Pharma’s Great Automation Migration

Companies should focus on the collection and curation of data because the variables selected could be the game changers when machine learning becomes standard

By David Torrone, Scientific Content Contributor, Nice Insight

1 of 2 < 1 | 2 View on one page

For drug manufacturers, Industry 4.0 will look very much like Industry 3.0, just faster. The time saved in production will be a slow road ahead, and will mostly come at the very end of the transition when machine learning algorithms quickly make adjustments to the manufacturing line and production scheduling. The industry should be wary of over-fitting facilities with heavy machinery that may make them less agile and therefore less able to respond to changing markets. At this historic moment for the industry, companies should shift their focus to feature selection: the collection and curation of data. The variables selected today could be the game changers when AI models become industry standard.

Although pharmaceutical firms have crossed galaxies discovering active pharmaceutical ingredients (APIs), the formulation of tablets hasn’t changed much in the last 50 years. “If we used a time machine to transport a pharmaceutical scientist from the 1960s into a current pharmaceutical production plant of today,” writes Lawrence Yu, FDA’s deputy director of the Office of Pharmaceutical Quality, Center for Drug Evaluation and Research, “it might be surprising to learn that they would already be very familiar with most of the processes and production techniques being used.”1

This is changing, however, and “what started with mortar and pestle has grown into more automated volume-controlled recipe processes that go through a quality check at each stage.”2 At first glance, the crux of the transition might appear to be the industry migrating from batch to continuous manufacturing. And indeed, several successful examples of this transition have made headlines in the last two years. Current rockstar Vertex was the first pharmaceutical company to use continuous manufacturing with its Orkambi drug, and in April of last year the FDA approved the first transition from batch to continuous manufacturing with Janssen’s HIV-1 treatment Prezista (darunavir).1 But, ultimately, the switch from batch to continuous is only a small part to the story. What is actually happening in the industry is more profound. Watching Industry 4.0 take over the horizon, many businesses are beginning to gut the processes incompatible with this new era of manufacturing.

Taking the right steps today can help prepare a pharmaceutical company for the great migration that will begin in tableting with the change from batch to continuous manufacturing, and ending with the implementation of machine learning.

Humans are still preferred to robots in many facets of manufacturing because they can quickly transition to meet ever-changing production demands.3 “Agile means that the company is quick to adjust to changes in the market,” says Joe Berish, senior manager for Oliver Wyman’s Digital and Manufacturing Operations Practices. “In the sense of manufacturing, if you set up your system with all of these expensive robots that are very heavy, very hard to move and complicated to reprogram, then that’s not agile. People are flexible.”4

Over-outfitting, over-automation of a facility is an easy-to-make misstep for a company eager to meet the possibilities of Industry 4.0. Rather than buying-in, in regards to robots and automation, particularly in a manufacturing process as (arguably) rudimentary as tablet formulation, the first step a company should take is assessing and understanding the variables that will be fed into a machine learning model. “The biggest challenge companies will face in the implementation of machine learning to manufacturing is a lack of talent,” says Berish. “Humans are still the conduit for teaching machines what to learn, so it takes a special kind of “tweener” who understands the business and the algorithms. Even the latest trend of ‘machine learning for machine learning’ to help scale up quickly, still needs a starting point.”

Many of the discussions about how to implement machine learning technologies to improve production — which at this stage should remain more on the philosophical end of the spectrum — too quickly devolve into the practical aspects of purchasing equipment, software packages, etc. Companies should understand that what might feel like a need to move from batch to continuous manufacturing, if done correctly, is actually a traditional business process reengineering (BPR), with a digital transformation of the company as the focal point. This is how tablet manufacturers will become part of a new industrial era. In the past, these BPRs took 10 to 15 years to rollout. However, becoming part of Industry 4.0 must be faster — much faster. By conservative estimates, it will take a quarter of this time. “If a company is ‘all-in,’” says Berish, “a digital transformation can put them at the forefront of the industry in as little as two or three years’ time.” He continues to explain that a true digital transformation would first include everything except manufacturing. “I don’t want someone to get the impression that if they do the manufacturing part, then they will be a leader.”

As we will be the conduits for these software suites, now is the time for the collection and organization of big data, or in the language of algorithmic modeling: feature selection. Instead of retrofitting a manufacturing line with the latest sensors and robotics, a business that takes the time and effort to find the best variables to feed into the model will dictate the speed at which machine learning algorithms can make adjustments that instill lasting changes to the manufacturing process.

Drugmakers have been working with units of operation to mix, grind, test and mill in different batches since the 19th century.2 In this way, tablet formulation is akin to cooking and therefore must rely somewhat on the intuition of experts. But in Industry 4.0, the art of tablet formulation must be quantifiable. Furthermore, anecdotal and experiential knowledge must keep pace with technological advancements. For example, particle shapes and sizes of an API and its excipients, as known by formulation experts, might first have to be translated to new technological advancements in particle characterization before being considered valid variables for the model — this transition is the time to exchange antiqued variables for more accurate descriptors.

1 of 2 < 1 | 2 View on one page
Show Comments
Hide Comments

Join the discussion

We welcome your thoughtful comments.
All comments will display your user name.

Want to participate in the discussion?

Register for free

Log in for complete access.


No one has commented on this page yet.

RSS feed for comments on this page | RSS feed for all comments