Revolutionising immunotherapy with precision biosimulation and next generation analytics
Empowering therapeutic development with highly detailed mechanistic models utilising Hybrid-AI for 100x better prediction accuracy with a 20-fold reduction in data requirements.
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DRUG DISCOVERY TO DRUG ENGINEERING
The high failure rate in drug development has plagued the pharma industry for too long.
90
%
failure
- Unable to test all 'what if' scenarios needed
- Limited computational tools to interpret complex data
- Experimental assays provide partially complete data
- Linear drug development pathway limits usage of data
Introducing, Hybrid-AI
Unlock the information contained in your vast datasets generated in immunotherapy research & development
100
X
More accurate predictions
20
X
Less data required
Hybrid-AI overcomes the challenges with AI & QSP
Artificial Intelligence / ML
- Black box predictions:
- AI models often lack interpretability, making it challenging to understand how predictions are derived
- Relies solely on data it is trained on:
- Leading to bias & broad predictions that overlook biological complexity and patient variability
- High data requirement:
- AI’s efficacy depends on high-quality data, and inconsistencies across assays or clinical trials can hinder results.
Quantiative Systems Pharmacology
- Unreliable predictions in complexity:
- Current methods are very poor at inferring correct parameters (measure of accuracy) from available data
- Time and cost intensive:
- Often takes 3-5 years to model complex biological systems in detail
- High data requirement:
- Need significant biological and mechanistic data, which can be difficult to source and integrate
Hybrid-AI
- 100x more accurate with 20-fold less data:
- Siloed data from multiple scenarios is integrated into one single mathematical framework
- Account for biological complexity:
- Hybrid-AI models better resemble the human system vs AI / QSP / In-vitro/vivo (molecular to cellular, tissue to patient)
- Detailed Mechanistic insight:
- Understand how and why predictions are made to inform key decisions
Solving challenges across your entire development pipeline
Bioleap's Hybrid-AI platform has been validated across 10,000 conditions in our POC
Accelerate pre-clinical research
- Simulate thousands of pre-clinical scenarios simultaneously to explore therapeutic possibilities beyond traditional methods – saving time and costs while uncovering robust solutions
Extract insights from existing data
- Unearth critical information about therapy mechanisms to understand why treatments succeed or fail, providing data-driven recommendations for improved outcomes
Enhance patient stratification
- Simulate diverse patient responses to identify cohorts with the highest likelihood of success, ensuring more effective trials and reducing failure rates
Optimise dosing strategies
- Leverage precision simulation to refine dosing for complex therapies, maximizing therapeutic efficacy and reducing risk
Simulate combination therapies
- Model the combined impact of therapies, such as CAR-T with checkpoint inhibitors, to predict and enhance outcomes for solid tumours
Support data-driven clinical trials
- Design smarter trials by integrating hybrid-AI insights, optimising every phase from patient selection to trial endpoints and maximise reverse translation
Partnerships
forward-thinking cell therapy companies