Aitia partners with leading biopharma companies to simulate their drug candidates across our cohort of Digital Twins in specific diseases to prioritize drug targets, discover optimal combination therapies, and discover responding versus non-responding patient groups and corresponding biomarkers. Aitia brings the full power of the world’s most accurate Digital Twins to help advance and de-risk promising drug candidates. Current Gemini Digital Twins are used in multiple myeloma, prostate cancer, Alzheimer’s Disease, Parkinson’s Disease, and Huntington’s Disease.
Oncology
Immunology
Neurodegeneration
Gemini Digital Twin Key Functionalities
Discovery &
Translation
- Prognostic Marker Discovery
- Combo Therapies
Clinical
Development
- Prognostic-Novel Drivers of
Progression - Precision Patient Stratification for
I/E criteria - Response Markers/Sub-Population
(SoC Drugs) - In Silico Head-to-Head trial
- Response Markers/Sub-Population
(drug candidate in RCT) - Historical Control Arms
Fueling Translational Research
- 01. Discovering prognostic markers for Multiple Myeloma
- 02. Revealing new targets and prognostic biomarkers of survival for Prostate Cancer
Unmet Need: Lack of strong predictive markers beyond MMSET for stratifying MM patients with high-risk of progression, hampering clinical trial design and optimal patient care
Insights: Causal Network simulations linked patient characteristics and gene expressions to novel biological pathways and clinical outcomes, revealing PHF19 as a new marker of high-risk disease
Impact: Established PHF19 as a stronger predictor of MM progression than the conventional high-risk marker MMSET, enabling better designed clinical trials that better achieve clinical endpoints. Additionally, PHF19 is now part of a 4-factor predicitive model that can effectively stratify patients in clinical settings
Unmet Need: Limited understanding of the mechanistic pathways around Androgen Receptor (AR) for developing next gen treatments for castrate sensitive and castrate resistant prostate cancer and associated prognostic biomarkers of overall survival
Insights: Gemini’s hypothesis free approach revealed novel germline mutations that modulate the effect of Androgen Receptor (AR) Copy Number Gain and effects on AR gene overexpression in metastatic Castration-Resistant Prostate Cancer (mCRPC)
Impact: This causal approach reveals new potential targets as well as novel prognostic markers of survival. These findings will help both early research discovery efforts for new target discoveries and translational endeavors to design sophisticated clinical trials and simulations of control and efficacy arms