International Journal For Multidisciplinary Research

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Statistical Applications in Pharmaceutical Product Development: A Comprehensive Review

Author(s) SRK Raju Sagiraju, Erdi EKMEKCİ, Emre Erol ALDENİZ, Udaya DUDE
Country Turkey
Abstract The pharmaceutical industry often depends on statistics to improve product quality, optimize the compositions, streamline operations, and conform to regulatory requirements. In Bio Equivalency investigations, Statistics plays an integral role in decision-making while demonstrating the similarity between the test and reference products. In this review, we explore how statistics play a crucial role in pharmaceutical product development.
We start with examining experiments, where statistics help scientists find the best combinations of ingredients and manufacturing conditions. We explored different types of experiments, like factorial designs, Plackett-Burman designs, Box-Behnken designs, and Taguchi designs. While optimizing the formulations and process parameters, these methodologies assist researchers in making meaningful conclusions supported by statistical inference.
The next sections include Response Surface Methodology (RSM) and Mixture Designs, which help in the development of drug formulations and the comprehension of complex responses. The development of pharmaceuticals is made simpler by these approaches. Then, we examine optimal designs like A-optimal, I-optimal, and D-optimal. A-optimal designs save resources, I-optimal designs focus on precise estimates, and D-optimal designs are great for screening and getting accurate results. These designs help scientists make decisions based on data.
The starting point for demonstrating dissolution similarity between generic and reference products is the statistical approach. This paper examines the use of model-dependent techniques, such as zero-order, first-order, Higuchi, and Weibull models, and model-independent techniques such as F1, F2, Bootstrap, MSD, and PCA techniques, it analyzes important parameters and bounds that are essential for determining the dissolution similarity.
In the field of biostatistics, we discuss how statistics ensures that generic drugs are safe and work just as efficiently as brand-name drugs. Both regulatory approval and patient safety rely on this.
We briefly discuss statistical process control (SPC) in closing. SPC checks that drugs/drug products are consistently of quality using graphs and figures. It facilitates the production of consistently effective pharmaceuticals by organizations.
Advanced statistical techniques, including data mining, machine learning, and artificial intelligence, are revolutionizing the pharmaceutical industry. These tools enhance drug discovery, optimize manufacturing processes, and pave the way for personalized medicine. As real-time monitoring and predictive analytics become integral, the future holds exciting possibilities for improving patient outcomes.
This review shows how statistics are like a guiding tool in the pharmaceutical industry. Whether it's in the lab or in manufacturing, statistics play a big role in making sure patients get the best medicines possible.
Keywords Design of Experiments, Statistical process control, Biostatistics, Dissolution similarity, Data science, Pharmaceuticals
Field Medical / Pharmacy
Published In Volume 6, Issue 1, January-February 2024
Published On 2024-02-10
Cite This Statistical Applications in Pharmaceutical Product Development: A Comprehensive Review - SRK Raju Sagiraju, Erdi EKMEKCİ, Emre Erol ALDENİZ, Udaya DUDE - IJFMR Volume 6, Issue 1, January-February 2024. DOI 10.36948/ijfmr.2024.v06i01.13216
DOI https://doi.org/10.36948/ijfmr.2024.v06i01.13216
Short DOI https://doi.org/gthqpx
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