Oncology
Immunology
Neurodegeneration
Gemini Virtual Patient and 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
(candidate drug vs SOC) - Response Markers/Sub-Population
(drug candidate in RCT)
Market Access
- Historical Control Arms
- Line of Therapy change
- In Silico Head-to-Head trial
(comparative effectiveness) - Treatment Sequence
Optimization
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
Driving Market Access
- 01. Comparative Effectiveness and Line of Therapy Switch in Multiple Myeloma
- 02. Identifying optimal drug sequences for Multiple Myeloma patients
- 03. Identifying causal drivers of care costs for Multiple Myeloma patients
Unmet Need: A 2nd line multiple myeloma drug has the potential to outperform a current 1st line standard of care therapy (at least in a significant subset of patients) and needs a way to generate evidence of the potential superior comparative performance before committing to a Phase IV trial
Insights: Gemini models containing all marketed multiple myeloma drugs including the 1st and 2nd lines drugs in question were created and a “head to head” in silico trial was conducted; Results showed that over a large majority of patients the 2nd line therapy resulted in a several months overall survival benefit vs the 1st line standard of care drug
Impact: This evidence serves as the guidepost to conducting a Phase IV trial that has the potential to change clinical practice and drive better patient outcomes
Unmet Need: The sequencing of treatment across an increasing number of lines of therapy and across a heterogeneous patient population creates a challenge for clinicians to deliver optimal care and a challenge for biopharma companies in designing clinical trials to optimize the use of their current drugs and drug candidates in development
Insights: Enhancing Gemini multiple myeloma models with a time-dependent line of therapy data allowed for the ability to simulate alternative treatment sequences for individual patients and ask “What if any of the clinically relevant treatment sequences were used instead of the observed treatment for a given patient?” which is not realistic to perform clinical trials to answer; the results were that a significant percentage of patients were receiving a sub-optimal treatment sequence that resulted in up to a 20-month survival reduction relative to the optimal sequence
Impact: It is now possible to explore optimal treatment sequencing on an individual patient basis without having to perform a clinical trial that would not be possible in the real world; also allows biopharma companies to optimize the clinical trial design in the context of increasingly complex treatment sequencing
Unmet Need: Lack of understanding of the drivers of supportive care costs and total cost of care for multiple myeloma patients makes it challenging to strategize on cost of care and reimbursement and demonstrate superior value being created and delivered by new therapies
Insights: Gemini multiple myeloma models with 17 current drug treatments and supportive costs and total cost of care were simulated to reveal that several factors are the primary drivers of supportive costs
Impact: Controlling these factors creates significant value when done in conjunction with use of certain therapies, and creates an important opportunity for bundled payments and value-based care in multiple myeloma
