The Impact of Smoking Status on the Efficacy of Erlotinib in Patients with Advanced Non-small Cell Lung Cancer

Background and objective Erlotinib is a targeted treatment for advanced non-small cell lung cancer. Smoking status may be one of influencing factors of the efficacy of erlotinib. The aim of this study is to explore the impact of smoking status on the efficacy of erlotinib in patients with advanced non-small cell lung cancer. Methods Patients with nonsmall cell lung cancer who had been previously treated with at least one course of platinum based chemotherapy received 150 mg oral doses of erlotinib once daily until disease progression. Response rate, progression-free survival, overall survival were analyzed in the different smoking status groups. Kaplan-Meier method was used to analyze the survival rate. Results Fortyeight patients were enrolled into the study from December 2005 to September 2006. We followed up these patients until 28th December, 2008. Median follow up time was 30 months. The compliance rate was 100%. The response rate was 32.1% in the smoking group and 35% in the never smoking group (P=0.836); The median progression-free survival was 3 months and 9 months, respectively (P=0.033). The median overall survival was 5 months and 17 months, respectively (P=0.162). Conclusion Erlotinib is an effective drug for advanced non-small cell lung cancer patients with different smoking status. Progressionfree survival is better in the never smoking patients than the smoking patients

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

Characterization of AcMNPV with a deletion of ac69 gene

<p>ORF69 (Ac69) of <em>Autographa californica</em> multiple nucleopolyhedrovirus (Ac<em>M</em>NPV) is conserved in some baculovirus genomes. Although it has been shown that Ac69 has cap 0-dependent methyltransferase activity and is not required for budded virus production in <em>Spodoptera frugiperda</em> Sf-9 cells, its role in occlusion-derived virus synthesis and virus oral infectivity is not known. This paper describes generation of an <em>ac69</em> knockout Ac<em>M</em>NPV bacmid mutant and analyses of the influence of <em>ac69</em> deletion on the viral infectivity in Sf-9 cells and <em>Trichoplusia ni</em> larvae so as to investigate the role of <em>ac69 in the viral life cycle. Results indicated that ac69</em> deletion has little effect on the production rates and morphogenesis of budded virus and occlusion-derived virus in Sf-9 cells. In addition, animal experiment revealed that the deletion mutant did not affect Ac<em>M</em>NPV infectivity for <em>Trichoplusia ni</em> larvae in LD₅₀ and LT₅₀ bioassay when administered orally. These results suggest that <em>ac69</em> may be dispensable for viral infectivity both in vitro and in vivo.</p