Various drugs affect body weight as a side effect. We conducted this systematic review and meta-analysis to summarize the evidence about commonly prescribed drugs and their association with weight change. MEDLINE, DARE, and the Cochrane Database of Systematic Reviews were searched to identify published systematic reviews as a source for trials. We included randomized trials that compared an a priori selected list of drugs to placebo and measured weight change. We extracted data in duplicate and assessed the methodological quality using the Cochrane risk of bias tool. We included 257 randomized trials (54 different drugs; 84 696 patients enrolled). Weight gain was associated with the use of amitriptyline (1.8 kg), mirtazapine (1.5 kg), olanzapine (2.4 kg), quetiapine (1.1 kg), risperidone (0.8 kg), gabapentin (2.2 kg), tolbutamide (2.8 kg), pioglitazone (2.6 kg), glimepiride (2.1 kg), gliclazide (1.8 kg), glyburide (2.6 kg), glipizide (2.2 kg), sitagliptin (0.55 kg), and nateglinide (0.3 kg). Weight loss was associated with the use of metformin (1.1 kg), acarbose (0.4 kg), miglitol (0.7 kg), pramlintide (2.3 kg), liraglutide (1.7 kg), exenatide (1.2 kg), zonisamide (7.7 kg), topiramate (3.8 kg), bupropion (1.3 kg), and fluoxetine (1.3 kg). For many other remaining drugs (including antihypertensives and antihistamines), the weight change was either statistically nonsignificant or supported by very low-quality evidence. Several drugs are associated with weight change of varying magnitude. Data are provided to guide the choice of drug when several options exist and institute preemptive weight loss strategies when obesogenic drugs are prescribed.

Background: Pancreatic neuroendocrine tumors (pNETs) are rare cancers with outcomes determined by multiple factors including grade, stage, and clinical presentation. In this study, we aimed to determine the prognosis of patients with pNETs using a large population-based database. Materials and Methods: In this population-based study, we identified patients with pNETs from the SEER 18 registry (2000-2016) using a combination of ICD-O-3 and histology codes. We calculated age-adjusted incidence rates using SEER*Stat 8.3.5. In addition, we analyzed overall survival (OS) using the Kaplan-Meier method, and investigated prognostic factors using a multivariable Cox proportional hazards model. Results: A total of 8944 pNETs patients were identified. Annual incidence rates increased from 0.27 to 1.00 per 100 000. This was largely explained by an increase in number of patients diagnosed with localized disease in more recent years (2012-2016). Median OS was 68 months (95% CI [64, 73]) and 5-year OS rates in localized, regional, and metastatic disease were 83%, 67%, and 28%, respectively. There was a significant improvement in OS for patients diagnosed between 2009 and 2016 (median OS 85 months) compared with those diagnosed between 2000 and 2008 (median OS 46 months) (HR 0.66; 95% CI [0.62, 0.70]). This improvement in OS was consistent across all stages. Conclusions and Relevance: This study shows a steady increase pNETs incidence with notable stage migration to earlier stages in recent years. This increase in incidence is accompanied by a significant improvement in survival across different disease stages.

Background: Patients with solid organ transplants (SOTs) have been excluded from programmed death protein-1 (PD-1)/programmed death ligand-1 (PD-L1) inhibitor clinical trials due to concern for allograft rejection. The use of immune checkpoint inhibitor therapy remains controversial in transplant patients. Methods: A retrospective pilot evaluation was conducted to assess the safety and efficacy of PD-1 inhibitors in patients with liver transplantation (LT). The primary endpoint was the rate of allograft rejection. Secondary endpoints included overall response rate (ORR), progression free survival (PFS) and overall survival (OS). Translational objectives included evaluation of tumor PD-L1, tumor infiltrating lymphocytes (TILs) and allograft PD-L1 expression. Results: Seven metastatic cancer patients with a history of LT who received PD-1 inhibitor therapy were included [hepatocellular carcinoma (HCC), n=5; melanoma, n=2]. Rejection was observed in 2 of 7 patients. When rejection occurs it appears to be an early event with a median time to rejection of 24 days in our cohort. One patient achieved a complete response (CR), 3 patients had progressive disease (PD) and 3 patients discontinued therapy prior to restaging assessments. Two of five patients with available tissue had PD-L1 expression in the allograft and both developed rejection. One of five evaluable patients had abundant TILs. Two of five evaluable patients had PD-L1 tumor staining. The single patient with both abundant TILs and PD-L1 staining obtained a response. The median OS and PFS were 1.1 (0.3–21.1) and 1.8 (0.7–21.1) months, respectively. Conclusions: In this pilot evaluation both preliminary efficacy (1 of 4) and allograft rejection (2 of 7) were exhibited in evaluable patients. Larger, prospective trials are needed to elucidate optimal patient selection.

Importance To date, an empirical evaluation of the quality of control arms in randomized clinical trials (RCTs) leading to anticancer drug approvals by the US Food and Drug Administration (FDA) has not been undertaken. Objective We sought to estimate the percentage of RCTs that used a control arm deemed suboptimal and led to FDA approval of anticancer drugs from January 1, 2013, to July 31, 2018. Design, Setting, and Participants This quality improvement study included 143 anticancer drug approvals granted by the FDA from January 1, 2013, to July 31, 2018. All approvals based on single-arm studies (47 approvals) were excluded. Approvals based on RCTs were further investigated and each trial was analyzed for design, time of patient accrual, control arm, and primary end point. Standard-of-care therapy was determined by evaluating the literature and published guidelines 1 year prior to the start of trial enrollment. The percentage of approvals based on RCTs that used suboptimal control arms was then calculated. The quality of the control arm was deemed suboptimal if the choice of control agent was restricted to exclude a recommended agent, the control arm was specified but the recommended agent was unspecified, and if prior RCT data had demonstrated that the control agent was inferior to an available alternative. Main Outcomes and Measures Estimated percentage of RCTs that used suboptimal control arms that led to FDA approval of anticancer agents between January 1, 2013, to July 31, 2018. Results A total of 145 studies that led to 143 drug approvals between January 1, 2013, and July 31, 2018, were included. Of these studies, 47 single-arm studies were excluded. The remaining 98 studies led to 96 drug approvals. Of these 96 approvals, 16 (17%) were based on RCTs with suboptimal control arms; 15 were international trials, and 1 was conducted in the United States. The type of approval was regular in 14 trials and accelerated in 2 trials. When categorized by the nature of suboptimal control, 4 (25%) trials omitted active treatment in control arm by limiting investigator’s choice, 10 (63%) trials omitted active treatment in the control arm by using a control agent known to be inferior to other available agents or not allowing combinations, and 2 (13%) trials used a previously used treatment in the control arm with a known lack of benefit associated with reexposure. Conclusions and Relevance Although anticancer drug approvals are increasing, a proportion of these drugs are reaching the market without proven superiority to what is considered the standard of care at the time of patient enrollment in pivotal trials. The choice of control arm should be optimized to ensure that new anticancer agents being marketed are truly superior to what most clinicians would prescribe outside a clinical trial setting.