COMPARISON OF COMPUTATIONAL MODELS IN THE PREDICTION OF ADVERSE DRUG REACTION

Soyemi, Jumoke (2015) COMPARISON OF COMPUTATIONAL MODELS IN THE PREDICTION OF ADVERSE DRUG REACTION. In: 3rd International Conference on Genomics & Pharmacogenomics, September 21-23, 2015, San Antonio, USA.

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Abstract

Computational pharmacology is the application of bioinformatics and computational biology with relevance to pharmacology, including understanding of drug action, adverse drug reaction, identification of drug targets and drug design. Early and accurate identification of adverse drug reactions (ADR) is critically important for drug development and clinical safety. Often times the adverse effect of drugs are not discovered until years later after the drugs’ release to the market. The post hoc analysis is usually unable to detect rare or delayed on-set ADR until clinical evidence accumulates. The process of drug development and ADRs discovery takes years, meaning that a lot of harm would have been caused to lives before evidences are accumulated, therefore developing a computational pharmacology model that can be used to make informed decisions so as to reduce the rate of attrition in drugs under development and increase the number of drugs with an acceptable benefit/risk ratio is paramount. This paper reviews the computational methods that have been used so far to address this issue and also compare them.

Item Type:	Conference or Workshop Item (Paper)
Subjects:	Q Science > Q Science (General) Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science
Depositing User:	Dr J. Soyemi
Date Deposited:	30 May 2020 14:55
Last Modified:	31 May 2020 06:02
URI:	http://eprints.federalpolyilaro.edu.ng/id/eprint/274

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