EXPLORE BIOMEDANA
Resource directory
A visible guide to BioMedAna’s product information, scientific explainers, comparisons and learning resources.
Solutions
BioMedAna Twin use cases
- Explore Twin decision workflows
- Choose the next scale with engineering evidence
- Make the receiving-site transfer decision explicit
- Find the process change worth testing next
- Connect a new CDMO facility’s design basis to ramp-up evidence
- Locate the constraint behind fill-finish and packaging capacity
- Evaluate a 2,000 L single-use to 10,000 L stainless-steel scale-up
White papers and ebooks
- Governed Scientific AI for Pharma 4.0
- The Pharma 4.0 Decision Playbook
- Applied AI in Biologics: The Executive Playbook
- FDA–EMA Good AI Practice for Pharma and Biologics
- FDA–EMA Good AI Practice for Pharma and Biologics — request PDF
- EU GMP Annex 22 and AI in Pharma and Biologics
- EU GMP Annex 22 and AI in Pharma and Biologics — request PDF
ISPE & Pharma 4.0
- Explore the ISPE series
- Advanced kinetic modelling: what a stability prediction must prove
- Pharma 4.0 is an operating model: people, process, culture and scientific intelligence
- Beyond the data tax: what a biopharma handoff must preserve
- Human-in-the-loop AI needs a decision right, not an approval button
- AI-enabled validation: automate the paperwork without losing the assurance
- RAG in GxP: the answer is only one part of the evidence
- Beyond the digital-twin label: physics, chemistry and hybrid models for Pharma 4.0
- From periodic review to continuous insight: who owns the exception?
- A 90-day Pharma 4.0 pilot should earn a decision, not just a demo
Enterprise use cases
- Explore all use cases
- Investigating a CHO process transfer across manufacturing sites
- Separating fermentation productivity loss from recovery loss
- Investigating an API impurity shift after CDMO transfer
- Preserving protein activity through enterprise purification
- Separating starting-material variability from cell-process performance
- Reconciling vector yield, recovery and potency across AAV campaigns
- Balancing oligonucleotide purity and recovery across synthesis campaigns
- Investigating vaccine harvest and recovery variability
- Balancing mAb titer and quality from the next experiment to scale-up
- From Batch Variability to Predictable Biomanufacturing
BioMedAna Industry Insights
- All Industry Insights
- Pythia designs antibodies that change receptor activity. What should a founder advance?
- Pinal turns words into protein candidates. The experimental brief still needs an owner.
- RFD4-Proteina makes protein search more capable. Who chooses what counts as better?
- APM in action: kinetic modelling in BioMedAna Twin
- Advanced kinetic modelling: what a stability prediction must prove
- Pharma 4.0 is an operating model: people, process, culture and scientific intelligence
- Beyond the data tax: what a biopharma handoff must preserve
- Human-in-the-loop AI needs a decision right, not an approval button
- AI-enabled validation: automate the paperwork without losing the assurance
- RAG in GxP: the answer is only one part of the evidence
- Beyond the digital-twin label: physics, chemistry and hybrid models for Pharma 4.0
- From periodic review to continuous insight: who owns the exception?
- A 90-day Pharma 4.0 pilot should earn a decision, not just a demo
- GSK’s seed-transfer digital shadow: the product is a better-timed decision
- Lonza’s dual-payload ADC expansion: two payloads need more than one headline
- JFAB’s AI lab update asks a harder question: what did the experiment teach you?
- AstraZeneca is moving AI deeper into biologics. Who owns the handoff?
- Six months to IND readiness? Read the decisions behind the timeline.
- Lonza and Onava put expression data inside the AI loop. That is the signal.
- Hansa and Cradle: buying AI is the easy part. Changing the workflow is harder.