data science Archives - Medical Design Sourcing

Genmab’s data-driven strategies speed up drug commercialization

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Genmab’s senior vice president, global head of data science and AI, Hisham Hamadeh, describes the company’s journey to becoming “a data-driven decision-making company.” In one sense, there is little choice but to do so. “We’re swimming in data like never before. We’ve seen the volumes of data, the ability to compute on that data, and the type of algorithms that are emerging,” he said.

The initiative has the firm support of the company’s CEO Jan G. J. van de Winkel, who expects AI to help enhance R&D efficiency in the coming years. Already, the company has succeeded in winning FDA approval for the antibody epcoritamab in lymphoma within five years after the first patient was dosed. The approval timeline was “a world record,” van de Winkel told Medwatch, adding that the company believes its investments in data science will unlock further breakthrou…

Nvidia exec: Generative AI can turn every biologist into a computer scientist — and vice versa

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On a recent visit to Nvidia’s headquarters in Santa Clara, I had the chance to speak with Kimberly Powell, the company’s vice president and general manager of healthcare. After asking about the pharma industry’s surging interest in AI, Powell remarked, “Every pharma knows who Nvidia is now.” Curious, I asked her, “How long has that been the case?” Without batting an eye, she replied: “Since December.” While some R&D professionals in pharma were aware of the company, awareness was not pervasive until recently. The December timing is not a coincidence. OpenAI’s ChatGPT rolled out on November 30, 2022. ChatGPT, which runs on Nvidia hardware, has prompted a reprioritization of AI in the industry. As Powell described it: “From the top down, every group is saying, go do at least two things using AI, and report back. They are really just starting to make it part of the DNA of the c…

A checklist for unlocking the promise of AI in clinical trials

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AI algorithms offer a myriad of advantages for clinical trials. AI techniques can, for instance, support patient enrollment and site selection, improve data quality and enhance patient outcomes. AI algorithms — combined with an effective digital infrastructure — can also help aggregate and manage clinical trial data in real time, as Deloitte has noted. Last week, a startup revealed an AI system that can accurately predict clinical trial outcomes.

Yet for organizations to fully realize AI’s promise is not simple. The task requires oversight, transparency and diverse collaboration. Core considerations include educating users to build trust in AI tools and ensuring the clinical precision of medical-grade AI algorithms. From unraveling the ‘black box’ of algorithms to safeguarding patient privacy, this article provides a checklist to help organizations responsibly incorporat…

Synthetica Bio lates to tap generative AI to spur drug discovery innovation

Interest in generative AI has skyrocketed in 2023. While the generative AI market is already substantial, worth $10 billion in 2022 according to Grand View Research, it is set to balloon by a compound annual growth rate (CAGR) of 35.6% from 2023 to 2030. It’s no wonder that a slew of companies — including Exscientia, Insilico Medicine, Atomwise and BenevolentAI — are eager to tap the technology to expedite drug discovery.

Enter Synthetica Bio

The latest entrant in the generative AI scene is Laguna Beach, California-based Synthetica Bio. The startup aims to explore generative AI and large language models (LLMs) to enable real-time data processing. The company, founded by Simon Arkell and Alex Dickinson, entrepreneurs with a successful track record in companies like the predictive analytics firm Predixion, digital pathology startup Deep Lens, biotech Helixis and the molecular diagnostics firm Chromacode, has formed strategic partnerships to propel its platform de…

A deep dive into AWS’s strategy with generative AI and ML in life sciences

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The AI market is witnessing meteoric growth, with projections hinting at a potentially staggering increase over the next decade. Against this backdrop of rapid AI evolution, we recently spoke with Tehsin Syed, general manager of AWS Health. Syed shared that Amazon Web Services (AWS) is seeing growing interest from Big Pharma firms. “Nine of the top 10 pharma companies in the world have a large majority of the workloads running on AWS,” Syed said. The AWS cloud hosts more than 100,000 customers across industries.

AWS aims to provide a broad and deep range of AI services and takes an end-to-end approach to AI that includes infrastructure, software, hardware and services.In an increasingly competitive AI landscape, AWS recently launched a $100 million initiative, the AWS Generative AI Innovation Center, to accelerate enterprise generative AI adoption. As Syed put it, “AWS has a pe…

Working backwards: AWS’s strategy for pharma’s cloud-enabled transformation

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The meteoric rise of the AI sector is hard to fathom. Projections from Precedence Research suggest that the global AI market could balloon by 2600% from 2022 to 2032, hitting $1.87 trillion — an annual growth rate of 39%.

In pharma, AI and data science demand is surging though overall growth is sluggish. Despite advances such as the rapid development of COVID-19 vaccines, the broader sector has struggled to keep up with the overall stock market. But the future could tell a different story. McKinsey projects that the pharma and medical products sector could gain 9% in EBITDA from cloud computing.

To tap this potential, cloud vendors are allying with pharma companies. “Nine of the top 10 pharma companies in the world have a large majority of the workloads running on AWS,” noted Tehsin Syed, general manager of AWS Health. These workloads encompass diverse areas including IT infras…

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 …

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…

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…

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 …

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. …

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? <…