Can Next-Generation PI3K Inhibitors Unlock the Full Potential of the Class in Patients With B-Cell Lymphoma?
Bibliographic record
Abstract
Although outcomes after first-line therapy for patients with indolent or aggressive non-Hodgkin lymphoma (NHL) are continually improving, relapse is still common. Current treatment options for patients with relapsed or refractory disease have limited efficacy, and various targeted therapies are under investigation to help improve outcomes in this patient population. The phosphatidylinositol 3-kinase (PI3K) pathway was identified as being involved in hematologic malignancies, leading to significant research for potential therapeutic agents. This has led to 3 PI3K inhibitors (idelalisib, copanlisib, and duvelisib) being approved for the treatment of patients with relapsed or refractory follicular lymphoma who have received at least 2 prior systemic therapies, with reported response rates of 40% to 59%. With potential class-specific and PI3K isoform-related toxicities that may limit clinical utility, the safety of the approved PI3K inhibitors has been carefully evaluated to weigh the risk/benefit ratio of therapy. Currently, there are no approved PI3K inhibitors for patients with aggressive NHL. A number of newer PI3K inhibitors are in clinical development for the treatment of relapsed or refractory NHL, aiming to improve treatment benefit for patients. We discuss a number of attributes that are important to increase the therapeutic potential of newer PI3K inhibitors. More promising results may come from combination trials with these newer PI3K inhibitors, developed to limit toxicities (including long-term adverse events), and other antitumor agents.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".