The maker of Ozempic and Wegovy is suing a rival firm for selling what it says are unsafe, knock-off versions of its weight-loss drugs in the US. Danish company Novo Nordisk asked US courts on Monday to ban Hims & Hers' range of weight-loss pills and injections, which it says are not approved by US authorities and infringe on its patent. The legal drama began on Friday after Hims & Hers launched a new weight-loss pill, leading to an initial threat from Novo Nordisk. Over the weekend, Hims & Hers said it would stop selling the pill. On Monday, its share price slumped as it called Novo Nordisk's decision to press ahead with the lawsuit a blatant attack.

Novo Nordisk's semaglutide will soon be available in a daily pill Americans can take for weight loss. The US Food and Drug Administration today approved a pill version of the blockbuster anti-obesity drug Wegovy. Made by Novo Nordisk, the pill is taken once a day. The company's original version of Wegovy is a weekly injection. Both drugs contain the same active ingredient, semaglutide.

This Digital Health News industry roundup includes a new online course for young people to build skills for a future career in care, a milestone for digital-first healthcare-at-home company Cera and the integration of Ibex Medical Analytics' AI platform with Source BioScience's pathology network. A partnership between Babyl – a subsidiary of Babylon – and Novo Nordisk will help contribute to the expansion of diabetes awareness and care in Rwanda through community engagement and skills building using digital technology. Babyl's existing infrastructure and digital tech will be used to offer digital consultations to patients across Rwanda. Patients who then receive a confirmed diagnosis will be guided to the correct level of care by a doctor or nurse. This could include medication or a referral for further tests.

Pre-trained protein language models have demonstrated significant applicability in different protein engineering task [1, 2]. A general usage of these pre-trained transformer models producing latent representation is to use a mean pool across residue positions to reduce the feature dimensions to further downstream tasks such as predicting bio-physical properties or other functional behaviours. In this paper we provide a two-fold contribution to machine learning (ML) driven drug design. Firstly, we demonstrate the power of sparsity by promoting penalization of pretrained transformer models to secure more robust and accurate melting temperature (Tm) prediction of single-chain variable fragments with a mean absolute error of 0.23 C. Secondly, we demonstrate the power of framing our prediction problem in a probabilistic framework. Specifically, we advocate for the need of adopting probabilistic frameworks especially in the context of ML driven drug design.

We are seeking outstanding candidates with strong analytical and problem solving skills, who are strong in written and oral communication (in English), and have documented experience in the development of complex compute systems. The applicant should have provable skills in the state-of-the-art web-development frameworks, virtualization techniques as well as database technologies. Expertise in clinical data science and machine learning, as well as computer security and data privacy are welcome. A large roadblock of medical research is the difficult access to sensitive data which therefore hinders the training of complex and powerful machine learning concepts. This issue is amplified when considering rare diseases with low incidence numbers per hospital.

According to Søren Smed Østergaard, Vice President, Digital Health of Novo Nordisk, the most significant innovations in the diabetes space centered around hardware, artificial intelligence (AI) and data. He believes that having access to more accurate data on individual behavior and medication usage could positively impact people living with diabetes. "We know there is a huge discrepancy between how people should use medication and how they're using it," said Østergaard. "In 2003, the World Health Organisation (WHO) said improving medication adherence will have a more significant impact on the population's health than improvements to specific medical treatments. "Healthcare data today is often incomplete and too sparse to use for effective decision-making; we need to solve that first, but with this comes a plethora of ethical implications," said Østergaard. "People must have confidence that their data is being kept secure and used responsibly. Data sharing – creating a complete picture using data from different parties and devices – has the potential to revolutionize healthcare and outcomes, but robust data privacy policies must underpin it.