Tissue fibrosis, or scar formation, is the common final pathway of virtually all progressive diseases and inflicts damage in every major organ including kidney, heart, bone marrow, lung, liver, muscle and skin. Scar tissue can form after an acute injury, or gradually as a result of chronic agitation or damage from a separate underlying malady such as diabetes or hypertension over several years.

While deposition of fibrotic matrix immediately after injury sustains tissue integrity, unchecked fibrotic matrix deposition, in particular during chronic or repetitive injury, slowly disrupts tissue architecture and drives maladaptive tissue remodeling, ultimately leading to loss of functional tissue and organ failure.

As such, fibrosis destroys the

• kidneys of patients with hypertension, diabetes or various immune diseases,
• livers of patients with cirrhosis due to obesity (non-alcoholic fatty liver disease, NAFLD), hepatitis or alcoholism,
• hearts of patients with hypertension, diabetes or coronary artery disease,
• lungs of patients with idiopathic pulmonary fibrosis, and
• the bone marrow of patients with myelofibrosis.

In sum, nearly 45% of all deaths in the developed world can be attributed to an underlying fibrotic disease (Henderson NC, Rieder F, Wynn TA. Fibrosis: from mechanisms to medicines. Nature. 2020 Nov;587(7835):555-566).

Sequantrix^TM identifies and validates novel, anti-fibrotic targets by leveraging one of the world’s largest human single-cell, multi-modal datasets in the field of fibrotic diseases and aims to develop anti-fibrotic drug candidates up to clinical proof of concept.

We have generated high-quality single-cell human fibrotic datasets that allow us to map fibrosis at unprecedented resolution across major human organs. In-depth bioinformatic dissection of these datasets will enable a better understanding of the complex mechanisms driving myofibroblast activation, expansion, cellular-crosstalk and matrix deposition. Our datasets are being extensively annotated and analyzed, thereby leveraging state of the art bioinformatic pipelines as well as proprietary algorithms to identify promising target candidates for drug development.

Single-cell data provides an unparalleled window into cellular states associated with disease. However, successful discovery of the most biologically relevant anti-fibrotic targets, that are simultaneously well-suited for successful drug development, requires a deep understanding of the relationship between cellular specificity derived from single-cell datasets, disease biology, phenotypic consequence, molecular and genetic factors, and therapeutic potential. We tackle this challenge through a truly data-driven approach based on our propietary FibroDecoder AI-platform that scores targets and nominates them for experimental validation.

The top scoring targets from our FibroDecoder pipeline are directly validated in human disease using state-of-the-art organoid-based disease models. Validation of drug candidates in animals is complex and time-consuming, but also limited due to inter-species differences that can prevent direct translation of insights gained from animals to human disease. We aim at reducing the need for animal experiments, increasing throughput and saving time by using proprietary human organoid systems and modelling complex human organs in a dish to validate top scoring targets from our FibroDecoder pipeline directly in human disease.