Manufacturing Opportunity in COVID-19 Treatments


Going on year 3 of COVID-19, new variants continue to ravage the world and disrupt manufacturing. Last year, we covered how the virus affected manufacturing operations across the world in 2020 (year 1). In 2021 (year 2), leading pharmaceutical companies developed new COVID vaccines and treatments and started to scale manufacturing to meet the demand. I suspect manufacturing capacity will continue to constrain the widespread availability of COVID treatments. The pharmaceutical companies that can get the required approvals and deliver on scaling manufacturing have a great opportunity ahead.

NOTE: You can revisit the breakthroughs in how manufacturers responded to the COVID-19 pandemic thus far by browsing the topic archive for COVID-19.

Let’s take a look at a few COVID treatments and their planned production ramp and financial impact as judged by revenues.

Gilead’s VEKLURY (Remdesivir) Early Lead

According to documents, manufacturing of Remdesivir takes roughly 180 days from raw materials to finished goods. The bulk of that time is to go from raw materials to ‘active pharmaceutical ingredients’ which takes about 150 days. The raw materials are sourced from at least 10 countries around the world and the production of these materials was the biggest bottleneck to ramping production. By early 2021, Gilead projected to have produced over 1 million treatment courses (11 doses per course) up from an inventory of only 5,000 in January 2020.

Being the first approved treatment, some of the financial impact is known. So far, I count about $7 Billion in revenue.

Quarter Revenue ($B) of VEKLURY
2020 Q3 $0.873
2020 Q4 $1.9
2021 Q1 $1.46
2021 Q2 $0.829
2021 Q3 $1.9

Reference Links

Regeneron’s REGEN-COV (casirivimab and imdevimab) Monoclonal Antibody Cocktail

Monoclonal antibodies are used to treat a variety of diseases including COVID-19. Supply limitations are hampering the availability of this treatment. It appears to be manufactured outside New York City in Tarrytown, New York with the help of over $1.5 billion in funding from the US federal government. Little else is known on the production plans or scaling difficulties for REGEN-COV.

After receiving the emergency use authorization in August of 2020, Regeneron sold about $185 million. As for 2021, sales exceeded $4 billion worldwide (including partner Roche).

Reference Links

Pfizer’s PAXLOVID - The First Pill

Building off the success of its COVID vaccine (COMIRNATY), Pfizer developed an oral antiviral (PAXLOVID) with a treatment course that consists of two daily 150-mg doses for five days. They pledge to invest up to approximately $1 billion to scale manufacturing and distribution. Just this week Pfizer raised its production projections by 50% from 80 million to 120 million treatment courses by the end of 2022. I could not find much data on the planned ramp, but it appears their plants in Ringaskiddy (Cork), Ireland and Ascoli, Italy are set to produce the pills.

Some financial analysts estimate Paxlovid could contribute $24.2 billion to Pfizer’s 2022 revenue.

Reference Links

Summary of Opportunity in COVID-19 Treatments

Being first to market with a vaccine or treatment during a pandemic can dramatically change the trajectory of a company and their suppliers. Two capabilities are needed: product research and development and manufacturing and distribution. The treatment products that have been first to market have made the most money, but have been constrained by manufacturing. As more companies get authorization for their products and the ones that are able to ramp production have the greatest opportunity ahead.

Assembly Line

How pioneering deep learning is reducing Amazon’s packaging waste

📅 Date:

✍️ Author: Sean O'Neill

🔖 Topics: Machine Learning, Computer Vision, Convolutional Neural Network, Sustainability, E-commerce

🏢 Organizations: Amazon

Fortunately, machine learning approaches — particularly deep learning — thrive on big data and massive scale, and a pioneering combination of natural language processing and computer vision is enabling Amazon to hone in on using the right amount of packaging. These tools have helped Amazon drive change over the past six years, reducing per-shipment packaging weight by 36% and eliminating more than a million tons of packaging, equivalent to more than 2 billion shipping boxes.

“When the model is certain of the best package type for a given product, we allow it to auto-certify it for that pack type,” says Bales. “When the model is less certain, it flags a product and its packaging for testing by a human.” The technology is currently being applied to product lines across North America and Europe, automatically reducing waste at a growing scale.

Read more at Amazon Science

John Deere’s self-driving tractor lets farmers leave the cab — and the field

📅 Date:

✍️ Author: James Vincent

🔖 Topics: artificial intelligence

🏭 Vertical: Agriculture

🏢 Organizations: John Deere

The technology to support autonomous farming has been developing rapidly in recent years, but John Deere claims this is a significant step forward. With this technology, farmers will not only be able to take their hands off the wheel of their tractor or leave the cab — they’ll be able to leave the field altogether, letting the equipment do the work without them while monitoring things remotely using their smartphone.

The big difference with this new technology is that drivers will now be able to set-and-forget some aspects of their self-driving tractors. The company’s autonomy kit includes six pairs of stereo cameras that capture a 360-degree view around the tractor. This input is then analyzed by machine vision algorithms, which spot unexpected obstacles.

Read more at The Verge

The New Isaac AMR Platform (Full Version)

Digital twins improve real-life manufacturing

📅 Date:

✍️ Author: James Vincent

🔖 Topics: digital twin

🏢 Organizations: Siemens, Tesla, Boeing

Real-world data paired with digital simulations of products—digital twins—are providing valuable insights that are helping companies identify and resolve problems before prototypes go into production and manage products in the field, says Alberto Ferrari, senior director of the Model-Based Digital Thread Process Capability Center at Raytheon.

The concept has started to take off, with the market for digital-twin technology and tools growing by 58% annually to reach $48 billion by 2026, up from $3.1 billion in 2020. Using the technology to create digital prototypes saves resources, money, and time. Yet the technology is also being used to simulate far more, from urban populations to energy systems to the deployment of new services.

Read more at MIT Technology Review Insights

Rodelta Optimizes Pumps for Cavitation-Free, Max-Impact/Min-Consumption Performance with Omnis CFD

How to Build Scalable Data and AI Industrial IoT Solutions in Manufacturing

📅 Date:

✍️ Authors: Bala Amavasai, Vamsi Krishna Bhupasamudram, Ashwin Voorakkara

🔖 Topics: IIoT, manufacturing analytics

🏢 Organizations: Databricks, Tredence

Unlike traditional data architectures, which are IT-based, in manufacturing there is an intersection between hardware and software that requires an OT (operational technology) architecture. OT has to contend with processes and physical machinery. Each component and aspect of this architecture is designed to address a specific need or challenge, when dealing with industrial operations.

The Databricks Lakehouse Platform is ideally suited to manage large amounts of streaming data. Built on the foundation of Delta Lake, you can work with the large quantities of data streams delivered in small chunks from these multiple sensors and devices, providing ACID compliances and eliminating job failures compared to traditional warehouse architectures. The Lakehouse platform is designed to scale with large data volumes. Manufacturing produces multiple data types consisting of semi-structured (JSON, XML, MQTT, etc.) or unstructured (video, audio, PDF, etc.), which the platform pattern fully supports. By merging all these data types onto one platform, only one version of the truth exists, leading to more accurate outcomes.

Read more at Databricks Blog

Manufacturing Shifts To AI Of Things

📅 Date:

✍️ Author: John Koon

🔖 Topics: IIoT, edge computing

🏢 Organizations: Infineon, Flex Logix, Silicon Labs

Preventive maintenance is an important part of smart manufacturing, but this is just the beginning. AIoT can be deployed in many different areas in a factory to further increase productivity. For example, it can be used for incoming inspection. Traditionally, the quality control department performs sample inspection. Instead of inspecting 100% of the components used to build a device, only a sample — say 10% — will be audited. With the installation of a 3D HD camera, AIoT can inspect 100% of the components and screen out defective parts at an early stage. Additionally, a robotic arm can pick out defective components or those of different colors and/or shapes, further reducing reject rates.

AIoT also can be used to improve worker safety, resulting in lower worker compensation payments. For example, a warehouse can be equipped with AIoT cameras to ensure only authorized workers wearing appropriate safety equipment can enter the warehouse.

Read more at Semiconductor Engineering

Why Tesla Soared as Other Automakers Struggled to Make Cars

📅 Date:

✍️ Author: Jack Ewing

🏭 Vertical: Automotive

🏢 Organizations: Tesla

GM and Ford closed one factory after another — sometimes for months on end — because of a shortage of computer chips, leaving dealer lots bare and sending car prices zooming. Yet Tesla racked up record sales quarter after quarter and ended the year having sold nearly twice as many vehicles as it did in 2020 unhindered by an industrywide crisis.

“Tesla, born in Silicon Valley, never outsourced their software — they write their own code,” said Morris Cohen, a professor emeritus at the Wharton School of the University of Pennsylvania who specializes in manufacturing and logistics. “They rewrote the software so they could replace chips in short supply with chips not in short supply. The other carmakers were not able to do that.”

Read more at New York Times (Paid)

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