Biology subjects

Bogumil, D.

Publications and source records attributed to Bogumil, D..

2 recordsLinked to original sources

bioRxiv2021

Genome-wide Association Study of Pancreatic Fat: The Multiethnic Cohort Adiposity Phenotype Study

Several studies have found associations between higher pancreatic fat content and adverse health outcomes, such as diabetes and the metabolic syndrome, but investigations into the genetic contributions to pancreatic fat are limited. This genome-wide association study, comprised of 804 participants with MRI-assessed pancreatic fat measurements, was conducted in the ethnically diverse Multiethnic Cohort-Adiposity Phenotype Study (MEC-APS). Two genetic variants reaching genome-wide significance, rs73449607 on chromosome 13q21.2 (Beta = -0.67, P = 4.50x10-8) and rs7996760 on chromosome 6q14 (Beta = -0.90, P = 4.91x10-8) were associated with percent pancreatic fat on the log scale. Rs73449607 was most common in the African American population (13%) and rs79967607 was most common in the European American population (6%). Rs73449607 was also suggestively associated with lower risk of type 2 diabetes (OR = 0.95, 95% CI = 0.89-1.00, P = 0.047) in the Population Architecture Genomics and Epidemiology (PAGE) Study and the DIAbetes Genetics Replication and Meta-analysis (DIAGRAM), which included substantial numbers of non-European ancestry participants (53,102 cases and 193,679 controls). Rs73449607 is located in an intergenic region between GSX1 and PLUT, and rs79967607 is in intron 1 of EPM2A. PLUT, a linkRNA, regulates transcription of an adjacent gene, PDX1, that controls beta-cell function in the mature pancreas, and EPM2A encodes the protein laforin, which plays a critical role in regulating glycogen production. If validated, these variants may suggest a genetic component for pancreatic fat and a common etiologic link between pancreatic fat and type 2 diabetes.

cell biology↗

bioRxiv2020

An Evaluation of the Efficacy of Preclinical Models of Lung Cancer Drugs

BackgroundPreclinical cell models are the mainstay in the early stages of drug development. We sought to explore the preclinical data that differentiated successful from failed therapeutic agents in lung cancer. Methods134 failed lung cancer drugs and 27 successful lung cancer drugs were identified. Preclinical data were evaluated. The outcome variable for cell model experiments was the half maximal inhibitory concentration (IC50), and for murine model experiments was tumor growth inhibition (TGI). A logistic regression was performed on quartiles (Q) of IC50s and TGIs. ResultsWe compared odds of approval among drugs defined by IC50 and TGI quartile. Compared to drugs with preclinical cell experiments in highest IC50 quartile (Q4, IC50 345.01-100,000nM), those in Q3 differed little, but those in the lower two quartiles had better odds of being approved. However, there was no significant monotonic trend identified (P-trend 0.4). For preclinical murine models, TGI values ranged from -0.3119 to 1.0000, with a tendency for approved drugs to demonstrate poorer inhibition than failed drugs. Analyses comparing success of drugs according to TGI quartile produced interval estimates too wide to be statistically meaningful, although all point estimates accord with drugs in Q2-Q4 (TGI 0.5576-0.7600, 0.7601-0.9364, 0.9365-1.0000) having lower odds of success than those in Q1 (-0.3119-0.5575). ConclusionThere does not appear to be a significant linear trend between preclinical success and drug approval, and therefore published preclinical data does not predict success of therapeutics in lung cancer. Newer models with predictive power would be beneficial to drug development efforts.

cancer biology↗