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

Abstract

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.

Keywords

• 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

Abstract

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.

Keywords

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

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