The endless frontier? The recent increase of R&D productivity in pharmaceuticals

Background: Studies on the early 2000s documented increasing attrition rates and duration of clinical trials, leading to a representation of a "productivity crisis" in pharmaceutical research and development (R&D). In this paper, we produce a new set of analyses for the last decade and report a recent increase of R&D productivity within the industry. Methods: We use an extensive data set on the development history of more than 50,000 projects between 1990 and 2017, which we integrate with data on sales, patents, and anagraphical information on each institution involved. We devise an indicator to quantify the novelty of each project, based on its set of mechanisms of action. Results: First, we investigate how R&D projects are allocated across therapeutic areas and find a polarization towards high uncertainty/high potential reward indications, with a strong focus on oncology. Second, we find that attrition rates have been decreasing at all stages of clinical research in recent years. In parallel, for each phase, we observe a significant reduction of time required to identify projects to be discontinued. Moreover, our analysis shows that more recent successful R&D projects are increasingly based on novel mechanisms of action and target novel indications, which are characterized by relatively small patient populations. Third, we find that the number of R&D projects on advanced therapies is also growing. Finally, we investigate the relative contribution to productivity variations of different types of institutions along the drug development process, with a specific focus on the distinction between the roles of Originators and Developers of R&D projects. We document that in the last decade Originator-Developer collaborations in which biotech companies act as Developers have been growing in importance. Moreover, we show that biotechnology companies have reached levels of productivity in project development that are equivalent to those of large pharmaceutical companies. Conclusions: Our study reports on the state of R&D productivity in the bio-pharmaceutical industry, finding several signals of an improving performance, with R&D projects becoming more targeted and novel in terms of indications and mechanisms of action

Discovering Contextual Association Rules in Relational Databases

Contextual association rules represent co-occurrences between contexts and properties of data, where the context is a set of environmental or user personal features employed to customize an application. Due to their particular structure, these rules can be very tricky to mine, and if the process is not carried out with care, an unmanageable set of not significant rules may be extracted. In this paper we survey two existing algorithms for relational databases and present a novel algorithm that merges the two proposals overcoming their limitations

Context Schema Evolution in Context-Aware Data Management

Pervasive access - often by means of mobile devices - to the massive amount of available (Web) data suggests to deliver, anywhere at any time, exactly the data that are needed in the current specific situation. The literature has introduced the possibility to describe the context in which the user is involved, and to tailor the available data on its basis. In this paper, after having formally defined the context schema - a representation for the contexts which are to be expected in a given application scenario - a strategy to manage context schema evolution is developed, by introducing a sound and complete set of operators

Mining Context-Aware Preferences on Relational and Sensor Data

The increasing amount of available digital data motivates the development of techniques for the management of the information overload which risks to actually reduce people’s knowledge instead of increasing it. Research is concentrating on topics related to the problem of filtering/suggesting a subset of available information that is likely to be of interest to the user, besides this subset may vary and is often determined by the context the user is currently in. We cannot actually expect only a collaborative approach, where users manually specify the long list of preferences that might be applied to all available data; that is why in this paper we propose a preliminary methodology, described by using a realistic running example, that tries to combine the following research topics: context-awareness, data mining, and preferences. In particular, data mining is used to infer contextual preferences from the previous user’s querying activity on static data and on available dynamic values coming from sensors

Efficiently using contextual influence to recommend new items to ephemeral groups

Group recommender systems suggest items to groups of users that want to utilize those items together. These systems can support several activities that can be performed together with other people and are typically social, like watching TV or going to the restaurant. In this paper we study ephemeral groups, i.e., groups constituted by users who are together for the first time, and for which therefore there is no history of past group activities. Recent works have studied ephemeral group recommendations proposing techniques that learn complex models of users and items. These techniques, however, are not appropriate to recommend items that are new in the system, while we propose a method able to deal with new items too. Specifically, our technique determines the preference of a group for a given item by combining the individual preferences of the group members on the basis of their contextual influence, the contextual influence representing the ability of an individual, in a given situation, to guide the group's decision. Moreover, while many works on recommendations do not consider the problem of efficiently producing recommendation lists at runtime, in this paper we speed up the recommendation process by applying techniques conceived for the top-K query processing problem. Finally, we present extensive experiments, evaluating: (i) the accuracy of the recommendations, using a real TV dataset containing a log of viewings performed by real groups, and (ii) the efficiency of the online recommendation task, exploiting also a bigger partially synthetic dataset

A Data-Mining Approach to Preference-Based Data Ranking Founded on Contextual Information

The term information overload was already used back in the 1970s by Alvin Toffler in his book Future Shock, and refers to the difficulty to understand and make decisions when too much information is available. In the era of Big Data, this problem becomes much more dramatic, since users may be literally overwhelmed by the cataract of data accessible in the most varied forms. With context-aware data tailoring, given a target application, in each specific context the system allows the user to access only the view which is relevant for that application in that context. Moreover, the relative importance of information to the same user in a different context or, reciprocally, to a different user in the same context, may vary enormously; for this reason, contextual preferences can be used to further refine the views associated with contexts, by imposing a ranking on the data of each context-aware view. In this paper, we propose a methodology and a system, PREMINE (PREference MINEr), where data mining is adopted to infer contextual preferences from the past interaction of the user with contextual views over a relational database, gathering knowledge in terms of association rules between each context and the relevant data

Disjunctive constraints in RDF and their application to context schemas

RDF is a data model whose relevance is growing in the last years. Recently, some proposals have enriched the model with integrity constraints, well known within relational databases. In this work we extend an existing framework with two new types of integrity constraints of disjunctive nature, inspired by similar kinds of dependencies studied for the relational model. The problem of the logical implication for the two novel categories is also analyzed. Moreover, as an application scenario, we propose a complete and independent set of constraints to model the context in RDF, where the context is a notion employed in databases to perform information filtering on the basis of the user's current situation

ADaPT: Automatic data personalization based on contextual preferences

This demo presents a framework for personalizing data access on the basis of the users' context and of the preferences they show while in that context. The system is composed of (i) a server application, which "tailors" a view over the available data on the basis of the user’s contextual preferences, previously inferred from log data, and (ii) a client application running on the user’s mobile device, which allows to query the data view and collects the activity log for later mining. At each change of context detected by the system the corresponding tailored view is loaded on the client device: accordingly, the most relevant data is available to the user even when the connection is unstable or lacking. The demo features a movie database, where users can browse data in different contexts and appreciate the personalization of the data views according to the inferred contextual preferences

Enhancing domain-aware multi-truth data fusion using copy-based source authority and value similarity

Data fusion, within the data integration pipeline, addresses the problem of discovering the true values of a data item when multiple sources provide different values for it. An important contribution to the solution of the problem can be given by assessing the quality of the involved sources and relying more on the values coming from trusted sources. State-of-the-art data fusion systems define source trustworthiness on the basis of the accuracy of the provided values and on the dependence on other sources, and recently it has been also recognized that the trustworthiness of the same source may vary with the domain of interest. In this paper we propose STORM, a novel domain-aware algorithm for data fusion designed for the multi-truth case, that is, when a data item can also have multiple true values. Like many other data-fusion techniques, STORM relies on Bayesian inference. However, differently from the other Bayesian approaches to the problem, it determines the trustworthiness of sources by taking into account their authority: Here, we define authoritative sources as those that have been copied by many other ones, assuming that, when source administrators decide to copy data from other sources, they choose the ones they perceive as the most reliable. To group together the values that have been recognized as variants representing the same real-world entity, STORM provides also a value-reconciliation step, thus reducing the possibility of making mistakes in the remaining part of the algorithm. The experimental results on multi-truth synthetic and real-world datasets show that STORM represents a solid step forward in data-fusion research