Modulating the Binding Kinetics of Bruton’s Tyrosine Kinase Inhibitors through Transition-State Effects
Bibliographic record
Abstract
Optimization exercises strive toward increasing the efficacy and selectivity of small molecules toward the target of interest while simultaneously phasing out design elements that lead to off-target interactions. Given the nonequilibrium nature of biological systems, greater reliance should be placed on engineering kinetic selectivity in addition to equilibrium thermodynamic selectivity; however, the rational design of kinetic selectivity is a challenging endeavor. This study presents a systematic knowledge-based approach to the design of inhibitors that vary in their binding kinetics for Bruton’s tyrosine kinase (BTK), a target for treating B-cell malignancies and autoimmune diseases. A detailed kinetic assessment was performed on existing BTK inhibitors, which, together with structural studies, provided critical insights into BTK-inhibitor interactions that control the kinetics of enzyme inhibition. Subsequently, a series of pyrazolopyrimidines was designed with the objective of modifying interactions between the inhibitor and the regulatory (R) spine in the kinase back pocket, which were hypothesized to modulate the stability of the transition state on the binding reaction coordinate. This resulted in the development of BTK inhibitors with extended residence time in which the variation in k on and k off was uncoupled from equilibrium thermodynamic affinity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".