Examining association between cohesion and diversity in collaboration networks of pharmaceutical clinical trials with drug approvals

Gary Lin; Sauleh Siddiqui; Jen Bernstein; Diego A Martinez; Lauren Gardner; Tenley Albright; Takeru Igusa

doi:10.1093/jamia/ocaa243

Examining association between cohesion and diversity in collaboration networks of pharmaceutical clinical trials with drug approvals

J Am Med Inform Assoc. 2021 Jan 15;28(1):62-70. doi: 10.1093/jamia/ocaa243.

Authors

Gary Lin^{1

2}, Sauleh Siddiqui³, Jen Bernstein⁴, Diego A Martinez^{1

2}, Lauren Gardner^{5

6}, Tenley Albright⁷, Takeru Igusa^{5

6}

Affiliations

¹ Department of Emergency Medicine, Johns Hopkins University, Baltimore, Maryland, USA.
² Center for Data Science in Emergency Medicine, Johns Hopkins University, Baltimore, Maryland, USA.
³ Department of Environmental Science, American University, Washington, DC, USA.
⁴ Center for Leadership Education, Johns Hopkins University, Baltimore, Maryland, USA.
⁵ Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, Maryland, USA.
⁶ Center for Systems Science and Engineering, Johns Hopkins University, Baltimore, Maryland, USA.
⁷ MIT Collaborative Initiatives, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.

Abstract

Objective: Clinical trials ensure that pharmaceutical treatments are safe, efficacious, and effective for public consumption, but are extremely complex, taking up to 10 years and $2.6 billion to complete. One main source of complexity arises from the collaboration between actors, and network science methodologies can be leveraged to explore that complexity. We aim to characterize collaborations between actors in the clinical trials context and investigate trends of successful actors.

Materials and methods: We constructed a temporal network of clinical trial collaborations between large and small-size pharmaceutical companies, academic institutions, nonprofit organizations, hospital systems, and government agencies from public and proprietary data and introduced metrics to quantify actors' collaboration network structure, organizational behavior, and partnership characteristics. A multivariable regression analysis was conducted to determine the metrics' relationship with success.

Results: We found a positive correlation between the number of successful approved trials and interdisciplinary collaborations measured by a collaboration diversity metric (P < .01). Our results also showed a negative effect of the local clustering coefficient (P < .01) on the success of clinical trials. Large pharmaceutical companies have the lowest local clustering coefficient and more diversity in partnerships across biomedical specializations.

Conclusions: Large pharmaceutical companies are more likely to collaborate with a wider range of actors from other specialties, especially smaller industry actors who are newcomers in clinical research, resulting in exclusive access to smaller actors. Future investigations are needed to show how concentrations of influence and resources might result in diminished gains in treatment development.

Keywords: clinical research; collaboration network; complex system; network science; science of science.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Clinical Trials as Topic / organization & administration*
Cooperative Behavior
Drug Approval / organization & administration*
Drug Industry / organization & administration*
Humans
Multivariate Analysis
Pharmaceutical Preparations*
Regression Analysis

Substances

Pharmaceutical Preparations