Effect of comedication of bupropion and other antidepressants on body mass index.

BACKGROUND: Weight gain as an adverse effect of monotherapy of antidepressant has been well-studied. The effects of augmentation therapy involving multiple antidepressants, on weight changes needs to be adequately addressed. OBJECTIVE: To study the co-medication effects of bupropion in combination with six individual antidepressants on body mass index (BMI) using EMR based data analysis. METHODS: Allscripts data warehouse was used to identify patients on monotherapy of five selective serotonin reuptake inhibitor (SSRI) drugs, escitalopram, sertraline, citalopram, paroxetine, fluoxetine, one selective norepinephrine reuptake inhibitor (SNRI) duloxetine and the aminoketone, bupropion for at least 180 days. We also identified patients on co-medication of SSRI/SNRI drugs with bupropion. Six ANCOVA models were built to compare the short term effects on BMI, among monotherapy and co-medication groups. The patients' clinical conditions and demographics were included to account for confounding effects. RESULTS: Monotherapy of all the SSRI/SNRI drugs showed significant weight increase, consistent with that of previous studies. The co-medication of bupropion and escitalopram showed a significantly higher increase in BMI than monotherapy (P = 0.0102). The increase in BMI in the other five co-medication groups was not significantly different from their respective monotherapy groups. CONCLUSION: Our study reports an adverse weight gain on co-medication of escitalopram and bupropion, which warrants further validation studies. Considering co-medication effects of antidepressants on weight is important to design robust depression treatment plans.

Co-medication of pravastatin and paroxetine-a categorical study.

Electronic Medical Records (EMRs) are wealthy storehouses of patient information, to which data mining techniques can be prudently applied to reveal clinically significant patterns. Detecting patterns in drug-drug interactions, leading to adverse drug reactions is a powerful application of EMR data mining. Adverse effects of drug treatments can be investigated by mining clinical laboratory tests data which are reliable indicators of abnormal physiological functions. We report here the co-medication effects of pravastatin (HMG-CoA reductase inhibitor) and paroxetine (selective serotonin reuptake inhibitor (SSRI) anti-depressant) on significant clinical parameters, identified through a data mining analysis conducted on the Allscripts data warehouse. We found that the concomitant drug treatments of pravastatin and paroxetine increased the mean values of glucose serum from 113.2 to 132.1 mg/dL and international normalized ratio (INR) from 2.18 to 2.52, respectively. It also decreased the mean values of estimated glomerular filtration rate (eGFR) from 43 to 37 mL/min/1.73 m(3) and blood CO2 levels from 24.8 to 23.9 mEq/L respectively. Our findings indicate that co-medication of pravastatin and paroxetine might have significant impact on blood anti-coagulation, kidney function, and glucose homeostasis. Our methodology can be applied to any EMR data set to reveal co-medication effects of any drug pairs.

Improvement of the crystallizability and expression of an RNA crystallization chaperone.

Crystallizing RNA has been an imperative and challenging task in the world of RNA research. Assistive methods such as chaperone-assisted RNA crystallography (CARC), employing monoclonal antibody fragments (Fabs) as crystallization chaperones have enabled us to obtain RNA crystal structures by forming crystal contacts and providing initial phasing information. Despite the early successes, the crystallization of large RNA-Fab complex remains a challenge in practice. The possible reason for this difficulty is that the Fab scaffold has not been optimized for crystallization in complex with RNA. Here, we have used the surface entropy reduction (SER) technique for the optimization of ΔC209 P4-P6/Fab2 model system. Protruding lysine and glutamate residues were mutated to a set of alanines or serines to construct Fab2SMA or Fab2SMS. Expression with the shake flask approach was optimized to allow large scale production for crystallization. Crystal screening shows that significantly higher crystal-forming ratio was observed for the mutant complexes. As the chosen SER residues are far away from the CDR regions of the Fab, the same set of mutations can now be directly applied to other Fabs binding to a variety of ribozymes and riboswitches to improve the crystallizability of Fab-RNA complex.