LONZA Accelerating development through experimentation and data Once potential synthetic routes are identified, researchers must optimize process chemistry. High-throughput 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 trial-and-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 smallscale 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 multiobjective 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. Salts or crystal coformers are, for example, often used to improve the bioavailability of poorly soluble compounds. However, the robust selection of the appropriate solid state requires extensive solid form screening, including evaluation of stability, solubility, polymorphism, etc. Failures at this stage can lead to significant delays, as evidenced historically by drugs like ritonavir, where solid-state issues required costly reformulation and regulatory approval. Lonza’s approach to solid form screening integrates automated workflows and high-throughput capabilities, allowing multiple solid forms and formulations to be assessed simultaneously. This comprehensive strategy ensures that the most promising candidates are identified early, reducing downstream risk and accelerating the path toward clinical development. In particular, Lonza has recently enhanced its toolbox with a new AI enhanced conformer selection tool enabling the identification of the most likely co-formers for any API. This predictive approach can significantly reduce the number of experiments by guiding screen design and enhance probably of success of identify of suistable solid state form of the API in record time. Understanding behavior in the body: Physiologically Based Biopharmaceutics Modeling (PBBM) Once a stable and bioavailable solid form has been selected, understanding how the molecule behaves in vivo is the next step. PBBM offers a powerful predictive tool to simulate how a drug will distribute, metabolize, and eliminate in humans. Unlike traditional PK studies, PBBM integrates physiological, chemical, and biochemical data to predict drug behavior across different populations, including pediatric and elderly patients, and under varied dosing conditions. By incorporating absorption, distribution, metabolism, and excretion (ADME) data, PBBM can identify potential challenges early, such as food-drug 21 DAILY NEWS CPHI 2025 FRANKFURT DAY 1 | 28th October 2025
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