The UK Medicines and Healthcare products Regulatory Agency has opened a new initiative focused specifically on the use of artificial intelligence in medicines safety evaluation.
On 16 September 2026, MHRA launched the Beyond ADMET: AI for Medicines Safety stakeholder survey.
The initiative will support development of a future regulatory sandbox examining how AI could be used to improve prediction and assessment of medicines safety.
This is important because the regulator is not simply publishing general principles for responsible AI.
MHRA is actively asking pharmaceutical, biotechnology, toxicology and AI organisations:
What prevents AI models from being used confidently in regulatory medicines-safety evaluation?
Moving Beyond Classical ADMET Assessment
ADMET traditionally refers to:
- Absorption;
- Distribution;
- Metabolism;
- Excretion;
- Toxicity.
These characteristics are fundamental to understanding how a medicine behaves in the body and whether potential safety problems may occur.
Traditional safety evaluation relies on multiple sources of evidence, including:
- preclinical studies;
- toxicology;
- clinical trials;
- pharmacokinetic information;
- post-marketing information.
MHRA’s initiative explores whether AI can integrate a broader range of information, including traditional preclinical data together with clinical and real-world data.
The objective is to improve understanding of how medicines behave:
- across different populations;
- in different disease states;
- under different clinical conditions;
- in real-world use.
Potentially, this could help identify safety risks earlier.
The Regulator Is Explicitly Asking About Validation
One of the most important aspects of the initiative is the scope of the questions MHRA wants industry and researchers to address.
The Agency specifically identifies areas including:
- model development;
- validation;
- access to data;
- data sharing;
- regulatory considerations.
This is significant.
The central problem with AI in regulated pharmaceutical science is increasingly not whether sophisticated models can be built.
The more difficult question is:
What evidence is necessary before the regulator can rely on their output?
That requires moving from model development toward model credibility.
AI Performance Alone Is Not Enough
A highly accurate AI model may still be unsuitable for regulatory use if the organisation cannot adequately explain:
- what data were used;
- which population the data represent;
- how the model was validated;
- where its limitations are;
- whether performance generalises beyond the development dataset;
- how model changes are controlled.
This is particularly important for medicines safety.
A model can perform well overall while performing poorly for a specific:
- patient population;
- disease state;
- rare adverse event;
- drug class;
- clinical context.
Average performance therefore does not necessarily demonstrate regulatory credibility.
Data May Be the Bigger Problem Than the Algorithm
MHRA specifically identifies access to data and data sharing as areas of interest.
This reflects a broader problem in pharmaceutical AI.
Medicines-safety information is distributed across multiple sources:
- preclinical datasets;
- clinical trials;
- electronic health records;
- pharmacovigilance systems;
- real-world evidence;
- literature;
- laboratory data.
Connecting those datasets creates significant challenges involving:
- data quality;
- standardisation;
- missing data;
- population bias;
- privacy;
- interoperability;
- provenance.
A sophisticated AI model cannot compensate automatically for poor or non-representative data.
This leads to an increasingly important regulatory principle:
AI model credibility begins with data credibility.
Why a Regulatory Sandbox Is Important
The stakeholder survey will inform development of the Beyond ADMET regulatory sandbox, which MHRA announced earlier in 2026.
A regulatory sandbox is different from conventional guidance development.
Instead of attempting to define all regulatory expectations in advance, a sandbox allows regulators and innovators to explore real technologies and identify regulatory problems through practical cases.
This can help answer questions such as:
- What evidence is sufficient?
- Which validation approaches are practical?
- How should uncertainty be communicated?
- What documentation does a regulator need?
- How should model changes be controlled?
- Which data-quality limitations are acceptable?
For rapidly developing AI technologies, this iterative regulatory approach may be particularly valuable.
The Important Shift: From AI Principles to Regulatory Evidence
Over the last several years, many AI frameworks have converged around principles such as:
- human oversight;
- transparency;
- risk-based governance;
- data quality;
- lifecycle management.
Those principles are important.
But pharmaceutical regulation eventually requires something more concrete:
evidence.
The question becomes:
What evidence must a company provide to demonstrate that an AI model is sufficiently reliable for a particular regulatory purpose?
The Beyond ADMET initiative appears to be moving directly toward that problem.
This Is Closely Related to Context of Use
The acceptable level of AI uncertainty depends heavily on how the model is used.
For example, AI could be used to:
- prioritise compounds for additional evaluation;
- identify potential safety signals;
- support toxicology assessment;
- integrate multiple data sources;
- predict possible adverse effects;
- support regulatory evidence.
These applications do not carry identical consequences.
The validation strategy should therefore reflect:
Intended Use → Context of Use → Decision Impact → Required Credibility
The closer the AI output is to a consequential regulatory or safety decision, the stronger the evidence supporting its reliability is likely to need to be.
Lifecycle Control Will Also Matter
AI models do not necessarily remain static.
Performance can change because of:
- new data;
- changing populations;
- model retraining;
- algorithm updates;
- software changes;
- changes in clinical practice.
This creates another important regulatory question:
When does a change require reassessment or revalidation?
For regulated pharmaceutical AI, initial validation cannot necessarily be treated as the end of the assurance process.
A more realistic model may become:
Model Development
↓
Validation
↓
Defined Context of Use
↓
Deployment
↓
Performance Monitoring
↓
Change Assessment
↓
Revalidation Where Necessary
Why This Matters Beyond Safety Prediction
The Beyond ADMET programme is not GMP guidance.
However, the regulatory questions being investigated are closely related to questions facing AI throughout pharmaceutical development and manufacturing:
- How should AI models be validated?
- What data are acceptable?
- How should model limitations be documented?
- How should uncertainty be managed?
- How should changes be controlled?
- When can a regulator rely on AI-generated evidence?
These questions are also relevant to AI used in:
- manufacturing models;
- quality systems;
- process monitoring;
- analytical applications;
- regulatory submissions.
The specific application changes, but the underlying credibility problem remains similar.
MHRA Is Asking Industry for Practical Experience
Importantly, MHRA is not limiting participation to organisations already using advanced AI systems.
The Agency is seeking input from organisations across the medicines-development pathway, including:
- toxicology;
- preclinical research;
- clinical research;
- life sciences;
- biotechnology;
- AI technology.
Organisations can contribute experience concerning current use, future plans, barriers and unmet needs.
The survey remains open until:
22 December 2026 at 23:59 UK time.
For pharmaceutical companies developing AI-based safety models, this represents a direct opportunity to influence the regulator’s understanding of practical validation and data challenges.
Why This Development Is Important
The Beyond ADMET initiative is another indication that regulators are moving from general AI policy toward application-specific regulatory science.
The progression can be seen as:
AI Principles
↓
Specific Use Cases
↓
Regulatory Sandboxes
↓
Evidence and Validation Methods
↓
Future Regulatory Expectations
This is a significant development for pharmaceutical companies.
Future AI regulation may not consist of one universal AI validation standard.
Instead, regulators may develop different evidence expectations for different contexts of use.
Regulatory Status Note
The Beyond ADMET stakeholder survey is an MHRA call for evidence.
It does not establish a new regulatory requirement and does not represent final guidance on validation or acceptance of AI models for medicines-safety evaluation.
Its findings will inform development of the Beyond ADMET regulatory sandbox and future MHRA work in this area.
The initiative should therefore be viewed as an important regulatory-development signal rather than a current compliance requirement.
Source
MHRA – Beyond ADMET: AI for medicines safety stakeholder survey, published 16 September 2026: