The Emergence of Oligonucleotide Building Blocks in the Multispecific Proximity-Inducing Drug Toolbox of Destruction
Bibliographic record
Abstract
Oligonucleotides are a rapidly emerging class of therapeutics. Their most well-known examples are informational drugs that modify gene expression by binding mRNA. Despite inducing proximity between biological machinery and mRNA when applied to modulating gene expression, oligonucleotides are not typically labeled as "proximity-inducing" in literature. Yet, they have recently been explored as building blocks for multispecific proximity-inducing drugs (MPIDs). MPIDs are unique because they can direct endogenous biological machinery to destroy targeted molecules and cells, in contrast to traditional drugs that inhibit only their functions. The unique mechanism of action of MPIDs has enabled the targeting of previously "undruggable" molecular entities that cannot be effectively inhibited. However, the development of MPIDs must ensure that these molecules will selectively direct a potent, destruction-based mechanism of action toward intended targets over healthy tissues to avoid causing life-threatening toxicities. Oligonucleotides have emerged as promising building blocks for the design of MPIDs because they are sequence-controlled molecules that can be rationally designed to program multispecific binding interactions. In this Review, we examine the emergence of oligonucleotide-containing MPIDs in the proximity induction space, which has been dominated by antibody and small molecule MPID modalities. Moreover, examples of oligonucleotides developed as MPID candidates in immunotherapy and protein degradation are discussed to demonstrate the utility of oligonucleotides in expanding the scope and selectivity of the MPID toolbox. Finally, we discuss the utility of programming "AND" gates into oligonucleotide scaffolds to encode conditional responses that have the potential to be incorporated into MPIDs, which can further enhance their selectivity, thus increasing the scope of this drug category.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".