How Does AI Support Product Improvement in Drug Development?

Product innovation in the life sciences sector can take many forms. For some, it might mean creating something so entirely new that it may lead to patent filling, for others, it could be a slightly improved formula. Furthermore, innovative companies may also focus on optimizing production processes to enhance product properties or achieve cost or time savings. According to a Deloitte report, over 60% of life science companies plan to spend more than $60 million USD on AI initiatives. Of these, 28% will focus on enhancing existing products, 27% on creating new products and services, and 22% on making processes more efficient.

It is evident that AI will profoundly impact drug discovery, new therapies, vaccines development, diagnostics, treatment choices, medicine manufacturing, and the supply chain in the coming years. This article will answer one crucial question: How can AI enhance existing products in the life sciences industry right now?

What is a product in life sciences?

In the life sciences sector, a ‘product’ extends beyond traditional pharmaceuticals to encompass a wide array of innovations and services. These include drugs, medical devices, diagnostic products, vaccines, cell therapies, antibodies, biologics, and more. Services such as Contract Research Organizations (CROs) and Contract Development and Manufacturing Organizations (CDMOs) play a significant role in the development and manufacturing processes. Additionally, software solutions like Laboratory Information Management Systems (LIMS), Electronic Data Capture (EDC) systems, and Health Information Systems (HIS) are essential for managing the vast amounts of data generated in life sciences research and also to ensure patient safety.

Biotechnology software development solutions also play an important role. As are life science databases, which are becoming even more valuable with AI advancements. These databases include biomedical and pharmacological data sets like clinical data, chemical data, patent databases, literature databases, and market insights. Databases are an integral part of the life sciences ecosystem.

Read also: AI in Life Sciences: Use Cases

Biotech product improvement with AI

Enhancing a product after a drug has completed the rigorous process of clinical trials can be challenging. However, AI can be used in Post-Marketing Surveillance (PMS) to improve drug safety and efficacy monitoring and analysis after a drug has been approved. According to Reniewicz et al. (2024), AI adoption in PMS significantly reduces human error, offers consistent and faster searches, and more effectively identifies relevant articles compared to manual approaches. Research on one AI system showed it consistently outperformed manual searches with precision rates significantly higher across various diagnostic assays, for example, boosting a RGQ MDx (Rotor-Gene Q MDx -QIAGEN) result precision rate from 42.86% (manual) to 98.21% (AI).

Scaling up production is another area where AI can have a significant impact. AI can optimize various stages of the manufacturing process, from raw material sourcing to final product packaging. Machine learning models are able to analyze patterns in production data and identify potential quality issues before they result in product defects. This proactive approach ensures higher product quality, reduces waste and lowers operational costs. For supply chain operations, AI systems analyze historical data and real-time information to predict demand, manage inventory levels, and optimize delivery routes, thereby ensuring products are delivered on time and in the right quantities – while reducing waste and improving efficiency.

For instance, AI algorithms can predict equipment failures before they happen, schedule maintenance during downtime, and ensure that machinery operates within optimal parameters. AI predictive maintenance helps prevent costly halts in production and ensures a consistent supply of high-quality drugs. One documented example created by Sanofi utilizes AI algorithms to optimize production scheduling – leading to a 30% reduction in production time and a 15% increase in throughput.

Data types

AI supports drug development

AI can greatly enhance the design and planning of clinical trials, as traditional methods of trial design are often time-consuming and based on limited data. AI, in contrast, can analyze vast amounts of data from previous trials, scientific literature, and patient records to identify optimal trial designs and patient populations. For example, AI can simulate various trial scenarios to determine the most effective design, so companies avoid costly and time-consuming trial modifications. This approach has been shown to reduce trial duration and costs significantly while increasing the likelihood of regulatory approval. Harrer et al. point out that AI has demonstrated a 32% reduction in patient recruitment failure rates in Phase III trials.

AI also identifies new therapeutic usage for existing drugs and suggests combination therapies. By analyzing vast amounts of biomedical data, AI can uncover hidden connections between drugs and diseases, while simultaneously facilitating drug repurposing and the development of combination therapies that might not have been considered otherwise. An example that should not be missed here is Insilico Medicine, which created the first drug using generative AI and is now administering the first dose of INS018_055 to patients in a Phase II clinical trial.

Patient recruitment is one of the most critical and challenging aspects of clinical trial management. AI can streamline this process by analyzing electronic health records (EHRs) to identify patients who meet a trial’s eligibility criteria. Natural language processing (NLP) algorithms can sift through extensive amounts of unstructured data in medical records to find suitable candidates quickly and efficiently. AI-driven patient monitoring and adherence control systems can reduce dropout rates by up to 30%, thereby increasing the likelihood of trial completion and success.

Read also: What is software for medical devices?

Artificial intelligence can also support regulatory services by developing and submitting applications to respective authorities, such as IND (Investigational New Drug) or NDA (New Drug Application) and managing compliance. AI platforms will also automate the compilation of necessary documentation, ensuring that submissions are complete and deferent with regulatory requirements. This reduces the risk of errors and the time required to prepare and submit applications.

AI-based solutions play a crucial role in Electronic Data Capture (EDC) systems and the statistical analysis of clinical trial data. EDC systems powered by AI can automatically validate data as it is entered, which reduce errors and ensure data integrity. AI algorithms can also analyze clinical trial data more quickly and accurately than traditional methods, identifying trends and insights that might be missed by human analysts.

Pharma companies use AI for data management to integrate and analyze data from diverse sources, including clinical trials, real-world evidence, and scientific literature. This comprehensive data analysis helps make informed drug development and faster market entry, ultimately leading to better patient outcomes.

Driving life sciences business with AI

Patents are another critical aspect that needs comprehensive control in the life-sciences sector. While CEOs or drug developers focus on specific drugs or therapies, they often concentrate on a narrow field, maintain insight through close contact with top researchers, and stay updated with the latest literature. They are even taking advantage of alerts covering specific keywords to stay up to date if necessary. However, in some cases, this is insufficient. Sometimes inventors purposefully avoid easy patent recognition to ensure additional patent security. Staying abreast of such data in these situations is not possible without additional human resources, but AI can offer a more thorough search and control mechanism that identifies potential overlaps and ensures robust patent protection strategies.

AI-powered business intelligence tools are revolutionizing commercial strategies in the pharmaceutical industry. AI enhances customer engagement and optimizes marketing and sales strategies by analyzing customer data and interactions to predict the most effective next steps in engaging healthcare professionals (HCPs) and patients.

For example, AI can determine the best time and channel to contact HCPs, recommend personalized content based on previous interactions, and suggest optimal follow-up actions. This data-driven approach not only improves the efficiency of sales and marketing efforts but also enhances the overall customer experience. By providing tailored recommendations and timely information, pharmaceutical companies can build stronger relationships with HCPs and increase the adoption of their products.

AI will prepare you to face future challenges

Artificial intelligence is revolutionizing product improvement in drug development across various stages, from discovery and clinical trials to manufacturing and post-market surveillance. By automating processes, enhancing data analysis, and improving accuracy, AI is helping pharmaceutical companies bring safer, more effective drugs to market faster and more efficiently. As the industry continues to adopt and integrate AI technologies, the potential for innovation and improvement in drug development will only grow, promising better health outcomes for patients worldwide.

In conclusion, embracing AI in drug development is not just an option but a necessity for staying competitive in a fast-evolving pharmaceutical landscape. If you are interested in investing in AI technologies to be better positioned to meet future challenges, contact us using this form and seize new opportunities in the quest to improve global health.