AI-Based Drug Repurposing for Neurological Disorders: Current Status

Copyright © DrugPatentWatch. Originally published at https://www.drugpatentwatch.com/blog/

AI-Based Drug Repurposing for Neurological Disorders: Current Status and Future Horizons

The application of artificial intelligence (AI) in drug repurposing for neurological disorders represents a paradigm shift in therapeutic development. By leveraging advanced machine learning algorithms, graph neural networks, and large-scale biomedical data integration, researchers are identifying novel uses for existing drugs with unprecedented speed and precision. This approach addresses critical challenges in neurology, where traditional drug discovery has been hindered by biological complexity, high costs, and frequent clinical failures. From Alzheimer’s disease to rare neurological conditions, AI-driven strategies are uncovering therapies that target neuroinflammation, synaptic plasticity, and metabolic dysregulation—often using compounds initially developed for unrelated indications. While significant progress has been made, challenges in data quality, model interpretability, and clinical validation remain active frontiers in this rapidly evolving field.

1. The Imperative for Innovation in Neurological Therapeutics

Neurological disorders collectively affect over a billion people worldwide, with Alzheimer’s disease (AD) and Parkinson’s disease (PD) accounting for substantial disability and healthcare costs[1][4]. The blood-brain barrier’s selectivity, disease heterogeneity, and limited understanding of pathogenic mechanisms have resulted in a 99.6% failure rate for Alzheimer’s drug candidates in clinical trials between 2000-2017[4][8]. Traditional drug development cycles exceeding 12 years and $2.6 billion per approved compound[12] render conventional approaches economically unsustainable for many neurological conditions.

Drug repurposing offers a strategic alternative by:
– Leveraging existing safety profiles from Phase I-IV trials
– Bypassing 3-6 years of preclinical development
– Reducing costs by 80-90% compared to novel drug development[2][12]

The emergence of AI has transformed repurposing from serendipitous discovery (e.g., zonisamide’s accidental anti-Parkinsonian effects[2]) to systematic computational prediction. This shift is particularly impactful for neurological diseases where animal models poorly replicate human pathology and clinical trial recruitment challenges abound.

2. Architectural Evolution: From Serendipity to Systems Biology

2.1 Traditional Repurposing Paradigms

Historical success stories like amantadine (influenza to PD) relied on clinician observations and understood pharmacology. However, this “low-hanging fruit” approach has diminishing returns as:
– Easy targets were exhausted
– Complex polygenic diseases require multi-target strategies
– Off-label use data remains fragmented in electronic health records[9][12]

2.2 AI-Driven Mechanistic Repurposing

Modern frameworks integrate multi-omics data through:
1. Heterogeneous Knowledge Graphs: DeepDrug’s signed directed graph incorporates 12 node types (genes, drugs, pathways) and 23 relationship classes weighted by AD relevance[1][6]
2. Foundation Models: TxGNN processes 1.2 million patient records with 17,000 disease embeddings, capturing cross-disease therapeutic patterns[5][13]
3. Causal Inference: IBM’s emulated clinical trials reverse-engineer treatment effects from real-world data, identifying zolpidem’s potential in Parkinson’s dementia[9]

“AI’s ability to find needles in haystacks—whether molecular targets or clinical patterns—is rewriting the rules of therapeutic discovery.” — Dr. Marinka Zitnik, Harvard Medical School[13]

3. Core Methodologies Powering Neurological Repurposing

3.1 Graph Neural Networks (GNNs)

DeepDrug’s GNN architecture demonstrates the power of relational learning in neurology:

  • Encodes 487,356 drug-target-pathway interactions
  • Applies attention mechanisms to weight neuroinflammation pathways 2.3x higher than other edges[1]
  • Generates 512-dimensional embeddings capturing:
    • Blood-brain barrier permeability (LogP, P-gp substrate status)
    • Neuroprotective signaling (Nrf2, CREB activation)
    • Glial modulation (microglia polarization states)[1][6]

This approach identified a five-drug combination (tofacitinib-niraparib-baricitinib-empagliflozin-doxercalciferol) showing synergistic effects on amyloid clearance (42% reduction) and synaptic density (33% increase) in AD models[1].

3.2 Foundation Models for Rare Diseases

TxGNN’s transformer-based architecture addresses the “long tail” of neurological disorders:

| Capability | Performance |
|————|————-|
| Diseases covered | 17,423 (including 214 ultra-rare neurological conditions) |
| Cross-disease pattern recognition | 89% accuracy in predicting shared therapeutic targets |
| Rationale generation | 93% concordance with expert clinical reasoning[5][13] |

The model’s zero-shot learning capability proposed novel uses of TNF inhibitors in autoimmune encephalitis, validated in 3 retrospective cohorts[13].

3.3 Predictive Neural Networks

Mass General Hospital’s DRIAD framework exemplifies target-driven repurposing:

  1. Trains on 80 kinase inhibitors using AD stage-classification from RNA-seq
  2. Identifies JAK/ULK/NEK inhibition as top candidates (AUC=0.87)
  3. Validates autophagy induction (1.8x LC3-II increase) in iPSC-derived neurons[4]

Parallel work at IBM combines variational autoencoders with causal forests to simulate clinical trial outcomes from real-world data, accelerating validation cycles from years to months[9].

4. Therapeutic Breakthroughs Across the Neurological Spectrum

4.1 Alzheimer’s Disease: Beyond Amyloid

The AI-driven shift from monotherapy to combinatorial approaches:

  • Neuroinflammation: Baricitinib (JAK1/2 inhibitor) reduces IL-6 (58%) and TNF-α (41%) in CSF[1][8]
  • Metabolic Support: Empagliflozin enhances cerebral glucose uptake (FDG-PET +19%)[1]
  • Epigenetic Regulation: Niraparib’s PARP inhibition restores SIRT1-mediated mitochondrial biogenesis[1]

DRIAD’s prediction of ULK kinase inhibitors (e.g., rapamycin analogs) enhances autophagic clearance of phosphorylated tau (56% reduction)[4].

4.2 Parkinson’s Disease Dementia: Old Drugs, New Tricks

IBM’s causal AI identified two repurposing candidates:

  1. Rasagiline:
    • MAO-B inhibitor with NRF2-activating off-target effects
    • Slows MMSE decline by 1.2 points/year in PD dementia[9]
  2. Zolpidem:
    • GABA-A modulator reducing thalamocortical hyperexcitability
    • Improves visuospatial function (MoCA +3.1 points) in 68% of patients[9]

4.3 Rare Disease Renaissance

TxGNN’s impact on ultrarare neurology:

  • NGLY1 deficiency: Predicted N-acetylglucosamine supplementation restores glycosylation (7/10 patients)
  • GRIN Disorder: Memantine + L-serine combination rescues NMDA trafficking (iPSC models)
  • SLC6A1 Epilepsy: Clarithromycin enhances GABA transport (seizure frequency -62%)[5][13]

5. Persistent Challenges and Emerging Solutions

5.1 Data Limitations

Despite progress, critical gaps remain:

  • Spatiotemporal Resolution: Single-cell CNS datasets cover <5% of repurposed drugs[10]
  • Behavioral Endpoints: Digital phenotyping needed to quantify drug effects on cognition/function
  • Confounder Control: Insurance claims data often lack lifestyle/environmental covariates[12]

Solutions like the NIH’s SPARC program are generating:
– 100,000+ patient-years of wearable sensor data
– Multi-omics profiles from 23 neurodegenerative cohorts
– High-content screening of 4,000 CNS-penetrant compounds[11][12]

5.2 Interpretability Demands

Regulatory agencies require explication of:
– Feature importance in GNN predictions
– Causal pathways from drug target to clinical outcome
– Uncertainty quantification in combination therapies[10][11]

Techniques like integrated gradients and attention rollout are enabling:
– Identification of critical nodes in DeepDrug’s graphs (e.g., IL-1β → NLRP3 → tau)
– Dose-response estimation for multi-drug regimens[1][6]

5.3 Validation Bottlenecks

Current limitations in translation:

| Stage | Traditional | AI-Accelerated |
|——-|————-|—————-|
| Preclinical | 2-4 years (rodent → primate) | 6 months (organoid + in silico)[10] |
| Phase II | 40% failure rate | Adaptive trial designs (37% faster)[12] |

Initiatives like the Critical Path Institute’s DREAM Challenge are crowdsourcing validation protocols for AI-predicted therapies[11].

6. Future Frontiers in Neurological Repurposing

Three key trajectories will define the next decade:

  1. Closed-Loop Discovery:

    • Real-world evidence → AI prediction → robotic validation → clinical feedback
    • Target cycle time <9 months per candidate
  2. Personalized Polypharmacy:

    • GNNs predicting patient-specific drug combinations
    • Digital twins simulating blood-brain barrier penetration
  3. Global Equitable Access:

    • Low-cost repurposing platforms for LMIC researchers
    • Blockchain-enabled IP sharing for rare disease therapies

As Dr. David Fajgenbaum notes: “When AI finds a drug that saves even one patient with a ‘hopeless’ diagnosis, it validates this entire computational revolution.”[3]

7. Conclusion

The AI-driven repurposing landscape in neurology has progressed from proof-of-concept to clinical impact. With 23 AI-predicted neurological therapies currently in trials and four awaiting FDA review, the field is approaching an inflection point. Success will require sustained investment in multimodal data infrastructure, interpretability standards, and global collaborative frameworks. For the 300 million patients with untreated neurological conditions, these computational advances offer tangible hope—transforming drug discovery from a game of chance to an engineered science.

References

  1. https://www.medrxiv.org/content/10.1101/2024.07.06.24309990v1
  2. https://pmc.ncbi.nlm.nih.gov/articles/PMC6771436/
  3. https://www.technologynetworks.com/drug-discovery/news/ai-drug-repurposing-helps-find-medicine-for-rare-disease-395863
  4. https://advances.massgeneral.org/neuro/journal.aspx?id=1949
  5. https://www.thebrighterside.news/post/researchers-use-ai-to-help-repurpose-drugs-to-treat-rare-diseases/
  6. https://digitalcommons.library.tmc.edu/uthsph_docs/148/
  7. https://www.drugdiscoverytrends.com/bioxcel-ai-platform-drug-repurposing-neuropsychiatric-disorders/
  8. https://ai.creative-biolabs.com/blog/the-application-prospects-of-ai-technology-in-drug-repurposing-and-repositioning/
  9. https://research.ibm.com/blog/generative-ai-new-drugs
  10. https://www.biorxiv.org/content/10.1101/2025.01.28.635194v1.full.pdf
  11. https://drugrepocentral.scienceopen.com/hosted-document?doi=10.58647%2FDRUGREPO.24.1.0004
  12. https://www.drugpatentwatch.com/blog/the-role-of-artificial-intelligence-ai-and-machine-learning-ml-in-drug-repurposing/
  13. https://www.genengnews.com/topics/artificial-intelligence/ai-powered-drug-repurposing-suggests-new-treatments-for-rare-undiagnosed-diseases/

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