Nanomedicine is a field of medicine that uses nanotechnology to develop new diagnostic tools and therapies for a wide range of medical conditions. It encompasses a variety of different approaches, including the use of nanoparticles, nano-robots, and nanodevices. Some examples of how nanotechnology is being used in medicine include drug delivery, diagnostics imaging, tissue engineering, and biomedical devices. One of the main advantages of using nanomedicine-based drug delivery is the ability to deliver drugs at fixed and controlled doses. This is achieved by engineering nanoparticles to release drugs at a specific rate, which can be attuned to match the desires of the patient. Determining the optimal combination of nanotherapy, dosages, and administration schedule can be challenging, as it requires a thorough understanding of the pharmacokinetics and pharmacodynamics of the drugs, as well as the patient's individual physiognomies and the stage of the disease. To overcome this challenge, researchers and practitioners are increasingly using computational models, Artificial intelligence and machine learning algorithms, and personalized medicine approaches to predict the optimal drug combination, dosage, and administration schedule for each patient. In this review, we were given the outline of the different Artificial intelligence tools for the prediction of nanomedicine in the field of various medical applications. 

Strategies of Artificial intelligence tools in the domain of nanomedicine

The emergence of Artificial Intelligence (AI) in drug discovery marks a pivotal shift in pharmaceutical research, blending sophisticated computational techniques with conventional scientific exploration to break through enduring obstacles. This review paper elucidates the multifaceted applications of AI across various stages of drug development, highlighting significant advancements and methodologies. It delves into AI's instrumental role in drug design, polypharmacology, chemical synthesis, drug repurposing, and the prediction of drug properties such as toxicity, bioactivity, and physicochemical characteristics. Despite AI's promising advancements, the paper also addresses the challenges and limitations encountered in the field, including data quality, generalizability, computational demands, and ethical considerations. By offering a comprehensive overview of AI's role in drug discovery, this paper underscores the technology's potential to significantly enhance drug development, while also acknowledging the hurdles that must be overcome to fully realize its benefits.

The Role of AI in Drug Discovery.

According to the World Health Organization (WHO), Parkinson’s disease (PD) is a neurodegenerative disease of the brain that causes motor symptoms including slower movement, rigidity, tremor, and imbalance in addition to other problems like Alzheimer’s disease (AD), psychiatric problems, insomnia, anxiety, and sensory abnormalities. Techniques including artificial intelligence (AI), machine learning (ML), and deep learning (DL) have been established for the classification of PD and normal controls (NC) with similar therapeutic appearances in order to address these problems and improve the diagnostic procedure for PD. In this article, we examine a literature survey of research articles published up to September 2022 in order to present an in-depth analysis of the use of datasets, various modalities, experimental setups, and architectures that have been applied in the diagnosis of subjective disease. This analysis includes a total of 217 research publications with a list of the various datasets, methodologies, and features. These findings suggest that ML/DL methods and novel biomarkers hold promising results for application in medical decision-making, leading to a more methodical and thorough detection of PD. Finally, we highlight the challenges and provide appropriate recommendations on selecting approaches that might be used for subgrouping and connection analysis with structural magnetic resonance imaging (sMRI), DaTSCAN, and single-photon emission computerized tomography (SPECT) data for future Parkinson’s research.

/pdf/a-computerized-analysis-with-machine-learning-techniques-for-36gna4m4.pdf

A Computerized Analysis with Machine Learning Techniques for the Diagnosis of Parkinson’s Disease: Past Studies and Future Perspectives

Artificial Intelligence (AI) has emerged as a powerful tool in various domains, and the field of drug formulation and development is no exception. This review article aims to provide an overview of the applications of AI in drug formulation and development and explore its future prospects. The article begins by introducing the fundamental concepts of AI, including machine learning, deep learning, and artificial neural networks and their relevance in the pharmaceutical industry. Furthermore, the article discusses the network and tools of AI and its applications in the pharmaceutical development process, including various areas, such as drug discovery, manufacturing, quality control, clinical trial management, and drug delivery. The utilization of AI in various conventional as well as modified dosage forms has been compiled. It also highlights the challenges and limitations associated with the implementation of AI in this field, including data availability, model interpretability, and regulatory considerations. Finally, the article presents the future prospects of AI in drug formulation and development, emphasizing the potential for personalized medicine, precision drug targeting, and rapid formulation optimization. It also discusses the ethical implications of AI in this context, including issues of privacy, bias, and accountability. 

Artificial Intelligence in Drug Formulation and Development: Applications and Future Prospects.

Nanobiotechnology is the integration of nanotechnology with biotechnology, with a specific emphasis on the utilisation of nanoscale techniques in biological systems. Nanobiotechnology significantly contributes to the field of drug delivery by utilising nanoscale materials, such as nanoparticles and liposomes, to enhance the effectiveness of medications. Nanoparticles possess a reduced size and altered properties that provide accurate drug localisation, regulated release, and enhanced bioavailability. This offers distinctive resolutions to challenges encountered in conventional drug administration techniques. Herein we highlight the significance of achieving optimal drug delivery and subsequently explore the potential of biotechnology to address these challenges. We explore contemporary methodologies, including the utilisation of nanotechnology for targeted drug delivery. Additionally, we discuss intelligent drug delivery systems, including those that are triggered by specific inputs or guided by artificial intelligence. The development of biodegradable implants, the use of artificial intelligence for personalised treatment, and the creation of microbes for the administration of drugs are also discussed. The chapter includes coverage of cutting-edge technology such as 3D printing for the customisation of medication forms and continuous monitoring devices, highlighting the important ethical inquiries and safety hazards linked to these novel methods. Finally, it provides a concise overview of the key progress made and anticipated breakthroughs in drug delivery that have the potential to transform healthcare. 

Drug Delivery in Biotechnology: Present and Future

Artificial Intelligence (AI) had transfigured different sectors in society, where the pharmaceutical sector is not an exceptional case. Pharmaceutical sectors have reached new heights with the emergence of these sophisticated technologies. The evolution of artificial intelligence in the pharmaceutical industry is in a growth phase opening the possibilities of discovering many new drugs. The diseases affecting humans are increasing tremendously whereas the drugs which are available to treat or cure are very much minimal. But this kind of scenario will not be present in the future because of the combination of artificial intelligence and the pharmaceutical industry which results in faster discovery of drugs with increased clinical outcomes. There was a shift in the paradigm of various stages in drug discovery because of the utilization of artificial intelligence. Each stage of drug discovery involves a certain timeline that can be cut down with the help of artificial intelligence. Many pharma companies are engaged with AI-based drug discovery approaches for treating various diseases like Parkinson’s disease diabetes, Alzheimer’s, Obsessive Compulsive Disorder, etc., AI is also being employed in product development for the fabrication of nanomedicines and nanorobots. Few AI-based drugs are already in the phase of clinical trials which indicates the growth of AI-driven drug discovery. In this review, we have highlighted the application of AI in drug discovery and product development of pharmaceuticals. ABSTRACT Mr. Ravi Ram Narayanan is a M. Pharmacy (Pharmaceutics) final semester student at Chennai His field of interest is NDDS and herbal research. He has presented posters in 2 national and 2 international conferences.

pdf/impact-of-artificial-intelligence-ai-on-drug-discovery-and-2nxjlv8k.pdf

Impact of Artificial Intelligence (AI) on Drug Discovery and Product Development

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Impact of Artificial Intelligence (AI) on Drug Discovery and Product Development