MAXIMIZE THE CHANCE OF SUCCESS OF MY ADC PROGRAM

Select cancer sub-indications or molecular biomarkers predictive of payload and payload combination sensitivity

Identify payloads or payload combinations with the highest chance of efficacy in a heterogeneous, biomarker-positive model population

Understand and address mechanisms driving resistance in the clinic

vLab Core

Simulate experiments on public data harmonized and enhanced by Turbine

1400 oncology cell lines

112 drugs and drug-like molecules, including 18 payloads
KO alterations across 3500+ genes

Robust interpretation toolkit for practical filtering and analysis of in silico results

vLab Custom

In silico predictions leveraging your proprietary datasets and Turbine expertise

Add your sample library

Add your drug(s)

Interpretation Services

Virtual PDX and PDO library of -400 samples

Wet lab perturbation data generation

Wet lab validation of key hypotheses

Why trust us?

Accurately predict drug responses using limited data

Compound responses with known mechanisms can be predicted at high accuracy, across 1,400+ cancer cell lines (0.75 Pearson correlation of unseen cell line responses).

Match novel payloads with responder indications

Turbine correctly predicted the sensitivity order of subtypes for microtubule payloads and produced closely aligned rankings for TOP1 inhibitors, potentially guiding trial strategy and avoiding dead-end subtypes.

Design dual-payload ADCs rationally, not by trial-and-error

Turbine predicts GDSC2 Topo1i – PARPi synergy from training only on monotherapy data.

Identify predictive biomarkers early and link payload response to mechanistic drivers of resistance

Turbine’s in silico pharmacology and biomarker screens suggested viable patient selection hypothesis for Topo1 ADCs in NSCLC (to be validated preclinically).

Learn more about our technology and proof points

Approach Science

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