Discovery of a novel hybrid coumarin-hydroxamate conjugate targeting the HDAC1-Sp1-FOSL2 signaling axis for breast cancer therapy.

BACKGROUND: Breast cancer is one of the most lethal cancers in women. Despite significant advances in the diagnosis and treatment of breast cancer, many patients still succumb to this disease, and thus, novel effective treatments are urgently needed. Natural product coumarin has been broadly investigated since it reveals various biological properties in the medicinal field. Accumulating evidence indicates that histone deacetylase inhibitors (HDACIs) are promising novel anti-breast cancer agents. However, most current HDACIs exhibit only moderate effects against solid tumors and are associated with severe side effects. Thus, to develop more effective HDACIs for breast cancer therapy, hydroxamate of HDACIs was linked to coumarin core, and coumarin-hydroxamate hybrids were designed and synthesized. METHODS: A substituted coumarin moiety was incorporated into the classic hydroxamate HDACIs by the pharmacophore fusion strategy. ZN444B was identified by using the HDACI screening kit and cell viability assay. Molecular docking was performed to explore the binding mode of ZN444B with HDAC1. Western blot, immunofluorescent staining, cell viability, colony formation and cell migration and flow cytometry assays were used to analyze the anti-breast cancer effects of ZN444B in vitro. Orthotopic studies in mouse models were applied for preclinical evaluation of efficacy and toxicity in vivo. Proteomic analysis, dual-luciferase reporter assay, chromatin immunoprecipitation, co-immunoprecipitation, immunofluorescent staining assays along with immunohistochemical (IHC) analysis were used to elucidate the molecular basis of the actions of ZN444B. RESULTS: We synthesized and identified a novel coumarin-hydroxamate conjugate, ZN444B which possesses promising anti-breast cancer activity both in vitro and in vivo. A molecular docking model showed that ZN444B binds to HDAC1 with high affinity. Further mechanistic studies revealed that ZN444B specifically decreases FOS-like antigen 2 (FOSL2) mRNA levels by inhibiting the deacetylase activity of HDAC1 on Sp1 at K703 and abrogates the binding ability of Sp1 to the FOSL2 promoter. Furthermore, FOSL2 expression positively correlates with breast cancer progression and metastasis. Silencing FOSL2 expression decreases the sensitivity of breast cancer cells to ZN444B treatment. In addition, ZN444B shows no systemic toxicity in mice. CONCLUSIONS: Our findings highlight the potential of FOSL2 as a new biomarker and therapeutic target for breast cancer and that targeting the HDAC1-Sp1-FOSL2 signaling axis with ZN444B may be a promising therapeutic strategy for breast cancer.

Comprehensive Analysis of the Function and Prognostic Value of TAS2Rs Family-Related Genes in Colon Cancer.

In the realm of colon carcinoma, significant genetic and epigenetic diversity is observed, underscoring the necessity for tailored prognostic features that can guide personalized therapeutic strategies. In this study, we explored the association between the type 2 bitter taste receptor (TAS2Rs) family-related genes and colon cancer using RNA-sequencing and clinical datasets from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). Our preliminary analysis identified seven TAS2Rs genes associated with survival using univariate Cox regression analysis, all of which were observed to be overexpressed in colon cancer. Subsequently, based on these seven TAS2Rs prognostic genes, two colon cancer molecular subtypes (Cluster A and Cluster B) were defined. These subtypes exhibited distinct prognostic and immune characteristics, with Cluster A characterized by low immune cell infiltration and less favorable outcomes, while Cluster B was associated with high immune cell infiltration and better prognosis. Finally, we developed a robust scoring system using a gradient boosting machine (GBM) approach, integrated with the gene-pairing method, to predict the prognosis of colon cancer patients. This machine learning model could improve our predictive accuracy for colon cancer outcomes, underscoring its value in the precision oncology framework.

The role of corneal endothelium in macular corneal dystrophy development and recurrence.

Macular corneal dystrophy (MCD) is a progressive, bilateral stromal dystrophic disease that arises from mutations in carbohydrate sulfotransferase 6 (CHST6). Corneal transplantation is the ultimate therapeutic solution for MCD patients. Unfortunately, postoperative recurrence remains a significant challenge. We conducted a retrospective review of a clinical cohort comprising 102 MCD patients with 124 eyes that underwent either penetrating keratoplasty (PKP) or deep anterior lamellar keratoplasty (DALK). Our results revealed that the recurrence rate was nearly three times higher in the DALK group (39.13%, 9/23 eyes) compared with the PKP group (10.89%, 11/101 eyes), suggesting that surgical replacement of the corneal endothelium for treating MCD is advisable to prevent postoperative recurrence. Our experimental data confirmed the robust mRNA and protein expression of CHST6 in human corneal endothelium and the rodent homolog CHST5 in mouse endothelium. Selective knockdown of wild-type Chst5 in mouse corneal endothelium (ACsiChst5), but not in the corneal stroma, induced experimental MCD with similar extracellular matrix synthesis impairments and corneal thinning as observed in MCD patients. Mice carrying Chst5 point mutation also recapitulated clinical phenotypes of MCD, along with corneal endothelial abnormalities. Intracameral injection of wild-type Chst5 rescued the corneal impairments in ACsiChst5 mice and retarded the disease progression in Chst5 mutant mice. Overall, our study provides new mechanistic insights and therapeutic approaches for MCD treatment by high-lighting the role of corneal endothelium in MCD development.

Oleanolic acid derivative alleviates cardiac fibrosis through inhibiting PTP1B activity and regulating AMPK/TGF-ß/Smads pathway.

Cardiac fibrosis (CF) in response to persistent exogenous stimuli or myocardial injury results in cardiovascular diseases (CVDs). Protein tyrosine phosphatase 1B (PTP1B) can promote collagen deposition through regulating AMPK/TGF-ß/Smads signaling pathway, and PTP1B knockout improves cardiac dysfunction against overload-induced heart failure. Oleanolic acid (OA) has been proven to be an inhibitor of PTP1B, and its anti-cardiac remodeling effects have been validated in different mouse models. To improve the bioactivity of OA and to clarify whether OA derivatives with stronger inhibition of PTP1B activity have greater prevention of cardiac remodeling than OA, four new OA derivatives were synthesized and among them, we found that compound B had better effects than OA in inhibiting cardiac fibrosis both in vivo in the isoproterenol (ISO)-induced mouse cardiac fibrosis and in vitro in the TGF-ß/ISO-induced 3T3 cells. Combining with the results of molecular docking, surface plasmon resonance and PTP1B activity assay, we reported that OA and compound B directly bound to PTP1B and inhibited its activity, and that compound B showed comparable binding capability but stronger inhibitory effect on PTP1B activity than OA. Moreover, compound B presented much greater effects on AMPK activation and TGF-ß/Smads inhibition than OA. Taken together, OA derivative compound B more significantly alleviated cardiac fibrosis than OA through much greater inhibition of PTP1B activity and thus much stronger regulation of AMPK/TGF-ß/Smads signaling pathway.

Comprehensive analysis of novel cancer prediction genes and tumor microenvironment infiltration in colon cancer

Background Colon cancer with high incidence and mortality is a severe public health problem. As an emerging therapy, immunotherapy has played an active clinical role in tumor treatment, but only a small number of patients respond. Methods By univariate Cox regression analysis of 165 novel cancer prediction genes (NCPGs), 29 NCPGs related to prognosis were screened. Based on these 29 NCPGs and 336 differentially expressed genes, we constructed two colon cancer subgroups and three gene clusters and analyzed prognosis, activation pathways, and immune infiltration characteristics under various modification patterns. Then each patient was scored and divided into high or low NCPG_score groups. A comprehensive evaluation between NCPG_score and clinical characteristics, tumor microenvironment (TME), tumor somatic mutations, and the potential for immunotherapy was conducted. Results Patients with high NCPG_score were characterized by high tumor mutation burden and high microsatellite instability and were more suitable for immunotherapy. Conclusions This study screened 29 NCPGs as independent prognostic markers in colon cancer patients, demonstrating their TME, clinicopathological features, and potential roles in immunotherapy, helping to assess prognosis and guiding more personalized immunotherapy (AU)

Identification of immunotherapy and chemotherapy-related molecular subtypes in colon cancer by integrated multi-omics data analysis.

Background: Colon cancer is a highly heterogeneous disease, and identifying molecular subtypes can provide insights into deregulated pathways within tumor subsets, which may lead to personalized treatment options. However, most prognostic models are based on single-pathway genes. Methods: In this study, we aimed to identify three clinically relevant subtypes of colon cancer based on multiple signaling pathways-related genes. Integrative multi-omics analysis was used to explain the biological processes contributing to colon cancer aggressiveness, recurrence, and progression. Machine learning methods were employed to identify the subtypes and provide medication guidance for distinct subtypes using the L1000 platform. We developed a robust prognostic model (MKPC score) based on gene pairs and validated it in one internal test set and three external test sets. Risk-related genes were extracted and verified by qPCR. Results: Three clinically relevant subtypes of colon cancer were identified based on multiple signaling pathways-related genes, which had significantly different survival state (Log-Rank test, p<0.05). Integrative multi-omics analysis revealed biological processes contributing to colon cancer aggressiveness, recurrence, and progression. The developed MKPC score, based on gene pairs, was robust in predicting prognosis state (Log-Rank test, p<0.05), and risk-related genes were successfully verified by qPCR (t test, p<0.05). An easy-to-use web tool was created for risk scoring and therapy stratification in colon cancer patients, and the practical nomogram can be extended to other cancer types. Conclusion: In conclusion, our study identified three clinically relevant subtypes of colon cancer and developed a robust prognostic model based on gene pairs. The developed web tool is a valuable resource for researchers and clinicians in risk scoring and therapy stratification in colon cancer patients, and the practical nomogram can be extended to other cancer types.

Comprehensive analysis of novel cancer prediction genes and tumor microenvironment infiltration in colon cancer.

BACKGROUND: Colon cancer with high incidence and mortality is a severe public health problem. As an emerging therapy, immunotherapy has played an active clinical role in tumor treatment, but only a small number of patients respond. METHODS: By univariate Cox regression analysis of 165 novel cancer prediction genes (NCPGs), 29 NCPGs related to prognosis were screened. Based on these 29 NCPGs and 336 differentially expressed genes, we constructed two colon cancer subgroups and three gene clusters and analyzed prognosis, activation pathways, and immune infiltration characteristics under various modification patterns. Then each patient was scored and divided into high or low NCPG_score groups. A comprehensive evaluation between NCPG_score and clinical characteristics, tumor microenvironment (TME), tumor somatic mutations, and the potential for immunotherapy was conducted. RESULTS: Patients with high NCPG_score were characterized by high tumor mutation burden and high microsatellite instability and were more suitable for immunotherapy. CONCLUSIONS: This study screened 29 NCPGs as independent prognostic markers in colon cancer patients, demonstrating their TME, clinicopathological features, and potential roles in immunotherapy, helping to assess prognosis and guiding more personalized immunotherapy.

Eclampsia with hypothyroidism complicated with posterior reversible encephalopathy syndrome-a case report.

BACKGROUND: Posterior reversible encephalopathy syndrome (PRES) is a rare neurological disorder with complex physiopathological mechanisms that have not been fully understood. Early identification is of great prognostic significance, of which the symptoms and radiological abnormalities can be completely reversed. If the diagnosis and treatment are delayed, ischemia and massive infarction may be developed in some patients. Posterior reversible encephalopathy syndrome (PRES) has been reported mainly in association with postpartum eclampsia, which have been rarely reported, while the association with hypothyroidism has not been reported at home or abroad. CASE PRESENTATION: Here we report on a pregnant 29-year-old with multipara and a chief complication of hypothyroidism. She presented in the emergency department with frequent attacks of severe headache symptoms resulting from reversible cerebral vasoconstriction syndrome (RCVS), accompanied with prenatal eclampsia. PRES was determined by radiological examination. CONCLUSION: To the best of our knowledge, this is the first case of PRES complicated by hypothyroidism and prepartum eclampsia.Clinicians should be alert for the co-occurence of eclampsia, PRES, and RCVS when patients have convulsions after a typical throbbing headache. Moreover, regular monitoring of thyroid function during pregnancy should also occupy certain special attention.

Adaptive best subset selection algorithm and genetic algorithm aided ensemble learning method identified a robust severity score of COVID-19 patients.

We used an integrated ensemble learning method to build a stable prediction model for severity in COVID-19 patients, which was validated in multicenter cohorts.

MLSP: A bioinformatics tool for predicting molecular subtypes and prognosis in patients with breast cancer.

The molecular landscape in breast cancer is characterized by large biological heterogeneity and variable clinical outcomes. Here, we performed an integrative multi-omics analysis of patients diagnosed with breast cancer. Using transcriptomic analysis, we identified three subtypes (cluster A, cluster B and cluster C) of breast cancer with distinct prognosis, clinical features, and genomic alterations: Cluster A was associated with higher genomic instability, immune suppression and worst prognosis outcome; cluster B was associated with high activation of immune-pathway, increased mutations and middle prognosis outcome; cluster C was linked to Luminal A subtype patients, moderate immune cell infiltration and best prognosis outcome. Combination of the three newly identified clusters with PAM50 subtypes, we proposed potential new precision strategies for 15 subtypes using L1000 database. Then, we developed a robust gene pair (RGP) score for prognosis outcome prediction of patients with breast cancer. The RGP score is based on a novel gene-pairing approach to eliminate batch effects caused by differences in heterogeneous patient cohorts and transcriptomic data distributions, and it was validated in ten cohorts of patients with breast cancer. Finally, we developed a user-friendly web-tool (https://sujiezhulab.shinyapps.io/BRCA/) to predict subtype, treatment strategies and prognosis states for patients with breast cancer.

The genetic algorithm-aided three-stage ensemble learning method identified a robust survival risk score in patients with glioma.

Ensemble learning is a kind of machine learning method which can integrate multiple basic learners together and achieve higher accuracy. Recently, single machine learning methods have been established to predict survival for patients with cancer. However, it still lacked a robust ensemble learning model with high accuracy to pick out patients with high risks. To achieve this, we proposed a novel genetic algorithm-aided three-stage ensemble learning method (3S score) for survival prediction. During the process of constructing the 3S score, double training sets were used to avoid over-fitting; the gene-pairing method was applied to reduce batch effect; a genetic algorithm was employed to select the best basic learner combination. When used to predict the survival state of glioma patients, this model achieved the highest C-index (0.697) as well as area under the receiver operating characteristic curve (ROC-AUCs) (first year = 0.705, third year = 0.825 and fifth year = 0.839) in the combined test set (n = 1191), compared with 12 other baseline models. Furthermore, the 3S score can distinguish survival significantly in eight cohorts among the total of nine independent test cohorts (P < 0.05), achieving significant improvement of ROC-AUCs. Notably, ablation experiments demonstrated that the gene-pairing method, double training sets and genetic algorithm make sure the robustness and effectiveness of the 3S score. The performance exploration on pan-cancer showed that the 3S score has excellent ability on survival prediction in five kinds of cancers, which was verified by Cox regression, survival curves and ROC curves together. To enable its clinical adoption, we implemented the 3S score and other two clinical factors as an easy-to-use web tool for risk scoring and therapy stratification in glioma patients.

An immunity and pyroptosis gene-pair signature predicts overall survival in acute myeloid leukemia.

Treatment responses of patients with acute myeloid leukemia (AML) are known to be heterogeneous, posing challenges for risk scoring and treatment stratification. In this retrospective multi-cohort study, we investigated whether combining pyroptosis- and immune-related genes improves prognostic classification of AML patients. Using a robust gene pairing approach, which effectively eliminates batch effects across heterogeneous patient cohorts and transcriptomic data, we developed an immunity and pyroptosis-related prognostic (IPRP) signature that consists of 15 genes. Using 5 AML cohorts (n = 1327 patients total), we demonstrate that the IPRP score leads to more consistent and accurate survival prediction performance, compared with 10 existing signatures, and that IPRP scoring is widely applicable to various patient cohorts, treatment procedures and transcriptomic technologies. Compared to current standards for AML patient stratification, such as age or ELN2017 risk classification, we demonstrate an added prognostic value of the IPRP risk score for providing improved prediction of AML patients. Our web-tool implementation of the IPRP score and a simple 4-factor nomogram enables practical and robust risk scoring for AML patients. Even though developed for AML patients, our pan-cancer analyses demonstrate a wider application of the IPRP signature for prognostic prediction and analysis of tumor-immune interplay also in multiple solid tumors.

Conformational Constrained 4-(1-Sulfonyl-3-indol)yl-2-phenylaminopyrimidine Derivatives as New Fourth-Generation Epidermal Growth Factor Receptor Inhibitors Targeting T790M/C797S Mutations.

Tertiary C797S mutation of epidermal growth factor receptor (EGFR)-mediated resistance in non-small-cell-lung-cancer (NSCLC) patients is still an unmet clinical need. Several classes of adenosine 5'-triphosphate-competitive or allosteric EGFRT790M/C797S inhibitors and degraders have been developed, but none of them have received approval from the regulatory agencies. Herein, we report the structure-based design of conformational constrained 4-(1-ethylsufonyl-3-indolyl)-2-phenylaminopyrimidines as new EGFRT790M/C797S inhibitors by using a macrocyclization strategy. Representative compound 18j potently inhibited EGFR19del/T790M/C797S and EGFRL858R/T790M/C797S mutants with IC50 values of 15.8 and 23.6 nM and suppressed Ba/F3-EGFRL858R/T790M/C797S and Ba/F3-EGFR19del/T790M/C797S cells with IC50 values of 0.036 and 0.052 µM, respectively, which is 10-20-fold more potent than brigatinib. 18j also potently inhibited the EGFR19del/T790M/C797S-mutated PC-9-OR NSCLC cell proliferation with an IC50 value of 0.644 µM but was less potent for parental Ba/F3 and A431 cells. This study provides a new lead compound for drug discovery to combat EGFRC797S-mediated resistance in NSCLC patients.

Optimization of Brigatinib as New Wild-Type Sparing Inhibitors of EGFRT790M/C797S Mutants.

A series of brigatinib derivatives were designed and synthesized as new potent and selective EGFRT790M/C797S inhibitors. One of the most potent and selective compounds 18k strongly suppressed the EGFRL858R/T790M/C797S and EGFR19Del/T790M/C797S kinases with IC50 values of 0.7 and 3.6 nM, respectively, which were over 54-fold more potent than the lead compound. 18k also demonstrated promising EGFRT790M/C797S mutant selectivity, and was 94-fold less potent against the wild type EGFR. A cocrystal structure of EGFRT790M/C797S with a close derivative 18f was solved to provide insight on the inhibitor's binding mode. Moreover, compound 18k was orally bioavailable and demonstrated highly desirable PK properties, making it a promising lead compound for further structural optimization.

A new ALK inhibitor overcomes resistance to first- and second-generation inhibitors in NSCLC.

More than 60% of nonsmall cell lung cancer (NSCLC) patients show a positive response to the first ALK inhibitor, crizotinib, which has been used as the standard treatment for newly diagnosed patients with ALK rearrangement. However, most patients inevitably develop crizotinib resistance due to acquired secondary mutations in the ALK kinase domain, such as the gatekeeper mutation L1196M and the most refractory mutation, G1202R. Here, we develop XMU-MP-5 as a new-generation ALK inhibitor to overcome crizotinib resistance mutations, including L1196M and G1202R. XMU-MP-5 blocks ALK signaling pathways and inhibits the proliferation of cells harboring either wild-type or mutant EML4-ALK in vitro and suppresses tumor growth in xenograft mouse models in vivo. Structural analysis provides insights into the mode of action of XMU-MP-5. In addition, XMU-MP-5 induces significant regression of lung tumors in two genetically engineered mouse (GEM) models, further demonstrating its pharmacological efficacy and potential for clinical application. These preclinical data support XMU-MP-5 as a novel selective ALK inhibitor with high potency and selectivity. XMU-MP-5 holds great promise as a new therapeutic against clinically relevant secondary ALK mutations.

Alteration of MDM2 by the Small Molecule YF438 Exerts Antitumor Effects in Triple-Negative Breast Cancer.

Triple-negative breast cancer (TNBC) exhibits a high mortality rate and is the most aggressive subtype of breast cancer. As previous studies have shown that histone deacetylases (HDAC) may represent molecular targets for TNBC treatment, we screened a small library of synthetic molecules and identified a potent HDAC inhibitor (HDACi), YF438, which exerts effective anti-TNBC activity both in vitro and in vivo. Proteomic and biochemical studies revealed that YF438 significantly downregulated mouse double minute 2 homolog (MDM2) expression. In parallel, loss of MDM2 expression or blocking MDM2 E3 ligase activity rendered TNBC cells less sensitive to YF438 treatment, revealing an essential role of MDM2 E3 ligase activity in YF438-induced inhibition of TNBC. Mechanistically, YF438 disturbed the interaction between HDAC1 and MDM2, induced the dissociation of MDM2-MDMX, and subsequently increased MDM2 self-ubiquitination to accelerate its degradation, which ultimately inhibited growth and metastasis of TNBC cells. In addition, analysis of clinical tissue samples demonstrated high expression levels of MDM2 in TNBC, and MDM2 protein levels closely correlated with TNBC progression and metastasis. Collectively, these findings show that MDM2 plays an essential role in TNBC progression and targeting the HDAC1-MDM2-MDMX signaling axis with YF438 may provide a promising therapeutic option for TNBC. Furthermore, this novel underlying mechanism of a hydroxamate-based HDACi in altering MDM2 highlights the need for further development of HDACi for TNBC treatment. SIGNIFICANCE: This study uncovers the essential role of MDM2 in TNBC progression and suggests that targeting the HDAC1-MDM2-MDMX axis with a hydroxamate-based HDACi could be a promising therapeutic strategy for TNBC.

Expression and Prognostic Characteristics of m6A RNA Methylation Regulators in Colon Cancer.

Colon cancer is a common and leading cause of death and malignancy worldwide. N6-methylation of adenosine (m6A) is the most common reversible mRNA modification in eukaryotes, and it plays a crucial role in various biological functions in vivo. Dysregulated expression and genetic changes of m6A regulators have been correlated with tumorigenesis, cancer cell proliferation, tumor microenvironment, and prognosis in cancers. This study used RNA-seq and colon cancer clinical data to explore the relationship between N6-methylation and colon cancer. Based on the seven m6A regulators related to prognosis, three molecular subgroups of colon cancer were identified. Surprisingly, we found that each subgroup had unique survival characteristics. We then identified three subtypes of tumors based on 299 m6A phenotype-related genes, and one subtype was characterized as an immunosuppressive tumor and patients in this subtype may be more suitable for immunotherapy than other subtypes. Finally, using m6A-related genes and clinical information from The Cancer Genome Atlas cohort, we constructed a prognosis model, and this model could be used to predict the prognosis of patients in clinics.

Structural Basis of AZD9291 Selectivity for EGFR T790M.

AZD9291 (Osimertinib) is highly effective in treating EGFR-mutated non-small-cell lung cancers (NSCLCs) with T790M-mediated drug resistance. Despite the remarkable success of AZD9291, its binding pose with EGFR T790M remains unclear. Here, we report unbiased, atomic-level molecular dynamics (MD) simulations in which spontaneous association of AZD9291 with EGFR kinases having WT and T790M mutant gatekeepers was observed. Simulation-generated structural models suggest that the binding pose of AZD9291 with T790M differs from its binding pose with the WT, and that AZD9291 interacts extensively with the gatekeeper residue (Met 790) in T790M but not with Thr 790 in the WT, which explains why AZD9291 binds T790M with higher affinity. The MD simulation-generated models were confirmed by experimentally determined EGFR/T790M complex crystal structures. This work may facilitate the rational design of drugs that can overcome resistance mutations to AZD9291, and more generally it suggests the potential of using unbiased MD simulation to elucidate small-molecule binding poses.

The Stability Maintenance of Protein Drugs in Organic Coatings Based on Nanogels.

Protein drugs are often loaded on scaffolds with organic coatings to realize a spatiotemporal controlled release. The stability or activity of protein drugs, however, is largely affected by the organic coating, particularly with organic solvents, which can dramatically reduce their delivery efficiency and limit their application scope. In spite of this, little attention has been paid to maintaining the stability of protein drugs in organic coatings, to date. Here, we used catalase as a model protein drug to exploit a kind of chemically cross-linked nanogel that can efficiently encapsulate protein drugs. The polymeric shells of nanogels can maintain the surface hydration shell to endow them with a protein protection ability against organic solvents. Furthermore, the protection efficiency of nanogels is higher when the polymeric shell is more hydrophilic. In addition, nanogels can be dispersed in polylactic acid (PLA) solution and subsequently coated on scaffolds to load catalase with high activity. To the best of our knowledge, this is the first use of hydrophilic nanogels as a protection niche to load protein drugs on scaffolds through an organic coating, potentially inspiring researchers to exploit new methods for protein drug loading.