Drug Repurposing and Combination Therapy
Repurposing and combination therapy sit between discovery and translation. They start with assets, mechanisms, signatures, or drug pairs that already have some evidence, then ask whether a new indication or regimen is biologically plausible. AI adds value when it narrows a search space and clarifies mechanism, not when it turns a computational match into a therapeutic claim.
Use this chapter to:
- Find new uses or combinations for existing drugs by linking signatures, networks, targets, phenotypes, and clinical context.
- Repurposing is constrained by mechanism, dose, exposure, intellectual property, regulatory pathway, and evidence in the new indication.
Summary: Find new uses or combinations for existing drugs by linking signatures, networks, targets, phenotypes, and clinical context. Signature matching, knowledge graphs, and synergy screens support hypothesis generation; efficacy still needs disease-specific validation.
Key point: Repurposing is constrained by mechanism, dose, exposure, intellectual property, regulatory pathway, and evidence in the new indication. Open question: whether computational leads remain plausible after dose, exposure, mechanism, and new-indication evidence are checked.
Bottom line: Repurposing connects chemical biology, target discovery, real-world evidence, clinical trials, oncology, infectious disease, and translational failure analysis.
What is this field trying to solve? Find new uses or combinations for existing drugs by linking signatures, networks, targets, phenotypes, and clinical context.
What is the core idea? Repurposing is constrained by mechanism, dose, exposure, intellectual property, regulatory pathway, and evidence in the new indication.
What is the current state of the field? Signature matching, knowledge graphs, and synergy screens support hypothesis generation; efficacy still needs disease-specific validation.
What do we know, and what remains open? Known reference points include Connectivity Map, L1000, LINCS, DrugBank, ChEMBL, Open Targets, network medicine resources, synergy screens, COVID-19 repurposing studies, and trial registries. What remains open is whether computational leads remain plausible after dose, exposure, mechanism, and new-indication evidence are checked.
Why does this matter? Repurposing connects chemical biology, target discovery, real-world evidence, clinical trials, oncology, infectious disease, and translational failure analysis.
Introduction
Drug repurposing starts from existing pharmacology, known chemistry, prior safety data, or clinical experience. That starting point can reduce uncertainty, but efficacy still has to be shown in the new context. Combination therapy adds dose, schedule, toxicity, tissue context, resistance, and mechanism constraints.
The scientific discipline is to keep the claim small until validation expands it. A transcriptional match is a hypothesis. A graph path is a hypothesis. A synergy score is a hypothesis. A prior label is context, not proof for a new indication.
What is demonstrated?
Transcriptional Signature Matching
The original Connectivity Map showed that gene-expression signatures could connect small molecules, genes, and disease states (Lamb et al., 2006). The L1000 expansion made perturbational signature matching a central repurposing method by profiling transcriptional responses to many perturbagens at scale (Subramanian et al., 2017). The logic is simple: if a disease state has a gene-expression signature, a compound that reverses or phenocopies that signature may be mechanistically relevant.
The demonstrated value is hypothesis generation. Signature matching can suggest mechanisms, compounds, and follow-up experiments. The main constraints are cell type, dose, exposure time, assay platform, disease model, and whether the signature is causal or downstream noise. Signature reversal is not efficacy.
Knowledge Graph and Network Repurposing
Biomedical knowledge graphs and networks connect drugs, targets, diseases, pathways, phenotypes, and evidence sources. Hetionet, PrimeKG, and Open Targets are covered in the knowledge-graph chapter because they form the evidence layer for repurposing workflows. Drug-repurposing reviews emphasise that successful repositioning depends on mechanism, safety, intellectual-property position, clinical feasibility, and evidence quality, not only computational ranking (Pushpakom et al., 2019).
Hetionet integrated biomedical entities and edge types to prioritise repurposing hypotheses (Himmelstein et al., 2017). PrimeKG later expanded disease-rooted precision-medicine graph coverage across diseases, drugs, proteins, pathways, anatomy, and phenotypes (Chandak et al., 2023). Network-proximity approaches add another layer by asking whether drug targets sit near disease proteins in the interactome (Guney et al., 2016). Cheng and colleagues paired network predictions with pharmacoepidemiology and in-vitro follow-up, which is closer to the evidence pattern required for repurposing decisions (Cheng et al., 2018).
Graph methods help identify neighbourhoods, missing edges, and convergent evidence. TxGNN is a peer-reviewed example of graph-based zero-shot drug repurposing across rare and common diseases (Huang et al., 2024). Its evidence supports hypothesis generation and prioritisation, not clinical efficacy. Graph methods also make weak evidence look strong if all edge types are flattened. A co-mention edge, a genetic association, a pathway membership, a trial mention, and a causal perturbation should never receive the same interpretation.
Combination Synergy Prediction
Combination therapy requires more than ranking single agents. Synergy depends on dose, sequence, timing, cell state, genotype, tissue context, and toxicity. DeepSynergy predicted anti-cancer drug synergy using deep learning on drug and cell-line features (Preuer et al., 2018). SynergyFinder 2.0 provides visual analytics for multi-drug combination synergies (Ianevski et al., 2020).
Network-based combination prediction can also ask whether drug targets cover complementary neighborhoods of a disease module (Cheng et al., 2019). That is mechanistic triage, not regimen evidence. A computationally complementary pair still needs matrix screening, exposure feasibility, resistance logic, and toxicity assessment.
These tools support screening design and hypothesis prioritisation. They do not establish a usable regimen. A combination that works in a cell line may fail because the exposure is not achievable, toxicity overlaps, schedule is incompatible, or the relevant tissue state is absent.
COVID-19 as a Repurposing Stress Test
COVID-19 showed both the speed and limits of repurposing. A SARS-CoV-2 protein-interaction map identified potential drug targets and repurposing candidates early in the pandemic (Gordon et al., 2020). Large randomized trials then filtered candidate therapies. The WHO Solidarity interim results found no clear mortality benefit for several repurposed antivirals in hospitalized patients (WHO Solidarity Trial Consortium, 2021), while the RECOVERY trial established a mortality benefit for dexamethasone in hospitalized patients requiring oxygen or ventilation (RECOVERY Collaborative Group, 2021).
The lesson is not that repurposing failed. The lesson is that fast computational and mechanistic hypotheses need fast, rigorous clinical filtering. Repurposing speed must be paired with evidence speed.
Industrial Platform Context
Recursion and Insilico Medicine illustrate industrial platform approaches that combine high-throughput biology, computational modelling, and therapeutic development. In this chapter they matter as platform context, not as proof of any particular repurposing or combination claim.
For diligence, the useful questions are specific: which asset, which indication, which mechanism, which evidence, which assay, which clinical stage, and which decision was changed by the computational method?
What is theoretical?
Mechanism-First Repurposing
The strongest repurposing logic starts with mechanism. If a compound has known target engagement, known pharmacology, known human exposure, and a disease biology rationale, AI can help prioritise context. The remaining challenge is proving that the mechanism matters in the new disease and that the dose required is tolerable.
Multi-Omic Combination Design
Combination design may improve when transcriptomics, proteomics, CRISPR screens, single-cell perturbation data, pathway models, and pharmacology are considered together. The theoretical value is high because combinations often aim to block compensation, resistance, or parallel pathways.
The hard part is validation. A multi-omic rationale can still fail if the drug exposure is wrong, the tissue context is absent, or toxicity dominates benefit.
Portfolio-Level Repurposing Intelligence
Companies with large internal datasets can search across failed assets, negative assays, historical screens, safety packages, and disease programs. That institutional memory may be more valuable than public-data ranking. The theoretical advantage comes from knowing what failed, not only what was published.
What is beyond current capability?
Efficacy from Computational Match Alone
No computational match establishes therapeutic efficacy. Signature reversal, graph proximity, structural plausibility, and synergy scores remain upstream evidence until tested in the right biological and clinical context.
General Combination Prediction Across Diseases
Combination effects do not transfer cleanly across diseases, genotypes, tissues, immune contexts, or dose schedules. General combination prediction remains beyond current capabilities for decision-grade therapeutic selection.
Repurposing Without Commercial and Regulatory Reality
A repurposing hypothesis may be scientifically plausible and commercially unusable. Patent position, formulation, dose, route, manufacturing, exclusivity, safety monitoring, and trial feasibility determine whether a program can proceed.
What would make this more promising?
Repurposing and combination work become more promising when a computational lead becomes a doseable, testable therapeutic hypothesis in the new context.
| Claim | Evidence that raises or lowers confidence |
|---|---|
| “This drug is plausible for a new indication” | Mechanism, target engagement, disease-relevant assay response, achievable exposure, and safety margin support the new use |
| “This signature match is meaningful” | Cell type, dose, time point, disease model, and orthogonal assay show that the signature tracks causal biology |
| “This graph path is credible” | Edge provenance, evidence type, directionality, genetics, perturbation, or prior clinical evidence are readable separately |
| “This combination is usable” | Matrix screening, dose schedule, sequence, exposure feasibility, resistance logic, and toxicity assessment support the regimen |
| “This program should advance” | Prospective validation, trial feasibility, commercial path, and regulatory route are explicit |
Known human exposure lowers some uncertainty. It does not establish efficacy, dose, schedule, or benefit-risk in the new setting.
What should researchers, biotech teams, funders, and program leaders do with this?
Write the claim in one sentence: new indication, old asset, mechanism, population, dose logic, and evidence source.
Separate evidence types. Do not merge signature reversal, graph proximity, prior human exposure, and trial evidence into a single confidence score without showing the components.
Run orthogonal validation before advancing a program. Use disease-relevant assays, dose-response work, target engagement, combination matrices, and toxicity checks.
For combinations, document dose, schedule, order, tissue context, and toxicity. A synergy score without those details is not a regimen.
Treat company platform claims as leads for due diligence. Ask for asset-level evidence, not platform-level language.