ADCs: Opportunities and Challenges

Abstract: Antibody–drug conjugates (ADCs) represent a promising class of targeted cancer therapies that combine monoclonal antibodies with cytotoxic payloads through specialized linkers. They enable selective tumor cell killing while minimizing damage to healthy tissues. Despite their clinical potential, ADCs face several challenges, including off-target toxicity, limited tumor penetration, and tumor antigen heterogeneity. Recent advances in linker chemistry, multi-payload strategies, and AI-assisted design are addressing these limitations. Furthermore, integration with personalized medicine and real-time biomarker monitoring is enhancing their clinical precision. Future developments in ADCs will rely on a deeper understanding of the tumor microenvironment, optimized drug delivery systems, and more robust translational models to bridge preclinical findings with patient outcomes.

ADC drugs

ADCs are a novel anti-cancer therapy that combines the targeting properties of antibodies with the lethality of chemotherapy drugs. They use antibodies to recognize specific antigens on the surface of tumor cells, precisely delivering cytotoxic drugs to the tumor site, thereby minimizing damage to normal tissue. The well-known trastuzumab (Enhertu) is an ADC. Compared to traditional cytotoxic drugs, ADCs can precisely identify tumors and kill them within them.

ADC Structure

ADCs consist of an antibody, a linker, and a cytotoxic drug.

① Antibody: Recognizes and binds to specific antigens on the surface of cancer cells;

② Linker: Conjugates the antibody and cytotoxic drug;

③ Payload: The cytotoxic drug with therapeutic effects.

Anti-tumor mechanism of ADC

① The antibody recognizes and binds to a specific antigen on the surface of the tumor cell;

② The ADC is endocytosed by the tumor cell and enters the cell interior;

③ The ADC is transported to the lysosome;

④ In the lysosome, the linker cleaves, releasing the drug;

⑤ The drug kills the tumor cell, and some of the drug may also spread to neighboring tumor cells, creating a “bystander effect.”

Challenges of ADC Drugs

Despite sophisticated ADC design, practical applications still face the following bottlenecks:

① Antibody off-target effects: Antibodies may bind to antigens in non-tumor tissues, leading to off-target toxicity;

② Limited drug penetration: Antibody binding to tumor surface antigens may hinder drug penetration into the tumor, resulting in uneven ADC distribution within the tumor;

③ Tumor heterogeneity: Cancer cells may express inconsistent surface antigens, leading to uneven ADC distribution;

④ Antigen downregulation: Cancer cells may reduce antigen expression, evading ADC recognition.

Solution

Issues Related to Antibodies and Targets

Main Issues:

1. Poor reliability of immunohistochemistry-based efficacy predictions

2. Poor distribution of antibodies within the tumor microenvironment (TME)

3. Target expression detection is limited to localized locations and lacks systematicity

Solutions:

1. Improve biomarker systems (including information on TME function, etc.)

2. Enhance antibody distribution within the TME (e.g., bispecific ADCs, combinations with naked antibodies, etc.)

3. Introduce systematic, dynamic detection methods (e.g., radiolabeled ADC imaging)

Linker-Related Issues

Main Issues:

1. The optimal balance of linker stability remains unclear.

2. Non-tumor-specific cleavages can occur, leading to off-target toxicity.

3. The trade-off between hydrophilicity and bystander effects.

Solutions:

1. Exploring novel cleavage triggering mechanisms (e.g., TME-responsive, bioorthogonal reactions).

2. Optimizing spatial structure (e.g., multi-arm linkers, biomimetic linkers).

3. Optimizing pharmacokinetic (PK) (e.g., drug-antibody ratio, hydrophilicity modulation).

Payload-Related Issues

Main Issues:

1. Cytotoxic payloads exhibit platform toxicity

2. Single payload mechanism, prone to drug resistance

3. Low success rate of preclinical model to clinical translation (e.g., novel payloads like ISAC)

Solutions:

1. Explore payloads with diverse mechanisms (e.g., DAC, RDC, etc.)

2. Research combination therapy and sequential therapy strategies (integrated with AI models)

3. Develop multi-payload ADCs and drug resistance inhibitors