Abstract IA007: Preclinical Approaches to Discovery and Development of Novel Combination Therapies
Bibliographic record
Abstract
Drug combinations provide an important tool to address the complexity of tumor biology, and the ability to drive deeper and more durable responses has led to many successful combination regimens in clinical use today. There are more possible novel combination regimens than can be feasibly explored clinically, and preclinical studies have become a common tool to identify and prioritize combinations partners for clinical investigation. Historically, these approaches have focused heavily on identification of synergy, however, a growing body of research is beginning to indicate that synergy may not be a key predictor of clinical utility. To better understand the role of synergy in combinations, we experimentally explored acquired resistance to combination therapies in preclinical models, and counterintuitively, find that combinations which derive activity through synergy rather than additivity can lead to less undesirable outcomes. Specifically, we find that the combinations which rely on synergy are more likely to develop resistance as well as demonstrate greater loss of combination activity upon acquisition of resistance than an equally active additive combination. We propose that instead of relying on synergy for combinations discovery, that a framework based on the 5Rs can be employed which emphasizes a more holistic view of combination rationale, development, and proposed clinical utility. We demonstrate the application of these principles to discovery of novel therapeutic agents in analysis of a large-scale pan-tumor screen, and how these factors can be used to identify novel rational combination partners linked to specific disease segments which drive activity in vivo. As more agents with novel mechanisms enter clinical development, preclinical studies will play an increasingly important role in designing combination regimens which can attack the tumor from multiple angles, leading to better activity, and present a more favorable therapeutic index. Citation Format: Jerome T. Mettetal Preclinical Approaches to Discovery and Development of Novel Combination Therapies. [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Optimizing Therapeutic Efficacy and Tolerability through Cancer Chemistry; 2024 Dec 9-11; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(12_Suppl):Abstract nr IA007
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".