TECHNOLOGY

Inside the technology that turns cells into predictions

Human iPSC-derived and primary cell models, CRISPR-Cas9 genome engineering, high-content Cell Painting, and machine learning - this is the technology stack behind every study we deliver, and the same stack pixlOS runs on.

Trusted by leading biopharma partners for predictive biology and custom studies.

AI-Driven

Accelerated Discovery

Compound Screening

De-Risking

Early Prediction

Human-Relevant Data

Why this technology

Built to be human-relevant, automated, and precise

Pixlbio combines human cell models, automated imaging, and AI-powered phenomics to reveal what others miss — delivering speed, accuracy, and predictive clarity in every experiment.

Human-relevant biology

iPSC-derived models that reflect real patient biology, reducing the gap between preclinical discovery and clinical translation.

pixCells

Scalable automation

Imaging through functional analysis runs automated end to end, enabling high-throughput screening without sacrificing precision.

Direct access, later this year

Enabling partners to track experiments in real-time and run in-silico phenomic predictions—through secure, API-based access.

Plate Details interface showing a grid of cell images labeled by rows A-H and columns 01-17 with zoom, brightness, and filter controls.

Predictive analysis

Tox Oracle analyzes phenotypic data to reveal changes invisible to manual review, surfaced by Alchemist on request.

pixCellPainting

pixCellServices

HOW IT WORKS

From cells to a decision your team can act on

Four stages. Each one is a real technical step, not a marketing metaphor and each one is also a product you can order on its own (see pixlbio.com/products).

Powers Lens

Biology, captured

Every discovery starts with a cell - iPSC-derived (pixCells), primary, or a line you already work with. Automated, high-content imaging quantifies thousands of morphological features per cell across healthy, diseased, or custom-engineered contexts.

AI-Powered

Precision

Phenomics

End-to-End

Surfaced through Lens

Biology, translated

Computer vision converts raw imagery into structured, quantitative phenotypic profiles - a measurable, comparable digital signature for how a cell responds to a given condition.

This is Tox Oracle

Prediction, amplified

Tox Oracle integrates chemical structure, phenotypic data and human clinical outcomes to deliver calibrated clinical risk predictions, bridging preclinical models to clinical-relevant readouts. Now researchers can make earlier, more confident progress/halt decisions and avoid costly late-stage failures.

Data Stream

Algorithm

Imaging

Automation

Analysis

pixlOS · Alchemist

Insight, delivered

pixlOS consolidates imaging and prediction outputs into reports tailored to your biology, with Alchemist available to query and interpret results across the stack. Today, our team operates this on your behalf; direct self-serve access to pixlOS opens later this year.

Predictive

Efficacy

Insights

Readouts

Output

Scroll to explore

Automation at the Core

Clarity and consistency at scale

Automating data collection, analysis, and physical operations reduces time, human error, and increases the accuracy of every result.

Automated Experimentation

Integrated robotics handle setup, perturbations, and cell painting with consistency, minimizing variability and human intervention.

Intelligent Data Generation

Automated imaging and quality-control pipelines flag batch effects and technical artifacts before data leaves the pipeline, supporting reproducible results.

Predictive Modeling

AI models analyze phenotypic data to uncover mechanisms of action, flag toxicities, and predict drug responses.

Real-world applications

Where partners are using this today

Early toxicity screening

Predict hepatotoxicity and other off-target effects prior to animal studies, using our validated liver-focused cell models.

Mechanism of action discovery

Map phenotypic fingerprints to known mechanisms, or use Tox Oracle and Alchemist to investigate new ones.

Hit triage & prioritisation

Rank candidates across donors and disease backgrounds using standardized, quantitative phenotypic readouts.

Complex disease modeling

Evaluate compounds in MASLD, A1ATD, PFIC2, or UCD or engineer a custom disease-relevant genotype with pixCRISPR.

Problems We Solve

Reactive guesswork, or predictive intelligence

Drug discovery has run on reaction for decades. Here's the difference the technology above makes, row by row.

Biological Relevance

Data Depth

Timeline

Cost

Mechanistic

Scale

Customization

The old way

Animal models, limited translatability

Single-point readouts, basic understanding

Late-stage failure, years wasted

Failed candidates strain budgets

Black-box phenotypic shifts

Manual, low throughput

Standardized models only

The pixlbio way

Human iPSC-derived models, built for translatable biology

AI-driven phenomics, quantitative mechanistic insight

Early insights, rapid iteration

Early de-risking and triage at high throughput

Clear, quantitative phenotypic fingerprints

Automated, high-throughput

Tailored disease models, including custom genotypes via pixCRISPR

“pixlbio’s unique combination of large scale automated Cell Painting capability, the high quality of the generated data, as well as their expertise in AI and predictive modelling, made them an obvious choice for AstraZeneca.”

Bjarki Johannesson

Director of Safety Innovation, AstraZeneca

“pixlbio, was selected for the Sanofi iDEA-TECH Awards Program to receive Sanofi’s support for its project due to their demonstrated ability to develop high-quality predictive models from their exceptional data. Within the funded project, the pixlbio team showcased profound expertise in Cell painting for morphological profiling, automation, AI and predictive modelling, which truly validates them as deserving awardees.”

Bodo Brunner

Head of In Vitro Biology and Cell Sample Technologies, Sanofi

“pixlbio’s cellular models have significantly improved our Go-No/Go translational decisions across multiple programs over the years at Deep Genomics. The hepatic cell systems are far superior and physiologically relevant compared to commonly used cell lines. More recently, we have begun testing disease variant carrying models, further improving translatability of our screening systems. Working with both the BD and technical support teams at pixlbio has been an excellent experience. Response times have been very fast and technical support and information provided has been above and beyond on a regular basis. We are looking forward to working on additional systems coming from the pixlbio team.”