Artificial intelligence (AI) has emerged as a powerful tool that harnesses anthropomorphic knowledge and provides expedited solutions to complex challenges. Remarkable advancements in AI technology and machine learning present a transformative opportunity in the drug discovery, formulation, and testing of pharmaceutical dosage forms. By utilizing AI algorithms that analyze extensive biological data, including genomics and proteomics, researchers can identify disease-associated targets and predict their interactions with potential drug candidates. This enables a more efficient and targeted approach to drug discovery, thereby increasing the likelihood of successful drug approvals. Furthermore, AI can contribute to reducing development costs by optimizing research and development processes. Machine learning algorithms assist in experimental design and can predict the pharmacokinetics and toxicity of drug candidates. This capability enables the prioritization and optimization of lead compounds, reducing the need for extensive and costly animal testing. Personalized medicine approaches can be facilitated through AI algorithms that analyze real-world patient data, leading to more effective treatment outcomes and improved patient adherence. This comprehensive review explores the wide-ranging applications of AI in drug discovery, drug delivery dosage form designs, process optimization, testing, and pharmacokinetics/pharmacodynamics (PK/PD) studies. This review provides an overview of various AI-based approaches utilized in pharmaceutical technology, highlighting their benefits and drawbacks. Nevertheless, the continued investment in and exploration of AI in the pharmaceutical industry offer exciting prospects for enhancing drug development processes and patient care.

Plant engineering technology has been working effectively since last 30 years. Commercialization of different product using plant engineering is encouraging us to develop effective treatment and this progress takes too much effort and time, but still many candidate vaccines for use in humans are in clinical trials. Virus-like particles (VLPs) are basically self-constructed structures departed from viral antigens which copy the organization of similar viruses but without viral genome. This technology offers several pros in terms of safety, immunogenicity and stability in production over vaccines derived from pathogen formulation. Now, many pharmaceutical companies are working in this technology to develop effective treatment against various diseases. This review discusses how plant engineering technology works for diseases and regulations relevant to the development of plant-based vaccines in the treatment of viruses like Hepatitis B, Ebola, Papilloma, Norwalk, Influenza, HIV and Covid-19. Keywords: Plant engineering technology, Virus-like Particles, Pathogens, Antibodies.