Inside a laboratory in Cambridge, Massachusetts, Shawn Walker, Global Head of Synthetic Chemistry, Manufacturing and Controls (CMC) Development, gestures toward one of his most distinctive team members. Known as “Solutron,” the system is a sophisticated network of robotic arms, joints, and cables, engineered by Sanofi scientists to autonomously prepare chemical solutions from precisely measured ingredients. As Solutron hums through a sequence of programmed movements, Walker explains its role succinctly: when tasks are repetitive, robotics are the preferred solution.

Solutron represents far more than a single automation success. It is a tangible example of Walker’s broader vision for the future of pharmaceutical development—one in which artificial intelligence (AI), machine learning, and robotics work alongside scientists to accelerate discovery, improve precision, and free human expertise for the most complex challenges. In this evolving environment, AI agents support data analysis and experimental decision-making, while automated laboratories execute high-volume, high-precision experiments with unprecedented consistency.

CMC Development: Where Wet Labs Meet Digital Intelligence

Developing a scalable manufacturing process for a new medicine is an intricate and time-intensive endeavor. Scientists must carefully balance empirical expertise with advanced instrumentation to design experiments that account for the physical and chemical properties of both drug candidates and raw materials. Every variable matters, and small deviations can ripple through later manufacturing stages.

AI and robotics are fundamentally reshaping this process. By introducing digital precision into traditionally manual workflows, they are changing not only the pace of development, but also its reliability. One critical example is solubility—the extent to which a compound can dissolve in a given solvent. Solubility directly affects impurity removal, formulation efficiency, and ultimately how much of an active ingredient is bioavailable to patients. Optimizing it requires thousands of measurements across varying conditions, solvents, and formulations.

Conducting these experiments manually across a broad drug portfolio would demand enormous time and resources. Solutron now performs this work at scale, generating highly reproducible experimental data that continuously feeds AI models. As Walker notes, transferring repetitive laboratory tasks to digital and robotic platforms allows scientists to focus on innovation rather than execution—on questions that cannot yet be automated.

Self-Sharpening Tools and Smarter Experimentation

Automation is only one component of a rapidly evolving CMC toolkit. AI and machine learning initiatives have enabled the creation of digital tools that inform experiments before they ever reach the lab. One such tool, “Solvify,” integrates extensive public datasets with Sanofi’s proprietary chemical data to predict solubility behavior. These predictions narrow the experimental space, guiding Solutron toward the most informative tests.

This feedback loop—prediction, experimentation, learning—creates what Walker describes as “self-sharpening tools.” Each cycle improves model accuracy, reduces wasted effort, and accelerates development timelines. Scientists and machines become collaborators, refining outcomes with every iteration.

As experimental throughput increases, so does data complexity. Advanced analytics platforms are transforming how results are interpreted. Sanofi’s Kinetic AI platform helps scientists understand reaction mechanisms, identify rate-limiting steps, and anticipate impurity formation. At the same time, large language models are simplifying interactions between researchers, robotic systems, and data environments, making it easier to design experiments and extract insights at scale.

From Discovery to Manufacturing Scale-Up

The journey from molecule discovery to patient use does not end in the lab. Scaling production for clinical trials and commercial manufacturing introduces a new level of complexity. Manufacturing at scale is not a simple multiplication of laboratory recipes; it requires re-engineering processes to ensure consistency, safety, and efficiency across much larger volumes.

Christian Airiau, Global Head of CMC Data Sciences and Global CMC Development, emphasizes the importance of AI-driven modeling during this transition. By applying predictive tools before large-scale production begins, teams can design processes that are robust from the outset. Although the models used during development differ from those deployed in manufacturing operations, the insights they generate are equally critical.

Digital Twins and Virtual Manufacturing

One of the most transformative advances in this phase is the adoption of process digital twins—virtual replicas of manufacturing equipment and chemical or biological processes. Similar to how digital twins are used in clinical research to simulate patient outcomes, process digital twins allow scientists to evaluate countless manufacturing scenarios without running physical experiments.

These models can identify early signals that a process requires optimization, test alternative conditions, and provide fine-grained control over production parameters. The result is a dramatic reduction in costly trial-and-error experimentation and a faster path to optimized manufacturing.

Real-Time Problem Solving Across Networks

As medicines approach commercialization, ensuring seamless technology transfer between internal teams and external manufacturing partners becomes essential. AI-enabled monitoring, predictive modeling, and generative tools support quality assurance, maximize output, and accelerate troubleshooting.

In one recent case, a partner preparing to manufacture its first batches of a Sanofi product encountered unexpected issues. Rather than replicating the problem through months of physical testing, a Sanofi scientist used a digital process twin to analyze the partner’s real-world data. Within days, the model identified the root cause, tested corrective actions virtually, and refined equipment settings—demonstrating the power of digital approaches to resolve complex challenges rapidly.

Data as the Foundation of Transformation

Underlying every AI model and digital twin is data. Reliable, consistent, and accessible information across the entire CMC and manufacturing lifecycle is essential. Cross-functional teams are working to reduce data fragmentation across systems, equipment, and global sites, ensuring real-time access to high-quality data across the network.

This transformation is not merely about modernizing laboratories or factories. It is about building a connected, intelligent ecosystem capable of delivering innovative, high-quality medicines to patients faster than ever before. With decades of experience in manufacturing, Sanofi’s leaders see AI and digitalization not as incremental improvements, but as the foundation for a fundamentally new way of working—one that will define the future of pharmaceutical development and production.

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