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Reach out for a call
 
Over 100 ADC programs have been discontinued, many due to payload-related toxicity or limited efficacy.
Understanding which payloads work best in which cancer subtypes, and why resistance occurs, remains a major challenge.
Traditional approaches rely heavily on trial-and-error, wasting time, money, and patient opportunity.

Accelerate ADC Success with Predictive Payload Modeling

Predict which cancer subtypes will respond to different payloads.
Identify effective payload combinations at scale
Explain resistance mechanisms through causal inference
Unique benefits only delivered by Turbine
Predict Payload Sensitivity
Quickly identify which cancer subtypes are most responsive to your ADC payload, even those the model has never seen before.
Discover Synergistic Payload Combinations
Model combinations of your payload with other agents to reveal synergy across cancer types before investing in in vitro screens.
Decode Resistance Mechanisms
Go beyond correlation to understand why resistance emerges and how to overcome it with rational design.
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Why trust us?

75
%
Simulated Cell can identify more than 75% of in vitro biomarkers from drug modifier screens
0.70
Turbine’s platform accurately ranks cancer subtypes for payload sensitivity (rank correlation =0.70)
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Learn more about our technology and proof points
Approach Science

How can you work with us?

INPUT

Viability Data: IC50 values for a subset of payloads tested across 10-40 cell lines, if available.

Omlcs Data: Pre- and post-treatment transcriptomics (RNA-seq), if available, for additional training and validation.

Contextual Information: Payload mechanism of action and any known resistance or sensitivity data (e.g., efflux pump involvement).

VIRTUAL LAB

in silico pharmacology screen of your payloads across a diverse panel of 1400+ virtual cell lines

In silico drug modifier KO screen of your payloads, across a diverse panel of 1400+ virtual cell lines

DELIVERY

ptOmlcs Data: Pre-and post-treatment transcriptomics (RNA-seq), if available, for additional training and validation.

pt Viability Data: IC50 values for a subset of payloads tested across 10-40 cell Iines, if available.

Causality Insights: Payload mechanism of action and any known resistance or sensitivity data (e.g., efflux pump involvement).

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Reach out for a call
Turbine's Virtual Lab is available
for ADC Payload Selection.
Pre-register
Turbine's Virtual Lab is available
for ADC Payload Selection.
Pre-register

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