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Role of AI and automation in quality control in the pharmaceutical industry
Articles- Oct 26,24
In recent months, the Indian pharma industry, accounting for 20% of the global generic exports, has been facing problems related to quality from global & local regulators. By leveraging AI and automation, the industry can not only overcome quality challenges, but can also help it reach the target of $ 130 billion by 2030, says Sanskriti Ramachandran.
In an industry where quality can be the difference between life and liability, Indian pharmaceuticals are embracing Artificial Intelligence (AI) and automation to redefine standards. From predictive algorithms that anticipate production issues to robotic systems that eliminate human error, technology is reshaping the way medicines are made and monitored. As India’s pharmaceutical exports surge, these solutions are ensuring compliance with global regulations, enhancing product safety, and building trust worldwide. The fusion of innovation and automation is not just improving quality—it is revolutionising the future of healthcare.
India: Pharmacy of the world
With over 20% share in the global generics market, India has emerged as a significant player in the pharmaceutical domain. The Indian pharmaceutical industry is poised for substantial growth, with ambitions to reach $130 billion by 2030 and $450 billion by 2047. However, with this anticipated growth come challenges—most notably, the need to maintain high standards of quality assurance (QA) to meet both domestic and international regulatory requirements.
Figure 1: Indian pharmaceutical industry’s growth projection
Source: EY Report
Maintaining quality across such a vast industry can no longer be solely reliant on human oversight. Human errors, process inefficiencies, and counterfeit medicines remain significant risks, all of which can have dire consequences for patients and reputations. This is where AI and automation are stepping in as transformative technologies, revolutionising the ways in which quality is ensured in pharmaceutical production. As India seeks to become a global leader in both innovation and volume, AI and automation offer unparalleled solutions to the ongoing challenges in quality assurance.
India is the third-largest pharmaceutical producer in the world by volume and ranks 14th in value. The country is responsible for around 10% of global pharmaceutical production, which includes both domestic sales and exports to over 200 countries. Indian pharmaceuticals are heavily exported to regulated markets like the US, Europe, Japan, and Australia, making quality control not just an internal requirement but a global mandate.
Figure 2: The rising pharma exports from India
Source: PIB
The market value of the Indian pharmaceutical industry stood at approximately $42 billion in 2021 and is expected to grow at a CAGR of 10.54% , reaching around $48.54 billion by 2033. However, this growth is not just quantitative; quality is the key differentiator as Indian companies compete with international players, especially in regulated markets that impose stringent quality checks and audits.
According to Kinjal Shah, Senior Vice President & Co-Group Head-Corporate Ratings, ICRA, “Revenues from the US market are expected to increase by 9-11% during FY25, while European and domestic markets will grow by 7-9% each. However, the industry faces continual pressure from regulatory agencies such as the US FDA, with inspections of Indian plants increasing. Form 483 observations from the US FDA for quality lapses can jeopardise access to lucrative markets, which emphasises the need for robust quality assurance.”
The US FDA's Form 483, also known as a 'Notice of Inspectional Observations', lists any concerns or observations which may violate the Food, Drug, and Cosmetic (FD&C) Act.
Why does quality matter?
The importance of quality assurance in the pharmaceutical industry cannot be overstated. Drugs, unlike other consumer goods, directly affect human health. A minor quality lapse can lead to catastrophic outcomes, ranging from ineffective treatment to severe adverse effects and even fatalities.
Recent developments in the Indian pharmaceutical sector highlight the need for stronger quality assurance systems. Many pharmaceutical manufacturing facilities in India have received Form 483 observations from the USFDA after their routine inspection process. The observations maybe procedural in nature, but it underlines the constant scrutiny that Indian pharmaceutical companies face in global markets.
India has the largest number of US FDA-approved plants (603) outside of the US (as of 2023). Over the last five years, the US FDA has carried out 672 drug quality assurance inspections in India and has identified data integrity issues (considered to be a very serious lapse) at various facilities. As the Indian pharmaceutical sector continues to expand its reach in regulated markets, companies cannot afford to overlook quality compliance.
Besides quality adherence issues, counterfeit drugs are also a grave cause of concern to the industry. Highlighting the importance of combating counterfeit drugs, Manoj Kochar, President, Authentication Solution Providers Association (ASPA), comments, "In the pharmaceutical industry, about 10-20% of medicines are thought to be counterfeit, posing a serious health risk. The more unorganised a sector is, greater the chances of counterfeiting. The World Health Organization (WHO) has also highlighted the prevalence of falsified medical products in India, making it a critical issue." It also puts an enormous burden on pharmaceutical companies to ensure that their products meet stringent quality standards, especially as Indian drugs are exported to regulated markets where non-compliance can lead to import bans and costly recalls.
Navigating the regulatory maze with AI
The pharmaceutical industry is among the most heavily regulated industries in the world. Agencies such as the US FDA (United States Food and Drug Administration), EMA (European Medicines Agency), and CDSCO (Central Drugs Standard Control Organization) in India set high standards for the safety, efficacy, and quality of drugs. In this context, AI and automation have the potential to both meet and exceed regulatory expectations by increasing transparency and accuracy in quality control processes.
AI can revolutionise the pharmaceutical industry by automating complex tasks and reducing human error. Quality control is one of the areas most impacted by AI, with predictive analytics and real-time monitoring providing unprecedented levels of oversight and precision.
Predictive analytics, powered by AI, can forecast potential quality issues before they occur. As Bharat Shah, Managing Director, S Kant Healthcare, explains, “AI and automation are revolutionising quality control and assurance processes within the pharmaceutical industry by enhancing efficiency, precision, and compliance. Automation has already replaced many manual processes that were prone to human error, improving the consistency and reliability of QC procedures. AI takes this a step further by enabling real-time monitoring and predictive analytics. For instance, AI algorithms can detect anomalies during the production process, allowing for swift corrective actions that prevent the release of defective products. Automation systems handle tasks like raw material sampling and in-process testing with a level of precision and speed that is impossible for human operators. Additionally, AI can analyse large datasets from the production floor to predict equipment failures or quality issues before they arise, thus minimising downtime and improving overall production efficiency. These technologies also streamline compliance reporting by automating the collection and analysis of data required for regulatory submissions, reducing the time and effort needed for audits.”
This capability is especially important in a highly regulated industry like pharma, where even minor defects can result in recalls, regulatory action, and reputational damage.
According to a 2022 study by McKinsey & Company, companies that implement AI in quality control experience a 15-20% reduction in manufacturing costs due to decreased downtime and reduced waste.
Mudit Agarwal, Founder and CEO, Agrim Tech Services, notes, "AI is playing a transformative role in drug discovery and development by significantly speeding up processes that traditionally took years or even decades. AI can analyse large biological datasets to identify and validate potential drug targets faster. AI is also being used to design new drug molecules, optimise clinical trial designs, and predict patient responses to treatments.”
In the future, AI will increasingly be used to analyse real-world data collected from patients, including data from wearables, electronic health records (EHRs) and patient surveys. “This will lead to better understanding of treatment outcomes and help generate real-world evidence that supports drug development and regulatory approvals. AI can help pharmaceutical companies track drug safety in post-market environments by analysing real-world usage data, detecting potential side effects and enabling real-time pharmacovigilance,” adds Agarwal.
Several Indian pharmaceutical companies have begun implementing AI and automation in their quality control processes with remarkable results. For instance, Dr Reddy’s Laboratories has integrated AI into its quality management system to monitor and predict deviations in its production lines. By doing so, the company has significantly reduced its error rates and increased the efficiency of its manufacturing processes.
Another example is Cipla, which has deployed automation and AI tools in its manufacturing plants to improve consistency in drug production. Cipla’s automated systems handle tasks like raw material sampling, in-process testing, and final product inspection, ensuring that each product meets rigorous quality standards.
These real-world implementations show that AI and automation are not just theoretical solutions but practical tools that can deliver tangible results. By reducing human error, improving consistency, and providing real-time insights, these technologies are transforming how quality control is managed in Indian pharma.
Making supply chain robust
Counterfeit drugs are another issue making headlines. Despite being a leading producer of legitimate drugs, India struggles with a significant counterfeit drug problem. As noted in a report by the Office of the United States Trade Representative (USTR), approximately 20% of all pharmaceutical goods sold in India are counterfeit. This problem is not only a regulatory challenge but also a massive public health concern.
AI and automation, particularly in track-and-trace systems, can help resolve the problem of counterfeit drugs by ensuring that drugs are authentic and safe before they reach patients. These technologies enable pharma companies to track every step of the production and distribution process, ensuring transparency and quality control at all stages.
“AI is set to transform quality control and supply chain management by leveraging data to predict and prevent failures in production, minimising errors and reducing rejections. AI also optimises supply chains by accurately forecasting demand, enabling Just-in-Time (JIT) inventory, reducing costs, and improving customer service. Beyond manufacturing, AI enhances marketing, sales forecasting, and operational planning, aligning production with market needs to improve satisfaction and ensure high-quality products.
Future advancements include AI in predictive data management, which helps pre-empt quality issues and non-compliances, allowing companies to address issues proactively. AI will further streamline supply chains, enabling JIT inventory management for optimal financial and operational efficiency. However, regulatory compliance remains essential, with a strong focus on ethical data management and transparency. By embracing AI responsibly, companies can build more sustainable and compliant operations, positioning themselves as future leaders in the pharmaceutical industry,” opines Sanjeev Dharwadkar, Current CEO, Pharma Tech Consulting LLP and Former Director, Sanofi.
Maximising AI potential
Ensuring automation systems comply with regulatory standards throughout manufacturing is a primary challenge, as regulators require proof that automated processes are risk-free and transparently reflect production data. “This scrutiny demands that all data be real-time and accurate. Another challenge lies in choosing automation tools that not only enhance efficiency but also meet these regulatory demands. Companies must develop in-house expertise to validate these tools, ensuring consistent quality and efficiency; without it, automation adoption can become resource-intensive,” adds Dharwadkar.
One of the primary regulatory challenges involves ensuring that AI systems are both explainable and transparent. Sudipta Ghosh, Partner, PwC, highlights this challenge, “Regulatory challenges in AI and automation within the pharmaceutical industry primarily revolve around compliance, data security, transparency, and ethical considerations. This highly regulated sector requires adherence to strict standards from agencies like the US FDA, EMA (Europe), and others to ensure patient safety, data integrity, and validation processes. Integrating AI and automation into this framework introduces several complexities.”
First, data privacy and security are major concerns, as AI systems rely on vast datasets that often contain sensitive patient information. Ensuring compliance with regulations like GDPR (Europe) or HIPAA (USA) is crucial. AI algorithms must handle this data securely, adhering to anonymisation and encryption protocols to protect patient confidentiality.
Another challenge is validation and explainability. Regulatory bodies demand transparency in AI decision-making, requiring the technology to be explainable and interpretable. Black-box AI models, whose processes are opaque, are unsuitable for pharma because their decisions cannot easily be justified. There is a need for robust validation methodologies to ensure the accuracy and reliability of AI systems.
Ethical considerations, such as avoiding bias, are also essential. AI algorithms must be designed to prevent discriminatory decisions, especially in areas like clinical trials and patient treatment recommendations. Regulatory guidelines are increasingly pushing for frameworks that minimise bias, ensuring fair and equitable treatment for all patient groups.
Compliance with evolving standards is another hurdle. AI technology evolves rapidly, often outpacing the development of regulatory standards. This creates uncertainty for pharmaceutical companies on how to implement AI innovations while staying compliant. Constant engagement with regulators to shape policies that accommodate these technological advances is crucial to closing this gap.
Government funding can play a pivotal role in overcoming these challenges and advancing AI and automation in the pharmaceutical industry. Investment in this area can lead to breakthroughs in drug discovery, manufacturing efficiency, and healthcare accessibility. Government-backed grants and funding initiatives can support collaborative research and development (R&D) projects between academia, AI tech firms, and pharma companies. This would accelerate AI-driven drug discovery and personalised medicine, reducing the time and cost involved in bringing new drugs to market.
Vandana Iyer, Research Director, TechVision, Frost & Sullivan shares, “Regulatory agencies such as the US FDA are already drafting guidelines such as ‘Using Artificial Intelligence & Machine Learning in the Development of Drug and Biological Products’ to ensure structured regulatory standards which helps improve compliance. Continuous monitoring of quality standards, partnering with vendors that have the necessary regulatory compliance, such as the 21 CFR Part 11 for US FDA helps improve outcomes for automated processes. Ensuring and implementing GAMP5 (Good Automated Manufacturing Practice) guidelines and following ALCOA+ principles (Attributable, Legible, Contemporaneous, Original, Accurate, plus Complete, Consistent, Enduring, and Available) help improve regulatory compliance for automation. Conducting regular audits, securing virtual data repositories, rigorous documentation and periodic risk-based validation are also some practices that can be adopted to improve regulatory compliance.”
The AI-amplified growth
The integration of AI and automation in India’s pharmaceutical sector is proving to be a decisive step towards enhanced quality assurance and global competitiveness. AI-driven predictive analytics and automated processes are minimising human error, improving consistency, and optimising efficiency across the supply chain. By investing in infrastructure, talent, and public-private partnerships, India’s pharmaceutical industry can continue to leverage AI innovations responsibly. As we position ourselves at the forefront of global pharma production, AI and automation will play pivotal roles in achieving sustainable growth, high-quality outputs, and a healthier world.
AI
Pharmaceuticals
Automation
Quality Control
Drugs
healthcare
Pharma
Sudipta Ghosh
Sanjeev Dharwadkar
Kinjal Shah
Mudit Agarwal
Manoj Kochar
Bharat Shah
Sanskriti Ramachandran
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