Our Solutions
Gemini The in silico Patient™
Oncology
Immunology
Neurodegeneration
Gemini in silico Patient™ and key functionalities
Fueling Drug Discovery
Uncovering novel drug targets for Alzheimer’s disease
Unmet Need: Lack of understanding of the true biological mechanisms and corresponding targets for AD. Tau biology and other known mechanisms only offer a small piece of the Alzheimer’s puzzle
Insights: Simulation of reverse-engineered causal networks connecting gene expressions, metabolites and clinical data revealed 10 novel and unique drug targets that are causal to several AD disease clinical endpoints that were validated in independently patient cohort
Impact: Billions of dollars have been invested in clinical drug programs against targets and mechanisms which were only part of full complexity of AD. These uniquely derived and validated targets are seeding new drug candidate pipelines that offer an orthogonal data-driven and hypothesis-free approach to curing this devastating disease development
Discovering prognostic markers for Multiple Myeloma
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: Simulation of causal networks linking patient characteristics and gene expressions to novel biological pathways and to clinical outcomes. Revealed 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 predictive model that can effectively stratify patients in clinical settings
Revealing prognostic biomarkers of survival for Prostate Cancer
Unmet Need: Lack of understanding prognostic biomarkers of overall survival for castrate sensitive and castrate resistant prostate cancer
Insights: Gemini’s hypothesis free approach revealed multiple novel prognostic markers of survival as well as validated the findings of the Phase 3 LATITUDE and PREVAIL trials
Impact: These novel findings will help in design of sophisticated clinical trials and simulations of control and efficacy arms
Accelerating Clinical Trials
Finding new sub-populations of progression and differential response to Erbitux vs Avastin
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
Identification of sub-populations of Multiple Myeloma patients for stem cell transplant
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
Discovering predictive biomarkers of Parkinson’s progression to improve clinical trial design
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)
Driving Market Access
Comparative Effectiveness and Line of Therapy Switch in Multiple Myeloma
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
Identifying optimal drug sequences for Multiple Myeloma patients
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
Identifying causal drivers of care costs for Multiple Myeloma patients
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
Our key Biopharma and Payer Customers, Patient Foundations and Data Partners
