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The acceptance criteria for dissolution profile data are anchored in Q values, representing the percentage of drug dissolved within a specified period. This assessment unfolds in three stages:First Stage: The test passes if all six drug dosage units are equal to or greater than Q plus 5%; otherwise, the sample proceeds to the second stage.Second Stage: The average of twelve units must be equal to or greater than Q, with no unit falling below Q - 15% to pass; if not, it progresses to the final...
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Metabolic Profile Analysis of Zebrafish Embryos
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Company Profile: Selventa, Inc.

David A Fryburg1, Louis J Latino1, John Tagliamonte1

  • 1Selventa, Inc., One Alewife Center, Cambridge, MA 02140, USA.

Personalized Medicine
|May 18, 2018
PubMed
Summary
This summary is machine-generated.

Selventa utilizes in silico reverse-engineering to uncover disease mechanisms and discover predictive biomarkers for personalized healthcare. Their unique platform aids in developing novel diagnostic tests for autoimmune and oncologic diseases.

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Area of Science:

  • Biomedical research
  • Biomarker discovery
  • Personalized healthcare

Background:

  • Selventa, Inc. (formerly Genstruct, Inc.) has evolved from a service provider to a partner in diagnostic test development.
  • The company rebranded to Selventa to signify its new mission in personalized medicine.
  • Selventa employs a unique platform functioning as a molecular dashboard for disease activity.

Purpose of the Study:

  • To leverage in silico reverse-engineering methods for determining biological causality.
  • To generate new insights into disease mechanisms using a curated knowledge base.
  • To focus on developing predictive biomarkers for response in autoimmune and oncologic diseases.

Main Methods:

  • Utilizing in silico reverse-engineering to identify biological mechanisms explaining observed data.
  • Employing a large and carefully curated knowledge base.
  • Generating predictive biomarkers for therapy response.

Main Results:

  • Gained new insights into disease-driving mechanisms.
  • Enabled biomarker discovery applicable to novel diagnostics.
  • Focused on autoimmune and oncologic diseases for biomarker development.

Conclusions:

  • Selventa's in silico methods provide a unique approach to understanding disease causality.
  • The company is actively developing a portfolio of biomarker projects.
  • The goal is to create diagnostic tests that predict patient response to therapy.