Drug combinations, offering increased therapeutic efficacy and reduced toxicity, play an important role in treating multiple complex diseases. Yet, our ability to identify and validate effective combinations is limited by a combinatorial explosion, driven by both the large number of drug pairs as well as dosage combinations. Here we propose a network-based methodology to identify clinically efficacious drug combinations for specific diseases. By quantifying the network-based relationship between drug targets and disease proteins in the human protein–protein interactome, we show the existence of six distinct classes of drug–drug–disease combinations. Relying on approved drug combinations for hypertension and cancer, we find that only one of the six classes correlates with therapeutic effects: if the targets of the drugs both hit disease module, but target separate neighborhoods. This finding allows us to identify and validate antihypertensive combinations, offering a generic, powerful network methodology to identify efficacious combination therapies in drug development.

 

Network-based prediction of drug combinations
Feixiong Cheng, István A. Kovács & Albert-László Barabási 
Nature Communications volume 10, Article number: 1197 (2019)

Source: www.nature.com

In this guest blog Nele van Hooste from Board of Innovation speaks about the 6 mistakes they made while introducting a network of sel-managing teams.

Source: corporate-rebels.com

The insertion of robotic and artificial intelligent (AI) systems in therapeutic settings is accelerating. In this paper, we investigate the legal and ethical challenges of the growing inclusion of social robots in therapy. Typical examples of such systems are Kaspar, Hookie, Pleo, Tito, Robota,Nao, Leka or Keepon. Although recent studies support the adoption of robotic technologies for therapy and education, these technological developments interact socially with children, elderly or disabled, and may raise concerns that range from physical to cognitive safety, including data protection. Research in other fields also suggests that technology has a profound and alerting impact on us and our human nature. This article brings all these findings into the debate on whether the adoption of therapeutic AI and robot technologies are adequate, not only to raise awareness of the possible impacts of this technology but also to help steer the development and use of AI and robot technologies in therapeutic settings in the appropriate direction. Our contribution seeks to provide a thoughtful analysis of some issues concerning the use and development of social robots in therapy, in the hope that this can inform the policy debate and set the scene for further research.

 

“I’ll take care of you,” said the robot

Reflecting upon the legal and ethical aspects of the use and development of social robots for therapy

Eduard Fosch-Villaronga Jordi Albo-Canals

Paladyn, Journal of Behavioral Robotics

Source: www.degruyter.com

The concept of social interaction is at the core of embodied and enactive approaches to social cognitive processes, yet scientifically it remains poorly understood. Traditionally, cognitive science had relegated all behavior to being the end result of internal neural activity. However, the role of feedback from the interactions between agent and their environment has become increasingly important to understanding behavior. We focus on the role that social interaction plays in the behavioral and neural activity of the individuals taking part in it. Is social interaction merely a source of complex inputs to the individual, or can social interaction increase the individuals’ own complexity? Here we provide a proof of concept of the latter possibility by artificially evolving pairs of simulated mobile robots to increase their neural complexity, which consistently gave rise to strategies that take advantage of their capacity for interaction. We found that during social interaction, the neural controllers exhibited dynamics of higher-dimensionality than were possible in social isolation. Moreover, by testing evolved strategies against unresponsive ghost partners, we demonstrated that under some conditions this effect was dependent on mutually responsive co-regulation, rather than on the mere presence of another agent’s behavior as such. Our findings provide an illustration of how social interaction can augment the internal degrees of freedom of individuals who are actively engaged in participation.

 

Embodied Dyadic Interaction Increases Complexity of Neural Dynamics: A Minimal Agent-Based Simulation Model

Madhavun Candadai, Matt Setzler, Eduardo J. Izquierdo and Tom Froese

Front. Psychol., 21 March 2019 | https://doi.org/10.3389/fpsyg.2019.00540

Source: www.frontiersin.org

A good example of interactions generating relevant novel information that is not present in initial nor boundary conditions, inherently limiting the predictability of complex systems.