Assessment of papain-induced lung injury in isolated lungs by measurements of aerosol deposition and mixing.

Aerosol bolus inspirations were used to assess lung injury in 15 isolated dog lungs exposed to low (0-375 units) or high doses (600-1,200 units) of papain. Effective air space size (EAD) was determined from aerosol deposition during a 5-s breath hold. Convective mixing was assessed by the spreading of the expired bolus with respect to expired volume, quantified by a coefficient of dispersion (CD) equal to the square root of the difference in the variances of the expired and inspired boluses divided by the volumetric penetration of the bolus. After exposure, CD measured with deeply penetrating boluses increased by an average of 2.5% in the low-exposure group (P greater than 0.05) and 28.0% in the high-exposure group (P less than 0.0001). CD measured with shallowly penetrating boluses decreased by 4.3% (P less than 0.0001) in the low-exposure group and increased by an average of 18.3% in the high-exposure group (P less than 0.05). Papain exposure caused EAD to increase in some lungs and decrease in others. For deep bolus penetrations, EAD changed by an average of -0.8% in the low-exposure group (P greater than 0.05) and +21.1% in the high-exposure group (P greater than 0.05). Both EAD and CD appeared to be sensitive to lung injury. However, changes in EAD were less consistent than those in CD, possibly due to changes caused by lung injury in the regional distribution of inspired aerosol.     

Drug2ways: Reasoning over causal paths in biological networks for drug discovery

Elucidating the causal mechanisms responsible for disease can reveal potential therapeutic targets for pharmacological intervention and, accordingly, guide drug repositioning and discovery. In essence, the topology of a network can reveal the impact a drug candidate may have on a given biological state, leading the way for enhanced disease characterization and the design of advanced therapies. Network-based approaches, in particular, are highly suited for these purposes as they hold the capacity to identify the molecular mechanisms underlying disease. Here, we present drug2ways, a novel methodology that leverages multimodal causal networks for predicting drug candidates. Drug2ways implements an efficient algorithm which reasons over causal paths in large-scale biological networks to propose drug candidates for a given disease. We validate our approach using clinical trial information and demonstrate how drug2ways can be used for multiple applications to identify: i) single-target drug candidates, ii) candidates with polypharmacological properties that can optimize multiple targets, and iii) candidates for combination therapy. Finally, we make drug2ways available to the scientific community as a Python package that enables conducting these applications on multiple standard network formats.