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Pharmaceutical supply chains (PSCs) struggle with inventory management (IM) due to unpredictable demand patterns and variable lead times associated with restocking. This complexity is further compounded by the finite shelf lives of pharmaceutical products, which necessitate a delicate balance between adequate stock and minimal waste. These intertwined factors create a complex optimization problem that requires sophisticated inventory strategies to ensure both product availability and PSC efficiency. This study aims to develop an optimal inventory replenishment policy for pharmaceutical products that can handle the stochasticity arising from uncertain demand and variable PSC conditions. The objective is to maximize the profitability of the PSC while maintaining a high patient service level. We formulate the problem as a Markov decision process and propose a deep reinforcement learning (DRL) approach, specifically, a hybrid asynchronous advantage actor critic distributed proximal policy optimization (A3C DPPO)algorithm. The A3C DPPO algorithm is tailored to handle the continuous action space inherent in IM. The numerical results demonstrate that the proposed algorithm adaptively upda

Herd immunity is shaped not only by the infection capacity of a spreading epidemic or the contact structure of the hosting population, but also by how and under what circumstances individuals acquire immunity. Immunization strategies may interact with ongoing non-pharmaceutical interventions, which commonly aim to reduce social contact numbers. We demonstrate that these interactions can induce unexpectedly strong and counterintuitive effects on herd immunity. We explore these phenomena on spatially embedded contact networks and uncover a reversal in the relative effectiveness of disease- versus vaccine-induced immunization schemes, highlighting the average number of contacts as a critical determinant of emerging herd immunity. In sparse geometric networks with limited degree heterogeneity, uniform vaccination proves most effective; however, as average contact numbers increase, naturally acquired immunity ultimately becomes the better strategy. We show that this phenomenon may emerge not only in synthetic networks but also in real-world mixing networks, observed during non-pharmaceutical intervention periods across multiple states of the United States.

The survival of unorganized pharmacies is increasingly challenging in the face of growing competition from organized and e-pharmaceutical retail channels in emerging economies. A theoretical model is developed to capture the triple-channel interactions among unorganized, organized and e-retailing in emerging markets, taking into account the essential features of the pharmaceutical retail landscape, consumers, retailers and pharmaceutical products. Given the retailer and customer-specific factors, the price-setting game between the triple-channel retailers yielded the optimal prices for these retailers. The analysis found that the product category level demand has no influence on optimal pricing strategies of the retailers. The analysis also reveals counterintuitive results, for instance, (i) an increase in customer acceptance of unorganized retailers will result in a decrease in profits of both unorganized and organized retailers; (ii) as the distance and transportation cost to unorganized retailers increases for the consumers, the profit of the unorganized retailer increases; and (iii) consumers marginal utility of money has no influence on the optimal price, but have an influence

We present a Japanese domain-specific language model for the pharmaceutical field, developed through continual pretraining on 2 billion Japanese pharmaceutical tokens and 8 billion English biomedical tokens. To enable rigorous evaluation, we introduce three new benchmarks: YakugakuQA, based on national pharmacist licensing exams; NayoseQA, which tests cross-lingual synonym and terminology normalization; and SogoCheck, a novel task designed to assess consistency reasoning between paired statements. We evaluate our model against both open-source medical LLMs and commercial models, including GPT-4o. Results show that our domain-specific model outperforms existing open models and achieves competitive performance with commercial ones, particularly on terminology-heavy and knowledge-based tasks. Interestingly, even GPT-4o performs poorly on SogoCheck, suggesting that cross-sentence consistency reasoning remains an open challenge. Our benchmark suite offers a broader diagnostic lens for pharmaceutical NLP, covering factual recall, lexical variation, and logical consistency. This work demonstrates the feasibility of building practical, secure, and cost-effective language models for Japanes

Data scarcity in pharmaceutical research has led to reliance on labour-intensive trial-and-error approaches for development rather than data-driven methods. While Machine Learning offers a solution, existing datasets are often small and noisy, limiting their utility. To address this, we developed a Variationally Encoded Conditional Tabular Generative Adversarial Network (VECT-GAN), a novel generative model specifically designed for augmenting small, noisy datasets. We introduce a pipeline where data is augmented before regression model development and demonstrate that this consistently and significantly improves performance over other state-of-the-art tabular generative models. We apply this pipeline across six pharmaceutical datasets, and highlight its real-world applicability by developing novel polymers with medically desirable mucoadhesive properties, which we made and experimentally characterised. Additionally, we pre-train the model on the ChEMBL database of drug-like molecules, leveraging knowledge distillation to enhance its generalisability, making it readily available for use on pharmaceutical datasets containing small molecules, an extremely common pharmaceutical task. W

Controlling the size of powder particles is pivotal in the design of many pharmaceutical forms and the related manufacturing processes and plants. One of the most common techniques for particle size reduction in process industry is powder milling, whose efficiency relates to the mechanical properties of powder particles themselves. In this work, we first characterize the elastic and plastic response of different pharmaceutical powders by measuring their Young modulus, the hardness and the brittleness index via nano-indentation. Subsequently, we analyze the behavior of those powder samples during comminution via jet-mill at different process conditions. Finally, the correlation between single particle mechanical properties and milling process results is illustrated; the possibility to build a predictive model for powder grindability, based on nano-indentation data, is critically discussed

In this work we present, for the first time, a computational fluid dynamics tool for the simulation of the metered discharge in a pressurized metered dose inhaler. The model, based on open-source software, adopts the Volume-Of-Fluid method for the representation of the multiphase flow inside the device and a cavitation model to explicitly account for the onset of flashboiling upon actuation. Experimental visualizations of the flow inside the device and measurements of the mixture density and liquid and vapor flow rates at the nozzle orifice are employed to validate the model and assess the sensitivity of numerical results to modeling parameters. The results obtained for a standard device geometry show that the model is able to quantitatively predict several aspects of the dynamics and thermodynamics of the metered discharge. We conclude by showing how, by allowing to reproduce and understand the fluid dynamics upstream of the atomizing nozzle, our computational tool enables systematic design and optimization of the actuator geometry.

Many drugs have been withdrawn from the market worldwide, at a cost of billions of dollars, because of patient fatalities due to them unexpectedly disturbing heart rhythm. Even drugs for ailments as mild as hay fever have been withdrawn due to an unacceptable increase in risk of these heart rhythm disturbances. Consequently, the whole pharmaceutical industry expends a huge effort in checking all new drugs for any unwanted side effects on the heart. The predominant root cause has been identified as drug molecules blocking ionic current flows in the heart. Block of individual types of ionic currents can now be measured experimentally at an early stage of drug development, and this is the standard screening approach for a number of ion currents in many large pharmaceutical companies. However, clinical risk is a complex function of the degree of block of many different types of cardiac ion currents, and this is difficult to understand by looking at results of these screens independently. By using ordinary differential equation models for the electrical activity of heart cells (electrophysiology models) we can integrate information from different types of currents, to predict the effect

Many pharmaceutical companies face concerns with the maintenance of desired revenue levels. Sales forecasts for the current portfolio of products and projects may indicate a decline in revenue as the marketed products approach patent expiry. To counteract the potential downturn in revenue, and to establish revenue growth, an in-flow of new projects into the development phases is required. In this article, we devise an approach with which the in-flow of new projects could be optimized, while adhering to the objectives and constraints set on revenue targets, budget limitations and strategic considerations on the composition of the company's portfolio.

We present PharmaShip, a real-world Chinese dataset of scanned pharmaceutical shipping documents designed to stress-test pre-trained text-layout models under noisy OCR and heterogeneous templates. PharmaShip covers three complementary tasks-sequence entity recognition (SER), relation extraction (RE), and reading order prediction (ROP)-and adopts an entity-centric evaluation protocol to minimize confounds across architectures. We benchmark five representative baselines spanning pixel-aware and geometry-aware families (LiLT, LayoutLMv3-base, GeoLayoutLM and their available RORE-enhanced variants), and standardize preprocessing, splits, and optimization. Experiments show that pixels and explicit geometry provide complementary inductive biases, yet neither alone is sufficient: injecting reading-order-oriented regularization consistently improves SER and EL and yields the most robust configuration, while longer positional coverage stabilizes late-page predictions and reduces truncation artifacts. ROP is accurate at the word level but challenging at the segment level, reflecting boundary ambiguity and long-range crossings. PharmaShip thus establishes a controlled, reproducible benchmark

The present study considers the rural pharmaceutical retail sector in India, where the arrival of organized retailers and e-retailers is testing the survival strategies of unorganized retailers. Grounded in a field investigation of the Indian pharmaceutical retail sector, this study integrates primary data collection, consumer conjoint analysis and design of experiments to develop an empirically grounded agent-based simulation of multi-channel competition among unorganized, organized and e-pharmaceutical retailers. The results of the conjoint analysis reveal that store attributes of price discount, quality of products offered, variety of assortment, and degree of personalized service, and customer attributes of distance, degree of mobility, and degree of emergency are key determinants of optimal store choice strategies. The primary insight obtained from the agent-based modeling is that the attribute levels of each individual retailer have some effect on other retailers performance. The field-calibrated simulation also evidenced counterintuitive behavior that an increase in unorganized price discounts initially leads to an increase in average footprint at unorganized retailers, but

The pharmaceutical supply chain faces escalating cybersecurity challenges threatening patient safety and operational continuity. This paper examines the transformative potential of zero trust architecture for enhancing security and resilience within this critical ecosystem. We explore the challenges posed by data breaches, counterfeiting, and disruptions and introduce the principles of continuous verification, least-privilege access, and data-centric security inherent in zero trust. Real-world case studies illustrate successful implementations. Benefits include heightened security, data protection, and adaptable resilience. As recognized by researchers and industrialists, a reliable drug tracing system is crucial for ensuring drug safety throughout the pharmaceutical production process. One of the most pivotal domains within the pharmaceutical industry and its associated supply chains where zero trust can be effectively implemented is in the management of narcotics, high-health-risk drugs, and abusable substances. By embracing zero trust, the pharmaceutical industry fortifies its supply chain against constantly changing cyber threats, ensuring the trustworthiness of critical medica

Pharmaceutical research and development has accumulated vast and heterogeneous archives of data. Much of this knowledge stems from discontinued programs, and reusing these archives is invaluable for reverse translation. However, in practice, such reuse is often infeasible. In this work, we introduce DiscoVerse, a multi-agent co-scientist designed to support pharmaceutical research and development at Roche. Designed as a human-in-the-loop assistant, DiscoVerse enables domain-specific queries by delivering evidence-based answers: it retrieves relevant data, links across documents, summarises key findings and preserves institutional memory. We assess DiscoVerse through expert evaluation of source-linked outputs. Our evaluation spans a selected subset of 180 molecules from Roche's research and development repositories, encompassing over 0.87 billion BPE tokens and more than four decades of research. To our knowledge, this represents the first agentic framework to be systematically assessed on real pharmaceutical data for reverse translation, enabled by authorized access to confidential archives covering the full lifecycle of drug development. Our contributions include: role-specialized

Climate change is increasingly recognized as a driver of health-related outcomes, yet its impact on pharmaceutical demand remains largely understudied. As environmental conditions evolve and extreme weather events intensify, anticipating their influence on medical needs is essential for designing resilient healthcare systems. This study examines the relationship between climate variability and the weekly demand for respiratory prescription pharmaceuticals in Greece, based on a dataset spanning seven and a half years (390 weeks). Granger causality spectra are employed to explore potential causal relationships. Following variable selection, four forecasting models are implemented: Prophet, a Vector Autoregressive model with exogenous variables (VARX), Random Forest with Moving Block Bootstrap (MBB-RF), and Long Short-Term Memory (LSTM) networks. The MBB-RF model achieves the best performance in relative error metrics while providing robust insights through variable importance rankings. The LSTM model outperforms most metrics, highlighting its ability to capture nonlinear dependencies. The VARX model, which includes Prophet-based exogenous inputs, balances interpretability and accurac

This research employs Gaussian Process Regression (GPR) with an ensemble kernel, integrating Exponential Squared, Revised Matérn, and Rational Quadratic kernels to analyze pharmaceutical sales data. Bayesian optimization was used to identify optimal kernel weights: 0.76 for Exponential Squared, 0.21 for Revised Matérn, and 0.13 for Rational Quadratic. The ensemble kernel demonstrated superior performance in predictive accuracy, achieving an \( R^2 \) score near 1.0, and significantly lower values in Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). These findings highlight the efficacy of ensemble kernels in GPR for predictive analytics in complex pharmaceutical sales datasets.

Traceability, security and freshness monitoring are crucial to the food and pharmaceutical industries. Currently, barcodes and sensors are almost exclusively located on product packaging. Making them edible and introducing them into edible products could significantly enhance their functions. Here, several types of microlasers made entirely out of edible substances were developed. It is striking that olive oil already contains enough chlorophyll to be used as a laser when dispersed in water as droplets. The edible lasers can be embedded directly into edible products and serve as barcodes and sensors. Due to their much narrower spectral lines compared to fluorescent or color-changing sensors, they are significantly more sensitive to various environmental factors. The edible lasers were employed to sense sugar concentration, pH, the presence of bacteria, and exposure to too-high temperatures. They can also encode tens of data bits, such as manufacturer's information and expiration date. The microlasers are entirely safe for consumption, do not change the appearance and taste of food considerably, and are environmentally friendly. The developed barcodes and sensors could also be appli

The advent of 3D printing has transformed the pharmaceutical industry, enabling precision drug manufacturing with controlled release profiles, dosing, and structural complexity. Additive manufacturing (AM) addresses the growing demand for personalized medicine, overcoming limitations of traditional methods. This technology facilitates tailored dosage forms, complex geometries, and real-time quality control. Recent advancements in drop-on-demand printing, UV curable inks, material science, and regulatory frameworks are discussed. Despite opportunities for cost reduction, flexibility, and decentralized manufacturing, challenges persist in scalability, reproducibility, and regulatory adaptation. This review provides an in-depth analysis of the current state of AM in pharmaceutical manufacturing, exploring recent developments, challenges, and future directions for mainstream integration.

A stability chamber is essential for pharmaceutical facilities to test the stability and quality of products over time by exposing them to different environmental conditions. This paper introduces an IoT-enabled stability chamber designed for the pharmaceutical industry. We constructed four stability chambers by leveraging the existing infrastructure within a manufacturing facility. Each chamber is controlled using a state-of-the-art Proportional Integral Derivative (PID) system based on the Siemens S7-1200 PLC. The Siemens WinCC Runtime Advanced platform, compliant with FDA 21 CFR Part 11, was used for visualizing chamber data. Additionally, an Internet of Things (IoT) application was developed to remotely monitor sensor data through any client application. This research aims to enhance the performance of traditional stability chambers by integrating IoT functionalities, making them more cost-effective and user-friendly.

This study investigates the impact of significant health events on pharmaceutical stock performance, employing a comprehensive analysis incorporating macroeconomic and market indicators. Using Ordinary Least Squares (OLS) regression, we evaluate the effects of thirteen major health events since 2000, including the Anthrax attacks, SARS outbreak, H1N1 pandemic, and COVID-19 pandemic, on the pharmaceutical sector. The analysis covers different phases of each event beginning, peak, and ending to capture their temporal influence on stock prices. Our findings reveal distinct patterns in stock performance, driven by market reactions to the initial news, peak impact, and eventual resolution of these crises. We also examine scenarios with and without key macroeconomic (MA) and market (MI) indicators to isolate their contributions. This detailed examination provides valuable insights for investors, policymakers, and stakeholders in understanding the interplay between major health events and health market dynamics, guiding better decision-making during future health related disruptions.