Author: Intelligence Ventures
Original publication here.
Small molecule manufacturing is a complex realm that bears many similarities to other types of industrial manufacturing (from automobiles to consumer packaged goods), but has historically been difficult to integrate end-to-end processing (i.e., supply chain) because of its unique challenges. Regulatory pressure from the FDA has generated a great deal of research into how small molecule synthesis and purification can mimic their industrial counterparts, however the industry still has not been able to implement a continuous manufacturing solution that solves the technical challenges associated with traditional batch-to-batch production. This sector is begging for AI/ML disruption which will increase production efficiency (material cost and manufacturing time), improve drug purity, reduce the probability of contamination, optimize formulation in silico, and reduce the footprint of industrial manufacturing facilities. But to truly grasp the value AI/ML brings, we need a comprehensive understanding of the small molecule lifecycle.
AI is trained on historical datasets of molecular structure and physiochemistry, identifying correlates between these factors and activity against known targets and putative substrates. Ultimately, the goal is to form predictions about the potential efficacy and safety of de novo drug candidates. In small molecule manufacturing, you will frequently see these two terms:
AI streamlines processes, cuts costs, boosts purity, optimizes production schedules, and minimizes contamination risks. 
AI predicts a drug product's in vitro behavior, determining physical characteristics like hardness and moisture content, which will affect the dissolution, diffusion, and distribution (pharmacokinetics) In turn, this will inform the route of administration and material composition of the final formulation (e.g., fillers, excipients, and co-factors).
Traditional replenishment times, which average 75 days in the pharmaceutical sector, can be optimized. AI/ML provides warehouses with better planning tools for delivery routes, real-time monitoring of market supply and demand, and automation that takes into account drug expiration dates and specific storage requirements. 
AI/ML can replace human efforts and seamlessly integrate into the production line. Recently Pfizer has adopted AI-based imaging analysis tools to detect defects of their tablet coating.
In the face of global challenges, such as the COVID pandemic and resulting Tylenol shortages, it becomes evident that pharmaceutical companies need robust systems to monitor and meet market demands.  AI/ML is that robust system.
The most transformative element perhaps is the blend of AI with the principles of Industry 4.0 – the current trend of automation and data exchange in manufacturing technologies. This blend is backed by real-time sensors, automatic robotics, digitalization of factory controls, and the Internet of Things. 
A significant portion of manufacturing is outsourced to Contract Manufacturing Organizations (CMO). With AI/ML's entry, the face of this sector is bound to change. By 2028, the CMO market size is projected to reach $210.5 Billion. 
In recent years, Continuous Manufacturing (CM) has been replacing traditional Batch Manufacturing due to its drastically improved efficiency, simplicity, quality assurance, and reduced probability of contamination. CM involves streamlining each technical aspect (e.g., synthesis, crystallization, filtration, and downstream processing - see Figure below) into a single end-to-end process that obviates the clunky “start-and-stop” nature of Batch Manufacturing, continuously monitoring the physical properties of the equipment and chemical properties of the raw material/active pharmaceutical ingredient without human intervention. Not only does this speed up production time, it improves the purity of the final drug product, reduces the chances of contamination, reduces inventory/storage, and requires a significantly smaller manufacturing plant footprint.The advantages are obvious, but there are still technical issues to work out (for example, how do we guarantee that the process is proceeding as intended if we can’t take samples at each step to diagnose problems? Can we trust the continuous monitoring? And if so, how do we know if a deviation has ruined the batch without manual intervention and testing?).
Perhaps AI/ML can help… 
“In order to entirely exploit the advantages of CM, the mainly separately developed processes need to be integrated to form end-to-end systems from the raw materials to the final dosage forms. However, even the integration of two technological steps is a challenging task. The development of end-to-end systems requires deep process understanding and a holistic approach toward process development and optimization.”
This need for a “deep process understanding” can be facilitated by AI/ML to fully integrate each step of standard batch processing into a single end-to-end system (CM). For example, spectroscopy (using in-line spectrometer probes) in combination with ML can “non-invasively” monitor process parameters such as metabolite and raw material concentrations (which, according to the article below, cannot be measured directly using Raman linear regression), obviating the material sampling/testing steps taken in standard batch processing. Similarly, in-line probes with high-frequency sensors and be installed to measure the physical factors that relate to manufacturing efficiency, such as sound, vibration, and electricity consumption. This physical data can be combined with ML to predict the latest possible time for maintenance or repair of an asset.
The largest Contract Drug Manufacturing Organizations in the world are working furiously to implement AI/ML into their process to turn traditional Batch Manufacturing on its head.
In an article titled “Growth of Artificial Intelligence in Pharma Manufacturing”, Lonza highlights the profound impact of AI/ML in drug manufacturing. Their idea of steering the process towards a “golden batch” perfectly encapsulates the potential of AI interventions, whether in small molecule production or biologic.
In today's rapidly evolving technological landscape, certain tides are reshaping industries in ways previously unimagined. Biopharma and manufacturing stand at the cusp of such a transformation, driven by the juggernaut of artificial intelligence (AI).
At Intelligence Ventures, we have identified and met with some of the most promising trailblazers involved in this revolution. Let's venture into this landscape, discreetly navigating the waters of the most promising AI-driven endeavors in this realm (without revealing our treasure map, of course!).
What do these endeavors spell for biopharma and manufacturing? A silent revolution, perhaps. The numbers reflect not just investor confidence but also the impending wave of transformation these enterprises could usher. The silent signals we've been picking up, here at Intelligence Ventures, from this landscape are loud and clear: AI's potential in healthcare and manufacturing is monumental. And while we cherish our unique vantage point, we're equally eager to champion the change these players might bring about.
Leading the AI Renaissance in Pharma Manufacturing:
In the intricate tapestry of pharmaceutical manufacturing, the infusion of AI isn't just another thread but the very loom on which the future is being woven. Understanding its integration is one thing; mastering it is another. At Intelligence Ventures, our depth of knowledge isn't just based on theories or observations. We possess a precise and comprehensive grasp of exactly how AI will redefine pharmaceutical manufacturing's every facet.
This isn't just about being ahead of the curve; it's about defining it. As the vanguards of this revolution, we don't merely have front row seats – we are directing the play. With Intelligence Ventures, stakeholders aren't merely watching a transformative epoch unfold; they're partnering with the very architects of this change. Embrace the expertise that can turn vision into reality. Join us, and be instrumental in crafting the pharmaceutical industry's future.
Intelligence Ventures is an emerging venture capital firm dedicated to cultivating innovation at the intersection of artificial intelligence and healthcare within the United States. Their commitment lies in the strategic investment and nurturing of pre-seed, seed, and Series A companies, fueling their growth and fostering the next generation of industry leaders.
Their first fund, AI Health Fund I, is focused on companies that use artificial intelligence to increase efficiencies and/or solve computationally intractable problems that place a ceiling on our ability to develop new drugs, advance them through clinical trials, and ultimately diagnose and treat patients. They are industry vertical agnostic and believe that generative AI and more specific ML models can be used to accelerate innovation in biotech, pharma, medtech, and diagnostics.
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