Big Data and Pharmaceutical Industry: Applications and Priorities

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Extended Abstract
Background
Since 1990, the world has experienced an unprecedented explosion in the capacity to produce, store, and communicate data, primarily in digital formats. Simultaneously, access to computing technologies in personal computers, smartphones, and other handheld devices has mirrored this growth. With new and varied sources of data available, companies are looking to integrate all sources into a big pool of data. Also, 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 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
The current study was conducted in two phases using the mixed methods approach in winter 2023. In the first phase, the applications of big data technology were identified by library search and assessed by content analysis. Content analysis is the study of documents and artifacts, which might be texts of various formats. Scientists use content analysis to examine patterns in communication in a replicable and systematic manner. In the second phase, applications were ranked by a panel of experts, including 17 experts who worked in the pharmaceutics industry in Iran. The Stepwise Weight Assessment Ratio Analysis (SWARA) method was used for ranking the application of big data in pharmaceutical industry. SWARA is a powerful method in the top level of decision and policy making. The main advantage of this method in decision making is that, in some problems, priorities are defined based on policies of companies or countries, and there is no need for evaluation to rank the criteria.
Results
The obtained results revealed the importance of drug discovery (0.263), clinical study analysis (0.224), drug efficacy and performance (0.197), drug safety (0.170), and drug personalization (0.146) in the applications of big data in pharmaceutics. A better understanding of how big data helps to delineate personalized approaches in severe mental illness and the provision of a quantitative synthesis of big data approaches for metabolomics in severe mental illness is necessary. Notably, big data has the potential to improve our understanding of the developmental trajectories of mental disorders. Big data can be used in pediatric drug development. The use of big data for clinical trial design, efficiency, and safety of data has been attained in clinical trials. Therefore, exploring the current opportunities and challenges of big data in future pediatric drug development must be enriched. Big data can be used in drug research to determine efficacy and safety signals. It can also leverage and improve clinical decisions at the point of care, uncovering or validating drug efficacy and safety. Also, big data is significant for medical use and requires re-thinking, regarding the data storage infrastructure, the analysis growth, and the associated tools to drive advancements in the considered field.
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 better understand the effects of drugs and other chemicals on the human body, which can help to improve the safety and efficacy of drugs and other chemicals. Also, it 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 human health, and big data may significantly impact the betterment of these domains. Moreover, the health sector policymakers need appropriate investment in information technology and should strengthen the platform of telecommunication networks, hardware, and software for big data technology in pharmaceutics. The fields of pharmacology, toxicology, and pharmaceutics are still at an early stage of big data adoption, and there are many research challenges to be overcome to effectively employ big data and address specific issues.