7 Use Cases of Artificial Intelligence in the Pharmaceutical Industry

John Ward
March 1, 2024
5 min read

Artificial intelligence has caused a digital revolution across healthcare. Nowhere is this clearer than in pharma. AI algorithms optimize everything from discovering new blockbuster drugs to managing clinical trials and accelerating regulatory approvals.

In this post, we'll explore the transformation. We'll look at seven use cases showing how AI boosts efficiency across the value chain. Let's dive in.

What is AI in the Pharma Industry

First, what do we mean by AI? It's the science of training algorithms to learn from data. AI includes everything from basic rules-based systems to advanced neural networks.

The pharma industry deals in huge volumes of data: patient records, lab tests, genomic profiles, and more. Advances let computers process data faster than humans do. It opens new frontiers in healthcare.

According to Statista, the global healthcare AI market's projected growth is forecasted to increase from approximately one billion to over 28 billion U.S. dollars by 2025. Where is this value coming from? How is AI being used from early discovery through commercialization? Read on to find out.

1. Drug Discovery and Development

Historically, discovering new medications was slow. It relied on luck and trial and error. It was testing endless combinations of chemicals by hand.

Today, AI does the heavy lifting. It predicts interactions between proteins and drug compounds and whether a substance could work safely in humans.

Algorithms trawl databases to identify candidates with therapeutic potential. This works for both new molecules and repurposing existing drugs and reducing timelines by up to 70%.

Let's explore breakthrough applications across the development cycle:

Design and Synthesis

Creating custom organic compounds was restricted by human imagination. AI expands possibilities. Algorithms generate novel molecular structures with desired properties. They are enabling targeted drug design.

Companies like Exscientia are pioneering next-gen platforms. In 2021, they used AI to discover a migraine compound swiftly. Now in Phase 1 trials. They are showing huge disruptive potential.

Preclinical and Toxicity Screening

Once designed, thousands of drug prospects get filtered down before clinical testing. Eliminating unsafe or ineffective ones through assays measuring attributes like:

  • Absorption
  • Metabolism
  • Interactions

Running so many lab tests consumed resources. AI changes this. Algorithms predict interactions between drugs and proteins or genes. And whether toxicity problems seem likely. They are dramatically reducing screen costs.

Clinical Trial Enrollment

Enrolling participants for testing new drugs is notoriously tricky. It was made worse by stricter privacy laws. AI is helping solve this, too.

Platforms use machine learning and massive datasets to match volunteers. They are streamlining recruiting for everything from cancer to rare diseases and getting trials up and running faster.

Trial Design and Operation

Running efficient clinical studies is complex. Researchers must pick optimal interventions. Sites, objectives, and methods. Then, adjust along the way as data comes in.

It's easy to make expensive mistakes. By crunching more variables with more precision, AI spots the best trial designs. So sponsors get answers efficiently. It saves money and time while gathering robust evidence.

2. Optimizing Clinical Trials

Speaking of getting drugs to market quicker, let's dive deeper into AI's impact on trials. Turning these critical studies into data-driven machines.

Clinical testing costs pharma billions every year. And failure rates hover around 95% due to safety or efficacy issues. It's no wonder AI solutions attract so much investment.

Algorithms boost enrollment, participant retention, protocol adherence, and real-world evidence generation. Let's explore some applications:

Matching Volunteers

As we covered above, identifying willing, eligible subjects is a big barrier. But analytical engines solve this using demographics, health records, genomic profiles, and more.

One startup, Deep 6 AI, boosted recruitment 10x for a cancer trial. Getting it fully enrolled in under six weeks. Speedy enrollment means faster access for more patients.

Monitoring Adherence

Ensuring subjects follow protocols is critical for credible findings. However, tracking behavior manually at scale can be nearly impossible. Some adherence issues even invalidate entire studies.

AI fixes this. Wearables capture clinical-grade data on things like activity, heart rate, sleep quality, etc. Algorithms analyze trends for protocol deviations. So researchers intervene early, keeping things on track.

Modifying Dosing

Administering optimal drug dosages is tricky. Too little shows no efficacy. There are too many risks, side effects, and safety failures. Both scenarios waste resources.

Machine learning adapts doses automatically. As more data comes in, the algorithms get smarter about ideal interventions. Ensuring subjects get effective treatment. At the same time, issues that could halt testing should be avoided.

Flagging Safety Signals

Finding dangerous side effects quickly is imperative. Getting potentially harmful drugs off the market ASAP. However, current methods delay adverse event detection by years in some cases.

By continuously crunching global health records, AI spots safety signals faster and accelerates reaction times from months down to weeks, in some instances protecting more patients.

3. Revolutionizing Manufacturing

Making drugs at scale after approval is complex, too. Requiring air-tight supply chains, huge factories, and regulatory sign-offs. AI is hitting this sector as well.

Let's walk through some innovative use cases:

Predictive Maintenance

Pharmaceutical plants are full of temperamental hardware. When equipment fails, it halts production—leading to shortages or dumping entire contaminated batches. Both cost millions.

AI anticipates problems before they occur. Algorithms ingest sensor and operations data for early failure detection. So, teams fix or replace parts proactively. Boosting uptime.

Intelligent Logistics

Coordinating material transport and inventory across global distribution networks could be more manageable. The more nodes, the more chance for breakdowns. Which batch goes where and when is tough to track.

AI systems overlay order visibility—prescriptive analytics route shipments efficiently in real-time. And anticipate future demands using models. Smoothing flows across the cadena.

Automated Quality Control

Verifying raw ingredients, components, and end products conform strictly to specifications is crucial. However, replicated manual inspections have limits.

Instead, AI checks everything. Computer vision powered by machine learning validates quality. Monitoring manufacturing from end-to-end. Dramatically lowering defect rates and reducing risks.

4. Optimizing the Supply Chain

Sticking with distribution, AI has plenty more contributions here, too:

Demand Forecasting

The pandemic highlighted weak links in supply continuity. Medical shortfalls led to panics, stockpiling, and price gouging. All worsened by primitive statistical forecasts.

Sophisticated ML algorithms make better predictions using multiple data sources and stabilizing inventories of essential medicines when needs shift unexpectedly.

Production Scheduling

Planning batch production is enormously complex. Requires syncing up supplies, capacity, labor, orders, and lead times across a web of sites and coordinating changeovers between product types.

AI juggles all these variables. Both optimizing individual plant schedules and synchronizing everything globally. Helping avoid bottlenecks or overproduction.

Automated Warehousing

Managing scale and throughput manually in big warehouses slows down order processing. Introduces picking errors. And has capacity limits before expansions are needed.

Instead, AI automates. Robots handle physical materials fetching. At the same time, the software tracks inventory digitally across the ecosystem—multiplied productivity and efficiency gains.

5. Revolutionizing Marketing and Sales

Now, let's switch gears. After working so hard to discover, test, make, and distribute new therapies, how does AI impact selling them?

Data-Driven Campaigns

All marketing hinges on resonating messaging. But traditionally, efforts were scattered. Using broad, ineffective tactics like mass emails or print ads.

Machine learning mines prescribing habits, attitudes, and demographic data for tailored outreach. AI shapes regional awareness campaigns, pricing models, and even how reps engage—wasting less budget.

Conversational Engagement

Answering repetitive sales questions eats reps' time. It frustrates physicians to need quick info between patients. So, chatbots now fill this gap.

NLP algorithms handle the most common inquiries. While learning continuously to take on more complex queries. 24/7 access and responses in seconds. Leading to warmer sales meetings.

So, pharma sells more by leveraging AI for sales and marketing efficiency gains. And doctors get the data they need effortlessly to make informed choices for better patient outcomes.

6. Monitoring Drug Safety

Once on the market, getting continuous feedback is critical. But how's AI improving pharmacovigilance?

Surface Insights from Data

Pharmacos manually reviews adverse event reports from fragmented sources. But it takes ages to pull signals from so much unstructured data spanning countries and delaying risk detection.

AI changes this by crunching all data faster. Processing physician notes, call center tickets, hospitalization records, genomics, and more. Accelerating safety review cycles from months down to hours in some trials and helping regulators make quicker decisions by rapidly surfacing insights.

Review Safety Proposals

Assessing new ingredients, formulations, doses, etc, requires tons of oversight. Experts cross-reference proposals against global regulations to avoid problems. But the books keep growing more complex.

Instead of just chasing rules, AI learns too. NLP algorithms reference documentation while answering common questions for reviewers, freeing up valuable human capital and accelerating approval timelines for safer patient access.

7. Streamlining Regulatory Submissions

Speaking of getting treatments to more people, AI also smooths regulatory approvals. Let's discuss two cases:

Automated Documentation Reviews

Companies submit lengthy technical dossiers covering manufacturing, preclinical and clinical data, etc, when proposing new therapies. But manual agency reviews delay launch decisions.

AI is far faster at crunching paperwork. Algorithms scan documents in hours versus weeks taken by human teams. Accelerating approval timelines by over 70% in some estimates.

Accelerated Audits

Even after launch, sites face ongoing inspections and audits to ensure continued compliance with standards. These heavily manual checks also slow down processes. And they represent one of the highest compliance costs.

Instead, AI handles much of the auditing legwork automatically. Algorithms continually crosscheck data flows, data integrity, training logs, and corrective actions. Identify gaps in near real-time vs. waiting for external auditors to schedule on-site assessments. Ultimately reducing risks while boosting efficiency.

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As we've explored, algorithms and analytics are infiltrating nearly every function within pharma, from early discovery to commercialization and post-market monitoring. And this post just scratched the surface of applications. The creativity of unlocking transformative tools using troves of data is limitless. Ultimately, these efficiency gains translate down to patients by getting safe, affordable therapies to market faster and, at the same time, optimizing ongoing oversight and availability. So, from revolutionizing R&D models to reimagining each downstream capability, AI and machine learning will define the future of pharmaceutical innovation.

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Frequently Asked Questions (FAQs)

Q: What are some innovative use cases of artificial intelligence in the pharmaceutical industry?

A: Artificial intelligence is being used in the pharmaceutical industry for various purposes, such as personalized treatment, drug discovery processes, process automation, predictive forecasting, and personalized medicine.

Q: How is artificial intelligence transforming the pharmaceutical industry?

A: Artificial intelligence is transforming the pharmaceutical industry by streamlining the drug development process, improving patient care through personalized treatment plans, and automating tasks like data analysis and medical image processing.

Q: Which AI technologies are commonly used in the pharmaceutical sector?

A: AI technologies such as machine learning algorithms, natural language processing, computer vision, and deep learning are commonly used in the pharmaceutical sector to analyze patient data, develop new drugs, and optimize the entire value chain.

Q: Can AI help pharmaceutical companies with predictive forecasting?

A: Yes, AI can help pharmaceutical companies with predictive forecasting by analyzing large amounts of data to predict trends, optimize production processes, and improve decision-making.

Q: What role does AI play in personalized medicine within the pharmaceutical industry?

A: AI plays a crucial role in personalized medicine within the pharmaceutical industry by analyzing patient data, medical histories, and genetic information to create personalized treatment plans tailored to individual patients.

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John Ward
February 29, 2024
5 min read