Pharmaceutical Industry Research Articles

The Reporting of a Disproportionality Analysis for Drug Safety Signal Detection Using Individual Case Safety Reports in PharmacoVigilance (READUS-PV): Development and Statement.

Disproportionality analyses using reports of suspected adverse drug reactions are the most commonly used quantitative methods for detecting safety signals in pharmacovigilance. However, their methods and results are generally poorly reported in published articles and existing guidelines do not capture the specific features of disproportionality analyses. We here describe the development of a guideline (REporting of A Disproportionality analysis for drUg Safety signal detection using individual case safety reports in PharmacoVigilance [READUS-PV]) for reporting the results of disproportionality analyses in articles and abstracts. We established a group of 34 international experts from universities, the pharmaceutical industry, and regulatory agencies, with expertise in pharmacovigilance, disproportionality analyses, and assessment of safety signals. We followed a three-step process to develop the checklist: (1) an open-text survey to generate a first list of items; (2) an online Delphi method to select and rephrase the most important items; (3) a final online consensus meeting. Among the panel members, 33 experts responded to round 1 and 30 to round 2 of the Delphi and 25 participated to the consensus meeting. Overall, 60 recommendations for the main body of the manuscript and 13 recommendations for the abstracts were retained by participants after the Delphi method. After merging of some items together and the online consensus meeting, the READUS-PV guidelines comprise a checklist of 32 recommendations, in 14 items, for the reporting of disproportionality analyses in the main body text and four items, comprising 12 recommendations, for abstracts. The READUS-PV guidelines will support authors, editors, peer-reviewers, and users of disproportionality analyses using individual case safety report databases. Adopting these guidelines will lead to more transparent, comprehensive, and accurate reporting and interpretation of disproportionality analyses, facilitating the integration with other sources of evidence.

Developmental Formulation Principles of Food Preservatives by Nanoencapsulation-Fundamentals, Application, and Challenges.

The role of food additives is to preserve food by extending shelf life and limiting harmful microorganism proliferation. They prevent spoilage by enhancing the taste and safety of food by utilizing beneficial microorganisms and their antimicrobial metabolites. Current advances in food preservation and processing utilize green technology principles for green preservative formulation, enhancing nutrition and supplying essential micronutrients safely, while also improving quality, packaging, and food safety. Encapsulation is gaining attention for its potential to protect delicate materials from oxidative degradation and extend their shelf life, thereby ensuring optimal nutrient uptake. Nanoencapsulation of bioactive compounds has significantly improved the food, pharmaceutical, agriculture, and nutraceutical industries by protecting antioxidants, vitamins, minerals, and essential fatty acids by controlling release and ensuring delivery to specific sites in the human body. This emerging area is crucial for future industrial production, improving the sensory properties of foods like color, taste, and texture. Research on encapsulated bioactive compounds like bacteriocins, LAB, natamycin, polylysine, and bacteriophage is crucial for their potential antioxidant and antimicrobial activities in food applications and the food industry. This paper reviews nanomaterials used as food antimicrobial carriers, including nanoemulsions, nanoliposomes, nanoparticles, and nanofibers, to protect natural food antimicrobials from degradation and improve antimicrobial activity. This review discusses nanoencapsulation techniques for biopreservative agents like nisin, poly lysine, and natamycin, focusing on biologically-derived polymeric nanofibers, nanocarriers, nanoliposomes, and polymer-stabilized metallic nanoparticles. Nanomaterials, in general, improve the dispersibility, stability, and availability of bioactive substances, and this study discusses the controlled release of nanoencapsulated biopreservative agents.

BiSpec Pairwise AI: guiding the selection of bispecific antibody target combinations with pairwise learning and GPT augmentation.

Bispecific antibodies (BsAbs), capable of targeting two antigens simultaneously, represent a significant advancement by employing dual mechanisms of action for tumor suppression. However, how to pair targets to develop effective and safe bispecific drugs is a major challenge for pharmaceutical companies. Using machine learning models, we refined the biological characteristics of currently approved or in clinical development BsAbs and analyzed hundreds of membrane proteins as bispecific targets to predict the likelihood of successful drug development for various target combinations. Moreover, to enhance the interpretability of prediction results in bispecific target combination, we combined machine learning models with Large Language Models (LLMs). Through a Retrieval-Augmented Generation (RAG) approach, we supplement each pair of bispecific targets' machine learning prediction with important features and rationales, generating interpretable analytical reports. In this study, the XGBoost model with pairwise learning was employed to predict the druggability of BsAbs. By analyzing extensive data on BsAbs and designing features from perspectives such as target activity, safety, cell type specificity, pathway mechanism, and gene embedding representation, our model is able to predict target combinations of BsAbs with high market potential. Specifically, we integrated XGBoost with the GPT model to discuss the efficacy of each bispecific target pair, thereby aiding the decision-making for drug developers. The novelty of this study lies in the integration of machine learning and GPT techniques to provide a novel framework for the design of BsAbs drugs. This holistic approach not only improves prediction accuracy, but also enhances the interpretability and innovativeness of drug design.

DeepAVP-TPPred: identification of antiviral peptides using transformed image-based localized descriptors and binary tree growth algorithm.

Despite the extensive manufacturing of antiviral drugs and vaccination, viral infections continue to be a major human ailment. Antiviral peptides (AVPs) have emerged as potential candidates in the pursuit of novel antiviral drugs. These peptides show vigorous antiviral activity against a diverse range of viruses by targeting different phases of the viral life cycle. Therefore, the accurate prediction of AVPs is an essential yet challenging task. Lately, many machine learning-based approaches have developed for this purpose; however, their limited capabilities in terms of feature engineering, accuracy, and generalization make these methods restricted. In the present study, we aim to develop an efficient machine learning-based approach for the identification of AVPs, referred to as DeepAVP-TPPred, to address the aforementioned problems. First, we extract two new transformed feature sets using our designed image-based feature extraction algorithms and integrate them with an evolutionary information-based feature. Next, these feature sets were optimized using a novel feature selection approach called binary tree growth (BTG) Algorithm. Finally, the optimal feature space from the training dataset was fed to the Deep neural network (DNN) to build the final classification model. The proposed model DeepAVP-TPPred was tested using stringent 5-fold cross-validation and two independent dataset testing methods, which achieved the maximum performance and showed an enhanced efficiency over existing predictors in terms of both accuracy and generalization capabilities. https://github.com/MateeullahKhan/DeepAVP-TPPred. Supplementary data are available at Bioinformatics online.

Pharmaceutical exports and patents in India – a systems approach

Purpose The Indian pharmaceutical industry currently faces multiple challenges, including rising costs and slowing export growth, which in turn have limited its ability to expand presence in global canvas. Given the nature of sectoral dynamics, a pharmaceutical firm must undertake huge investments in R&D to introduce product innovation, in turn enhancing market share and sustaining profit streams. The development of novel medicines, confirmed by the granting of patent rights, provides a pharma company edge over its competitors. In addition, presence of innovator firms within the industry invigorates the sectoral value chain and raises efficiency. Hence, it is important to analyze whether granting patent rights enhances the exports of pharmaceutical products in the Indian context. Design/methodology/approach The current study explored this question using a simultaneous-equation framework. Specifically, the authors use the methods developed by Davidson and MacKinnon (1993) and Greene (2003) to obtain heteroscedasticity-consistent estimates. The time-series properties of the data were further probed, and robust estimates were used to test the theory. Methods developed by Baltagi (1981) have been used further to refine the authors’ estimations. Findings After controlling for relevant variables, it is observed that granting of patents caused a significantly positive impact on pharmaceutical exports. Furthermore, the change in the patent administration regime had a significant impact on patent fillings, which further impacted their exportability. Compared to patents granted patents filed had a higher impact on pharmaceutical exports. Originality/value This study attempts to apply the framework developed by Goldstein and Khan (1978) with necessary modifications to suit the context of a developing country. The application of the 3SLS method to estimate the export supply equation for pharmaceutical products is a novel approach to the research question in general and to the Indian context in particular. System autocorrelation and heteroscedasticity tests were performed to refine the results further.

Leveraging Language Model Multitasking To Predict C-H Borylation Selectivity.

C-H borylation is a high-value transformation in the synthesis of lead candidates for the pharmaceutical industry because a wide array of downstream coupling reactions is available. However, predicting its regioselectivity, especially in drug-like molecules that may contain multiple heterocycles, is not a trivial task. Using a data set of borylation reactions from Reaxys, we explored how a language model originally trained on USPTO_500_MT, a broad-scope set of patent data, can be used to predict the C-H borylation reaction product in different modes: product generation and site reactivity classification. Our fine-tuned T5Chem multitask language model can generate the correct product in 79% of cases. It can also classify the reactive aromatic C-H bonds with 95% accuracy and 88% positive predictive value, exceeding purpose-developed graph-based neural networks.

Pharma Supply Chain Management System Using Blockchain

A drug traceability system's objective is to track or trace a drug's movement along the drug supply chain, which is essential for both public drug security and the operations of pharmaceutical companies. Data integrity, privacy, system resilience, and adaptability expectations have not been met by traditional centralized server-client technology solutions. We have presented a whole new blockchain method for drug traceability. Compared to other solutions now on the market, this one is more secure and scalable. The proposed system can also efficiently prune its storage, producing a reliable and practical blockchain storage solution.

A Pilot, Predictive Surveillance Model in Pharmacovigilance Using Machine Learning Approaches.

The identification of a new adverse event (AE) caused by a drug product is one of the key activities in the pharmaceutical industry to ensure the safety profile of a drug product. Machine learning (ML) has the potential to assist with signal detection and supplement traditional pharmacovigilance (PV) surveillance methods. This pilot ML modeling study was designed to detect potential safety signals for two AbbVie products and test the model's capability of detecting safety signals earlier than humans. Drug X, a mature product with post-marketing data, and Drug Y, a recently approved drug in another therapeutic area, were selected. Gradient boosting-based ML approaches (e.g., XGBoost) were applied as the main modeling strategy. For Drug X, eight true signals were present in the test set. Among 12 potential new signals generated, four were true signals with a 50.0% sensitivity rate and a 33.3% positive predictive value (PPV) rate. Among the remaining eight potential new signals, one was confirmed as a signal and detected six months earlier than humans. For Drug Y, nine true signals were present in the test set. Among 13 potential new signals generated, five were true signals with a 55.6% sensitivity rate and a 38.5% PPV rate. Among the remaining eight potential new signals, none were confirmed as true signals upon human review. This model demonstrated acceptable accuracy for safety signal detection and potential for earlier detection when compared to humans. Expert judgment, flexibility, and critical thinking are essential human skills required for the final, accurate assessment of adverse event cases.

Molecular Assessments of Antimicrobial Protein Enterocins and Quorum Sensing Genes and Their Role in Virulence of the Genus Enterococcus.

Enterococcus has emerged as an opportunistic pathogen because of its antibiotic resistance and virulence profile, which makes it a causative agent of several diseases like endocarditis, surgical site, and urinary tract infections. Currently, species of this genus are the 2nd most frequently isolated microorganisms from hospital-acquired infections. Significant association with hospitals and unhygienic conditions of the environments has made them resistant to a wide range of antibiotics. On the brighter side, enterococci have the ability to produce antimicrobial proteins (i.e., enterocins) that exhibit wide antagonistic activity, thus making them useful microbes in the food and pharmaceutical industries. Enterocins are also involved in niche control in gut microbiota which is regulated by the quorum sensing (QS) system. A bacterial communication system that is controlled by the fsr operon in enterococci consists of FsrABDC, ef1097, and GelE/SprE genes. Hence, the present study was conducted for molecular assessment of enterocins and quorum sensing genes, inter-environmental correlation, and species prevalence of enterococci isolated from different environmental niches of Karachi, Pakistan. Obtained results revealed the highest prevalence of E. faecium and E. faecalis in all environments. Bacterial antagonism and enterocin genes were observed significantly high in poultry environments. The inter-environmental correlation indicated a strong positive correlation of freshwater with sewage and soil environments. Similarly, the fsr regulatory system was mostly identified in poultry-related environments, and a significant correlation between QS system and biofilm formation was established. In conclusion, this study confirmed the high prevalence of E. faecium in all tested sources, high enterocin production in non-clinical environments, and more fsr regulatory genes in poultry-related environments.

Impact of silver nanoparticles on secondary metabolite composition and toxicity in anise (Pimpinella anisum L.) callus culture

BackgroundThere are numerous challenges associated with producing desired amounts of secondary metabolites (SMs), which are mostly unique and cannot be chemically synthesized. Many studies indicate that nanoparticles (NPs) can boost the production of SMs. Still, the precise manner in which NPs induce metabolic changes remains unidentified. This study examines the influence of eco-friendly silver NPs (AgNPs) on the chemical makeup and toxicity of Pimpinella anisum L. (anise).ResultsAgNPs were introduced into anise callus cultures at different concentrations (0, 1.0, 5.0, 10, and 20 mg/L). The induced oxidative stress was tracked over intervals of 7, 14, 28, and 35 days. Chemical composition evaluations were carried out on the 35th day. Within the first 14 days, plant stress was evident, though the plant adapted to the stress later on. Notably, the plant showed high tolerance at 1 mg/L and 5 mg/L concentrations despite increased toxicity levels. However, relatively high toxicity levels were identified at 10 and 20 mg/L. The AgNP-induced stress significantly impacted anise SMs, particularly affecting fatty acid content. In the 10 and 20 mg/L AgNP groups, essential metabolites, including palmitic and linoleic acid, showed a significant increase. Polyunsaturated (omega) and monounsaturated fatty acids, vital for the food and pharmaceutical industries, saw substantial growth in the 1 and 5 mg/L AgNP groups. For the first time, vanillyl alcohol and 4-Hydroxybenzoic acid were detected along with various phenolic compounds, such as t-anethole, Salicylic acid, and Thiamazole.ConclusionAgNPs can function as an elicitor to efficiently generate essential SMs such as omegas and phenolic compounds in anise callus culture. This study explores the application of AgNPs as plant elicitors in anise SM production, offering invaluable insight into potential uses.

Evaluation of Anti‐inflammatory Activity of Garlic Extracts in 3D Bioprinted Skin Equivalents

Ongoing obstacles in preclinical drug testing have raised significant concerns within the pharmaceutical industry. Recently, utilizing the potential of three‐dimensional (3D) bioprinting offers a solution for creating tissue models for screening of the effectiveness and safety of new drugs. In this study, the anti‐inflammatory potential of garlic extracts is assessed, specifically N‐Benzyl‐N‐methyl‐dodecan‐1‐amine (BMDA), using a 3D bioprinted artificial skin model. Comprehensive physicochemical characterization and immunocytochemical analysis demonstrate that the 3D bioprinted skin model exhibits structures and functions resembling human skin. BMDA treatment in a prepared skin model reveals its capacity to mitigate H2O2‐induced oxidative stress and trigger anti‐inflammatory responses. Notably, BMDA reduces the expression of pro‐inflammatory cytokines and chemokines by downregulating NF‐κB and mitogen‐activated protein kinase inflammatory signaling pathways. In summary, our findings highlight the potential of an artificial skin model as a robust platform for the development of new drugs.

Evolving Characteristics of Gadolinium-Based Contrast Agents for MR Imaging: A Systematic Review of the Importance of Relaxivity.

Gadolinium-based contrast agents (GBCAs) are widely and routinely used to enhance the diagnostic performance of magnetic resonance imaging and magnetic resonance angiography examinations. T1 relaxivity (r1) is the measure of their ability to increase signal intensity in tissues and blood on T1-weighted images at a given dose. Pharmaceutical companies have invested in the design and development of GBCAs with higher and higher T1 relaxivity values, and "high relaxivity" is a claim frequently used to promote GBCAs, with no clear definition of what "high relaxivity" means, or general concurrence about its clinical benefit. To understand whether higher relaxivity values translate into a material clinical benefit, well-designed, and properly powered clinical studies are necessary, while mere in vitro measurements may be misleading. This systematic review of relevant peer-reviewed literature provides high-quality clinical evidence showing that a difference in relaxivity of at least 40% between two GBCAs results in superior diagnostic efficacy for the higher-relaxivity agent when this is used at the same equimolar gadolinium dose as the lower-relaxivity agent, or similar imaging performance when used at a lower dose. Either outcome clearly implies a relevant clinical benefit. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 3.

Estimating Physico-chemical Properties of Drugs for Prostate Cancer using Degree-Based and Neighbourhood Degree-Based Topological Descriptors

Abstract Prostate cancer is one of the most alarming diseases among men and leads to various illnesses. The main flaw in this disease is that there is not a single test that can find it. Prostate-specific antigens and prostate biopsies are used to identify this type of cancer. Many pharmaceutical industries do active research to identify non-steroidal drugs that can be used to treat prostate cancer effectively. QSPR analysis is one of the best practices that aid scientists in getting a deep insight into the physico-chemical properties of drugs without actually performing experiments in a wet lab, thereby cutting down on the cost of manufacturing such drugs. In this article, we perform a QSPR analysis of nineteen drugs primarily used to treat prostate cancer by using a set of ten degree-based indices and another set of ten novel neighbourhood-degree-based indices. In addition, we have done a comparative study of the findings obtained through these two different sets of indices over thirteen physico-chemical properties of prostate cancer drugs.

Dereplication tools for Rhizophora mangle extracts from different mangrove areas and their potential against Staphylococcus aureus.

Rhizophora extracts have several potential biological activities, and their metabolites can be used in the pharmaceutical industry. Extracts of Rhizophora species obtained from mangroves have shown prospective activity against Staphylococcus aureus. This study aimed to investigate the chemical profile of Rhizophora mangle leaves from fringe, basin, and transition mangrove zones and their bactericidal/bacteriostatic potential against S. aureus. R. mangle leaves were collected monthly in 2018 from litterfall in three different zones of the mangrove of Guaratiba State Reserve: fringe, basin, and transition. Extracts were prepared from the material collected in October and December for LC-HRMS/MS analysis, and dereplication was performed using a molecular library search and the classical molecular networkingGNPS platform. The minimum inhibitory concentrations (MICs) of the aqueous extract of R. mangle against S. aureus were determined. No S. aureus growth was observed compared to the control for extracts collected from September to December. Different compounds were annotated in each region, yet a marked presence of phenolic compounds was noted, among them glycosylated flavonoid derivatives of quercetin and kaempferol. The results suggest bactericidal/bacteriostatic activity for extracts of R. mangle leaves collected in 2018 from three mangrove forest zones.

Metabolic Engineering of Escherichia coli for High-Level Production of l-Phenylalanine.

l-Phenylalanine (l-Phe) is widely used in the food and pharmaceutical industries. However, the biosynthesis of l-Phe using Escherichia coli remains challenging due to its lower tolerance to high concentration of l-Phe. In this study, to efficiently synthesize l-Phe, the l-Phe biosynthetic pathway was reconstructed by expressing the heterologous genes aroK1, aroL1, and pheA1, along with the native genes aroA, aroC, and tyrB in the shikimate-producing strain E. coli SA09, resulting in the engineered strain E. coli PHE03. Subsequently, adaptive evolution was conducted on E. coli PHE03 to enhance its tolerance to high concentrations of l-Phe, resulting in the strain E. coli PHE04, which reduced the cell mortality to 36.2% after 48 h of fermentation. To elucidate the potential mechanisms, transcriptional profiling was conducted, revealing MarA, a DNA-binding transcriptional dual regulator, as playing a crucial role in enhancing cell membrane integrity and fluidity for improving cell tolerance to high concentrations of l-Phe. Finally, the titer, yield, and productivity of l-Phe with E. coli PHE05 overexpressing marA were increased to 80.48 g/L, 0.27 g/g glucose, and 1.68 g/L/h in a 5-L fed-batch fermentation, respectively.

The End of the Beginning? Temporality and Bioagency in Pandemic Research.

This paper deals with the ways in which the intellectual and political history of AIDS can assist in the chronological conceptualization of a pandemic such as COVID-19 as it is unfolding. It problematizes the idea of pandemic "beginnings" and "ends" to show that such definitions are shaped by the disciplinary location and thematic foci of relevant scholars. Central to this analysis is the notion that ethical and political contexts affect research on a pandemic in different ways at national and global levels at various points in its trajectory. The article develops this argument in relation to two main themes: firstly, with reference to the history of AIDS research in South Africa; secondly, with the philosophical concept of bioagency to understand the ways in which viruses and humans co-shape the course of epidemics over time. I first make the case for the development of historically informed, long-term ethnographic studies of COVID-19. Using bioagency as a point of departure to consider viruses as social actors, the essay then critiques the notion of bioinformationalism as catalyzing the widening accessibility of biomedical research. Instead, I discuss the biotechnology and pharmaceutical industries as protagonists in the operation of biocapital. I argue that the history of AIDS in South Africa can provide methodological and theoretical insights into how to interpret an unfolding epidemic, outlining an ambitious transdisciplinary research agenda for thinking about the temporality of a pandemic spanning the different, interconnected, scales of life.

Materials and methods for the microencapsulation of substances of food and agricultural interest

Microencapsulation involves trapping solid, liquid, or gas particles within an inert cover to protect them from the environment. This technique has numerous biotechnological applications in the food, agricultural, pharmaceutical, and other industries. Microcapsules are relevant for obtaining viable products and optimizing their efficacy, stability, safety, and ease of application. There is a wide range of coating materials and techniques used to microencapsulate various substances of interest. This article presents a review of encapsulating materials and microencapsulation methods used in the food and agricultural industries.

Abstract PO2-26-04: Nature and Trends of Pharmaceutical Payments to the Board Certified Breast Cancer Specialists in Japan between 2016 and 2019

Abstract Background The field of breast cancer care has experienced a rapid influx of new treatments, making it a potential focus for promotional activities by pharmaceutical companies. These financial relationships could turn into significant sources of conflict of interest within the healthcare sector, possibly leading to undue influence on breast cancer care. Despite the potential ramifications of these ties, there is a noticeable lack of understanding about the details of financial relationships between pharmaceutical companies and breast cancer specialists. The aim of this investigation was to understand the extent and distribution of financial relationships between pharmaceutical companies and breast cancer specialists, along with their trends over recent years in Japan. Methods Our study included all 1733 board-certified breast cancer specialists registered with the Japan Breast Cancer Society as of May 2021. We obtained data on the payments they received, along with the intended purpose of each payment, from 92 leading Japanese pharmaceutical companies between 2016 and 2019. This data was disclosed by the companies following the transparency guidelines set forth by the Japan Pharmaceutical Manufacturers Association. Firstly, we conducted a descriptive analysis of the extent and distribution of payments to specialists, including a calculation of the Gini index to illustrate the disparity of payments among these specialists. Secondly, we assessed the overall trends and individual-based payment patterns over a four-year period. Finally, we explored whether the introduction of new agents was associated with variations in payment values at the company level. Results In total, 64 companies made 11,774 payments, collectively amounting to 13,329,919 USD, with speaking fees comprising 81.4% of this total. Among the 1733 specialists, 1306 (75.4%) received at least one payment from these companies. The average payment per physician was 7,692 USD (with a standard deviation of 24,162), and the median payment was 2,044 USD (with an interquartile range from 798 to 5,207 USD). Among these specialists, 27 received payments of 100,000 USD or more. The Gini index was 0.99, indicating a significant disparity in payment distribution among the specialists. There was a rising trend in total payment values, from 2,883,954 USD in 2016, to 3,037,888 USD in 2017, 3,589,583 USD in 2018, and finally 3,820,041 USD in 2019. A notable increase in payment value occurred with Pfizer in 2018, following the launch of Palbociclib in December 2017. Similarly, Eli Lilly, which launched Abemaciclib in November 2018, also increased its payments to specialists from 2018 onward. Conclusion: The majority of board-certified specialists in Japan received payments from pharmaceutical companies between 2016 and 2019. However, these payments were concentrated among only a small fraction of the specialists. During the study period, the overall value of payments increased. This surge likely resulted from the launch of key drugs within the same class and with similar indications, specifically CDK4/6 inhibitors. Citation Format: Akihiko Ozaki, Kenji Gonda, Masahiro Wada, Hiroaki Saito, Erika Yamashita, Tetsuya Tanimoto. Nature and Trends of Pharmaceutical Payments to the Board Certified Breast Cancer Specialists in Japan between 2016 and 2019 [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-26-04.

PHARMACEUTICAL INDUSTRY ANALYSIS: A COMPREHENSIVE REVIEW OF MARKET TRENDS, COMPETITIVE LANDSCAPE, AND FUTURE OUTLOOK

The pharmaceutical industry, a cornerstone of modern healthcare, undergoes constant evolution to meet the dynamic demands of the healthcare landscape. This comprehensive review delves into the multifaceted dimensions of the industry, encompassing market trends, competitive dynamics, and future projections. Market trends reveal a shift towards personalized medicine, leveraging advancements in genomics and biotechnology, alongside a growing focus on specialty drugs catering to niche patient populations. The prevalence of chronic diseases and an aging populace accentuate the necessity for innovative therapies, while the digital revolution introduces new paradigms in patient care and drug development, propelled by telemedicine, digital therapeutics, and artificial intelligence. Simultaneously, regulatory pressures and pricing constraints exert significant influence, necessitating adaptability among industry players. In the competitive landscape, established pharmaceutical giants vie for market dominance amidst patent expirations and generic competition, while biotech startups and niche players disrupt traditional models. Mergers and acquisitions serve as strategic imperatives for pipeline fortification and market expansion. Looking forward, the industry must navigate a terrain characterized by rapid technological advancements, evolving patient needs, and regulatory complexities, all while striving for innovation, accessibility, and sustainability in the delivery of healthcare solutions. Keywords: Pharmaceutical industry, healthcare landscape, market trends, personalized medicine, specialty drugs, chronic diseases.

Respiratory syncytial virus vaccination during pregnancy for improving infant outcomes.

Respiratory syncytial virus (RSV) is a major cause of lower respiratory tract infections (LRTIs) in infants. Maternal RSV vaccination is a preventive strategy of great interest, as it could have a substantial impact on infant RSV disease burden. In recent years, the clinical development of maternal RSV vaccines has advanced rapidly. To assess the efficacy and safety of maternal respiratory syncytial virus (RSV) vaccination for preventing RSV disease in infants. We searched Cochrane Pregnancy and Childbirth's Trials Register and two other trials registries on 21 October 2022. We updated the search on 27 July 2023, when we searched MEDLINE, Embase, CENTRAL, CINAHL, and two trials registries. Additionally, we searched the reference lists of retrieved studies and conference proceedings. There were no language restrictions on our searches. We included randomised controlled trials (RCTs) comparing maternal RSV vaccination with placebo or no intervention in pregnant women of any age. The primary outcomes were hospitalisation with clinically confirmed or laboratory-confirmed RSV disease in infants. The secondary outcomes covered adverse pregnancy outcomes (intrauterine growth restriction, stillbirth, and maternal death) and adverse infant outcomes (preterm birth, congenital abnormalities, and infant death). We used standard Cochrane methods and assessed the certainty of evidence using the GRADE approach. We included six RCTs (25 study reports) involving 17,991 pregnant women. The intervention was an RSV pre-F protein vaccine in four studies, and an RSV F protein nanoparticle vaccine in two studies. In all studies, the comparator was a placebo (saline, formulation buffer, or sterile water). We judged four studies at overall low risk of bias and two studies at overall high risk (mainly due to selection bias). All studies were funded by pharmaceutical companies. Maternal RSV vaccination compared with placebo reduces infant hospitalisation with laboratory-confirmed RSV disease (risk ratio (RR) 0.50, 95% confidence interval (CI) 0.31 to 0.82; 4 RCTs, 12,216 infants; high-certainty evidence). Based on an absolute risk with placebo of 22 hospitalisations per 1000 infants, our results represent 11 fewer hospitalisations per 1000 infants from vaccinated pregnant women (15 fewer to 4 fewer). No studies reported infant hospitalisation with clinically confirmed RSV disease. Maternal RSV vaccination compared with placebo has little or no effect on the risk of congenital abnormalities (RR 0.96, 95% CI 0.88 to 1.04; 140 per 1000 with placebo, 5 fewer per 1000 with RSV vaccination (17 fewer to 6 more); 4 RCTs, 12,304 infants; high-certainty evidence). Maternal RSV vaccination likely has little or no effect on the risk of intrauterine growth restriction (RR 1.32, 95% CI 0.75 to 2.33; 3 per 1000 with placebo, 1 more per 1000 with RSV vaccination (1 fewer to 4 more); 4 RCTs, 12,545 pregnant women; moderate-certainty evidence). Maternal RSV vaccination may have little or no effect on the risk of stillbirth (RR 0.81, 95% CI 0.38 to 1.72; 3 per 1000 with placebo, no difference with RSV vaccination (2 fewer to 3 more); 5 RCTs, 12,652 pregnant women). There may be a safety signal warranting further investigation related to preterm birth. This outcome may be more likely with maternal RSV vaccination, although the 95% CI includes no effect, and the evidence is very uncertain (RR 1.16, 95% CI 0.99 to 1.36; 6 RCTs, 17,560 infants; very low-certainty evidence). Based on an absolute risk of 51 preterm births per 1000 infants from pregnant women who received placebo, there may be 8 more per 1000 infants from pregnant women with RSV vaccination (1 fewer to 18 more). There was one maternal death in the RSV vaccination group and none in the placebo group. Our meta-analysis suggests that RSV vaccination compared with placebo may have little or no effect on the risk of maternal death (RR 3.00, 95% CI 0.12 to 73.50; 3 RCTs, 7977 pregnant women; low-certainty evidence). The effect of maternal RSV vaccination on the risk of infant death is very uncertain (RR 0.81, 95% CI 0.36 to 1.81; 6 RCTs, 17,589 infants; very low-certainty evidence). The findings of this review suggest that maternal RSV vaccination reduces laboratory-confirmed RSV hospitalisations in infants. There are no safety concerns about intrauterine growth restriction and congenital abnormalities. We must be careful in drawing conclusions about other safety outcomes owing to the low and very low certainty of the evidence. The evidence available to date suggests RSV vaccination may have little or no effect on stillbirth, maternal death, and infant death (although the evidence for infant death is very uncertain). However, there may be a safety signal warranting further investigation related to preterm birth. This is driven by data from one trial, which is not fully published yet. The evidence base would be much improved by more RCTs with substantial sample sizes and well-designed observational studies with long-term follow-up for assessment of safety outcomes. Future studies should aim to use standard outcome measures, collect data on concomitant vaccines, and stratify data by timing of vaccination, gestational age at birth, race, and geographical setting.