NAMs for genotoxicity testing, Foundation models transforming biology, EMA consultation, de novo antibodies with generative AI and more
News on non-animal methods
FEBRUARY 24 - 28, 2025NEWS, REPORTS & POSITION STATEMENTS
1. Advancing alternative methods for genotoxicity testing
A new Opinion of the EURL ECVAM Scientific Advisory Committee (ESAC) on the scientific validity of the Reconstructed human Skin (RS) Comet and Micronucleus (MN) in vitro test methods was just released. The committee reviews the appropriateness of study design and management, the quality of results obtained and the plausibility of the conclusions drawn.
The ESAC concludes that the evidence provided on the RS Comet assay is sufficient and adequate to support its scientific validity for use with chemicals that test positive in the Ames or mammalian cell gene mutation tests, including chemicals that require bioactivation. These assays are proposed as a follow up to the traditional genotoxicity in vitro test battery without the need for confirmatory animal tests.
2. How complex in vitro models are addressing the challenges of predicting DILI
Predicting which drugs might have the potential to cause drug-induced liver injury (DILI) is highly complex and the current methods, 2D cell-based models and animal tests, are not sensitive enough to prevent some costly failures in clinical trials or to avoid all patient safety concerns for DILI post-market.
On 24 April 2023 the Alliance for Human Relevant Science hosted a workshop at the Royal Society, London entitled Drug-Induced Liver Injury (DILI): Can Human-Focused Testing Improve Clinical Translation ? A perspective article, written by the participants, builds on those discussions to provide a ‘state of play’ on liver complex in vitro models with recommendations for how to encourage their greater uptake by the pharmaceutical industry.
3. OASIS, a new European research programme to optimise antibody-drug conjugates
The OASIS project, for Optimal methods to characterize ADC resistance in Solid tumours and Identify clinically useful biomarkers, was awarded a €9.9 million grant from the European Commission for 5 years to focus on advancing antibody-drug conjugates (ADCs), a new class of cancer therapies. These innovative treatments target cancer cells with precision, reducing harm to healthy tissues.
Led by Dr Barbara Pistilli at Gustave Roussy, the project aims to develop tools to help clinicians select the most suitable ADC for each patient, taking into account their clinical characteristics and the biology of their tumour. To achieve this, the project relies on multicentric clinical trials integrating different cutting-edge technologies, such as new nuclear medicine techniques, digital pathology, liquid biopsies, and the generation of organoids derived from the tumours of the patients included.
INTERVIEWS, NOMINATIONS & AWARDS
4. AI, MPS and the end of animal testing ?
From 11th to 13th February the WORD+ 25 on organoids and OOC took place in Cambridge, UK. Prof Thomas Hartung delivered a talk entitled “Evidence-Based Animal Alternatives for Drug Discovery”, where he explored the urgent need for more human-relevant models in pharmaceutical development.
The journal Technology Networks caught up with Prof. Hartung after the event to discuss his insights on the future of drug testing, the role of AI in toxicology and the regulatory and ethical challenges in moving away from animal models.
5. Orakl Oncology has been awarded “Le Point” Innovation Award in Healthcare
Dr Fanny Jaulin, CEO and co-founder at Orakl Oncology, has been awarded “Le Point” Innovation Award in Healthcare in the ‘med tech’ category. Her work combines cell biology, AI and engineering to reproduce the response to drugs in the laboratory.
“This recognition is a testament to the hard work, passion, and commitment of my cofounders Diane-Laure and Gustave and all Oraklees who push the boundaries of innovation every day to revolutionize oncology drug development.” — she says.
Read the article in “Le Point” (FR)
6. Foundation model series : transforming biology with Zelda Mariet from Bioptimus
Zelda Mariet, Co-Founder and Principal Research Scientist at Bioptimus was recently part of a series of conversations on the vast possibilities and diverse applications of foundation models, AI neural networks trained on massive unlabeled datasets.
Zelda shares insights into Bioptimus’ work and why it’s so critical in this field. She breaks down the three core components involved in building these models and explains what sets their histopathology model apart from the many others being published today. “Bioptimus is building foundation models for biology. Foundation models are essentially machine learning models that take an extremely long time to train [and] are trained over an incredible amount of data.” — Zelda Mariet
TOOLS, PLATFORMS, CALLS
7. EMA consultation on the qualification of non-mutagenic impurities
In December 2024, the EMA published a reflection paper that considers the safety evaluation of non-mutagenic impurities (NMI) in chemically synthesised pharmaceuticals, intended to establish a framework to facilitate future discussions among stakeholders.The reflection paper discusses different non-animal approaches, which may provide more compound-specific information than animal studies.
On 30 January 2025, EMA launched a public consultation on the draft reflection paper, with the aim of gathering input and feedback from stakeholders. Deadline for comments is April 30th 2025.
INDUSTRY, BIOTECH & PARTNERSHIPS
8. Massive foundation model for now available via NVIDIA BioNeMo
Scientists everywhere can now access Evo 2, a powerful new foundation model that understands the genetic code for all domains of life. Unveiled today as the largest publicly available AI model for genomic data, it was built on the NVIDIA DGX Cloud platform in a collaboration led by nonprofit biomedical research organization Arc Institute and Stanford University.
Trained on an enormous dataset of nearly 9 trillion nucleotides — the building blocks of DNA and RNA — Evo 2 can be applied to biomolecular research applications including predicting the form and function of proteins based on their genetic sequence, identifying novel molecules for healthcare and industrial applications, and evaluating how gene mutations affect their function.
9. Insilico Medicine reports benchmarks for its AI-designed therapeutics
Insilico Medicine has released benchmarks detailing the timelines and development metrics for 22 preclinical candidate (PCC) nominations achieved between 2021 and 2024. These self-reported metrics offer a view of the company’s drug discovery workflow, demonstrating an average 13-month timeline to PCC nomination, a reduction from the traditional 2.5- to 4‑year process.
The company attributes these timelines to its AI-driven platform, which integrates deep learning models for molecular design, target selection, and preclinical validation. By publishing detailed data on development timelines and molecular synthesis rates, Insilico seeks to initiate broader industry efforts toward establishing clearer validation standards for AI-designed therapeutics, addressing a gap in standardized benchmarking within AI-driven drug discovery.
10. Advancing immunology drug discovery with human 3D tissue models
Developing effective therapies for immunological diseases remains a major challenge due to the complexity of the immune system and limitations in traditional preclinical models. Many promising drug candidates fail in clinical trials due to a lack of predictive accuracy in existing in vitro and in vivo models.
The biotech Mimetas released a whitepaper that examines how advanced 3D tissue models can improve translational relevance in immunology drug discovery, addressing key challenges in diseases such as inflammatory bowel disease (IBD) and chronic kidney disease (CKD).
SCIENTIFIC DISCOVERIES & PROTOCOLS
11. Create de novo antibodies with generative AI
Traditional antibody discovery is time and resource intensive, screening large immune or synthetic libraries. Offering little control over the output sequences, that can result in lead candidates with suboptimal binding or poor developability attributes.
A new technical poster from absci demonstrates the potential of zero-shot generative AI to greatly increase the speed, quality, and controllability of antibody design.
Read more and download the poster
