Digital and artificial intelligence (AI)-enabled tools are increasingly being applied across pharmaceutical Chemistry, Manufacturing and Controls (CMC) activities, yet adoption, benefits and regulatory barriers remain incompletely characterised. The Digital CMC Centre of Excellence in Regulatory Science and Innovation (CERSI), led by the University of Strathclyde and supported by the UK Medicines and Healthcare products Regulatory Agency (MHRA), conducted a survey to assess the landscape and inform priorities. A survey was distributed to industry stakeholders, including innovator and generic manufacturers, non-prescription medicine manufacturers, and contract development and manufacturing organisations. Responses were analysed descriptively to evaluate adoption patterns, drivers, barriers, perceptions and support needs. Most respondents reported experience with digital CMC tools, and many reported experiences with AI-enabled tools, across small and large molecule drug substance (DS) and drug product (DP) development. Adoption was more prevalent in small molecule applications, with greater implementation in drug product than drug substance. Fewer than 15% of tools had been included in submissions. Key drivers included process robustness, efficiency, time to market and cost reduction. Principal challenges included culture change, skills gaps and uncertainty regarding regulatory expectations, particularly for AI-enabled applications and evidence requirements for submissions and inspections. Respondents generally agreed that identifiable patient data are not used in Quality/GMDP AI applications, but there was no consensus that current regulatory frameworks fully address AI governance principles. Strong support was expressed for harmonised guidance, training and dialogue. Digital CMC tool adoption is increasing but remains uneven; greater regulatory clarity, harmonisation and collaboration are critical for scalable implementation.

The pharmaceutical industry is undergoing a rapid digital transformation. These changes are reshaping drug substance and drug product development and manufacturing, creating both opportunities and challenges that span pharmaceutical sciences, data science, and automation engineering. However, significant skills gaps persist, limiting the sector's ability to fully leverage digital technologies and sustain innovation. This paper explores the shifting digital and data science skills needs within the pharmaceutical industry, with a focus on industrial pharmacy and pharmaceutical sciences in the UK and Europe. We examine the key technological transformations reshaping the sector, the evolution of job roles, and the attributes required of the future workforce. Building on these insights, we propose an integrated approach to skills development that spans the entire career lifecycle - from embedding digital competencies in higher education to supporting lifelong learning through flexible, industry-aligned continuing professional development. Addressing these skills gaps requires coordinated action from academia, industry, and policymakers. By fostering a collaborative, interdisciplinary, and adaptive learning ecosystem, the pharmaceutical sector can equip its workforce to thrive in a rapidly changing landscape and continue improving global health and wellbeing.

Abstract Advances in drug discovery and clinical research have shifted the bottleneck in medicines development to chemistry, manufacturing, and controls activities, a critically step for regulatory approval. This includes formulation and process development of a new drug product, which traditionally requires extensive resources, often leading to suboptimal outcomes. These development processes must adapt to follow the advances in drug discovery and clinical research and ultimately shorten timelines while ensuring product quality and safety. In this work, we present an integrated platform for tablet formulation and process development that couples a digital formulator, an in-silico optimisation tool using a predictive material-to-tablet model, with a self-driving tableting data factory, which applies Bayesian optimisation within an automated, fully integrated per-tablet manufacturing to testing workflow. The results demonstrate a reduction in the time from material characterisation to in-specification tablets to 6 h and a reduction in API material use by 65% compared to current state-of-the-art methods.

Tensile strength loss during scale-up is commonly attributed to strain rate sensitivity, where shorter dwell times are assumed to weaken tablets. Since compression speed and feed frame paddle speed are typically increased together in rotary presses, decoupling their individual impact on tensile strength remains challenging. This study decoupled their impact using a compaction simulator. Materials with different deformation behaviours were studied. Tablets for each material were compressed at two dwell times (lab- and industrial-scale) across different feed frame speeds. Matched in-die porosity was maintained for each material across all conditions to ensure similar in-die densification, and a machine-independent porosity-tensile strength approach was used for analysis. In unlubricated excipients (with and without die-wall lubrication), reducing dwell time from 150 to 15ms showed no reduction in tensile strength; however, hard-brittle dibasic calcium phosphate showed a slight increase. Upon internal lubrication with different magnesium stearate loads, only starch showed a reduction in tensile strength, driven by lubrication and increased elastic recovery at shorter dwell time. Unexpectedly, feed-frame shear at a fixed 1% lubrication load resulted in greater strength loss than increasing lubrication loads for viscoelastic, plastic, and brittle materials. This study reveals that dwell time effects were limited, with only lubricated starch showing a reduction, whereas feed frame shear induced lubrication plays a major role in tensile strength loss during scale-up. These findings enable formulators to implement feed frame focused strategies that maintain tensile strength and support reliable scale-up.

This study presents an integrated approach utilising optical coherence tomography (OCT) to investigate the drug release mechanisms of hot-melt extruded (HME) amorphous solid dispersions (ASDs) of ritonavir and Soluplus®. Ritonavir-Soluplus® extrudates were prepared via HME using a twin-screw extruder and characterized during dissolution using a custom-designed 3D-printed flow cell, which enabled in-situ OCT imaging and continuous UV-vis monitoring of drug release. OCT provided high-resolution, time resolved visualization of structural transformations within the dissolving extrudates, while UV-vis spectroscopy quantified active pharmaceutical ingredient (API) release kinetics. Results revealed a multiphase dissolution mechanism involving sequential surface film formation, polymer swelling, delamination, and erosion. Increasing drug loading (10-30% w/w) produced marked effects on dissolution behaviour: formulations above 14% exhibited delayed release onset, extended swelling phases, and reduced overall release efficiency. OCT data showed that drug loadings above 14% led to slower erosion rates, greater swelling, and prolonged structural integrity, correlating with delayed UV-vis release profiles. Image processing using a machine learning segmentation model enabled quantitative extraction of sample cross-sectional area, confirming load-dependent swelling and erosion dynamics. Together, these findings establish a mechanistic link between structural evolution and release kinetics in HME ASDs and demonstrate the capability of OCT to provide real-time, non-destructive insight into solid dosage form dissolution. This methodology offers a powerful framework for optimizing ASD formulations and enhancing the predictive understanding of drug release mechanisms.

Understanding how active pharmaceutical ingredients (APIs) inuence tablet disintegration remains essential for optimising oral drug product performance. In this work, we present an integrated, data-driven framework that combines deep-learning based image segmentation, automated regime detection and mechanistic modelling to characterise liquid absorption and swelling behaviours from sessile drop experiments. Using this workflow, we investigated the effect of incorporating griseofulvin into directly compressed tablets containing a variety of excipient combinations. Across all formulations, the presence of API significantly reduced wettability and maximum swelling size, consistent with replacing hygroscopic excipients with a poorly soluble, hydrophobic drug. Bayesian hierarchical modelling confirmed credible decreases inswelling capacity for all formulations and formulation-dependent changes in initial swelling rate. Notably, MCC/lactose tablets exhibited faster initial swelling and shorter disintegration times despite reduced porosity, suggesting API-induced microstructural changes that enhance access of liquid to the swellable excipients. Overall, the proposed framework provides a rapid (<6 min), mechanistic and minimally subjectivemethod for probing formulation-dependent disintegration behaviour. Its ability to sensitively detect API-induced changes in absorption and swelling highlights its potential as a high-throughput decision tool to inform formulation screening and anticipate impacts on disintegration and dissolution performance.

Developing directly-compressed formulations remains a resource-intensive task, requiring substantial experimental effort to characterise the compressibility and compactability of a formulation space. This study extends a global optimisation of mixture rules to a ternary formulation space (API-brittle filler-elastic filler) and investigates the potential for reducing experimental burden whilst maintaining the predictive accuracy of empirical compression and compaction models. Three grades each of paracetamol and ibuprofen, combined with a consistent placebo base, were used to evaluate the approach. The global optimisation outperformed the traditional line of best fit approach, achieving strong predictive performance for the Kawakita model (R2>0.94; RMSE<0.01) and more variable fits for the Ryshkewitch-Duckworth model (R2 = 0.93 - 0.95 and RMSE = 0.23 - 0.39 MPa for paracetamol; R2 = 0.70 - 0.83 and RMSE = 0.31 - 0.37 MPa for ibuprofen). The optimisations performance was found to improve when the training dataset considered only drug-loaded blends. The exploration of reducing experimental burden considered a Model-Based Design of Experiments (MBDoE) which was benchmarked against random experiment selection. Integrating MBDoE with optimised mixture rules reduced API consumption by over 30% across all formulations, with median savings of 75%-95% under Acceptable and Good performance thresholds. Savings decreased with increasing threshold stringency, with the greatest variability observed in ibuprofen formulations. The Kawakita model supported reductions across all threshold levels, whilst the Ryshkewitch-Duckworth model showed limited capacity beyond the Acceptable threshold. The MBDoE did not outperform the random selection of experiments, however the optimisation framework for populating empirical compression and compaction models offers a resource-efficient approach to predicting tablet porosity and tensile strength of ternary API loaded blends.

Physical stability is a critical aspect of tablet formulation, with excipients significantly influencing long-term tablet performance, especially under humid conditions. This study investigated storage-induced changes in increasingly complex tablet formulations by sequentially incorporating croscarmellose sodium (CCS), magnesium stearate (MgSt), and lactose into microcrystalline cellulose (MCC)-based tablets prepared at different porosities and stored at 50°C/75% RH for up to 42 days. Storage induced changes occurred through two distinct stages. The first stage was governed by moisture uptake, with most sorption occurring within the first day of storage, resulting in tablet swelling and tensile strength reductions of 16-51% depending on formulation and porosity. The rate of these structural changes was strongly correlated with sorption kinetics. The second stage occurred after moisture uptake had largely stabilised and was characterised by continued changes in liquid penetration and swelling behaviour (quantified by sessile drop analysis), and disintegration performance. These later changes were highly formulation dependent, with MgSt containing formulations showing the greatest deterioration, where disintegration times increased from approximately 40 s to 500 s after storage. DVS measurements successfully predicted moisture uptake and tensile strength evolution across formulations and porosity levels. The results demonstrate that tablet stability is governed by a sequence of moisture driven structural changes followed by slower performance related changes, providing a practical framework for predicting stability assessment and formulation development from short-term measurements.

The transformation of anhydrous tablet excipients into their hydrated forms occurs simultaneously with film coating dissolution, yet the kinetics of this process remain poorly understood. Water molecules diffuse through the immediate release film coating and form hydrogen bonds with the anhydrous components within the tablet core, creating a thermodynamic driving force that competes with the hydration of the coating polymer. This study investigates the influence of film coating properties, including the base polymer (polyvinyl alcohol versus hydroxypropyl methylcellulose), coating thickness, and coating density, alongside tablet core properties such as porosity and excipient type. Using polymer-coated tablets containing anhydrous lactose or magnesium sulphate, we demonstrate that terahertz pulsed imaging (TPI) captures this diffusion process non-destructively. We characterise the anhydrous-to-hydrate transformation based on subtle refractive index changes at the interface. We identify distinct hydration mechanisms for channel hydrates versus ion-coordinated hydrates, providing further evidence that the core formulation actively regulates water ingress prior to coating failure. These findings offer critical insights for the predictive modelling of film-coated tablet disintegration and dissolution.

Traditional centralised manufacturing offers efficient economies and broad market reach but faces increasing limitations with the rise of complex products requiring rapid localised delivery and greater supply chain resilience. The logistics demands of hospital-compounded therapies expose vulnerabilities in existing infrastructure, accentuating the need for rigorous evaluation of alternative paradigms. This study investigates the comparative performance of centralised and decentralised pharmaceutical manufacturing models, applying an agent-based simulation framework designed for specialised or time-sensitive drug product orders. The work implements an agent-based simulation to model both centralised and decentralised scenarios using key structural, resource, and demand parameters identified within the supply chain ecosystem. Comparison criteria include labour requirements, sustainability (as measured by environmental emissions and operational efficiency), and end-to-end supply chain lead times, informed by the geospatial distribution of manufacturers, hospitals, and clinics. The centralised case considers a single facility supplying major hospitals and several clinics from a designated hub, while the decentralised case models multiple smaller production sites supplying care centres more directly. Demand frequency, emergency inventory buffers, personnel allocations, and practical constraints are explicitly built into the simulation inputs. Preliminary simulation results reveal trade-offs between manufacturing paradigms across multiple performance dimensions. The decentralised model shows potential advantages in reducing supply chain lead times and improving responsiveness to localised demand surges, while the centralised model demonstrates efficiency gains in resource utilisation under steady-state conditions. The framework enables quantitative comparison of throughput, cost implications, delivery timeliness, and system resilience under varied operational scenarios. These findings will inform strategic design decisions for patient-centric and resilient pharmaceutical supply chains, facilitating adoption of flexible models capable of meeting modern healthcare delivery needs within the medicines manufacturing and supply ecosystem.

Assessing polymorphic form changes in active pharmaceutical ingredients (APIs) during the early stage is critical for selecting the best polymorphic form required for drug product development. However, this assessment can be constrained by limited API availability. Present methods to assess risk in industry relies on larger volume equipment such as a compaction simulator or texture analyser (TA) that requires larger quantities of API. In the present study, a diamond anvil cell (DAC) was used, that reduces the quantity of material even further to micrograms to investigate the impact of pressure using Hydrochlorothiazide (HCT) as a model API. The powdered API was directly loaded into the DAC sample chamber without a pressure-transmitting medium (PTM), and Raman spectroscopy was used to monitor form changes. A polymorphic transition begins to be observed at 300 MPa pressure in the DAC that is commensurate with the findings in the TA (500 MPa) from Raman and X-ray powder diffraction (XRPD). XRPD analysis revealed that trituration likely causes a transformation back to the original phase as undisturbed samples can be stored for months. In conclusion, the study highlights the effectiveness of the DAC as a material sparing technique for assessing pressure induced polymorphic transition in APIs at the time of compression enabling a real-time monitoring of the process. The DAC successfully detected the polymorphic transition in tabletting compression range requiring significantly less material than the TA. The minimal consumption of API making DAC a preferred method for polymorphic screening particularly at the early stage of development when material availability is limited.

Disintegration and dissolution play a key role in drug release from oral immediate-release products. An improved understanding of these processes, the impact of process parameters and the critical material attributes are required to develop robust formulations and manufacturing processes. This study demonstrates an in-situ disintegration and dissolution monitoring system capable of capturing quantitative swelling and erosion data in a paddle dissolution apparatus. The system utilises optical coherence tomography integrated with a bathless direct heating vessel and overhead stirrer. A bespoke sample holder was developed to keep the tablets in place and allow swelling and erosion to be captured. Tablet swelling and erosion were captured and quantified for different paracetamol and ibuprofen formulations. For slowly disintegrating tablets (no disintegrant) consistent swelling that correlated with drug release was observed, with 10 % porosity tablets swelling up to 1000 µm in 2 min while 20 % porosity tablets swelled up to 1900 µm. For rapidly disintegrating tablets, phases of swelling and erosion dominated the disintegration process, the length and extent of which increased with increasing porosity. This new system has multiple academic and industrial applications, including identifying performance-controlling disintegration mechanisms in problem formulations and developing a deeper understanding of stability study results.

Terahertz time-domain spectroscopy (THz-TDS) is a vital tool for scientific and industrial analysis, however, many commonly analyzed products, such as those found in pharmaceutical, agriculture, and mining sectors, are produced as powders or granular materials, and these sample morphologies have been reported to produce anomalous spectral features which can often obscure known material resonances. The cause of these anomalous features has been poorly understood, making it difficult to predict their presence and limiting the applicability of THz-TDS for such materials. Here, we systematically study how the sample morphology of granular compacts produces anomalous spectral features by performing extensive experimental measurements on two-part powder compacts with varying microsphere size and concentration. Further, we employ ray-tracing simulations to identify the physical mechanism whereby these spectral features arise owing to variations in optical path length within the heterogeneous sample. We believe this is the first time that the physical cause of spurious spectral features within powder samples has been adequately explained and that a robust method has been presented for modeling this effect. By understanding these features, we propose that instead of being seen as a parasitic effect, their presence can be utilised to extract morphological properties of the samples, thereby enhancing the utility of THz-TDS for granular materials.

THz TDS is a vital tool in many industrial sectors, however commonly analyzed samples are often produced as powders or granular materials; these morphologies have been demonstrated to produce anomalous spectral features which appear at unpredictable locations and can hamper spectral analysis. Here we systematically study the effects of sample morphology on the appearance of anomalous spectral features and present a physical mechanism to describe their behavior. Two-part powder compacts of PTFE and Borofloat glass are prepared at a range of microsphere sizes and concentrations for TDS measurement. Further, a computational model utilizing raytracing through heterogeneous material is developed, which is able to accurately predict the anomalous spectral features. By attributing a physical mechanism to this phenomenon, we are able to predict their occurrence, and utilize their presence to deduce sample morphology.

Pharmaceutical tablet formulation and process development, traditionally a complex and multi-dimensional decision-making process, necessitates extensive experimentation and resources, often resulting in suboptimal solutions. This study presents an integrated platform for tablet formulation and manufacturing, built around a Digital Formulator and a Self-Driving Tableting DataFactory. By combining predictive modelling, optimisation algorithms, and automation, this system offers a material-to-product approach to predict and optimise critical quality attributes for different formulations, linking raw material attributes to key blend and tablet properties, such as flowability, porosity, and tensile strength. The platform leverages the Digital Formulator, an in-silico optimisation framework that employs a hybrid system of models - melding data-driven and mechanistic models - to identify optimal formulation settings for manufacturability. Optimised formulations then proceed through the self-driving Tableting DataFactory, which includes automated powder dosing, tablet compression and performance testing, followed by iterative refinement of process parameters through Bayesian optimisation methods. This approach accelerates the timeline from material characterisation to development of an in-specification tablet within 6 hours, utilising less than 5 grams of API, and manufacturing small batch sizes of up to 1,440 tablets with augmented and mixed reality enabled real-time quality control within 24 hours. Validation across multiple APIs and drug loadings underscores the platform's capacity to reliably meet target quality attributes, positioning it as a transformative solution for accelerated and resource-efficient pharmaceutical development.

We present a shared industry-academic perspective on the principles and opportunities for Quality by Digital Design (QbDD) as a framework to accelerate medicines development and enable regulatory innovation for new medicines approvals. This approach exploits emerging capabilities in industrial digital technologies to achieve robust control strategies assuring product quality and patient safety whilst reducing development time/costs, improving research and development efficiency, embedding sustainability into new products and processes, and promoting supply chain resilience. Key QbDD drivers include the opportunity for new scientific understanding and advanced simulation and model-driven, automated experimental approaches. QbDD accelerates the identification and exploration of more robust design spaces. Opportunities to optimise multiple objectives emerge in route selection, manufacturability and sustainability whilst assuring product quality. Challenges to QbDD adoption include siloed data and information sources across development stages, gaps in predictive capabilities, and the current extensive reliance on empirical knowledge and judgement. These challenges can be addressed via QbDD workflows; model-driven experimental design to collect and structure findable, accessible, interoperable and reusable (FAIR) data; and chemistry, manufacturing and control ontologies for shareable and reusable knowledge. Additionally, improved product, process, and performance predictive tools must be developed and exploited to provide a holistic end-to-end development approach.

Physical stability testing is crucial in pharmaceutical development, requiring a thorough understanding of how moisture interacts with materials to ensure tablet integrity and performance. This study explores how tablet porosity and the inclusion of swelling excipients influence moisture uptake and stability across different length and time scales, from particle-level interactions to tablet behaviour and from short-term moisture sorption using dynamic vapour sorption (DVS) to long-term storage effects. Two tablet formulations were tested: pure MCC and MCC-CCS (8%), each with four different porosities. A predictive framework was developed to estimate porosity changes in stored tablets by accounting for particle swelling. The study highlights rapid structural changes, including reductions in tensile strength, bulk density, and porosity, which occur mostly within the first day of storage. By linking powder and tablet DVS data and establishing correlations between DVS and real-storage data, this study provides a framework for predicting moisture sorption behaviour in tablets over time. This approach enhances the predictability of tablet stability, reducing the need for extensive long-term storage studies and enabling faster, more reliable stability assessments. Ultimately, these findings provide a stronger foundation for optimising stability testing and formulation performance.

Particle size variations during manufacturing are common in many industries, including pharmaceuticals, construction & paints/emulsions. Monitoring particle sizes is crucial for adherence to product specifications and for quality control. In this work, we report the application of terahertz time-domain spectroscopy in transmission to non-destructively detect particle size changes in multicomponent granular compacts. These compacts consisted of borosilicate microspheres of various particle size distributions and concentrations suspended in a PTFE matrix. The method employs a simple power law and linear fitting (R2 = 0.99, RMSE = 0.01 cm 1) to terahertz scattering data of granular compacts, enabling the quantification of particle size changes. We discuss in detail the advantages and limitations of the technique and highlight its applicability for process monitoring.

Scattering in water-paraffin oil emulsions was studied by terahertz time-domain spectroscopy (THz-TDS) with the aim of estimating droplet size and concentration. The loss coefficients in water-oil emulsion systems were measured at very low concentrations of water (≤ 1% v/v) to establish the sensitivity of THz-TDS. As expected, the scattering losses increased linearly with water concentration. The scattering spectra exhibited a quadratic dependence on frequency, indicating that Mie scattering was the dominant scattering loss mechanism in this system.

Moisture sensitivity poses a challenge in formulating oral dosage forms, particularly when considering disintegrants' swelling due to prior moisture exposure, impacting performance and physical stability. This study utilises dynamic vapour sorption to simulate real-world storage scenarios, investigating the equilibrium moisture content and dynamics of eight commonly used excipients in oral solid dosage forms. A model was developed to determine the kinetic rate constant of moisture sorption and desorption for different storage conditions. Dynamic vapour sorption tests revealed that excipients with higher moisture-binding capacities showed slower equilibration to the target relative humidity (RH). Elevated temperatures accelerated the moisture sorption/desorption process for all excipients, reducing the equilibrated moisture content for most, except mannitol and lactose. Particle imaging over a 14-day accelerated storage period quantified swelling, indicating approximately 6% increase in particle diameter for croscarmellose sodium (CCS) and sodium starch glycolate (SSG), and a lesser 2.7% for microcrystalline cellulose (MCC), predominantly caused by the humidity. All excipients reached their swelling peak within the first day of storage, with permanent particle size enlargement for CCS and SSG, whereas MCC displayed a partial reversibility post-storage. Enhancing our understanding of excipients' stability and interaction with moisture and the resulting particle swelling contributes to the rational design of oral solid dosage formulations and promotes a better understanding of their long-term physical stability.