Aligning AI Toxicity Predictions with Wet-Lab Biology for PFOA Toxicity in SH-SY5Y Cells

Abstract

Perfluorooctanoic acid (PFOA) is a highly persistent per- and polyfluoroalkyl substance (PFAS) widely detected in the environment and biological systems. Its resistance to degradation and bioaccumulative behavior make it a critical toxicological and public health concern. The present study investigates whether probability based artificial intelligence (AI) toxicity predictions align with experimental in vitro findings in human SH-SY5Y neuroblastoma cells. Cells were exposed to PFOA at concentrations ranging from 0 to 2000 µM for 24, 48, and 72 hours, and cell viability was determined using the MTT assay. The resulting IC₅₀ values419.52 µM, 174.97 µM, and 104.64 µM, respectively demonstrated a clear time-dependent increase in apparent cytotoxic potency (~4.01-fold from 24 to 72 h). These empirical data were compared against AI-derived toxicity probabilities from two external platforms: ProTox and CompTox/invitrodb. Calibration between predicted probabilities and observed biological outcomes was assessed using the Brier score. ProTox showed good calibration (Brier = 0.102), whereas CompTox/invitrodb yielded poor alignment (Brier = 0.537), highlighting the importance of endpoint- and time-matched probabilities. The results emphasize that AI models lacking temporal or biological context may underestimate toxicity, particularly when effects manifest gradually over prolonged exposures. This study presents a reproducible, curve-free workflow for integrating AI predictions with time-resolved in vitro toxicity data, providing a framework to enhance biological realism in computational toxicology and guide future PFAS risk assessments.

Keywords

• PFOA
• SH-SY5y
• Artificial Intelligence
• Cytotoxicity

SH-SY5Y Hücre Hattında PFOA Toksisitesi İçin Yapay Zekâ Toksisite Tahminlerinin Islak Laboratuvar Biyolojisi ile Eşleştirilmesi

Abstract

Perflorooktanoik asit (PFOA), çevrede ve biyolojik sistemlerde yaygın olarak tespit edilen, yüksek derecede kalıcı bir per- ve polifloroalkil madde (PFAS)’dır. Bozunmaya karşı gösterdiği direnç ve biyobirikim özelliği, onu önemli bir toksikolojik ve halk sağlığı sorunu haline getirmektedir. Bu çalışmada, olasılığa dayalı yapay zekâ (AI) toksisite tahminlerinin, insan SH-SY5Y nöroblastoma hücrelerinde elde edilen deneysel in vitro bulgularla uyumlu olup olmadığı araştırılmıştır. Hücreler, 0–2000 µM aralığındaki PFOA konsantrasyonlarına 24, 48 ve 72 saat süreyle maruz bırakılmış ve hücre canlılığı MTT testi ile belirlenmiştir. Elde edilen IC₅₀ değerleri sırasıyla 419.52 µM, 174.97 µM ve 104.64 µM olup, görünür sitotoksik etkinin zamana bağlı olarak yaklaşık 4 kat arttığını göstermiştir (24 saatten 72 saate). Bu ampirik veriler, iki harici platformdan elde edilen yapay zekâ kaynaklı toksisite olasılıklarıyla (ProTox ve CompTox/invitrodb) karşılaştırılmıştır. Tahmin edilen olasılıklar ile gözlenen biyolojik sonuçlar arasındaki kalibrasyon, Brier skoru kullanılarak değerlendirilmiştir. ProTox iyi bir kalibrasyon göstermiştir (Brier = 0.102), buna karşın CompTox/invitrodb zayıf bir uyum sergilemiştir (Brier = 0.537). Bu bulgular, zamansal veya biyolojik bağlamdan yoksun yapay zekâ modellerinin toksisiteyi hafife alabileceğini ortaya koymaktadır. Bu çalışma, yapay zekâ tahminlerinin zaman çözünürlüklü in vitro toksisite verileriyle entegrasyonuna yönelik yeniden üretilebilir, eğriye bağımlı olmayan bir yaklaşım sunmakta ve hesaplamalı toksikolojide biyolojik gerçekliğin güçlendirilmesi ile PFAS risk değerlendirmelerinde daha güvenilir öngörüler yapılabilmesi için bir çerçeve önermektedir.

Keywords

• PFOA
• SH-SY5Y
• Yapay Zekâ
• Sitotoksisite

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