Rabinarayan Parhi

Background and purpose: Quality by design (QbD) has revolutionized pharmaceutical development by shifting from a conventional quality-by-testing approach to a science-based approach that incorporates quality into products and processes from inception. The integration of artificial intelligence (AI), machine learning (ML), automation, and digital technologies with QbD offers new opportunities to enhance process understanding, optimize critical quality attributes (CQAs) and support the Pharma 4.0 industry framework. Experimental approach: A systematic review of studies published between 2016 and 2025 was conducted to evaluate the application of supervised learning algorithms, deep neural networks, reinforcement learning, and digital twin technologies within QbD. Regulatory frameworks, including International Council for Harmonisation (ICH) quality guideline Q8 through Q14, the U.S. Food and Drug Administration’s Emerging Technology Program and the European Medicines Agency quality initiative, together with representative industrial case studies, were also examined. Key results: This review demonstrates that AI-driven QbD improves process understanding, CQA optimization, batch-to-batch consistency, process robustness, and real-time quality control. Further, advanced computational models enable predictive process monitoring, intelligent decision-making, and in silico experimentation, reducing manufacturing costs, development time and experimental burden. However, challenges related to data quality, model interpretability, validation, and regulatory harmonisation remain a critical barrier to widespread implementation. Conclusion: The convergence of AI, ML and QbD provides a robust framework for intelligent pharmaceutical manufacturing (PM). AI-enabled QbD supports the development of high-quality products through predictive, data-driven, and in silico approaches while accelerating development and improving manufacturing efficiency. These technologies offer a practical roadmap toward Pharma 4.0 and the future of digital PM.

The current study set out to assess the impact of microsphere formulation variables on the drug release and entrapment efficiency of a model medication, amlodipine besylate. A three-factor, three-level Box-Behnken design was used to examine the main and interaction impacts of several independent formulation factors, including the ratio of drug to polymer, the concentration of surfactant, and the amount of acetone. Entrapment efficiency (EE) and cumulative percentage drug release (CPR) were the dependent variables. In order to maximise amlodipine entrapment efficiency and provide a desirable, sustained release over a 12-hour period, formulation optimisation was done. The drug release, entrapment efficiency, size, and shape of the prepared microspheres were evaluated. To explain the effect of all components and their co-linearities on the %EE and CPR of amlodipine, respectively, mathematical relationships were obtained: EE = 78.95 –1.67A+3.18B +1.50C and CPR = +81.75 –7.72A +5.15B +0.9500C +2.68AB –0.1100AC –0.1800BC –11.79A2 –1.14B2 –0.1775C2 (r2 = 0.9970). A new batch was made using the optimised formulation, and there was a strong agreement between the observed and expected values of CPR and EE. We were able to understand the effect of formulation parameters on the entrapment efficiency and obtain optimal and sustained drug dissolving through the use of the Box-Behnken experimental design.

Research on shape memory materials (SMM) or smart materials, along with advancements in printing technology, has transformed three-dimensional (3D) printing into what we now refer to as 4D printing. In this context, the addition of time as a fourth dimension enhances 3D printing. 4D printing involves the creation of 3D-printed objects that can change their shapes into complex geometries when influenced by external stimuli such as temperature, light, or pH over time. Currently, the use of smart materials in 4D printing is being explored extensively across various fields, including automotive, wearable electronics, soft robotics, food, mechatronics, textiles, biomedicine, and pharmaceuticals. A particular focus is on designing and fabricating smart drug delivery systems (DDS). This review discusses the evolution of 3D printing into 4D printing, highlighting the differences between the two. It covers the history and fundamentals of 4D printing, the integration of machine learning in 4D printing, and the types of materials used, such as stimuli-responsive materials (SRMs), hydrogels, liquid crystal elastomers, and active composites. Moreover, it presents various 4D printing techniques. Additionally, the review highlights several smart DDS that have been fabricated using 4D printing techniques. These include tablets, capsules, grippers, scaffolds, robots, hydrogels, microneedles, stents, bandages, dressings, and other devices aimed at esophageal retention, gastro-retention, and intravesical DDS. Lastly, it elucidates the current limitations and future directions of 4D printing.

Stimuli-responsive, smart, or intelligent polymers are materials that significantly change their physical or chemical properties when there is a small change in the surrounding environment due to either internal or external stimuli. In the last two decades or so, there has been tremendous growth in the strategies to develop various types of stimuli-responsive polymer (SRP) materials/systems that are suitable for various fields, including biomedical, material science, nanotechnology, biotechnology, surface and colloid sciences, biochemistry, and the environmental field. The wide acceptability of SRPs is due to their availability in different architectural forms such as scaffolds, aggregates, hydrogels, pickering emulsions, core-shell particles, nanogels, micelles, membranes, capsules, and layer-by-layer films. The present review focuses on different types of SRPs, such as physical, chemical, and biological, and various important applications, including controlled drug delivery (CDD), stabilization of colloidal dispersion, diagnostics (sensors and imaging), tissue engineering, regenerative medicines, and actuators. The applications of SRPs have immense potential in various fields, and the author hopes these polymers will add a new field of applications through new concepts.

Background and Purpose: Safe and effective drug delivery is crucial for the treatment of cancer, which is quite impossible to achieve through traditional methods. Among all types of cancer, skin melanoma is known for its aggressive metastasizing ability and an unprecedented higher degree of lethalness, limiting the overall therapeutic efficacy. Here, we focus on the different types of nanomaterials (NMs) and their drug delivery applications against melanoma. Experimental Approach: All relevant publications, including research papers, reviews, chapters and patents, were assessed using search engines such as Scopus and PubMed, up to the end of August of 2023. The keywords used in the search were: nanomaterials, melanoma, drug delivery routes for melanoma, and nanomaterial-based drug delivery systems (DDS). Most of the publications out of 234 cited in this review are from the last five years. Key Results: The recent advancement and mechanism of action of various NMs against melanoma, including inorganic metallic and carbon-based NMs, organic polymeric and lipid-based NMs, and cell-derived vesicles are discussed. We also focus on the application of different NMs in the delivery of therapeutic agents for melanoma therapy. In addition, the skin and melanoma, genetic mutation and pathways for melanoma, conventional treatment options, and delivery routes for therapeutic agents are also discussed briefly. Conclusion: There are few NM-based DDS developed in the lab set up recently. The findings of this review will pave the path for the development of NM-based DDS on an industrial scale and help in the better management of skin melanoma.