Why use AI in pharma, and how to get it right

You will notice that it’s common for pharma companies to team up with tech innovators to successfully deploy AI. Accenture conducted a survey where 61% of the respondents reported at least a 5% increase in profit after partnering with a tech vendor, with 76% of pharma executives citing effective partnership as a key success factor.

Here are 5 top applications of artificial intelligence in pharmaceutics.

The Congressional Budget Office reports that the R&D costs of developing a new drug can exceed $2 billion, which includes research and clinical trials.

Deploying AI in pharma enables researchers to sift through enormous datasets, such as small molecule libraries and spot disease patterns, and learn which chemical compositions can be a good fit for various biological targets. AI can generate chemical compounds either as a text string or as a graph architecture. It’s important to validate the resulting compounds, as many of them will not make sense, could be toxic, or could contain a component that shouldn’t be a part of any drug.

In addition to discovering candidate compositions, scientists can use AI algorithms to parse medical literature on how to best synthesize the drug and design clinical trials. Research shows that pharmaceutical artificial intelligence can cut drug synthesizing and screening time by 50%, saving the pharma sector up to $26 billion in annual expenses.

There are many great examples of pharma companies deploying AI solutions to facilitate drug discovery. For instance, GSK, a British pharmaceutical company headquartered in London, partnered with California’s Vir Biotechnology during the pandemic to accelerate COVID-19 antibody discovery with the help of AI and a human gene editing tool, CRISPR. Vir already had an antibody platform that it deployed to discover drugs for different respiratory pathogens in the past. And now, in this collaboration, they discovered sotrovimab, an antibody that binds to a SARS-CoV-2 epitope to neutralize COVID-19.

In another example of collaboration between Europe and the US, a French pharma and healthcare company Sanofi partnered with California-based biotech innovator Atomwise to discover and synthesize drug compounds for five different targets. Sanofi wanted to steer clear of the traditional drug discovery approach and paid Atomwise $20 million upfront for their innovation and AI capabilities.

You can find more information on AI in drug discovery on our blog.

AI has many applications in clinical trials. One of them is identifying the right candidate participants. The technology can analyze patient data, genetic information, doctor notes, and other information, and pick people who are eligible for a particular trial. AI can even help decide on the optimal population size based on the existing description of similar trials.

86% of clinical trials fail to recruit enough patients within their target time frame. One-third of phase Ⅲ clinical trials have to stop due to recruitment-associated challenges.

For instance, IBM Watson relies on analytics and natural language processing (NLP) to analyze patient information. The tool can handle unstructured data, like doctor’s notes, and produce an insightful patient summary. Clinical researchers use these highlights to select and recruit patients.

As AI helps pharma companies to find patients, it also works the other way around. Antidote, a clinical trial patient recruitment platform, uses NLP to analyze their text and screen them for trial inclusion/exclusion criteria. It requires patients to answer a few simple questions on its platform and suggests a list of trials that the person can join.

Deploying AI in the pharmaceutical industry offers multiple opportunities to improve the drug production process. The technology could:

The pharma sector largely depends on sales. Companies aim to reach as many customers as possible while offering a distinctive user experience and a customized approach. Artificial intelligence in pharma can facilitate drug marketing by:

AI can analyze large quantities of unstructured patient data and calculate the optimal dosage of a particular drug for this person to achieve the best possible results with minimal side effects. Artificial intelligence models in the pharma industry can analyze the following information:

When the optimal dosage is calculated, the technology can monitor its effectiveness and make adjustments when needed.

To give a real-life example, a California-based company Dosis built an AI-driven personalized medicine dosing platform that dialysis clinics can use to manage chronic drug intake. In his interview with HealthcareITNews, Dosis’ CEO Shivrat Chhabra mentioned this platform helped clients reduce drug consumption by 25% while improving patient outcomes.

Some of these obstacles are specific to the field, and some are more general and apply to all projects involving this technology. One of the key challenges is the enormous costs associated with artificial intelligence. This is particularly hard as the expenses associated with drug development are already rather high. You can turn to experienced AI consultants to learn how to cut down on costs and still get a viable product.

Here are other prominent challenges that you can face during pharmaceutical AI implementation.

According to a recent study by McKinsey, the lack of integrated data sources was the chief obstacle on the way to applying analytics in the healthcare field.

Pharma AI models typically require large datasets to learn. However, it’s a challenge to obtain a sufficient dataset for each disease, especially the rare ones. So, as training datasets are getting smaller, the data that an AI-powered drug development tool has to handle is rather complex. Think of patient data. It includes historical information, genetic makeup, doctor notes, medical scans, etc. Under these conditions, it’s a challenge to build accurate algorithms.

When training data is lacking, it’s possible to use synthetic data generators for some pharma applications. For instance, Mostly AI claims it can generate data suitable for pharmaceutical usage. Healthcare data is among the most sensitive data types, and privacy is of the essence in such applications. Synthetic datasets can solve this issue. As Andreas Ponikiewicz, VP of Global Sales at Mostly AI, puts it, “With generative AI based synthetic healthcare data, that contains all the statistical patterns, but is completely artificial, the data can be made available without privacy risk.”

Another option for acquiring data for experimenting with AI and pharma is to become a part of a specialized collaboration. For example, the Massachusetts Institute of Technology initiated the Machine Learning for Pharmaceutical Discovery and Synthesis Consortium. 13 pharma companies joined the consortium to design and build AI algorithms for small molecule discovery.

You need to make sure that the data used within pharmaceutical applications is all realistic. But it’s rather costly to verify that, as it requires the intervention of human experts.

There are still multiple healthcare IT standards and regulations, which means that each hospital can adopt a standard of their choice for data storage and formatting. This makes it hard to integrate and use patient data needed for drug-related research from different medical facilities.

These issues of AI in the pharma industry can be addressed on the governmental level. For instance, the Swiss Personalized Health Network (SPHN) is a health data unifying initiative by the Swiss government. The SPHN was set to build a national infrastructure that streamlines medical data exchange among Swiss hospitals, research institutes, and regulatory bodies.

On an individual level, pharma researchers can benefit from platforms like Deep 6 AI, which uses NLP to scan and extract data from heterogeneous electronic health record (EHRs) systems.

“All data is biased. This is not paranoia. This is fact.”

– Dr. Sanjiv Narayan, professor of medicine at Stanford University.

AI-powered models can easily develop bias if their training dataset wasn’t representative of the target population. Data bias has specifically been a problem in the pharmaceutical and healthcare sectors. Research shows that only a few AI-powered products submitted for FDA approval offer evidence on covering the bias issue.

Some medical professionals believe that it will help reduce bias if data scientists work more closely with clinicians and learn more about data while building the algorithms. They can request information, such as where the data came from and what was the original goal of gathering it. Then engineers can make tweaks to the algorithms to address any population misrepresentation.

Algorithms can also acquire bias as they continue to learn on the job. Hence, systematic audits are essential to ensure that all AI-based tools are still relevant and work as expected.

Deploying AI in pharma implies integrating it with the existing platforms and applications. Many pharma companies still rely on outdated legacy systems that are not designed to work with AI or deal with a large amount of data. Such systems use their proprietary protocols and are hard to integrate with modern applications.

Pharma companies that want to use modern technology alongside legacy systems can benefit from custom pharma software solutions designed to fit seamlessly with the existing legacy systems.

The use cases of artificial intelligence in the pharmaceutical industry are rather complex, and there is a large room for error in the predictions that the technology makes. Here is what makes pharma so intricate:

Deloitte reports that only a few of the 7,000 rare diseases that we know have witnessed some progress over the past years. And the consultancy believes AI in pharma can change this. In addition to the applications mentioned above, AI can help pharma companies achieve compliance, which is vital in this field.

If you want to incorporate this advanced technology into your business, you are likely to have to team up with a tech vendor of your choice. Also, it’s a good practice to:

Speaking of the future of artificial intelligence in the pharmaceutical industry, PwC predicts the emergence of a new digital health ecosystem that will include the following players:

And according to the consultancy, firms who will still refuse to make AI a part of their operations will turn into a mere “contract manufacturers” for the rest of the ecosystem. So, if you haven’t yet considered enhancing your business processes with AI, this seems like a good time to experiment with the technology.