Pranay Wal

Introduction: The demand for inventive approaches to drug discovery has increased due to the rising costs, time, and failure rates in pharmaceutical research. Large Language Models (LLMs), with their sophisticated natural language processing and generative capabilities, have become potent instruments that have the potential to revolutionize biomedical research. The function of LLMs in different phases of drug development is methodically examined in this article. Methods: The PRISMA 2020 principles were adhered to in this systematic study. A thorough search for research published between 2018 and 2025 was done using PubMed, Scopus, Web of Science, and Google Scholar. The search terms "large language model," "transformer," "drug discovery," and important sub-domains (such as "de-novo design" and "ADMET") were merged, and two reviewers independently screened the results. Predetermined inclusion and exclusion criteria were used to filter studies for relevance. 98 studies out of the 1,285 records that were initially retrieved met the requirements for the final qualitative synthesis. Results: 98 studies that demonstrated the use of LLMs in various drug discovery domains were found during the review. These covered molecular generation, genomics, protein-ligand modeling, ADME/T and toxicity profiling, drug-target interaction and DTI prediction, and biomedical text mining. 42 different LLM-based tools were mapped, including BioBERT, SciSpacy, Drug- LLM, DNA-BERT, GPT-4, and ChatGPT. Predictive accuracy, hypothesis creation, target prioritization, and multi-modal data integration all showed notable gains with these techniques. Discussion: By providing scalable, precise, and effective solutions for data-driven drug discovery, LLMs are revolutionizing the pharmaceutical industry. They allow for the creation of hypotheses and individualized insights across multi-modal biological data, and they perform better than conventional approaches in a number of subdomains. Improvements in performance were task-dependent; the most consistent gains occurred for biomedical text mining, disease-genedrug relationship mapping and drug-target interaction prediction tasks. Yet most evidence for clinical applications is still derived from retrospective studies and benchmark datasets, suggesting a higher need for prospective validation. Conclusion: There is revolutionary potential in incorporating LLMs into drug discovery processes. Clinical translation and regulatory uptake will depend heavily on collaborative validation, ethical deployment, and standardization as models become more multimodal and interpretable. Before normal use, extensive prospective benchmarking and head-to-head comparisons with established chemoinformatics pipelines are necessary.

Breast cancer remains one of the leading causes of morbidity and mortality among women worldwide, underscoring the critical need for early and accurate diagnostic methodologies. This review describes an advanced breast cancer detection system that integrates Artificial Intelligence (AI) with sophisticated imaging mechanisms to improve diagnostic precision. The system employs highresolution imaging technology to non-invasively visualize and isolate successive layers of breast tissue, thereby providing detailed, multi-planar views of targeted anatomical regions. This approach facilitates deeper examination of tissue architecture. Through subsequent AI-driven computational analysis, potential malignant areas, including microcalcifications and subtle structural distortions, are identified and highlighted in real time. This integrated approach enables faster and more reliable interpretation of complex imaging data while reducing the cognitive burden on radiologists. The core mechanism enhances diagnostic accuracy by detecting aberrant tissue patterns and generating comprehensive layered insights, including quantitative assessments of tissue density and vascularization, to support clinical decision-making and risk stratification. By combining depth-resolved visualization with automated interpretation, this approach has the potential to advance beyond conventional twodimensional screening paradigms. The integration of AI and advanced imaging contributes to a more sensitive and specific diagnostic workflow. Consequently, this review presents SDSND as a hypothetical and emerging framework integrating AI and nanoscale detection. However, its clinical applicability remains unvalidated and requires experimental and clinical investigation.

1. 1.1. A highly productive conceptual basis for passive tumor targeting was given by the EPR effect first reported by Matsumura and Maeda in 1986 to elucidate the preferential accretion of macromo...

The advancement of topical pharmaceutical delivery systems has been significantly boosted by the development of polymer-based microsponge technologies. These microstructured, porous polymeric systems enable controlled, sustained, and site-specific release of active phar-maceutical ingredients (APIs), thereby enhancing therapeutic efficacy while reducing systemic side effects. Microsponge delivery systems encapsulate APIs within spherical polymer matrices that gradually release their contents upon dermal application. This controlled release mechanism has proven effective for a range of therapeutic agents, including anti-inflammatory, antifungal, and cosmetic actives. This review provides a comprehensive overview of recent innovations in polymeric microsponge systems for topical drug delivery. Emphasis is placed on critical formula-tion techniques such as solvent evaporation, quasi-emulsion solvent diffusion, and liquid-liquid suspension polymerization. The review also examines the biocompatibility, physicochemical sta-bility, and dermatological applicability of microsponge systems, supporting their integration into both pharmaceutical and cosmetic formulations. Furthermore, the role of nanotechnology in en-hancing microsponge performance is explored, particularly through nano-microsponge formula-tions that offer improved skin permeation and precise drug targeting. In summary, polymeric mi-crosponge technology represents a promising platform for topical drug delivery, with potential for significant clinical and commercial impact. Ongoing research in natural polymers, nanocarri-ers, and advanced formulation strategies is expected to expand the utility and effectiveness of these systems in the near future.

Diabetic Neuropathy (DN) is among the most prevalent and debilitating microvascular complications of diabetes mellitus, with Distal Symmetric Polyneuropathy (DPN) representing the predominant phenotype. Despite decades of research, diagnostic precision at early stages remains inadequate, and existing therapies are largely confined to symptomatic management rather than disease modification. This review critically evaluates recent advances in the mechanistic understanding, early diagnostic innovations, and evolving therapeutic paradigms of DN. Special emphasis is placed on emerging biomarkers, digital diagnostics, regenerative interventions, and neuromodulatory strategies that hold potential to transform clinical practice. A Literature survey was conducted across the PubMed, Scopus, and Web of Science databases to identify published studies. Eligible peer-reviewed articles included clinical trials, systematic reviews, meta-analyses, and translational research focusing on early detection modalities, molecular biomarker discovery, pharmacological interventions, and novel regenerative or neuromodulatory therapies. Key findings were synthesised and analysed narratively. Recent diagnostic innovations enable sensitive detection of small fibre neuropathy, often preceding overt clinical manifestations. On the therapeutic front, a multimodal strategy is gaining ground. Established pharmacological agents such as pregabalin and duloxetine remain central, but adjunctive interventions, including antioxidant-enriched nutritional strategies and neuromodulation techniques (e.g., transcutaneous electrical nerve stimulation, spinal cord stimulation), provide additional benefit. Regenerative approaches, notably stem cell therapy, exosome-based delivery, and CRISPR/Cas-mediated gene modulation, are emerging as promising avenues for reversing neuronal injury rather than merely alleviating symptoms. Advances in precision diagnostics and integrative therapeutics are redefining the clinical landscape of DN. Early identification through sensitive digital and molecular tools, combined with multimodal and regenerative treatment strategies, underscores a paradigm shift from reactive symptomatic care toward preventive neurology and personalised medicine. Harnessing these innovations may ultimately alter the trajectory of DN, reducing disability and improving long-term quality of life in patients with diabetes.

Nanosuspensions are an effective way to improve the delivery of drugs that do not dissolve well in water, especially those classified in the Biopharmaceutics Classification System (BCS) Classes II and IV. These drugs often have poor bioavailability. Traditional methods, like salt formation, solid dispersion, and reducing particle size, can help, but they also have drawbacks such as high cost, drug instability, and difficulties in large-scale production. Nanosuspensions are minuscule particles of pure drug, dispersed in a liquid with the help of stabilizers like surfactants or polymers. These systems are easy to prepare and can be scaled up for industrial use. They also work for both hydrophilic and lipophilic drugs. Common methods to make nanosuspensions include high-pressure homogenization, wet milling, and emulsification-solvent evaporation. Newer methods, such as bottom-up (precipitation) and top-down (grinding) technologies, are also being explored. By reducing the particle size, nanosuspensions increase the drug's surface area, which leads to faster and improved dissolution. This helps the drug work more effectively in the body. Nanosuspensions can be administered in different ways, like oral routes, injections, or applied to the skin, eyes, and lungs. They can also be designed to release the drug slowly over time or target specific parts of the body, which reduces side effects and how often a drug needs to be taken. This review focuses on how nanosuspensions are made, how they are tested and characterized, and how they are being used to improve the effectiveness of poorly soluble drugs. Their ability to improve solubility, stability, and bioavailability makes them a valuable option in modern drug delivery.

Background: Psychosis is a debilitating mental disorder characterized by a profound disconnection from reality, manifesting in symptoms such as hallucinations, delusions, and disorganized thinking. The pathophysiology of psychosis is multifaceted, involving an interplay of neurobiological, genetic, and environmental factors. Neurobiologically, dysregulation of neurotransmitter systems particularly dopaminergic, serotonergic, and glutamatergic pathways-plays a central role, with excessive dopaminergic activity linked to positive symptoms and glutamatergic dysfunction implicated in cognitive impairments. Genetic predisposition, evidenced by significant heritability and associations with specific genetic variants, intersects with environmental factors such as stress, trauma, and substance use to influence the onset and progression of psychosis. Cognitive disruptions, including deficits in attention, memory, and executive function, exacerbate the disorder’s impact. This phenomenon has led to short-term as well as long-term psychosocial and mental health implications for all. Objective: To determine the antipsychotic activity of pharmaceuticals, numerous antipsychotic screening models are available. Determining the optimal animal model for measuring antipsychotic activity is the primary goal of this study. Methods: A search for literature was carried out using several keywords, including "Antipsychotic," "In-vivo models," "In-vitro models," and "Behavior models," on the databases Science Direct and PubMed. For the purpose of obtaining the most appropriate articles to fulfil the goal of this review article, the search was customized by using the necessary filters. Results: Research and review articles based on neuroleptic screening models are available to determine the antipsychotic activity of novel pharmacological compounds. Conclusion: Following our research, we identified several helpful models for evaluating the antipsychotic activity of pharmaceuticals and proposed that combining invitro and in-vivo techniques with behavioral methodologies might yield the most appropriate results for our field of study.

Diabetes mellitus is a common long-term illness that requires steady medical treatment. Standard oral medicines for diabetes often have problems like low absorption, being broken down by the liver before they work, and patients forgetting to take their doses. Buccal drug delivery, which means giving medicine through the inside lining of the mouth, is a promising alternative. It helps control how fast the drug is released and avoids breakdown by the liver. This review examines the structure and function of the buccal mucosa, its drug-absorption capacity, and its utility for drug delivery. Different systems, such as tablets, patches, films, gels, and sticky polymers, are discussed, along with methods to help drugs pass through the mouth lining more easily. The article also explains how buccal drug delivery affects the behavior of medicines in the body, especially for diabetes drugs like Metformin and Glibenclamide. Clinical benefits, recent research, and future opportunities with new technologies are highlighted. Buccal drug delivery is convenient and can improve absorption, but it has limitations, such as poor absorption, mouth irritation, a small absorption area, salivary washout, low permeability, and commercial unavailability for some drugs. Similarly, many oral diabetes medicines, like metformin and alpha-glucosidase inhibitors, can irritate the stomach and intestines, making it harder for patients to continue treatment. These irritations can occur due to changes in pH, changes in gut motility and bile salts, fermentation of food, causing extra fluid in the intestines, or infections such as fungal overgrowth with SGLT2 inhibitors. This review aims to give a clear overview of buccal drug delivery for diabetes and its potential to improve treatment.

Neurodegenerative diseases present a considerable challenge to healthcare systems worldwide, prompting the exploration of innovative treatment strategies. Heterocyclic compounds, specifically those originating from the indole and imidazole structures, have garnered increasing interest due to their potential to protect neurons. Based on an in-depth literature survey in this review, we have compiled various derivatives of indole and imidazole scaffolds with potential in neurodegenerative disorders. This review also emphasized the Structure-Activity Relationship (SAR) and pharmacokinetics to reveal the active pharmacophores of various indole and imidazole analogs. We delve into the underlying molecular and cellular mechanisms involved in neurodegeneration, highlighting how indole and imidazole derivatives exert neuroprotective effects by modulating oxidative stress, inflammation, protein misfolding, inhibiting cholinesterase, and neuroinflammation. Finally, we address the challenges and prospects in translating these findings into clinical therapies, underscoring the need for continued research to optimize the safety and efficacy of heterocyclic compounds in the treatment of neurodegenerative disorders.

Background: A condition associated with neurodegeneration is termed Alzheimer's, characterized by mental as well as memory deterioration. Amyloid beta (Aβ) is a type of peptide that accumulates within the central nervous system and causes plaque, cerebral amyloid angiopathy (CAA), and neurodegeneration (loss of acetylcholine). These peptide forms have a major impact on neuronal damage. Objective: Our goal is to provide an overview of Aβ's potential involvement in developing Alzheimer's condition. Methods: An extensive literature review was done using the standard keywords “amyloid beta”, “amyloid beta precursor protein”, “secretase”, and “Alzheimer’s disease” from the databases SCOPUS, PubMed, Elsevier, and Web of Science. Conclusion: Amyloid-β precursor protein (AβPP) is converted to Aβ through a series of divisions by β and γ secretases, which are found in lipid rafts. The control of these elements is believed to have a significant role in Aβ production throughout the aetiology of Alzheimer's disease (AD). In conclusion, research on Aβ aggregation and clearance has been active in preventing and curing Alzheimer's disorders. Thus, the purpose of this article was to describe the pharmacological function of Aβ peptide plus its process of aggregation.