Among non-oil and in trade arena, drug has always been strategic importance and most government especially industrialized countries pay special attention to its production and trade issues. Thus, having a comprehensive view from economic perspective to this section is essential for suggesting intervention. This was a descriptive-analytical and panel study. In this study, gravity model is used to estimate Iran’s bilateral intra-INDUSTRY trade in PHARMACEUTICAL products in the 2001-2012 periods. To illustrate the extent of PHARMACEUTICAL’s intra-INDUSTRY trade between Iran and its major trading partners, the explanatory variables of market size, income, factor endowments, distance, cultural contributions, and similarities and also special trade arrangements have been applied. Analysis of factors affecting Iran’s bilateral intra-INDUSTRY trade in PHARMACEUTICAL INDUSTRY showed that the average GDP and cultural similaritieshad a significant positive impact on Iran’s bilateral IIT, while the difference in GDP has a negative and significant effect. Coefficients obtained for the geographical distance and the average ratio of total capital to the labor force is not consistent with theoretical expectations. Special trade arrangements did not have significant impact on the extent of bilateral intra-INDUSTRY trade between Iran and its trading partners. The knowledge of the intra-INDUSTRY trade between Iran and its trade partners make integration between the countries. Factors affecting this type of trade pattern underlie its development in trade relationship. Therefore, the findings of this study would be useful in helping to develop and implement policies for the expansion of the PHARMACEUTICAL trade.

Background. Hospitals, patients, researchers, and healthcare organizations are producing enormous amounts of data in both the healthcare and drug detection sectors. With continued development of cheap data storage and availability of smart devices in the world, the influence of big data (BD) will continue to grow. This influence has also carried over to healthcare. The volumes of data available in the fields of pharmacology, toxicology, and pharmaceutics are constantly increasing. Therefore, the present study aimed to identify the applications of big data technology in the field of pharmaceutics. Methods. Using the mixed methods approach, this study was conducted in two phases in winter 2023. In the first phase, the applications of big data technology were identified by library search and assessed by content analysis. In the second phase, applications were ranked by a panel of experts, including 17 experts who worked in the Iranian PHARMACEUTICAL INDUSTRY. The stepwise weight assessment ratio analysis (SWARA) method was used for ranking the application of big data in pharmaceutics. Results. The present study examined the importance of big data applications in pharmaceutics. The results showed that drug discovery (0. 263) and clinical study analysis (0. 224) had the highest importance, followed by drug efficacy and performance (0. 197), drug safety (0. 170) and drug personalization (0. 146). Conclusion. The present study detailed a substantial attempt to review the existing literature regarding the implementation of big data and to rank big data applications in the PHARMACEUTICAL INDUSTRY. Big data can help researchers to better discover and develop drugs and understand the effects of drugs and other chemicals on the human body. As a result, it can help improve the safety and efficacy of drugs and other chemicals. Also, big data can help to improve the accuracy of predictions regarding the effects of drugs and chemicals, which can improve safety in drug development and help to avoid potential adverse drug interactions. The applications considered in this study are ultimately necessary for humanity, and big data may significantly impact the betterment of these domains. Big data has a revolutionary potential, providing new ways to understand and predict the effects of drugs. BD will possibly play an important role in pharmaceutics in future, critically helping to drug discovery and improve drug safety and efficacy.

Background: The potentials of artificial intelligence (AI) have permeated all industries and fields, and the advantages of AI are extensively employed. This technology offers a wide range of benefits in the PHARMACEUTICAL INDUSTRY, including reducing human interventions and increasing the speed and accuracy of tasks. This can expedite time-consuming activities, such as drug discovery, production, clinical trials, research and development, and ultimately, determining a drug’s position in target markets. Methods: A comprehensive scope review was performed in this descriptive, applied study on the applications of AI in the PHARMACEUTICAL INDUSTRY in Iran. Relevant data were meticulously gathered and extracted from diverse sources, including various search engines, key databases, such as Medline, PubMed, Elsevier, and the Iranian Center for Scientific Information and Documentation, and information databases, reference books, and reports from the World Health Organization. These results represent our research on key themes, including AI, the PHARMACEUTICAL INDUSTRY, drug production, innovation, and evolution. Our main focus lies on the application of AI in the manufacturing part of the PHARMACEUTICAL INDUSTRY, with a deliberate decision not to delve into the technical aspects. This approach allowed us to prioritize a comprehensive understanding of the practical implications and advancements in the manufacturing processes facilitated by artificial intelligence. Results: The retrieved studies showed that AI has the potential to enhance crucial processes in PHARMACEUTICAL companies across various dimensions. These potential capabilities are observed in areas such as quality control, human resource management, research and development, finance, supply chain management, logistics, data management, operations management, customer relationship management, and commerce, which are further discussed. Conclusions: PHARMACEUTICAL companies can utilize the tools provided by AI in various value-creating processes to enhance their efficiency and effectiveness. This requires the adoption and integration of this innovative technology at various levels of organizational planning so that these companies can harness the greater potential it offers with appropriate investments.