Blog: Introducing: OmicsLogic for Clinical Research
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2.4 years ago by
Pine Biotech70
New Orleans, LA
Pine Biotech70 wrote:

If they can be harnessed, the massive amounts of biomedical data generated by healthcare interventions, clinical trials of pharmaceuticals, and biomedical research have a real potential to speed up drug discovery while driving down healthcare costs. Especially, this is true with the growing utilization of multi-omics data that provides unprecedented precision about sub-cellular processes that are critical in disease diagnostics and therapeutics development.

With affordable whole-patient scale precision data just around the corner, the challenge has now moved into the realm of extracting meaningful insights from the data. To get the most value from multi-omics data analysis in clinical applications, Pine Biotech is developing an omics-first machine learning platform, OmicsLogic.

Pine Biotech’s machine learning platform for biomedical data goes beyond analytics, integrating clinical knowledge with multi-omics raw data analysis for biomarker discovery and personalized molecular studies.

Precision Therapeutics

According to the Global Oncology Trend Report, global spending on cancer medications rose 10.3 percent in 2014, bringing the total to $100 billion, up from $75 billion in 2010. The rising cost of cancer treatment is linked to the emergence of precision therapeutics, which are costly to develop and often fail before they reach the market. While more effective, they target a smaller population that is hard to identify. Using these treatments requires training and education, and the adoption of new technologies. For efficiency, the pharmaceutical industry is turning to theoretical and computational modelling to improve the drug discovery process2 . With the right technological innovations, the wealth of data that is accumulating can actually be used to increase efficiency at every step in drug development and in integrating health care, thereby reducing costs.

Real World Evidence

Real World Data (RWD) covers data that is obtained from a wide variety of sources, everything from clinical trial data, pharma data, survey data, wearables, Electronic Medical Records (EMRs), and omics data. Real World Evidence (RWE) is the interpretation of RWD in a way that benefits drug discovery and healthcare by complementing the information available from traditional sources. The incorporation of “real-world evidence”—that is, evidence derived from data gathered from actual patient experiences, in all their diversity— in many ways represents an important step toward a better fundamental understanding of states of disease and health.

In addition to progress in research into diseases and their treatment, RWE represents a potential for significant cost savings by reducing costs at every step of the drug development pipeline. In providing care, RWE reflects the likely conditions in which a drug would be used, by factoring patient, biological, and cost information, and it contributes to greater efficiency of care by identifying those patients most likely to benefit from a given drug.

The Promise of Personalized Medicine

Personalized medicine is the effort to prescribe the most appropriate drug for each individual patient based on their specific biology. Genetics explain some of the variations in responses seen during clinical trials. By employing RWE in routine patient exams, clinicians can analyze how the clinical symptoms of a disease in a patient correlate with their specific biology, resulting in a more effective treatment, while also providing data that can be applied across all patients.

What’s in Store

The potential exists to identify early indicators of disease, including cancer, in the form of biomarkers for early detection of a disease, through effective analysis and integration of the biomedical data becoming available. Within the realm of healthcare services alone, and also with data amassed by pharma, payers, and government, a wealth of data is generated, and should be exploited, ultimately to improve care, at times in astonishing ways, and benefit consumers.

As the field evolves and data continues to become available, algorithmic innovation is poised to be a transformative force in solving healthcare ecosystem challenges.

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