Novartis will be presenting work done in collaboration with SciCross at the 15th Open Scientific EIP Symposium on Immunogenicity of Biopharmaceuticals in Lisbon later this month. The poster to be presented can be found here. This work describes how drug/pathogen analogy can be considered in the immunogenicity risk assessment of biologics.
SciCross has been awarded a grant from VINNOVA, with support from Medtech4Heath and Swelife, for the development of a novel way to treat ANCA vasculitis (an autoimmune blood vessel disease). The project will be run in collaboration with Toleranzia AB, Lund University’s Kidney Research Lab and Region Skåne. Additional information can be found in this press release (https://www.toleranzia.com/mfn_news/toleranzia-receives-funding-from-vinnova-for-anca-vasculitis-project/).
In collaboration with UCL, Charles University in Prague and the Karolinska Institute we have a poster at ISMB 2022 (Madison US) with the title “Using Metabolomic and Transcriptomic profiles to Predict Development of Anti- IFNβ Antibodies in people with Relapsing Remitting Multiple Sclerosis”.
Abstract
Anti-drug antibodies (ADA) reduces treatment efficacy in multiple sclerosis (MS) patients. Predisposing factors for ADA are poorly characterised, creating an unmet need for biomarkers to predict ADA and subsequent treatment failure. A multiomic approach was used to predict ADA development in interferon-beta (IFNβ) treated MS patients.
Serum was collected from MS patients, through the ABIRISK consortium, before and 3 months after IFNβ treatment. ADA status was determined after 12 months on IFNβ. Supervised machine learning, including logistic regression and sPLS-DA, were used to predict ADA status using metabolomic data (n=82), while transcriptomic profiles were examined in matched patients (n=11).
Before treatment, machine learning models classified patients with an accuracy ≈84%, while lipid metabolism gene counts clustered patients by ADA status, suggesting disrupted lipid metabolism amongst ADA+ patients. Strong correlations between these gene counts and metabolite concentrations were observed. Furthermore, ADA+ patients had distinct responses to IFNβ at month 3, showing differentially regulated metabolites and a down regulation of interferon signalling genes previously associated with response to IFNβ. This could predict ADA early in the treatment course.
Metabolomics and transcriptomics are promising tools for prediction of ADA in IFNβ treated MS patients and could provide novel insight into mechanisms of immunogenicity.
See pubmed for detail, https://pubmed.ncbi.nlm.nih.gov/35349756/.
Together with scientist from UCL, we have written a review article covering AI technologies in the field of chronic inflammatory disease.
Machine Learning Techniques for Personalised Medicine Approaches in Immune-Mediated Chronic Inflammatory Diseases: Applications and Challenges. Peng J, Jury EC, Dönnes P, Ciurtin C. Front Pharmacol. 2021 Sep 30;12:720694. doi: 10.3389/fphar.2021.720694. eCollection 2021.
Check out the latest Covid-19 related publications. SciCross main contributions in these projects has been the design of Sars-CoV-2 peptides used to study T-cell responses.
An evaluation of a FluoroSpot assay as a diagnostic tool to determine SARS-CoV-2 specific T cell responses, PLoS One. 2021 Sep 30;16(9):e0258041, https://pubmed.ncbi.nlm.nih.gov/34591918/
Robust humoral and cellular immune responses and low risk for reinfection at least 8 months following asymptomatic to mild COVID-19, J Intern Med. 2021 Aug 30, https://pubmed.ncbi.nlm.nih.gov/34459525/
In a collaboration with Novartis, we (SciCross) has developed a method for improved HLA class II binding prediction based on MAPPs data. The results till be presented at the Immunogenicity and Bioassay Summit (https://www.immunogenicitysummit.com/), which in 2020 will be hosted as a virtual conference. And yes, the performance accuracy of the methods is great!
New paper published in Lancet Rheumatology on immune cell signatures in JSLE patients, discovered by machine learning techniques AND extensive laboratory work AND clinical knowledge. Really happy to have the privilege to collaborate with this amazing group of people!
https://www.thelancet.com/journals/lanrhe/article/PIIS2665-9913(20)30168-5/fulltext
#Machinelearning #AI #UCL #JSLE
SciCross partners with scientists from Uppsala University, KTH and SciLifeLab in developing Covid-19 treatments in immune suppressed individuals. The project is called “An adaptable therapeutic technology platform to treat SARS-CoV infections in immune suppressed individuals” and has received funding from SciLifeLab and Knut and Alice Wallenberg Foundation (https://kaw.wallenberg.org/). SciCross focus will be on identifying parts in the SARS-CoV-2 genome that can elicit an immune response towards the virus. More details about the project will follow soon.
Great news before Christmas. SciCross is part of a project receiving funding from the Swedish Knowledge Foundation. The project title is “Biomedical AI-driven data analytics” (BIO-AID) and will start in 2020. The project will focus on how AI/Machine learning approaches can be designed and applied in Life Science. Coordinated by the University of Skövde, other project members include Merck, Takarabio, MultiD and TATAA Biocenter.
Read more at: https://www.mynewsdesk.com/se/his/pressreleases/miljonregn-oever-projekt-paa-hoegskolan-i-skoevde-2954981 (Swedish only)