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  5. Volume 30, Issue 34, 2024

Current Pharmaceutical Design - Volume 30, Issue 34, 2024

Volume 30, Issue 34

Volume 30, Issue 34, 2024

    • Dental Caries: Unveiling the State-of-the-art Insights and Crafting Hypotheses for Oral Health

      https://doi.org/10.2174/0113816128318101240708095951

      The pathophysiological understanding of dental caries explains that the primary factor responsible is linked to an imbalance in microbial composition within the oral cavity, stemming from both artificial and natural sources. () is the most accountable and prevalent pathogen for caries development among the diverse pool. , an acidogenic bacterium, lowers oral pH through the metabolic conversion of dietary sugar into organic acids, leading to enamel demineralization and dental caries. Numerous antibacterial interventions have been employed in the past to address this issue. However, adopting such an approach poses the risk of exacerbating concerns related to Antimicrobial Resistance (AMR) and long-term oral cytotoxicity. In response to this, a sustainable strategy is suggested, involving the utilization of L-Arginine (L-Arg) as a probiotic nutrient supplement for non-pathogenic microbes. It will help in creating a natural competitive environment against the pathogenic microbes responsible for initiating dental caries. The hypothesis involves utilizing a combination of a nutrient supplement and the repurposed drug Piceatannol, specifically for its anti-biofilm properties. This combination synergistically improves the effectiveness of the therapy by converting the complex microbial biofilm into a planktonic state.

    • Prescription Precision: A Comprehensive Review of Intelligent Prescription Systems

      https://doi.org/10.2174/0113816128321623240719104337

      Intelligent Prescription Systems (IPS) represent a promising frontier in healthcare, offering the potential to optimize medication selection, dosing, and monitoring tailored to individual patient needs. This comprehensive review explores the current landscape of IPS, encompassing various technological approaches, applications, benefits, and challenges. IPS leverages advanced computational algorithms, machine learning techniques, and big data analytics to analyze patient-specific factors, such as medical history, genetic makeup, biomarkers, and lifestyle variables. By integrating this information with evidence-based guidelines, clinical decision support systems, and real-time patient data, IPS generates personalized treatment recommendations that enhance therapeutic outcomes while minimizing adverse effects and drug interactions. Key components of IPS include predictive modeling, drug-drug interaction detection, adverse event prediction, dose optimization, and medication adherence monitoring. These systems offer clinicians invaluable decision-support tools to navigate the complexities of medication management, particularly in the context of polypharmacy and chronic disease management. While IPS holds immense promise for improving patient care and reducing healthcare costs, several challenges must be addressed. These include data privacy and security concerns, interoperability issues, integration with existing electronic health record systems, and clinician adoption barriers. Additionally, the regulatory landscape surrounding IPS requires clarification to ensure compliance with evolving healthcare regulations. Despite these challenges, the rapid advancements in artificial intelligence, data analytics, and digital health technologies are driving the continued evolution and adoption of IPS. As precision medicine gains momentum, IPS is poised to play a central role in revolutionizing medication management, ultimately leading to more effective, personalized, and patient-centric healthcare delivery.

    • Recent Research Trends against Skin Carcinoma - An Overview

      https://doi.org/10.2174/0113816128307653240710044902

      Skin cancer is a prevalent and sometimes lethal cancer that affects a wide range of people. UV radiation exposure is the main cause of skin cancer. Immunosuppression, environmental factors, and genetic predisposition are other contributing variables. Fair-skinned people and those with a history of sunburns or severe sun exposure are more likely to experience this condition. Melanoma, squamous cell carcinoma (SCC), and basal cell carcinoma (BCC) are the three main forms. Melanoma poses a bigger hazard because of its tendency for metastasis, while SCC and BCC have limited metastatic potential. Genetic mutations and changes to signalling pathways such as p53 and MAPK are involved in pathogenesis. Early diagnosis is essential, and molecular testing, biopsy, dermoscopy, and visual inspection can all help. In addition to natural medicines like curcumin and green tea polyphenols, treatment options include immunotherapy, targeted therapy, radiation, surgery, and chemotherapy. Reducing the incidence of skin cancer requires preventive actions, including sun protection and early detection programs. An overview of skin cancers, including their forms, pathophysiology, diagnosis, and treatment, highlighting herbal therapy, is given in this review.

    • Ameliorates Oral Mucositis HIF and TNF Pathways: A Network Pharmacology Approach

      https://doi.org/10.2174/0113816128312694240712072959
      Background

      Oral mucositis is the most common and troublesome complication for cancer patients receiving radiotherapy or chemotherapy. Recent research has shown that , an important economic crop widely grown in China, has epithelial protective effects in several other organs. However, it is unknown whether or not can exert a beneficial effect on oral mucositis. Network pharmacology has been suggested to be applied in “multi-component-multi-target” functional food studies. The purpose of this study is to evaluate the effect of on oral mucositis through network pharmacology, molecular docking and experimental validation.

      Aims

      To explore the biological effects and molecular mechanisms of in the treatment of oral mucositis through network pharmacology and molecular docking combined with experimental validation.

      Methods

      Based on network pharmacology methods, we collected the active components and related targets of from public databases, as well as the targets related to oral mucositis. We mapped protein-protein interaction (PPI) networks, performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment, and constructed a 'components-disease-targets' network and 'components-pathways-targets' network using Cytoscape to further analyse the intrinsic molecular mechanisms of against oral mucositis. The affinity and stability predictions were performed using molecular docking strategies, and experiments were conducted to demonstrate the biological effects and possible mechanisms of against oral mucositis.

      Results

      A network was established between 49 components and 61 OM targets. The main active compounds were quercetin, beta-carotene, palmatine, and cyanin. The predicted core targets were IL-6, RELA, TP53, TNF, IL10, CTNNB1, AKT1, CDKN1A, HIF1A and MYC. The enrichment analysis predicted that the therapeutic effect was mainly through the regulation of inflammation, apoptosis, and hypoxia response with the involvement of TNF and HIF pathways. Molecular docking results showed that key components bind well to the core targets. In both chemically and radiation-induced OM models, significantly promoted healing and reduced inflammation. The experimental verification showed targeted the key genes (IL-6, RELA, TP53, TNF, IL10, CTNNB1, AKT1, CDKN1A, HIF1A, and MYC) through regulating the HIF and TNF signaling pathways, which were validated using the RT-qPCR, immunofluorescence staining and western blotting assays.

      Conclusion

      In conclusion, the present study systematically demonstrated the possible therapeutic effects and mechanisms of on oral mucositis through network pharmacology analysis and experimental validation. The results showed that could promote healing and reduce the inflammatory response through TNF and HIF signaling pathways.

    • Weight, Genotype, and Voriconazole Co-administration Influence Tacrolimus Initial Dosage in Pediatric Lung Transplantation Recipients with Low Hematocrit based on a Simulation Model

      https://doi.org/10.2174/0113816128318672240807112413
      Objective

      The method of administering the initial doses of tacrolimus in recipients of pediatric lung transplantation, especially in patients with low hematocrit, is not clear. The present study aims to explore whether weight, genotype, and voriconazole co-administration influence tacrolimus initial dosage in recipients of pediatric lung transplantation with low hematocrit based on safety and efficacy using a simulation model.

      Methods

      The present study utilized the tacrolimus population pharmacokinetic model, which was employed in lung transplantation recipients with low hematocrit.

      Results

      For pediatric lung transplantation recipients not carrying and without voriconazole, the recommended tacrolimus doses for weights of 10-13, 13-19, 19-22, 22-35, 35-38, and 38-40 kg are 0.03, 0.04, 0.05, 0.06, 0.07, and 0.08 mg/kg/day, which are split into two doses, respectively. For pediatric lung transplantation recipients carrying and without voriconazole, the recommended tacrolimus doses for weights of 10-18, 18-30, and 30-40 kg are 0.06, 0.08, 0.11 mg/kg/day, which are split into two doses, respectively. For pediatric lung transplantation recipients not carrying and with voriconazole, the recommended tacrolimus doses for weights of 10-20 and 20-40 kg are 0.02 and 0.03 mg/kg/day, which are split into two doses, respectively. For pediatric lung transplantation recipients carrying and with voriconazole, the recommended tacrolimus doses for weights of 10-20, 20-33, and 33-40 kg are 0.03, 0.04, and 0.05 mg/kg/day, which are split into two doses, respectively.

      Conclusion

      The present study is the first to recommend the initial dosages of tacrolimus in recipients of pediatric lung transplantation with low hematocrit using a simulation model.

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