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The Study on Extraction Process and Analysis of Components in Black Pepper and White Pepper
(Book Rivers, 2026) Saimah Khan, Afifa Baig, Abdul Rahman Khan
Black pepper is one of the most commonly consumed spices, and its pungency is due to the presence of an alkaloid known as piperine, volatile chemical constituents, and essential oils. Piperine is found in black pepper (piper nigrum), white pepper, and long pepper (piper longum) belonging to the family Piperaceae. Piperine represents diverse biological activities, such as anti-inflammatory, anticancer, antiviral, anti-larvicidal, pesticide, anti- ortantly piperine is known as the bioavailability enhancer. The study aims to explore the extraction of piperine from different species of pepper, its total synthesis, pharmacokinetic study and various biological activities of piperine. In this study the black pepper seeds were collected from local market of Lucknow, India. The extraction was done by using two extraction process - Steam distillation process and Soxhlet extraction process by optimizing conditions that affect the extraction process. Result demonstrated that in steam distillation process 50 gram of black pepper and white pepper dissolved in 300 ml distilled water undergo double distillation process for 3hours the obtained essential oil yield is 9.2% and 7.88% of black pepper and white pepper respectively whereas from soxhlet process the extracted oil yield for 3 hours is 12.03% and 10.5% of black pepper and white pepper respectively. From the result it was conducted that the % yield of essential oil (EO) obtained from both black pepper and white pepper using both the extraction follows the order:
% yield EO using soxhlet extraction > % yield of EO using steam distillation.
It was also concluded that the %yield of the EO obtained from black pepper is greater than that of the white pepper.
GC-MS Profiling and Antioxidant Activity of Musa paradisiaca L. Peels Fractions
(Book Rivers, 2026) Azeem Fatima, Mohd Arsh Khan, Shahzadi Bano, Abdul Rahman Khan, Jamal Akhtar Ansari
This study investigates the phytochemical composition and antioxidant activity of Musa paradisiaca L. (banana) peel extracts. Using GC-MS profiling, various chemical compounds within the peels were identified, including multiple phytochemicals with potential medicinal properties. Extraction and fractionation were performed with solvents of varying polarities, and antioxidant activity was measured using DPPH radical scavenging assays. The findings reveal a significant presence of compounds such as alkaloids, flavonoids, and reducing sugars, with high antioxidant potential observed particularly in chloroform and ethyl acetate fractions. This study highlights the potential of Musa paradisiaca peels as a source of bioactive compounds for pharmaceutical and nutraceutical applications, suggesting further research on their therapeutic benefits.
A Computational Study of Chalcone Derived Cu(II) and Zn (II) Complexes
(Book Rivers, 2026) Supreet, Tahmeena Khan, Mohammad Imran Ahmad
The simplicity of chalcone chemistry, coupled with the ease of structural modification and straightforward synthesis, has made them a focal point for medicinal chemists exploring a wide range of biological applications. Chalcone derivatives, renowned for their diverse biological activities, have gained significant attention in recent years. In this chapter, we employed computational methods to investigate the physicochemical properties of Cu(II) and Zn(II) complexes derived from various chalcone derivatives which lead to the identification of drug likeness. Computational predictions offer a powerful tool for accelerating drug discovery. By predicting molecular structure, properties, and potential side effects, these techniques can help identify promising lead compounds and optimize their design. Additionally, computational methods can aid in the identification of disease-specific targets, streamlining the development process. Careful consideration of ADMET properties, pharmacokinetics, metabolism, and safety profiles is essential to ensure the clinical viability of these compounds. The results obtained provide insights into the factors influencing the stability, reactivity, and potential biological activity of these metal complexes.
Computational and artificial intelligence methodologies for big data analytics in structural biology
(Academic Press, 2026) Mohammad Kalim Ahmad Khan , Umme Aiman, Salman Akhtar
High-throughput technologies have rapidly advanced, transforming structural biology and bioinformatics into data-intensive areas. Next-generation sequencing, cryo-electron microscopy (cryo-EM), high-resolution mass spectrometry, and large-scale interactomes generate unprecedented amounts of genomic, transcriptomic, proteomic, structural, and imaging data. This exponential growth of biological data poses significant computational and analytical hurdles, necessitating robust frameworks capable of integrating diverse data sources, minimizing noise, ensuring reproducibility, and providing biologically valuable insights. Protein structure prediction, drug discovery, functional annotation, and system-level studies have all been revolutionized by the emergence of machine learning, deep learning, and sophisticated statistical modeling as effective methods for handling this complexity. Despite these gains, many obstacles remain, including computing cost, data quality variance, algorithmic transparency, and concerns about data privacy and security concerns. This chapter reviews the major sources of biological big data, their defining characteristics, and the computational methodologies employed for their analysis, while surveying key applications across structural biology and bioinformatics. Special emphasis is placed on artificial intelligence (AI)-driven structure prediction, cryo-EM data processing, integrative modeling, multiomics analytics, and their growing impact on drug discovery and precision medicine. Emerging trends, such as explainable AI, quantum-enabled computation, and federated learning, are also discussed for their potential to enable interpretable, scalable, and privacy-preserving analytical frameworks. The synergy between big data analytics and experimental technologies will continue to speed up discoveries by enhancing mechanistic knowledge and facilitating translational advancements as it becomes an essential component of contemporary biological research.
Cancer therapy depending on tumor stroma
(Academic Press, 2026) Pranesh Kumar, Anurag Kumar Gautam, Abdul Baseer Khan
Cancers are made up of complex “ecosystems” of different cell types rather than just cancer cells. With significant involvement in tumor development, growth, and metastasis, the tumor stroma is an essential part of the tumor microenvironment. Although the tumor stroma can encourage cancer-cell resistance to anticancer treatments, the great majority of these treatments are designed to specifically kill cancer cells. Therefore, both antitumor and antistroma therapeutics should be included in future treatment strategies. The tumor stromal components can be exploited for targeted drug delivery by incorporating certain stromal components (ECM, fibroblast, cancer-associated fibroblast, osteoblasts, and chondrocytes) and homologously targeting stromal cells. The tumor microenvironment can also be changed by directly targeting components of the tumor stroma. In the present chapter, a thorough analysis of the latest developments in the understanding of the intricate reciprocal interactions between cancer cells and the tumor stroma was provided to develop potent treatment approaches for enhanced patient outcomes.