Revolutionising immunotherapy with Precision Biosimulation

Empowering therapeutic development utilising Hybrid-AI for 100x better prediction accuracy with a 20-fold reduction in data required to build our highly detailed mechanistic models.

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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 research & development
Hybrid-AI overcomes the challenges in 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:
    • In-silico 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
Blue circle abstract
Partnerships
forward-thinking cell therapy companies