Biopharma Analytics

Your bioprocess deserves more than basic statistics

Graphtal is a specialist analytics and data science firm built for biopharma, CDMO, cell therapy, and emerging biotech where science meets data intelligence.

GT
Graphtal Analytics Aug 01, 2026 7 min read
#Biopharma #Analytics #HybridModelling #CFD #ProcessDevelopment
40%
Faster development with hybrid modelling
60%
Fewer physical experiments needed
10+
Years of combined bioprocess experience
3×
Higher productivity vs. fed-batch

Biopharma pipelines run on data but most organizations are drowning in it rather than benefiting from it. Fragmented datasets, small and noisy experimental runs, and machine learning initiatives with no clear strategy quietly slow down development programs. Graphtal exists to fix that.

Who We Are
An analytics partner built for life sciences

We're a multidisciplinary team of statisticians, data scientists, and bioprocess engineers pairing deep statistical expertise with real bioprocess understanding that only comes from working inside CMC and process development.

Statistical Expertise
Core statisticians with genuine bioprocess depth from DoE to MVDA to SPC.
Cross-Functional Team
Bioprocess engineers, process developers, and data designers working as one.
Regulatory Alignment
GxP-ready, submission-quality outputs built for FDA, EMA, and ICH expectations.
Growth-Oriented
Analytics that enable strategic decisions not just compliance outputs.
The Problem
Challenges that slow down your pipeline

Ask almost any biopharma organization about their data challenges, and the same themes surface again and again. Individually, each is a nuisance. Together, they compound into slower programs and expensive surprises at scale-up.

Fragmented & poor-quality data
Small, noisy, expensive datasets
Can't extract insight from multivariate data
Weak real-time process analytics
Weak statistical depth in documents
Limited in-house statistical expertise
ML without a clear strategy
Experimental overload, classical methods
No CFD-driven scale-up understanding
Our Solutions
Deep expertise at every stage of your bioprocess
Deep Statistical Expertise
Seasoned biopharma statisticians who understand process development, CMC, and biological variability.
Hybrid Modelling & CFD
Cuts experimental volume via transfer learning and kinetic + data + CFD hybrid models with virtual bioreactor simulation.
Embedded Partner & Training
We act as an embedded quantitative partner within your team, building lasting capability.
ML Strategy
Structured, explainable AI frameworks tailored to biopharma realities no black-box solutions.
Biopharma CDMO Cell Therapy Cultured Meat
Statistical Services
Comprehensive analytics across the full lifecycle
Design of Experiments (DoE)
Screening, optimization & RSM across upstream, downstream & formulation.
Multivariate Data Analysis
PCA, PLS, OPLS uncover patterns, identify CPPs, build predictive models.
Statistical Process Control (SPC)
SPC charts, APQR trending, batch comparison & Cpk reporting.
Hypothesis Testing & ANOVA
Scale-to-scale comparison, stability analysis & ARD for CMC submissions.
Modelling & Simulation
The digital twin foundation

Four modelling approaches, combined into one framework that accelerates development, reduces experiments, and predicts outcomes at any scale.

Hybrid Modelling
Kinetic + data + CFD combined the foundation for digital twins.
Mechanistic Modelling
Predicts cell culture kinetics & antibody formation via first-principles equations.
Data-Based Modelling
ML/ANN models enabling real-time predictions & closed-loop control.
CFD Modelling
Simulates fluid environment for mixing, mass transfer & shear stress limits.

Kinetic Modelling + Data-Driven Model + CFD = Digital Twin Foundation. One integrated framework to de-risk scale-up and accelerate commercialisation.

Bioprocess Development by Hybrid Modelling
Speed up development by up to 40%
Traditional Approach
150 – 300 Days
Initial Setup — 1–2 months
Development Run 1 — 3–4 weeks, physical
Development Run 2 — 3–4 weeks, repeat cycle
Development Run 3+ — up to 6 months total cycling
Confirmation Run — 1–2 months
Hybrid Modelling
90 – 180 Days
01 · Hypothesis & Initial Experiments — minimal physical runs capture training data
02 · AI Prediction & Simulation — thousands of conditions explored in silico, zero extra wet-lab runs
03 · Confirmation Run & Decision — one physical run confirms the prediction; rapid go/no-go
Save 60 – 120 days per molecule vs. the traditional approach
40%
Faster development physical run cycles cut from 3–6 months to under 2.
60%
Fewer experiments transfer learning cuts wet-lab volume before scale-up.
24/7
In-silico screening thousands of conditions simulated overnight.
3×
Higher productivity perfusion & continuous processing vs. fed-batch.
Why Partner With Graphtal
The quantitative advantage for your programme
Faster decision making
Informed choices at every stage of your pipeline.
Time & cost savings
Reduced experimental volume, accelerated timelines.
Competitive advantage
Analytics capability that helps you outpace peers.
Submission-ready quality
Every output built to regulatory standards from day one.
Dedicated resource allocation
Experts embedded in your programme, not shared consultants.
Credit rollover
Unused project credits carry into the following year.

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