How Artificial Intelligence Is Transforming Biopharma (And What It Means For Marketers)

How Artificial Intelligence Is Transforming Biopharma (And What It Means For Marketers)

Value founder Jessica is the Digital Transformation Advisor for Rare Advocacy Movement’s Health Equity Diagnostic Infrastructure Initiative.

Emerging technologies such as artificial intelligence (AI) and machine learning (ML) are making their mark on every aspect of our lives. Biopharmaceutical companies are beginning to use both technologies to offer treatments for serious diseases. As the authorized digital transformation advisor for the Rare Advocacy Movement (RAM), I find it encouraging to see scientists experimenting with data to discover more applications for the treatment of rare diseases.

How AI and ML Can Support Biopharma

Artificial intelligence and machine learning are two of the leading emerging technologies that will change the way people live, work and scientific progress. IBM defines AI as a technology that “uses computers and machines to mimic the problem-solving and decision-making abilities of the human mind.”

Machine learning is a subfield of AI, and both technologies are often mentioned in conjunction with each other. ML algorithms can not only process large volumes of data, but are also able to learn from their results without further human input.

AI and ML can help biopharmaceutical companies overcome some of the challenges associated with drug research and development. By integrating these advanced approaches, the pharmaceutical industry can bring life-saving medicines to market faster and more cost-effectively. In a recent article, the consulting firm McKinsey & Company points to the potential of AI in biopharmaceutical research. The consultants highlight the slow and inefficient progress of current drug research and development.

Artificial intelligence can streamline and accelerate this process by identifying the most promising drug candidates for specific target diseases.

Companies making their mark

Pharmaceutical companies in the United States continue to dominate the industry globally, dominating nearly half of total pharmaceutical spending (paywall) in 2021. Furthermore, McKinsey research shows that around 270 pharmaceutical companies around the world are working on the AI-driven drug discovery industry. About 50% of these are based in the United States, with hubs originating in Southeast Asia and Western Europe. According to McKinsey, few AI-driven companies have drug candidates in the preclinical development stages.

One company taking a technology-driven approach to drug discovery is Anavex Life Sciences. Anavex is developing a drug to help Alzheimer’s patients. The company has taken a precision medicine approach and recently announced positive results in its Phase 2B/3 clinical trial. The company used Ariana Pharma’s AI technology to illustrate the promising results of its drug.

Another forward-thinking company is pharmaceutical giant Bayer, which has strengthened its ties with healthcare technology company Huma. Together, they will “develop a machine learning model that can distinguish between different forms of non-small cell lung carcinoma in image data.”

What do these developments mean for Biopharma marketers?

Artificial intelligence has the potential to change the way the world currently approaches medicine. Making digital technologies integral to drug discovery and research can enable biopharmaceutical companies to accelerate drug development. Data-driven approaches can support research into additional uses of existing drugs or those in development and bring hope to rare disease sufferers. These developments will also change the way biopharma marketers must approach their work.

In an era where misinformation is common, scientific and pharmaceutical developments must be backed up by factual results beyond claims. One of the most credible ways to do this is by being open about the developer’s expectations of a clinical trial and how it compares to the results. Digital marketers have several roles to play in this process.

Communicating research outcomes often requires some translation from scientific English into more accessible language. Emphasizing potential patient benefits is one way to reach a wider audience. Accurate statistics are important, but benefits can be memorable and recognizable.

Reaching out to clinicians early is equally important to ensure engagement and buy-in. Doctors who work with patients on a daily basis may bring a new perspective to the research and the brand’s communication of test results. During the later stages of clinical trials, clinicians can also refer patients to participate in the trial.

In addition, clinicians in positions of authority can lend weight to marketing communications about biopharmaceutical developments through quotes or video appearances. Additionally, those quotes can be useful as testimonials on the biopharma company’s website and in other marketing materials.

Test results are only one aspect of biopharmaceutical communication. It is equally important to provide details of the development process and compare the effectiveness of previous treatments with the effectiveness and speed of the current treatment.

Further implications for marketers

The use of AI and machine learning is transforming digital marketing in the same way as biopharma. Algorithms power some of the leading B2C and B2B marketing channels. Data analytics allows marketers to make data-driven predictions rather than relying on historical results to make recommendations.

As with biopharma, digital marketing and advertising use data-driven technology. For example, a programmatic advertising campaign is an effective way to build brand awareness and deliver relevant messages at the right time. Through AI and machine learning, marketers can build models and personas and optimize campaigns with a wide variety of targeting capabilities that allow them to reach and scale to niche audiences.

Another example is branding. Audiences associate a brand with tangible and intangible factors. Consistent, authentic branding – also for biopharmaceutical companies – can help build audience trust and establish long-term customer relationships. For example, the data companies get from using AI and machine learning for research can strengthen a brand’s credibility.

Plus, by focusing on how these technologies are changing the research process, digital marketers have the opportunity to demystify drug development and remove barriers between patients and biopharma leaders.

With all that said, we are only at the beginning of implementing AI and ML in both biopharma and digital marketing. As these technologies further penetrate both industries, they will create opportunities for and change the approaches of scientists, manufacturers, and communicators simultaneously.

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