Flexible Fitting of PROTAC Concentration–Response Curves with Changepoint Gaussian Processes

Semenova, Elizaveta; Guerriero, Maria Luisa; Zhang, Bairu; Höck, A.; Hopcroft, Philip; Kadamur, Ganesh; Afzal, Avid M.; Lazic, Stanley E.

doi:10.1177/24725552211028142

Cited by 4 publications

(4 citation statements)

References 16 publications

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“…First, concentrations that are greater than the concentration of maximal degradation (

) must often be excluded from the analysis to make the

model converge [ 31 ]. This fitting of experimental data to a default model that however lacks a sound mechanistic justification, is already questionable in its own right.…”

Section: Discussionmentioning

confidence: 99%

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A Mechanistic Pharmacodynamic Modeling Framework for the Assessment and Optimization of Proteolysis Targeting Chimeras (PROTACs)

Haid

Reichel

2023

Pharmaceutics

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The field of targeted protein degradation is growing exponentially. Yet, there is an unmet need for pharmacokinetic/pharmacodynamic models that provide mechanistic insights, while also being practically useful in a drug discovery setting. Therefore, we have developed a comprehensive modeling framework which can be applied to experimental data from routine projects to: (1) assess PROTACs based on accurate degradation metrics, (2) guide compound optimization of the most critical parameters, and (3) link degradation to downstream pharmacodynamic effects. The presented framework contains a number of first-time features: (1) a mechanistic model to fit the hook effect in the PROTAC concentration-degradation profile, (2) quantification of the role of target occupancy in the PROTAC mechanism of action and (3) deconvolution of the effects of target degradation and target inhibition by PROTACs on the overall pharmacodynamic response. To illustrate applicability and to build confidence, we have employed these three models to analyze exemplary data on various compounds from different projects and targets. The presented framework allows researchers to tailor their experimental work and to arrive at a better understanding of their results, ultimately leading to more successful PROTAC discovery. While the focus here lies on in vitro pharmacology experiments, key implications for in vivo studies are also discussed.

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“…First, concentrations that are greater than the concentration of maximal degradation (

) must often be excluded from the analysis to make the

model converge [ 31 ]. This fitting of experimental data to a default model that however lacks a sound mechanistic justification, is already questionable in its own right.…”

Section: Discussionmentioning

confidence: 99%

“…This fitting of experimental data to a default model that however lacks a sound mechanistic justification, is already questionable in its own right. It introduces an element of subjectivity into data analysis, as it might not always be obvious which data points to exclude [ 31 ].…”

Section: Discussionmentioning

confidence: 99%

A Mechanistic Pharmacodynamic Modeling Framework for the Assessment and Optimization of Proteolysis Targeting Chimeras (PROTACs)

Haid

Reichel

2023

Pharmaceutics

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“…Gaussian Processes have accurately predicted lipophilicity, ADMET properties, and PROTAC potency in prior reports. 6,22,23 Both models are non-parametric, making them ideal ML models for our "Swiss cheese" framework.…”

Section: Resultsmentioning

confidence: 99%

Deconvoluting low yield from weak potency in direct-to-biology workflows with machine learning

McCorkindale,

Filep,

London

et al. 2024

RSC Med. Chem.

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“…Gaussian Processes have accurately predicted lipophilicity, ADMET properties, and PROTAC potency in prior reports. 5,15,16 Both models are non-parametric, making them ideal ML models for our "Swiss Cheese" framework.…”

Section: Lower Chance Of Missing Promising Compoundsmentioning

confidence: 99%

Deconvoluting Low Yield from Weak Potency in Direct-to-Biology Workflows with Machine Learning

McCorkindale,

Filep,

London

et al. 2023

Preprint

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High throughput and rapid biological evaluation of small molecules is an essential factor in drug discovery and development. Direct-to-Biology (D2B), whereby compound purification is foregone, has emerged as a viable technique in time efficient screening, specifically for PROTAC design and biological evaluation. However, one notable limitation is the prerequisite of high yielding reactions to ensure the desired compound is indeed the compound responsible for biological activity. Herein, we report a machine learning based yield-assay deconfounder capable of deconvoluting low yield from low potency to identify false negatives. We validated this approach by identifying promising SARS-CoV-2 main protease inhibitors with nanomolar activity that rivaled potency observed from the standard D2B workflow. Furthermore, we show how our framework can be utilized in a broad, in silico screen with to produce compounds of similar potency as a D2B assay.

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Flexible Fitting of PROTAC Concentration–Response Curves with Changepoint Gaussian Processes

Cited by 4 publications

References 16 publications

A Mechanistic Pharmacodynamic Modeling Framework for the Assessment and Optimization of Proteolysis Targeting Chimeras (PROTACs)

A Mechanistic Pharmacodynamic Modeling Framework for the Assessment and Optimization of Proteolysis Targeting Chimeras (PROTACs)

Deconvoluting low yield from weak potency in direct-to-biology workflows with machine learning

Deconvoluting Low Yield from Weak Potency in Direct-to-Biology Workflows with Machine Learning

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