International Journal of Modern Pharmacy and Life Sciences

Impact of the Bioconversion Process to Increase the Yield of Camptothecin (CPT)

Al-Baraa Akram El-Sayed — 2026-04-30

Background: One of the leading causes of death is cancer. So, there is a continuing need for the development of novel, reasonably priced, and efficiently acting natural anticancer medications. Camptothecins, which are produced from the bark of the divided Camptotheca acuminata plant, are among of the most effective anti-cancer medications that attack different cell types.Objective: Bioconversion is the ability of microorganisms to modify a wide variety of organic compounds to produce useful products. In this paper, they will explain how biotransformation can be helpful to increase the yield of Camptothecin from its resources similar to the process of cortisone production from Rhizopus nigricans which played a vital role in reducing the price of cortisone. This chemical product is a natural and very potent anti-cancer medication.Methods: DNA, RNA, or protein sequences can be arranged using sequence alignments to find related areas: may result from relationships that are structural, functional, or evolutionary. Three or more sequences are involved in multiple sequence alignments. Placing homologous positions of homologous sequences in the same column is the aim of multiple sequence alignments.Results: The active component is mostly found in sections of young plants that undergo photosynthesis. Although the alignment result is not very high, Endophytic fungi are a source of camptothecin indicates that biotransformation of Camptotheca acuminate is feasible if the fungi are properly cultivated. Conclusion: For obtaining the maximum and effective yield of camptothecin, this method has a number of advantages over chemical production. These benefits include the rations' region- and/or stereo-specificity and the processes' eco-friendliness.

Pharmacological Management of Benign Thyroid Nodules: A Comprehensive Systematic Review of Repurposed Therapeutics and Molecular-Targeted Strategies

Mahmoud Younis — 2026-02-26

Background: Benign thyroid nodules are highly prevalent, affecting approximately 50–60% of adults on high-resolution ultrasonography. Despite their frequency, management remains limited to surveillance, minimally invasive ablative techniques, or surgery. Earlier levothyroxine-based thyroid-stimulating hormone (TSH) suppression showed inconsistent efficacy and adverse metabolic effects, limiting its role in current clinical practice. Objective: This systematic review synthesizes evidence from clinical trials, preclinical studies, and epidemiological data to evaluate emerging pharmacological therapies for benign thyroid nodules. It focuses on TSH-modulating agents, selective THR-β agonists, mTOR pathway modulators, antifibrotic and vascular-targeted therapies, and mutation-specific agents, with particular emphasis on drug repurposing. Methods: A comprehensive search of PubMed, Scopus, Web of Science, and Embase (January 2000–December 2025) identified relevant clinical, preclinical, and observational studies. Data extraction focused on nodule volume reduction, safety profiles, and mechanistic insights from thyroid cell cultures, organoid models, and murine experiments. Study quality was assessed using standardized tools. Results: Levothyroxine achieved a 10–20% mean volume reduction in 1,245 patients, but caused hyperthyroidism in 14–19% of patients. Selective THR-β agonists produced a 21% reduction with improved safety over 24 weeks (n=48). Mutation-directed therapies targeting RET, BRAF, and NTRK yielded 25–32% reductions in preliminary cohorts. mTOR inhibitors achieved an 18% reduction with acceptable tolerability (n=12). Repurposed agents such as metformin (16% reduction, n=67) and statins (22% reduced prevalence in observational studies) showed moderate efficacy and favorable safety. Antifibrotic therapies demonstrated promising preclinical results, reducing fibrotic markers by 35–45%. Conclusion: Evidence supports a shift from observation-only strategies toward precision pharmacotherapy for selected benign thyroid nodules. Repurposing established drugs with known safety profiles offers the most feasible near-term approach, while molecular stratification may improve targeted treatment effectiveness and address an unmet need in endocrinology practice.

Integration of Artificial Intelligence, QSAR, and In Silico Approaches in Modern Drug Discovery

Satish Kumar Sarankar — 2026-04-30

The process of discovering new drugs involves intricate steps that demand significant time, resources, and often face high failure rates along with rising expenses. Combining Artificial Intelligence (AI), Quantitative Structure-Activity Relationship (QSAR) analysis, and sophisticated computational methods offers a promising solution to these issues. Techniques powered by AI, such as machine learning algorithms and deep neural networks, allow for streamlined processing of vast chemical and biological data sets, speeding up processes like target selection, virtual compound screening, and novel drug molecule creation. QSAR methodologies, encompassing classical, 2D, 3D, and group-based approaches, provide quantitative insights into structure activity relationships, whereas in silico tools such as molecular docking, molecular dynamics simulations, pharmacophore modeling, and ADME/Tox prediction enhance the evaluation and optimization of drug candidates. The integration of these methodologies through hybrid modeling approaches significantly improves predictive accuracy, reduces experimental burden, and accelerates the drug discovery pipeline. Real-world examples, such as AI-guided discovery of kinase inhibitors, antiviral agents developed amid the COVID-19 crisis, and innovative anticancer compounds, showcase the tangible benefits of these computational methods. Applications targeting diseases such as cancer, viral infections, and malaria, along with research on pyrimidine-based compounds, further validate the value of combined in silico strategies in practical settings. However, persistent hurdles, including inconsistent data quality, limited model transparency, and regulatory hurdles, continue to demand attention. Future perspectives emphasize the development of explainable AI, integration of multi-omics data, and the emergence of automated and cloud-based drug discovery platforms. Overall, the synergy of AI, QSAR modeling, and computational techniques establishes a powerful, adaptable platform ready to transform contemporary drug discovery and advance innovative therapies.

Genome Editing Beyond Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR): Comparative Insights into Alternative Tools

Nandita Kanodia — 2026-01-30

Genome editing has been revolutionized by the introduction of CRISPR–Cas systems, which, in a very short time, became the main tools in both research and clinical pipelines. Nevertheless, CRISPR-based methods have their limitations. Unintended targeting, protospacer adjacent motif (PAM) requirements, delivery difficulties, and possible immunogenicity are still the main challenges for a widely safe application. These limitations have led to the revival of older genome-editing technologies such as meganucleases, zinc-finger nucleases (ZFNs), and transcription activator-like effector nucleases (TALENs), as well as to newer inventions like base editors and prime editors. This review covers alternative genome-editing platforms that go beyond the use of classical CRISPR nucleases. The discussion covers molecular mechanisms, applications, and performance compared to CRISPR systems. Delivery strategies, such as viral, non-viral, and mRNA/RNP approaches, are examined along with their suitability for ex vivo and in vivo applications. Clinical case studies, including CCR5 editing for HIV, hemoglobinopathies, and in vivo therapies for liver and eye disorders, demonstrate real-world applications. Ethical and regulatory issues are also addressed, particularly regarding germline versus somatic editing, patient consent, long-term monitoring, and international policies. Analysis reveals that no single approach is best in all cases, and the choice of tools depends largely on the biological question or therapeutic goal. Next steps for these technologies include combining and refining methods and further developing the genome-editing toolkit to ensure safe and efficient clinical translation.