ProductLife Group (PLG) – a provider of regulatory and compliance services for the life sciences industry – has become part of EIT Health’s regulatory hub, the Venture Centre of Excellence (VCoE).

EIT Health will consequently recommend PLG to those early-stage biotech, medtech and digital health start-ups requiring support to accelerate their journey to market through the VCoE programme.

The role of the VCoE is to increase and accelerate market opportunities across Europe, by matching young companies with the necessary funding, professional services expertise and contacts. The wider aim is to make Europe a more attractive and lucrative market for a new generation of life sciences start-ups, whether they are entering the market for the first time or looking to expand their activities in other continents.

Indeed, when the European Commission announced its €150m anchor investment into the programme in October 2020, it set out an ambition to facilitate 2 billion euros in investment while bringing 200 fledgling start-ups to market over a period of 15 years.

That investment level has already been achieved and more than 60 start-ups have already benefited, including those emerging from university laboratories.

Optibrium and Lhasa – developers of software and AI solutions for drug discovery and development – have announced the publication of a peer-reviewed study in the Journal of Medicinal Chemistry.

The paper, Predicting Regioselectivity of AO, CYP, FMO and UGT Metabolism Using Quantum Mechanical Simulations and Machine Learning, describes how the team used existing experimental results, in combination with quantum mechanics and machine learning, in order to build predictive models for drug metabolism.

The study will subsequently underpin the development of new capabilities that better determine the metabolic fate of drug candidates and streamline the preclinical drug discovery process.

Unexpected metabolism can cause the failure of many late-stage drug candidates, or even the withdrawal of approved drugs, making metabolism prediction essential for potential drug candidates.

Current predictive models of metabolism usually target the human cytochrome P450 (CYP) enzyme family, due to its well-characterised role in the metabolism of drug-like compounds. There is, however, an increasing need to predict metabolism for other enzymes, such as human aldehyde oxidates, flavin-containing monooxygenases and Uridine 5’-diphospho glucuronosyltransferases.

The study also demonstrates novel predictive models for these enzymes, while extending the existing model for CYP metabolism to preclinical species. Meanwhile, expanding the portfolio of predictive models beyond CYPs will allow drug discovery scientists to establish a compound’s metabolic fate more accurately. This will help to design better drugs and identify toxicity earlier in the project.