Biosimilars: An update on clinical trials (review of published and ongoing studies)

Biosimilars represent a new trend in the treatment of many immune-mediated inflammatory diseases. Regulatory requirements for approval of biosimilars are different from those of originators and rely mostly on the evidence generated from bioequivalence studies and in particular from RCTs. Our goal in this review was to search for relevant studies from randomized controlled trials on the biosimilars adalimumab, etanercept, infliximab and ustekinumab compared with their reference medication (publication in Medline) and ongoing studies in clinical trial registries. For infliximab biosimilars, we found data on patients with ankylosing spondylitis rheumatoid arthritis indicating no clinically relevant differences regarding efficacy and safety, as well as data on inflammatory bowel diseases and psoriasis. In addition, three registered studies of adalimumab biosimilars and just one study of an etanercept biosimilar were being carried out in patients with psoriasis. Ongoing studies on adalimumab, etanercept, and infliximab biosimilars in patients with rheumatoid arthritis were also identified. The conclusion seems to be that there are only 4 clinical trials on psoriasis (3 for the adalimumab biosimilar and 1 for etanercept biosimilar) and 1 clinical trial for Pso, CD, UC, RA, and AS (with the Infliximab biosimilar). Thus, the real and unique advantage of biosimilars is the low price derived from the special design studies despite the high technology used in fabrication process. Although not all ongoing biosimilar trials may have been registered, the present situation in terms of registered trials is quite unsatisfactory and provision of further clinical data and inclusion of patients in patient registries will be crucial.  </p

Probing the Limits of Social Data:Biases, Methods, and Domain Knowledge

Online social data has been hailed to provide unprecedented insights into human phenomena due to its ability to capture human behavior at a scale and level of detail, both in breadth and depth, that is hard to achieve through conventional data collection techniques. This has led to numerous studies that leverage online social data to model or gain insights about real world phenomena, as well as to inform system or methods design for performance gains, or for providing personalized services. Alas, regardless of how large, detailed or varied the online social data is, there are limits to what can be discerned from it about real-world, or even media- or application-specific phenomena. This thesis investigates four instances of such limits that are related to both the properties of the working data sets and of the methods used to acquire and leverage them, including: (1) online social media biases, (2) assessing and (3) reducing data collection biases, and (4) methods sensitivity to data biases and variability. For each of them, we conduct a separate case study that enables us to systematically devise and apply consistent methodologies to collect, process, compare or assess different data sets and dedicated methods. The main contributions of this thesis are: (i) To gain insights into media-specific biases, we run a comparative study juxtaposing social and mainstream media coverage of domain-specific news events for a period of 17 months. To this end, we introduce a generic methodology for comparing news agendas online based on a comparison of spikes of coverage. We expose significant differences in the type of events that are covered by the two media. (ii) To assess possible biases across data collections, we run a transversal study that systematically assembles and examines 26 distinct data sets of social media posts during a variety of crisis events spanning a 2 years period. While we find patterns and consistencies, we also uncover substantial variability across different event data sets, highlighting the pitfalls of generalizing findings from one data set to another. (iii) To improve data collections, we introduce a method that increases the recall of social media samples, while preserving the original distribution of message types and sources. To locate and monitor domain-specific events, this method constructs and applies a domain-specific, yet generic lexicon, automatically learning event-specific terms and adapting the lexicon to the targeted event. The resulted improvements also show that only a fraction of the relevant data is currently mined. (iv) To test the methods sensitivity, to data biases and variability we run an empirical evaluation on 6 real-world data sets dissecting the impact of user and item attributes on the performance of recommendation approaches that leverage distinct social cues--explicit social links vs. implicit interest affinity. We show performance variations not only across data sets, but also within each data set, across different classes of users or items, suggesting that global metrics are often unsuited for assessing recommendation systems performance. The overarching goal of this thesis is to contribute a practical perspective to the body of research that aims to quantify biases, to devise better methods to collect and model social data, and to evaluate such methods in context

Tell Me Your Ads and I'll Tell You Who You Are

Google AdSense, one of the most popular Online Advertise Networks, uses a mix of two techniques to deliver ads: contextual ads and behavioral ads, but it is not clear when or how much of the both they actually use. In this work, we raise several concerns regarding behavioral ads. End-users have very little control: once behavioral ads are allowed (which is the default setting), it is hard to predict what part about a user's past interests they will reveal. Our goal in this work is to understand and quantify the loss of online privacy through behavioral advertising

Interdependent and Multi-Subject Privacy: Threats, Analysis and Protection

In Alan Westin's generally accepted definition of privacy, he describes it as an individual's right 'to control, edit, manage, and delete information about them[selves] and decide when, how, and to what extent information is communicated to others.' Therefore, privacy is an individual and independent human right. The great Mahatma Gandhi once said that 'interdependence is and ought to be as much the ideal of man as selfsufficiency. Man is a social being.' To ensure this independent right to inherently social beings, it will be difficult, if not impossible. This is especially true as today's world is highly interconnected, technology evolves rapidly, data sharing is increasingly abundant, and regulations do not provide sufficient guidance in the realm of interdependency. In this thesis, we explore the topic of interdependent privacy from an adversarial point of view by exposing threats, as well as from an end-user point of view, by exploring awareness, preferences and privacy protection needs. First, we quantify the effect of co-locations on location privacy, considering an adversary such as a social-network operator that has access to this information: Not only can a user be localized due to her reported locations and mobility patterns, but also due to those of her friends (and the friends of her friends and so on). We formalize this problem and propose effective inference algorithms that substantially reduce the complexity of localization attacks that make use of co-locations. Our results show that an adversary can effectively incorporate co-locations in attacks to substantially reduce users' location privacy; this exposes a real and severe threat. Second, we investigate the interplay between the privacy risks and the social benefits of users when sharing (co-)locations on OSNs. We propose a game-theoretic framework for analyzing users' strategic behaviors. We conduct a survey of Facebook users and quantify their benefits of sharing vs. viewing information and their preference for privacy vs. benefits. Our survey exposes deficits in users' awareness of privacy risks in OSNs. Our results further show how users' individual preferences influence, sometimes in a negative way, each other's decisions. Third, we consider various types of interdependent and multi-subject data (photo, colocation, genome, etc.) that often have privacy implications for data subjects other than the uploader, yet can be shared without their consent or awareness. We propose a system for sharing such data in a consensual and privacy-preserving manner. We implement it in the case of photos, by relying on image-processing and cryptographic techniques, as well as on a two-tier architecture. We conduct a survey of Facebook users; it indicates that there is interest in such a system, and that users have increasing privacy concerns due to prejudice or discrimination that they have been or could still easily be exposed to. In conclusion, this thesis provides new insights on users' privacy in the context of interdependence and constitutes a step towards the design of novel privacy-protection mechanisms. It should be seen as a warning message for service providers and regulatory institutions: Unless the interdependent aspects of privacy are considered, this fundamental human right can never be guaranteed