Our Platform

AI Powered, Computational Pathology and Multi-omics platform to assist Pharmaceutical and Healthcare Industries
with faster, cost-effective drug development and scalable personalized therapy design

In spite of billions of dollars spent in cancer drug development, high rates of attrition limit the number of new drug approvals. Current methods for patient selection in clinical trials are inefficient and time consuming due to lack of reliable prognostic and predictive biomarker(s)

Due to extensive tumor heterogeneity, genomic analysis of whole tumor tissue based on average molecular signal of many cells, only identifies an overly expressed driver across the large majority of cells in the tissue. Molecular signals from a handful of cells that often drive tumor progression are lost in the current genomic analysis of the total tumor section homogenate

In addition, current analyses do not capture the critical spatial distribution of proteogenomic abnormalities in the tumor and its microenvironment, a key contributor in tumor progression and therapy resistance

An AI-enabled platform capable of characterizing and integrating high dimensional morphometric and multi-omics data analysis of patient tissues is warranted. PathomIQ’s AI-powered Computational Pathology and Multi-omics platform can accurately capture the spatio-temporal heterogeneity of the tumor, identifying critical spatial interactions of tumor with its microenvironment that leads to accurate prediction of disease outcome to therapy. This helps identify new treatments, improving chances of developing successful anticancer therapies

Machine Learning Developer

Accurate and Efficient Cancer Diagnosis & Prognosis

Standardize cancer grading to reduce inter-observer variability amongst pathologists worldwide and for highly accurate and faster diagnosis

Harmonization of histopathology data across cancer care centres and clinical trial sites, accelerating standardized patient selection across sites

Reliable Novel Biomarker Identification to Therapy Response

Early identification of non-responders who may be and are eligible for new therapy

Informed clinical trial patient selection with increased accrual rate for cost-effective and faster therapy approval

Discovery of reliable biomarkers and early identification of responders to novel therapy leading to more successful and faster drug approvals

Novel Target identification for informed Drug discovery

Machine Learning Developer

Computational Pathology and Multi-omics Integrated Platform for Therapy Outcome Prediction

Accelerate reliable biomarker discovery, therapeutic development, companion diagnostic to market
Scalable personalized treatment design to improve clinical outcomes

Machine Learning Developer