To face SARS-CoV-2 pandemic various attempts are made to identify potential effective treatments by repurposing available drugs. Among them, indomethacin, an anti-inflammatory drug, was shown to have potent in-vitro antiviral properties on human SARS-CoV-1, canine CCoV, and more recently on human SARS-CoV-2 at low micromolar range. Our objective was to show that indomethacin could be considered as a promising candidate for the treatment of SARS-CoV-2 and to provide criteria for comparing benefits of alternative dosage regimens using a model-based approach. A multi-stage model-based approach was developed to characterize % of recovery and viral load in CCoVinfected dogs, to estimate the PK of indomethacin in dog and human using published data after administration of immediate (IR) and sustained-release (SR) formulations, and to estimate the expected antiviral activity as a function of different assumptions on the effective exposure in human. Different dosage regimens were evaluated for IR formulation (25 mg and 50 mg three-times-a-day, and 25 mg four-times-a-day), and SR formulation (75 mg once and twice-a-day). The best performing dosing regimens were: 50 mg three-times-a-day for the IR formulation, and 75 mg twice-a-day for the SR formulation. The treatment with the SR formulation at the dose of 75 mg twice-a-day is expected to achieve a complete response in three days for the treatment in patients infected by the SARS-CoV-2 coronavirus. These results suggest that indomethacin could be considered as a promising candidate for the treatment of SARS-CoV-2 whose potential therapeutic effect needs to be further assessed in a prospective clinical trial.

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SUMMARYWith the progress of ubiquitous technology, ubiquitous learning presents new opportunities to learners. Situations of a learner can be grasped through analyzing the learner's actions collected by sensors, RF-IDs, or cameras in order to provide support at proper time, proper place, and proper situation. Training for acquiring skills and enhancing physical abilities through exercise and experience in the real world is an important domain in u-learning. A training program may last for several days and has one or more training units (exercises) for a day. A learner's performance in a unit is considered as short term state. The performance in a series of units may change with patterns: progress, plateau, and decline. Long term state in a series of units is accumulatively computed based on short term states. In a learning/training program, it is necessary to apply different support strategies to adapt to different states of the learner. Adaptation in learning support is significant, because a learner loses his/her interests easily without adaptation. Systems with the adaptive support usually provide stimulators to a learner, and a learner can have a great motivation in learning at beginning. However, when the stimulators reach some levels, the learner may lose his/her motivation, because the long term state of the learner changes dynamically, which means a progress state may change to a plateau state or a decline state. In different long term learning states, different types of stimulators are needed. However, the stimulators and advice provided by the existing systems are monotonic without changeable support strategies. We propose a mutual adaptive support. The mutual adaptation means each of the system and the learner has their own states. On one hand, the system tries to change its state to adapt to the learner's state for providing adaptive support. On the other hand, the learner can change its performance following the advice given based on the state of the system. We create a ubiquitous pet (u-pet) as a metaphor of our system. A u-pet is always with the learner and encourage the leaner to start training at proper time and to do training smoothly. The u-pet can perform actions with the learner in training, change its own attributes based on the learner's attributes, and adjust its own learning rate by a learning function. The u-pet grasps the state of the learner and adopts different training support strategies to the learner's training based on the learner's short and long term states.

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