 145-163.
23. Parasrampuria, D. A., Benet, L. Z., & Sharma, A. (2018). Why drugs fail in late stages of development: case study analyses from the last decade and recommendations. The AAPS journal, 20(3), 46.
24. Kaplowitz, N. (2005). Idiosyncratic drug hepatotoxicity. Nature reviews Drug discovery, 4(6), 489-499.
25. Ulrich, Roger G. "Idiosyncratic toxicity: a convergence of risk factors." Annu. Rev. Med. 58.1 (2007): 17-34.
26. Spławiński, J., Kuźniar, J., Filipiak, K., & Zieliński, W. (2006). Evaluation of drug toxicity in clinical trials. Science and Engineering Ethics, 12(1), 139-145.
27. Guengerich, F. P. (2011). Mechanisms of drug toxicity and relevance to pharmaceutical development. Drug metabolism and pharmacokinetics, 26(1), 3-14.
28. Onakpoya, I. J., Heneghan, C. J., & Aronson, J. K. (2016). Post-marketing withdrawal of 462 medicinal products because of adverse drug reactions: a systematic review of the world literature. BMC medicine, 14(1), 10.
29. Rao, N., & Powar, R. (2023). Post-marketing drug withdrawals: a review. Pharmaceutical Chemistry Journal, 57(7), 1138-1146.
30. Ballesteros, J., & Ransom, J. (2006). In-target versus off-target allosteric modulators of GPCRs. Drug Discovery Today: Therapeutic Strategies, 3(4), 445–450.
31. Raschi, E., Vasina, V., Poluzzi, E., & De Ponti, F. (2008). The hERG K+ channel: target and antitarget strategies in drug development. Pharmacological Research, 57(3), 181–195.
32. Mahé, J., de Campaigno, E. P., Chené, A.-L., Montastruc, J.-L., Despas, F., & Jolliet, P. (2018). Pleural adverse drugs reactions and protein kinase inhibitors: Identification of suspicious targets by disproportionality analysis from VigiBase. British Journal of Clinical Pharmacology, 84(10), 2373–2383.
33. Brunetti, L., Francavilla, F., Leopoldo, M., & Lacivita, E. (2024). Allosteric modulators of serotonin receptors: a medicinal chemistry survey. Pharmaceuticals, 17(6), 695.
34. Hilfiger, L., Zhao, Q., Kerspern, D., Inquimbert, P., Andry, V., Goumon, Y., ... & Charlet, A. (2020). A nonpeptide oxytocin receptor agonist for a durable relief of inflammatory pain. Scientific Reports, 10(1), 3017.
35. Lagunas-Rangel, F. A., Liepinsh, E., Fredriksson, R., Alsehli, A. M., Williams, M. J., Dambrova, M., Jönsson, J., & Schiöth, H. B. (2024). Off-target effects of statins: molecular mechanisms, side effects and the emerging role of kinases. British Journal of Pharmacology, 181(20), 3799–3818.
36. Chen, L., He, Y., Wang, X., Ge, J., & Li, H. (2021). Ventricular voltage‐gated ion channels: Detection, characteristics, mechanisms, and drug safety evaluation. Clinical and Translational Medicine, 11(10), e530.
37. Atabakhsh, E., & Schild-Poulter, C. (2012). RanBPM is an inhibitor of ERK signaling. PLoS One, 7(10), e47803.
38. Benton, Jennifer. Chemoenzymatic applications and catalytic mechanism of 7-epi-zingiberene synthase. Diss. Cardiff University, 2022.
39. Onakpoya, I. J., Heneghan, C. J., & Aronson, J. K. (2016). Worldwide withdrawal of medicinal products because of adverse drug reactions: a systematic review and analysis. Critical reviews in toxicology, 46(6), 477-489.
40. Lexchin, J. (2005). Drug withdrawals from the Canadian market for safety reasons, 1963–2004. Cmaj, 172(6), 765-767.
41. Craveiro, N. S., Lopes, B. S., Tomás, L., & Almeida, S. F. (2020). Drug withdrawal due to safety: a review of the data supporting withdrawal decision. Current drug safety, 15(1), 4-12.
42. Ji, Z. L., Han, L. Y., Yap, C. W., Sun, L. Z., Chen, X., & Chen, Y. Z. (2003). Drug Adverse Reaction Target Database (DART): Proteins related to adverse drug reactions. Drug Safety, 26(10), 685–690.
43. Mohseni, S., Tabatabaei-Malazy, O., Peimani, M., Ejtahed, H. S., Khodaeian, M., Nazeri, E., ... & Larijani, B. (2021). Withdrawal reasons of randomized controlled trials on type 2 diabetes: a systematic review. DARU Journal of Pharmaceutical Sciences, 29(1), 39-50.
44. Rao, M. S., Gupta, R., Liguori, M. J., Hu, M., Huang, X., Mantena, S. R., ... & Van Vleet, T. R. (2019). Novel computational approach to predict off-target interactions for small molecules. Frontiers in big data, 2, 25.
45. Naga, D., Muster, W., Musvasva, E., & Ecker, G. F. (2022). Off-targetP ML: an open source machine learning framework for off-target panel safety assessment of small molecules. Journal of Cheminformatics, 14(1), 27.
46. Velez Rueda, A. J., Palopoli, N., Zacarías, M., Sommese, L. M., & Parisi, G. (2019). ProtMiscuity: a database of promiscuous proteins. Database, 2019, baz103.
47. Liu, T., & Altman, R. B. (2015). Relating essential proteins to drug side-effects using canonical component analysis: a structure-based approach. Journal of chemical information and modeling, 55(7), 1483-1494.
48. Wang, R., Fang, X., Lu, Y., Yang, C. Y., & Wang, S. (2005). The PDBbind database: methodologies and updates. Journal of medicinal chemistry, 48(12), 4111-4119.
49. Huang, L. H., He, Q. S., Liu, K., Cheng, J., Zhong, M. D., Chen, L. S., ... & Ji, Z. L. (2018). ADReCS-Target: target profiles for aiding drug safety research and application. Nucleic acids research, 46(D1), D911-D917.
50. Soldatos, T. G., Taglang, G., & Jackson, D. B. (2018). In silico profiling of clinical phenotypes for human targets using adverse event data. High-throughput, 7(4), 37.
51. Iwata, H., Mizutani, S., Tabei, Y., Kotera, M., Goto, S., & Yamanishi, Y. (2013). Inferring protein domains associated with drug side effects based on drug-target interaction network. BMC systems biology, 7(Suppl 6), S18.
52. Hindle, S. J., Munji, R. N., Dolghih, E., Gaskins, G., Orng, S., Ishimoto, H., ... & Bainton, R. J. (2017). Evolutionarily conserved roles for blood-brain barrier xenobiotic transporters in endogenous steroid partitioning and behavior. Cell reports, 21(5), 1304-1316.
53. Galletti, C., Bota, P. M., Oliva, B., & Fernandez-Fuentes, N. (2021). Mining drug–target and drug–adverse drug reaction databases to identify target–adverse drug reaction relationships. Database, 2021, baab068.
54. Mizutani, S., Pauwels, E., Stoven, V., Goto, S., & Yamanishi, Y. (2012). Relating drug–protein interaction network with drug side effects. Bioinformatics, 28(18), i522-i528.
55. Kuhn, M., Al Banchaabouchi, M., Campillos, M., Jensen, L. J., Gross, C., Gavin, A. C., & Bork, P. (2013). Systematic identification of proteins that elicit drug side effects. Molecular systems biology, 9(1), 663.
56. Pérez-Nueno, V. I., Souchet, M., Karaboga, A. S., & Ritchie, D. W. (2015). GESSE: predicting drug side effects from drug–target relationships. Journal of Chemical Information and Modeling, 55(9), 1804-1823.
57. Berg, E. L. (2019). Human cell-based in vitro phenotypic profiling for drug safety-related attrition. Frontiers in Big Data, 2, 47.
58. Liu, J., Gui, Y., Rao, J., Sun, J., Wang, G., Ren, Q., Qu, N., Niu, B., Chen, Z., Sheng, X., et al. (2024). In silico off-target profiling for enhanced drug safety assessment. Acta Pharmaceutica Sinica B, 14(7), 2927–2941.
59. Brial, F., Puel, G., Gonzalez, L., Russick, J., Auld, D., Lathrop, M., ... & Gauguier, D. (2024). Stimulation of insulin secretion induced by low 4-cresol dose involves the RPS6KA3 signalling pathway. PloS one, 19(10), e0310370.
60. Aluja, D., Delgado-Tomás, S., Barrabés, J. A., Miró-Casas, E., Ruiz-Meana, M., Rodríguez-Sinovas, A., ... & Inserte, J. (2024). Efficacy of a cysteine protease inhibitor compared with enalapril in murine heart failure models. Iscience, 27(10).
61. Evans, R., Bolduc, P. N., Pfaffenbach, M., Gao, F., May-Dracka, T., Fang, T., ... & Peterson, E. A. (2024). The Discovery of 7-Isopropoxy-2-(1-methyl-2-oxabicyclo [2.1. 1] hexan-4-yl)-N-(6-methylpyrazolo [1, 5-a] pyrimidin-3-yl) imidazo [1, 2-a] pyrimidine-6-carboxamide (BIO-7488), a Potent, Selective, and CNS-Penetrant IRAK4 Inhibitor for the Treatment of Ischemic Stroke. Journal of Medicinal Chemistry, 67(6), 4676-4690.
62. Baudry, M., Wang, Y., Bi, X., Luo, Y. L., Wang, Z., Kamal, Z., ... & Coulter, G. (2024). Identification and neuroprotective properties of NA‐184, a calpain‐2 inhibitor. Pharmacology Research & Perspectives, 12(2), e1181.
63. Burnett, G. L., Yang, Y. C., Aggen, J. B., Pitzen, J., Gliedt, M. K., Semko, C. M., ... & Gill, A. L. (2022). Discovery of RMC-5552, a selective bi-steric inhibitor of mTORC1, for the treatment of mTORC1-activated tumors. Journal of medicinal chemistry, 66(1), 149-169.
64. Santra, P., Ghosh, M., Ganguly, D., Basuchowdhuri, P., & Naskar, S. K. (2025). HF-RAG: Hierarchical Fusion-based RAG with Multiple Sources and Rankers. In Proceedings of the 34th ACM International Conference on Information and Knowledge Management (pp. 5202–5207). ACM.
65. Cormack, G. V., Clarke, C. L., & Buettcher, S. (2009, July). Reciprocal rank fusion outperforms condorcet and individual rank learning methods. In Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval (pp. 758-759).
66. Samuel, S., DeGenaro, D., Guallar-Blasco, J., Sanders, K., Eisape, S., Reddy, A., ... & Kriz, R. (2025, July). Mmmorrf: Multimodal multilingual modularized reciprocal rank fusion. In Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 4004-4009).
67. Bruch, S., Gai, S., & Ingber, A. (2023). An analysis of fusion functions for hybrid retrieval. ACM Transactions on Information Systems, 42(1), 1-35.
68. Sun, J., Jeliazkova, N., Chupakhin, V., Golib-Dzib, J. F., Engkvist, O., Carlsson, L., ... & Chen, H. (2017). ExCAPE-DB: an integrated large scale dataset facilitating Big Data analysis in chemogenomics. Journal of cheminformatics, 9(1), 17.
69. Dimova, D., Stumpfe, D., Hu, Y., & Bajorath, J. (2016). Analog series-based scaffolds: computational design and exploration of a new type of molecular scaffolds for medicinal chemistry. Future Science OA, 2(4), FSO149.
70. Benham, R., & Culpepper, J. S. (2017). Risk-Reward Trade-offs in Rank Fusion. In Proceedings of the 22nd Australasian Document Computing Symposium (ADCS ’17). ACM. https://doi.org/10.1145/3166072.3166084
71. Bresalier, R. S., Sandler, R. S., Quan, H., Bolognese, J. A., Oxenius, B., Horgan, K., Lines, C., Riddell, R., Morton, D., Lanas, A., Konstam, M. A., & Baron, J. A.; Adenomatous Polyp Prevention on Vioxx (APPROVe) Trial Investigators. (2005). Cardiovascular events associated with rofecoxib in a colorectal adenoma chemoprevention trial. New England Journal of Medicine, 352(11), 1092–1102.
72. Taggart, H. M., & Alderdice, J. M. (1982). Fatal cholestatic jaundice in elderly patients taking benoxaprofen. BMJ (Clinical Research Edition), 284(6326), 1372–1374.
73. Zanettini, R., Antonini, A., Gatto, G., Gentile, R., Tesei, S., & Pezzoli, G. (2007). Valvular heart disease and the use of dopamine agonists for Parkinson’s disease. New England Journal of Medicine, 356(1), 39–46.
74. Monahan, B. P., Ferguson, C. L., Killeavy, E. S., Lloyd, B. K., Troy, J., & Cantilena, L. R., Jr. (1990). Torsades de pointes occurring in association with terfenadine use. JAMA, 264(21), 2788–2790.
75. Staffa, J. A., Chang, J., & Green, L. (2002). Cerivastatin and reports of fatal rhabdomyolysis. New England Journal of Medicine, 346(7), 539–540.
76. Scheen, A. J. (2001). Thiazolidinediones and liver toxicity. Drug Safety, 24(11), 873–888.
77. Chávez-Tapia, N. C., Hernández-Calleros, J., Tellez-Avila, F. I., et al. (2009). Systematic review and meta-analysis on the adverse events of rimonabant treatment: considerations for its potential use in hepatology. BMC Gastroenterology, 9, 75