New Basic Part

CD47/HER2 bispecific antibody

Part I: Design of active monoclonal antibodies targeting specific receptorepitopes.

Figure 1. Screening flowchart for active monoclonal antibodies.

Phase 1: De novo antibody generation based on the localization of antigenic epitope hotspot residues.

Figure 2. AI-designed antibody fragments. A: Structural diagram of the designed active antibody fragment for CD47 (the active antibody fragment is shown in yellow). B: Structural diagram of the designed active antibody fragment for HER2 (the active antibody fragments are shown in green and blue).

In the initial stage, our goal was to design a bispecific fusion protein-drug conjugate targeting CD47 and HER2, which are upregulated in tumor cells, especially in cases of gemcitabine and trastuzumab resistance. We aimed to design a construct composed of two nanobodies—one for CD47 and one for HER2. Therefore, we first needed to design and screen active antibody fragments for HER2 and CD47 respectively (as shown in Figure 1).

Using AI tools (RFdiffusion, RoseTTAFold2, ProteinMPNN), we designed 10 active antibody fragments targeting HER2 and CD47. For HER2, we drew on the binding modes of trastuzumab and pertuzumab, selecting different hotspot residue regions to guide the design. For CD47, the design was based on the binding site of Lemzoparlimab. By specifying hotspot residues, we successfully guided the designed antibody fragments to the intended antigenic epitopes (Figure 2).

Phase 2: Screening of active fragments based on protein-protein docking and molecular dynamics simulations.

Figure 3. RMSD analysis from MD simulation.

Figure 4. Free energy landscape.

Figure 5. Alphafold3 predicted structures of active antibody fragments for subsequent active bispecific antibody design. (A) Structure of the active CD47 antibody fragment. (B) Structure of the active HER2 antibody fragment (single chain).

To select the optimal antibody fragments, we integrated AI design with existing databases. We utilized Alphafold3 to predict structures, followed by an initial screening via protein docking. The top 10% of candidate fragments, ranked by Glide Score, were selected. A secondary screening was then conducted using molecular dynamics (MD) simulations and MM-GBSA calculations. For HER2, because the two candidate fragments (HER2_001, HER2_002) had similar scores, we performed a 500 ns MD simulation. Free energy landscape analysis confirmed that HER2_002 had more stable binding (Figure 4), as it exhibited a narrower and deeper free energy landscape valley, indicating that its low-energy conformations were more clustered and stable. For CD47, the optimal fragment, CD47_001, was directly selected via MM-GBSA. The finally selected HER2_002 and CD47_001 both demonstrated stable binding with their targets in computational simulations (Figure 3) and can be used for the subsequent design of the bispecific antibody.

Transient transfection of purified plasmids into CHO cells for protein expression and purification.

Figure 6. SDS-PAGE and SEC-HPLC of the antibody fusion protein.

The purified plasmids were transiently transfected into CHO cells for protein expression, followed by purification and validation using SDS-PAGE and SEC-HPLC. We found that our synthesized antibody not only met the required molecular weight but also achieved a purity of 98.74%.

Activity Testing of the Bispecific Antibody Fusion Protein

Phase 1: Effect on trastuzumab-resistant tumor cells (HCC1954 cells)

We investigated the inhibitory effect of the bispecific antibody on trastuzumab-resistant tumor cells by testing the cell viability under different treatments. As shown in the results, HCC1954 cells exhibit a degree of resistance to trastuzumab. However, upon the addition of our designed bispecific antibody, NanosphinX, the tumor cell viability was significantly reduced. Therefore, our designed active bispecific antibody, NanosphinX, can inhibit trastuzumab-resistant tumors, thus addressing the issue of trastuzumab resistance.

Figure 7. Effect of NanosphinX on HCC1954.

Phase 2: Effect of NanosphinX on PANC-1

Figure 8. Effect of NanosphinX on PANC-1.

We explored the inhibitory effect of the bispecific antibody on gemcitabine-resistant tumor cells by testing cell viability under different treatments. As shown, PANC-1 cells are resistant to gemcitabine. However, when our designed bispecific antibody NanosphinX was added, tumor cell viability significantly decreased. This indicates that NanosphinX can inhibit gemcitabine-resistant tumor cells, offering a solution to gemcitabine resistance. Furthermore, the results from the Gemcitabine + NanosphinX treatment show that NanosphinX can be used in combination with gemcitabine to enhance its efficacy and overcome resistance.

Phase 3: Effect of NanosphinX on MDA-MB-231

Figure 9. Effect of NanosphinX on MDA-MB-231.

Figure 10. Growth of MDA-MB-231 after NanosphinX treatment.

Triple-negative breast cancer has long been a major challenge regarding drug resistance, as its surface lacks many biological targets, making it difficult to treat with targeted therapies. We further investigated whether our bispecific antibody could inhibit triple-negative breast cancer cells through its ability to alter the tumor immune microenvironment. As shown in Figure 18, the inhibition of MDA-MB-231 cells by NanosphinX was significantly stronger than that of trastuzumab. Additionally, Figure 19 shows that the number of MDA-MB-231 cells was markedly reduced after the addition of NanosphinX, indicating significant growth inhibition.

Phase 4: Changes in Chemokine CXCL9 and CXCL10 Secretion after NanosphinX Addition

Figure 11. Changes in chemokine CXCL9 and CXCL10 secretion after NanosphinX treatment.

Having understood the superiority of NanosphinX in addressing drug resistance, we wanted to further investigate whether NanosphinX could combat cancer not only by directly attacking tumor cells but also by mobilizing the patient's own immune system. Through literature research, we found that antibodies can potentially increase the secretion of chemokines CXCL9 and CXCL10 by macrophages, thereby modulating the tumor microenvironment to strongly attract immune killer cells like T cells and NK (Natural Killer) cells into the tumor tissue, achieving an anti-tumor effect. Therefore, we designed a co-culture experiment with NanosphinX, tumor cells, and macrophages. The results (Figure 11) showed that NanosphinX can effectively induce an increase in the secretion of chemokines CXCL9 and CXCL10 by macrophages in the tumor microenvironment, thereby enhancing our tumor-killing effect.

In summary, the bispecific antibody fusion protein we designed, NanosphinX, can effectively address the problem of drug resistance in cancer therapy by targeting two overexpressed molecular targets on the surface of resistant tumor cells. Furthermore, it can further inhibit tumor cells by modulating the tumor microenvironment. The experimental results clearly demonstrate that NanosphinX has a potent inhibitory effect on both trastuzumab-resistant and gemcitabine-resistant cells, providing a new approach for overcoming resistance to these commonly used drugs and for combination therapy. Designing rational bispecific antibody fusion proteins that target overexpressed molecules on the surface of resistant cells appears to be a promising breakthrough in solving the problem of tumor drug resistance. We will continue to validate our designed bispecific antibody, NanosphinX, in multiple animal studies.