Pharmaceutical
This industry comprises establishments primarily engaged in one or more of the following: (1) manufacturing biological and medicinal products; (2) processing (i.e., grading, grinding, and milling) botanical drugs and herbs; (3) isolating active medicinal principals from botanical drugs and herbs; and (4) manufacturing pharmaceutical products intended for internal and external consumption in such forms as ampoules, tablets, capsules, vials, ointments, powders, solutions, and suspensions.
Assembly Line
Autonomous synthesis robot uses AI to speed up chemical discovery
Chemists of the University of Amsterdam (UvA) have developed an autonomous chemical synthesis robot with an integrated AI-driven machine learning unit. Dubbed ‘RoboChem’, the benchtop device can outperform a human chemist in terms of speed and accuracy while also displaying a high level of ingenuity. As the first of its kind, it could significantly accelerate chemical discovery of molecules for pharmaceutical and many other applications. RoboChem’s first results were published on 25 January in the journal Science.
RoboChem was developed by the group of Prof. Timothy Noël at the UvA’s Van ‘t Hoff Institute for Molecular Sciences. Their paper shows that RoboChem is a precise and reliable chemist that can perform a variety of reactions while producing minimal amounts of waste. Working autonomously around the clock, the system delivers results quickly and tirelessly. Noël: ‘In a week, we can optimise the synthesis of about ten to twenty molecules. This would take a PhD student several months.’ The robot not only yields the best reaction conditions, but also provides the settings for scale-up. ‘This means we can produce quantities that are directly relevant for suppliers to the pharmaceutical industry, for example.’
NJM turnkey integrated solid dose line
Digital Twins and AI Reshape Biopharmaceutical Manufacturing
The foundation of any control strategy is process understanding. And, according to the ICH’s Q8 guidance,1 modeling is the best way to generate process understanding and meet regulators’ quality-by-design expectations. The models should describe the relationship between process parameters and drug quality and performance attributes.
Statistical models—predictions based on available data—have proven to be the most popular approach so far. Many manufacturers have used data-based models to guide development, scale-up, and process control. But their predictive power is limited to the range of data available, and they require significant experimental effort.
For this reason, mechanistic models—assumptions based on known principles rather than just data—are gaining in popularity. Mechanistic models “can provide a full description of the system, higher prediction power, as well as the potential to extrapolate well outside of calibration space,” Li explains. “They are valuable tools for predicting scale-up process performance, thereby de-risking large-scale manufacturing runs.”
Pharma 4.0™ Demystified: Navigating Improvement Opportunities with Solutions from Grantek
Mexican manufacturing: so far from EU, so close to US
Kearney’s US Reshoring Index (April 2021) shows that many US manufacturing executives perceive nearshoring to Mexico or Canada as even more advantageous than reshoring to the US. The index also noted that US manufacturers will specifically strive to reduce dependence on China for manufacturing, another positive sign for nearshoring operations to Mexico. Since 2020, Covid-19 related supply chain disruptions have caused many US companies to take steps to bring some of their manufacturing closer to home.
The US pharma market is the largest worldwide. There is a great incentive for North American countries to trade with each other (and promote nearshoring, given lower overhead costs) since the US-Mexico-Canada Agreement (USMCA), a free trade agreement between Canada, Mexico and the US, came into force in July 2020. At the same time, the US has had trade disputes with a major pharma exporter, China, in recent years. Many of the world’s largest pharma companies already operate facilities in Mexico, and nearshoring could increase international investment further.
Merck Teams Up With Rival J&J to Help Produce Its Covid Vaccine
Blockchain and the Pharmaceutical Industry
“Blockchain can help ensure that there is reliable, accurate data and a single reliable version of the truth that is shared by every participant in the pharmaceutical manufacturing and supply chain. It can increase end-to-end visibility of the supply chain with data. Blockchain can bridge barriers between stakeholders by giving all parties the same, real-time, accurate view of the supply chain. This is an important factor for ensuring manufacturing and supply chain robustness,” says Arul Joseph, Senior Director, Pharmaceutical Development and Clinical Supply Chain, Avanir Pharmaceuticals.
Pharma Sets a Foundation for Greener API Manufacturing
To contribute to the reduction of CO2 and GHG emissions, all drug developers and manufacturers need to seriously consider measures to improve sustainability throughout each phase of their industrial processes, according to Weng. “The pharmaceutical industry is due for a major overhaul in all aspects of its unit operations. Essentially, the pharmaceutical industry should be evaluating sustainable alternatives for all current exercises that rely on fossil fuel inputs,” he sates.
The best time to consider optimal, sustainable production solutions is during the design of the synthetic route to an intermediate/API, notes Martin, because once these processes are validated, it is very challenging to introduce any changes, even if they offer significant improvements in productivity and sustainability.
Pfizer’s Edge in the COVID-19 Vaccine Race: Data Science
Pfizer dominated news headlines and family dinner conversations last December when it became the first company to bring a COVID-19 vaccine to the U.S. market. The pharma giant accomplished the feat in record time: less than a year after the disease was first identified.
Integral to that effort was the work of Pfizer’s informatics and digital technology team for its vaccine R&D business. Led by Frank DePierro, this group of researchers crunched and chronicled all of the clinical trial data that led to a green light from the U.S. Food and Drug Administration (FDA), and a safeguard for millions of people.
Are my (bio)pharmaceutical assay performances reliable? Only probability of success counts!
Gage R&R studies are often conducted in the industry to determine the operating performance of a measurement system and determine if it is capable to monitor a manufacturing process. Several metrics are commonly associated with Gage R&R studies, such as the precision-to-tolerance ratio (P/T), the precision-to-total-variation ratio (%RR), the Signal to noise ratio (SNR), the %Reproducibility and the %Repeatability. While these metrics may suit well the overall industry, they could be problematic once applied in drug manufacturing sector for several reasons, (1) (bio)pharmaceutical assays are often more variable than common physico-chemical measurement systems and the usual criteria are too restrictive for the pharma industry, (2) analytical methods cannot always be improved once qualified, and (3) measurements are usually costly and time consuming, which makes difficult to have enough data to estimate all sources of variance with high precision.
Assisting Continued Process Verification with AI
Patterns of behavior reflected in the data from equipment sensors can give insight into these performance affecting factors. In many cases, these patterns develop before product quality is significantly affected. Putting in place analytics that can detect these patterns gives the plant operations team actionable warning before CPV limits indicate a problem. This warning can be used to limit costly production impacts. Importantly, because the CPV process itself is untouched, these kinds of pattern detection analytics can be implemented without additional filings or regulatory delay. Assisting CPV does not mean replacing or even changing CPV.
How and Why Pharmaceutical Manufacturers Are Applying Artificial Intelligence
“Opportunities to reduce manufacturing costs exist across all stages of the product lifecycle. Advanced analytics can reveal those opportunities, allowing pharma companies to take informed action to save money,” said Richard Porter, global director, pharmaceuticals, at AspenTech. “Whether using multivariate analytics to identify process degradation and its impact on quality or predicting final product quality to reduce lab testing lag times, these techniques offer pharmaceutical companies a competitive advantage.”
A purified water system at a pharmaceutical manufacturing facility.“The company tried to avoid batch losses—with each batch valued between $250,000-$300,000—as frequent shutdowns to replace the seals limited capacity,” said Porter. “As the company needed to ramp up capacity, it purchased two additional mills. Adopting Aspen Mtell, which connects to OPC UA supported devices, for predictive maintenance allowed the company to reduce supply chain disruptions from seal replacements and cut lifecycle maintenance costs by 60%. In addition, the company reduced capital expenditures and associated lifecycle maintenance costs by 50%.”
Vaccine production: Marburg has the right stuff
BioNTech manufactures BNT162b2 in collaboration with US pharmaceutical specialist Pfizer. The company has started manufacturing at the production site in Marburg, in the German state of Hesse. The plant there comes with an ultramodern production facility for recombinant proteins. The relevant expertise is also available, since BioNTech also acquired a highly qualified employee base along with the production facility, all of whom are experienced in developing new technologies.
The facility in Marburg had been producing influenza vaccines based on flu cell culture, then changed over to recombinant proteins for cancer treatments and now manufactures mRNA vaccine.
All the improvements at the Marburg plant are Industry 4.0-compatible. One of the challenges with the conversion was the fact that it involved switching from rigid to mobile production with many single-use components. At the same time, working with mRNA meant a higher clean room class than was previously required in the facility. Paper is now an avoidable “contamination factor” that doesn’t arise with digital production. That was the basis for opting for the Opcenter Execution Pharma solution from Siemens as the new MES. This solution enables complete paperless manufacturing and fully electronic batch recording.
How Pfizer Makes Its Covid-19 Vaccine
“This is where the magic happens.”– Patrick McEvoysenior director of operations and engineering
A rack of 16 pumps precisely controls the flow of the mRNA and lipid solutions, then mixes them together to create lipid nanoparticles.
When the lipids come into contact with the naked strands of mRNA, electric charge pulls them together in a nanosecond. The mRNA is enveloped in several layers of lipids, forming an oily, protective vaccine particle.
Synchronizing eight pairs of pumps is not an ideal solution, but Pfizer engineers chose to scale up existing technology instead of trying to build a larger, unproven type of precision mixing device.
The newly made vaccine is filtered to remove the ethanol, concentrated and filtered again to remove any impurities, and finally sterilized.
Why We Can't Make Vaccine Doses Any Faster
The Trump administration deployed the Defense Production Act last year to give vaccine manufacturers priority in accessing crucial production supplies before anyone else could buy them. And the Biden administration used it to help Pfizer obtain specialized needles that can squeeze a sixth dose from the company’s vials, as well as for two critical manufacturing components: filling pumps and tangential flow filtration units. The pumps help supply the lipid nanoparticles that hold and protect the mRNA — the vaccines’ active ingredient, so to speak — and also fill vials with finished vaccine. The filtration units remove unneeded solutions and other materials used in the manufacturing process.
These highly precise pieces of equipment are not typically available on demand, said Matthew Johnson, senior director of product management at Duke University’s Human Vaccine Institute, who works on developing mRNA vaccines, but not for COVID-19. “Right now, there is so much growth in biopharmaceuticals, plus the pinch of the pandemic,” he said. “Many equipment suppliers are sold out of production, and even products scheduled to be made, in some cases, sold out for a year or so looking forward.”
Inside one of the new, quick-build factories making the Moderna vaccine
The race to produce as much of the new vaccine as possible goes through these factories, which were spun up much faster than usual by building the shells before the vaccine production process was finalized.
The story of mRNA: How a once-dismissed idea became a leading technology in the Covid vaccine race
The liquid that many hope could help end the Covid-19 pandemic is stored in a nondescript metal tank in a manufacturing complex owned by Pfizer, one of the world’s biggest drug companies. There is nothing remarkable about the container, which could fit in a walk-in closet, except that its contents could end up in the world’s first authorized Covid-19 vaccine.
Pfizer, a 171-year-old Fortune 500 powerhouse, has made a billion-dollar bet on that dream. So has a brash, young rival just 23 miles away in Cambridge, Mass. Moderna, a 10-year-old biotech company with billions in market valuation but no approved products, is racing forward with a vaccine of its own. Its new sprawling drug-making facility nearby is hiring workers at a fast clip in the hopes of making history — and a lot of money.