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Abstract Cigarette smoking is a major preventable cause of morbidity and mortality. While quitting smoking is the best option, switching from cigarettes to non-combustible alternatives (NCAs) such as e-vapor products is a viable harm reduction approach for smokers who would otherwise continue to smoke. A key challenge for the clinical assessment of NCAs is that self-reported product use can be unreliable, compromising the proper evaluation of their risk reduction potential. In this cross-sectional study of 205 healthy volunteers, we combined comprehensive exposure characterization with in-depth multi-omics profiling to compare effects across four study groups: cigarette smokers (CS), e-vapor users (EV), former smokers (FS), and never smokers (NS). Multi-omics analyses included metabolomics, transcriptomics, DNA methylomics, proteomics, and lipidomics. Comparison of the molecular effects between CS and NS recapitulated several previous observations, such as increased inflammatory markers in CS. Generally, FS and EV demonstrated intermediate molecular effects between the NS and CS groups. Stratification of the FS and EV by combustion exposure markers suggested that this position on the spectrum between CS and NS was partially driven by non-compliance/dual use. Overall, this study highlights the importance of in-depth exposure characterization before biological effect characterization for any NCA assessment study.

In the past few years, technological advancements enabled the development of novel electronic nicotine delivery systems (ENDS). Several empirical measures such as “nicotine flux” are being proposed to evaluate the abuse liability potential of these products. We explored the applicability of nicotine flux for clinical nicotine pharmacokinetics (PK) and 52-week quit success from cigarettes for a wide range of existing nicotine delivery systems. We found that the differences in nicotine flux for various nicotine delivery systems are not related to changes in PK, as nicotine flux does not capture key physiological properties such as nicotine absorption rate. Further, the 52-week quit success and abuse liability potential of nicotine nasal sprays (high nicotine flux product), and nicotine inhalers (nicotine flux similar to ENDS) are low, suggesting that nicotine flux is a poor metric for the assessment of nicotine delivery systems. PK indices are more dependable for characterizing nicotine delivery systems, and a nicotine plasma CmaxTmax > 1 could improve 52-week quit success from cigarettes. However, a single metric may be inadequate to fully assess the abuse liability potential of nicotine delivery systems and needs to be further studied. A combination of in vitro and in silico approaches could potentially address the factors influencing the inhaled aerosol dosimetry and resulting PK of nicotine to provide early insights for ENDS assessments. Further research is required to understand nicotine dosimetry and PK for ad libitum product use, and abuse liability indicators of nicotine delivery systems. This commentary is intended to (1) highlight the need to think beyond a single empirical metric such as nicotine flux, (2) suggest potential PK-based metrics, (3) suggest the use of in vitro and in silico tools to obtain early insights into inhaled aerosol dosimetry for ENDS, and (4) emphasize the importance of considering comprehensive clinical pharmacology outcomes to evaluate nicotine delivery systems.

The COVID-19 (Coronavirus disease 2019) global pandemic has upended the normal pace of society at multiple levels—from daily activities in personal and professional lives to the way the sciences operate. Many laboratories have reported shortage in vital supplies, change in standard operating protocols, suspension of operations because of social distancing and stay-at-home guidelines during the pandemic. This global crisis has opened opportunities to leverage internet of things, connectivity, and artificial intelligence (AI) to build a connected laboratory automation platform. However, laboratory operations involve complex, multicomponent systems. It is unrealistic to completely automate the entire diversity of laboratories and processes. Recently, AI technology, particularly, game simulation has made significant strides in modeling and learning complex, multicomponent systems. Here, we present a cloud-based laboratory management and automation platform which combines multilayer information on a simulation-driven inference engine to plan and optimize laboratory operations under various constraints of COVID-19 and risk scenarios. The platform was used to assess the execution of two cell-based assays with distinct parameters in a real-life high-content screening laboratory scenario. The results show that the platform can provide a systematic framework for assessing laboratory operation scenarios under different conditions, quantifying tradeoffs, and determining the performance impact of specific resources or constraints, thereby enabling decision-making in a cost-effective manner. We envisage the laboratory management and automation platform to be further expanded by connecting it with sensors, robotic equipment, and other components of scientific operations to provide an integrated, end-to-end platform for scientific laboratory automation.

Abstract The molecular mechanisms of IBD have been the subject of intensive exploration. We, therefore, assembled the available information into a suite of causal biological network models, which offer comprehensive visualization of the processes underlying IBD. Scientific text was curated by using Biological Expression Language (BEL) and compiled with OpenBEL 3.0.0. Network properties were analysed by Cytoscape. Network perturbation amplitudes were computed to score the network models with transcriptomic data from public data repositories. The IBD network model suite consists of three independent models that represent signalling pathways that contribute to IBD. In the “intestinal permeability” model, programmed cell death factors were downregulated in CD and upregulated in UC. In the “inflammation” model, PPARG, IL6, and IFN-associated pathways were prominent regulatory factors in both diseases. In the “wound healing” model, factors promoting wound healing were upregulated in CD and downregulated in UC. Scoring of publicly available transcriptomic datasets onto these network models demonstrated that the IBD models capture the perturbation in each dataset accurately. The IBD network model suite can provide better mechanistic insights of the transcriptional changes in IBD and constitutes a valuable tool in personalized medicine to further understand individual drug responses in IBD.

Many flavor ingredients are often used in potentially reduced-risk tobacco products (such as e-vapor products). Although most are “generally recognized as safe (GRAS)” when used in food, there is limited information available on their long-term health effects when delivered by inhalation. While obtaining route-of-exposure-specific toxicological data on flavor ingredients is critical to product evaluation, the large number of individual flavor ingredients available and their potential combinations render classical toxicological assessment approaches impractical, as they may require years of preclinical investigations and thousands of laboratory animals. Therefore, we propose a pragmatic approach in which flavor ingredients are initially assigned to groups of structurally related compounds (Flavor Groups), from which flavor group representatives (FGR) are then selected and tested individually and as a mixture in vitro and in vivo. The premise is that structurally related compounds would have comparable metabolic and biological activity and that the data generated using FGRs could support the toxicological assessment of other structurally related flavor ingredients of their respective Flavor Groups. This approach is explained in a step-wise manner and exemplified by a case study, along with its strengths, limitations as well as recommendations for further confirmatory testing. Once completed, this FGR approach could significantly reduce the time and resources required for filling the data gap in understanding the health risks of many flavor ingredients while also minimizing the need for laboratory animals.