AstraZeneca has decided to abandon the pledge of not-for profit made during the pandemic and aims to make a modest profit from COVID-19 vaccinations.
AstraZeneca PLC, LSE:AZN, has changed its strategy for the Coronavirus (COVID-19 vaccine) vaccine. It now plans to make a modest profit on sales as new orders are received. This is a departure from what the drug giant had previously said as it began to roll out the first batches of life-saving injections co-developed with the University of Oxford on a non-profit basis. Nevertheless, it is unlikely that the change will affect the COVID-19 sales of Pfizer. Pfizer expects to record revenues of US$36bn after its inoculation.
This update was presented alongside third quarter results, in which AZN maintained its guidance for the year. It anticipates earnings in the US$5.05 to US$5.50 range per share, while revenues will grow by ‘a low percentage’.
The forecast will include a contribution from Alexion, a rare disease business, which was purchased in the summer for US$38bn. However, it will not take into account the impact of sales of COVID-19 vaccine.
AZN is seeing the fruits of R&D investments made over the last decade, with eight positive phase III results from June. According to the company, Alexion integration is progressing well. Revenues were up 32% to US$25.4bn for the year, while core earnings per share rose 22% to US$3.59. The shares of the company fell below 9,000 during early trading Friday, but were up 4% just after noon at 9,038p.
When one considers the abuse suffered from Europe during its overly generous offer at the start of the pandemic and the likelihood of this being an ongoing pandemic one can hardly blame Astrazeneca from wanting to make a small profit for delivering a world class vaccine.
DeepMatter has partneredl with AstraZeneca to improve the productivity and reproducibility of compound synthesis by combining the pharma giant’s automated compound synthesis platform with DeepMatter’s DigitalGlassware™ data collection and structuring technology.
DeepMatter, a big data analysis and research company based in Glasgow, Scotland, announced that the two companies have teamed up to increase productivity when synthesizing single compounds or compound libraries. DeepMatter claims that this work will be based upon unique structured data collected via DigitalGlassware. This is designed to allow chemical experiments to be recorded and coded and then entered into a shared cloud.
DigitalGlassware allows users to record and analyze chemical reactions in compound synthesis, including temperature, solvent, catalysts, and more. The reaction vessel is equipped with a multi-sensor probe that provides real-time data, including temperature, pressure, UV levels and other relevant information. An environmental sensor records the ambient conditions. Software application programming interfaces (APIs) can also record data from external laboratory hardware.
Structured data is collected in the cloud and stored alongside every reaction. This helps to contextualize user actions in the lab. The data can be interrogated in multiple views. This allows for analysis of reactions and the re-playing syntheses.
DeepMatter and AstraZeneca believe that machine learning and AI algorithms can be used to save time and money, as well as provide new insights into drug chemistry.
“To get potential new medicines to patients faster, we need to reduce the cycle time for lead identification and optimization and look forward to working with DeepMatter to assess the potential of DigitalGlassware to help with this,” Michael Kossenjans, associate director, Discovery Sciences, R&D, AstraZeneca, said yesterday in a statement. “Our goal is to transform drug design using innovative digital technologies in combination with automation and AI.”
DeepMatter CEO Mark Warne, PhD stated: “We’ve been impressed with the automated chemistry platforms developed at AstraZeneca sites for autonomous delivery of new lead series. We see an opportunity to draw together knowledge from the DigitalGlassware platform to enable machine learning and AI technologies to increase the certainty of producing a high quality and choice of candidate drug molecules.”
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