Turbine Launches Collaboration with AstraZeneca, Leveraging Turbine's Virtual Disease Models to Rationalize ADC Discovery
Turbine announced a collaboration with AstraZeneca (AZN) on October 9, 2025 to apply Turbine's virtual disease models to antibody-drug conjugate (ADC) discovery.
The partnership will use a lab-in-the-loop workflow where Turbine recommends a strategic subset of cell lines for wet-lab testing, then predicts outcomes across thousands of in silico models using AstraZeneca's ADC datasets, covering single-agent and combination studies. Goals include reducing large-scale cell line screening, improving speed and efficiency of ADC discovery, and delivering mechanistic insights by modeling cell survival and gene-expression changes to inform ADC positioning and clinical translatability.
Turbine ha annunciato una collaborazione con AstraZeneca (AZN) il 9 ottobre 2025 per applicare i modelli virtuali di malattia di Turbine alla scoperta di coniugati anticorpo-farmaco (ADC).
La partnership utilizzerà un flusso di lavoro lab-in-the-loop in cui Turbine raccomanda un sottoinsieme strategico di linee cellulari per i test di laboratorio, quindi prevede esiti attraverso migliaia di modelli in silico utilizzando i set di dati ADC di AstraZeneca, coprendo studi con agente singolo e con combinazioni. Gli obiettivi includono ridurre la scrematura su larga scala delle linee cellulari, migliorare la velocità e l'efficienza della scoperta di ADC e fornire intuizioni meccanistiche modellando la sopravvivenza cellulare e i cambiamenti nell'espressione genica per informare l'inquadramento degli ADC e la traduzione clinica.
Turbine anunció una colaboración con AstraZeneca (AZN) el 9 de octubre de 2025 para aplicar los modelos virtuales de enfermedad de Turbine al descubrimiento de conjugados anticuerpo-fármaco (ADC).
La asociación utilizará un flujo de trabajo lab-in-the-loop en el que Turbine recomienda un subconjunto estratégico de líneas celulares para pruebas en laboratorio, y luego predice resultados en miles de modelos in silico utilizando los conjuntos de datos de ADC de AstraZeneca, cubriendo estudios con fármaco único y combinaciones. Los objetivos incluyen reducir la cribación a gran escala de líneas celulares, mejorar la velocidad y la eficiencia del descubrimiento de ADC y proporcionar ideas mechanísticas al modelar la supervivencia celular y los cambios en la expresión génica para informar el posicionamiento de los ADC y su translación clínica.
Turbine은 AstraZeneca (AZN)와 2025년 10월 9일에 협력하여 Turbine의 가상 질병 모델을 항체-약물 결합체(ADC) 발견에 적용한다고 발표했습니다.
이 파트너십은 랩-인-더-루프 워크플로우를 사용합니다. Turbine은 젖은 실험실(testing)에서의 전략적 셀 라인 하위 집합을 권장하고, AstraZeneca의 ADC 데이터 세트를 사용한 수천 개의 인 실리코 모델에서 결과를 예측하며 단일 제제 및 조합 연구를 모두 다룹니다. 목표는 대규모 셀 라인 선별을 줄이고 ADC 발견의 속도와 효율을 개선하며, 세포 생존 및 유전자 발현 변화의 기전적 정보를 모델링하여 ADC의 위치 선정과 임상 번역 가능성을 inform하는 것입니다.
Turbine a annoncé une collaboration avec AstraZeneca (AZN) le 9 octobre 2025 afin d’appliquer les modèles virtuels de maladie de Turbine à la découverte de conjugués anticorps-médicament (ADC).
Le partenariat utilisera un flux de travail lab-in-the-loop où Turbine recommande un sous-ensemble stratégique de lignées cellulaires pour les tests en laboratoire, puis prédit les résultats sur des milliers de modèles in silico en utilisant les ensembles de données ADC d’AstraZeneca, couvrant les études en monothérapie et en combinaison. Les objectifs incluent la réduction du criblage à grande échelle des lignées cellulaires, l’amélioration de la vitesse et de l’efficacité de la découverte des ADC et la fourniture d’intuitions mécanistiques en modélisant la survie cellulaire et les changements d’expression génétique pour informer le positionnement des ADC et leur translational clinique.
Turbine kündigte eine Zusammenarbeit mit AstraZeneca (AZN) am 9. Oktober 2025 an, um Turbines virtuelle Krankheitsmodelle auf die Entdeckung von Antikörper-Wirkstoff-Konjugaten (ADC) anzuwenden.
Die Partnerschaft wird einen lab-in-the-loop Arbeitsablauf nutzen, bei dem Turbine eine strategische Teilmenge von Zelllinien für Labortests empfiehlt und dann Ergebnisse über Tausende von in silico Modellen unter Verwendung der ADC-Datensätze von AstraZeneca vorhersagt, wobei Einzelwirkstoff- und Kombinationsstudien abgedeckt werden. Ziele sind die Reduzierung der großangelegten Zelllinien-Screenings, eine schnellere und effizientere ADC-Entdeckung sowie die Bereitstellung mechanistischer Einsichten, indem Zellüberleben und Veränderungen der Genexpression modelliert werden, um die Positionierung von ADCs und deren klinische Übertragbarkeit zu informieren.
تورن باه تعاونًا مع AstraZeneca (AZN) في 9 أكتوبر 2025 لتطبيق نماذج الأمراض الافتراضية التابعة لـ Turbine على اكتشاف قناتي الأجسام المضادة-العقاقير (ADC).
ستستخدم الشراكة سير عمل مختبري-في-الحلقة حيث توصي Turbine بمجموعة فرعية استراتيجية من خطوط الخلايا للاختبارات المخبرية، ثم تتنبأ بالنتائج عبر آلاف النماذج الحاسوبية باستخدام مجموعات بيانات ADC من AstraZeneca، مع تغطية دراسات وحدتها الدوائية وتراكيبها. تشمل الأهداف تقليل فحص خطوط الخلايا على نطاق واسع، وتحسين السرعة والكفاءة في اكتشاف ADC، وتقديم رؤى ميكانيكية من خلال نمذجة بقاء الخلية وتغيرات التعبير الجيني لإعلام تحديد موقع الـ ADC وقابلية ترجمته klinياً.
Turbine 于 2025 年 10 月 9 日 宣布与 AstraZeneca (AZN) 的合作，将 Turbine 的虚拟疾病模型应用于抗体药物偶联物（ADC）的发现。
此次合作将采用一个 lab-in-the-loop 工作流程，其中 Turbine 针对湿实验测试推荐一组策略性细胞系，然后利用 AstraZeneca 的 ADC 数据集在数千个体内计算模型（in silico）中预测结果，覆盖单药及联合研究。目标包括减少大规模细胞系筛选、提高 ADC 发现的速度与效率，并通过建模细胞存活和基因表达变化来提供机理性洞察，以告知 ADC 的定位及临床转化潜力。
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Insights
Collaboration aims to use Turbine's in silico models with AstraZeneca to streamline ADC discovery and add mechanistic insight.
Turbine's virtual biology platform will recommend a smaller, strategic set of cell lines for wet‑lab testing and then predict outcomes across thousands of in silico models using AstraZeneca's ADC datasets, including single‑agent and combination studies. This approach targets the core bottleneck in ADC discovery described: costly, large‑scale cell line and PDX screening.
The main dependencies and risks are clear from the announcement: model fidelity to real biology and the quality/representativeness of AstraZeneca's datasets will determine translational value. The lab‑in‑the‑loop design reduces pure simulation risk by validating model recommendations experimentally, but success depends on how well predicted mechanisms (including gene expression changes) match observed resistance and response patterns.
Watch for concrete milestones over the next 6–18 months such as published validation results, metrics showing reduced screen size or concordance rates between in silico predictions and wet‑lab outcomes, and any extension of the method to patient‑derived models or clinical translation. The
ADCs are targeted cancer therapies that deliver potent drug payloads directly to tumor cells, but discovery can be slowed by the need to identify effective payloads across diverse tumor types and patient populations through costly, large-scale screening of hundreds of cell lines and patient-derived xenografts (PDXs). Through this collaboration, Turbine and AstraZeneca will address the in vitro challenge by implementing a lab-in-the-loop approach where Turbine's platform recommends a strategically chosen subset of cell lines for testing, then predicts outcomes across thousands of in silico models using AstraZeneca's ADC datasets, including both single-agent and combination studies. This brings discovery closer to outcomes, with the long-term aim of extending the same approach to patient derived models and ultimately clinical care. Beyond reducing experimental burden, the platform also provides mechanistic insights that enhance clinical translatability, modeling not only cell survival but also changes in gene expression, to understand why cells respond or resist treatment.
"By implementing a lab-in-the-loop approach, we can move beyond broad experimental screening toward a more efficient, targeted strategy that selects the ADC combinations most likely to succeed in patients," said Daniel Veres, MD, PhD, CSO and Co-Founder of Turbine. "This also lays the groundwork for deeper integration of our Virtual Lab into discovery workflows, helping ensure that the right experiments are run to generate the greatest impact for patients."
Turbine and AstraZeneca previously collaborated to use Turbine's Simulated Cell™ platform to identify and understand mechanisms of resistance to therapy in hematological cancers and to predict combination synergy and relevant biomarker candidates involving DNA Damage Repair mechanisms.
About Turbine
Turbine is virtualizing experiments with AI to accelerate discovery and enhance clinical translatability. They've spent the last decade building virtual disease models that they believe can become second only to the patient in predicting drug response. By simulating how cells and tissues behave under treatment, Turbine helps pharma identify the right therapeutic ideas smarter and faster, cutting years of dead-end research and reducing late-stage clinical failure caused by poor efficacy. Scientists can now run billions of virtual experiments to uncover risk, design smarter trials, and scale decisions across entire pipelines. Validated through partnerships with Bayer, MSD, AstraZeneca and others, Turbine's platform has supported nearly 30 research programs. Backed by Accel, MSD Global Health Innovation Fund, Turbine is putting predictive simulations in the hands of every scientist.
For more information, visit www.turbine.ai or follow our LinkedIn page.
Corporate Inquiries:
Luca Bárdió
Turbine
+36 30 675 7099
luca.bardio@turbine.ai
Media Inquiries:
EvolveMKD
turbine@evolvemkd.com
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SOURCE Turbine
FAQ
What did Turbine and AstraZeneca announce on October 9, 2025 about ADC discovery for AZN?
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Does the collaboration with Turbine cover single-agent and combination ADC studies for AZN?
What mechanistic insights will Turbine provide to AstraZeneca (AZN)?
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