Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca’s large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.Peer reviewe

Recording the vortex beam parameters in turbulent atmosphere

In the second part of the paper the Hartmann sensor model is introduced into design diagram; it allows determining its influence on optical system operation accuracy and optimizing the device parameters. In the closing paragraph the accuracy of detecting the optical vortex in turbulent medium using the sensor is compared with the accuracy of the results of the ideal optical system operation

Recording the vortex beam parameters in turbulent atmosphere

Based on the methods of the numerical experiments the authors study the possibility of determining the characteristics of vortex radiation expanding in turbulent medium. To obtain the estimation results given in the first part of the paper the parameters are determined using gradients of phase distribution of light field; the ideal optical system is considered

LPV design of fault-tolerant control for road vehicles

The aim of the paper is to present a supervisory decentralized architecture for the design and development of reconfigurable and fault-tolerant control systems in road vehicles. The performance specifications are guaranteed by local controllers, while the coordination of these components is provided by a supervisor. Since the monitoring components and FDI filters provide the supervisor with information about the various vehicle maneuvers and the different fault operations, it is able to make decisions about necessary interventions into the vehicle motions and guarantee reconfigurable and fault-tolerant operation of the vehicle. The design of the proposed reconfigurable and fault-tolerant control is based on an LPV method that uses monitored scheduling variables during the operation of the vehicle