How do AI technologies transform biotech and pharma?

According to MIT, only 13% of biopharma’s drug development efforts get US FDA approval. Usually, these efforts involve multimillion-dollar clinical trials that rely on human intelligence for success. With AI support, biopharma executives are finding less risky and more cost-effective ways to achieve drug development success. Giant biopharma companies from Pfizer, GSK, and Sanofi, to Johnson & Johnson, have taken great steps in acquiring their in-house AI systems.

By 2025, half of the biopharma and healthcare players are expected to jump on board. In this article, we look at unique examples of how AI technologies are transforming biotech and pharma.

How AI technologies transform biotech and pharma:

1. Innovation & Research Efficiency

While many people may await an AI future, it already started. Researchers have begun using AI tools to scan scientific literature for research and manage clinical trial data for pharma.

In 2018, MIT’s partnership with Pfizer and Novartis showed us how AI and machine learning programs could improve drug discovery research in biopharma. E.g. Novartis is a biopharma AI that groups digital images of cells while also capturing details of their interactions with different compounds. Novartis AI algorithms classify the compounds with similar effects and then distribute them as organized data to the researchers. The researchers then use insights from this data to influence their research.

Traditionally, testing compounds on sampled diseased cells can be time-consuming. Scientists can mistakenly omit biologically active compounds that require further investigation. With AI, the screening is faster, images can be used to determine which untested compounds need attention.

When AI technologies support pharmaceutical efforts, research is more efficient and drugs are produced at a faster, and less-costly rate.

2. Clinical Diagnostics

AI also proves effective in improving how diseases are detected. Many medical applications are proudly using AI to enhance diagnosis. In 2020, the medical tech and AI vendor Eyenuk used their AI EyeArt Artificial Intelligence Eye Screening to detect diseases by scanning retinal images. Over 900 patients across 15 medical facilities were involved in the clinical trial and the AI’s sensitivity for detecting diabetic retinopathy was 95%. The company revealed that EyeArt’s algorithms were trained with over 2 million images to increase the AI’s data intelligence scope.?

When it comes to humans our memories have limits. Memorizing 2 million images is beyond us. But with intelligent computers diagnosis can reach newer heights.?

For people with diabetes, expert guidelines recommend annual screening for diabetic retinopathy.? Globally, the UK leads the chart for screening over 80% of its diabetes population. This percentage means UK’s medical professionals would have to screen and review retinal images of almost 2.5 million persons each year. That’s a lot of work.

In 2021, the UK National Screening Committee Report reported EyeArt as the only diabetic eye screening AI technology ready for live clinical implementation in its National Health Service.?

And in August 2020, the EyeArt team disclosed their AI technology had gotten approval from the United State’s FDA. AI is indeed breaking new grounds for biopharma and healthcare.

3. Data Analysis & Drug Discovery

Using traditional approaches to analyze data for drug discovery works when the data is simple and homogenous. A good example is when a patient reports only one case and requires just a single treatment plan and clinical visit. However, it becomes very exhausting when the data is complex. The method has to consider patients whose cases include multiple diagnoses, complex treatment plans, and even require more frequent visits with physicians.?

Artificial intelligence [AI] can collect multiple data, analyze them and produce stratified patient groups. Sensyne Health a clinical AI medical company in its UK NHS partnership, explained how AI could identify subcategories of heart failures. Traditional methods reveal just two categories. AI is able to uncover multiple subpopulations of patients. Now drug discovery teams will be able to create more effective therapies when the clinical trial data from AI includes those omitted groups. Left to the research team, the clinical trial would cost additional dollars and time to extract results. This traditional system of operations cannot keep biopharma companies in business for long. Many drug discovery opportunities are missed when we approach complex clinical data [which is usually diverse] with basic methods.

AI can also launch virtual clinical trials which will not require highly expensive human trials. Researchers will be able to access key drug information before testing it on humans. They will be able to experiment with actual-world data, connect them with patient sample data, and reapply the information in the clinical trial.

With AI,? biopharma executives will not rely on just abstract ideas to make it to clinical trials. They will be able to know what is likely to work before approving full clinical work.

4. Pharmaceutical Research

A successful drug development investment relies on effective pharmaceutical research. Usually, pharma companies spend a lot gathering data from diverse sources for their clinical trial investigators. The investigators will then use these data to make research decisions. This can be challenging as multiple teams must work for weeks organizing the data extracted from different sources. Most of these data [images & sequencing data] needed for quality control usually come in disconnected formats.

AI can organize all these abundant data resources into several data sets. The researcher would find it easier to perform biology analysis in minutes using insights from the AI’s machine learning. The medical business AI QuartzBio is an example of an AI tool that collects bio-clinical data from multiple sources. It then provides insights that can be used for drug development research. With AI, data extraction and analyses for research become better.

Indeed, it is exciting to see biopharma companies explore AI for next-level drug development and industry success. The much-awaited future is here. Of course, there are countless things that can be achieved with AI. It is a matter of time. But for us to fully determine the potential of AI, we must be willing to put it to use.

Want to experience a live demo of how AI can impact research and development at your institution or organization? Knowledgator is making AI research tools accessible to biopharma players like you. Request your demo at www.knowledgator.com.