78 TWENTYFOURSEVENBIOPHARMA Issue 3 / October 2025 PHARMATECH ASSOCIATES Regulatory framework Global regulatory authorities have evolved from cautious observers to active promoters of predictive modelling. The FDA’s MIDD framework provides a clear pathway for model-based submissions, emphasising context of use definition, systematic validation and transparent documentation. Model credibility, defined as demonstrated trustworthiness for intended regulatory applications, is now assessed through established protocols rather than ad hoc review. The EMA and FDA co-lead joint MIDD initiatives are fostering cross-agency alignment, while ICH guidelines increasingly integrate model-based approaches across the product lifecycle. This regulatory evolution reflects a fundamental recognition: when properly validated and scientifically justified, predictive models are no longer experimental, they are regulatory enablers, which derisk evaluation by regulators. Success requires robust model credibility frameworks addressing data governance, validation protocols and uncertainty quantification. First-principle and mechanistic modelling offer significant advantages by embedding established scientific relationships directly into model structures, providing interpretable, causally-linked predictions that extend beyond available training data. These approaches align with ICH Q8(R2) and FDA continuous manufacturing guidance while supporting regulatory submissions with higher scientific justification. This maturation has been fuelled by three converging trends: the exponential growth in high-quality pharmaceutical data, the refinement of mechanistic and hybrid models and regulatory willingness to accept model-based justifications when built on scientifically sound and validated frameworks. AI and machine learning models are now routinely used for target identification, de novo molecule generation, high-throughput screening, formulation optimisation and synthetic route scouting. More critically, these models increasingly simulate complex biological and manufacturing systems supporting or replacing wet-lab experiments in areas where testing is expensive, slow or difficult to perform. In process development, digital twins can simulate manufacturing behaviour across scales, reducing the need for multiple process scale-up runs and increasing confidence in the final design and control space. In synthetic route design, i.e. the process of identifying optimal pathways from available starting materials to target molecules, AI tools can evaluate feasible approaches and prioritise them based on yield, cost, impurity profile and environmental impact well before laboratory work begins. These tools no longer operate as black boxes. There is growing emphasis on explainability, validation and contextual alignment, particularly in regulatory settings. Hybrid models that combine firstprinciple understanding with AI-driven pattern recognition enable higher confidence in predictive output, making it easier for sponsors to defend modelling results in regulatory submissions. The figure compares traditional experimental-first development with a predictive first-principles approach. The model-driven workflow shows reduced time and cost investment in preclinical, formulation and CMC phases; modelling is used to direct experimentation rather than follow it. Streamlining drug development Predictive modelling is transforming multiple critical domains, streamlining drug development and manufacturing to enable faster, more cost-effective and higher-quality outcomes. This revolution spans from the earliest stages of research and development to advanced manufacturing, generics formulation and chemistry, manufacturing, and controls (CMC) optimisation. As regulatory agencies, investors and manufacturers align around these capabilities, predictive modelling is shifting from a promising tool to a regulatory and commercial imperative and a foundational strategy for building competitive, capital-efficient pipelines. At the forefront of innovation, predictive modelling, powered by artificial intelligence and machine learning, is reshaping how new drug candidates are discovered and validated. By simulating molecular interactions and biological responses in silico, researchers can identify non-viable candidates early. The hope is as the data supporting these relationships continues to build, it will substantially reduce costly failures during later stages. While the opportunity here is to improve upon our industry’s woeful track record of only one in nine products making it to market. For drug sponsors and investors, the biggest opportunity for time and capital efficiency lies in applying modelling as you move down the drug development lifecycle. The adoption of in silico modelling across pharmaceutical R&D and manufacturing marks a fundamental shift in the approach to drug development. The biggest barrier often lies in the fact that the necessary expertise, systems and organisational capabilities do not typically reside within traditional drug development frameworks. A deliberate strategy is required to acquire, build or integrate the expertise and digital infrastructure needed to fully leverage modelling and predictive
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