47 TWENTYFOURSEVENBIOPHARMA Issue 3 / October 2025 LONZA but also for process feasibility and economic viability beyond the lab. Predictive insights into pricing and supply chain reliability enable researchers to anticipate potential disruptions due to geopolitical tensions, natural disasters, or raw material shortages, mitigating risk before scale-up. By incorporating these tools early, emerging biotech companies can increase readiness for IND (Investigational New Drug) applications. Evaluating routes comprehensively before clinical and commercial manufacturing also minimizes risks during later-stage process development. Accelerating development through experimentation and data Once potential synthetic routes are identified, researchers must optimize process chemistry. Highthroughput experimentation (HTE) allows many reactions to be conducted in parallel under varying conditions, providing rapid insights into optimal reagents, solvents, and reaction parameters. This approach reduces time, effort, and resource requirements compared to traditional iterative trialand-error methods. HTE requires specialized expertise, robotics, and analytical instruments. Lonza’s dedicated robotics system enables automated, round-the-clock experimentation across a wide range of reaction conditions. By conducting small-scale reactions in 96-well plates and scaling promising options to 20 mL, researchers gain comprehensive insights into reaction kinetics and scalability. For highly potent APIs, specialized glovebox and purge systems allow safe experimentation under low-moisture, lowoxygen conditions. HTE accelerates the development of robust synthetic routes while mitigating scale-up risks, in particular when combined with AI route scouting. Statistical design of experiments and Design2Optimize™ Optimizing process chemistry requires understanding how multiple variables interact. Traditional methods of changing one variable at a time fail to capture these interactions. Statistical Design of Experiments (DoE) is a well-established method that provides a structured approach to evaluate multiple parameters simultaneously, identifying critical process parameters (CPPs) that may influence yield, purity, and stability. Lonza’s proprietary Design2Optimize™ platform extends DoE principles by integrating existing experimental data with physicochemical modeling. This reduces the number of experiments required to optimize reactions while providing predictive insights into complex chemistries. Researchers can model the impact of temperature, pH, and other parameters, creating a digital twin of the synthetic process. This enables exploration of hypothetical scenarios, such as adjusting process conditions to maximize yield, but also the multi-objective optimisation of parameters vs multiple targets without additional experimental work (e.g. aiming to achieve a sweet spot for yield and cycle time). Solid form screening Solid form selection is a critical early-phase decision, often sitting on the critical path to IND with profound downstream implications. The physical form of an active pharmaceutical ingredient (API), including salts, polymorphs, hydrates, solvates, and amorphous solid dispersions (ASDs) can impact solubility, stability, and bioavailability. Early identification of the optimal solid form ensures consistent performance, regulatory compliance, and intellectual property protection.
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