Targeted Nanomedicine
Targeted Nanomedicine — Nanoparticle Delivery · Nanomedicine
Deliver drugs only where they're needed — design nanoparticles that navigate the body and accumulate in target tissue, sparing the rest.
📋 The problem
Most drugs flood the whole body; nanoparticles can be engineered to navigate it and accumulate where needed — tumors, specific organs — sparing healthy tissue. Designing particles for targeted delivery is a grand pharmacological challenge.
🧗 Why it's a grand challenge
Biodistribution depends on size/charge/ligand and a protein corona we barely model; the EPR effect is variable; in-vivo data are scarce and noisy.
🧮 Governing model
∂C/∂t = ∇·(D∇C) − v·∇C − k_clear·C + k_on·R·C; uptake = ∫_target C dVWhole-body nanoparticle pharmacokinetics: convection–diffusion–reaction transport with size/charge/ligand-dependent clearance and receptor binding; tumor uptake via the EPR effect and active targeting.
Current best: Ligand-targeted nanoparticles + PBPK delivery models
🧭 Possible approaches
- PBPK + ML surrogates of biodistribution
- Generative design of particle physicochemistry / ligands
- Active learning over in-vivo screens
🎯 Build the benchmark
Predict the percent injected dose reaching the target tissue from particle design (within ~1% absolute); inverse-design for maximal targeting.
Metric: target_dose_pct — % injected dose reaching target (higher better)
Datasets to start from: Nanoparticle biodistribution corpus, PBPK organ time-course set, Ligand-receptor binding panel
🤖 Build an AI agent to solve it
An agent that designs nanoparticles for a target organ/tumor and predicts delivery efficiency and off-target load.
Once a benchmark exists, an AI4Science agent can iterate solutions against it — every verified solution earns PWM.
This is a frontier framing page — an open problem, not yet benchmarked or verified, unlike PWM's mature computational-imaging benchmarks.