As the volume and complexity of nonclinical toxicology data continue to grow, the industry is seeing a shift toward predictive modelling, an approach that uses standardized datasets and machine learning to enhance the interpretation of study results. The upcoming ACT Annual Meeting Symposium: Developing Predictive Models to Facilitate Interpretation of Toxicology Study Results promises to explore how these emerging tools are reshaping the way toxicologists assess safety and make data-driven decisions.

At Attentive Science, we’re excited to attend this session and join the conversation on how data standardization and predictive modelling are advancing the field of toxicology. These innovations align closely with our commitment to smarter, safer, and more transparent research solutions for our sponsors.

Leveraging CDISC-SEND Data for Predictive Insights

One of the key enablers of predictive modelling is the availability of standardized CDISC-SEND datasets. SEND has long been recognized for its role in improving the quality and consistency of nonclinical data submissions. But as explored in our previous blog, "To SEND or Not to SEND", its value extends far beyond regulatory compliance.

By harmonizing toxicology data across studies and programs, SEND enables the development of predictive algorithms that can identify patterns correlating with expert toxicologist conclusions such as target organs of toxicity, dose-response relationships, and adversity classification. Leveraging SEND continues to be a major step forward in transforming how we interpret study outcomes.

From Conventional Analysis to Predictive Interpretation

Traditionally, toxicology interpretation relies heavily on expert review of study reports, clinical pathology, and histopathology data. Predictive models built on SEND data are expediting streamlining this process by helping identify potential safety concerns earlier and more efficiently.

These models can:

• Predict adversity and NOAELs with increased consistency.
• Flag potential safety concerns in early development, supporting go/no-go decisions.
• Enhance translatability by correlating nonclinical findings with potential clinical outcomes.
• Enable exploratory analyses, such as structure activity relationships, to anticipate toxicity profiles for related compounds.

As noted in our previous post, "AI in Toxicology & SEND Services: Exploring Its Potential Role", the integration of AI and standardized data opens the door to deeper, more automated insight generation; helping researchers focus their expertise where it matters most.

A Step Toward Predictive, Data-Driven Toxicology

Predictive modelling doesn’t replace the critical thinking and contextual judgment of experienced toxicologists, it enhances it. By offering new perspectives grounded in data, these tools support reproducible interpretations and more confident decision-making, particularly across large datasets or multi-study programs.

At Attentive Science, we see predictive modelling as a natural extension of the phase-specific and GLP/non-GLP strategies we apply in toxicology studies. As outlined in our earlier piece, "Phase-Specific Strategies for GLP, Non-GLP Studies and SEND", building data continuity across study phases lays the groundwork for predictive, translational insights that can drive safer and more efficient drug development.

Meet Us at ACT 2025

We look forward to connecting with colleagues and industry partners at ACT 2025 to explore how predictive models are shaping the future of toxicology. Stop by to discuss how Attentive Science is integrating data innovation and SEND expertise to enhance study design, interpretation, and decision-making.

Let’s collaborate to make toxicology not just descriptive but predictive.