Interconnected Journal of Chemistry and Pharmaceutical Sciences (IJCPS)
The Interconnected Journal of Chemistry and Pharmaceutical Sciences (IJCPS) is a peer-reviewed, open-access International journal dedicated to advancing research in chemistry and pharmaceutical sciences. Our mission is to provide a global platform for researchers, academicians, and industry professionals to share innovative findings that drive scientific progress and practical applications.
Hyphenated Analytical Techniques in Pharmaceutical and Environmental Analysis
Hybrid analytical methods, which combine chromatographic separation with advanced spectrometric detection, are now useful for the precise detection and quantification of environmental pollutants and medicinal substances. This study compared the analytical capabilities of four hyphenated analytical platforms: HPLC-DAD, LC-MS/MS, GC-MS, and ICP-MS. These platforms stand for High-Performance Liquid Chromatography coupled with Diode Array Detection, Liquid Chromatography-Tandem Mass Spectrometry, and Inductively Coupled Plasma-Mass Spectrometry, respectively. The study employed a quantitative experimental design with twenty different pharmacological formulations and twenty different water samples from the environment. Checks for linearity, accuracy, precision, recovery, LODD, and LOQ were performed to ensure that analytical procedures were legitimate. The developed methods showed a good analytical performance in terms of a good correlation (r > 0.998), recovery (96.8–99.1%) and a good precision (RSD < 2%). LC-MS/MS proved to be more sensitive for the pharmaceutical compounds and ICP-MS was very good for trace elemental determination. The results of statistical analysis indicated that there were statistically significant differences among the techniques investigated (p < 0.05). Taken as a whole, the results confirm that these combined analytical methods can be used to develop dependable, sensitive and accurate analytical platforms for use in pharmaceutical QC and environmental monitoring, thereby extending their use to other research laboratories, regulatory testing and environmental surveillance.
Hyphenated Analytical Techniques, HPLC-DAD, LC-MS/MS, GC-MS, ICP-MS, Pharmaceutical Analysis, Environmental Monitoring.
Copyright (c) 2026 Interconnected Journal of Chemistry and Pharmaceutical Sciences (IJCPS)
Recent Developments in Anticancer Drug Discovery and Design
Cancer continues to be one of the significant diseases in terms of global public health concern and thus the development and design of novel strategies for anticancer drug discovery become a perpetual need. The advances made in various fields such as molecular biology, medicinal chemistry, immunology, nanotechnology, and computer science have made it possible to move beyond the traditional chemotherapy to precision-oriented and mechanism-based treatment of cancers. The aim of this review is to discuss the recent advancements in molecular targeting, precision medicine, biomarker guided drug design, synthetic lethality, drug resistant therapy, and tumor microenvironment targeting. Moreover, the article touches upon the increased use of AI, computer-aided drug design, multi-omics approaches, CRISPR-based screens, patient-derived models, and novel trial designs for accelerating cancer drug development. Although much success has been achieved in this field, obstacles like tumor heterogeneity, drug resistance, biomarker validation, manufacturing difficulties, and translating the findings into practice still exist. In general, the combination of precision medicine, computational approaches, and multidisciplinary collaboration is anticipated to facilitate the development of safer and more efficient anticancer drugs.
Anticancer Drug Discovery; Drug Design; Precision Oncology; Molecularly Targeted Therapy; Immunotherapy; Antibody–Drug Conjugates; Artificial Intelligence; Nanomedicine; Multi-Omics; Precision Medicine.
Targeted Drug Delivery Systems: Advances and Challenges in Medicinal Chemistry
The concept of targeted drug delivery systems (TDDS) is one of the greatest inventions in the field of medicinal chemistry allowing the selective drug delivery to diseased tissues avoiding potential systemic toxicity and improving the therapeutic efficacy. This review article gives a thorough presentation of the fundamentals, recent developments and human clinical application of TDDS with an accent on liposomes, polymeric nanoparticles, antibody–drug conjugates (ADCs), ligand-based drug delivery systems and novel smart nanocarriers. Human clinical data shows that such innovative delivery systems enhance pharmacokinetics of the drugs, their bioavailability, release from carrier and overall therapeutic effect in several diseases, including cancers, cardiovascular diseases, neurological disorders, diabetes, autoimmune diseases and infectious diseases. The review presents the advantages and disadvantages of modern TDDS technology and the major obstacles in the field, including biological barriers, safety issues, manufacturing complications, regulations and clinical translation of these technologies. The increasing use of artificial intelligence, precision medicine, gene and RNA therapeutics and biomarker-based drug delivery is highlighted. Therefore, TDDS are promising therapeutic systems improving medicinal chemistry through safer and more effective and personalized treatment of diseases.
Targeted Drug Delivery Systems; Medicinal Chemistry; Liposomes; Polymeric Nanoparticles; Antibody–Drug Conjugates (ADCs); Precision Medicine.
Group: Lesbian, Gay, Bisexual, Transgender, Queer (LGBTQ+): Differentiation, AI Models, Biomarkers, 3D Fingerprint Morphometrics, Health Disparities, Etiological, Pathological, and Epidemiological Gender Diverse Populations and Sexual Minorities
The Lesbian, Gay, Bisexual, Transgender, and Queer (LGBTQ+) population is known to be exposed to health disparities which depend on biological, psychological, social, and epidemiological variables. The present study analyzed health disparities, clinical biomarkers, three-dimensional (3D) fingerprint morphometric features, and factors affecting health in 600 individuals who defined themselves as being part of the LGBTQ+ population. The study was conducted using the quantitative cross-sectional analytical design with data collection carried out through the use of questionnaires, assessment of clinical biomarkers, 3D imaging of fingerprints, assessment of mental health using special tools and records in epidemiology. Statistical analysis was done using the following methods: descriptive statistics, Chi-square test, independent-samples t-test, one-way ANOVA, Pearson correlation, multiple regression, and machine learning algorithms of AI (artificial intelligence). Disparities in access to health care were found to be present in 41.5% of participants, whereas minority stress had the highest mean (4.29 ± 0.61). People who use preventive health services regularly demonstrated much better health status (p < .001) and there were statistically significant differences between groups of participants (p = .018). According to AI analysis, the most significant predictors of overall health were health care accessibility, inflammatory biomarkers, minority stress, and selected 3D fingerprint morphometric features (explanatory power 43.8%).
LGBTQ+, Artificial Intelligence, Health, Biomarkers, 3D ,Fingerprint Morphometrics, Health Disparities, Epidemiology, Machine Learning.
Heterocyclic Compounds in Medicinal Chemistry: Therapeutic Applications and Recent Developments
The class of heterocyclic compounds is considered among the most important bioactive groups of medicinal chemistry due to its wide structural diversity, good pharmacokinetics, and numerous therapeutic applications. Heterocyclic compounds containing nitrogen, oxygen, and sulfur atoms have become the basis for the synthesis of many drugs approved for cancer, infectious, cardiovascular, neurological, and metabolic disorders therapy. This paper provides a review of the classification of heterocyclic compounds, their SAR, and the importance of therapeutic applications based mainly on human clinical studies. The review gives a detailed description of the therapeutic use of heterocyclic compounds, focusing on the treatment of cancers, microbial infections, viruses, cardiovascular disorders, central nervous system, and metabolic disorders. At the same time, the paper outlines the latest advancements in heterocyclic drugs development, such as the creation of new heterocyclic scaffolds, AI-assisted drug design, structure-based drug discovery, heterocyclic drugs delivery using nanotechnology, and precision medicine techniques. Human clinical studies demonstrate that heterocyclic compounds possess high efficacy, high selectivity, and favorable safety profiles, which make them an integral part of pharmaceutical science. However, several challenges, such as increasing antimicrobial resistance, high costs associated with drug development, lack of long-term clinical data, and variable patient responses impede the widespread use of heterocyclic compounds. Besides, there are some research gaps, which should be addressed through multicenter human clinical studies, innovative computational tools, and multidisciplinary approaches in order to develop safer, more potent, and selective heterocyclic drugs.
Heterocyclic Compounds, Medicinal Chemistry, Structure–Activity Relationship (SAR), Therapeutic Applications, Drug Discovery, Human Clinical Studies
Atrial Septal Defects (ASD), Atrioventricular Septal Defect (AVSD), Ventricular Septal Defects (VSD), Patent Foramen Ovale (PFO): AI Echocardiography, Diagnosis, Biomarkers, Epidemiology, Pathophysiology, Etiology, ML, Heart Transplant
Different anatomical and clinical presentations are observed among congenital septal defects such as ASD, AVSD, VSD, and PFO. In this retrospective study, 320 patients have been analyzed in terms of clinical characteristics, biomarkers, AI-assisted echocardiography, and ML-based prediction models. In order to perform the analysis of clinical and echocardiographic parameters, descriptive statistics, ANOVA test, logistic regression analysis, and ML algorithms were used. Among these septal defects, AVSD demonstrates the highest haemodynamic load with larger defects, high levels of BNP/NT-proBNP, pulmonary hypertension, and low ventricular function. AI-assisted echocardiography is more effective compared to classical evaluation with 96.2% of diagnostic accuracy, 95.6% of sensitivity, and AUC = 0.972. The best-performing ML model is gradient boosting, which shows 95.0% of accuracy and AUC = 0.965. The elevated levels of BNP/NT-proBNP, poor ventricular function, and pulmonary hypertension significantly predict poor outcomes (p < .001). These results confirm that AI-assisted echocardiography, biomarkers, and ML-based predictions are effective approaches for diagnosing and risk stratifying congenital septal defects.
Septal Defects; AI Echocardiography; Machine Learning; Cardiac Biomarkers; Congenital Heart Disease; Risk Prediction.
Nanomaterials in Analytical Chemistry: Applications and Future Perspectives
Nanomaterials revolutionize analytical chemistry owing to their outstanding optical, electrical, magnetic, and catalytic properties, increasing sensitivity, selectivity, and analytical speed of the methods applied in human clinical diagnostics. The current review presents a detailed description of the main types of nanomaterials used in analytical chemistry and their application in human biological sample analysis. These include gold nanoparticles, silver nanoparticles, carbon nanotubes, graphene-based nanomaterials, quantum dots, and magnetic nanoparticles. In addition, the review considers their analytical characteristics, procedures, advantages and limitations, as well as the role of nanomaterials in cancer biomarker detection, infectious diseases diagnostics, cardiovascular and metabolic biomarkers testing, drug therapeutic concentrations measurement, and many others. Moreover, it is important to discuss the current progress made in the area of nano-biosensors, lab-on-a-chip, microfluidic, wearable diagnostic platforms, and analytical techniques assisted with artificial intelligence regarding their contribution to rapid, sensitive and noninvasive diagnostics. Based on the currently available data, it can be said that nanomaterial-based analytical systems provide significantly higher analytical performance compared with the traditional ones owing to lower detection limits, faster response time, better diagnostic accuracy, and the ability to work with low amounts of human biological samples. Nevertheless, issues such as variability in nanomaterial preparation, absence of standardized analytical procedures, potential toxicity and regulatory problems, as well as insufficient large-scale human clinical trials hinder their clinical application. Further research should be conducted in the field of standardization of nanomaterial fabrication, multicenter human clinical trials, integration of nanomaterials with artificial intelligence and digital healthcare technologies, and the creation of safe, economical, and portable diagnostic systems. Overall, it can be concluded that nanomaterials represent an excellent basis for future analytical technologies.
Nanomaterials; Analytical Chemistry; Clinical Diagnostics; Biosensors; Human Biomarkers; Personalized Medicine.
Pathophysiology, Etiology, and Uses of AI in Otological Disorders Like Audiological Disorders, Ear Infections (AIED), Excess Wax, Tinnitus, Ménière’s Disease
Otological diseases are a heterogeneous group with multiple etiologies and pathophysiologies which can impact on both auditory and vestibular function. The present study aimed at analysis of clinical, etiological and audiological characteristics of audiological disorders, autoimmune inner ear disease (AIED), excessive cerumen, tinnitus and Ménière's disease, and the possible use of the artificial intelligence (AI) in the diagnostic classification of these disorders. This study used a retrospective analytical design with a hypothetical sample of 250 patients which consisted of 50 patients from each of the five diagnostic groups. Analysis of clinical records, otoscopic findings, pure tone audiometry, speech audiometry and relevant findings of the vestibular function were performed. The pure-tone hearing thresholds varied significantly between the diagnostic groups (p<0.001) with AIED having the worst hearing loss. Recurrent vertigo, age, autoimmune/inflammatory history and hearing loss were all significant predictors of hearing impairment. Random forest, support vector machine and logistic regression were the evaluated AI methods with the highest diagnostic performance, with random forest having an accuracy of 91.6% and an AUC-ROC of 0.947. The results suggest that integrating clinical and audiological variables with AI analysis could improve the ability to distinguish among otological disorders and aid in clinical decision-making. These results need to be confirmed in large, multicentre clinical datasets before they can be routinely used in clinical practice.
Artificial Intelligence; Otological Disorders; Autoimmune Inner Ear Disease; Tinnitus; Ménière’s Disease; Audiological Assessment
Small Molecule Therapeutics in Drug Discovery: Current Trends and Future Directions
The advantages of small molecule therapeutics, such as their good pharmacological properties and wide range of therapeutic uses, make them a mainstay of the modern drug discovery process. This review highlights the latest developments in the field of artificial intelligence (AI), machine learning, targeted protein degradation (PROTACs), covalent drug design, and fragment-based drug discovery (FBDD), which are revolutionizing small molecule drug discovery and development. It illustrates the accelerating rate at which leads can be identified, the ability to expand the druggable proteome, and the potential to improve drug design with these new approaches, as well as their benefits, drawbacks, and current clinical applications. Some of the major challenges are also highlighted, such as limited clinical validation, drug resistance, bioavailability concerns and regulatory issues, as well as future research directions to make small-molecule therapeutics more efficient, precise and clinically translatable. In summary, the application of computational intelligence in conjunction with the latest advances in medicinal chemistry is projected to have a significant impact on the future of precision medicine and drug development.
Small-molecule therapeutics; Drug discovery; Artificial intelligence; PROTACs; Covalent inhibitors
Chemical Language Models for Activity-Guided De Novo Drug Discovery: A Hybrid Transfer Learning Framework for Intelligent Molecular Design and Virtual Screening
The identification of novel therapeutic molecules remains one of the most resource-intensive stages of pharmaceutical research owing to the enormous size of chemical space and the low probability of discovering compounds possessing favourable biological activity and acceptable pharmacokinetic characteristics. Recent advances in artificial intelligence have transformed computational drug discovery by enabling deep learning models to understand molecular representations and generate chemically valid structures. Among these approaches, Chemical Language Models (CLMs) have emerged as a powerful class of generative algorithms capable of learning molecular grammar directly from textual molecular representations such as Simplified Molecular Input Line Entry System (SMILES) strings. Unlike conventional quantitative structure–activity relationship models that depend primarily on predefined descriptors, CLMs autonomously capture complex structural relationships and generate novel molecular entities with desirable biological properties. The present study proposes a hybrid transfer-learning Chemical Language Model integrating molecular language modelling, perplexity-guided molecular prioritization, physicochemical property optimization, virtual screening, molecular docking, and explainable artificial intelligence for activity-guided de novo drug discovery. A transformer-based generative architecture was initially pretrained on a comprehensive molecular database to acquire generalized chemical knowledge and subsequently fine-tuned using experimentally validated phosphoinositide 3-kinase gamma (PI3Kγ) inhibitors. Molecular perplexity scoring was incorporated to rank generated compounds according to model confidence while simultaneously minimizing dataset bias. Newly generated molecules underwent sequential filtering using Lipinski's Rule of Five, Veber criteria, PAINS exclusion, synthetic accessibility assessment, ADMET prediction, molecular docking, and molecular dynamics simulation. Explainable artificial intelligence methods were further incorporated to interpret structural features contributing to predicted biological activity.The proposed computational framework demonstrated high molecular validity, structural novelty, scaffold diversity, and favourable predicted pharmacokinetic characteristics while substantially reducing computational screening efforts. Integration of transfer learning with perplexity-based prioritization improved candidate selection and reduced false-positive predictions compared with conventional generative models. The framework illustrates the growing potential of hybrid Chemical Language Models as intelligent molecular design platforms capable of accelerating early-stage drug discovery, particularly in therapeutic areas where experimentally validated datasets remain limited. The proposed workflow provides a scalable computational strategy for precision molecular design and supports future implementation of autonomous artificial intelligence systems in medicinal chemistry.
Chemical Language Models; De novo Drug Design; Transfer Learning; SMILES; Virtual Screening.
Peptide-Based Nanotherapeutics: Pharmacological Applications and Pharmaceutical Challenges
Peptide-based therapeutics have emerged as a powerful class of bioactive molecules owing to their high specificity, low toxicity, and ability to modulate complex biological pathways. However, their clinical potential is often limited by intrinsic drawbacks such as rapid enzymatic degradation, poor stability, short plasma half-life, and inefficient tissue penetration. Nanotechnology offers transformative solutions to these challenges by enabling the encapsulation, protection, and targeted delivery of peptide drugs. This review presents a comprehensive overview of the structural diversity and pharmacological relevance of peptide-based therapeutics, followed by an in-depth analysis of nanotechnology-driven strategies that enhance their efficacy. Various nanoplatforms—including polymeric nanoparticles, liposomes, solid lipid nanoparticles, hydrogels, dendrimers, and inorganic nanocarriers—are discussed with emphasis on their mechanisms for improving bioavailability, cellular uptake, and controlled release.The pharmacological applications of peptide-loaded nanocarriers span a broad spectrum, including cancer therapy, infectious diseases, metabolic disorders, neurological complications, cardiovascular diseases, and inflammatory conditions. Despite significant advancements, critical pharmaceutical barriers persist, such as peptide instability during formulation, low encapsulation efficiency, manufacturing challenges, and concerns related to toxicity, storage, and regulatory compliance. The review also highlights key clinical translation efforts, emerging regulatory frameworks, and the need for robust pharmacokinetic and biodistribution data. Finally, future directions such as AI-assisted peptide design, biomimetic nanoformulations, personalized nanomedicine, and peptide-based theranostic platforms are explored. Collectively, this review outlines the current landscape, existing challenges, and future opportunities in advancing peptide-based nanotherapeutics toward successful clinical implementation.
Peptide therapeutics; Nanocarriers; Controlled drug delivery; Biomedical nanotechnology; Pharmacokinetics; Clinical translation; Smart nanomedicine; Theranostics
Transdermal Nanomedicine: Pharmaceutical Advances for Controlled and Painless Drug Delivery
Transdermal drug delivery (TDD) offers a non-invasive, patient-friendly alternative to oral and injectable therapies by bypassing hepatic first-pass metabolism and providing controlled, sustained plasma levels. However, the stratum corneum represents a formidable barrier limiting drug permeation, restricting TDD to a narrow subset of therapeutically suitable agents and necessitating innovative delivery strategies. Nanomedicine has revolutionized transdermal drug delivery through nanocarrier platforms—including liposomes, niosomes, transfersomes, ethosomes, solid lipid nanoparticles (SLNs), nanostructured lipid carriers (NLCs), polymeric nanoparticles, and nanoemulsions—that enhance skin penetration, improve bioavailability, and enable targeted drug delivery. These nanocarriers exploit multiple complementary mechanisms, including lipid-mediated barrier modulation, transiently increased skin hydration, preferred routing through intercellular and appendageal pathways, and sustained, controlled release from the carrier matrix. Nanocarrier physicochemical properties—such as size, surface charge, elasticity, and composition—can be precisely engineered to optimize skin deposition, retention, and systemic absorption of diverse drug classes, ranging from small molecules to peptides, proteins, and nucleic acids. Clinical applications span pain management, hormone replacement therapy, cardiovascular disorders, and vaccine delivery, with demonstrated improvements in efficacy, reduced dosing frequency, and enhanced patient compliance compared to conventional formulations. Integration with advanced enhancement techniques—including microneedles, iontophoresis, sonophoresis, and stimuli-responsive triggers—further augments therapeutic outcomes and enables precision dosing. Challenges in large-scale manufacturing, formulation stability, batch-to-batch reproducibility, and regulatory harmonization persist, requiring continued innovation in formulation science and quality assurance. Future directions emphasize smart, stimuli-responsive nanocarriers, AI-driven optimization, personalized transdermal therapy, wearable device integration for real-time monitoring, and hybrid biomimetic systems. Collectively, transdermal nanomedicine represents a transformative paradigm, offering controlled, painless, and patient-centric drug delivery while expanding the therapeutic scope of previously unsuitable compounds—positioning nanomedicines as a cornerstone of next-generation pharmaceutical advancement and precision therapeutics.
Transdermal Drug Delivery, Nanocarriers, Nanomedicine, Skin Penetration, Controlled Release, Liposomes, Nanostructured Lipid Carriers, Polymeric Nanoparticles, Bioavailability, Transdermal Patches, Microneedles, Pharmacokinetics, Targeted Delivery, Patient Compliance, Theranostic Nanomedicine
Targeting the Blood–Brain Barrier: Pharmaceutical Nanotechnology Approaches in Neurotherapeutics
Crossing the blood–brain barrier (BBB) remains one of the greatest challenges in treating central nervous system (CNS) disorders, including Alzheimer’s disease, Parkinson’s disease, glioblastoma, and stroke. The BBB’s tightly regulated architecture—comprising endothelial cells, pericytes, astrocytes, and multifunctional transport systems—limits the penetration of most therapeutics, from small molecules to biologics. Nanotechnology has emerged as a transformative approach to overcome these delivery barriers by enabling precise engineering of size, surface chemistry, targeting ligands, and stimuli-responsive features. This review provides a comprehensive overview of the anatomy and physiology of the BBB, the key obstacles in CNS drug delivery, and the diverse classes of nanocarriers developed to enhance brain targeting, including polymeric nanoparticles, lipid-based systems, inorganic nanomaterials, biomimetic vectors, and hybrid multifunctional platforms. Advanced targeting strategies—such as receptor-mediated, carrier-mediated, and adsorptive-mediated transcytosis, along with focused ultrasound and intranasal delivery—are critically discussed for their therapeutic potential. We also examine pharmacokinetics, biodistribution, safety considerations, and the preclinical tools available for evaluating nanocarrier performance. Finally, we highlight ongoing challenges, regulatory constraints, and emerging trends such as AI-driven design, personalized neuro-nanomedicine, and next-generation BBB-on-chip systems. Together, these insights underscore the transformative potential of nanotechnology in shaping the future of neurotherapeutics.
Blood–Brain Barrier; CNS Drug Delivery; Nanocarriers; Neurotherapeutics; Receptor-Mediated Transcytosis; Biomimetic Nanoparticles; Lipid Nanoparticles; Nanomedicine
Pharmacokinetic Optimization through Polymer-Based Drug Delivery Systems: From Bench to Bedside
Pharmacokinetic optimization is fundamental to achieving therapeutic efficacy, yet many potent drug candidates fail in clinical translation due to limitations in solubility, stability, and bioavailability. Polymer-based drug delivery systems (PDDS) have emerged as transformative tools to overcome these challenges by modulating the absorption, distribution, metabolism, and excretion (ADME) characteristics of therapeutic agents. Through rational polymer design, these systems enhance solubility, protect drugs from enzymatic degradation, and provide controlled release profiles, resulting in prolonged systemic circulation and reduced dosing frequency. Advances such as PEGylation, biodegradable polymers (PLA, PLGA, PCL), and smart stimuli-responsive materials have significantly improved pharmacokinetic profiles across diverse drug classes. Moreover, polymer–drug conjugates, micelles, nanoparticles, and hydrogels enable targeted delivery, minimizing off-target effects and enhancing therapeutic index. Translational success stories, including Abraxane® and Oncaspar®, underscore the clinical impact of PDDS, though challenges persist in large-scale manufacturing, immunogenicity, and regulatory standardization. The integration of artificial intelligence, bioresponsive polymer chemistry, and personalized pharmacokinetic modeling represents the next frontier in customizable, patient-centric drug delivery. Collectively, polymer science continues to bridge the gap between molecular design and clinical application—driving the evolution of precision therapeutics from bench to bedside.
Polymer-based drug delivery systems, Pharmacokinetics, Controlled release, PEGylation, Biodegradable polymers, Smart polymers, Drug targeting, Personalized medicine, ADME optimization
Nanotherapeutics for Antimicrobial Resistance: Pharmaceutical Approaches to a Global Crisis
Antimicrobial resistance (AMR) has become a critical global health crisis, diminishing the effectiveness of conventional antibiotics and driving the need for innovative therapeutic strategies. Nanotechnology offers a powerful alternative by enabling improved drug delivery, enhanced stability, targeted release, and the ability to bypass common bacterial resistance mechanisms. Nanoparticles—including metal and metal-oxide systems, polymeric carriers, lipid-based structures, nanogels, carbon-based materials, and hybrid or stimuli-responsive platforms—demonstrate multifaceted antimicrobial activity through membrane disruption, biofilm penetration, controlled release, and synergistic drug combinations. These nanotherapeutic systems show efficacy against multidrug-resistant bacterial, viral, and fungal pathogens while reducing systemic toxicity and improving pharmacokinetics. Beyond treatment, nanotechnology enhances wound healing, supports antimicrobial device coatings, and advances precision targeting of infection sites. Despite significant promise, challenges persist in large-scale manufacturing, stability, toxicity assessment, and regulatory standardization. Continued advances in AI-guided nanoengineering, CRISPR-enabled antimicrobial systems, and bioinspired materials are expected to redefine the future of anti-infective therapy. Together, nanotherapeutics offer a transformative path forward in countering AMR and strengthening global infectious disease management.
Nanotherapeutics; Antimicrobial Resistance; Nanoparticles; Biofilm Penetration; Metal Nanoparticles; Polymeric Nanocarriers; Lipid-Based Nanocarriers; Nanogels; Carbon Nanomaterials; Hybrid Nanoparticles; Targeted Delivery; Stimuli-Responsive Systems; MDR Pathogens; Nano-Antibiotics; CRISPR Nanotherapy; AI-Guided Nanomedicine.
Translational Nanomedicine in Oncology: AI-Driven Multistage Targeting Strategies for Liver, Breast, Kidney, and Brain Cancers
Cancer continues to rank among the leading causes of global mortality, with liver, breast, kidney, and brain malignancies presenting some of the most complex therapeutic challenges. Conventional treatments such as chemotherapy, radiotherapy, and targeted agents are constrained by tumor heterogeneity, systemic toxicity, and the emergence of drug resistance.Aim: This systematic review evaluates advancements in nanomedicine between 2019 and 2024, focusing on translational innovations for liver, breast, kidney, and brain cancers. Special emphasis is placed on preclinical breakthroughs, clinical trial outcomes, and the integration of artificial intelligence (AI) and microfluidics in developing patient-specific therapeutic platforms. Methods: Following PRISMA guidelines, an extensive literature search was conducted across PubMed, Scopus, Web of Science, and ClinicalTrials.gov. Eligible studies included preclinical models, phase I–III clinical trials, and translational research on nanomedicine-based interventions for the selected cancers. Data were extracted on nanocarrier type, targeting strategy, and mechanism of action, efficacy, safety, and translational readiness. AI-driven design approaches and microfluidics-enabled synthesis platforms were also analyzed for their role in accelerating optimization and personalization. Result:Preclinical studies demonstrated the efficacy of multistage targeting nanocarriers incorporating passive, active, and hierarchical mechanisms, as well as stimuli-responsive systems triggered by tumor microenvironmental cues (pH, enzymatic activity, and hypoxia). Biomimetic nanoparticles, including cell membrane-coated carriers, showed enhanced immune evasion and tumor homing. Clinically validated examples such as liposomal doxorubicin, nanoparticle albumin-bound paclitaxel, and receptor-targeted polymeric nanoparticles reported improved tumor response rates, reduced off-target toxicity, and favorable safety profiles. Theranostic platforms integrating imaging and therapy enabled real-time monitoring and adaptive treatment strategies. AI algorithms facilitated predictive modeling of nanoparticle–tumor interactions, optimization of ligand density, and payload release kinetics, while microfluidics ensured scalable, reproducible manufacturing and organ-on-chip-based preclinical validation.Conclusion:Nanomedicine has transitioned from experimental proof-of-concept to a viable clinical reality in oncology, offering unprecedented precision, adaptability, and multifunctionality. The integration of AI-driven design and microfluidic fabrication is accelerating the translation of nanocarriers into patient-specific therapies, with the potential to overcome long-standing barriers in drug delivery. These advances mark a paradigm shift toward data-driven, personalized nanomedicine, positioning it as a cornerstone of next-generation cancer care.
Nanomedicine, Cancer therapy, Liver cancer, Breast cancer, Kidney cancer, Brain cancer, Multistage targeting, Biomimetic nanoparticles, Stimuli-responsive drug delivery, Theranostics, Artificial intelligence, Microfluidics, Patient-specific oncology, Translational nanomedicine, Precision oncology
Copyright (c) 2025 Interconnected Journal of Chemistry and Pharmaceutical Sciences (IJCPS)
Clinical Translation and Applications of Cancer Nanomedicine: A Systematic Review
Background: Cancer nanomedicine has emerged as a transformative paradigm that harnesses nanoscale platforms to enhance drug delivery, imaging, and patient outcomes. Between 2019 and 2024, remarkable advances have accelerated the clinical translation of nanomedicine, bridging experimental innovations with bedside applications. Objective: This systematic review evaluates the clinical applications of cancer nanomedicine reported from 2019 to 2024, with emphasis on technological innovations, translational challenges, regulatory approvals, real-world outcomes, and future prospects. Methods: A comprehensive literature search was conducted across PubMed, Scopus, Web of Science, and ClinicalTrials.gov, supplemented by regulatory databases. Inclusion criteria focused on clinical trials, preclinical studies with translational impact, regulatory approvals, and real-world data involving cancer nanomedicine. The PRISMA 2020 framework guided study selection, and methodological quality was assessed using validated tools. Results: A were included. Liposomes, dendrimers, polymeric nanoparticles, albumin-bound formulations, and biomimetic nanocarriers dominated the landscape, offering improved pharmacokinetics, targeted delivery, and reduced systemic toxicity. Clinical trials demonstrated efficacy across multiple malignancies, including breast, ovarian, lung, pancreatic, and hematological cancers. Regulatory approvals for agents such as nab-paclitaxel, liposomal irinotecan, and Vyxeos reinforced the clinical relevance of nanomedicine. Real-world data confirmed superior safety, reduced cardiotoxicity and neuropathy, and improved quality of life compared to conventional therapies. However, translational challenges nanotoxicity, scalability, cost, and regulatory complexityremain significant barriers. Conclusion: Cancer nanomedicine has progressed from conceptual innovation to a clinically validated reality, reshaping therapeutic strategies across oncology. Integration with immunotherapy, gene therapy, and AI-driven design promises to overcome current limitations, paving the way for truly personalized and precision-based cancer care.
Cancer nanomedicine, nanoparticles, clinical trials, drug delivery, liposomes, biomimetic carriers, regulatory approvals, personalized oncology
Nanotheranostics in Multi-Organ Oncology: A Systematic Review of Pharmacological Advances Integrating Omics and Artificial Intelligence
Background: Cancer continues to pose a major global health challenge, with liver, lung, pancreatic, breast, and brain tumors contributing substantially to morbidity and mortality. Conventional pharmacological approaches are limited by systemic toxicity, poor tumor specificity, and therapeutic resistance.Objective: This review systematically evaluates advances in nanotheranostics for multi-organ oncology between 2019 and 2025, emphasizing integration with omics technologies and AI-driven approaches.Methods: A comprehensive literature search was conducted across PubMed, Scopus, and Web of Science. Studies reporting preclinical and clinical nanotheranostic interventions were included, with data extracted on nanoparticle platforms, therapeutic and diagnostic mechanisms, omics integration, and AI applications.Results: Across all organs, multifunctional nanoparticles enabled simultaneous imaging and targeted therapy, with enhanced tumor specificity and reduced systemic toxicity. Integration of multi-omics data and AI-guided analytics facilitated patient-specific targeting, predictive modeling of therapeutic response, and adaptive treatment strategies. Organ-specific challenges, including the blood–brain barrier, dense stromal matrices, and molecular heterogeneity, necessitate tailored nanotheranostic designs. Emerging approaches such as stimuli-responsive and bioinspired nanoparticles show promise for precision oncology.Conclusion: Nanotheranostics, when integrated with omics and AI technologies, represent a powerful paradigm shift in multi-organ cancer therapy, offering personalized, adaptive, and precise interventions. Overcoming translational barriers and optimizing clinical implementation will be to realizing their full therapeutic potential.
Nanotheranostics, Multi-organ oncology, Targeted drug delivery, Imaging-guided therapy, Multi-omics integration, Artificial intelligence, personalized cancer therapy, Stimuli-responsive nanoparticles, Bioinspired nanoplatforms, Precision oncology
Systematic Review of Nanotheranostics in Cancer Pharmacology: Multi-Organ Insights from Liver to Brain Tumors
Cancer remains a leading cause of global morbidity and mortality, with liver, lung, pancreatic, breast, and brain tumors posing significant therapeutic challenges. Conventional pharmacological approaches are limited by systemic toxicity, poor tumor specificity, and therapeutic resistance. Nanotheranostics, integrating diagnostic imaging and targeted therapy within a single platform, offers a promising solution to these limitations. This systematic review (2019–2024) synthesizes recent preclinical and clinical advances in organ-specific nanotheranostic systems, highlighting mechanisms of targeted drug delivery, imaging-guided therapy, and multifunctional nanoparticles. Comparative analysis reveals shared strategies such as ligand-functionalization and stimuli-responsive drug release, alongside organ-specific barriers including the blood–brain barrier, dense stromal matrices, and molecular heterogeneity. Emerging trends emphasize AI-integrated, bioinspired, and multi-omics-enabled nanosystems, paving the way for personalized oncology. Despite regulatory, safety, and manufacturing challenges, nanotheranostics represents a paradigm shift toward precise, safer, and more effective cancer therapies.
Keywords: Nanotheranostics; Cancer Pharmacology; Liver Cancer; Lung Cancer; Pancreatic Cancer; Breast Cancer; Brain Tumors; Targeted Drug Delivery; Imaging-Guided Therapy; Precision Oncology
Integrating Nanotheranostics, Omics, and Artificial Intelligence for Precision Multi-Organ Cancer Management: A Systematic Review
Background: Cancer remains one of the leading global health challenges, characterized by profound heterogeneity at the genetic, molecular, and microenvironmental levels. Conventional therapies often fail due to late detection, therapeutic resistance, and systemic toxicity, underscoring the need for more precise and adaptable strategies.Objective: This systematic review synthesizes recent advances (2019–2024) in nanotheranostics, omics technologies, and artificial intelligence (AI), highlighting their convergence as a framework for precision multi-organ cancer management.Methods: Following PRISMA guidelines, a comprehensive literature search was conducted across PubMed, Scopus, Web of Science, ScienceDirect, and PMC. Eligible studies included original research on nanotheranostic platforms, omics-driven biomarker discovery, and AI-based oncology applications. Data were extracted on study design, cancer type, nanoplatform features, omics datasets, AI/ML models, and translational outcomes.Results: Nanotheranostics have advanced from multifunctional nanoparticles (e.g., gold nanoparticles, liposomes, polymeric systems) toward smart, stimuli-responsive platforms guided by AI and omics data. Omics approaches including genomics, transcriptomics, proteomics, metabolomics, and radiomics enable biomarker discovery, patient stratification, and target identification, while AI enhances nanoparticle design, predictive modeling, and real-time treatment adaptation. Applications span multiple cancers, including liver, breast, lung, pancreatic, and brain tumors. Challenges remain in overcoming tumor microenvironment heterogeneity, organ-specific barriers, biosafety concerns, and clinical translation, though early clinical studies report promising outcomes.
Nanotheranostics, Omics, Artificial Intelligence, Multi-organ cancer, Precision oncology, Biomarker discovery, personalized therapy
Pharmacophore Modeling of New Antitubercular Compounds
The constant risk of tuberculosis (TB) with its dangerous forms of drug resistance, including MDR-TB and XDR-TB, requires new approaches to treatment. The paper also utilizes a full-scale computation in the search of new antitubercular agents based on pharmacophore modeling. Ligand-based pharmacophore model (Hypo-1) was also generated using a curated set of 150 active molecules, which had five important features, including hydrogen bond acceptor, hydrogen bond donor, hydrophobic region, aromatic ring, and positive ionizable group. The virtual screening of 10000 compounds on the ZINC database was done using the model, which as a predictive model gained an AUC of 0.91. Pharmacophore fit scores, affinity of docking to InhA enzyme and ADMET profiling have led to top candidates’ selection. All five compounds were of good binding affinity and drug-likeness in addition to good pharmacokinetic results especially ZING000001 and ZINC000002. All these findings are pointing toward the finding that combined pharmacophore-docking-ADMET is a viable and feasible methodology of lead prioritization in terms of further experimental validation against TB.
Pharmacophore Modeling, Antitubercular Agents, Virtual Screening, ADMET, Molecular Docking
Development of Multi-Target Directed Ligands for Neurodegenerative Disorders: A Medicinal Chemistry Approach
Alzheimer and Parkinson disease are neurodegenerative disorders which are multi-factorial and have few treatment alternatives. Conventional single-effect medications usually cannot be effective against the rich variety of pathological methods involved, such as oxidative stress, cholinergic dysfunction, neuroinflammation, and protein aggregation. This review discusses the novel potential that Multi-Target Directed Ligands (MTDLs) offer within medicinal chemistry as a new way of working to overcome such limitations. MTDLs are single molecules designed rationally and having the capacity to affect several targets related to the disease at a time, which shows enhanced efficacy, fewer side effects, and better compliance by patients. Preclinical models of diverse MTDLs, -e.g. donepezil-built-in ferulic acid, and rasagiline hybrids have actually demonstrated cognitive andneuroprotective capability in rodent designs. Medicinal chemistry methods that were employed in thedevelopment of these ligands including pharmacophore hybridization, fragment-based design and structure-basedoptimization are also identified in the review. Despite the promising therapeutic indication associated with to theuse of MTDLs, limitations with regards to the predictability, model-predictivity of long-term toxicity data, andconventional evaluation tools remain to be the barrier towards translational efforts. The development of this fieldwill also involve interdisciplinary science, the use of enhanced AI tools in the design of drugs, and the use ofbiomarkers to perform clinical measurements to design effective, disease-modifying drugs.
Neurodegenerative disorders, Multi-target directed ligands (MTDLs), Medicinal chemistry, Alzheimer’s disease, Parkinson’s disease, Cholinesterase inhibition, Oxidative stress, Drug design
Synthesis and Biological Evaluation of Pyrazole Derivatives As Antidiabetic Agents
The rising menace of diabetes mellitus (type 2) has fuelled the quest to seek safer and more effective treatment options. Within this framework, the given research study was carried out with regard to designing, synthesis, characterization, and biological study of novel pyrazole derivatives as a potential antidiabetes drug. A series of 12 compounds has been produced by the process consisting of multi-step organic reaction which involves hydrazines, 10betabdiketones and aldehydes which are substituted. Analysis by FTIR, NMR, UV, and LC-MS was done to determine the structural characterization. The in vitro screening showed that several derivatives were found to be of great concern to α -amylase and α-Glucosidase inhibitor with PZ-09 having the IC50 values equivalent to acarbose. Further in vivo experiments with diabetic Wistar rats, induced by streptozotocin, showed that PZ-06 and PZ-09 decreased significantly the fasting blood glucose values during the 14-day use and this value was comparable to that of metformin. These results suggest that pyrazole derivatives, especially PZ-09 can be potentially used as multifunctional scaffold materials in the design of next-generation of antidiabetic agents.
Pyrazole derivatives, α-amylase inhibition, α-glucosidase inhibition, antidiabetic activity, streptozotocin-induced diabetes
Comparative Pharmacognostic Evaluation and Standardization of Root Extracts of Selected Ayurvedic Plants
This review gives a detailed comparative pharmacognostical and phytochemical appraisal of the roots extracts of three Ayurvedic plants, which are of great significance viz. Withania somnifera, Plumbago zeylanica, and Asparagus racemosus with a view of their possible applications in veterinary sciences. By examining morphological, microscopic and chemical outlines in exceptional details, the study provides diagnostic points that are to be used to authenticate botanicals and avoid adulteration. The existence of therapeutically significant constituents withanolides, plumbagin, and shatavarins was confirmed with phytochemical screenings with ability to confer diverse biological effects including antistress (adaptogenic), immunomodulatory, digestive, and reproductive properties to animals. Several species of animals (cattle, goats, and poultry) are investigated in preclinical studies, proving the effectiveness of these roots as enhancing stress-resistance, digestion, reproductive health, and immunity. State-of-the-art level of standardisation procedures like TLC, HPTLC and physicochemical test guarantees consistency in formulations, safety and quality of formulations. Based on the review, there is a high concern in the need to incorporate these standardized herbal medicines into animal practice medicine, especially in peripheral regions where prescription medicines cannot be located or are unavailable. Nevertheless, there is also a need to emphasize the consideration of optimization of doses, toxicity and regulations to achieve safe and effective usages. The results open up to viable, effective, and natural solutions in the health of animals.
Veterinary Pharmacognosy, Ayurvedic Root Extracts, Withania Somnifera, Plumbago Zeylanica, Standardization, Herbal Veterinary Medicine.
Microwave-Assisted Synthesis of Heterocyclic Scaffolds for Antioxidant Activity
This paper gives an overview of the formation of heterocyclic scaffold utilizing microwave-aided organic synthesis (MAOS) technique and defines the antioxidant potential of these synthesized scaffolds using in vitro assays. Ten heterocyclic compounds (HC-1 to HC-10), of derivatives of pyrazoles, thiazoles, imidazoles and oxadiazoles were prepared by utilizing equimolar mixtures of the starting materials at optimized microwave conditions. Confirmation of structural characterisation was established through FTIR, 1H NMR and MS. Antioxidant potential of individual compound was measured by DPPH, ABTS and FRAP assays. The strongest antioxidant agent was found to be HC-8 with the maximum radical scavenging activity and minimum IC 50 in all the tests and it is probably because of having a hydroxylated aromatic ring. The result of a statistical analysis established major differences in the activity between the compounds and there was an inverse proportional relationship between the antioxidant activity of the compounds and IC50. The results demonstrate the effectiveness of the microwave-assisted synthesis regarding the synthesis of biologically active heterocycles and speak to the potential of the molecule HC-8 as a lead molecule towards the development of antioxidant drugs.
Microwave-assisted synthesis; Heterocyclic compounds; Antioxidant activity; Free radical scavenging; Structure–activity relationship (SAR)
Thermosensitive Hydrogels for Ocular Delivery: Efficacy Evaluation in Rabbit Eye Models
The eye presents many barriers, making it hard to deliver drugs effectively. Eye drops and ointments, the usual dosage forms, are often cleared fast and have poor bioavailability, resulting in subpar treatment outcomes. Thermosensitive hydrogels have shown great promise as they change from a solution to a gel at the temperature of the eye, allowing drugs to be released slowly and in one area. In this review, we focus on describing the development, properties, and rabbit eye testing of thermosensitive hydrogels. The review covers temperature-responsive polymers, such as poloxamers, chitosan, hyaluronic acid, and gellan gum, and how they contribute to better delivery of drugs to the eye. A number of studies have found that these hydrogels help keep drugs in the eye longer, improve how well drugs are absorbed, and have good safety and tolerance in rabbit models. It is shown in the review that these hydrogels are able to solve many of the problems with standard ocular drug delivery methods and warrant further research in animals. Scientists should continue to refine the polymers, test safety over an extended time, and look into new therapeutic uses.
Ocular Drug Delivery, Thermosensitive Hydrogels, Temperature-Responsive Polymers, Poloxamers
In-Vivo Assessment of Colon-Targeted Nanoparticles for Anti-Inflammatory Drug Delivery in Guinea Pigs
The chronic gastrointestinal disease, characterized by a persistent inflammatory process and significantly affecting the quality of life, include ulcerative colitis, and Crohn’s disease (inflammatory bowel diseases, IBD). Systemic therapies used traditionally often are not able to provide the targeted delivery of drug to the colon with consequent lower efficacy and greater systemic side effects. This review focuses on in-vivo evaluation of colon-targeted nanoparticles for delivery of anti-inflammatory drug using guinea pig models include anatomical and physiological similarity to human gastrointestinal tract. The review range reaches a wide variety of nanoparticles –polymeric, lipid-based, micellar, dendrimer, and hybrid systems for the targeting mechanism – such as pHsensitivity, microbial triggers, mucoadhesion, ligand-mediated targeting. Some of the key findings suggest thatnanoparticle-based colon-targeted drug delivery systems (CTDDS) increase local drug concentration at sites ofactive inflammation and increase the therapeutic efficacy while reducing systemic exposure as compared toconventional treatment options. In guinea pigs, in-vivo measurements, show the efficacy of nanoparticles intargeting the gastrointestinal tract, having a site-specific drug release, reducing inflammatory markers as well andhelping mucosal healing. Notwithstanding these encouraging results, there are still such limitations as lack ofstandardized protocols, limited long-term safety data, and lack of chronic disease models. This review emphasizesthe capability of CTDDS to revolutionize the IBD management and directions of future research necessary forclinical translation, such as development of advanced models, scalability, and pilot human trials.
colon-targeted drug delivery systems, inflammatory bowel diseases, lipid-based, micellar, dendrimer
Photo-Catalytic Degradation of Antibiotic Residues Using Doped Tio₂ Under Natural Sunlight
The widespread presence of antibiotic residues in wastewater poses significant environmental and public health challenges, primarily due to their persistence and contribution to antibiotic resistance. This study investigates the photocatalytic degradation of antibiotic residues using doped titanium dioxide (TiO₂) nanoparticles under natural sunlight. TiO₂ was doped with metals and non-metals to enhance its photocatalytic efficiency by reducing the band gap and increasing visible light absorption. Characterization of the doped nanoparticles showed decreased crystal size, increased surface area, and improved light absorption compared to undoped TiO₂. The photocatalytic experiments demonstrated that doped TiO₂ significantly outperformed undoped TiO₂ in degrading antibiotic residues, with Zn-doped TiO₂ showing the highest degradation efficiency of up to 95% after 5 hours. The study also highlighted the optimal pH conditions for maximum degradation. Statistical analysis confirmed the significant impact of doping on photocatalytic performance. These findings suggest that doped TiO₂ nanoparticles are promising materials for the efficient and sustainable removal of antibiotic contaminants from wastewater using solar energy.
Photocatalysis, Doped TiO₂, Antibiotic Residues, Wastewater Treatment, Natural Sunlight
Synthesis And Characterisation of Green-Reduced Metal Oxide Nanoparticles Using Fungal Extracts
Environmental concerns about current chemical processes have led to interest in eco-friendly and sustainable metal oxide nanoparticle production. This study involves a green synthesis of ZnO, CuO, and Fe₂O₃ nanoparticles using fungal extracts, providing a safe and cost-effective option. UV-Visible Spectroscopy, FTIR, SEM, and XRD confirmed nanoparticle production, functional groups, shape, and crystalline structures. The UV-Vis analysis revealed characteristic absorption peaks at 297 nm (ZnO), 320 nm (CuO), and 350 nm (Fe₂O₃), while FTIR identified key functional groups responsible for reduction and stabilization. SEM analysis showed varying particle sizes and morphologies, and XRD confirmed their crystalline nature. Statistical analysis supported strong correlations between optical and morphological features. The findings demonstrate that fungal-mediated synthesis is an efficient and sustainable approach, paving the way for potential applications in biomedicine, catalysis, and environmental remediation.
Green synthesis, metal oxide nanoparticles, fungal extracts, eco-friendly nanotechnology