Accelerate breakthroughs at every stage of the pharmaceutical life cycle with trustworthy AI

Speed the identification of new molecules for drug development with AI, streamlining the science-heavy process that requires millions of data points.

The value of this solution:

• Faster decision making.
• Trustworthy insights.
• Competitive advantages.

AI techniques used in this solution:

• Machine learning is used to analyze large amounts of data to improve the efficiency and success rate in drug discovery.
• Large language models (LLMs) are used to identify drug targets and predict drug interactions.
• Synthetic data is used to fill data gaps, simulate trials and protect patient privacy.

How AI helps:

• Analyze large data sets using AI to more rapidly identify drug targets.
• Generate and capitalize on synthetic data to understand molecular interaction.
• Predict the safety and efficacy of drug candidates to help prioritize compounds and streamline pre-clinical testing.

The AI models provide:

• Synthetic data offers a unique opportunity to gather more insights, quicker.
• Algorithms are based on scientific relevancy.
• Models help to improve the drug development process materially.

Use AI and LLMs to automate and accelerate FDA readiness and improve data quality, including support for standardized data generation and validation, as well as synthetic data creation.

The value of this solution:

• Reduce manual effort, accelerate submission timelines and handle complex data from OMICS and digital health devices.
• Enhance code validation efficiency and accuracy.
• Improve accuracy, reduce errors and enhance compliance in clinical trials.
• Accelerate submission processes and improve documentation quality.
• Inform strategic decisions for efficient data transformation.

AI techniques used in this solution:

• Metadata-driven automation.
• LLMs.
• Automation and synthetic data generation.
• Multi-agent framework.

How AI helps:

• Enable continuous Study Data Tabulation Model (SDTM) and Analysis Data Model (ADaM) updates, reduce coding needs and support efficient data review.
• Automate code and outcome review, reducing manual validation.
• Detect inconsistencies and automate validation workflows.
• Reduce time for Tables, Listings and Figures (TLF) creation, enable early code validation and improve data transparency.

The AI models provide:

• Automated SDTM, ADaM, and TLF/ADRG generation and configuration management.
• AI-assisted code validation and compliance checking.
• Automated error detection and validation logic.
• Synthetic data generation, anomaly detection and documentation automation.
• Strategic insights for SDTM, ADaM and TLF/ADRG transformation architecture.

Improve the efficiency of clinical research while protecting the safety of study participants with predictive analytics using AI and digital twins.

The value of this solution:

• Improve safety.
• Accelerate innovation.
• Make decisions faster.

AI techniques used in this solution:

• AI agents can flag early indicators of adverse events and recommend protocol adjustments in real time
• Digital twins are used to simulate drug interactions, identify candidates for drug repurposing and understand alternate pathways for patients.
• Predictive analytics is used to identify which patient populations will respond best to future drugs.

How AI helps:

• Improved understanding of diseases, patient populations and drug interactions and efficacy.
• Accelerated clinical research while ensuring patient safety.
• AI agents can protect the safety of clinical trial participants by embedding intelligent, autonomous systems into the design, monitoring and decision-making processes of clinical research.

The AI models provide:

• Digital twins power simulations that researchers use to anticipate risks and optimize trial protocols before exposing real patients to experimental treatments.
• Predictive models help identify which patient populations are most likely to benefit from or be harmed by a treatment. This allows researchers to intervene earlier, improving safety while maintaining trial integrity.

Extended control arms are alternatives to traditional control groups in clinical trials. Instead of enrolling patients into a placebo or standard-of-care group, researchers use existing data, such as historical clinical trial data, real-world data or synthetic data, to simulate the control group.

The value of this solution:

• Simulate control groups when traditional control groups are not possible due to insufficient data or time and ethical concerns. For example, extended control arms are especially valuable in trials for rare diseases, life-threatening conditions and oncology.

AI techniques used in this solution:

• Artificially generated data – that mimics real patient data but does not correspond to actual individuals – is used to extend control arms.
• AI agents can ingest large amounts of real-world data (RWD), match patients in a treatment arm to similar patients from historical trial data and simulate outcomes for the extended control group.
• AI agents create a digital twin to mirror patients under control conditions.

How AI helps:

• Synthetic data can fill data gaps by simulating underrepresented patient profiles in real-world datasets to improve the diversity and robustness of control arms.
• AI-generated synthetic data sets allow researchers to model thousands of trial scenarios before enrolling a single patient. This accelerates trial design by reducing failure rates and optimizing design.
• By reducing the need for placebo groups, extended control arms ensure more patients receive potentially life-saving treatments while maintaining scientific rigor.
• Synthetic control arms have been used to support accelerated drug approvals by the FDA, especially when randomized controlled trials (RCTs) are infeasible.
• Digital twin technologies, virtual patient replicas, are being used to model drug interactions and predict adverse events at scale.

The AI models provide:

• SAS Data Maker generates high-quality synthetic data using a variety of AI models and techniques.
• The models evaluate how closely synthetic data resembles the original data while ensuring privacy thresholds are maintained.
• The models assess similarity metrics, privacy risk scores and synthetic data quality indicators, including SMOTE (Synthetic Minority Over-sampling Technique), GANs (Generative Adversarial Networks) and Privacy Risk and Similarity Scoring Models.

Use models to streamline the clinical trial protocol process by transferring information and making material “protocol-ready” to fit into a template, saving hours of manual drafting for clinical project managers, trial designers and medical leads.

The value of this solution:

• Accelerate innovation.
• Achieve greater productivity through automation and repeatable documented processes.

AI techniques used in this solution:

• LLMs enable researchers and protocol creators to more quickly develop materials and content.
• Small LLMs ensure protocol creators are using a limited, specific context window to templatize the protocol.
• Intelligent decisioning provides a transparent and automated workflow that meets business requirements and guides the AI agents.

How AI helps:

• LLMs and small language models can be tailored in compliance with various regulations.
• Researchers can create, edit and update their protocols more quickly and with less human error.

The AI models provide:

• Large and small language models help condense the protocol development process, saving valuable time for creators.
• Large and small language models help populate templates and automate the creation of protocol components for greater efficiency.
• Large and small language models help fine-tune protocols to support regulatory compliance.

Create chatbots to engage with patients, sites, investigators and research teams more efficiently and effectively.

The value of this solution:

• Resolve issues faster.
• Improve customer service.
• Support 24/7 personalized outreach across large patient and member populations.

AI techniques used in this solution:

• LLMs and natural language processing are used to train chatbots to effectively engage with patients and research teams.
• Intelligent decisioning helps increase engagement quality.

How AI helps:

• Optimize resources and improve engagement effectiveness.
• Increase patient, site and stakeholder satisfaction while maintaining data privacy.
• Be better prepared to respond quickly in times of disruption and uncertainty.

The AI models provide:

• Conversational AI provides scalable support. Use LLM- and SLM-powered chatbots to provide 24/7 support for patients, sites and research teams, answering questions about side effects, logistics, protocol documentation and more.
• Intelligent decisioning enables agents for personalized outreach. Embedded AI agents segment patients by behavior and risk, score them for outreach, and recommend personalized next-best actions for care teams.
• AI agents power intelligent workflows that identify at-risk populations, automate triage and follow-up, and continuously learn from outcomes to improve coordination and reduce manual effort.
• All AI systems include built-in governance with a human in the loop, including human oversight for high-risk decisions, explainability, auditability, and alignment with regulatory standards to ensure trust and compliance.

Use chatbots and AI agents powered by LLMs to optimize SKU-level warehouse inventory and dynamically adjust scenarios based on updated demand forecasts.

The value of this solution:

• Optimize inventory.
• Forecast stock with high accuracy.
• Make decisions faster.
• Scale effectively.

AI techniques used in this solution:

• AI-driven machine learning models analyze vast amounts of data, recognize patterns and continuously adapt to provide accurate, automated stock forecasts and risk assessments.
• GenAI using small language models and natural language processing (NLP) enables efficient, human-like interactions by understanding user input, generating contextual responses and automating communication tasks in a resource-efficient manner.
• Intelligent decisioning provides a transparent and automated workflow that meets business requirements and guides the AI agents.

How AI helps:

• Through insights generated by machine learning, the entire supply chain becomes more efficient as inventory levels are optimized based on data-driven predictions, reducing waste, cutting costs and improving overall performance.
• The models help optimize inventory levels by considering factors like lead times, storage costs, expiration dates and supplier reliability, ensuring that the right amount of stock is available when needed.

The AI models provide:

• Machine learning models analyze historical sales data, seasonal trends and external factors, like market demand and regulatory changes, to predict future inventory needs more accurately. This helps prevent stockouts or overstocking.
• Machine learning models learn and adapt over time based on new data inputs, improving forecasting accuracy as market conditions, product demand or supply chain factors change.
• Models continuously monitor and assess risks, such as supply chain disruptions, changes in demand or supplier delays. They provide real-time, dynamic risk assessments.
• The AI agent manages inventory and predicts disruptions.
• The AI agent automates carrier ordering and improves supply chain efficiency.

Use AI agents and NLP to automate the extraction and refinement of patient groups from large and varied data sets.

The value of this solution:

• Generate more real-world evidence.
• Make decisions faster.
• Reduce recruitment time and costs.
• Protect patient safety.
• Support decentralized trial methods.
• Shift human effort to high-value tasks.

AI techniques used in this solution:

• AI agents in a multi-agent system streamlining the entire cohort and creating a monitoring process.
• NLP to contextualize non-standard data formats to speed analysis.
• NLP to accelerate the identification of patient characteristics.

How AI helps:

• Analyze diverse data sources and automate queries using NLP to improve the efficiency and accuracy of cohort generation for clinical studies.
• Reduce the time and expertise needed to build cohorts for clinical trials, outcomes research, and operational analytics.

The AI models provide:

• NLP plays a pivotal role in patient cohort generation by automating data extraction, improving accuracy of cohort definitions and enabling the integration of diverse data sources.
• As the AI models advance, they will enhance research capabilities, streamline clinical trials and improve patient outcomes.
• AI agents for patient recruitment scan EHRs and social media to identify eligible participants.
• AI agents for candidate selection analyze biomarkers and comorbidities to optimize cohort composition.