What Does AI Mean for Drug Manufacturing & Development?

There’s no denying the recent transformative potential of artificial intelligence (AI). When harnessed effectively, AI's success in drug discovery and identification is well-celebrated. I’ve noticed, however, that its application in drug manufacturing and development remains a budding arena – one which is teeming with equal promise and challenges. 

Where CDMOs often act as the pillars for many pharmaceutical giants, they also bridge resource gaps, offer specialised expertise, and ensure the safe, efficient delivery of drugs from labs to pharmacies. Yet, despite their centrality, many CDMOs still rely on traditional, manually intensive processes. With the implementation of AI technologies, comes the promise of precision, efficiency, and cost-effectiveness, but is this as easily actionable as it seems? 

AI's Potential in Drug Manufacturing and Development. 

AI can revolutionise how drugs are developed and produced – from ensuring consistent drug quality through real-time monitoring and corrections, to using predictive analytics for equipment maintenance. Alongside this, AI helps with factors such as optimising the supply chain by analysing global demand patterns and advancing R&D through predictive modelling in clinical trials. Where both these benefits could entirely reshape the industry's landscape, the journey to such transformation comes with a number of notable challenges. 

Key Challenges. 

1. Regulatory Scrutiny: The pharmaceutical sector is one of the most heavily regulated. Integrating AI means that systems and algorithms will need to meet stringent regulatory standards, ensuring both safety and efficacy. This can slow down adoption and require significant documentation and validation. 
2. Data Integrity and Storage: AI thrives on data. The vast amounts of data needed can pose challenges in terms of storage, security, and management. Ensuring data integrity, especially when patient information is involved, is paramount. 
3. Standardisation Issues: The pharmaceutical industry spans the globe, with varying standards and protocols. Designing AI systems that cater to these diverse standards, and can be seamlessly integrated across different platforms, is a significant challenge. 
4. Reliability and Trust: Transitioning from human-led processes to AI-driven ones requires trust in the algorithms. Ensuring that these algorithms are reliable, transparent, and can be audited is essential to gain industry-wide acceptance. 
5. Skill Gap: The integration of AI demands a workforce skilled in both pharmaceutical processes and AI technologies. Bridging this skill gap is crucial for the successful adoption of AI in drug manufacturing and development.

Companies Changing the Game. 

There are a few companies pioneering the integration of AI in drug manufacturing and development. Who, despite these challenges, have harnessed AI's potential to create amazing innovative solutions for the market. They offer valuable insights into the future of an AI-driven pharmaceutical world, showcasing both the strides made and the hurdles we still face as we move from drug discovery to manufacturing and development, bringing both promise and complexity in equal measure.  

Atomwise

Atomwise is a technology-enabled pharmaceutical company leveraging the power of AI to revolutionise small molecule drug discovery. Through the invention of a technology which uses deep learning for structure-based drug design; this technology underpins Atomwise’s best-in-class AI discovery engine, differentiated by its ability to find, and optimise novel chemical matter. 

Through extensive validation of its discovery engine, delivering hit ID success in over 200 academic and collaboration projects to date including a wide variety of protein types and numerous “hard-to-drug” targets, Atomwise is building a wholly owned pipeline of small-molecule drug candidates. It currently has two programs in lead optimisation and programs in immunology and oncology in the discovery stage. Unsurprisingly, the company has recently secured over $174 million from leading venture capital firms to advance its mission, helping fuel its work to “make better medicines, faster”. 

Insilico

Having collaborated with major industry players and prestigious institutions, Insilico Medicine is a clinical-stage end-to-end drug discovery using its innovative AI platform, driven by machine learning (ML), to discover and design novel targets and molecular structures. Backed by an impressive range of notable investors, Insilico strives to provide discovery and development options for a range of innovative drugs, including solutions for cancer, fibrosis, immunity, central nervous system (CNS), and aging-related diseases. 

Exscientia

Exscientia is a precision medicine company harnessing AI to revolutionise drug discovery and development. Its pioneering and functional precision oncology platform has not only improved patient outcomes, but advanced AI-designed small molecules into clinical settings. With a primary focus on oncology, Exscientia is committed to leveraging precision medicine to create innovative therapies for untreatable diseases. So, what’s next? The company also plans to develop a pipeline of key partners to extend its impact to various therapeutic areas in the coming years. Watch this space. 

BenevolentAI

BenevolentAI prides itself on empowering biopharmaceutical firms and the scientists within them to leverage data and AI, to spearhead the next era of scientific advancements. Its AI-driven drug discovery engine, known as the Benevolent Platform™, unlocks the potential of vast fields of biomedical data to offer a comprehensive view of human biology across all diseases. 

At the heart of its technology lies the ‘BenAI Engine’, which transforms fragmented scientific data into actionable insights. This engine is designed to ingest, organise, and harness biomedical data, providing a nuanced understanding of disease biology. Using this approach, the company hopes to enhance the likelihood of success in clinical trials, which, if successful, could quickly lead to the roll-out of life-changing treatments for patients.

What’s missing?

While researching companies in the space, I noticed a gap in AI-driven drug manufacturing and development. Digging deeper, I came across a few companies with a truly innovative approach to AI-use supporting the CDMO and drug delivery space. 

Insight68 offers AI-driven solutions tailored for cell and gene therapy manufacturing and development. Its platform supports clinical trials, scheduling, planning, manufacturing, and logistics, specifically addressing the unique requirements of Cell and Gene Therapy manufacturers. 

Sanofi and Aily Labs are using AI to revolutionise drug development and manufacturing. Their AI-powered pharma strategy, known as "plai" provides personalised predictive analytics to guide company-wide decisions. Sanofi's global presence and commitment to improving lives as a prerogative makes this initiative particularly impactful. 

AbSci is pushing boundaries with its AI-based antibody development. Harnessing generative AI to create better biologics for patients faster, these guys have a dedicated team and cutting-edge facilities to boot. Its approach not only accelerates the drug development process but also increases the chances of success. 

Innowise is a consulting company implementing AI and ML into manufacturing processes, bridging the gap between technology and industry. The group's focus is on custom software development for healthcare and pharmaceutical sectors, and so far has contributed significantly to digital solutions in these areas. Plus, Veratrak focuses on software solutions for pharmaceutical supply chain management, with a platform streamlining the complexities of the global supply chain to ensure compliance with industry regulatory requirements. 

The Future Industry Landscape.

Not only do these companies share the common goal of accelerating tech innovation and adoption in the pharmaceutical industry but do so to facilitate life-changing medical solutions. While they have their unique niches and areas of focus, through recognising the potential for AI to optimise processes, enhance patient outcomes, and drive efficiency, there is a common ground with the pursuit of excellence in an industry that impacts lives across the globe.