S-Space College of Medicine/School of Medicine (의과대학/대학원) Dept. of Biomedical Sciences (대학원 의과학과) Theses (Ph.D. / Sc.D._의과학과)
Variability among personal genome sequences as a measure of drug safety
약물 안전성 척도로서의 개인 유전체 염기서열 다양성
- 의과대학 의과학과
- Issue Date
- 서울대학교 대학원
- Adverse Drug Reactions ; Safety-based Drug Withdrawal ; Product Recalls and Withdrawals ; Genetic Variation/Genetics ; Pharmacogenetics ; Ethnic Groups ; Organization for Economic Cooperation and Development ; Health Expenditure ; Socioeconomic Factors
- 학위논문 (박사)-- 서울대학교 대학원 : 의과학과, 2017. 2. 김주한.
- Introduction: Despite substantial premarket efforts, a significant proportion of approved drugs are withdrawn from the market for safety reasons. A drug that has proven clinical efficacy in many patients often fails to work in other patients, or may even cause serious side effects including death. The person-to-person variability in a drug response is a major challenge in current clinical practice, drug development, and drug regulation.
Methods: To determine the personal genetic and government regulatory features associated with drug withdrawal, we analyzed two different types of data. At first, to evaluate the factors associate with drug regulation at each government, the relationship between the number of withdrawn/restricted drugs and socioeconomic, health, and welfare indicators were investigated in a comprehensive review of drug regulation information in the United Nations (UN) countries. A total of 362 drugs were withdrawn and 248 were restricted during 1950–2010, corresponding to rates of 12.02 ± 13.07 and 5.77 ± 8.69 (mean ± SD), respectively, among 94 UN countries. A socioeconomic, health, and welfare analysis was performed for 33 OECD countries for which data were available regarding withdrawn/restricted drugs. Next, person-to-person variability between 2,504 individual genomic sequences for the genes involved in drug pharmacokinetics (PK) and pharmacodynamics (PD) was used to assess the effect of genomic diversity on populations for drug safety. The deleterious impact of nonsynonymous substitutions predicted by the SIFT algorithm on structure and function of drug-related proteins was evaluated for 2504 personal genomes. The selected 1,041 drugs were classified into withdrawn, precautionary (by the Beers criteria or US FDA), and other drugs
the impact of nonsynonymous substitutions on protein structure and function was predicted by the SIFT algorithm.
Results: The gross domestic product (GDP) per capita, GDP per hour worked, health expenditure per GDP, and elderly population rate were positively correlated with the numbers of withdrawn and restricted drugs (P < 0.05), while the out-of-pocket health expenditure payment rate was negatively correlated. The number of restricted drugs was also correlated with the rate of drug-related deaths (P < 0.05). The World Bank data cross-validated the findings of 33 OECD countries. The lists of withdrawn/restricted drugs showed markedly poor international agreement between them (Fleisss kappa=–0.114). Twenty-seven drugs that had been withdrawn internationally by manufacturers are still available in some countries. The wide variation in the numbers of drug withdrawals and restrictions among countries indicates the need to improve drug surveillance systems and regulatory communication networks.
Drugs withdrawn from the market (n = 154) and precautionary drugs from the Beers list (n = 90) and US FDA pharmacogenomic labeled drug list (n = 96) showed significantly lower population deleterious gene variant scores (P < 0.001) compared to other drugs (n = 752). Furthermore, the rates of drug withdrawals and precautions significantly correlated with the population deleterious gene variant scores (P < 0.01)
this trend was confirmed for all drugs included in the withdrawal and precaution lists by the United Nations, European Medicines Agency, DrugBank, Beers criteria, and US FDA.
Conclusions: The interchange of the nation-wide adverse drug reaction occurrence and regulatory decision information should be more active when considering the variety and severity of ADRs. Given the variability of the damaging profile of drug-associated genes of each person and ethnic group, we could estimate how variable of the combinations of dangerous drugs for every people. Otherwise, certain drugs are predicted to be the most dangerous drugs with no exception for every people and every ethnicity. If the relationships of PK/PD genes and drugs would be further accumulated, the accuracy of this prediction method will even higher.