Massimo Riccaboni

The productivity crisis in pharmaceutical R&D

Advances in the understanding of the molecular basis of diseases have expanded the number of plausible therapeutic targets for the development of innovative agents in recent decades. However, although investment in pharmaceutical research and development (R&D) has increased substantially in this time, the lack of a corresponding increase in the output in terms of new drugs being approved indicates that therapeutic innovation has become more challenging. Here, using a large database that contains information on R&D projects for more than 28,000 compounds investigated since 1990, we examine the decline of R&D productivity in pharmaceuticals in the past two decades and its determinants. We show that this decline is associated with an increasing concentration of R&D investments in areas in which the risk of failure is high, which correspond to unmet therapeutic needs and unexploited biological mechanisms. We also investigate the potential variations in productivity with regard to the regional location of companies and find that although companies based in the United States and Europe differ in the composition of their R&D portfolios, there is no evidence of any productivity gap.

A Comparison of U.S. and European University-Industry Relations in the Life Sciences

We draw on diverse data sets to compare the institutional organization of upstream life science research across the United States and Europe. Understanding cross-national differences in the organization of innovative labor in the life sciences requires attention to the structure and evolution of biomedical networks involving public research organizations (universities, government laboratories, nonprofit research institutes, and research hospitals), science-based biotechnology firms, and multinational pharmaceutical corporations. We use network visualization methods and correspondence analyses to demonstrate that innovative research in biomedicine has its origins in regional clusters in the United States and in European nations. But the scientific and organizational composition of these regions varies in consequential ways. In the United States, public research organizations and small firms conduct R&D across multiple therapeutic areas and stages of the development process. Ties within and across these regions link small firms and diverse public institutions, contributing to the development of a robust national network. In contrast, the European story is one of regional specialization with a less diverse group of public research organizations working in a smaller number of therapeutic areas. European institutes develop local connections to small firms working on similar scientific problems, while cross-national linkages of European regional clusters typically involve large pharmaceutical corporations. We show that the roles of large and small firms differ in the United States and Europe, arguing that the greater heterogeneity of the U.S. system is based on much closer integration of basic science and clinical development.

Technological Change and Network Dynamics: Lessons from the Pharmaceutical Industry

In this paper, we investigate how underlying relevant technological conditions induce distinguishable patterns of change in industry structure and evolution. A mapping is detected between the specific nature of problem decompositions and research techniques at the micro level of knowledge bases, and patterns of structural evolution at the macro level of the industry network. The graph-theoretic techniques we introduce map major technological discontinuities on changes observed at the level of dominant organization forms. They might have applications in other domains, whenever the identification of structural breaks and homological relationships between technological and industrial spaces are important issues.

Does co-location matter for formal knowledge collaboration in the Swedish biotechnology-pharmaceutical sector?

This article addresses the validity of assumptions about the importance of co-locality for innovation, by analyzing whether or not co-location matters for formal knowledge collaboration in the Swedish biotechnology–pharmaceutical sector, or biotech–pharma sector. The population of Swedish biotech–pharma firms has been defined, based on the three criteria of geographical location, their engagement in active knowledge development, and their specialized knowledge/product focus. The firms’ patterns of regional, national and international collaboration with other firms and with universities is analyzed, as well as the differing collaborative patterns of small versus large firm. In addressing the theoretical questions about the relative importance of co-location for innovation, the article also provides an empirical overview of the Swedish biotech–pharma sector, especially trends over time. This paper thus contributes to the literature by expanding our empirical knowledge about one European biotech–pharma sectoral system, e.g. Sweden, as well as addressing the theoretical question about the relative importance of co-location for formal knowledge collaboration.

The growth of business firms: Theoretical framework and empirical evidence

We introduce a model of proportional growth to explain the distribution P(g)(g) of business-firm growth rates. The model predicts that P(g)(g) is exponential in the central part and depicts an asymptotic power-law behavior in the tails with an exponent zeta = 3. Because of data limitations, previous studies in this field have been focusing exclusively on the Laplace shape of the body of the distribution. In this article, we test the model at different levels of aggregation in the economy, from products to firms to countries, and we find that the predictions of the model agree with empirical growth distributions and size-variance relationships.

Reputation and impact in academic careers

Significance Over a scientist’s career, a reputation is developed, a standing within a research community, based largely upon the quantity and quality of his/her publications. Here, we develop a framework for quantifying the influence author reputation has on a publication’s future impact. We find author reputation plays a key role in driving a paper’s citation count early in its citation life cycle, before a tipping point, after which reputation has much less influence relative to the paper’s citation count. In science, perceived quality, and decisions made based on those perceptions, is increasingly linked to citation counts. Shedding light on the complex mechanisms driving these quantitative measures facilitates not only better evaluation of scientific outputs but also a more transparent evaluation of the scientists producing them. Reputation is an important social construct in science, which enables informed quality assessments of both publications and careers of scientists in the absence of complete systemic information. However, the relation between reputation and career growth of an individual remains poorly understood, despite recent proliferation of quantitative research evaluation methods. Here, we develop an original framework for measuring how a publication’s citation rate Δc depends on the reputation of its central author i, in addition to its net citation count c. To estimate the strength of the reputation effect, we perform a longitudinal analysis on the careers of 450 highly cited scientists, using the total citations Ci of each scientist as his/her reputation measure. We find a citation crossover c×, which distinguishes the strength of the reputation effect. For publications with c < c×, the author’s reputation is found to dominate the annual citation rate. Hence, a new publication may gain a significant early advantage corresponding to roughly a 66% increase in the citation rate for each tenfold increase in Ci. However, the reputation effect becomes negligible for highly cited publications meaning that, for c ≥ c×, the citation rate measures scientific impact more transparently. In addition, we have developed a stochastic reputation model, which is found to reproduce numerous statistical observations for real careers, thus providing insight into the microscopic mechanisms underlying cumulative advantage in science.

Persistence and uncertainty in the academic career

Understanding how institutional changes within academia may affect the overall potential of science requires a better quantitative representation of how careers evolve over time. Because knowledge spillovers, cumulative advantage, competition, and collaboration are distinctive features of the academic profession, both the employment relationship and the procedures for assigning recognition and allocating funding should be designed to account for these factors. We study the annual production ni(t) of a given scientist i by analyzing longitudinal career data for 200 leading scientists and 100 assistant professors from the physics community. Our empirical analysis of individual productivity dynamics shows that (i) there are increasing returns for the top individuals within the competitive cohort, and that (ii) the distribution of production growth is a leptokurtic “tent-shaped” distribution that is remarkably symmetric. Our methodology is general, and we speculate that similar features appear in other disciplines where academic publication is essential and collaboration is a key feature. We introduce a model of proportional growth which reproduces these two observations, and additionally accounts for the significantly right-skewed distributions of career longevity and achievement in science. Using this theoretical model, we show that short-term contracts can amplify the effects of competition and uncertainty making careers more vulnerable to early termination, not necessarily due to lack of individual talent and persistence, but because of random negative production shocks. We show that fluctuations in scientific production are quantitatively related to a scientist’s collaboration radius and team efficiency.

The Evolution of Knowledge and the Dynamics of an Industry Network

The paper moves a step forward in the direction of establishing a connection between the structure and evolution of knowledge bases and the structure and evolution of organizational forms in innovative activities in a science-intensive industry. The paper has an explicit focus on the dynamics of the network of collaborative agreements in R&D in the pharma/biotech industry after the “molecular biology revolution”. Using a comprehensive dataset, built by the authors integrating several sources in the industry, the dynamics of the network over time is extensively analyzed. With regards to network structure, it is found that, while the size of the network increases over time due to net flows of entry, its topological properties remain relatively unchanged. The evolution of the network has occurred without relevant deformations in the core-periphery profile. With regards to age-dependent propensity to collaborate, the paper finds that the extent of inter-generational collaboration is much more significant than intra-generational collaboration. In addition, the propensity of firms of a given generation to enter into collaboration with firms of a different generation increases with the distance between the two, while the total number of intra-generational collaborations decreases over time and, moreover, tends to decrease for most recent generations. In the paper a unitary and coherent explanation of the evidence is developed, coming to reveal the existence of a striking isomorphism between structural properties of the dynamics of knowledge and of the evolution of network structure.

On Size and Growth of Business Firms

We study size and growth distributions of products and business firms in the context of a given industry. Firm size growth is analyzed in terms of two basic mechanisms, i.e., the increase of the number of new elementary business units and their size growth. We find a power-law relationship between size and the variance of growth rates for both firms and products, with an exponent between −0.17 and −0.15, with a remarkable stability upon aggregation. We then introduce a simple and general model of proportional growth for both the number of firm independent constituent units and their size, which conveys a good representation of the empirical evidences. This general and plausible generative process can account for the observed scaling in a wide variety of economic and industrial systems. Our findings contribute to shed light on the mechanisms that sustain economic growth in terms of the relationships between the size of economic entities and the number and size distribution of their elementary components.

Is Europe Evolving Toward an Integrated Research Area

Despite efforts to integrate across borders, Europe remains a collection of national innovation systems. Efforts toward European research and development (R&D) integration have a long history, intensifying with the Fifth Framework Programme (FP) in 1998 (1–3) and the launch of the European Research Area (ERA) initiative at the Lisbon European Council in 2000. A key component of the European Union (EU) strategy for innovation and growth (4, 5), the ERA aims to overcome national borders through directed funding, increased mobility, and streamlined innovation policies.