The data to solve many pharmaceutical research problems already exists. We just need to harness it.

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In searching for new therapies, pharmaceutical research teams worldwide are conducting experiments daily and generating knowledge. Thanks to this constantly expanding pool of scientific data, we are starting projects with access to more information than ever before.

Data is good. The right data is better. But finding the right data is no easy task. Inaccessible data sources, the growing complexity of search terms required to attain appropriate results, and the multitude of databases available means that finding data – and then applying it to inform research – is taking up more and more valuable researcher hours. As a result, 80% of researcher time is dedicated to acquiring and reformatting data; time that could be much better spent on analysis and developing scientific insights.

It is, however, essential. What’s needed are methods to accelerate the search …

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How biologists of the future could displace some data scientists in drug development

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A decade ago, data scientist seemed like the sexiest job of the 21st century, to paraphrase an influential Harvard Business Review article.

In the pharmaceutical industry, data science certainly continues to have tremendous potential, but in years to come, data-savvy biologists could have as least as much of an impact on drug development as data scientists, according to David Harel, co-founder and president of Cytoreason, which has developed a computational disease model for drug developers.

“We call this the biologist of the future,” Harel said, referring to biologists with significant data science training received either in academia or on the job.

The consulting firm Gartner has espoused a similar idea, which it terms a citizen data science to refer to workers outside of statistics and analytics who create data science models based on predictive or prescrip…

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An intelligent approach to data cleaning 

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The collection of good quality data from clinical trials is essential to data analysis to produce robust results that meet the precise requirements for regulatory need. Data from clinical trials are increasingly complex, related to involved protocols, the geography of trial sites, increasing data streams and technological advances. Therefore, studies must be set up to be efficient, offer support and training to trial sites and ensure that the right data are collected correctly.

Data Management teams have historically reviewed data once source document verification (SDV) has taken place by the Clinical Monitors on an ongoing basis. Data issues can be actioned early in the trial, and corrective action put in place. This activity has been a very manual process, thorough and time-consuming and has left less time to focus on insightful data analysis.

Collaboration: Data science and da…
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3 ways to reduce implicit bias in predictive analytics for better health equity

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The COVID-19 pandemic has brought into sharp focus the racioethnic and socioeconomic disparities inherent in the U.S. healthcare system. These disparities take the form of increased adverse health outcomes and reduced quality of life for affected groups.

For example, a study of cities that reported COVID-19 deaths by race and ethnicity found that 34% of deaths were among non-Hispanic Black people. This group accounts for just 12% of the total U.S. population, according to the U.S. Centers for Disease Control and Prevention (CDC), citing “long-standing systemic health and social inequities” among the reasons for the racial and ethnic disparities in COVID-19 deaths.

This heightened awareness around inequities and disparities in healthcare has also resulted in some much-needed attention to similar bias-related problems in the growing sector of healthcare artificial intelligence …

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Why cyberattacks targeting pharma are ramping up

Cyberattacks targeting the pharma industry have ramped up during the pandemic, and insider threats and nation-state attacks are on the rise. Meanwhile, the average cost of a pharma breach in 2021 is $5.04 million, according to the IBM-sponsored Ponemon Institute’s Cost of a Data Breach Report. For context, an average data breach incurs damages of $4.24 million.

Pharmaceutical companies are beginning to allocate more resources to cybersecurity, according to Howard Ting, CEO of data detection and response business Cyberhaven (Palo Alto, Calif.).

Pharma companies’ data is increasingly decentralized

The traditional model for protecting sensitive data was to create the networking equivalent to a castle and moat. But in the pharmaceutical industry and elsewhere, sensitive data can no longer be stored under lock and key. Pharmaceutical companies’ data must “move and be shared,” Ting said. For example, a contract manufacturer might need access to sensitive data. …

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Q&A: Keys to unlock data science potential for drug discovery

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For all of its promise in healthcare and elsewhere, deploying artificial intelligence is frequently a challenging endeavor. “Close collaboration between data science teams, other project team members and stakeholders is essential,” said Jennifer Bradford, director of data science at Phastar, the London-headquartered contract research organization. While input from computational, statistical or medical experts could be essential to inform data science models, all stakeholders understand the requirements and are working “in sync with the project,” Branford said.

In the following interview, Bradford shares advice on how to collaborate effectively on data science projects, the impact of COVID-19 on data science in pharma and the potential for AI to accelerate R&D timelines. 

What comes next after alignment between different stakeholders on data science projects is confirmed? <…

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