Module 3

Antibody-Drug Conjugates

ADCs combine the target-selectivity of monoclonal antibodies with the potency of small-molecule cytotoxins. Kadcyla (T-DM1, 2013) was the breakthrough; Enhertu (T-DXd, 2019) redefined HER2-low breast cancer. This module covers linker chemistry, DAR optimisation, payload classes, and the bystander effect.

1. ADC Architecture

Three components: a tumour-antigen-targeting monoclonal antibody (trastuzumab for HER2, brentuximab for CD30, inotuzumab for CD22), a cleavable or non-cleavable linker, and a cytotoxic payload conjugated at specified residues (cysteines, lysines, or engineered tags). Upon antibody binding and internalisation, lysosomal proteolysis or pH-dependent cleavage releases the payload inside the tumour cell.

2. Linker Chemistry

Cleavable linkers (valine-citrulline for cathepsin B; hydrazones for lysosomal pH; disulfides for glutathione reduction) release the payload after antibody internalisation. Non-cleavable linkers (T-DM1 SMCC) require full antibody degradation, releasing payload-linker adducts. Next-generation self-immolative linkers (e.g., T-DXd GGFG) cleave in tumour extracellular environment to produce bystander-permeable payload that can kill neighbouring cells even if they lack the antigen.

3. DAR Optimisation

DAR = drug-antibody ratio. Classical stochastic conjugation yields a DAR distribution ~0–8 with mean ~3.5 (Kadcyla). Higher DAR improves per-molecule potency but increases hydrophobic aggregation, alters pharmacokinetics, and can increase off-target toxicity. Site-specific conjugation (ThioMab, GlycoCon, enzymatic SMARTag) enables homogeneous high-DAR products like Enhertu (DAR 8).

Simulation: DAR Balance

Python

script.py38 lines

import numpy as np, matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt

# DAR (drug-antibody ratio) vs efficacy / toxicity / aggregation
dar = np.arange(0, 10)
efficacy = 1 - np.exp(-dar/3)
aggregation = (dar/8)**2.5
# Therapeutic index = efficacy / toxicity proxy
tox = 0.3 + 0.1*dar
TI = efficacy / tox

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 5), facecolor='#0a0a1a')
for ax in (ax1, ax2):
    ax.set_facecolor('#111827'); ax.tick_params(colors='#cbd5e1')
    for s in ax.spines.values(): s.set_color('#334155')
    ax.grid(True, color='#334155', alpha=0.3)

ax1.plot(dar, efficacy, color='#2dd4bf', lw=2.6, marker='o', label='Efficacy')
ax1.plot(dar, aggregation, color='#dc2626', lw=2.6, marker='s', label='Aggregation propensity')
ax1.plot(dar, tox, color='#f87171', lw=2.6, marker='^', label='Off-target tox')
ax1.set_xlabel('DAR', color='#cbd5e1')
ax1.set_ylabel('Relative scale', color='#cbd5e1')
ax1.set_title('DAR balance: efficacy vs liabilities',
              color='#5eead4', fontweight='bold')
ax1.legend(facecolor='#1e293b', edgecolor='#334155', labelcolor='#cbd5e1')

ax2.bar(dar, TI, color='#fbbf24', edgecolor='#5eead4')
ax2.axvline(dar[np.argmax(TI)], color='#2dd4bf', ls='--',
            label=f'Optimal DAR ~ {dar[np.argmax(TI)]}')
ax2.set_xlabel('DAR', color='#cbd5e1')
ax2.set_ylabel('Therapeutic index', color='#cbd5e1')
ax2.set_title('Optimal DAR usually 3-4', color='#5eead4', fontweight='bold')
ax2.legend(facecolor='#1e293b', edgecolor='#334155', labelcolor='#cbd5e1')

plt.tight_layout()
plt.savefig('output.png', dpi=120, bbox_inches='tight', facecolor='#0a0a1a')
print('Kadcyla DAR ~3.5; Enhertu DAR ~8 (site-specific homogeneous conjugation)')

Click Run to execute the Python code

Code will be executed with Python 3 on the server

4. Payload Classes

Auristatins (MMAE, MMAF): tubulin inhibitors; Adcetris, Padcev.
Maytansinoids (DM1, DM4): tubulin inhibitors; Kadcyla, Elahere.
Calicheamicins: DNA double-strand break; Mylotarg, Besponsa.
Topo-I inhibitors (exatecan/DXd): lower potency but better therapeutic index; Enhertu, Trodelvy.
PBD dimers: DNA crosslinkers, ultra-potent; several in clinical development.

Key References

• Lewis Phillips, G. D. et al. (2008). “Targeting HER2-positive breast cancer with trastuzumab-DM1, an antibody-cytotoxic drug conjugate.” Cancer Res., 68, 9280–9290.

• Modi, S. et al. (2022). “Trastuzumab deruxtecan in previously treated HER2-low advanced breast cancer.” N. Engl. J. Med., 387, 9–20.

• Chau, C. H., Steeg, P. S. & Figg, W. D. (2019). “Antibody-drug conjugates for cancer.” Lancet, 394, 793–804.

• Nakada, T. et al. (2019). “The latest research and development into the antibody-drug conjugate, trastuzumab deruxtecan.” Chem. Pharm. Bull., 67, 173–185.

Share:X Reddit LinkedIn

← Module 2 Module 4: CAR-T →