Our Solutions

Gemini Virtual Patient

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

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

Forest with fog

Accelerating Clinical Trials

Unmet Need: Sidedness of colorectal tumors has been used as a predictor of prognosis and response to standard of care drugs Erbitux and Avastin

Insights: Gemini models fueled with DNA sequence and gene expression and clinical data have revealed the underlying molecular circuitry of “sidedness”. Simulations of these models reveal novel prognostic molecular drivers of differential response between left and right-sided tumors, and the subpopulations that achieve a survival benefit of 11 months when treated with Avatsin vs Eribitux

Impact: Better treatment of mCRC patients as it revealed that at least 5% of patients are likely being mistreated per current guidelines. These models are also utilized to better determine inclusion/exclusion criteria for mCRC trials

Unmet Need: There is an inability to determine ahead of treatment which patients will respond or not respond to stem cell transplant, an invasive and expensive procedure with serious side effects

Insights: Simulation of the causal network models revealed the CHEK1 gene expression levels, along with 2 other supporting genes, as strong predictors of response/non-response conveying ~20 months of survival benefit. An independent trial at Dana Farber Cancer Center validated the survival benefit of ~20 months

Impact: If further validation of this predictive marker is successful, this development would be transformative for patient care by allowing patients to avoid an invasive therapy that would be ineffective and potentially toxic to them, vs. earlier treatment with treatment that would be effective for them. This insight would also allow for better stratified clinical trials for therapies that could be used in early lines of treatment

Unmet Need: Parkinson’s disease progression is highly variable across patients, making it challenging to treat and design effective clinical trials. We aimed to discover the unknown molecular and genetic drivers of motor progression to design more efficient clinical trials

Insights: Parkinson’s disease models built from clinical, molecular, and genetic data confirmed known predictors and identified novel predictors of Parkinson’s motor progression, including the biological context of potential progression markers. Specifically, patients who carried minor alleles of both rs17710829 (in LINGO2; Leucine-rich protein, like LRRK2) and rs929897 (nearest gene is DPP10, seen in Alzheimer’s Disease) SNPs had substantially faster rate of motor decline. Findings were validated through an independent patient cohort and a method to prospectively differentiate rates of motor progression was discovered

Impact: Discoveries could reduce trial enrollment by 20%. Additionally, findings provided insight into the mechanisms of disease process, leading to potential for novel therapeutic intervention and improving patient care by aiding in clinical disease management. Results were published in high-impact journal Lancet Neurology (see link below)

Clinical Trials

Driving Market Access

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

Market Access

Gemini Virtual Patient Biopharma Partners