Professor Philipp Koellinger, co-founder and CEO of open science startup DeSci Labs, argues that peer review needs urgent reform and that AI can help pharma live up to its ideals.
DeSci Labs
Peer review is the core quality control mechanism of pharmaceutical research, determining what findings are published and what projects are funded. Yet, the system is surprisingly outdated and inefficient. Journal editors, including in pharmaceutical research face mounting pressure to find the right reviewers, but often rely on outdated tools or author-supplied information that can be biased. The result is a process that struggles with inefficiency, inconsistency and a lack of transparency, especially in how reviewers are chosen.
Peer review remains the cornerstone of validating scientific research, but the process of selecting reviewers is outdated, slow, and flawed. Editors, who rely heavily on traditional methods such as scanning reference lists or databases of past referees frequently end up reusing the same reviewers rather than finding the most suitable experts. In pharmaceutical research, where the reliability of preclinical and early clinical data underpins development pipelines, this limited reviewer pool can compromise the early detection of methodological flaws or exaggerated claims.
This limited and recycled pool can lead to biases or inadequate evaluations, as the chosen reviewers may not always have the ideal expertise or impartiality needed to rigorously assess the work. Despite advances in technology, many journals still depend on these aging systems, which restrict the diversity and quality of peer review and ultimately impact the fairness and reliability of how pharmaceutical research gets published and funded. In a recent survey across four scientific publications, editors described finding multiple suitable reviewers as the most frustrating part of their job above all other frustrations. The editors also expressed frustration with selecting the most appropriate expert for each manuscript.
Choosing the right reviewer
The quality and integrity of the peer review process hinges on choosing reviewers with the right expertise, impartiality and motivations. A well-qualified referee with knowledge of the studied pharmaceutical topic helps editors make informed and fair decisions by providing thorough, competent, and unbiased assessments of a submission. In pharmaceutical science, this is particularly important when evaluating studies that could influence clinical trial design, biomarker validation, or therapeutic positioning.
Conversely, selecting inappropriate reviewers can introduce bias, reduce the quality of the review and ultimately damage the credibility of the entire publishing process. Ensuring the right experts are involved is essential for maintaining trust in pharmaceutical research.
An ideal reviewer should have no conflicts of interest, they must not be recent co-authors, past or current supervisors or supervisees, nor affiliated with the same academic institution as the authors. They should possess relevant expertise, demonstrated by recent publications on the same topic or employing similar methods, ensuring they understand the nuances of the research. This rigour is essential when assessing pharmacological mechanisms, toxicology profiles, or novel therapeutic modalities, where subtle methodological issues can have major downstream effects.
Beyond expertise, reviewers must be objective and fair, and able to set aside biases that could influence their judgment. Lastly, the best reviewers are motivated and thorough, willing to dedicate the necessary time and effort to carefully assess a submission.
Limitations of current procedure
Editors and funding agencies play a crucial role in identifying and managing potential biases among reviewers. They carefully select and oversee the process to ensure that reviews are balanced, fair and focused solely on the quality of the research. Currently, editors typically spend up to two weeks identifying suitable reviewers within their specific field and reaching out to them. In pharmaceutical fields like oncology or infectious diseases, such delays can slow critical advancements and decision-making.
In addition, they often depend heavily on the references cited within a manuscript to identify potential reviewers, which saves time from searching elsewhere. However, this approach has notable drawbacks; authors can manipulate reference lists to steer suggestions toward certain reviewers, and important experts who are not cited may be overlooked entirely.
This method therefore results in a narrow or biased pool of reviewers, missing out on broader expertise that could improve the quality of peer review. A more expansive, data-driven approach is needed to ensure that critical pharmaceutical research benefits from cross-disciplinary scrutiny, particularly as it moves towards clinical application.
Additionally, the demands placed on editors and funding agencies are significant. Finding suitable reviewers is not just time-consuming but made more difficult by peer reviewers typically serving on a voluntary, unpaid basis. Consequently, editors face ongoing challenges in securing timely and thorough reviews, which can delay the publication process and affect the overall rigour of scientific assessment.
Revolutionising the procedure
AI tools have the potential to transform the reviewer selection process by rapidly analysing vast amounts of scientific literature to identify the most relevant experts. This is particularly valuable in pharmaceutical R&D, where novel targets or platforms often outpace traditional editorial expertise and require hyper-specific review insight.
These advanced systems can incorporate important features such as conflict-of-interest checks, expertise mapping that distinguishes between topic specialists and methodological experts and integration of researcher metadata like H-index and career stage. This also greatly expands the pool of qualified and capable reviewers, something DeSci Labs is actively addressing through its AI-driven Referee Finder tool.
Editors can tailor these algorithms to meet the unique requirements of each submission, ensuring a more precise match between reviewers and manuscripts. By addressing long-standing inefficiencies, AI-powered solutions can significantly enhance both the fairness and speed of peer review.
As pharmaceutical innovation increasingly relies on data integrity and cross-sector collaboration, transparent and scalable peer review systems are no longer optional, they're essential.