1,532 research outputs found

    Drug Adverse Event Detection in Health Plan Data Using the Gamma Poisson Shrinker and Comparison to the Tree-based Scan Statistic

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    Background: Drug adverse event (AE) signal detection using the Gamma Poisson Shrinker (GPS) is commonly applied in spontaneous reporting. AE signal detection using large observational health plan databases can expand medication safety surveillance. Methods: Using data from nine health plans, we conducted a pilot study to evaluate the implementation and findings of the GPS approach for two antifungal drugs, terbinafine and itraconazole, and two diabetes drugs, pioglitazone and rosiglitazone. We evaluated 1676 diagnosis codes grouped into 183 different clinical concepts and four levels of granularity. Several signaling thresholds were assessed. GPS results were compared to findings from a companion study using the identical analytic dataset but an alternative statistical method—the tree-based scan statistic (TreeScan). Results: We identified 71 statistical signals across two signaling thresholds and two methods, including closely-related signals of overlapping diagnosis definitions. Initial review found that most signals represented known adverse drug reactions or confounding. About 31% of signals met the highest signaling threshold. Conclusions: The GPS method was successfully applied to observational health plan data in a distributed data environment as a drug safety data mining method. There was substantial concordance between the GPS and TreeScan approaches. Key method implementation decisions relate to defining exposures and outcomes and informed choice of signaling thresholds

    Possibility of Multiple Drug-Drug Interactions in Patients Treated with Statins: Analysis of Data from the Japanese Adverse Drug Event Report (JADER) Database and Verification by Animal Experiments

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    Adverse drug events due to drug-drug interactions can be prevented by avoiding concomitant use of causative drugs; therefore, it is important to understand drug combinations that cause drug-drug interactions. Although many attempts to identify drug-drug interactions from real-world databases such as spontaneous reporting systems have been performed, little is known about drug-drug interactions caused by three or more drugs in polypharmacy, i.e., multiple drug-drug interactions. Therefore, we attempted to detect multiple drug-drug interactions using decision tree analysis using the Japanese Adverse Drug Event Report (JADER) database, a Japanese spontaneous reporting system. First, we used decision tree analysis to detect drug combinations that increase the risk of rhabdomyolysis in cases registered in the JADER database that used six statins. Next, the risk of three or more drug combinations that significantly increased the risk of rhabdomyolysis was validated with in vivo experiments in rats. The analysis identified a multiple drug-drug interaction signal only for pitavastatin. The reporting rate of rhabdomyolysis for pitavastatin in the JADER database was 0.09, and it increased to 0.16 in combination with allopurinol. Furthermore, the rate was even higher (0.40) in combination with valsartan. Additionally, necrosis of leg muscles was observed in some rats simultaneously treated with these three drugs, and their creatine kinase and myoglobin levels were elevated. The combination of pitavastatin, allopurinol, and valsartan should be treated with caution as a multiple drug-drug interaction. Since multiple drug-drug interactions were detected with decision tree analysis and the increased risk was verified in animal experiments, decision tree analysis is considered to be an effective method for detecting multiple drug-drug interactions.This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/). See http://ivyspring.com/terms for full terms and conditions

    Pharmacokinetic interactions of pioglitazone

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    Pioglitazone is a thiazolidinedione compound used in the treatment of type 2 diabetes. It has been reported to be metabolised by multiple cytochrome P450 (CYP) enzymes, including CYP2C8, CYP2C9 and CYP3A4 in vitro. The aims of this work were to identify the CYP enzymes mainly responsible for the elimination of pioglitazone in order to evaluate its potential for in vivo drug interactions, and to investigate the effects of CYP2C8- and CYP3A4-inhibiting drugs (gemfibrozil, montelukast, zafirlukast and itraconazole) on the pharmacokinetics of pioglitazone in healthy volunteers. In addition, the effect of induction of CYP enzymes on the pharmacokinetics of pioglitazone in healthy volunteers was investigated, with rifampicin as a model inducer. Finally, the effect of pioglitazone on CYP2C8 and CYP3A enzyme activity was examined in healthy volunteers using repaglinide as a model substrate. Study I was conducted in vitro using pooled human liver microsomes (HLM) and human recombinant CYP isoforms. Studies II to V were randomised, placebo-controlled cross-over studies with 2-4 phases each. A total of 10-12 healthy volunteers participated in each study. Pretreatment with clinically relevant doses with the inhibitor or inducer was followed by a single dose of pioglitazone or repaglinide, whereafter blood and urine samples were collected for the determination of drug concentrations. In vitro, the elimination of pioglitazone (1 µM) by HLM was markedly inhibited, in particular by CYP2C8 inhibitors, but also by CYP3A4 inhibitors. Of the recombinant CYP isoforms, CYP2C8 metabolised pioglitazone markedly, and CYP3A4 also had a significant effect. All of the tested CYP2C8 inhibitors (montelukast, zafirlukast, trimethoprim and gemfibrozil) concentration-dependently inhibited pioglitazone metabolism in HLM. In humans, gemfibrozil raised the area under the plasma concentration-time curve (AUC) of pioglitazone 3.2-fold (P < 0.001) and prolonged its elimination half-life (t½) from 8.3 to 22.7 hours (P < 0.001), but had no significant effect on its peak concentration (Cmax) compared with placebo. Gemfibrozil also increased the excretion of pioglitazone into urine and reduced the ratios of the active metabolites M-IV and M-III to pioglitazone in plasma and urine. Itraconazole had no significant effect on the pharmacokinetics of pioglitazone and did not alter the effect of gemfibrozil on pioglitazone pharmacokinetics. Rifampicin decreased the AUC of pioglitazone by 54% (P < 0.001) and shortened its dominant t½ from 4.9 to 2.3 hours (P < 0.001). No significant effect on Cmax was observed. Rifampicin also decreased the AUC of the metabolites M-IV and M-III, shortened their t½ and increased the ratios of the metabolite to pioglitazone in plasma and urine. Montelukast and zafirlukast did not affect the pharmacokinetics of pioglitazone. The pharmacokinetics of repaglinide remained unaffected by pioglitazone. These studies demonstrate the principal role of CYP2C8 in the metabolism of pioglitazone in humans. Gemfibrozil, an inhibitor of CYP2C8, increases and rifampicin, an inducer of CYP2C8 and other CYP enzymes, decreases the plasma concentrations of pioglitazone, which can necessitate blood glucose monitoring and adjustment of pioglitazone dosage. Montelukast and zafirlukast had no effects on the pharmacokinetics of pioglitazone, indicating that their inhibitory effect on CYP2C8 is negligible in vivo. Pioglitazone did not increase the plasma concentrations of repaglinide, indicating that its inhibitory effect on CYP2C8 and CYP3A4 is very weak in vivo.Diabeteslääke pioglitatsonin yhteisvaikutukset Aikuistyypin diabetesta sairastavat potilaat joutuvat usein käyttämään monia lääkkeitä samanaikaisesti ja ovat siten erityisen alttiita lääkeaineiden haitallisille yhteisvaikutuksille. Pioglitatsoni on uudehko tablettimuotoinen diabeteslääke, joka poistuu elimistöstä pääasiassa hajoamalla (metaboloitumalla) maksan sytokromi P450 (CYP) entsyymien välityksellä. Muut lääkkeet voivat estää (inhiboida) tai kiihdyttää (indusoida) näiden entsyymien kautta tapahtuvaa lääkeainemetaboliaa. Ennen tätä väitöstutkimusta pioglitatsonin yhteisvaikutuksista oli vain vähän julkaistua tutkimustietoa. Ensimmäisessä tutkimuksessa selvitettiin koeputkiolosuhteissa (in vitro -menetelmillä) pioglitatsonin metaboliaa eri CYP-entsyymien välityksellä sekä useiden CYP-estäjien vaikutusta pioglitatsonin metaboliaan. Siinä havaittiin pioglitatsonin metaboloituvan pääasiassa CYP2C8- ja jonkin verran myös CYP3A4-entsyymin välityksellä ja että pioglitatsonilla saattaisi olla merkittäviä yhteisvaikutuksia näitä entsyymejä estävien lääkkeiden kanssa. Kliinisissä lääketutkimuksissa (in vivo -tutkimukset) selvitettiin viiden eri lääkkeen vaikutusta pioglitatsonin pitoisuuksiin elimistössä. Lisäksi tutkittiin pioglitatsonin vaikutusta toisen diabeteslääkkeen, repaglinidin, pitoisuuksiin. Tutkimukset tehtiin terveillä vapaaehtoisilla koehenkilöillä käyttäen lumekontrolloitua vaihtovuoroista koeasetelmaa. Koehenkilöille annettiin tavanomaisina hoitoannoksina tutkimuslääkkeitä muutaman päivän ajan ja tämän jälkeen kerta-annoksena pioglitatsonia tai repaglinidia. Koehenkilöiltä kerättiin useita verinäytteitä, joista määritettiin lääkepitoisuudet herkillä massaspektrometrisillä menetelmillä. CYP2C8-entsyymiä estävä lipidilääke gemfibrotsiili keskimäärin yli kolminkertaisti pioglitatsonin pitoisuudet. Toisaalta CYP3A4-entsyymiä estävä sienilääke itrakonatsoli ei vaikuttanut pioglitatsonin pitoisuuksiin. Astmalääkkeet montelukasti ja tsafirlukasti (CYP2C8-estäjiä in vitro) eivät myöskään vaikuttaneet pioglitatsonin pitoisuuksiin, vaikka ne estivät hyvin voimakkaasti pioglitatsonin metaboliaa in vitro -tutkimuksissa. Rifampisiini-antibiootin aiheuttama CYP-entsyymien induktio pienensi pioglitatsonin pitoisuuksia keskimäärin yli 50 %. Pioglitatsoni ei vaikuttanut repaglinidin pitoisuuksiin (metaboloituu CYP2C8- ja CYP3A4-entsyymien välityksellä). Tehdyt havainnot ovat käytännön lääkehoidon kannalta merkityksellisiä. Gemfibrotsiilin tai muiden CYP2C8-entsyymiä estävien lääkkeiden yhteiskäyttö voi lisätä pioglitatsonin tehoa ja annosriippuvaisia haittavaikutuksia. Rifampisiinin tai muiden CYP2C8-entsyymiä indusoivien lääkkeiden aloittaminen pioglitatsonia käyttävälle potilaalle voi heikentää verensokeritasapainoa. Itrakonatsoli ei vaikuttanut pioglitatsonin pitoisuuksiin viitaten siihen, ettei CYP3A4-entsyymi ole tärkeä pioglitatsonin metaboliassa in vivo. Montelukasti ja tsafirlukasti eivät vaikuta normaaleilla hoitoannoksilla merkittävästi estävän CYP2C8-välitteistä lääkemetaboliaa. Pioglitatsoni ei myöskään itse vaikuta estävän CYP2C8- tai CYP3A4-välitteistä lääkemetaboliaa

    Pharmacoepidemiologic Exploration of Increased Eating Drives Associated with Antidiabetic Medications

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    학위논문(박사) -- 서울대학교대학원 : 의과대학 임상의과학과, 2021.8. 최형진.임상 개발 과정에서 대부분의 안전성 탐지, 분석 노력은 동물 독성 연구에서 얻은 중요한 결과 또는 1 차 및 2 차 약력학적 효과에 기반한 가설을 사람에서 유심히 관찰하는 것에 집중되어 있다. 약물이 시장에 출시된 후 약물 역학적 연구는 주로 사망률, 심각한 이환율 또는 객관적으로 정량화 할 수 있는 결과들 (예: 검사 수치, 영상 바이오마커)의 분석에 결과에 집중되어 있다. 현존하는 약물 안전의 제도는 임상 현장에서 혹은 실생활에서 환자의 주관적인 약물 경험을 조사하는 데 큰 관심을 보이지 않았다. 이 논문은 기존 약물 감시 데이터베이스 중 미국 FDA의 약물 부작용 보고 시스템 (FAERS)의 빅데이터가 환자의 주관적 약물 경험을 탐색할 수 있는지를 보고자 했다. 환자의 주관적 경험 중 배고픔, 식욕, 음식에 대한 갈망 등 식이 행동의 동기와 연관된 지각들이 약물 부작용으로 경험되고 보고되는지를 탐색했다. 탐색에는 미국 FDA의 시판 후 약물 부작용 데이터베이스에 보고된 당뇨약과 연관된 식사 동기를 증가시키는 부작용이 사용되었다. 미국의 약물 처방 자료를 참고하여 미국에서 많이 쓰이는 6개의 약물군에 속한 15개의 당뇨약과 식욕 증가와 관련된 부작용 용어가 조합된 보고를 추출했다. 부작용 용어로 배고픔 (hunger), 음식에 대한 갈망 (food craving), 식욕 증가 (increased appetite)가 사용되었다. 이 부작용 들은 개별적 신호 탐색에도 쓰였고 세 부작용 용어의 보고 빈도의 합으로도 탐색 되었다. 미 FDA 데이터베이스에서 1968년부터 2020년 12월 30일까지 전체 데이터에서 약물-부작용 조합을 추출하였다. 부작용 신호 탐색에 흔히 쓰는 기법 중 reporting odds ratio (ROR)을 사용하였다. 이는 다른 모든 약과 비교해서 특정 약물의 특정 부작용 보고 비율의 비교의 균형을 보는 불균형 계산 (disproportionality) 방법 중 하나로 이 값의 95% 신뢰 구간의 하부경계값이 1을 넘으면 부작용 신호로 해석했다. 모든 계열의 당뇨약이 식사 동기 증가 부작용과 2.00 [1.74, 2.31]에서 12.38 [11.81, 12.98] 범위의 ROR [95 % CI] 값의 유의한 연관을 보였다. 개별 당뇨약의 식사 동기 증가 부작용의 ROR [95 % CI]은 다음과 같았다: 메트포르민은 2.00 [1.74, 2.31], 리나글립틴은 2.29 [1.46, 3.59], 삭 사글립틴 1.85 [0.96, 3.55], 시타글립틴 3.20 [2.64, 3.89], 둘라글루타이드 4.69 [4.06, 5.42], 엑세나타이드 16.22 [15.31, 17.18], 리라글루타이드 12.55 [11.42, 13.78], 세마글루타이드 9.63 [7.50, 12.37], 카나글리플로진 2.98 [2.39, 3.73], 다파글리플로진 6.93 [5.17, 9.29], 엠파글리플로진 2.49 [1.84, 3.37], 글리메피리드 3.07 [2.12, 4.45], 글리피지드 5.03 [3.90, 6.48], 글리부라이드 3.31 [2.39, 4.57], 피오글리타존 3.06 [2.42, 3.87]. FAERS에는 상당한 수의 주관적인 환자 경험 ADR이 포함되었다. 20,000 개가 넘는 부작용 용어 중 세 개의 식사 동기 증가 용어가 전체 부작용 보고 사례의 0.1 %를 차지했다. 약물 주관적 경험은 의료인보다 소비자가 더 자주 보고하는 것으로 보인다. 식사 동기 증가 부작용의 보고자 중 69.33 %는 소비자, 23.94 %는 의료인이었다. 모든 계열의 당뇨약에서 의료인 (9.89-35.48 %)보다 소비자 (33.82-89.70 %)가 더 많은 보고를 하였고, 남성 (25.64-34.36 %)보다 여성 (57.26-72.45 %)에서 더 많은 식사 동기 증가가 보고되었다. FAERS는 환자의 주관적 경험에 대한 초기 신호 탐색 및 가설 생성을 위한 유용한 도구로 보여진다. 심각한 부작용이 더 선택적으로 보고되는 것으로 보여지나 환자의 주관적 불편함도 충분한 사례가 보고되어 있다. 환자의 주관적인 약물 경험은 의료인보다는 환자가 보고하는 경우가 많았는데 이것이 환자와 의사가 생각하는 치료의 목표와 그 과정에서 중요하게 생각하는 점의 불일치에서 기인하는지를 이해하는 것은 치료의 관계 및 약물 순응도에 중요할 것으로 보여진다. 약물감시체계가 이런 주관적인 환자 경험을 탐색할 수 있는 유용한 도구가 되기 위해서는 약물과 부작용 관리 시스템에 대한 환자의 지식과 이해를 필요로 한다. 미국 FDA 데이터베이스는 의미 있는 정보원이 될 수 있을 것으로 보인다.During clinical development, much of the safety detection and analysis effort is centered on assessing the human equivalent of significant findings from animal toxicology studies. Moreover, toxicities hypothesized from primary and secondary pharmacologic effects profiling are also used for safety analysis. After a drug launches in the market, postmarketing safety surveillance systems focus mainly on hard outcomes of mortality, serious morbidity, or objectively quantifiable outcomes (e.g., laboratory data, imaging biomarkers). Institutions using these pillars of drug safety have not had much interest in examining “soft signs” or subjective patient drug experiences. This study explores whether an existing pharmacovigilance database, namely the U.S. Food and Drug Administration’s Adverse Events Reporting System (FAERS), can be used to examine soft signals of subjective patient experiences, especially those related to motivational aspects of eating. Antidiabetic drugs were used to examine whether subjective patient drug experiences of increased eating drives could be detected using the FAERS. Referencing U.S. prescription data, 15 non-insulin, single agent antidiabetic drugs (ADDs) most frequently prescribed in the United States from 6 ADD classes were used. Event terms used to extract adverse drug reactions (ADRs) of increased eating drives were hunger, food craving, and increased appetite. An aggregate search was also performed combining the 3 event terms. Drug-event pairs were extracted for periods of FAERS existence from 1968 to December 31, 2020. The reporting odds ratio (ROR) was used for a disproportionality calculation in which a ROR with a lower margin of the 95% CI >1 was defined as a positive ADR signal. All ADD classes yielded positive safety signals of increased eating drives: ROR [95% CI] calculations ranging from 2.00 [1.74, 2.31] to 12.38 [11.81, 12.98]. For the individual ADDs, the RORs [95% CI] for increased eating drives were: 2.00 [1.74, 2.31] for metformin, 2.29 [1.46, 3.59] for linagliptin, 1.85 [0.96, 3.55] for saxagliptin, 3.20 [2.64, 3.89] for sitagliptin, 4.69 [4.06, 5.42] for dulaglutide, 16.22 [15.31, 17.18] for exenatide, 12.55 [11.42, 13.78] for liraglutide, 9.63 [7.50, 12.37] for semaglutide, 2.98 [2.39, 3.73] for canagliflozin, 6.93 [5.17, 9.29] for dapagliflozin, 2.49 [1.84, 3.37] for empagliflozin, 3.07 [2.12, 4.45] for glimepiride, 5.03 [3.90, 6.48] for glipizide, 3.31 [2.39, 4.57] for glyburide, and 3.06 [2.42, 3.87] for pioglitazone. The FAERS contained substantial numbers of subjective patient experience ADRs. Out of over 20,000 event terms, the three event terms for increased eating drives totaled 0.1% of all case reports in the FAERS. Soft signals seem to be more frequently reported by consumers than by healthcare providers. 69.33% of the reports of increased eating drives for all drugs were from consumers and 23.94% from healthcare providers. For all ADD classes, more reports of increased eating drives were received from consumers (33.82-89.70%) than healthcare providers (9.89-35.48%) and from women (57.26-72.45%) than men (25.64-34.36%). Patients may offer information about previously unknown ADRs that physicians cannot observe or quantify. Educated consumers can be valuable partner in the post-marketing surveillance of drug safety. Patient distressful drug experiences can affect treatment adherence and therapeutic. FAERS and other patient reporting systems might be useful tools in detecting adverse patient drug experiences.1. INTRODUCTION 1 1.1. Pharmacovigilance systems 1 1.1.1. Rationale for legislation: protection of public health 1 1.1.2. Definitions 2 1.1.3. Sources of report 2 1.1.4. Valid report 3 1.1.5. Coding of AEs: MedDRA 4 1.1.6. Causality 9 1.1.7. Non-clinical safety 11 1.1.8. Clinical trial safety data 12 1.1.9. Continuous characterization of safety in the post-market authorization 15 1.2. Spontaneous reporting systems 17 1.2.1. Limitations of spontaneous reports 18 1.2.2. Strengths of SRS 21 1.2.3. Databases 22 1.3. Signal detection 24 1.3.1. Disproportionality Analyses 26 1.4. Relatively benign soft ADRs 30 1.4.1. ABCDE Classification 30 1.4.2. Where do subjective patient experience ADRs fit in the ABCDE scheme? 31 1.4.3. Patient drug experience and adherence 31 1.4.4. Examples of soft signals detected from SRS 34 1.5. Increased eating drive as an ADR 35 1.5.1. Hunger, appetite, craving—wanting, liking, needing 36 1.6. Diabetes, antidiabetic drugs and the drive to eat 40 1.6.1. Glucostatic theory 41 1.6.2. Food addiction and diabetes 42 1.6.3. Biologically driven to eat more 45 1.7. Research question: antidiabetic drug—increased eating drives explored with FAERS data 45 2. METHODS 47 2.1. Source of spontaneous report database 47 2.1.1. FAERS 47 2.1.2. Characteristics of individual case safety reports in FAERS 47 2.2. Reaction terms examined 55 2.2.1. ADD-increased eating drives 55 2.3. Drug names 59 2.3.1. ADD-increased eating drives 59 2.4. Disproportionality analyses 62 2.5. Ethical statements 62 3. RESULTS 63 3.1. All cases for all drugs in the FAERS 63 3.2. ICSR characteristics of ADD classes 64 3.3. Disproportionality analyses 67 4. DISCUSSION 80 4.1. Summary of key findings 80 4.1.1. Appearance of a reporting bias of serious and lethal cases in FAERS 80 4.1.2. Numerous spontaneous ADR reports of increased eating drives were in FAERS 81 4.1.3. Three-fold more reports of increased eating drives are received from consumers (especially women) than healthcare professionals 81 4.1.4. Increased eating drives was a positive ADR signal for all ADD classes; strongest signal was observed with GLP1RAs 82 4.1.5. Increased drive to eat, behavioral output, and associated physical exam were not trending together in the FAERS 83 4.2. Interpretations 83 4.2.1. Bias toward reporting ADRs with serious outcomes 83 4.2.2. Patients, especially women, were more likely to report increased eating drives than physicians 84 4.2.3. What was unexpected—associations of GLP1RA and increased eating drives 86 4.2.4. Discordance in signal directions of eating drives, eating behavior, and weight increase 98 4.3. Limitations 104 4.3.1. Cannot assess comparative risk based on strength of the signal 104 4.3.2. The magnitude of the problem cannot be estimated 105 4.3.3. Association is not causation 107 4.4. Implications 108 4.4.1. FAERS can be used to detect signals of subjective patient experience ADRs 108 4.4.2. Consumer reports may be more sensitive to detect soft signals of subjective patient experiences 108 4.4.3. Informed consumers may be key to successful PV activities for signals that matter to patients 110 4.4.4. Increased eating drives associated with ADDs requires further evaluation 113 4.5. Recommendations for signal evaluation of increased eating drives associated with ADDs 114 4.5.1. For characterization 114 4.5.2. Susceptibility factors 114 5. CONCLUSION 115 REFERENCES 116 국문 초록 137박

    The utility of HepG2 cells to identify direct mitochondrial dysfunction in the absence of cell death.

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    Drug-induced mitochondrial dysfunction has been hypothesized to be an important determining factor in the onset of drug-induced liver injury. It is essential to develop robust screens with which to identify drug-induced mitochondrial toxicity and to dissect its role in hepatotoxicity. In this study we have characterised a mechanistically refined HepG2 model, using a panel of selected hepatotoxicants and non-hepatotoxicants. We have demonstrated that acute metabolic modification, via glucose-deprivation over a 4 h period immediately prior to compound addition, is sufficient to allow the identification of drugs which induce mitochondrial dysfunction, in the absence of cell death over a short exposure (2 – 8 h) using a plate-based screen to measure cellular ATP content and cytotoxicity. These effects were verified by measuring changes in cellular respiration, via oxygen consumption and extracellular acidification rates. Overall, these studies demonstrate the utility of HepG2 cells for the identification of mitochondrial toxins which act directly on the electron transport chain and that the dual assessment of ATP content alongside cytotoxicity provides an enhanced mechanistic understanding of the causes of toxicity

    Pharmacogenetics in type 2 diabetes:Influence on response to oral hypoglycemic agents

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    Adem Yesuf Dawed, Kaixin Zhou, Ewan Robert Pearson&nbsp;&nbsp;Division of Cardiovascular and Diabetes Medicine, Medical Research Institute, University of Dundee, Dundee, Scotland, UK Abstract: Type 2 diabetes is one of the leading causes of morbidity and mortality, consuming a significant proportion of public health spending. Oral hypoglycemic agents (OHAs) are the frontline treatment approaches after lifestyle changes. However, huge interindividual variation in response to OHAs results in unnecessary treatment failure. In addition to nongenetic factors, genetic factors are thought to contribute to much of such variability, highlighting the importance of the potential of pharmacogenetics to improve therapeutic outcome. Despite the presence of conflicting results, significant progress has been made in an effort to identify the genetic markers associated with pharmacokinetics, pharmacodynamics, and ultimately therapeutic response and/or adverse outcomes to OHAs. As such, this article presents a comprehensive review of current knowledge on pharmacogenetics of OHAs and provides insights into knowledge gaps and future directions. Keywords: pharmacogenetics, type 2 diabetes, oral hypoglycemic agents, pharmacokinetics, pharmacodynamics, respons

    New and emerging agents in the management of lipodystrophy in HIV-infected patients

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    Lipodystrophy remains a major long-term complication in human immunodeficiency virus-infected patients under antiretroviral (ARV) therapy. Patients may present with lipoatrophy or lipohypertrophy or both. The choice of treatments to improve fat redistribution depends on the form of lipodystrophy and its duration. Measures known to improve lipoatrophy are switches in ARV therapy (stavudine or zidovudine to abacavir or tenofovir) and filling interventions. Pioglitazone may be added to these measures, although any benefits appear small. Uridine and leptin were found to be disappointing so far. Regarding lipohypertrophy, diet and exercise, recombinant human growth hormone, and metformin may reduce visceral fat, but may worsen subcutaneous lipoatrophy. Surgical therapy may be required. Attractive pharmacologic treatments include growth hormone-releasing factor and leptin. Adiponectin and adiponectin receptors are promising therapeutic targets to explore
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