WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT• Many drugs inhibit or induce cytochrome P450 enzymes (CYP) to cause clinically significant changes in the concentrations of other drugs, i.e.'perpetrate' pharmacokinetic drug-drug interactions (PK-DDIs).• Tables that list the substrates, inhibitors and inducers of CYP are common, but they lack consistency and are constructed from evidence of variable quality. WHAT THIS STUDY ADDS• This is the first study to catalogue important perpetrators of PK-DDIs using objective criteria and clinical pharmacokinetic drug interaction studies. This information is intended to inform clinical decisions on PK-DDIs.• Existing tables of CYP inhibitors and inducers have low sensitivity and low positive predictive value in identifying the major perpetrators of PK-DDIs. • Several drugs were identified which potentially perpetrate CYP-mediated PK-DDIs, but quality clinical pharmacokinetic interaction studies are lacking. This information may be used to inform future research. AIMSTo catalogue the perpetrators of CYP-mediated pharmacokinetic drug-drug interactions (PK-DDIs) using clinically relevant criteria, and to compare this with an analogous catalogue. METHODSCandidate inhibitors and inducers of CYP1A2, CYP2C9, CYP2C19, CYP2D6 and CYP3A ('perpetrators') were evaluated using published clinical pharmacokinetic interaction studies. Studies were selected on the basis of Նsix human subjects, use of a validated in vivo probe substrate for the CYP enzyme, and clinically relevant dosing. Inhibitors were described according to the FDA classifications of strong, moderate or weak, whereas inducers were classified as major (Նtwofold decrease in AUC) or weak (

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Email is one of the most prevalent communication tools today, and solving the email overload problem is pressingly urgent. A good way to alleviate email overload is to automatically prioritize received messages according to the priorities of each user. However, research on statistical learning methods for fully personalized email prioritization (PEP) has been sparse due to privacy issues, since people are reluctant to share personal messages and importance judgments with the research community. It is therefore important to develop and evaluate PEP methods under the assumption that only limited training examples can be available, and that the system can only have the personal email data of each user during the training and testing of the model for that user. This paper presents the first study (to the best of our knowledge) under such an assumption. Specifically, we focus on analysis of personal social networks to capture user groups and to obtain rich features that represent the social roles from the viewpoint of a particular user. We also developed a novel semi-supervised (transductive) learning algorithm that propagates importance labels from training examples to test examples through message and user nodes in a personal email network. These methods together enable us to obtain an enriched vector representation of each new email message, which consists of both standard features of an email message (such as words in the title or body, sender and receiver IDs, etc.) and the induced social features from the sender and receivers of the message. Using the enriched vector representation as the input in SVM classifiers to predict the importance level for each test message, we obtained significant performance improvement over the baseline system (without induced social features) in our experiments on a multi-user data collection. We obtained significant performance improvement over the baseline system (without induced social features) in our experiments on a multiuser data collection: the relative error reduction in MAE was 31% in micro-averaging, and 14% in macro-averaging.

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