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• W4367367457 abstract "Abstract The value of an integrated mathematical modelling approach for protein degraders which combines the benefits of traditional turnover models and fully mechanistic models is presented. Firstly, we show how exact solutions of the mechanistic models of monovalent and bivalent degraders can provide insight on the role of each system parameter in driving the pharmacological response. We show how on/off binding rates and degradation rates are related to potency and maximal effect of monovalent degraders, and how such relationship can be used to suggest a compound optimization strategy. Even convoluted exact steady state solutions for bivalent degraders provide insight on the type of observations required to ensure the predictive capacity of a mechanistic approach. Specifically for PROTACs, the structure of the exact steady state solution suggests that the total remaining target at steady state, which is easily accessible experimentally, is insufficient to reconstruct the state of the whole system at equilibrium and observations on different species (such as binary/ternary complexes) are necessary. Secondly, global sensitivity analysis of fully mechanistic models for PROTACs suggests that both target and ligase baselines (actually, their ratio) are the major sources of variability in the response of non-cooperative systems, which speaks to the importance of characterizing their distribution in the target patient population. Finally, we propose a pragmatic modelling approach which incorporates the insights generated with fully mechanistic models into simpler turnover models to improve their predictive ability, hence enabling acceleration of drug discovery programs and increased probability of success in the clinic." @default.
• W4367367457 created "2023-04-30" @default.
• W4367367457 creator A5003972753 @default.
• W4367367457 creator A5018957059 @default.
• W4367367457 date "2023-04-29" @default.
• W4367367457 modified "2023-10-17" @default.
• W4367367457 title "An integrated modelling approach for targeted degradation: insights on optimization, data requirements and PKPD predictions from semi- or fully-mechanistic models and exact steady state solutions" @default.
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• W4367367457 doi "https://doi.org/10.1007/s10928-023-09857-9" @default.
• W4367367457 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37120680" @default.
• W4367367457 hasPublicationYear "2023" @default.
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