AI-powered translational safety intelligence for earlier drug safety decisions

Drug development teams are under growing pressure to make confident safety decisions earlier. The science is more complex – with novel drug modalities being developed — data volumes are increasing and scattered across multiple sources, and the consequences of identifying a safety liability too late can have a huge impact. For translational researchers and safety scientists, the central challenge is not simply whether relevant evidence exists. It is whether the right evidence can be found, interpreted and applied early enough to shape a program before risk becomes difficult to manage.

That challenge is intensifying. Overall drug approval rates remain low, at 6.7% of those in development, and safety liabilities contribute to approximately 30% of clinical trial failures. At the same time, relevant safety and toxicology evidence continues to grow year over year across publications, regulatory documents, company communications, clinical trial registries and postmarket sources. The result is a familiar tension: organizations need earlier predictive decision-making, but the information needed to support those decisions is fragmented, heterogeneous and difficult to assess quickly.

Why safety intelligence is a translational challenge

Safety assessment is rarely a single-question exercise. Teams must evaluate whether an observed toxicity is specific to a drug or to a class, mediated by a target (on- vs off-target), associated with a modality, linked to a combination strategy or concomitant medication, or explainable through a biological mechanism. These distinctions matter because they influence compound selection, animal model choice, clinical monitoring, mitigation planning and investment decisions.

Consider the example highlighted in the deck: anticipating the safety profile of a new bispecific agent directed at Met and PD-L1. A conventional search may identify known adverse events for individual drugs in each class. Translational safety requires a more integrated view. Teams need to understand target safety assessments for both biological pathways, distinguish class effects from molecule-specific findings, identify toxicities that could be exacerbated by dual targeting and determine which preclinical models are most informative for human translation. They may also need to benchmark competitor programs, including discontinued assets, and anticipate regulatory concerns before they appear in formal feedback.

These are evidence-integration problems. They require breadth across data sources, depth in toxicology interpretation and speed in workflow execution. They also require traceability. Safety scientists must be able to explain not only what an intelligence workflow suggests, but why the conclusion is reasonable and where the supporting evidence comes from.

AI is only as useful as the evidence it can reason over

The promise of AI in translational safety is not that it replaces toxicology expertise. It is that it can help experts work through complex evidence faster and more consistently. But that promise depends on the quality of the underlying data. In safety intelligence, a model grounded on incomplete, poorly structured or weakly contextualized information can produce responses that appear plausible while missing critical nuance.

For this reason, the data foundation is central, utilizing a translational safety intelligence approach built on multiple source types: peer-reviewed journals and conference outputs, regulatory documents, company communications and news, clinical trial registries, approval packages and other sources relevant to drug and target safety. AI can help capture and prioritize new content, but the workflow also depends on expert indexation, concise summaries and structured ontologies for drugs, targets, modalities and adverse events.

This combination matters. Ontologies help normalize language across sources that may describe the same concept differently. Expert curation helps distinguish meaningful safety signals from noise and extract relevant insights from the original publication sources. Structured evidence can support scoring approaches that estimate the level of association between a target class and an adverse event, between a drug and an adverse event, or between modality and risk. Together, these capabilities make safety evidence more computable without stripping it of scientific context.

Where AI can strengthen the safety workflow

AI-powered translational safety intelligence can strengthen established workflows in several practical ways. First, it can reduce the time required to locate relevant evidence. Instead of manually searching across disconnected sources, teams can use natural-language questions to interrogate a curated safety dataset. This matters when decisions are time-sensitive, such as during candidate nomination, clinical hold response preparation, due diligence or portfolio review.

Second, AI can help prioritize what deserves attention. Automated capture of new evidence, combined with alerting and analytic tools to assess it in the context of what was previously known, can help teams detect relevant changes in the evidence base. This is especially valuable for emerging targets, new modalities and competitive landscapes where new findings may alter the interpretation of risk.

Third, AI can support contextual analysis. A natural-language assistant grounded in curated safety intelligence can help users move from a broad question to a more precise one. A scientist might begin by asking which organ toxicities are associated with a target class, then follow up by identifying specific adverse events and asking whether those events appear in preclinical models, whether they have been observed clinically, which mechanisms may explain them and how competitor assets compare. The value is not simply faster search. It is faster reasoning across connected evidence.

Fourth, AI can make expert knowledge more accessible across functions. Translational safety decisions often require collaboration among toxicologists, pharmacologists, clinicians, regulatory strategists and portfolio leaders. When safety intelligence is surfaced in a structured, explainable way, cross-functional teams can align around the same evidence base. This can improve the quality of discussion and reduce reliance on isolated knowledge or incomplete searches.

Trust depends on grounding, traceability and evaluation

In drug safety, an AI response is useful only if it can be trusted. That trust depends on several conditions. The system must be grounded in relevant, curated evidence rather than open-ended general content. It must provide traceable outputs so users can inspect the underlying sources. It must operate in a secure environment, particularly when proprietary program information is involved. It must also be evaluated continuously against expert-defined benchmarks, not only for fluency but for accuracy, comprehensiveness and relevance.

At Clarivate, we built our OFF-X AI Safety Assistant with a multi-agent architecture, combining AI agents with deterministic tools to surface relevant safety information. We used commercial large language models, grounded in a curated translational safety dataset and housed within a secure and confidential environment. Accuracy and comprehensiveness are continually evaluated using more than 300 curated “golden” questions and answers. These design principles reflect a broader truth for AI in scientific workflows: the goal is not a conversational interface for its own sake. The goal is dependable decision support that fits the standards of evidence expected in drug development.

Evidence traceability is particularly important when safety insights may influence regulatory strategy or clinical monitoring. If an AI assistant suggests that a toxicity may be target-mediated, the user must be able to review the supporting evidence and assess its strength. If it identifies a potential class effect, the user must understand which assets, modalities and contexts contributed to that conclusion. Transparency supports scientific judgment rather than replacing it.

From reactive assessment to earlier anticipation

The most meaningful impact of AI-powered translational safety intelligence may be its ability to shift safety work upstream. Too often, safety analysis becomes most urgent after a signal appears: a concerning preclinical finding, an unexpected clinical adverse event, a competitor discontinuation or a regulatory question. Earlier intelligence can help teams ask better questions before those moments occur.

For discovery and preclinical teams, this may mean evaluating target biology in the context of known adverse events and pathway relationships. For translational scientists, it may mean selecting models that are more likely to illuminate human-relevant risk. For clinical teams, it may mean designing monitoring strategies informed by class history, target biology and modality-specific experience. For pharmacovigilance teams, it may mean staying on top of what has been reported across multiple sources for other drugs in the same class from early stages of development to postmarketing to anticipate or contextualize new signals. For business development teams, it may mean assessing whether a safety concern is manageable, differentiated or potentially program-limiting.

In each case, the customer challenge is the same: make decisions with incomplete certainty while avoiding decisions made with incomplete awareness. AI does not remove uncertainty from drug development. It can, however, help teams see more of the relevant evidence, organize it more coherently and identify risks that deserve closer examination.

A more connected safety intelligence model

AI-powered translational safety intelligence is best understood as a way to connect evidence, expertise and workflow. Its value comes from the combination of high-quality curated data, structured ontologies, analytic scoring, notifications and natural-language interaction. Used well, it complements established toxicology practice by helping teams move faster without giving up scientific rigor.

For pharmaceutical and biotech organizations, this matters because safety decisions shape both patient outcomes and portfolio efficiency. Anticipating drug safety issues earlier can help teams refine candidate selection, strengthen translational plans, prepare for regulatory questions and design more resilient clinical strategies. The objective is not to predict every outcome with certainty. It is to improve the quality and timing of the questions teams ask, and to ensure those questions are answered with the best available evidence.

As safety evidence continues to expand, organizations need ways to transform fragmented information into actionable insight. AI can help, but only when it is grounded in curated, traceable and scientifically meaningful data. In translational safety, intelligence is not simply about finding more information. It is about finding the right evidence soon enough to make a better decision.

Explore how translational safety intelligence can support target‑specific risk assessment and help de‑risk novel therapeutic modalities throughout discovery and development: OFF-X preclinical and clinical safety data | Clarivate