Use cases for AI in the pharmaceutical industry

The pharmaceutical industry is at a transformative juncture, driven by the confluence of scientific advancements and technological innovations.

Among these, AI stands out as a groundbreaking force. This whitepaper explores the multifaceted applications of AI in the pharmaceutical sector, highlighting how AI is accelerating progress in the space.

After reading this whitepaper, you will understand AI strategies, how to present them in the boardroom, and demonstrate how different AI use cases can propel your business forward.

Clinical trials optimization

Traditional clinical trial operations at pharmaceutical companies often prolong trial durations due to inefficient patient recruitment and manual data analysis. This approach can lead to higher costs and an increased likelihood of data handling errors. Without AI-driven insights, there is a reduced capacity to optimize trial protocols and adapt to regulatory changes swiftly, potentially impacting trial outcomes and overall efficiency.

How can AI optimize clinical trials?

Companies can de-risk early trial processes through simulated trial outcomes across diverse populations. The study timeline can also be improved by identifying relevant patients faster via datasets screened for relevant biomarkers. This streamlined recruitment process accelerates the trial and ensures that the most suitable candidates are enrolled, increasing the likelihood of successful outcomes. (Nature)

What are the challenges to AI-optimized clinical trials?

If you're using AI to simulate trials, it's crucial to have high-quality, accurate data to ensure results align with real-world outcomes. This means data must be carefully collected, cleaned, and validated. Errors or biases can affect the accuracy and generalizability of AI insights, potentially undermining the trial.

In addition to data management, companies face challenges like:

• Regulatory and ethical considerations: AI in clinical trials brings up ethical and regulatory issues, especially around patient privacy, data security, and algorithmic bias.
• Interpretability and explainability: Complex AI models can be hard to understand, which poses problems in clinical trials where transparency is key for trust and informed decision-making.
• Integration with existing systems: Many clinical research organizations still use legacy systems that don't easily work with modern AI technologies, making integration difficult.

AI clinical trial optimization: implementation, impact, and key players

If you're interested in working with an AI vendor specializing in clinical trial optimization, InSilicoTrials, QuantHealth, and Nova Discovery are pioneering companies specializing in computational and AI solutions for the healthcare and pharmaceutical industries.

• InSilicoTrials: Leverages computer-simulated trials to streamline drug development and regulatory processes by predicting drug efficacy and safety, thereby reducing the need for extensive physical trials.
• Nova Discovery: Focuses on in silico clinical trials, providing a simulation platform that integrates biological, pharmacological, and clinical data to create virtual patient cohorts for trial simulations.
• QuantHealth: Uses artificial intelligence and machine learning to optimize clinical trial design and execution. It analyzes data to identify optimal patient populations and predict trial outcomes.

Together, these companies enhance decision-making, improve efficiency, and lower costs in drug development and clinical trials.

Predictive maintenance

In the pharmaceutical industry, equipment that isn't working or being serviced can disrupt production schedules, leading to high costs and off-schedule deliveries. Traditional maintenance schedules are usually based on fixed time slots, resulting in over-maintenance, inflexible service, or unexpected breakdowns.

These methods fail to consider the actual condition of machinery, leading to unnecessary maintenance or, worse, unexpected failures that can jeopardize safety and damage equipment. To minimize these risks, pharmaceutical manufacturers require a more precise and proactive approach to predict and prevent equipment malfunctions.

How can predictive maintenance optimize maintenance schedules?

Within pharmaceutical manufacturing, AI-powered algorithms leverage real-time data from equipment sensors to monitor critical metrics like temperature, vibration, and pressure. By identifying patterns and anomalies in this data, AI can accurately forecast potential equipment failures, allowing for proactive maintenance scheduling and minimizing unexpected downtime.

This proactive maintenance approach, driven by AI's predictive capabilities, minimizes costly downtime and extends the lifespan of valuable machinery. As AI models continuously learn and refine their predictions, maintenance is conducted precisely when needed, optimizing resource allocation and enhancing overall operational efficiency.

What are the challenges to predictive maintenance?

Within the pharmaceutical industry, predictive maintenance (PdM) requires collecting and storing vast quantities of sensitive equipment data, necessitating a robust technological infrastructure and stringent data management practices. The specialized nature of this field amplifies these challenges:

• Demand for specialized expertise: Successful PdM implementation in pharmaceutical manufacturing hinges on a multidisciplinary team possessing expertise in data analytics, artificial intelligence, machine learning, and a deep understanding of complex pharmaceutical equipment.
• Data analysis at scale: The sheer volume and complexity of data generated by pharmaceutical equipment can overwhelm even skilled data analysts, requiring sophisticated tools and methodologies to derive actionable insights.
• Security risks: Pharmaceutical manufacturers utilizing PdM face increased vulnerability to cyber threats, including ransomware attacks, necessitating security measures to safeguard sensitive operational data and ensure uninterrupted production.

AI predictive maintenance for pharmaceuticals: implementation, impact, and key players

Setting up a PdM program can be costly, involving significant investments in sensors, data analytics tools, and the technical expertise to manage these systems. However, doing so can result in greater savings and efficiency for the company, as we see in GlaxoSmithKline (GSK)'s case, a British multinational pharmaceutical and biotechnology company:

• GSK: An unexpected breakdown in the water purification system caused a massive disruption, leading to a one-week shutdown of the entire facility. Predictive maintenance initiatives achieved a 50% reduction in associated lifecycle costs, a 60% reduction in CAPEX, a 25% increase in production capacity, and tens of millions of dollars saved from batch losses. (Aspen Tech)

• Aspen Mtell: Resolved GSK's issue using machine learning to identify the root cause in the deionizer equipment and detect early signs of asset failure based on late-stage deterioration patterns.

Quality control

Manual inspection processes in pharmaceutical manufacturing are labor-intensive and prone to human error, resulting in inconsistent product quality. Variability in manual inspections can lead to undetected deviations or defects, contributing to patient dissatisfaction and increased product recalls.

As production volumes escalate, maintaining stringent cGMP standards becomes increasingly difficult. Traditional inspection methods also fail to keep pace with the high throughput of modern production lines, making it challenging to ensure uniform quality across large batches and uphold brand integrity and regulatory compliance.

How can AI improve quality control?

AI-powered computer vision systems utilize high-resolution cameras and advanced machine learning algorithms to conduct real-time inspections of pharmaceutical products for defects. These systems can detect even minor drug deviations and defects that human inspectors might overlook, ensuring higher consistency in product quality and the delivery of more stable/effective medications.

Data collected from these inspections can be analyzed to identify common defect patterns, offering insights for continuous and proactive improvement. By automating quality control, pharmaceutical manufacturers can reduce reliance on manual inspections, enhance cGMP compliance, and improve overall operational efficiency.

What are the challenges to AI quality control?

Pharmaceutical manufacturers employing AI for quality control face unique challenges. Vast amounts of data and meticulous data management practices are essential to ensure AI models make accurate decisions based on high-quality information. This data-intensive nature, coupled with the stringent quality standards of the pharmaceutical industry, presents additional hurdles:

• High initial investment: Acquiring and implementing high-resolution cameras, sophisticated visual inspection systems, and powerful AI algorithms require a significant upfront financial commitment.
• Ongoing maintenance and training: AI models in pharmaceutical quality control demand continuous updates and training to adapt to evolving product specifications, new defect types, and regulatory changes.
• Integration complexities: Seamlessly integrating AI-powered quality control systems with existing pharmaceutical manufacturing lines can require careful coordination and technical expertise.

AI quality control for pharmaceutical manufacturing: implementation, impact, and key players

Several of the world's largest pharmaceutical manufacturers already use AI quality control to improve their products. One use case that exemplifies this is Amgen, a worldwide pioneer in biotechnology:

• Amgen: Alongside Syntegon Technology, they developed the first fully validated visual inspection system using artificial intelligence. The machine has 13 inspection stations, each performing a specific inspection task on syringes and their contents. Their particle detection rate increased by approximately 70%, while the false detection rate decreased by about 60%. Maximizing the defect detection rate enhances quality and safety for Amgen end customers. Reducing the number of false rejects minimizes waste, rework, and overall costs (Packaging Digest).

Procurement/supply chain optimization

Traditional procurement processes in pharma manufacturing rely heavily on manual labor and conventional communication methods. Procurement officers spend significant time identifying suppliers, negotiating contracts, and placing orders based on inventory levels and production schedules. Spreadsheets, phone calls, and in-person meetings are the primary tools for managing supplier relationships and ensuring the timely delivery of materials.

How can AI improve procurement?

By automating supplier selection and contract negotiations through advanced data analysis and predictive modeling, AI ensures optimal deals and timely deliveries of critical materials for manufacturing drugs and other pharmaceutical products.

Real-time inventory monitoring and demand forecasting enable AI to place orders, automatically preventing costly shortages or overstock situations. AI's ability to analyze supplier performance and market trends allows for continuous optimization of procurement strategies, ultimately leading to significant cost savings and enhanced operational efficiency for pharmaceutical companies.

What are the challenges to using AI for procurement?

High data fidelity from suppliers, CMOs, and internal LIMS is vital for AI-driven procurement insights. Integrating AI with legacy ERP and Manufacturing Execution Systems presents complex interoperability challenges. Inaccurate data can compromise supplier assessments and lead to inefficient inventory optimization, impacting overall supply chain robustness. Beyond that, pharma manufacturers can face problems with:

• Regulatory compliance: Procurement must ensure AI solutions comply with stringent GxP standards, including supplier Good Manufacturing Practice certification and API traceability. This requires ongoing AI system validation to align with evolving FDA, EMA, and ICH (International Council for Harmonisation) guidelines. Failure to adhere can lead to batch rejections, supply chain disruptions, and substantial regulatory sanctions.

• Algorithmic bias: AI algorithms can unintentionally perpetuate biases in supplier QbD (Quality by Design) assessments and risk stratification models. Addressing these biases is critical to maintaining balanced supplier diversity and adhering to ethical sourcing mandates. Regular model revalidation and bias audits are essential to ensure AI systems support equitable procurement strategies and align with corporate social responsibility goals.

AI procurement for pharmaceuticals: implementation, impact, and key players

For a representation of AI procurement, we return to GlaxoSmithKline (GSK). Within their pharma supply chain, multiple packaging lines handle upwards of 10,000 batches per year annually, producing 2.3 billion packs of medicine across a network of 30 sites in 18 countries. After implementing AI for their procurement and supply chain optimization, they achieved: (AspenTech)

• 35 days’ advance warning of potential issues.
• Tens of millions of USD in lost batches avoided.
• 50% reduction in lifecycle maintenance costs.

AI-Powered chatbots

Manually handling queries demands substantial human resources, often resulting in inconsistent service quality and slower response times.

How can using AI chatbots improve pharma manufacturing operations?

In pharmaceutical manufacturing, maintaining a steady supply of raw materials is crucial to keep medications abundant and available. AI-powered chatbots offer real-time inventory updates, reducing waste and ensuring timely restocking. By integrating with ERP systems, these chatbots streamline information retrieval and analyze supply usage patterns, optimizing inventory management through a single interface.

Chatbots, acting as virtual assistants, also facilitate recall procedures and resolve delivery issues. They enhance efficiency in multi-floor manufacturing plants by managing floor queries and providing real-time data on workload distribution, production capacity, and maintenance issues.

AI chatbots assist in product selection by analyzing historical data and using machine learning to make decisions based on customer preference. They provide personalized recommendations, improving the patient/doctor experience and driving sales. This intelligent capability ensures customers find suitable products quickly, boosting satisfaction and operational efficiency.

What are the challenges to AI chatbots for pharmaceutical companies?

To safeguard sensitive health data, robust encryption methods must be implemented for both data in transit and at rest. Strict access controls and authentication processes must be established to ensure that only authorized personnel can access patient information.

Regular compliance audits are also essential to verify adherence to data protection regulations such as HIPAA and GDPR. That's why regulatory compliance and the accuracy and reliability of these models have to be a deep consideration for any pharmaceutical company:

• Regulatory compliance: Continuously updating chatbots is necessary to keep pace with evolving pharmaceutical regulations and guidelines. Maintaining detailed records of chatbot interactions facilitates compliance verification during regulatory reviews. Furthermore, navigating varying regulatory environments across different countries requires making localized adjustments to ensure the chatbot meets all regional requirements.
• Accuracy and reliability: Keeping chatbots updated with the latest medical research and drug information is essential for providing accurate and reliable advice. Implementing robust error detection mechanisms helps swiftly identify and correct any misinformation. Moreover, involving medical professionals to review and validate chatbot responses ensures that the information meets high clinical standards.

AI chatbots for pharmaceuticals: implementation, impact, and key players

Medical chatbot buzz started in February 2023 when Open AI’s ChatGPT 3.5 was found to pass the US Medical Licensing Exam (USMLE) with similar scores to the average human, despite no field-specific training​​. By June 2023, Google’s medically tailored model — Med-PaLM 2 — outdid ChatGPT’s score by more than 25 percentage points and outperformed doctors at answering patient questions​ (Pharma Journal).

Customer targeting

In pharmaceutical companies, traditional approaches to customer targeting can be overly manual, slow, and often lack the precision and flexibility of AI-driven methods.

How does using AI for customer targeting work?

In pharmaceutical companies, using AI for customer targeting involves collecting data from sources like prescription history, medication usage patterns, and patient support interactions.

AI algorithms segment customers based on characteristics such as prescription frequency, medication volume, and treatment preferences. Predictive analytics identify patients and healthcare providers and anticipate their future needs, enabling personalized marketing campaigns with tailored offers, such as dosage discounts or customized medication recommendations.

What are the challenges to using AI for customer targeting?

Implementing AI for customer targeting in pharmaceutical companies involves challenges such as data privacy issues, as handling large volumes of personal health information can lead to breaches or misuse.

There's also the potential for algorithmic bias, where AI may unintentionally favor certain patient groups, causing inequitable treatment. Moreover, excessive reliance on AI might reduce human oversight, resulting in strategies lacking nuance and the empathy of human judgment.

What is the expected return on investing in AI for customer targeting?

One vendor that can help pharmaceutical companies target customers is Gemseek.

• Gemseek. Roche, a Swiss multinational holding healthcare company, works with Gemseek to use predictive analytics to find the root cause of dissatisfied customers. Using this model, Predictive NPS enabled Roche to identify more detractors than survey results could have uncovered. The model determines the data types that best predict customer happiness and then assigns a score to other customers with similar characteristics. The likelihood of identifying a “silent” Detractor has increased 3-4 times, creating opportunities to proactively treat and retain accounts worth millions annually. (Gemseek).

Data Quality as a foundation for AI

Nearly all use cases of AI in the pharmaceutical industry share a common challenge: the quality of data currently available within the business. Accurate assessments and effective utilization of AI depend heavily on high-quality data. However, many pharmaceutical companies struggle with data that is incomplete, inconsistent, or outdated. To fully harness the potential of AI, decision-makers must prioritize improving data quality first and foremost. This involves:

• Implementing robust data governance practices
• Investing in advanced data integration and cleaning technologies
• Fostering a culture that values data accuracy and completeness.

Only by addressing these foundational issues can pharmaceutical companies leverage AI to enhance quality control, optimize supply chains, and effectively target customers. We at Ataccama are leaders in helping pharmaceutical companies use high-quality data in a secure and governed way. Download this free ebook to begin building your end-to-end DQ framework.