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Record W4390966096 · doi:10.1021/acs.jmedchem.3c02095

Structure-Guided Design of a Highly Potent Partial RXR Agonist with Superior Physicochemical Properties

2024· article· en· W4390966096 on OpenAlexfundno aff
Max Lewandowski, Melania Carmina, Loris Knümann, Minh Sai, Sabine Willems, Till Kasch, Julius Pollinger, Stefan Knapp, Julian A. Marschner, A. Chaikuad, Daniel Merk

Bibliographic record

VenueJournal of Medicinal Chemistry · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRetinoids in leukemia and cellular processes
Canadian institutionsnot available
FundersStructural Genomics ConsortiumDeutsche Forschungsgemeinschaft
KeywordsRetinoid X receptorChemistryRetinoic acidAgonistPartial agonistPharmacologyLigand (biochemistry)Nuclear receptorBiochemistryReceptorTranscription factorBiology

Abstract

fetched live from OpenAlex

Retinoid X receptors (RXRs, NR2B1–3) hold therapeutic potential in oncology, neurodegeneration, and metabolic diseases, but traditional RXR agonists mimicking the natural ligand 9- cis retinoic acid exhibit poor physicochemical properties, pharmacokinetics, and safety profiles. Improved RXR ligands are needed to exploit RXR modulation as a promising therapeutic concept in various indications beyond its current role in second-line cancer treatment. Here, we report the co-crystal structure of RXR in complex with a novel pyrimidine-based ligand and the structure-informed optimization of this scaffold to highly potent and highly soluble RXR agonists. Focused structure–activity relationship elucidation and rigidization resulted in a substantially optimized partial RXR agonist with low nanomolar potency, no cytotoxic activity, and very favorable physicochemical properties highlighting this promising scaffold for the development of next-generation RXR targeting drugs.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.534

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.239
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations9
Published2024
Admission routes1
Has abstractyes

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