| Summary: | 碩士 === 國立臺灣大學 === 基因體與系統生物學學位學程 === 107 === The financial cost and time consumption of drug design are tremendous, yet adverse effects can cause drugs to be withdrawn during clinical trials or after the drugs are on the market. There had been no report directly investigating to what extent the chemical properties between drugs that have been withdrawn and those that are currently on the market are different. Understanding the factors connecting withdrawn drugs (WDs) and their adverse effects would help in preclinical toxicity studies. Knowing the distribution of properties between WDs and on market drugs (MDs) may provide new concepts for scientists to avoid developing a drug that may be withdrawn from the market or during clinical trials. In the works presented in this thesis, we systemically analyzed the chemical features of WDs and MDs. The result allowed us to propose ranges of chemical properties for “WD -like” and “MD-like” groups. The performance of filtering out WDs using previously established rules for drugs design such as Rule of Five, Ghose Filter, and MDDR-Like Rule was investigated and a new rule of a drug design “Rule of on market drugs” was proposed. We then developed a novel method to predict WDs and MDs by machine learning. Protein targets of WDs and MDs were also investigated. To explore the relationship between protein targets and drug-induced adverse effects, many features of adverse effect related proteins (ARPs) and non adverse effect related proteins (NARPs) were studied. We examined whether these features were significantly different between ARPs and NARPs. Machine learning was applied to classify ARPs and NARPs. The importance of each feature for the resulting model was assessed. The present studies of WDs and MDs, and also of ARPs and NARPs, allow us to develop some new guidelines for future drug development research to avoid or minimize the risk of adverse effect.
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