Precision Drug Screening. No Lab Required.

A computational platform that replicates how a patient's tumour responds to cancer drugs — delivering results in hours, not weeks.

0%
Reduction in cost per test
0
From biopsy data to drug response
0
Virtual compounds screened per hour

Traditional drug testing grows patient tumour cells in a laboratory — a process that takes 4 to 6 weeks, costs over $10,000 per panel, and produces inconsistent results. In Silico Organoid Twins replaces the laboratory with a validated computational model, using the patient's own genomic and imaging data to predict which drugs will work — and which will not.

Predictive Accuracy Comparison
Predictive Accuracy (%)
~50% accuracy
80-90% accuracy
Target ~95%
2D Cell Culture
Patient Organoids
In Silico Twin
Screening Method
Five Computational Layers

Each layer processes a different dimension of the patient's biological data.

01
Patient Data Input
The platform reads the patient's next-generation sequencing files and histological biopsy images. These files are the only clinical inputs required.
02
Organoid Geometry Reconstruction
A U-Net convolutional neural network analyses the biopsy image to reconstruct the three-dimensional architecture of the patient's tumour organoid. This geometry determines how drugs physically penetrate the tissue.
03
Cell Behaviour Simulation
Single-cell RNA sequencing data is fed into an agent-based model running on the PhysiCell framework. Each virtual cell is individually programmed to grow, divide, signal, or die according to the patient's gene expression profile.
04
Drug Diffusion Modelling
Partial differential equations compute how each candidate drug molecule moves through the virtual organoid volume, accounting for absorption, metabolism, and concentration gradients across the tumour.
05
Response Output
The platform outputs IC50 curves, dose-response profiles, and drug synergy maps. An oncologist receives a ranked list of effective drugs specific to that patient's tumour within four hours of data upload.
Virtual Organoid — Interactive Model

Rotate and zoom to explore the computational representation of a patient tumour organoid. Colour indicates cell activity level.

Low activity / Healthy cells
Moderate activity
High activity / Aggressive cells
Drug Response Heatmap

Predicted sensitivity of this patient's tumour to 12 candidate drugs across 6 biological pathway targets. Darker green indicates stronger tumour suppression.

Top recommended drugs for this patient: Erlotinib (EGFR, 0.91)  |  Olaparib (DDR, 0.93)  |  Palbociclib (CDK, 0.87)  |  Ribociclib (CDK, 0.91)
Patient Simulation — Live Demo

Enter patient parameters below to generate a mock drug screening report. This demonstrates the clinical workflow an oncologist would follow.

EGFR Mutation Detected
TP53 Mutation Detected
Drop NGS file here or click to select (.vcf, .fastq)
Results will appear here after simulation runs.