Healthcare: AI’s broader impact

And while much focus has been placed on artificial intelligence (AI) revolutionizing cancer research and healthcare investments in 2025, it's also imperative to shed light on how the benefits of AI extend far beyond cancer vaccines, offering a paradigm shift across the pharmaceutical pipeline.

For closed-end fund investors and prospective investors, understanding the broader implications of AI in the pharmaceutical pipeline is crucial. AI is poised to bring a paradigm shift in drug discovery and development, toxicology, and clinical trial design, promising to enhance efficiency, reduce costs, and improve patient outcomes.

Drug discovery and development

Traditional drug discovery is a lengthy and costly process, often taking over a decade and billions of dollars to bring a new drug to market, compounded by a high failure rate in clinical trials. AI has the potential to transform this landscape by accelerating the drug discovery process and improving success rates.

AI algorithms can quickly and accurately analyze vast datasets to identify potential drug candidates. For example, models like AlphaFold can predict protein structures, aiding in drug design.¹ AI also facilitates virtual screening and de novo drug design, optimizing molecular structures for specific biological properties.¹

By automating these processes, AI reduces the time and cost associated with drug discovery.

By automating these processes, AI reduces the time and cost associated with drug discovery. It can also identify compounds that bind to "undruggable targets," paving the way for treatments of previously untreatable diseases.² Additionally, AI minimizes false positives in candidate identification and significantly shortens the preclinical stage, enabling the rapid in silico screening of millions of compounds and optimizing lead compounds by predicting their pharmacokinetics and efficacy.

Toxicology

Thanks to AI, toxicology is advancing rapidly. Traditional methods often rely on animal testing, which can be time-consuming, costly, and ethically controversial. AI provides a more efficient and humane alternative.

Machine learning models can predict toxicity endpoints, analyze large datasets, and generate synthetic data, integrating information from various sources including legacy studies, literature, and high-throughput assays. By automating data analysis and prediction, AI speeds up quantitative risk assessments and accounts for uncertainties.³

Additionally, AI helps clarify mechanisms behind predictions, fostering regulatory trust in AI-based toxicology assessments. Multidisciplinary collaboration is crucial for developing interpretable and robust AI systems.³

When applied effectively, AI can transform toxicology into a more predictive and evidence-based discipline.

When applied effectively, AI can transform toxicology into a more predictive and evidence-based discipline, thereby enhancing both human and environmental safety. It also has the potential to predict compound toxicity earlier, helping to avoid costly late-stage failures.

Clinical trial design

Clinical trials are vital in the drug development process, but they often encounter high costs, lengthy timelines, and significant risks of failure. AI and machine learning are transforming these trials by improving patient recruitment, data management, and personalized treatment design.

AI addresses challenges in trial design by streamlining tasks like patient selection and data analysis.

AI addresses challenges in trial design by streamlining tasks like patient selection and data analysis.⁴ Machine learning models can predict risks and adverse events early, enhancing patient safety. Additionally, intelligent systems optimize trial protocols by simulating scenarios and adjusting parameters in real time.⁴ For example, AI can automate the review and analysis of regulatory documents, ensuring compliance with regulatory standards and accelerating the submission process.⁵

AI also automates regulatory document reviews, ensuring compliance and speeding up submissions. By increasing efficiency and accuracy in trial design, AI facilitates faster patient recruitment, real-time data analysis, and more tailored treatment approaches.⁴

Final thoughts

AI is reshaping the pharmaceutical pipeline, offering a paradigm shift in drug discovery and development, as well as in toxicology and clinical trial design. For closed-end fund investors and prospective investors, we believe understanding the broader implications of AI in healthcare is crucial. AI's ability to enhance efficiency, reduce costs, and improve patient outcomes presents significant investment opportunities. By shedding light on the transformative power of AI beyond cancer vaccines, investors can make informed decisions and capitalize on the potential of AI-driven pharmaceutical companies. Ultimately, we believe that AI is shaping the future of healthcare, and savvy investors will recognize and capitalize on this paradigm shift.

¹ "Integrating artificial intelligence in drug discovery and early drug development: a transformative approach." Biomarker Research, March 2025. https://biomarkerres.biomedcentral.com/articles/10.1186/s40364-025-00758-2.
² "AI in drug discovery - Benefits, drawbacks, and challenges." RoboticsBiz, April 2021. https://roboticsbiz.com/ai-in-drug-discovery-benefits-drawback-and-challenges/.
³ "Artificial intelligence (AI)-it's the end of the tox as we know it (and I feel fine)*." Springer Nature Link, January 2024. https://link.springer.com/article/10.1007/s00204-023-03666-2.
⁴ "Role of ML and AI in Clinical Trials Design: Use Cases, Benefits." Coherent Solutions. https://www.coherentsolutions.com/insights/role-of-ml-and-ai-in-clinical-trials-design-use-cases-benefits.
⁵ "Role of Artificial Intelligence in Clinical Trials and Healthcare Research." Appinventiv, March 2025. https://appinventiv.com/blog/artificial-intelligence-in-clinical-trials/.

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