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Traditionally, drugs were discovered by testing compounds synthesized in time consuming multi-step processes against a battery of in vivo biological screens. Promising compounds were then further studied in development, where their pharmaceutical properties, metabolism and potential toxicity were investigated. Here we present a study on herbal lead compounds and their potential binding affinity to the effectors molecules of major diseases like endometrial cancer. Clinical studies demonstrate a positive correlation between the extent of PDK1 and malignant progression of pre cancerous lesions in…mehr

Produktbeschreibung
Traditionally, drugs were discovered by testing compounds synthesized in time consuming multi-step processes against a battery of in vivo biological screens. Promising compounds were then further studied in development, where their pharmaceutical properties, metabolism and potential toxicity were investigated. Here we present a study on herbal lead compounds and their potential binding affinity to the effectors molecules of major diseases like endometrial cancer. Clinical studies demonstrate a positive correlation between the extent of PDK1 and malignant progression of pre cancerous lesions in endometrial. Therefore, identification of effective, well-tolerated anti cancer inhibitor represents a rational chemo preventive strategy. This study has investigated the effects of naturally occurring compounds that inhibits the target. Our results reveal that these compounds use less energy to bind to the target and inhibit its activity than the commercial drugs used. Their high legend binding activity to the target introduce the prospect for their use in chemo preventive applications in addition they are freely available natural compounds that can be safely used to prevent disease.
Autorenporträt
Jayadeepa. R. M, M. Sc: Estudió Bioinformática en el Dr. G. R. Damodaran College of Science, Coimbatore,Tamilnadu, India; Investigador científico en el Institute Of Computational Biology,Bangalore, India Áreas de interés: Diseño de fármacos insilico, aprendizaje automático, programación en R, redes biológicas, análisis de datos de micromatrices, biología de sistemas, análisis de secuencias.