Teaching pack: Drug Discovery Platforms

CRISPR functional genomics (DepMap), patient-derived organoids and xenografts, ex vivo drug sensitivity testing, structure-based and AI-driven design, degrader platforms, and conjugation chemistry.

• AI compute and model platforms for oncology: AI compute platforms are the GPUs, model libraries, and cloud services that pathology, radiology, and drug-design AI run on.
• AI-driven drug & target discovery: Using machine learning to pick targets, design molecules and antibodies, and predict which ADC will work.
• AlphaFold 3: Predicts the 3D shape of proteins together with DNA, RNA, small molecules and antibodies, the starting point for much modern drug design.
• Bacteriophage-based tumour delivery: Using viruses that infect bacteria, not human cells, as programmable delivery shells for cancer drugs and vaccines.
• BH3 profiling (functional apoptosis testing): A lab test that measures how close a leukaemia cell is to self-destructing, and which survival protein is holding it back, to predict response to venetoclax-type drugs.
• Biobanking and tissue procurement: Freezers full of consented tumour samples with matched clinical data, which every biomarker and drug programme depends on.
• BioEmu (Microsoft): Predicts the many shapes a protein moves between, not just one, thousands of times faster than simulation.
• Boltz-1 / Boltz-2 (MIT, open): Open-source structure models that match AlphaFold 3, with Boltz-2 also predicting how strongly a drug binds.
• Chai-1 / Chai-2: Structure and antibody-design models from Chai Discovery, with Chai-2 reporting high zero-shot antibody hit rates.
• Chemistry42 and Pharma.AI (Insilico): Generative chemistry platform behind the first AI-discovered drug to reach phase 2, plus oncology candidates.

• Radical oncology: what could change the war by 2035 · Ideas already being tested against a control arm (current)
• Radical oncology: what could change the war by 2035 · Living drugs, logic gates, and designed proteins reach decision points (emerging)
• Radical oncology: what could change the war by 2035 · Radiation and radiopharmaceuticals get a second act (emerging)
• Radical oncology: what could change the war by 2035 · Monitoring becomes continuous and selection becomes spatial (emerging)
• Radical oncology: what could change the war by 2035 · Writing to the genome and the epigenome inside a tumour (speculative)
• Radical oncology: what could change the war by 2035 · Treating the soil rather than the seed (speculative)

• ALASCCA (phase 3, n=626): Time to recurrence at 3 years, group A (PIK3CA exon 9/20): 7.7% vs 14.1%, HR 0.49
• ACE-Breast-02 (phase 3, n=441): Progression-free survival: 11.3 months vs 8.2 months, HR 0.64
• Low-dose olanzapine for cancer anorexia (Tata Memorial) (phase 3, n=124): Weight gain greater than 5% at 12 weeks: 60% vs 9%
• Add-Aspirin (phase 3, n=11,000): Ongoing; primary analyses pending.

1. What is drug-to-antibody ratio and why does it matter?
   Answer

   The number of payload molecules per antibody, typically 2-8. Higher DAR delivers more drug per binding event (T-DXd about 8) but increases hydrophobicity and clearance unless hydrophilic linkers are used.
2. What is DepMap for?
   Answer

   The Broad Institute's Cancer Dependency Map: genome-wide CRISPR and drug screens across more than 1,000 cancer cell lines showing which genes each line cannot live without, the source of synthetic-lethal targets like PRMT5/MTAP.