Parallel Biosciences, Inc.


Parallel Biosystems is dedicated to transforming drug discovery and development by leveraging immune organoids and AI technology. Their mission is to create safe, effective therapies without the need for traditional clinical trials, focusing on human immunology and replacing animal models with scalable, ethical, and precise in-vitro models. The company aims to accelerate the development of immunotherapies, reduce costs and time, and improve success rates by modeling human disease more accurately and at population scale.

Industries

biotechnology
life-science
pharmaceutical
therapeutics

Nr. of Employees

small (1-50)

Parallel Biosciences, Inc.

Cambridge, Massachusetts, United States, North America


Products

Human immune system organoid platform

A platform technology that reproduces human immune system function in vitro at scale for discovery, safety testing, and target identification.


Services

Trial-in-a-Dish service for preclinical safety and efficacy prediction

In vitro screening of candidate therapeutics across diverse human-derived organoids to predict safety and efficacy and reduce reliance on animal studies.

Disease modeling using patient-matched organoids

Generation of realistic disease models from human samples to study mechanisms and test interventions in a human-relevant context.

Target discovery from organoid datasets

Identification and prioritization of therapeutic targets using comparative experiments on healthy and disease organoids combined with computational analysis.

Platform access and collaborative discovery partnerships

Partnership engagements that apply the organoid platform, analytics, and automation to client drug pipelines.

Expertise Areas

  • Human immune organoid development
  • In vitro clinical trial modeling
  • AI/ML for drug discovery
  • High-throughput automation and lab robotics
  • Show More (4)

Key Technologies

  • Human immune organoids
  • High-throughput laboratory automation
  • Robotics for cell and tissue workflows
  • AI / machine learning on biological datasets
  • Show More (3)

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