48TWENTYFOURSEVENBIOPHARMA Issue 3 / October 2025 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 or acid-reducing drug interactions. For example, in developing posaconazole ASDs, PBBM was used to predict in vivo performance, guiding formulation strategies and dosing regimens. Regulatory agencies increasingly recognize PBBM data in IND applications, underscoring the importance of these models in accelerating clinical readiness. Lonza is at the forefront of deploying PBBM modelling tools such as Gastro+ in the CDMO industry guiding our customers decision and derisking their assets by ensuring appropriate drug exposure. Strategic benefits for biotech and pharma Integrating AI-enabled route scouting, HTE, Design2Optimize™, solid form screening and PBBM tools provides a holistic, data-driven foundation for drug development in early phase and forms the first wave of a digital design and optimisation toolbox being built at Lonza to enable our customers’ drug to reach the market faster. Key benefits include: - De-risking early development: Identifying optimal routes, formulations, and process parameters early reduces the likelihood of delays during scale-up. - Accelerating timelines: Faster process optimization and predictive modeling shorten the path to IND submission and clinical trials. - Cost efficiency: By selecting commercially viable routes and formulations early, developers reduce potential losses from late-stage failures. - Investor confidence: Robust, data-backed processes demonstrate maturity and reliability to potential investors and partners. Looking beyond process chemistry With the remarkable growth in AI and predictive tools, Lonza’s integrated approach is being extended well beyond chemistry optimization. In the optimisation area, the combination of highthroughput screening combined with learning tools like D2O, Bayesian optimisation or AI are driving a paradigm shift towards self-optimising platform in process development. In the technology transfer and manufacturing areas, digital twins of synthetic processes already allow exploration of hypothetical scenarios and process modifications before experimentation. Insights gained early inform later-stage development, such as scaleup and commercial manufacturing, ensuring that processes remain robust, scalable, and cost-effective. The ability to offer full digital technology transfer and manufacturing capabilities from lab to plant beyond current MES and LIMS system is also opening a wide range of opportunities for acceleration and optimisation of manufacturing of pharmaceuticals. Toward a data-driven development paradigm in early phase The convergence of AI-enabled route scouting, HTE, model-based process optimization, solid form science and PBBM, is transforming small-molecule drug development. By adopting a data-driven, integrated approach, drug developers can accelerate timelines, LONZA
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