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@article{181179,
        author = {Muri Chakrapani},
        title = {STATISTICAL APPLICATIONS IN MODERN PHARMACEUTICAL RESEARCH: METHODS AND CASE STUDIES-A CRITICAL REVIEW},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {3807-3813},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=181179},
        abstract = {Statistical methodologies are integral to modern pharmaceutical research, serving as foundational tools that guide decision-making, enhance data interpretation, and ensure scientific rigor throughout the drug development pipeline. This review comprehensively explores the applications of both classical and advanced statistical methods in pharmaceutical sciences. Keyareas include experimental design in formulation development, statistical analyses in preclinical and clinical trials, bioequivalence assessments, and quality control practices. Special attention is given to Pharmacokinetic /Pharmacodynamic (PK/PD) modeling and post-marketing pharmacovigilance, highlighting their reliance on robust statistical frameworks. Real-world case studies are embedded throughout to demonstrate practical implementations and outcomes. Furthermore, the article addresses the integration of emerging methodologies such as Bayesian statistics, real-world data analytics, and machine learning, which are reshaping the future of pharmaceutical research. Emphasis is placed on the alignment of statistical practices with regulatory guidelines and the growing importance of interdisciplinary collaboration. By consolidating theory, application, and future perspectives, this review underscores the indispensable role of statistics in achieving innovation, efficacy, and safety in pharmaceutical development.},
        keywords = {Statistics, Design of experiments, Clinical trials, Pharmacokinetics, Machine learning etc.},
        month = {June},
        }