Life Sciences

Updated April 2026

GxP-native

Computer-system-validation and 21 CFR Part 11 baked into delivery

Trial AI

Patient identification, site selection, and protocol simulation accelerated by AI

Discovery

Generative chemistry and target identification on proprietary corpora

ColdAI applies AI and data engineering across the pharma, biotech and medtech lifecycle: discovery research, clinical development, regulatory submissions, drug safety, manufacturing and supply. Regulation is the common thread. Each use is classified by its impact on patients, product quality and data integrity before anything is built, so systems that touch GxP records are designed, validated and monitored to stand up to inspection.

“Generative chemistry has moved from a research curiosity to a serious shortcut on the discovery timeline. The labs that operationalise it inside their existing GxP envelope will compound the lead.”

Dr. Shayan Salehi H.C.— Founder & CEO, ColdAI

In depth

Go deeper into AI for life sciences

Working guides for three regulated areas: validating AI under GxP, automating safety case intake and drafting traceable submission documents.

AI-Accelerated Life Sciences

Life sciences companies — pharmaceutical, biotechnology, and medical device organizations — are under immense pressure to bring safer, more effective therapies to market faster and at lower cost. AI is transforming every stage of the life sciences value chain: from target discovery and molecular design through clinical trials and commercial launch. ColdAI builds the computational platforms that accelerate drug development, optimize clinical operations, and enable precision medicine at scale.

Use Cases We Deliver

AI Drug Discovery

Machine learning models for target identification, molecular design, ADMET prediction, and lead optimization that reduce the discovery phase from years to months.

Clinical Trial Optimization

AI-powered patient recruitment, site selection, protocol design, and real-time monitoring that reduces trial duration and improves data quality.

Real-World Evidence Analytics

Analysis of electronic health records, claims data, and patient registries to generate real-world evidence for regulatory submissions and post-market surveillance.

Genomic Analysis Platforms

High-throughput genomic data processing, variant calling, and pharmacogenomic analysis that enables precision medicine approaches tailored to individual patients.

Regulatory Submission Automation

AI-assisted preparation of regulatory submissions, including automated document generation, cross-referencing, and compliance checking against FDA and EMA guidelines.

Supply Chain & Cold Chain Monitoring

Temperature-sensitive logistics monitoring, demand forecasting, and serialization tracking for pharmaceutical supply chains across global distribution networks.

How We Help Across Life Sciences

Segment	Challenge	ColdAI Solution
Pharma R&D	Drug discovery costs and timelines	AI drug discovery, molecular design, clinical trial optimization
Biotech	Target identification and validation	Genomic analysis, pathway modeling, biomarker discovery
Medical Devices	Regulatory pathway and clinical evidence	Clinical analytics, regulatory submission AI, post-market monitoring
CROs & CDMOs	Operational efficiency and data quality	Process optimization, data management, quality prediction

Our Life Sciences Delivery Process

Scientific & Regulatory Assessment

Review R&D workflows, clinical programs, and regulatory strategy to identify where AI can accelerate development timelines.

GxP-Compliant Architecture

Design systems that meet GLP, GCP, GMP, and 21 CFR Part 11 requirements with full validation documentation and audit readiness.

Model Development & Validation

Build and validate AI models using diverse, representative datasets with rigorous performance benchmarking and bias testing.

Regulatory Submission Support

Support regulatory submissions with model documentation, validation reports, and ongoing post-market monitoring capabilities.

Why Life Sciences Companies Choose ColdAI

Drug discovery AI that has accelerated candidate identification in therapeutic areas including oncology, neurology, and immunology.
Clinical trial platforms designed for GCP compliance with full audit trail and data integrity verification.
Genomic analysis pipelines that process whole-genome sequencing data with clinical-grade accuracy and turnaround.
Regulatory expertise spanning FDA, EMA, PMDA, and emerging AI/ML regulatory frameworks for SaMD.

Where AI fits from target discovery to patient supply

Each stage runs under its own good practice, and the stage shapes the controls more than the algorithm does.

Discovery
Target identification, molecular design and property prediction on proprietary and public data.
Nonclinical
Study data analysis and report preparation under good laboratory practice.
Clinical development
Protocol design, site selection, recruitment and trial data review under good clinical practice.
Regulatory submission
Drafting and checking submission documents with every figure traced to a source.
Drug safety
Adverse event intake, coding and signal support under good pharmacovigilance practices.
Manufacturing and supply
Inspection, process monitoring, cold chain and serialization under GMP and good distribution practice.

Conceptual map of AI use across the medicines lifecycle. It does not describe a specific program or a deployed system.

How regulatory weight changes across the lifecycle

Stage	Governing framework	Typical AI tasks	What raises the stakes
Discovery	Research quality systems; the EU AI Act excludes AI built and used solely for scientific research⁵	Target ranking, generative chemistry, property prediction	Outputs carried into regulated studies with no record of their data lineage
Nonclinical	Good laboratory practice	Pathology image analysis, study data checks	Model output becoming part of a GLP study record
Clinical	ICH E6(R3) good clinical practice, whose principles and Annex 1 took effect in the EU in July 2025⁶	Feasibility, recruitment, data review, medical monitoring support	Any influence on dosing, treatment assignment or participant safety
Submissions	Agency submission rules; FDA's draft AI guidance leaves out drafting that does not affect safety, quality or study reliability⁷	Drafting reports and summaries, cross-reference checks	Unreviewed numbers or claims reaching a submitted document
Safety	Good pharmacovigilance practices and national reporting rules	Case intake, coding, literature screening, signal support	Errors in seriousness assessment that move reporting deadlines
Manufacturing and supply	EU GMP Annex 11 and draft Annex 22¹¹; Part 11 in the US; good distribution practice	Visual inspection, process monitoring, demand and cold-chain forecasting	Models making batch or reject decisions without human review

EMA's reflection paper reserves the most scrutiny for uses of “high patient risk” or “high regulatory impact”⁸. The right-hand column shows where those terms tend to apply.

Choosing a first AI use in a regulated life sciences company

If
You want early results with a light validation burden.
Then
Start in discovery or in operational work outside GxP records, such as literature triage for research teams or internal knowledge search.
Controls can stay proportionate while teams learn how models behave on your own data.
If
Safety case volume is outgrowing the team.
Then
Automate the intake stages that prepare work for reviewers and keep validity and seriousness calls with qualified staff, as in pharmacovigilance case intake.
Reviewer time moves from transcription to judgment, while the calls that set deadlines stay human.
If
Submission timelines slip on document preparation.
Then
Build a structured content layer and add grounded drafting on top, following the AI regulatory writing architecture.
Traceable components cut rework across the many documents that repeat the same results.
If
A manufacturing site wants AI in inspection or batch decisions.
Then
Plan validation before building, using GxP AI validation as the frame.
Critical GMP uses carry the heaviest evidence burden, and retrofitting validation onto a finished pilot rarely works.
If
The software will diagnose, treat or monitor patients.
Then
Treat it as potential software as a medical device and start from the healthcare hub.
Device regulation, clinical evidence and HIPAA questions are handled there, not under GxP manufacturing rules.

What staying inside a GxP envelope means during delivery

ColdAI's life sciences work begins with a scientific and regulatory assessment of the workflow. It then designs systems against GLP, GCP, GMP and 21 CFR Part 11 requirements with validation documentation, develops and validates models on representative data with bias testing, and supports submissions with model documentation and validation reports¹.

Day to day, that comes down to three habits. Your quality unit sees the intended use and risk classification before the design is fixed, not at the end. Data lineage is recorded from the first experiment, so a discovery model that later informs a regulated study can show where its training data came from. And models that handle confidential or patient-level data run where you control them: the enterprise AI practice builds on-premise and private-cloud deployments with audit logging².

Engineering follows the development practice's two-week sprints, with design choices recorded as architecture decision records³, which hands validation teams a written trail. Questions that need research rather than delivery, such as a new computational biology method, go to the Frontier R&D practice, which fixes how results will be judged before experiments run⁴.

Why life sciences AI pilots stall before they reach validation

The pilot was built where it can never be validated

Early signalPersonal accounts or a public endpoint, no audit trail and no version pinning.

MitigationBuild pilots on infrastructure that could be qualified, even if formal validation waits.

Consent and contracts do not cover the reuse

Early signalTrial or real-world data used for training beyond what informed consent or data agreements allow.

MitigationCheck consent scope, data-sharing terms and the lawful basis under the General Data Protection Regulation before data reaches a model⁹.

Nobody owns the model after the project ends

Early signalOnly the vendor or project team understands thresholds and retraining.

MitigationName a system owner and a process expert for every model and hand over monitoring before go-live.

A hosted model changes underneath you

Early signalOutput quality shifts with no release on your side.

MitigationPin model versions contractually or self-host, and retest on a reference set before accepting updates.

Frequently asked questions

Does the EU AI Act apply to AI used in drug development?

It depends on the use. The AI Act does not apply to AI systems and models developed and put into service solely for scientific research and development⁵, which covers much discovery work. AI that is a medical device, or a safety component of one, can be classed as high-risk under the Act. Most other drug-development uses are governed mainly by GxP rules and agency guidance, but check each use against the Act's categories.

Do you build software that is itself a medical device?

Software that diagnoses, treats or monitors patients raises device regulation, clinical evidence and HIPAA questions, which the healthcare practice covers. This page concentrates on pharma, biotech and medtech operations: discovery, trials, submissions, safety and manufacturing, including the production and quality systems of device makers. If you are unsure which side a product falls on, start from its intended use.

Where does prior authorization automation fit?

Prior authorization is a payer and provider workflow rather than a life sciences one, so it has its own page on prior authorization automation. Manufacturers meet it indirectly through patient support and access programs; if that is your question, the use case explains how evidence packets are prepared and how their status is tracked.

How should patient-level trial data be protected during an AI project?

Pseudonymize data before it reaches a model wherever the use allows, limit access to named people and services, and run models in your own environment or a private deployment so prompts and outputs stay under your control. In the EU, data concerning health is a special category under the General Data Protection Regulation⁹, so confirm consent scope and data agreements before any reuse for training.

What does a first life sciences engagement look like?

A sensible start is a focused assessment of one workflow, which ColdAI runs as a consulting sprint of two to four weeks¹⁰. It maps the process, classifies each candidate AI use by its GxP impact, checks the data that would feed it and ends in a sequenced plan your quality and IT teams can review. Building and validation follow only if the plan holds up.

Can a discovery model be reused later in GxP work?

Yes, if its history allows it. A model trained during discovery can later support a regulated activity, but it then needs what any GxP system needs: documented training data provenance, a defined intended use, independent test data and change control. Keeping lineage and version records from the start costs little and avoids retraining from scratch when the tool moves into a regulated study.

Sources