Abstract
Computational tools for predicting toxicity have been envisioned to have the potential to broadly impact up on the attrition rate of compounds in pre-clinical drug discovery and development. An integrated approach of computerassisted, predictive, and physico-chemical properties of a compound, along with its in vitro and in vivo analysis, needs to be routinely exercised in the lead identification and lead optimization processes. Starting with a good lead can save a lot of money and it can significantly reduce the entire drug discovery process. The journey towards triple Rs- reduce, replace and refine, further proves to be successful in predicting adverse drug reactions in patients (or animals) enrolled in clinical trials. However, the impact of predictive toxicity analysis was modest and relatively narrow in scope, due to the limited domain knowledge in this field. It is important to note that advances within medical science and newer approaches in drug development will require predictive toxicology applications to be viable. The field of computational toxicology has been heading in a direction more relevant to human diseases by reducing the adverse drug reactions. Therefore, efforts must be directed to integrating these tools relevant to the goal of preventing undesired toxicity in pre-clinical trials followed by different phases of clinical trials.
Keywords: ADME/T, CYP450, hERG, REACH, toxicoinformatics, Drug Discovery, Toxicology, physico-chemical properties, in vivo, clinical trials
Combinatorial Chemistry & High Throughput Screening
Title: Impact of Computational Structure-Based Predictive Toxicology in Drug Discovery
Volume: 14 Issue: 5
Author(s): Chethampadi Gopi Mohan
Affiliation:
Keywords: ADME/T, CYP450, hERG, REACH, toxicoinformatics, Drug Discovery, Toxicology, physico-chemical properties, in vivo, clinical trials
Abstract: Computational tools for predicting toxicity have been envisioned to have the potential to broadly impact up on the attrition rate of compounds in pre-clinical drug discovery and development. An integrated approach of computerassisted, predictive, and physico-chemical properties of a compound, along with its in vitro and in vivo analysis, needs to be routinely exercised in the lead identification and lead optimization processes. Starting with a good lead can save a lot of money and it can significantly reduce the entire drug discovery process. The journey towards triple Rs- reduce, replace and refine, further proves to be successful in predicting adverse drug reactions in patients (or animals) enrolled in clinical trials. However, the impact of predictive toxicity analysis was modest and relatively narrow in scope, due to the limited domain knowledge in this field. It is important to note that advances within medical science and newer approaches in drug development will require predictive toxicology applications to be viable. The field of computational toxicology has been heading in a direction more relevant to human diseases by reducing the adverse drug reactions. Therefore, efforts must be directed to integrating these tools relevant to the goal of preventing undesired toxicity in pre-clinical trials followed by different phases of clinical trials.
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Cite this article as:
Gopi Mohan Chethampadi, Impact of Computational Structure-Based Predictive Toxicology in Drug Discovery, Combinatorial Chemistry & High Throughput Screening 2011; 14 (5) . https://dx.doi.org/10.2174/138620711795508395
DOI https://dx.doi.org/10.2174/138620711795508395 |
Print ISSN 1386-2073 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-5402 |
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