77 TWENTYFOURSEVENBIOPHARMA Issue 3 / October 2025 PHARMATECH ASSOCIATES supports modelling to waive clinical studies, while ICH guidelines increasingly endorse model-based approaches for paediatric extrapolation and lifecycle management. This confluence of maturing regulatory frameworks and intensifying capital constraints is creating a ‘perfect storm’ that compels drug sponsors toward smarter, more efficient development pathways. Regulators’ increasing openness and, in some cases, encouragement for sponsors to use digital evidence in place of costly, time-consuming experiments is now a powerful catalyst. In this environment, advanced in silico modelling and predictive analytics are rapidly transitioning from optional tools to essential capabilities for competitive survival and scientific progress. By leveraging these technologies, drug developers can conserve capital, reduce program risk and accelerate achievement of the technical and regulatory milestones needed for sustained investor support and value creation. This convergence of scientific innovation and regulatory openness marks a turning point in how new therapies can be developed, scaled and approved worldwide. The economic imperative Traditional pharmaceutical development follows a costly, sequential experimental approach where each phase builds incrementally on the previous one. Process development alone can consume 18-24 months and $5-15 million or more, before reaching manufacturing readiness, with stability studies adding another 12-18 months of real-time data generation. In silico modelling fundamentally rebalances this equation. By shifting from experimental-first to predictive-first approaches, sponsors can achieve dramatic reductions in development burden across the entire lifecycle. In CMC and formulation phases, digital twins, process simulation and kinetic modelling can reduce experimental effort by 6070%. Early molecule design benefits from AI/ML platforms that eliminate trial-and-error work, delivering 50% or more time and cost reductions. Even preclinical and clinical phases see 30-40% efficiency gains through dose optimisation, toxicity prediction, and bridging strategies, while tech transfer and manufacturing achieve 50% + reductions through mechanistic simulations and real-time release strategies. These improvements concentrate experimentation where it adds maximum value while reducing effort where models provide faster, more cost-effective insights. Most critically, this enables sponsors to ‘fail faster’ and pivot earlier, unlocking significant capital efficiency in today’s constrained funding environment. Figure 1. Shift from Experimental-First to Predictive-First Development Model6,7,8,9,10 Note: Values shown reflect relative time and cost burden in each development phase. Predictive-first estimates are based on industry case studies, FDA pilot data, and published reductions in experimental workload due to in-silico and AI-driven approaches
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