About SyzOnc

SyzOnc develops the STEM3 platform, a systems-level oncology solution that utilizes multi-modal human tissue data and AI-driven ecosystem models to identify druggable targets in matrix-rich solid tumors. The platform addresses the lack of effective treatment options for patients with aggressive cancers, such as liver and pancreatic cancer, by rapidly comparing genetic and compound phenotypes to discover improved drug candidates.

```xml <problem> Matrix-rich solid tumors, such as liver and pancreatic cancers, are difficult to treat due to the protective ecosystem surrounding the cancer cells. Current treatment options are often ineffective, leading to poor survival rates for patients with these aggressive cancers. A lack of effective methods for identifying druggable targets within the tumor microenvironment hinders the development of improved therapies. </problem> <solution> SyzOnc's STEM3 platform is a systems-level oncology solution designed to identify druggable targets in matrix-rich solid tumors. The platform utilizes multi-modal human tissue data and AI-driven ecosystem models to map the tumor landscape, model cancer type-specific ecosystems, and rapidly compare genetic and compound phenotypes. By integrating data from across the tumor ecosystem, STEM3 facilitates the discovery of novel biology and the identification of improved drug candidates, ultimately aiming for better clinical-stage results and improved patient outcomes. The platform's approach allows for targeting the tumor microenvironment, which protects cancer cells and contributes to treatment resistance. </solution> <features> - Multi-modal human tissue data integration for comprehensive tumor mapping - AI/ML models incorporating data from across the tumor ecosystem to create cancer type-specific models - Rapid comparison of genetic and compound phenotypes to identify compound starting points - Identification of novel biology within the tumor microenvironment - Validation using gold-standard in vivo models - Focus on matrix biology and phenomics to explore potential druggable matrix targets </features> <target_audience> The primary target audience includes pharmaceutical companies and research institutions focused on developing novel cancer therapeutics, particularly for matrix-rich solid tumors like liver, pancreatic, and glioblastoma cancers. </target_audience> ```

What does SyzOnc do?

Where is SyzOnc located?

SyzOnc is based in Philadelphia, United States.

When was SyzOnc founded?

SyzOnc was founded in 2023.

How much funding has SyzOnc raised?

SyzOnc has raised 800000.

Who founded SyzOnc?

SyzOnc was founded by Karin Eisinger and Ashley Fuller.

Karin Eisinger - CEO/Co-Founder
Ashley Fuller - Co-founder/Chief Scientific Officer

Location

Philadelphia, United States

Founded

2023

Funding

800000

Employees

5 employees

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SyzOnc

Score: 100/100

AI-Generated Company Overview (experimental) – could contain errors

Executive Summary

syzonc.com 300+

Crunchbase

Founded 2023 – Philadelphia, United States

Funding

Estimated Funding

$800K+

Team (5+)

Karin Eisinger

CEO/Co-Founder

Ashley Fuller

Co-founder/Chief Scientific Officer

Company Description

Problem

Matrix-rich solid tumors, such as liver and pancreatic cancers, are difficult to treat due to the protective ecosystem surrounding the cancer cells. Current treatment options are often ineffective, leading to poor survival rates for patients with these aggressive cancers. A lack of effective methods for identifying druggable targets within the tumor microenvironment hinders the development of improved therapies.

Solution

SyzOnc's STEM3 platform is a systems-level oncology solution designed to identify druggable targets in matrix-rich solid tumors. The platform utilizes multi-modal human tissue data and AI-driven ecosystem models to map the tumor landscape, model cancer type-specific ecosystems, and rapidly compare genetic and compound phenotypes. By integrating data from across the tumor ecosystem, STEM3 facilitates the discovery of novel biology and the identification of improved drug candidates, ultimately aiming for better clinical-stage results and improved patient outcomes. The platform's approach allows for targeting the tumor microenvironment, which protects cancer cells and contributes to treatment resistance.

Features

Multi-modal human tissue data integration for comprehensive tumor mapping

AI/ML models incorporating data from across the tumor ecosystem to create cancer type-specific models

Rapid comparison of genetic and compound phenotypes to identify compound starting points

Identification of novel biology within the tumor microenvironment

Validation using gold-standard in vivo models

Focus on matrix biology and phenomics to explore potential druggable matrix targets

Target Audience

The primary target audience includes pharmaceutical companies and research institutions focused on developing novel cancer therapeutics, particularly for matrix-rich solid tumors like liver, pancreatic, and glioblastoma cancers.