CAMBRIDGE, Mass.—GNS Healthcare Inc. in March announced the publication of results from a study focused on identifying novel drug targets for the one-third of rheumatoid arthritis patients who do not respond to leading anti-TNF therapies.
The paper, titled "Causal Modeling Using Network Ensemble Simulations of Genetic and Gene Expression Data Predicts Genes Involved in Rheumatoid Arthritis," by researchers at GNS Healthcare and Biogen Idec, was published in the journal PLoS Computational Biology. The paper describes the experimental and computational approach used by the researchers to integrate clinical, molecular and genetic data into dynamic models of disease progression and drug response. This approach helped predict individual patients' clinical responses to the shutting down of specific pathways, suggesting that accurate determinations can be made about patients' responses to existing drugs as well as to the inhibition of novel targets and pathways based on DNA sequence and gene expression data from a given patient's blood.
"The convergence of multiple layers of rich genomics and molecular profiling data, together with clinical outcomes, has reached a tipping point in the evolution of personalized medicine that is now enabling the automated discovery of dynamic disease models of individualized patient outcomes," says Colin Hill, CEO and co-founder of GNS Healthcare.
Starting from a patient dataset comprised of genotyping, whole-blood gene expression profiles and clinical measures such as tender joints, swollen joints and C-reactive protein, the researchers used GNS's supercomputer-driven reverse-engineering and forward-simulation (REFS) scientific computing platform to construct a comprehensive disease model directly from the raw data. This computer disease model enabled the team to conduct virtual clinical trials, simulating the clinical effect of inhibiting various drug targets and predicting novel and previously known alternative genetic targets to anti-TNFs.
"This project established that a relatively small, heterogeneous clinical trial dataset can be directly utilized to learn novel disease biology if one has access to a significantly powerful computational modeling platform," Hill says. "This is the first time that a patient-data driven, computer model of rheumatoid arthritis has been developed to generate patient-specific predictions to the response to existing drugs and the response to inhibiting novel targets and pathways of the disease."
GNS Healthcare and Biogen Idec say their publication sets forth an approach that may allow researchers to rapidly construct and interrogate computer models of drug and disease biology that reflect probabilistic cause-and-effect relationships directly from genetic, molecular and clinical data without requiring the use of prior biological knowledge. Such models, the companies say, potentially enable patient-specific predictions, accelerating the cycle of bench-to-bedside medicine by bringing bedside data back to the computational bench, and in turn, making additional insights available to researchers and clinicians.
The paper, titled "Causal Modeling Using Network Ensemble Simulations of Genetic and Gene Expression Data Predicts Genes Involved in Rheumatoid Arthritis," by researchers at GNS Healthcare and Biogen Idec, was published in the journal PLoS Computational Biology. The paper describes the experimental and computational approach used by the researchers to integrate clinical, molecular and genetic data into dynamic models of disease progression and drug response. This approach helped predict individual patients' clinical responses to the shutting down of specific pathways, suggesting that accurate determinations can be made about patients' responses to existing drugs as well as to the inhibition of novel targets and pathways based on DNA sequence and gene expression data from a given patient's blood.
"The convergence of multiple layers of rich genomics and molecular profiling data, together with clinical outcomes, has reached a tipping point in the evolution of personalized medicine that is now enabling the automated discovery of dynamic disease models of individualized patient outcomes," says Colin Hill, CEO and co-founder of GNS Healthcare.
Starting from a patient dataset comprised of genotyping, whole-blood gene expression profiles and clinical measures such as tender joints, swollen joints and C-reactive protein, the researchers used GNS's supercomputer-driven reverse-engineering and forward-simulation (REFS) scientific computing platform to construct a comprehensive disease model directly from the raw data. This computer disease model enabled the team to conduct virtual clinical trials, simulating the clinical effect of inhibiting various drug targets and predicting novel and previously known alternative genetic targets to anti-TNFs.
"This project established that a relatively small, heterogeneous clinical trial dataset can be directly utilized to learn novel disease biology if one has access to a significantly powerful computational modeling platform," Hill says. "This is the first time that a patient-data driven, computer model of rheumatoid arthritis has been developed to generate patient-specific predictions to the response to existing drugs and the response to inhibiting novel targets and pathways of the disease."
GNS Healthcare and Biogen Idec say their publication sets forth an approach that may allow researchers to rapidly construct and interrogate computer models of drug and disease biology that reflect probabilistic cause-and-effect relationships directly from genetic, molecular and clinical data without requiring the use of prior biological knowledge. Such models, the companies say, potentially enable patient-specific predictions, accelerating the cycle of bench-to-bedside medicine by bringing bedside data back to the computational bench, and in turn, making additional insights available to researchers and clinicians.
