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26th North American ISSX and JSSX Meeting

Honolul, Hawaii

September 15 – 18, 2024

If you are heading to Honolulu for this years joint ISSX and JSSX meetings then you can hear more about how to ‘Address common metabolism problems in drug discovery with In Silico methods’. Join Chris Khoury and Daniel Barr, PhD to learn about how Optibrium’s software, tailored to DMPK scientists, is changing the landscape of accurate metabolism insight generation.

Come and meet the team to learn about how Optibrium’s range of drug discovery software can increase the speed, efficiency, and productivity of your chemistry discovery programs.

Book a meeting with the team

Abstract:

Authors: Daniel A. Barr, Mario Öeren, Peter A. Hunt, Jonathan D. Tyzack, Tomáš Chrien, Tamsin E. Mansley, and Matthew D. Segall

In silico metabolism prediction can address critical questions to guide lead optimization. Using several case studies, we demonstrate the application of these models to address design challenges involving metabolic (in)stability, the formation of reactive and/or toxic intermediates, and to mitigate the risk of genetic polymorphisms and drug-drug interactions.

In addition, we illustrate how these models can inform the selection of in vitro and in vivo pre-clinical experiments to avoid surprises in late-stage trials. Furthermore, accurate predictions of metabolite profiles early in the discovery process provide essential guidance for drug design. Optibrium’s mechanistic metabolism models cover metabolism by P450, AOX, FMO, UGT, and SULT enzymes^1-5. By combining these models, metabolic pathway analysis proposes the most likely metabolites with greater precision than other methods, assisting in metabolite identification studies and enabling potentially active, reactive, or toxic metabolites to be identified⁶.

References:

Mario Öeren, Peter J. Walton, James Suri, David J. Ponting, Peter A. Hunt and Matthew D. Segall, (2022) J. Med. Chem. 65(20) pp. 1406-1408
Mario Öeren, Sylvia C. Kaempf, David J. Ponting, Peter A. Hunt and Matthew D. Segall, (2023) J. Chem. Inf. Model. 63(11) pp. 3340-3349
Mario Öeren, Peter J. Walton, Peter A. Hunt, David J. Ponting and Matthew D. Segall, (2021) J. Comput.-Aided Mol. Des. 35(4) pp. 541-555
Jonathan D. Tyzack, Peter A. Hunt and Matthew D. Segall, (2016) J. Chem. Inf. Model. 56(1) pp. 2180-2193
Peter A. Hunt, Matthew D. Segall & Jonathan D. Tyzack, (2018) J. Comput.-Aided Mol. Des., 32 pp. 537-546
Mario Öeren, Peter A. Hunt, Charlotte E. Wharrick, Hamed Tabatabaei Ghomi and Matthew D. Segall, (2023) Xenobiotica DOI: 10.1080/00498254.2023.2284251

Daniel Barr, PhD

Senior Application Support Scientist

Daniel Barr, Senior Application Scientist

Chris Khoury

Associate Director of Business Development

7th RSC-BMCS AI in chemistry conference

The Chemical Information & Computer Applications Group (CICAG) and Biological & Medicinal Chemistry Sector (BMCS) of the Royal Society of Chemistry are once again organising a conference to present the current advances in AI and machine learning in Chemistry.