Discovery and Characterization of a Novel Aryl Hydrocarbon Receptor Inhibitor, IK-175, and Its Inhibitory Activity on Tumor Immune Suppression
Bibliographic record
Abstract
Aryl hydrocarbon receptor (AHR) is a transcription factor that regulates the activity of multiple innate and adaptive immune cells subsequent to binding to numerous endogenous and exogenous ligands. For example, AHR is activated by the metabolite kynurenine, which is secreted into the tumor microenvironment by cancer cells leading to broad immunosuppression. Therefore, AHR inhibition provides a novel and ideal approach to stimulate immune-mediated recognition and subsequent eradication of tumor cells. We report here the discovery and characterization of IK-175, a novel, potent and selective AHR antagonist with favorable ADME and pharmacokinetic profiles in preclinical species. IK-175 inhibits AHR activity in experimental systems derived from multiple species including mouse, rat, monkey, and humans. In human primary immune cells, IK-175 decreased AHR target gene expression and anti-inflammatory cytokine release and increased proinflammatory cytokine release. Moreover, IK-175 led to a decrease in suppressive IL17A-, IL-22+ expressing T cells in a Th17 differentiation assay. IK-175 dose dependently blocks ligand-stimulated AHR activation of Cyp1a1 transcription in mouse liver and spleen, demonstrating on-target in vivo activity. IK-175 increases proinflammatory phenotype of the tumor microenvironment in mouse syngeneic tumors and in adjacent tumor-draining lymph nodes. As a monotherapy and combined with an anti-PD-1 antibody, IK-175 demonstrates antitumor activity in syngeneic mouse models of colorectal cancer and melanoma. IK-175 also demonstrates antitumor activity combined with liposomal doxorubicin in syngeneic mouse tumors. These studies provide rationale for targeting AHR in patients with cancer. IK-175 is being evaluated in a phase I clinical trial in patients with advanced solid tumors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".