Hasil untuk "Pharmaceutical industry"

Arefa Khan, Anamika Singh, S. Malviya et al.

This review provide an overview of the various documentation of quality management system, which includes deviations, OOS and CAPA. A detailed case study of deviations, out-of-Specification and CAPA generation is beneficial for improving pharmaceutical capabilities and understanding the documentation associated with a quality management system. It is essential for understanding deviations and out-of-spec in the pharmaceutical industry. The quality of medicines means that they meet the required specifications. The quality management system in the pharmaceutical industry is essential because the drugs or pharmaceutical products are delivered directly to the customer's body. Therefore, identity, purity, safety, and the quality of the products are critical. A Deviation can define as "a deviation from an approved instruction or established standard" The deviation process helps identify potential risks to product quality and patient safety and establish the root cause. Once the root cause identifies, appropriate corrective and preventive actions take to prevent reoccurrence. OOS defines as "A result that is outside the specifications or acceptance criteria established by the manufacturer or laboratory" As the industry moves to newer and more complicated products, quality control procedures must be in place to ensure consistent product quality. "CAPA defined by corrections.

J. DiMasi, H. Grabowski, R. Hansen

Jiang Wang, María Sánchez-Roselló, J. Aceña et al.

S. Paul, D. Mytelka, C. Dunwiddie et al.

I. Kola, John Landis

K. Campos, P. Coleman, J. Alvarez et al.

Synthetic innovation in drug development Chemical synthesis plays a key role in pharmaceutical research and development. Campos et al. review some of the advantages that have come from recent innovations in synthetic methods. In particular, they highlight small-molecule catalysts stimulated by visible light, enzymes engineered for versatility beyond their intrinsic function, and bio-orthogonal reactions to selectively modify proteins for conjugation. High-throughput techniques are also poised to accelerate methods optimization from small-scale discovery to large-scale production, and complementary machine-learning approaches are just coming into focus. Science, this issue p. eaat0805 BACKGROUND Over the past century, innovations in synthetic chemistry have greatly enabled the discovery and development of important life-changing medicines, improving the health of patients worldwide. In recent years, many pharmaceutical companies have chosen to reduce their R&D investment in chemistry, viewing synthetic chemistry more as a mature technology and less as a driver of innovation in drug discovery. Contrary to this opinion, we believe that excellence and innovation in synthetic chemistry continue to be critical to success in all phases of drug discovery and development. Moreover, recent developments in new synthetic methods, biocatalysis, chemoinformatics, and reaction miniaturization have the power to accelerate the pace and improve the quality of products in pharmaceutical research. Indeed, the application of new synthetic methods is rapidly expanding the realm of accessible chemical matter for modulating a broader array of biological targets, and there is a growing recognition that innovations in synthetic chemistry are changing the practice of drug discovery. We identify some of the most enabling recent advances in synthetic chemistry as well as opportunities that we believe are poised to transform the practice of drug discovery and development in the coming years. ADVANCES Over the past century, innovations in synthetic methods have changed the way scientists think about designing and building molecules, enabling access to more expansive chemical space and to molecules possessing the essential biological activity needed in future investigational drugs. In order for the pharmaceutical industry to continue to produce breakthrough therapies that address global health needs, there remains a critical need for invention of synthetic transformations that can continue to drive new drug discovery. Toward this end, investment in research directed toward synthetic methods innovation, furthering the nexus of synthetic chemistry and biomolecules, and developing new technologies to accelerate methods discovery is essential. One powerful example of an emerging, transformative synthetic method is the recent discovery of photoredox catalysis, which allows one to harness the energy of visible light to accomplish synthetic transformations on drug-like molecules that were previously unachievable. Furthermore, recent breakthroughs in molecular biology, bioinformatics, and protein engineering are driving rapid identification of biocatalysts that possess desirable stability, unique activity, and exquisite selectivity needed to accelerate drug discovery. Recent developments in the merging fields of synthetic and biosynthetic chemistry have sought to harness these molecules in three distinct ways: as biocatalysts for novel and selective transformations, as conjugates through innovative bio-orthogonal chemistry, and in the development of improved therapeutic modalities. The development of high-throughput experimentation and analytical tools for chemistry has made it possible to execute more than 1500 simultaneous experiments at microgram scale in 1 day, enabling the rapid identification of suitable reaction conditions to explore chemical space and accelerate drug discovery. Finally, advances in computational chemistry and machine learning in the past decade are delivering real impact in areas such as new catalyst design, reaction prediction, and even new reaction discovery. OUTLOOK These advances position synthetic chemistry to continue to have an impact on the discovery and development of the next generation of medicines. Key unsolved problems in synthetic chemistry with potential implications for drug discovery include selective saturation and functionalization of heteroaromatics; concise synthesis of highly functionalized, constrained bicyclic amines; and C-H functionalization for the synthesis of α,α,α-trisubstituted amines. Other areas, such as site-selective modification of biomolecules and synthesis of noncanonical nucleosides, are emerging as opportunities of high potential impact. The concept of molecular editing, whereby one could selectively insert, delete, or exchange atoms in highly elaborated molecules, is an area of emerging interest. Continued investment in synthetic chemistry and chemical technologies through partnerships between the pharmaceutical industry and leading academic groups holds great promise to advance the field closer to a state where exploration of chemical space is unconstrained by synthetic complexity and only limited by the imagination of the chemist, enabling the discovery of the optimal chemical matter to treat disease faster than ever before. Evolution of synthesis as a driver of innovation in drug discovery. Past, present, and future advances in synthetic chemistry are poised to transform the practice of drug discovery and development. Innovations in synthetic chemistry have enabled the discovery of many breakthrough therapies that have improved human health over the past century. In the face of increasing challenges in the pharmaceutical sector, continued innovation in chemistry is required to drive the discovery of the next wave of medicines. Novel synthetic methods not only unlock access to previously unattainable chemical matter, but also inspire new concepts as to how we design and build chemical matter. We identify some of the most important recent advances in synthetic chemistry as well as opportunities at the interface with partner disciplines that are poised to transform the practice of drug discovery and development.

M. Baumann, T. Moody, Megan Smyth et al.

Continuous flow manufacture is an innovative technology platform, which is gaining momentum within the pharmaceutical industry. The key advantages of continuous flow include faster and safer reacti...

P. Thorakkattu, Anandu Chandra Khanashyam, Kartik Shah et al.

Postbiotics are non-viable bacterial products or metabolic byproducts produced by probiotic microorganisms that have biologic activity in the host. Postbiotics are functional bioactive compounds, generated in a matrix during anaerobic fermentation of organic nutrients like prebiotics, for the generation of energy in the form of adenosine triphosphate. The byproducts of this metabolic sequence are called postbiotics, these are low molecular weight soluble compounds either secreted by live microflora or released after microbial cell lysis. A few examples of widely studied postbiotics are short-chain fatty acids, microbial cell fragments, extracellular polysaccharides, cell lysates, teichoic acid, vitamins, etc. Presently, prebiotics and probiotics are the products on the market; however, postbiotics are also gaining a great deal of attention. The numerous health advantages of postbiotic components may soon lead to an increase in consumer demand for postbiotic supplements. The most recent research aspects of postbiotics in the food and pharmaceutical industries are included in this review. The review encompasses a brief introduction, classification, production technologies, characterization, biological activities, and potential applications of postbiotics.

C. M. P. Freitas, J. Coimbra, V. G. Souza et al.

Pectin is a biocompatible polysaccharide with intrinsic biological activity, which may exhibit different structures depending on its source or extraction method. The extraction of pectin from various industrial by-products presents itself as a green option for the valorization of agro-industrial residues by producing a high commercial value product. Pectin is susceptible to physical, chemical, and/or enzymatic changes. The numerous functional groups present in its structure can stimulate different functionalities, and certain modifications can enable pectin for countless applications in food, agriculture, drugs, and biomedicine. It is currently a trend to use pectin to produce edible coating to protect foodstuff, antimicrobial bio-based films, nanoparticles, healing agents, and cancer treatment. Advances in methodology, use of different sources of extraction, and knowledge about structural modification have significantly expanded the properties, yields, and applications of this polysaccharide. Recently, structurally modified pectin has shown better functional properties and bioactivities than the native one. In addition, pectin can be used in conjunction with a wide variety of biopolymers with differentiated properties and specific functionalities. In this context, this review presents the structural characteristics and properties of pectin and information on the modification of this polysaccharide, its respective applications, perspectives, and future challenges.

F. Pognan, M. Beilmann, H. Boonen et al.

For decades, preclinical toxicology was essentially a descriptive discipline in which treatment-related effects were carefully reported and used as a basis to calculate safety margins for drug candidates. In recent years, however, technological advances have increasingly enabled researchers to gain insights into toxicity mechanisms, supporting greater understanding of species relevance and translatability to humans, prediction of safety events, mitigation of side effects and development of safety biomarkers. Consequently, investigative (or mechanistic) toxicology has been gaining momentum and is now a key capability in the pharmaceutical industry. Here, we provide an overview of the current status of the field using case studies and discuss the potential impact of ongoing technological developments, based on a survey of investigative toxicologists from 14 European-based medium-sized to large pharmaceutical companies. Investigative toxicology tools and strategies are used in pharmaceutical companies to reduce safety-related attrition in drug development. This Perspective article summarizes the key goals of investigative toxicology, highlights current approaches and discusses selected emerging technologies that have the potential to improve the current safety-testing paradigm.

Khizar Abbas, Muhammad Afaq, Talha Ahmed Khan et al.

From the last decade, pharmaceutical companies are facing difficulties in tracking their products during the supply chain process, allowing the counterfeiters to add their fake medicines into the market. Counterfeit drugs are analyzed as a very big challenge for the pharmaceutical industry worldwide. As indicated by the statistics, yearly business loss of around $200 billion is reported by US pharmaceutical companies due to these counterfeit drugs. These drugs may not help the patients to recover the disease but have many other dangerous side effects. According to the World Health Organization (WHO) survey report, in under-developed countries every 10th drug use by the consumers is counterfeit and has low quality. Hence, a system that can trace and track drug delivery at every phase is needed to solve the counterfeiting problem. The blockchain has the full potential to handle and track the supply chain process very efficiently. In this paper, we have proposed and implemented a novel blockchain and machine learning-based drug supply chain management and recommendation system (DSCMR). Our proposed system consists of two main modules: blockchain-based drug supply chain management and machine learning-based drug recommendation system for consumers. In the first module, the drug supply chain management system is deployed using Hyperledger fabrics which is capable of continuously monitor and track the drug delivery process in the smart pharmaceutical industry. On the other hand, the N-gram, LightGBM models are used in the machine learning module to recommend the top-rated or best medicines to the customers of the pharmaceutical industry. These models have trained on well known publicly available drug reviews dataset provided by the UCI: an open-source machine learning repository. Moreover, the machine learning module is integrated with this blockchain system with the help of the REST API. Finally, we also perform several tests to check the efficiency and usability of our proposed system.

Kampanart Huanbutta, K. Burapapadh, P. Kraisit et al.

The advent of artificial intelligence (AI) has catalyzed a profound transformation in the pharmaceutical industry, ushering in a paradigm shift across various domains, including drug discovery, formulation development, manufacturing, quality control, and post-market surveillance. This comprehensive review examines the multifaceted impact of AI-driven technologies on all stages of the pharmaceutical life cycle. It discusses the application of machine learning algorithms, data analytics, and predictive modeling to accelerate drug discovery processes, optimize formulation development, enhance manufacturing efficiency, ensure stringent quality control measures, and revolutionize post-market surveillance methodologies. By describing the advancements, challenges, and future prospects of harnessing AI in the pharmaceutical landscape, this review offers valuable insights into the evolving dynamics of drug development and regulatory practices in the era of AI-driven innovation.

Suyash Mishra, Qiang Li, Srikanth Patil et al.

Vision Language Models (VLMs) have shown strong performance on multimodal reasoning tasks, yet most evaluations focus on short videos and assume unconstrained computational resources. In industrial settings such as pharmaceutical content understanding, practitioners must process long-form videos under strict GPU, latency, and cost constraints, where many existing approaches fail to scale. In this work, we present an industrial GenAI framework that processes over 200,000 PDFs, 25,326 videos across eight formats (e.g., MP4, M4V, etc.), and 888 multilingual audio files in more than 20 languages. Our study makes three contributions: (i) an industrial large-scale architecture for multimodal reasoning in pharmaceutical domains; (ii) empirical analysis of over 40 VLMs on two leading benchmarks (Video-MME and MMBench) and proprietary dataset of 25,326 videos across 14 disease areas; and (iii) four findings relevant to long-form video reasoning: the role of multimodality, attention mechanism trade-offs, temporal reasoning limits, and challenges of video splitting under GPU constraints. Results show 3-8 times efficiency gains with SDPA attention on commodity GPUs, multimodality improving up to 8/12 task domains (especially length-dependent tasks), and clear bottlenecks in temporal alignment and keyframe detection across open- and closed-source VLMs. Rather than proposing a new "A+B" model, this paper characterizes practical limits, trade-offs, and failure patterns of current VLMs under realistic deployment constraints, and provide actionable guidance for both researchers and practitioners designing scalable multimodal systems for long-form video understanding in industrial domains.

Jenny Devenport, Tobias Mielke, Mouna Akacha et al.

Research and Development is the largest budget position in the pharmaceutical industry, with clinical trials being a critical, yet costly and time-consuming component to inform decisions. Beyond drug efficacy, the probability of success and efficiency of research and development are highly dependent on the approaches used for designing, analyzing, and interpreting clinical trials. Deep understanding of statistical methodology and quantitative approaches is therefore essential. Consequently, dedicated methodology groups have emerged in mid-size and large pharmaceutical companies and CROs. Their remit is to lead the conception and implementation of innovative quantitative methodologies in order to improve drug development, often by addressing complexities or offering more efficient designs. To achieve this, they collaborate internally and externally (e.g., with academics, regulators) to identify common challenges and tear down silos in order to invest in methods with the highest impact on efficiency and value to the portfolio. Given the immense financial stakes of drug development -- where delays carry massive implications -- these groups represent a critical strategic investment. However, to realize this business impact, statistical innovations must be rigorously validated and seamlessly integrated. This manuscript explores the setup, remit, and value of dedicated methodology groups, alongside the critical organizational considerations and success factors required to maximize their impact on the speed, efficiency, and probability of success.

Yanqing Xie, Wenjing Zhang

IntroductionThe Consistency Evaluation Policy of Generic Drugs is a major quality-oriented regulatory reform in China’s pharmaceutical manufacturing industry. Whether and how this policy facilitates the integration of the innovation chain and the industrial chain at the enterprise level remains insufficiently examined. This study evaluates the policy effect and investigates potential mechanisms.MethodsThis study used panel data on A-share listed pharmaceutical enterprises from 2013 to 2023. Enterprises were treated as the micro-level carriers of both the innovation chain and the industrial chain, and a enterprise-level index was constructed to measure their integration. A difference-in-differences (DID) design was employed to estimate the impact of the Consistency Evaluation Policy of Generic Drugs. Mechanism analyses focused on government subsidies and market concentration, and heterogeneity was assessed by market demand and total factor productivity (TFP).ResultsThe Consistency Evaluation Policy of Generic Drugs significantly promoted the integration of the innovation chain and the industrial chain. Mechanism tests suggested that the effect operated through two channels: increased government subsidies and higher market concentration. The positive effect was stronger among enterprises facing larger market demand. Moreover, the effect was significant for enterprises with higher TFP, while it was not statistically significant for enterprises with lower TFP.DiscussionThese findings suggest that policy implementation can be strengthened by (1) improving the depth and precision of the Consistency Evaluation Policy of Generic Drugs, (2) enhancing the targeting of government subsidies and supporting an appropriate degree of industry concentration where warranted, and (3) adopting differentiated guidance to stimulate enterprise vitality through multiple measures.

L. Belkhir, A. Elmeligi

Abstract Despite the heightened urgency of curbing carbon emissions around the world, the healthcare sector in general, and the pharmaceutical sector in particular have received very little attention from the sustainability community in terms of their contribution to the global carbon footprint. In this paper, we conduct an analysis of the overall contributions and the historical emissions trends of the pharmaceutical sector, as well as an industry-specific comparative analysis of the major pharmaceutical companies in the world. Surprisingly, our analysis reveals that the pharmaceutical industry is significantly more emission-intensive than the automotive industry. We also use a previously published mathematical framework linking national target emissions to the target emission intensity of the pharmaceutical sector to derive the emission intensity of the pharmaceutical sector required for the US to meet its reductions commitments per the now defunct Obama-administration commitments at the 2015 Paris Agreement. We identify the excess emitters among the top-15 Pharmaceutical companies, from those that are leading the pack with their emissions improvement efforts. The results are quite instructive as we find a far greater variability amongst the Top-15 pharmaceuticals than the Top-10 automotive companies, suggesting a very disparate set of environmental practices within the industry. The paper should elicit further in-depth studies of the environmental performance of the pharmaceutical sector and help inform policy makers, business leaders and academicians on how to help curb this unwarranted level of emissions in this important and growing industry sector.

Ruirui Ma, Juan Li, Ping Zeng et al.

With the advancement in membrane technology, membrane separation technology has been found increasingly widespread applications in the pharmaceutical industry. It is utilized in drug separation and purification, wastewater treatment, and the recycling of wastewater resources. This study summarizes the application history of membrane technology in the pharmaceutical industry, presents practical engineering examples of its applications, analyzes the various types of membrane technologies employed in the pharmaceutical sector, and finally, highlights the application cases of renowned international and Chinese membrane technology companies in the pharmaceutical field.

Xiaochen Zheng, Alvaro Serra, Ilya Schneider Chernov et al.

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 agent designs aligned with scientist workflows; human-in-the-loop support for reverse translation; expert evaluation; and a large-scale demonstration showing promising decision-making insights. In brief, across seven benchmark queries, DiscoVerse achieved near-perfect recall ($\geq 0.99$) with moderate precision ($0.71-0.91$). Qualitative assessments and three real-world pharmaceutical use cases further showed faithful, source-linked synthesis across preclinical and clinical evidence.

Georg Rottenwalter, Marcel Tilly, Victor Owolabi

Machine learning is an essential tool for optimizing industrial quality control processes. However, the complexity of machine learning models often limits their practical applicability due to a lack of interpretability. Additionally, many industrial machines lack comprehensive sensor technology, making data acquisition incomplete and challenging. Explainable Artificial Intelligence offers a solution by providing insights into model decision-making and identifying the most relevant features for classification. In this paper, we investigate the impact of feature reduction using XAI techniques on the quality classification of injection-molded parts. We apply SHAP, Grad-CAM, and LIME to analyze feature importance in a Long Short-Term Memory model trained on real production data. By reducing the original 19 input features to 9 and 6, we evaluate the trade-off between model accuracy, inference speed, and interpretability. Our results show that reducing features can improve generalization while maintaining high classification performance, with an small increase in inference speed. This approach enhances the feasibility of AI-driven quality control, particularly for industrial settings with limited sensor capabilities, and paves the way for more efficient and interpretable machine learning applications in manufacturing.

Gary R. Mirams

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 on whole heart cells and subsequent risk of side effects. The resulting simulations can provide a more accurate summary of the risk of a drug earlier in development and hence more cheaply than the pre-existing approaches.