How Pharma Companies Can Leverage AI Across the Entire Value Chain

The pharmaceutical industry has seen significant growth and change in recent decades due to advancements in scientific research, leading to the development of life-improving drugs and therapies. However, the industry faces challenges in leveraging its vast data resources, as information often exists in isolated silos and traditional approaches struggle to keep up with the scale and complexity of data. AI holds great promise in addressing these challenges by uncovering insights from large volumes of structured and unstructured data throughout the pharmaceutical value chain.

This article provides an overview of a few ways in which AI is already being applied in various areas, from identifying new drug targets to enhancing manufacturing and supply chain efficiency, and how a well-planned AI strategy can enable pharmaceutical companies to move towards more personalized, predictive, and preventive healthcare.

Scaling AI Across the Pharma Value Chain

Artificial intelligence has woven itself seamlessly into the fabric of the pharmaceutical industry, revolutionizing critical processes across the pharma AI value chain.

Its applications span a multitude of critical areas within the industry such as drug discovery, clinical research, manufacturing & quality control, supply chain & logistics, regulatory compliance and pharmacovigilance, medical affairs, market access, and sales & marketing, underpinning the transformative potential of this technology.

A. AI in Drug Discovery

Drug discovery is one of the most financially intensive processes in the pharmaceutical industry, often taking over a decade and costing more than $2.6 billion for the development of a new drug from initial research to final approval. The traditional trial-and-error method, which involves screening millions of compounds, has historically been a time-consuming and costly aspect of this process.

However, the emergence of big data and AI has made this process more efficient. Machine learning algorithms now efficiently analyze vast datasets containing chemical structures, genomic data, and previous clinical trial information, uncovering patterns and insights beyond human capability.

AI expedites target identification and lead optimization, predicting protein involvement in diseases and assessing compound properties like absorption, distribution, metabolism, excretion, and toxicity (ADMET) early on.

In fact, a study showcased AI reducing drug discovery time by over 50% for a major pharmaceutical company, optimizing target selection and lead evaluation. As more biological and chemical data becomes accessible, AI is poised to revolutionize drug R&D, enhancing each phase from hypothesis generation to clinical trial design.

B. AI in Clinical Trials

Clinical trials represent one of the most expensive phases in drug development, costing approximately $30-50 million and lasting 2-3 years on average for a Phase 3 trial. However, nearly half of these late-stage trials fail due to efficacy or safety concerns, leading to substantial resource wastage for pharmaceutical companies.

AI in pharma offers solutions to key challenges in clinical research. Machine learning algorithms analyze extensive data from past and ongoing trials to predict a drug’s success, ideal dosing regimens, and patient subgroups likely to benefit thus optimizing research protocols.

AI streamlines patient recruitment by utilizing real-world data and enhances retention by monitoring patient-reported outcomes. It also aids in the early detection of adverse events.

According to a report by IQVIA, the implementation of AI technologies in clinical trials is projected to save the industry billions of dollars by 2025. The report indicates that the use of AI in patient recruitment alone could save approximately 30% of clinical trial costs, amounting to around $18 billion annually. Moreover, by improving trial efficiency, AI can potentially bring new drugs to market faster, benefiting both pharmaceutical companies and patients awaiting novel treatment options.

C. AI in Supply Chain and Logistics

Pharmaceutical companies operate within a complex supply chain network bound by stringent regulations. AI plays a pivotal role in optimizing operations, enhancing efficiency, and automating decision-making processes. Utilizing machine learning algorithms fed with historical data, AI accurately forecasts demand, reducing risks of both stockouts and excess inventory.
Moreover, AI’s application in predictive maintenance minimizes unplanned downtimes. By analyzing performance data through sensors, it predicts machinery failures, allowing for scheduled repairs before breakdowns occur.

A study by McKinsey & Company revealed that AI applications in supply chain management could reduce forecasting errors by up to 50%, and overall supply chain costs by 5-10%.

D. AI in Manufacturing and Quality Control

The pharmaceutical industry’s core lies in robust manufacturing and stringent quality control to ensure the safety and effectiveness of every medication reaching patients. As demand for pharmaceuticals grows, integrating artificial intelligence (AI) in manufacturing and quality control has become pivotal in upholding and even elevating these standards.

AI empowers drug manufacturers to monitor production lines in real-time, detecting anomalies and preventing costly quality issues. Using advanced machine vision and deep learning, AI systems inspect every pill or vial on the production line at a pace surpassing human capabilities. Statistics indicate that AI-powered inspection catches up to 30% more defects compared to human-only inspection, reducing wasted batches and recalls. AI also automates repetitive tasks on the factory floor, freeing up workers for more skilled roles and enhancing workplace ergonomics.

AI is also making its mark in predictive maintenance by analyzing sensor data to detect early signs of equipment deterioration, minimizing unexpected downtime. These advantages enable drug companies to scale up production volumes while adhering to the stringent quality standards mandated by regulatory bodies.

A Deloitte report found that the integration of AI into quality control procedures resulted in a 90% reduction in defects for a pharmaceutical manufacturer, translating to significant savings and, more importantly, improved patient safety.

E. AI in Regulatory Compliance

Regulatory compliance is the bedrock of patient safety and trust in the pharmaceutical industry. The rigorous standards and protocols governing drug development, manufacturing, and distribution are non-negotiable. In this domain, artificial intelligence plays a vital role in not only ensuring compliance but also bolstering pharmacovigilance.

Compliance with regulations isn’t merely a checkbox; it’s the guarantee that medications are produced, tested, and marketed in line with the highest quality and safety standards. AI in pharma systems can bolster compliance by automating data validation, ensuring every step of the pharma AI value chain aligns with regulatory guidelines.

Notably, a study by KPMG reported that AI can reduce regulatory compliance costs in the pharma industry by up to 30%.

F. AI in Pharmacovigilance

Pharmacovigilance, responsible for monitoring and assessing adverse effects of pharmaceutical products, benefits significantly from AI.

AI algorithms analyze vast data sets, swiftly identifying potential safety concerns and patterns that might elude human review. Studies suggest AI could enhance pharmacovigilance efficiency by up to 50%, enabling quicker responses to emerging safety issues and ultimately safeguarding patient well-being.

For example, the FDA has embraced AI in its Sentinel System, which monitors the safety of FDA-approved drugs through the analysis of electronic health records.

G. AI in Medical Affairs

The volume and complexity of medical information have risen dramatically in recent years, and with this has risen the amount of work required by medical affairs teams.
Meta-studies and literature reviews constitute an important part of the medical affairs team process, but the process of selecting, filtering and reviewing research papers is time-consuming and costly, just as it is for safety reporting. Access to simple, powerful, and effective AMLM platforms for research institutions could dramatically increase the prevalence of such reviews while improving their findings. Indeed, any medical and pharmaceutical workflows that involve a literature review, from the scientific to the commercial, will benefit from access to simple, powerful and effective AMLM platforms.

H. AI in Market Access

Market access remains one of the most complex links in the pharmaceutical value chain, and studies show it has grown in complexity significantly in recent years. The advent and widespread adoption of new digital technologies is changing expectations from patients, practitioners, and payers, while fluctuating and novel market pressures make it difficult to devise useful strategies. Artificial intelligence (AI) is the most appropriate tool for cutting through this complexity and making data-driven market access decisions. An unprecedented quantity of data is now available to pharma providers, and those with the tools and knowledge to make use of it will quickly pull ahead.

Pricing is an important but highly complex element in market access. It involves analyzing large amounts of data from ever more diverse sources—something AI excels at. Rather than spending hours pouring over clinical trial and real-world data (RWD), past drug submissions and evaluations, and global, regional, and historical pricing data, market access teams can simply feed this data into an appropriately structured AI system for fast results, largely free from human error and easily translatable into relevant, compelling insights.
Outcome-based contracts (OBC) and value-based models or “value pricing” are promising approaches to pricing and reimbursement but require rapid analysis of large amounts of data to ensure advantageous reimbursement tiers are approved as quickly as possible and that providers aren’t left holding the bag when expectations aren’t met. AI can assist by better identifying qualifying populations with greater certainty around efficacy, informing manufacturers on where OBCs are most likely to be advantageous, and, of course, tracking and analyzing outcomes. By using advanced analytics from structured and unstructured patient data one can have faster, more accurate insights for both payers and providers.

I. AI in Sales and Marketing

Precision sales and marketing have become increasingly crucial in the pharmaceutical industry for effectively educating healthcare providers and patients about new treatment options. However, the old manual segmentation approaches of extensive databases and the determination of the most effective messaging strategies pose significant challenges through traditional methods alone.

In this context, AI in pharma showcases immense potential in optimizing marketing processes and demand forecasting. By leveraging advanced analytics of customer profiles, medical records, and past behaviours, AI systems proficiently segment HCP populations into detailed subgroups with distinct needs, preferences, and potential responsiveness to specific appeals. Machine learning algorithms continually refine these segments as new data is gathered. Equipped with these insights, pharmaceutical marketers can precisely target communications—be it digital marketing, details, or virtual engagement —tailored to resonate best with each specific individual.

According to a report by McKinsey & Company, pharma AI-driven customer segmentation and targeting can lead to a 10-15% increase in marketing efficiency. This is not merely about profit margins; it’s about ensuring that life-improving medications reach the individuals who require them.

How to Scale AI Across the Value Chain

To enhance pharmaceutical operations through the strategic deployment of artificial intelligence (AI), a multifaceted approach is key.

Scaling AI across the pharmaceutical value chain necessitates a combination of collaborations and partnerships, seamless data integration, and the development of a skilled workforce.

These three pillars serve as the bedrock for unlocking the full potential of AI in the pharma industry.

A. Collaborations and Partnerships

In a fast-paced technological landscape, collaboration is the key for the pharmaceutical industry to fully harness the transformative potential of AI. Partnerships offer numerous advantages. They cultivate an atmosphere of innovation and knowledge exchange, expediting the development and implementation of pharma AI solutions. Collaborations bring fresh perspectives and cross-industry expertise, opening doors for more resilient and adaptable applications of AI across pharmaceutical operations.

In an industry where innovation can mean the difference between life and death, these partnerships play a pivotal role in bolstering pharma’s position at the forefront of healthcare advancements.

B. Data Integration and Interoperability

One of the major hurdles in expanding AI across the pharmaceutical value chain is the intricate network of data, often isolated within different departments and systems. To unleash the complete potential of AI, integrating and making data interoperable is crucial. The effectiveness of AI heavily relies on data, and when data moves smoothly across departments, it powers more precise predictions and well-informed decisions.

The hurdles in data sharing encompass concerns about data security, privacy, and the need for standardized formats. However, solutions are emerging. Blockchain technology, for instance, provides a secure and transparent method of sharing and verifying data, potentially revolutionizing how pharmaceutical data is managed. Similarly, federated learning allows AI models to learn from dispersed data sources without disclosing sensitive information. Open standards and data governance frameworks are also under development to encourage data sharing while maintaining compliance with regulatory requirements.

C. Talent and Workforce Development

The pharmaceutical industry is experiencing a burgeoning demand for AI professionals. This shift towards AI requires a highly skilled workforce capable of developing, implementing, and maintaining AI solutions. Initiatives in training and skill development are vital to bridge this talent gap.

Companies are collaborating with universities and AI training institutes to equip their employees with the required skills. Additionally, the industry is nurturing a culture of continual learning, motivating employees to adapt and remain at the forefront of AI-driven innovations.

Case Studies of Pharma Companies Scaling AI

The transformative impact of AI in pharma is perhaps best exemplified through real-world case studies of pioneering companies that have successfully scaled AI in specific areas.

Moderna’s AI-Driven Supply Chain Optimization: Moderna, a trailblazer in mRNA-based vaccine technology, utilized AI to optimize its supply chain during the development and distribution of the COVID-19 vaccine. AI-driven demand forecasting models helped ensure a consistent supply of vaccines while minimizing waste. This was pivotal in meeting the urgent global demand for COVID-19 vaccinations. Moderna’s AI-enhanced supply chain management not only addressed immediate challenges but also set new standards for future vaccine distribution.

GlaxoSmithKline’s Smart Manufacturing: GlaxoSmithKline (GSK) has implemented AI for predictive maintenance in its manufacturing processes. By using AI algorithms to predict when equipment requires maintenance, GSK reduced unplanned downtime by nearly one-third. This increased efficiency has a direct impact on production, ensuring that the pharmaceutical giant can consistently deliver life-saving medications to patients.

These case studies are just a glimpse into the transformations happening in the pharmaceutical industry through AI.

The Global Impact of AI in Pharma

Artificial intelligence is profoundly reshaping the pharmaceutical industry and global healthcare. The AI pharma market has experienced significant growth and investment in recent years. According to a report by Accenture, pharmaceutical companies invested over $5 billion in AI in 2021, marking a 40% increase from the previous year. AI adoption and investment are on track to expand significantly over the next decade, as pharmaceutical firms recognize its potential to revolutionize drug discovery, clinical trials, and precision medicine.

Statistics illustrate the pharmaceutical industry’s rapid embrace of AI technologies. An IBM survey revealed that over 80% of pharmaceutical executives are either currently using AI or have plans to do so within the next two years. AI finds applications in various areas, such as designing new drug compounds through molecular simulations (50%), predicting medication side effects (40%), and enhancing clinical trial recruitment and monitoring (30%). AI algorithms can swiftly analyze patterns in vast datasets, expediting drug discovery by identifying promising drug targets in a fraction of the time it would take for humans. This holds the potential to transform patient care, providing faster access to personalized treatments tailored to an individual’s genetics.