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Educational articles and scientific resources related to herbal medicines, phytochemicals, pharmacy practice, and pharmaceutical sciences.
What a Pharmacist Should Know About Herbal Medicines
A practical scientific resource about herbal medicines and pharmacy practice.
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Understanding Phytochemicals in Simple Language
An educational article about phytochemicals and their biological relevance.
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Carbohydrate Biopolymers
Scientific resource related to carbohydrate biopolymers and pharmaceutical relevance.
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Biotechnological Innovations in Nano-Medicine
Exploring cutting-edge biotechnological advances in nanomedicine for targeted drug delivery, diagnostics, and therapeutic applications.
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Upcoming research articles in preparation
Abstract
The fields of pharmacognosy, ethnopharmacology, and natural-product research is rapidly transforming and reshaping from predominantly descriptive and fragmented disciplines into an integrated, predictive, and systems-level science through the use of different artificial intelligence (AI) systems and methodologies. This review presents a comprehensive synthesis of how this contemporary AI methodologies including machine learning, deep learning, graph-based modeling, and natural language processing are redefining the discovery, interpretation, and clinical translation of plant-derived therapeutics. Importantly, by integrating plant genomics, transcriptomics, metabolomics, spectral data, ecological context, and ethnobotanical knowledge into unified proven computational frameworks, AI enables the reconstruction of biosynthetic pathways, prioritization of bioactive taxa, acceleration of dereplication, and prediction of structure-function relationships across diverse chemical classes. This review further demonstrates that ethnobotanical knowledge, when digitized and analyzed through AI-driven semantic modeling, constitutes a structured informational layer that reveals chemocultural convergence, hidden therapeutic associations, and underexplored biosynthetic potential across cultures. Scientists can identify new natural products at higher rates and speed through the combination of AI-assisted genome mining with multimodal metabolomics and spectrum-biosynthetic gene cluster linkage systems. The combination of AI-based germplasm authentication with genomic selection helps to develop sustainable medicinal plant cultivation which simultaneously enhances their genetic potential. Similarly, the combination of artificial intelligence with pharmacogenomics creates a scientifically based system which enables precision herbal medicine through genotype-based predictions of treatment effectiveness, safety and drug-herb reaction potential in different population groups. This review shows that pharmacognosy functions as an AI-driven knowledge system by combining modern research methods with ethical considerations, while illustrating how plants link human communities to cultural traditions through connected data networks. Collectively, it outlines a forward-looking paradigm in which artificial intelligence functions as the organizing infrastructure of natural-product science, enabling predictive discovery, culturally informed interpretation, and clinically actionable personalization of herbal therapeutics.
Abstract
Natural products continue to be one of the most important sources of therapeutic scaffolds, but turning them into real drug candidates is rarely straightforward. Their development is often slowed by structural complexity, low abundance in nature, difficult isolation, incomplete knowledge of their biosynthetic origins, and the challenge of achieving stereoselective synthesis. This review looks at how artificial intelligence is helping move pharmacognosy toward a more synthesis-oriented discipline by linking ethnobotanical knowledge, multi-omic data, biosynthetic pathway prediction, retrosynthetic planning, and medicinal chemistry optimization. A key focus is placed on AI-driven platforms that can reconstruct natural-product biosynthesis and support synthetic or semi-synthetic route design for complex metabolites. Tools such as BioNavi-NP, graph-sequence enhanced transformers, NAG2G, RSGPT, RetroExplainer, and human-in-the-loop systems like DeepRetro show how transformer-based, graph-based, and large language model-assisted approaches can predict plausible precursors, retain molecular topology, suggest multi-step disconnections, and explore broad reaction spaces. These methods are especially useful for natural products with dense stereochemistry, unusual ring systems, multifunctional scaffolds, and enzyme-guided biosynthetic logic, features that conventional retrosynthetic approaches may not always capture well. Beyond route prediction, AI can help prioritize biosynthetic genes, support the optimization of scarce plant-derived compounds, and guide the design of more drug-like analogues with improved potency, selectivity, pharmacokinetic behavior, and synthetic accessibility. Still, several limitations remain. These include the lack of plant-specific reaction datasets, weak prediction of reaction conditions, incomplete modeling of stereochemistry and regioselectivity, benchmark-related problems, and the ongoing need for expert chemical validation. Overall, AI should not be seen as a replacement for pharmacognosists, synthetic chemists, or medicinal chemists. It is better understood as a decision-support layer that connects biodiversity, traditional knowledge, biosynthetic logic, and experimental synthesis. With more transparent, plant-specific, and experimentally validated AI systems, the discovery and responsible development of natural-product-based therapeutics could become faster and more reliable.
Abstract
Silk sericin, a hydrophilic protein historically discarded during silk degumming, is emerging as a bioactive biomacromolecule for tissue engineering and regenerative medicine. Unlike conventional natural polymers such as collagen, gelatin, and alginate, sericin possesses a high density of reactive functional groups together with intrinsic antioxidant, anti-inflammatory, immunomodulatory, and cell-interactive properties. This review addresses a critical gap in the field by establishing clear extraction-structure-function relationships, which remain poorly defined in existing literature. By systematically correlating degumming and purification strategies with molecular weight distribution, secondary structure, purity, degradation behavior, and biological performance, we resolve inconsistencies reported across prior studies. Importantly, this work integrates sericin molecular chemistry with advanced material design, including porous scaffolds, electrospun nanofibers, injectable hydrogels, cryogels, inorganic composites, and bioinks for three-dimensional bioprinting. Mechanistically, sericin is shown to actively regulate cell adhesion, proliferation, differentiation, angiogenesis, and immune responses through integrin-mediated signaling, redox modulation, and growth factor interactions, enabling bio-instructive microenvironments rather than passive support. Silk sericin has significant applications across wound healing and multiple tissue engineering domains, including bone, cartilage, nerve, vascular, and corneal regeneration. Finally, translational challenges such as batch variability, degradation control, sterilization, and regulatory considerations are critically discussed within a cross-scale framework linking processing to functional outcomes.
Abstract
Nonsteroidal anti-inflammatory drugs (NSAIDs) remain among the most widely used therapeutic agents for the treatment of pain, inflammation, and chronic inflammatory disorders; however, their long-term clinical utility continues to be limited by gastrointestinal, cardiovascular, renal, and systemic toxicities. This review examines the medicinal chemistry and structure-activity relationship (SAR) evolution of major NSAID classes, including salicylates, profens, acetic acid derivatives, oxicams, fenamates, and selective coxibs, with particular emphasis on the structural determinants governing cyclooxygenase (COX) inhibition, isoform selectivity, pharmacokinetic behavior, and safety outcomes. Across classical NSAID families, conserved anchoring interactions such as acidic carboxylate binding or enolic hydrogen-bonding networks remain central to COX recognition; however, increasing evidence demonstrates that the final pharmacological profile is strongly influenced by hydrophobic topology, electronic distribution, steric architecture, conformational flexibility, and side-pocket accessibility. Importantly, excessive COX-2 selectivity does not necessarily translate into superior clinical safety, as prolonged vascular COX-2 suppression may disrupt prostacyclin/thromboxane balance and contribute to cardiovascular risk. Consequently, modern NSAID optimization has shifted from maximizing isolated COX inhibitory potency toward achieving a more balanced integration of selectivity, tissue exposure, tolerability, and multi-target therapeutic activity. Recent medicinal chemistry strategies including hybrid pharmacophores, NO- and H₂S-releasing prodrugs, dual COX/LOX inhibitors, NSAID-carbonic anhydrase inhibitor conjugates, and metal-based NSAID complexes have further expanded the therapeutic potential of this scaffold class beyond conventional anti-inflammatory mechanisms. Particular attention is given to oxaprozin as a flexible medicinal chemistry platform whose diaryl-oxazole framework enables structural modifications associated with altered selectivity, improved pharmacokinetics, and emerging biological activities including antioxidant and anticancer potential. Overall, the available evidence suggests that next-generation NSAID design should move beyond a simplified "stronger COX inhibition" paradigm toward an integrated, exposure-sensitive, and safety-oriented medicinal chemistry framework. Future progress in NSAID development will likely depend on simultaneously optimizing scaffold architecture, bioisosteric interactions, ADME behavior, multi-target modulation, and organ-specific tolerability to achieve safer and more versatile anti-inflammatory therapies.
Abstract
This review critically synthesizes recent advances in nanomaterial-enabled subcellular organelle targeting, highlighting how precise intracellular delivery is redefining therapeutic paradigms in gene therapy, oncology, and metabolic disease management. By integrating principles of cellular trafficking with rational Nano-carrier engineering, diverse platforms including lipid nanoparticles, polymeric systems, dendrimers, inorganic nanostructures, and extracellular vesicles are evaluated for their capacity to selectively localize within the nucleus, mitochondria, lysosomes, endoplasmic reticulum, and Golgi apparatus. These systems exploit ligand-directed recognition (e.g., NLS, TPP), biomimetic surface modifications, and stimuli-responsive release mechanisms to overcome key biological barriers such as endosomal entrapment, nonspecific distribution, and intracellular degradation. Particular emphasis is placed on nuclear-targeted gene delivery, LNP-mediated genome editing, and mitochondria-specific nanotherapeutics, demonstrating how organelle-level precision enhances therapeutic efficacy while minimizing systemic toxicity. The review further examines the emerging role of cytoskeletal interactions, peptide-guided targeting, and engineered extracellular vesicles in improving intracellular navigation and spatiotemporal control of drug release. Despite substantial progress, critical limitations and translational challenges persist which are being addressed, including heterogeneity in cellular uptake pathways, scalability of nanomaterial synthesis, long-term safety, and regulatory constraints. Collectively, this work provides a comprehensive and forward-looking perspective on the design, functionality, and clinical potential of organelle-targeted nanomedicine, emphasizing that future breakthroughs will depend on the convergence of materials science, molecular biology, and precision therapeutics to achieve truly programmable intracellular drug delivery systems.
Abstract
Poor aqueous solubility continues to limit the oral performance of many contemporary drug candidates, particularly Biopharmaceutics Classification System (BCS) class II and IV compounds. Although numerous formulation and molecular strategies have been developed to enhance apparent solubility, in vitro gains frequently fail to translate into predictable in vivo exposure. This review critically synthesizes current solubility- and bioavailability-enhancing approaches including amorphous solid dispersions, salts, co-crystals, prodrugs, lipid-based systems, nanotechnologies, particle size reduction, and permeability-modifying strategies through a unified mechanistic lens. Rather than cataloging technologies descriptively, the analysis focuses on the dynamic determinants of oral absorption: supersaturation generation and maintenance, precipitation kinetics, dissolution-permeation interplay, gastrointestinal microenvironment variability, and drug-excipient-physiology interactions. Particular attention is given to transient and metastable solubilization states under biorelevant conditions, which often govern absorption outcomes yet remain inadequately captured by conventional dissolution testing. The translational limitations of current experimental models are discussed alongside emerging integrative frameworks that combine biorelevant dissolution, precipitation profiling, and physiologically based pharmacokinetic (PBPK) modeling to improve predictive accuracy. By comparatively evaluating advantages, constraints, and failure modes across platforms, this review highlights critical trade-offs among solubility enhancement, permeability, stability, manufacturability, and clinical robustness. Ultimately, sustainable oral exposure requires not merely increasing solubility, but precisely controlling the temporal and spatial availability of dissolved drug within the gastrointestinal milieu. This mechanistic perspective provides a rational foundation for strategy selection and more reliable development of poorly water-soluble therapeutics.
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