Eravacycline for treating cancer

Abstract

Methods for treating pancreatic cancer or metastases thereof by administering eravacycline or a derivative thereof are provided. Pharmaceutical compositions comprising eravacycline or a derivative thereof for use in treating pancreatic cancer or metastases thereof are also provided.

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of priority to U.S. Provisional Application No. 63 / 346,868, filed May 29, 2022, and U.S. Provisional Application No. 63 / 404,251, filed September 7, 2022, both entitled "IDENTIFICATION AND CHARACTERIZATION OF DRUGS WITH NOVEL ANTI-CANCER ACTIVITY, SELECTED BY COMPUTATIONAL DRUG REPURPOSING STUDY, USING ARTIFICIAL INTELLIGENCE (AI) DEEP LEARNING MODELS," and U.S. Provisional Application No. 63 / 430,466, filed December 6, 2022, entitled "ERAVACYCLINE FOR TREATING CANCER," both of which are incorporated herein by reference in their entireties.

[0002] The present invention is in the field of cancer treatment. [Background technology]

[0003] Pancreatic adenocarcinoma (PDAC) is a devastating disease with very few treatment options. It is recognized as one of the most lethal malignancies and is the leading cause of cancer-related deaths in Western countries. The resistance mechanisms of this cancer may be explained in part by the presence of multiple alterations in signaling pathways. The survival rate for pancreatic cancer patients is estimated to be an average of 5 years at best. Chemotherapy, radiation, and surgery are widely used, but these do not result in significant improvements in clinical outcomes. The lack of treatment options highlights the need for new approaches to treat and manage this deadly disease.

[0004] Repurposing drugs already approved by the Food and Drug Administration (FDA) for other indications has become a widely accepted approach to discovering new anticancer drugs, reducing costs, and eliminating the need for toxicological testing. Existing drugs may have a higher success rate than potential drugs in the FDA's new chemical entity (NCE) track. Deep learning approaches to identifying and predicting new indications for existing drugs have been extended to other areas. In the case of pancreatic cancer, where the disease mechanism remains unclear, this approach could be extremely beneficial. Drug repurposing for PDAC has received increasing attention in recent years, but research in this field has primarily been driven by hypotheses based on the partial overlap between existing pharmacological mechanisms of action (MOA) and the cause of the disease. Although some proposed drugs have shown promising anticancer activity, few have reported success.

[0005] As drug databases grow, machine learning (ML)-based approaches for drug reassignment have emerged. These tools identify new drug-disease interactions. ML-based approaches can then be optimized to repurpose drugs. Shortcomings of existing drug repurposing tools include the fact that they are typically not disease-specific and often contain data on drug mechanisms and pathways derived from diverse biological frameworks that are not available for related drugs. Tools that predict specific properties or activities based on chemical structure may yield more accurate predictions. New modalities for treating cancer in general and PDAC in particular are greatly needed. Summary of the Invention

[0006] In some embodiments, the present invention provides methods for treating or preventing pancreatic cancer or metastasis thereof by administering eravacycline or a derivative thereof to a subject in need thereof. Pharmaceutical compositions comprising eravacycline or a derivative thereof for use in treating pancreatic cancer or metastasis thereof in a subject in need thereof are also provided.

[0007] According to a first aspect, there is provided a method of treating or preventing pancreatic cancer or metastasis thereof in a subject in need thereof, the method comprising administering a therapeutically effective amount of eravacycline or a derivative thereof to the subject, thereby treating or preventing pancreatic cancer or metastasis thereof.

[0008] According to another aspect, there is provided a method for treating or preventing cancer involving expression of mutant p53 carrying a mutation in which tyrosine at position 220 is substituted with cysteine ​​(Y220C) in a subject in need thereof, the method comprising administering a therapeutically effective amount of eravacycline or a derivative thereof to the subject, thereby treating or preventing cancer involving expression of mutant p53 carrying the Y220C mutation in the subject.

[0009] According to another aspect, there is provided a pharmaceutical composition comprising a therapeutically effective amount of eravacycline or a derivative thereof for use in treating or preventing pancreatic cancer or metastasis thereof in a subject in need thereof.

[0010] According to another aspect, there is provided a pharmaceutical composition comprising a therapeutically effective amount of eravacycline or a derivative thereof for use in treating or preventing cancer comprising expression of mutant p53 harboring a Y220C mutation in a subject in need thereof.

[0011] According to some embodiments, the cancer is pancreatic cancer or a metastasis thereof.

[0012] According to some embodiments, eravacycline is represented by Formula I: Formula I: [ka]

[0013] According to some embodiments, the eravacycline is a salt or crystalline form of eravacycline.

[0014] According to some embodiments, the eravacycline is eravacycline dihydrochloride.

[0015] According to some embodiments, the pancreatic cancer is pancreatic ductal adenocarcinoma (PDAC).

[0016] According to some embodiments, the administration is systemic.

[0017] According to some embodiments, administering comprises administering a pharmaceutical composition comprising a therapeutically effective amount of eravacycline and a pharmaceutically acceptable carrier, excipient, or adjuvant.

[0018] According to some embodiments, the subject does not have an eravacycline-treatable bacterial infection.

[0019] According to some embodiments, the treatment includes at least one of increasing cancer cell apoptosis, increasing expression of truncated poly(ADP-ribose) polymerase 1 (cPARP1) in cancer cells, decreasing expression of DNA polymerase kappa (POLK) in cancer cells, decreasing expression of mutant p53 in cancer cells, and decreasing cancer cell migration in the subject.

[0020] According to some embodiments, the method further comprises administering at least one other conventional cancer treatment.

[0021] According to some embodiments, the pharmaceutical composition is formulated for systemic administration.

[0022] According to some embodiments, the pharmaceutical composition further comprises a pharmaceutically acceptable carrier, excipient, or adjuvant.

[0023] According to some embodiments, the treatment comprises at least one of increasing cancer cell apoptosis, increasing expression of cPARP1 in cancer cells, decreasing expression of POLK in cancer cells, and decreasing cancer cell migration in the subject.

[0024] According to some embodiments, the pharmaceutical composition is further used in combination with at least one other conventional cancer treatment.

[0025] Further embodiments and the full scope of applicability of the present invention will become apparent from the detailed description given hereinafter. It should be understood, however, that these detailed descriptions and specific examples, while indicating preferred embodiments of the invention, are given by way of example only, since various changes and modifications within the spirit and scope of the invention will become apparent to those skilled in the art from this detailed description. [Brief explanation of the drawings]

[0026] [Figure 1] Figure 1 contains a schematic diagram illustrating an embodiment of the method of the present invention for repurposing drugs with potential anticancer activity using machine learning. 1) Labeled datasets are compiled from DrugBank, cancer.com, ClinicalTrials.gov, and MeSH. 2) The dimensionality of drug-target and drug-drug interactions is reduced; this information serves as tabular features. 3) A message-passing neural network is trained to identify molecules with potential anticancer activity. The input consists of molecular structures and tabular features. Using this model, all approved drugs are ranked by the probability of anticancer activity. 4) The mechanism of action of approved drugs is predicted using a message-passing neural network trained on the ExCAPE-DB, which consists of 1.5 million molecules and 1,300 yeast targets. 5) Pharmacologists use the output of both models to identify promising candidates. 6) The anticancer activity of selected candidates is validated in vitro and in vivo. [Figure 2-1]Figures 2A-2F include graphs showing that eravacycline inhibits cell growth and proliferation of pancreatic cancer cells. (Figures 2A-2C) Line graphs of the cytotoxic effects of (2A) omadacycline, (2B) tigecycline, and (2C) eravacycline in AsPC-1, BxPC-3, A-549, MCF-7, and HT-29 cells over 72 hours. (Figure 2D) Line graph of the anticancer activity of tigecycline, eravacycline, and omadacycline in the BxPC-3 cell line. (Figure 2E) Line graph of the percent inhibition of two human PDAC cell lines and one human normal pancreatic cell line (HPNE) treated with increasing concentrations of eravacycline for 72 hours. (Figure 2F) Bar graph of the inhibition rate of BxPC-3 cell line treated with increasing concentrations of eravacycline, doxorubicin, and gemcitabine for 72 hours. Cell viability was determined by performing an XTT assay, which was performed to measure IC50 values. At least three independent experiments were performed. All data are shown as mean ± SD. Comparisons between two groups were performed using Student's t-test, and comparisons between multiple groups were performed using one-way ANOVA. ***P<0.001. ns, not significant. A P value <0.05 was considered statistically significant. [Figure 2-2] Same as above. [Figure 2-3] Same as above. [Figure 3]Figures 3A-3B contain micrographs and column graphs demonstrating that eravacycline inhibits cell migration in human PDAC cell lines. (Figure 3A) Micrographs of migration assessment assessed by wound healing assay of BxPC-3 cells after culturing in medium alone (control) or medium containing 10 μM eravacycline for the indicated times. Scale bar, 100 μm. (Figure 3B) Bar graph quantification of the effect of 10 μM eravacycline on wound closure in BxPC-3 cells. At least three independent experiments were performed. All data are shown as mean ± SD. Comparisons between two groups were performed using Student's t-test, and comparisons between multiple groups were performed using one-way ANOVA. **P<0.01, ***P<0.001. ns, not significant. A P value of <0.05 was considered statistically significant. [Figure 4-1] Figures 4A-4D contain plots, graphs, images, and tables demonstrating that eravacycline induces apoptosis in human pancreatic cancer cells. (Figures 4A-4B) Flow cytometry analysis of the apoptosis rate of BxPC-3 cells after 72 hours of exposure to control, 1 μM doxorubicin, 0.1 μM gemcitabine, 10 μM eravacycline, or 25 μM eravacycline shows a dot plot (Figure 4A) and a bar graph (Figure 4B). Statistical analysis of the apoptosis intensity in treated cells was performed in vitro. (Figures 4C-4D) BxPC-3 cells were treated with 1 μM doxorubicin, 0.01 μM gemcitabine, 0.1 μM gemcitabine, or increasing concentrations of eravacycline for 72 hours. (Figure 4C) Western blot assay was used to detect the expression of the cell apoptosis-related protein c-PARP1, and actin was used as a control. At least three independent experiments were performed. (Figure 4D) Bar graph quantification of C-PARP1 expression. All data are shown as mean ± SD. Comparisons between two groups were performed using Student's t-test, and comparisons between multiple groups were performed using one-way ANOVA. *P<0.05, **P<0.01, ***P<0.001. ns, not significant. A P value <0.05 was considered statistically significant. [Figure 4-2] Same as above. [Figure 4-3] Same as above. [Figure 5-1] Figures 5A-5I contain images and graphs demonstrating that eravacycline reduced POLK expression in BxPC-3 cells. (Figures 5A-5I) BxPC-3 cells were treated with increasing concentrations of eravacycline for 72 hours. (Figure 5A) Western blot assay was used to detect the expression of POLK-related proteins, with actin used as a control. (Figure 5B) Bar graph quantification of POLK protein expression. (Figures 5C-5D) Western blot analysis of baseline POLK protein expression levels in BxPC-3 and HPNE cells (Figure 5C). At least three independent experiments were performed. (Figure 5D) Bar graph quantification of POLK protein expression. All data are shown as mean ± SD. Comparisons between two groups were performed using Student's t-test, and comparisons between multiple groups were performed using one-way ANOVA. *P<0.05, ***P<0.001. ns, not significant. A P value of <0.05 was considered statistically significant. (Figure 5E) Western blot of p53 protein expression after treatment with various concentrations of eravacycline, gemcitabine, and doxorubicin. The upper band is p53. The lower band is actin. (Figure 5F) Bar graph quantification of the protein expression shown in Figure 5E. (Figure 5G) Western blot of p53 protein expression in pancreatic cancer cell lines BxPC-3 and Panc-1scr and healthy pancreatic cell lines HPNE and Panc1-p53ko. (Figure 5H) Bar graph quantification of the protein expression shown in Figure 5G. (Figure 5I) Line graph of the viability of various pancreatic cell lines after culture with eravacycline. [Figure 5-2] Same as above. [Figure 5-3] Same as above. [Figure 5-4] Same as above. [Figure 6-1]Figures 6A-6D include graphs and images demonstrating that eravacycline inhibits tumor growth in vivo in a xenograft model of human PDAC cells. (Figure 6A) Line graph of the mean mouse weight for each group. (Figure 6B) Photographs of tumors excised from mice in each group. (Figure 6C) Line graph of the percentage change in xenograft tumor size (volume) for each mouse group: control, gemcitabine (25 mg / kg), and eravacycline (10 mg / kg). (Figure 6D) Bar graph of the mean tumor weight for each group. At least three independent experiments were performed. All data are presented as mean ± SD. Comparisons between two groups were performed using Student's t-test, and comparisons between multiple groups were performed using one-way ANOVA. *P<0.05; ***P<0.001. A P value of <0.05 was considered statistically significant. [Figure 6-2] Same as above. DETAILED DESCRIPTION OF THE INVENTION

[0027] Detailed Description of the Invention In some embodiments, the present invention provides methods of treating or preventing cancer in a subject in need thereof by administering eravacycline to the subject. The present invention further relates to compositions comprising eravacycline for use in treating cancer.

[0028] Building on our recent success in identifying the novel antibiotic halicin using ML (see Stokes et al., "A deep learning approach to antibiotic discovery," Cell, 2020 Feb 20;180(4):688-702.e13 1; incorporated herein by reference in its entirety), we trained the same message-passing neural network (Chemprop) using drug data collected from various sources (e.g., DrugBank, clinicaltrials.gov) to predict the anticancer activity of small molecules. In addition to analyzing the chemical structure of drugs, we extended this anticancer prediction model to consider drug-target interaction (DTI) and drug-drug interaction (DDI) information and demonstrated the resulting improvement in model accuracy. We used this model to predict the anticancer activity of chemical structures and drugs that have not been tested in cancer clinical trials. Specifically, we used this model to predict the anticancer activity of all FDA-approved molecules as a means of identifying approved molecules with unknown potential anticancer properties. To explain the MOA behind the predicted anticancer drugs and complete the virtual screening process, an in silico yeast screening ML model was developed based on an extensive database of over 1.5 million molecules. This model was used to predict three possible outcomes (active, inactive, and irrelevant) for over 1,300 targets.

[0029] By analyzing the predictions of two models (i.e., the ML anticancer prediction model and the in silico yeast screening ML model) for a set of FDA-approved drugs, three antibacterial drugs from the tetracycline family, eravacycline, tigecycline, and omadacycline (listed in rank order), were found to have high anticancer activity scores. While eravacycline and omadacycline have never been tested for their potential anticancer activity, tigecycline, approved as an antibiotic in 2005, has demonstrated potential anticancer activity, exerting its action in PDAC through downregulation of CCNE2. Eravacycline and omadacycline were developed and approved in 2018 and demonstrated excellent antibacterial activity. Tetracycline family drugs are broad-spectrum antimicrobial agents widely used in human medicine. They are also used to treat various diseases and disorders, including cancer and inflammation.

[0030] In a first aspect, a method of treating or preventing cancer is provided, comprising contacting cells of the cancer with eravacycline, thereby treating the cancer.

[0031] In another aspect, a composition comprising eravacycline is provided for use in the treatment or prevention of cancer.

[0032] In some embodiments, the cancer is a solid cancer. In some embodiments, the cancer is a tumor. In some embodiments, the cancer is selected from hepatobiliary cancer, cervical cancer, genitourinary cancer (e.g., urothelial cancer), testicular cancer, prostate cancer, thyroid cancer, ovarian cancer, nervous system cancer, eye cancer, lung cancer, soft tissue cancer, bone cancer, pancreatic cancer, bladder cancer, skin cancer, intestinal cancer, liver cancer, rectal cancer, colorectal cancer, esophageal cancer, gastric cancer, gastroesophageal cancer, breast cancer (e.g., triple-negative breast cancer), renal cancer (e.g., renal carcinoma), skin cancer, head and neck cancer, leukemia, and lymphoma. In some embodiments, the cancer is pancreatic cancer. In some embodiments, the pancreatic cancer is pancreatic ductal adenocarcinoma (PDAC). In some embodiments, the cancer is metastatic cancer. In some embodiments, the cancer is metastasis. In some embodiments, the metastasis is pancreatic cancer metastasis. In some embodiments, the cancer is pancreatic cancer or a metastasis thereof. In some embodiments, the cancer is not lung cancer. In some embodiments, the cancer is not breast cancer. In some embodiments, the cancer is not colon cancer. In some embodiments, the cancer is selected from pancreatic cancer, esophageal cancer, colorectal cancer, head and neck cancer, and laryngeal cancer. In some embodiments, the cancer is selected from esophageal cancer, colorectal cancer, head and neck cancer, and laryngeal cancer.

[0033] In some embodiments, the cancer overexpresses POLK. In some embodiments, the method further includes determining POLK expression in the cancer and administering eravacycline to the POLK-overexpressing cancer. In some embodiments, the overexpression is in comparison to healthy cells or tissue. In some embodiments, the healthy cells or tissue is the same tissue or cell type as the cancer. In some embodiments, the healthy tissue or cell type is the pancreas or pancreatic cells. In some embodiments, overexpression includes an increase of at least 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100% relative to healthy cells or tissue, or any value or range therebetween. Each possibility represents a separate embodiment of the invention. In some embodiments, overexpression is an increase of at least 25%. In some embodiments, the cancer expresses mutant p53. In some embodiments, the cancer contains at least one cell expressing mutant p53. In some embodiments, the mutation is a gain-of-function mutation. In some embodiments, the mutation is a tyrosine 220 to cysteine ​​(Y220C) mutation. In some embodiments, the mutation is an equivalent mutation to the Y220C mutation that results in the same gain of function. In some embodiments, the mutation is not an arginine 273 to histidine (R273H) mutation or an equivalent mutation that results in the same gain of function. In some embodiments, the mutant p53 is oncogenic p53. Cancers harboring p53 mutations are well known in the art and can be found in all tissue types, although certain cancers have a particularly high incidence of such mutations. These include, for example, pancreatic cancer, esophageal cancer, colorectal cancer, head and neck cancer, and laryngeal cancer. In some embodiments, the method further includes determining mutant p53 expression in the cancer and administering eravacycline to cancers expressing mutant p53. In some embodiments, the method further comprises determining the mutation status of p53 in the cancer and administering eravacycline to cancers that express mutant p53.The method for measuring expression level in cancer is well known in the art, and any such method can be used.This includes, for example, PCR, Western blotting, and sequencing, but these are only a few examples.In some embodiments, the method comprises receiving a sample from a subject that contains cancer cells.In some embodiments, the POLK expression is the expression in the sample.In some embodiments, the mutant p53 is the mutant p53 in the sample.

[0034] Eravacycline is a halogenated tetracycline derivative with well-known antibacterial activity. The chemical structure of tetracyclines is formed by a linear fused tetracyclic ring (A-D) to which various pharmacophores are attached. Structural modifications at the C7 and C9 positions of the D ring of tetracyclines are considered the most promising approach to enhancing antibacterial activity, leading to the discovery of tigecycline, omadacycline, and eravacycline. These drugs contain a unique tail extension at the C9 position. The tail structures of eravacycline and tigecycline are more similar than those of eravacycline and omadacycline. The tails at the C9 position of eravacycline and tigecycline are nearly identical, containing an acidic hydrogen on the amide nitrogen and a lipophilic tail extension. The lack of a hydroxyl group at the C6 position results in greater lipid solubility in all three molecules. The essential difference between the structure of eravacycline and those of tigecycline and omadacycline is the fluorine atom at C7. Eravacycline is a new tetracyclic analogue with a fluorine atom at C7 of the D ring and a pyrrolidinoacetamido group at C9 of the D ring.

[0035] Eravacycline is also known as (4S,4aS,5aR,12aS)-4-(dimethylamino)-7-fluoro-3,10,12,12a-tetrahydroxy-1,11-dioxo-9-[2-(pyrrolidin-1-yl)acetamido]-1,4,4a,5,5a,6,11,12a-octahydrotetracene-2-carboxamide. It is commercially available as Xerava. It has the CAS number 1207283-85-9 and the chemical formula C 27 H 31 In some embodiments, eravacycline is represented by Formula I: FN4O8. Formula I: [ka]

[0036] In some embodiments, the eravacycline is a salt of eravacycline. In some embodiments, the eravacycline is eravacycline dihydrochloride. Eravacycline dihydrochloride is set forth in CAS number 1334714-66-7. It can also be represented by the chemical formula C 27 H 33 CL2FN4O8. In some embodiments, the eravacycline is crystalline eravacycline. Crystalline forms of eravacycline are described in International Patent Application WO2018 / 075767, which is incorporated herein by reference in its entirety.

[0037] In some embodiments, the eravacycline is a derivative of eravacycline. In some embodiments, the derivative contains a fluorine atom at the C7 position of the D ring. In some embodiments, the derivative contains a pyrrolidinoacetamide group at the C9 position of the D ring. As used herein, the term "derivative" refers to a therapeutic compound based on eravacycline that retains anti-cancer function. In some embodiments, the derivative is synthesized from eravacycline.

[0038] As used herein, the term "treatment" or "treating" a disease, disorder, or condition (e.g., cancer) includes alleviating at least one symptom thereof, reducing its severity, or inhibiting its progression. Treatment does not necessarily mean that the disease, disorder, or condition is completely cured. To be an effective treatment, a composition or method useful herein need only reduce the severity of the disease, disorder, or condition, reduce the severity of symptoms associated therewith, or provide an improvement in the quality of life of the patient or subject.

[0039] In some embodiments, the treating or preventing is treating. In some embodiments, the treating or preventing is preventing. In some embodiments, the treating comprises increasing cancer cell apoptosis. In some embodiments, the treating comprises increasing cancer cell death. In some embodiments, the treating comprises decreasing tumor size. In some embodiments, the tumor size is tumor volume. In some embodiments, the tumor size is tumor weight. In some embodiments, decreasing comprises a statistically significant change. In some embodiments, increasing comprises a statistically significant change. In some embodiments, a statistically significant change is at least a 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 92, 95, 97, 99, 100, 150, 200, 250, 300, 350, 400, 450, or 500% change, or any value or range therebetween. Each possibility represents a separate embodiment of the present invention. In some embodiments, the effect of eravacycline on cancer cell apoptosis / death and / or tumor size is greater than the effect produced by tigecycline. In some embodiments, the effect of eravacycline on cancer cell apoptosis / death and / or tumor size is greater than the effect produced by doxorubicin. In some embodiments, the effect of eravacycline on cancer cell apoptosis / death and / or tumor size is greater than the effect produced by gemcitabine. In some embodiments, the effect of eravacycline on cancer cell apoptosis / death and / or tumor size is greater than the effect produced by omadacycline.

[0040] In some embodiments, the treatment comprises increasing the expression of cleaved poly(ADP-ribose) polymerase 1 (cPARP1) in cancer cells. In some embodiments, the treatment comprises increasing the expression of cPARP1 in tumors. In some embodiments, the expression is protein expression. Since cleaved PARP1 is an indicator of cell death, an increase indicates increased cancer cell death. Methods for measuring cPARP1 expression levels are well known in the art and are disclosed below, and any such methods can be used.

[0041] In some embodiments, the treatment comprises decreasing expression of DNA polymerase kappa (POLK) in the cancer cells. In some embodiments, the treatment comprises decreasing expression of POLK in the tumor. In some embodiments, the expression is protein expression. In some embodiments, the expression is mRNA expression. In some embodiments, the decreasing comprises decreasing replication of the cancer cells. In some embodiments, the replication is DNA replication. In some embodiments, the replication is cell division. Methods for measuring POLK are well known in the art and are disclosed below, and any such methods can be used.

[0042] In some embodiments, treating comprises reducing cancer cell migration. In some embodiments, reducing migration comprises reducing metastasis. In some embodiments, reducing metastasis comprises reducing the metastatic rate. In some embodiments, reducing metastasis comprises reducing the number of metastases. In some embodiments, reducing metastasis comprises reducing the metastatic rate and reducing the number of metastases. Methods for measuring migration are well known in the art and are disclosed below, and any such method can be used.

[0043] In some embodiments, contacting the cancer cells with eravacycline comprises administering eravacycline to the subject. In some embodiments, treating cancer comprises administering eravacycline to the subject. In some embodiments, the subject is a mammal. In some embodiments, the subject is a human. In some embodiments, the subject has or is afflicted with cancer. In some embodiments, the subject is a subject in need of treatment. In some embodiments, the subject is a subject in need of cancer treatment. In some embodiments, the subject does not have an infection. In some embodiments, the infection is a bacterial infection. In some embodiments, the infection is an infection treatable with eravacycline. Eravacycline is approved for the treatment of complicated urinary tract infections (cUTI) and complicated intra-abdominal infections (cIAI) caused by multidrug-resistant gram-positive, gram-negative, and anaerobic bacteria. In some embodiments, the subject does not have a urinary tract or intra-abdominal infection.

[0044] As used herein, the terms "administering," "administration," and the like refer to any method, in sound medical practice, of delivering a composition containing an active agent to a subject in a manner that produces a therapeutic effect. One aspect of the present subject matter provides for intravenous administration of a therapeutically effective amount of eravacycline to a patient in need thereof. Other suitable routes of administration include parenteral, subcutaneous, oral, intramuscular, intratumoral, or intraperitoneal. Oral and intravenous formulations of eravacycline are commercially available and can be used in the methods of the present invention. In some embodiments, administration is systemic administration. In some embodiments, administration is intravenous administration. In some embodiments, administration is oral administration. In some embodiments, administration is intratumoral administration.

[0045] The dosage administered will depend upon the age, health, and weight of the recipient, type of concurrent treatment, if any, frequency of treatment, and the nature of the effect desired.

[0046] In some embodiments, the eravacycline is a therapeutically effective amount of eravacycline. In some embodiments, the therapeutically effective amount is a therapeutically effective dose. The term "therapeutically effective amount" refers to an amount of drug effective to treat a disease or disorder (e.g., cancer) in a mammal. The term "therapeutically effective amount" refers to an amount effective, at the necessary dosage and for the necessary period of time, to achieve a desired therapeutic or prophylactic result (e.g., cancer treatment). The exact dosage form and regimen will be determined by a physician depending on the patient's condition. In some embodiments, the dosage is that used to treat bacterial infections. In some embodiments, the dosage is equivalent to about 10 mg / kg body weight in mice. In some embodiments, the dosage is about 0.8 mg / kg body weight. In some embodiments, the dosage is about 1 mg / kg body weight. In some embodiments, the dosage is about 1.5 mg / kg body weight.

[0047] In some embodiments, administering comprises administering a composition comprising eravacycline. In some embodiments, the composition is a pharmaceutical composition. In some embodiments, the composition comprises a therapeutically effective amount of eravacycline. In some embodiments, the composition is formulated for administration to a subject. In some embodiments, the composition is formulated for systemic administration. In some embodiments, the composition is formulated for oral administration. In some embodiments, the composition is formulated for intravenous administration. In some embodiments, the composition is formulated for intratumoral administration.

[0048] In some embodiments, the composition further comprises a pharmaceutically acceptable carrier, excipient, or adjuvant. As used herein, the term "carrier," "excipient," or "adjuvant" refers to any component of a pharmaceutical composition that is not an active agent. As used herein, the term "pharmaceutically acceptable carrier" refers to a non-toxic, inert solid, semi-solid, or liquid filler, diluent, encapsulating material, any type of formulation auxiliary, or simply a sterile aqueous medium, such as saline. Some examples of materials which can function as pharmaceutically acceptable carriers include sugars such as lactose, glucose, and sucrose, starches such as corn starch and potato starch, cellulose and its derivatives such as sodium carboxymethylcellulose, ethylcellulose, and cellulose acetate, powdered tragacanth, malt, gelatin, talc, excipients such as cocoa butter and suppository waxes, oils such as peanut oil, cottonseed oil, safflower oil, sesame oil, olive oil, corn oil, and soybean oil, glycols such as propylene glycol, polyols such as glycerin, sorbitol, mannitol, and polyethylene glycol, esters such as ethyl oleate and ethyl laurate, agar, buffers such as magnesium hydroxide and aluminum hydroxide, alginic acid, pyrogen-free water, isotonic saline, Ringer's solution, ethyl alcohol, and phosphate buffer solutions, and other non-toxic, compatible substances used in pharmaceutical formulations. Non-limiting examples of materials that can function as carriers herein include sugars, starches, cellulose and its derivatives, powered tragacanth, malt, gelatin, talc, stearic acid, magnesium stearate, calcium sulfate, vegetable oils, polyols, alginic acid, pyrogen-free water, isotonic saline, phosphate buffer solution, cocoa butter (suppository base), emulsifiers, and other non-toxic, pharmaceutically compatible materials used in other pharmaceutical preparations. Wetting agents and lubricants such as sodium lauryl sulfate, as well as colorants, flavoring agents, excipients, stabilizers, antioxidants, and preservatives, may also be present. Any non-toxic, inert, and effective carrier may be used to formulate the compositions contemplated herein.In this respect, suitable pharmaceutically acceptable carriers, excipients and diluents are well known to those skilled in the art, such as those listed in "The Merck Index" 13th edition, edited by Budavari et al., Merck & Co., Inc., Rahway, New Jersey (2001), CTFA (Cosmetic, Toiletry, and Fragrance Association) "International Cosmetic Ingredient Dictionary and Handbook" 10th edition (2004), and "Inactive Ingredient Guide" U.S. Food and Drug Administration (FDA) Center for Drug Evaluation and Research (CDER) Office of Management, the contents of which are all incorporated herein by reference in their entirety.Examples of pharmaceutically acceptable carriers, carriers and diluents useful in the compositions of the present invention include distilled water, physiological saline, Ringer's solution, dextrose solution, Hank's solution and DMSO. These additional inactive ingredients, as well as effective formulation and administration procedures, are well known in the art and are described in standard textbooks such as "Goodman and Gillman's: The Pharmacological Bases of Therapeutics," 8th ed., Gilman et al., eds., Pergamon Press (1990), "Remington's Pharmaceutical Sciences," 18th ed., Mack Publishing Co., Easton, PA (1990), and "Remington: The Science and Practice of Pharmacy," 21st ed., Lippincott Williams & Wilkins, Philadelphia, PA (2005), each of which is incorporated herein by reference in its entirety. The compositions described herein may also be contained in engineered structures, such as liposomes, ISCOMs, slow-release particles, and other vehicles, that increase the half-life of the peptide or polypeptide in serum.Liposomes include emulsions, foams, micelles, insoluble monolayers, liquid crystals, phospholipid dispersions, lamellar layers, and the like. Liposomes for use with the peptides described herein are formed from standard vesicle-forming lipids, which generally include neutral and negatively charged phospholipids and one or more sterols, such as cholesterol. The choice of lipid is generally determined by considerations such as liposome size and blood stability. A variety of methods are available for preparing liposomes, as reviewed, for example, in Coligan, JE et al., "Current Protocols in Protein Science," 1999, John Wiley & Sons, Inc., New York. See also U.S. Patent Nos. 4,235,871, 4,501,728, 4,837,028, and 5,019,369.

[0049] The carrier may comprise from about 0.1% to about 99.99999% by weight of the pharmaceutical compositions provided herein. In some embodiments, the composition consists of eravacycline. In some embodiments, the composition consists essentially of eravacycline. In some embodiments, the composition includes eravacycline as the only therapeutic agent. In some embodiments, the therapeutic agent is a therapeutic anti-cancer agent. In some embodiments, the composition is devoid of another therapeutic agent other than eravacycline. In some embodiments, the eravacycline is essentially pure eravacycline. In some embodiments, the composition comprises an active agent and a carrier, wherein the active agent consists essentially of eravacycline.

[0050] As used herein, the term "consisting essentially of" indicates that a given compound or substance makes up the majority of a portion or fraction of the active ingredients of the composition.

[0051] In some embodiments, consisting essentially of means that eravacycline comprises at least 95%, at least 98%, at least 99%, or at least 99.9%, by weight, moles, or molar concentration of the composition, or any value and range therebetween, with each possibility representing a separate embodiment of the present invention.

[0052] In some embodiments, the subject is administered eravacycline as a monotherapy. In some embodiments, the subject is administered eravacycline as part of a combination therapy. In some embodiments, the method further comprises administering at least one other cancer therapy. In some embodiments, the other cancer therapy comprises a conventional cancer therapy. Conventional cancer therapy is well known in the art and includes, but is not limited to, chemotherapy, immunotherapy, radiation therapy, and targeted therapy. Any such cancer therapy may be combined with eravacycline as part of a combination therapy. In some embodiments, the composition comprising eravacycline is for use in combination with another cancer therapy.

[0053] As used herein, the term "about" in conjunction with a value refers to ±10% of the reference value. For example, a length of about 1,000 nanometers (nm) refers to a length of 1000 nm ±100 nm.

[0054] It should be noted that, as used in this specification and the accompanying drawings, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. Thus, for example, a reference to a "polynucleotide" includes a plurality of such polynucleotides, a reference to the "polypeptide" includes a reference to one or more polypeptides and equivalents thereof known to those skilled in the art, and so forth. It should be further noted that the claims may be drafted to exclude any optional element. Accordingly, this statement is intended to serve as a premise for the use of exclusive language such as "solely," "only," and the like, or the use of a "negative" limitation in connection with the recitation of claim elements.

[0055] When a convention similar to "at least one of A, B, and C, etc." is used, such syntax is generally intended in the sense that one of ordinary skill in the art would understand that convention (e.g., "a system having at least one of A, B, and C" would include, but is not limited to, systems having A only, B only, C only, A and B together, A and C together, B and C together, and / or A, B, and C together, etc.). Furthermore, one of ordinary skill in the art will understand that virtually any disjunctive word and / or phrase presenting two or more alternative terms, whether in the specification, claims, or drawings, should be understood to contemplate the possibility of encompassing one of those terms, either one of those terms, or both terms. For example, the phrase "A or B" is understood to encompass the possibilities of "A" or "B" or "A and B."

[0056] It is understood that certain features of the invention, which are, for clarity of description, described in the context of separate embodiments, may also be provided in combination in a single embodiment. Conversely, various features of the invention, which are, for clarity of description, described in the context of a single embodiment, may also be provided separately or in any suitable subcombination. All combinations of embodiments relevant to the present invention are specifically embraced by the present invention and are disclosed herein as if each and every combination were individually and expressly disclosed. In addition, all subcombinations of the various embodiments and elements thereof are specifically embraced by the present invention and are disclosed herein as if each and every subcombination were individually and expressly disclosed.

[0057] Additional objects, advantages, and novel features of the present invention will become apparent to those skilled in the art upon examination of the following non-limiting examples. Additionally, each of the various embodiments and aspects of the present invention as delineated hereinabove and as claimed in the claims section below finds experimental support in the following examples.

[0058] Various embodiments and aspects of the present invention as delineated hereinabove and as claimed in the claims section below find experimental support in the following examples. [Example]

[0059] Generally, the nomenclature used herein and the laboratory procedures utilized in the present invention include molecular, biochemical, microbiological and recombinant DNA techniques, which are fully explained in the literature.For example, "Molecular Cloning: A Laboratory Manual" by Sambrook et al. (1989); "Current Protocols in Molecular Biology" Vols. I to III, edited by Ausubel, RM (1994); "Current Protocols in Molecular Biology" by Ausubel et al., John Wiley & Sons, Baltimore, Maryland (1989); "A Practical Guide to Molecular Cloning" by Perbal, John Wiley & Sons, New York (1988); "Recombinant DNA" by Watson et al., Scientific American Books, New York; "Genome Analysis: A Laboratory Manual Series" Vols. 1 to 4, edited by Birren et al., Cold Spring Harbor Laboratory Press, New York (1998), the methods described in U.S. Pat. Nos. 4,666,828, 4,683,202, 4,801,531, 5,192,659, and 5,272,057, "Cell Biology: A Laboratory Handbook," Vols. I to III, edited by Cellis, JE (1994), "Culture of Animal Cells--A Manual of Basic Technique," Freshney, Wiley-Liss, NY (1994), 3rd Edition, "Current Protocols in Immunology," Vols. I to III, edited by Coligan JE (1994), "Basic and Clinical Immunology," edited by Stites et al. (8th Edition), Appleton & Lange, Norwalk, Connecticut (1994), "Strategies for Protein Purification and Characterization--A Laboratory Course Manual," CSHL (eds.) See Press (1996).All of these documents are incorporated herein by reference. Other general references are also listed throughout this document.

[0060] material and method An overview of the method used herein is presented in Figure 1. Anticancer training data was collected from various sources (Figure 1.1). Next, a feature manipulation process was performed (Figure 1.2), and a deep neural network was trained to rank drugs by their anticancer potential (Figure 1.3). Another model was used to predict MOA by performing in silico yeast screening (Figure 1.4). Finally, pharmacologists selected candidates (Figure 1.5), which were then validated through in vitro and in vivo experiments (Figure 1.6). The following subsections describe each phase of the method in detail.

[0061] Anti-cancer activity data collection To train the anticancer activity prediction model, a set of 451 positive drugs with known anticancer activity, along with their associated Medical Subject Heading (MeSH) terms, was collected from DrugBank 5.1.8, ClinicalTrials.gov, and cancer.com (Figure 1). The 800 negative drugs without anticancer activity were randomly sampled from the list of FDA-approved drugs after excluding drugs already investigated in cancer-related clinical trials (as reported on ClinicalTrials.gov) or drugs chemically similar to those drugs. Chemical similarity measures from the DrugBank website (drugbank.ca) were used. The collected lists of positive and negative drugs were then manually verified by pharmacologists.

[0062] Training a predictive model for anticancer activity The collected set of drugs was then used to train a directed message-passing neural network (Chemprop) model to predict the anticancer potential of molecules (Figure 1.3). The model was trained to solve a binary classification problem. When making a prediction, the model assigns a continuous score that quantifies the certainty of the prediction for each molecule. The Chemprop program is available at github.com / swansonk14 / chemprop.

[0063] Models were evaluated using a balanced scaffold-based split, which was repeated three times. An ensemble of two Chemprop models was used. The contribution of additional drug features was also evaluated (Figure 1.2): (1) drug-target interaction (DTI) information, obtained from DrugBank 5.1.8 and compressed using principal component analysis (PCA) from a binary matrix of 2,767 targets and 5,857 drugs to a 64-dimensional representation of the drugs; and (2) drug-drug interaction (DDI) information (also obtained from DrugBank 5.1.8), originally represented by a rectangular binary matrix of 3,377 drugs and reduced to a 256-dimensional compressed representation using AMFP, which performs factorization-like compression on matrices. Missing information was filled in based on the single most chemically similar drug. Similarity was calculated as described in the previous subsection. For molecules with no similar molecules at the DrugBank default threshold of 0.7, the average DTI and DDI information was used. The area under the receiver operating characteristic curve (AUC) was taken as the primary evaluation metric of the proposed model, but results are also based on the area under the precision-recall curve (AUPR) metric.

[0064] In silico yeast screening A second message-passing neural network was also trained for in silico yeast screening (see Ojima et al., "Use of fluorine in the medical chemistry and chemical biology of bioactive compounds—a case study on fluorinated taxane anticancer agents," Chembiochem 5, 628–635, incorporated herein by reference) that can account for drug MOA. The model was trained using the ExCAPE-DB20 database, which contains over 1.5 million molecules, 1,300 target yeast proteins, and 70 million DTIs (Figure 1.4). The model output consisted of a single outcome (active, inactive, and irrelevant) for each target. Due to dataset size and computational limitations, the model was evaluated by performing single scaffold-based train-validation-test splits of 70%, 10%, and 20%, respectively.

[0065] Inspired by test-time augmentation techniques, which are useful for creating accurate models, we used a weighted average prediction of similar drugs. Here, we focused on drugs from the tetracycline family (Figure 1.5). Therefore, we used predictions for seven antibiotics from the tetracycline family with known anticancer activity. The weights were based on the chemical similarity of the drugs. To set the classification threshold for each target, we calculated the prediction score of the nth ranked sample, where n is the number of positive samples for a given target in the test set.

[0066] Identifying drugs that can be repurposed The trained anticancer model was used to predict repurposed drugs with potential anticancer activity. Each approved drug found in DrugBank 5.1.8 was assigned an anticancer activity score by the trained anticancer model, and the drugs were ranked according to their scores. The ranked list was analyzed, and marketed drugs were given higher priority for further consideration (Figure 1.5). Some drugs, such as trofosfamide (ranked 15th) and fenretinide (ranked 34th), were found to have some known anticancer activity documented in the literature. These drugs were added to the training set and the model was retrained. Therefore, these drugs were not advanced to in vitro experiments. After considering their potential anticancer activity and commercial availability, eravacycline (ranked 17th out of 1,352 approved molecules) was selected for further investigation. A literature review indicated that eravacycline had not yet been investigated for oncological use.

[0067] cell culture Human PDAC (AsPC-1, BxPC-3, Panc-1 scr, Panc-1 p53 R273H knockout (KO), hTERT-HPNE) cell lines, breast cancer (MCF-7), lung cancer (A549), and colon cancer (HT-29) cell lines were purchased from the American Type Culture Collection (ATCC). MCF-7, A549, Panc-1 scr, Panc-1 p53 KO, and HT-29 cell lines were grown in Dulbecco's modified Eagle's medium (DMEM; Biological Industries, Beit HaEmek, Israel). BxPC-3 and AsPC-1 cell lines were cultured in Roswell Park Memorial Institute 1640 (RPMI-1640; BI, Israel). All media were supplemented with 10% fetal bovine serum (FBS), 200 μM L-glutamine (BI), and 1% penicillin-streptomycin (BI). Cells were cultured at 37°C in a 5% CO2 humidified incubator.

[0068] Tetracycline derivative treatment Eravacycline dihydrochloride (MedChem Express (MCE)) was dissolved in deuterium-depleted water (DDW). Tigecycline (MCE) and omadacycline (MCE) were dissolved in dimethyl sulfoxide (DMSO). All drugs were prepared as 100 mM stock solutions. All cell lines were treated with DMSO (<0.1%) as a control or with tigecycline, omadacycline, or eravacycline (at the indicated concentrations) for 72 hours. Cell viability and proliferation were determined using the XTT Cell Proliferation Assay Kit (Promega).

[0069] Cell proliferation assay Cell viability and proliferation were determined using an XTT cell proliferation assay kit. Cells were seeded into 96-well plates at a density of 4,000 cells per well and allowed to attach overnight at 37°C in a 5% CO2 incubator. After overnight incubation, cells were treated with culture medium or medium containing various concentrations (1, 2, 5, 10, 25, and 50 μM) of tigecycline, omadacycline, or eravacycline for the indicated periods. Next, 50 μl of XTT reaction solution (0.1 ml activation solution + 5 ml XTT reagent solution) was added to each well, and the plate was incubated at 37°C for 2 hours. The plate was gently shaken to distribute the dye throughout the wells, and the absorbance was measured using a spectrophotometer (Bio-RadiMark microplate absorbance reader) at a wavelength of 450 nm and a reference wavelength of 655 nm. Determination of the 50% inhibitory concentration (IC50) of eravacycline, tigecycline, and omadacycline

[0070] To evaluate the 50% inhibitory concentration (IC50) of tetracycline derivatives against five different cell lines (MCF-7, A549, HT-29, BxPC-3, and AsPC-1), cells were seeded and allowed to adhere overnight. All cells were washed with phosphate-buffered saline (PBS) and then cultured for 72 h in fresh medium containing increasing concentrations (0–50 μM) of the drugs tigecycline, omadacycline, or eravacycline. Inhibition of cell proliferation was determined using the XTT cell proliferation assay kit.

[0071] Cell migration and scratch wound healing assays BxPC-3 cells were cultured in 6-well plates until fully confluent. The cell monolayer was then scraped using a 200 μl pipette tip. Subsequently, floating and damaged cells were washed and removed with PBS, and medium alone or medium containing 10 μM eravacycline was added to the cells for further culture. Cell migration across the detached area was observed and captured at the indicated times using a Nikon ECLIPSE Ts2 fluorescence microscope equipped with a DS-Fi3 camera. The wound closure rate was also measured at the indicated time points.

[0072] Flow cytometry analysis Cells were cultured in 6-well plates containing different concentrations of eravacycline, gemcitabine, or doxorubicin (medium alone was used as a control) for 72 hours. Then, cells were harvested by trypsinization for flow cytometry analysis. Cells were resuspended in 100 μl of binding buffer and then stained with 5 μl of propidium iodide (PI) and 5 μl of Annexin V-FITC (Annexin V-FITC Apoptosis Detection Kit with PI - BioLegend) for 20 minutes at room temperature in the dark. Finally, cells were harvested and analyzed by FACS (Sony SP6800 Spectral Cell Analyzer), and data were analyzed using SP6800 Spectral Cell Analyzer software.

[0073] Protein extraction from Bxpc-3 cells BxPC-3 cells were incubated for 3 days with medium alone or medium containing various concentrations of eravacycline, gemcitabine, or doxorubicin. They were then harvested and lysed. BxPC-3 cells were briefly lysed in a buffer containing 20 mM HEPES (pH 7.4), 150 mM NaCl, 1 mM EGTA, 1 mM EDTA, 10% glycerol, 1 mM MgCl2, 1% Triton X-100, and 10 μL of protease and phosphatase inhibitors per mL of buffer. After incubation on ice for 45 minutes, the lysates were sonicated for 15 minutes (40% amplitude, no pulse), centrifuged at 12,000 × g at 4°C, and the supernatants were collected. The supernatants were analyzed for protein concentration using a Bradford assay. Absorbance at 595 nm was recorded using a microplate reader (Bio-RadiMark Microplate Absorbance Reader).

[0074] Western blot analysis To evaluate the expression of cellular apoptosis-related proteins, Western blot assays were used to detect cleaved poly(ADP-ribose) polymerase 1 (c-PARP1). Actin was used as a control. BxPC-3 cells were incubated for 3 days with medium alone or with medium containing various concentrations of eravacycline, gemcitabine, or doxorubicin. After incubation, the cells were harvested and lysed. Next, 20 μg of protein from each sample was extracted, mixed with Laemmli sample buffer (Bio-Rad, Hercules, CA, USA) containing 0.1% β-mercaptoethanol, boiled at 95°C for 5 minutes, and separated by 10% SDS-PAGE. Proteins were transferred to nitrocellulose membranes, blocked in 5% BSA containing 0.5% Tween 20, and then probed overnight with primary antibodies to detect c-PARP1 and analyze its expression levels. The membrane was incubated with anti-rabbit secondary antibody (1:1,000, Sigma-Aldrich) for 1 hour at room temperature. β-actin levels were determined using anti-β-actin (1:10,000, Sigma-Aldrich). Immune complexes were detected with chemiluminescence reagent (Thermo Fisher Scientific, Pierce ECL Plus substrate) and subsequently exposed to Kodak X-ray film (Rochester, NY, USA). Semiquantitative analysis was performed for all Western blot experiments using a computerized image analysis system (MacBiophotonics ImageJ software version 1.53k14).

[0075] Preparation and treatment of BxPC-3 cell xenograft tumor model Six-week-old female athymic nude mice were purchased from Envigo (Jerusalem, Israel), housed in pathogen-free conditions, and fed a standard sterile diet. Mice were housed under humidity- and temperature-controlled conditions with a 12-h light / dark cycle. The animal protocol was reviewed and approved by the Committee for the Ethical Care and Use of Animals in Research at Ben-Gurion University of the Negev in compliance with Israeli animal welfare laws. BxPC-3 cells (4 × 10 6 Mice were subcutaneously injected with 1000 mg of ... 2 After 10 days, the tumor was palpable and had a volume of 50-150 mm. 3 At the time of the tumor-bearing mice reaching 10 days, they were randomly assigned to a control group or an eravacycline group (n=6). Treatment was administered every two days for 10 days with either phosphate-buffered saline (PBS), eravacycline (10 mg / kg via i.p. injection), or gemcitabine (25 mg / kg via i.p. injection). Tumor growth and mouse weight were measured every other day during the treatment period. After the last treatment, mice were monitored for an additional six days before the experiment was terminated. Mice were then sacrificed, and tumors were excised, photographed, and measured, and tumor tissue was flash-frozen pending analysis.

[0076] Example 1 In silico candidate identification Table 1 shows the performance of the anticancer prediction models used in this study. A model that considered only the drug's molecular structure achieved an AUC of 0.83, but when the best-performing model was trained using the drug's DTI and DDI information as well, the enhanced model achieved an AUC of 0.909. Eravacycline was ranked 17th out of 1,371 molecules ranked. This drug was given higher priority due to the known anticancer activity of tetracyclines and their commercial availability. The PCA process reduced the dimensionality from 2,767 to 64 while retaining 45% of the variance.

[0077] Table 1: Area under the receiver operating characteristic curve (AUC) and area under the precision-recall curve (AUPR) of a message-passing neural network trained on the molecular structure of a drug for various different combinations of additional drug features. RDKIT 2D features are calculated by Chemprop using functions found in the RDKIT software. [Table 1]

[0078] Example 2 In silico yeast screening The in silico yeast screening model achieved an average AUC of 0.939 and an AUPR of 0.99 for identifying the absence of an interaction (note that the inverse of this class attribute can be used to identify the presence of any type of interaction), an AUC of 0.913 and an AUPR of 0.178 for identifying target activation, and an AUC of 0.95 and an AUPR of 0.19 for identifying target inactivation. On average, 3.6% of potential interactions were present in the test set. To identify candidate target proteins that could explain the predicted anticancer activity of eravacycline, we manually analyzed the first 30 predictions for eravacycline and compared each prediction to a threshold for each target. The threshold was the nth prediction score, where n is proportional to the number of positive examples in the training set (eravacycline was not part of the training set). The first predicted interaction for eravacycline was with CYP3A4, and this prediction was confirmed by searching the drug entry on the DrugBank website. Further targets were identified by analyzing the model output for eravacycline, focusing on targets known to be involved in anticancer MOA: P53, ALOX12, POLK, and BLM. POLK was the only target not associated with PDAC.

[0079] Example 3 Effects of tetracycline derivatives on the viability of human breast, lung, colon, and PDAC cancer cells To investigate the effects of tetracycline derivatives on cell proliferation in human breast, lung, colon, and PDAC cells, we treated all cell lines (MCF-7, A549, HT-29, BxPC-3, and AsPC-1) with various concentrations of tigecycline, omadacycline, and eravacycline (1, 2, 5, 10, 25, and 50 μM) for 72 hours, using medium as a control. XTT assays were then performed to determine the growth inhibition rate.

[0080] The results show that treatment with increasing concentrations of omadacycline had no significant effect on cell viability (Figure 2A). Treatment of all cell lines with tigecycline for 72 hours moderately inhibited the proliferation of human PDAC (BxPC-3, AsPC-1) and A549 cell lines by 60–65% in a concentration-dependent manner, whereas no significant effect was observed in MCF-7 and HT-29 cell lines (Figure 2B). Treatment of cells with eravacycline for 72 hours inhibited the proliferation of human PDAC (BxPC-3, AsPC-1) cells by 90–93% in a concentration-dependent manner (Figure 2C). When administered at a concentration of 25 μM, eravacycline was able to reduce the viability of human BxPC-3 cells more effectively than 25 μM tigecycline (93% and 60% reduction, respectively, p=0.001). No significant effect of eravacycline was observed in MCF-7, A549, or HT-29 cell lines (Figure 2C). These results indicate that cell viability in both PDAC cell lines was significantly reduced in a dose-dependent manner after treatment with eravacycline (Figure 2C). The IC50 values ​​for eravacycline in BxPC-3 and AsPC-1 cells were 3.57 μM and 7.07 μM, respectively (Figure 2C). In contrast, tigecycline required 9.9 μM and 4.58 μM to inhibit 50% of BxPC-3 and AsPC-1 cells, respectively, at the same time point (Figure 2B).

[0081] After 72 hours of treatment, eravacycline significantly reduced the growth of BxPC3-3 cells by 90% (P < 0.01) and 93% (P < 0.001) at concentrations of 25 μM and 50 μM, respectively (Figure 2C). Meanwhile, eravacycline reduced the growth of AsPC-1 cells by 75% (P < 0.05) and 89% (P < 0.001) at the same concentrations (Figure 2C). Thus, eravacycline was clearly superior to both omadacycline and tigecycline in killing PDAC cells (Figure 2D). The BxPC-3 cell line, which was relatively sensitive to eravacycline, was selected for further study.

[0082] To compare the effects of eravacycline on pancreatic cancer cells with those on normal cells, we examined the effects of eravacycline on cell proliferation in a type of human normal pancreatic cell line (HPNE cell line). Results revealed that HPNE cells were the least sensitive to eravacycline, exhibiting significantly lower inhibition than other PDAC cell lines (Figure 2E). To compare the activity of eravacycline with that of chemotherapeutic agents on BxPC-3 cancer cells, we examined the anticancer activity of doxorubicin and gemcitabine. After 72 hours of treatment, doxorubicin significantly reduced BxPC-3 cell growth by 67% (P<0.001) and 94% (P<0.001) at 1 μM and 10 μM concentrations, respectively (Figure 2F). The percentage of growth inhibition of BxPC-3 cells by gemcitabine was 77% and 80% at 1 μM and 10 μM, respectively (Figure 2F). Eravacycline reduced BxPC-3 cell growth by 27% and 63% at 1 μM and 10 μM concentrations, respectively (Figure 2F).

[0083] These results indicate that eravacycline significantly inhibited cell proliferation in BxPC-3 cells in a dose-dependent manner and was more cytotoxic to PDAC cells than tigecycline or omadacycline (Figure 2D).The results also show that the BxPC-3 cell line was the most sensitive to eravacycline, while its efficacy was relatively low in breast cancer cell lines (MCF-7), lung cancer cell lines (A549), and colon cancer cell lines (HT-29), as well as healthy pancreatic cells (Figure 2E).

[0084] Example 4 Effect of eravacycline on inhibiting cell migration in PDAC cells As one of the most aggressive gastrointestinal cancer cells, PDAC cells possess potent migration and invasion capabilities. Therefore, we investigated the effect of eravacycline on cell migration. Wound healing assays showed that BxPC-3 cells treated with 10 μM eravacycline for 0, 24, 48, and 72 hours exhibited significantly lower migration rates toward wounds introduced into confluent monolayers than the control group (Figure 3A-B). These results demonstrate that eravacycline inhibited cell migration in human PDAC cells.

[0085] Example 5 Effect of eravacycline on the induction of apoptosis in pancreatic cancer cells To investigate the effect of eravacycline on cell growth inhibition and the underlying mechanism, we investigated the induction of apoptosis activated by eravacycline treatment in BxPC-3 cells by performing an Annexin V-FITC (V-fluorescein isothiocyanate) apoptosis assay. The effect of treatment of BxPC-3 cells with increasing concentrations of eravacycline was evaluated after 72 hours. A positive control of 1 μM doxorubicin was evaluated at the same time point. Treatment of BxPC-3 cells with 10 μM eravacycline increased the number of apoptotic cells threefold (Figure 4A-B). 25 μM eravacycline resulted in nearly 70% apoptosis, which was superior to both gemcitabine (0.1 μM) and doxorubicin (1 μM) (Figure 4B).

[0086] C-PARP1 was assessed by Western blot. The results show that C-PARP1 was significantly increased after treatment with 15 μM eravacycline compared to the control group (Figures 4C and 4D). These data indicate that eravacycline strongly affected apoptosis in BxPC-3 cells at levels comparable to or greater than those of gemcitabine and doxorubicin.

[0087] Example 6 Eravacycline suppresses PDAC cell survival by inhibiting the expression of POLK and p53 genes To further explore the biological mechanism of eravacycline-induced BxPC-3 cell growth inhibition, we examined the expression of p53 and POLK-related proteins, two of the top targets identified in the in silico yeast screening model, by performing Western blot assays. We examined the effect of increasing concentrations of eravacycline on p53 and POLK gene expression in BxPC-3 cells over 72 hours. POLK and p53 protein expression was found to be significantly reduced in a dose-dependent manner after treatment with eravacycline compared to the control group (Figures 5A-5B and 5E-5F). Neither gemcitabine nor doxorubicin reduced p53 or POLK protein expression, indicating that eravacycline induces apoptosis through a different mechanism than these chemotherapeutic agents. We also found that baseline POLK protein expression was significantly higher in pancreatic cancer cells (BxPC-3 cell line) compared to normal pancreatic cells (HPNE cell line) (Figures 5C-5D). This may partly explain the increased effect of eravacycline on pancreatic cancer cells versus healthy pancreatic cells (Fig. 2E).

[0088] Example 7 Eravacycline suppresses PDAC cell survival by inhibiting mutant p53 gene expression TP53, which encodes the p53 protein, plays a central role in tumor suppression. The TP53 tumor suppressor gene is the most frequently mutated gene in human cancers, including over 50% of pancreatic adenocarcinomas. p53 is the most frequently mutated gene in human cancers, and more than half of human cancers contain p53 mutations. However, the majority of p53 mutations in cancer are missense mutations, resulting in the expression of full-length mutant p53 proteins. While the critical role of wild-type p53 in tumor suppression is firmly established, increasing evidence indicates that many tumor-associated mutant p53 proteins lose their wild-type p53 tumor suppressor function and acquire new activities that contribute to tumorigenesis, suggesting that the role of mutant p53 in carcinogenesis is through a gain-of-function mechanism. Mutant p53 proteins are often present at high levels in tumors and contribute to malignant progression. In recent years, scientists have recognized mutant p53 genes as attractive therapeutic targets for cancer. Numerous gain-of-function mutant p53s have been identified, including those that promote tumor cell proliferation, survival, migration, and invasion, improve chemotherapy resistance, disrupt normal tissue architecture, and promote cancer metabolism. Previous reports have also shown that mutant p53 gain-of-function, demonstrated in p53 knock-in mouse models, results in more aggressive or earlier-onset tumors compared with p53-null mice.

[0089] When we examined the effect of eravacycline on TP53 gene expression, we found that the expression of mutated p53 in the BxPC-3 cell line was significantly reduced at the protein level in a dose- and time-dependent manner after treatment with eravacycline (Figures 5E-F).

[0090] Mutant p53 protein was found to be present at higher levels in pancreatic cancer cell lines (Bxpc-3 and Panc-1 scr) compared with the normal pancreatic cell line hTERT-HPNE (Figure 5G-H). This observation is consistent with previous reports showing that mutant p53 protein is often present at high levels in tumors. This tumor accumulation may be important for mutant p53 to exert its gain of function in tumorigenesis and contribute to more advanced tumors. Furthermore, these data suggest that reduction of mutant p53 protein may play a potent role in eravacycline-induced inhibition of cancer cell proliferation.

[0091] Example 8 Effect of eravacycline on cell viability in different types of pancreatic cancer with different p53 mutation statuses To further investigate the effect of eravacycline on cancer cell viability in various types of pancreatic cancer p53 mutations, human PDAC (AsPC-1, BxPC-3, Panc-1 scr, Panc-1 p53-R273H knockout (KO)) cell lines were treated with various concentrations of eravacycline (1, 5, 10, 25, and 50 μM) for 72 hours, with medium used as a control. An XTT assay was then performed to determine the growth inhibition rate (Figure 5I). After 72 hours of treatment, eravacycline significantly reduced BxPC3-3 cell growth by 90% (P<0.01) and 93% (P<0.001) at concentrations of 25 μM and 50 μM, respectively. In contrast, it reduced AsPC-1 cell growth by 75% (P<0.05) and 89% (P<0.001) at the same concentrations (Figure 5I). No significant effect of eravacycline was observed in the Panc-1 cell line (Figure 5I). Eravacycline potently and selectively reduced viability in the p53-Y220C cancer cell line BXPC-3, whereas no toxicity was observed in the p53-R273H mutant cell line Panc-1 over the same concentration range. Without being bound by any particular theory, this suggests that eravacycline restored p53 function. Perhaps eravacycline binding stabilizes the structure of the p53 protein, affecting its DNA binding and / or thermodynamic stability, reactivating the apoptotic signaling pathway in tumor cells via either a transactivation-dependent or transactivation-independent pathway. Alternatively, blocking the MDM2-p53 interaction preserves wild-type p53 function and reactivates its tumor suppressor function.

[0092] Example 9 Effect of eravacycline on the growth of PDAC xenografts To further evaluate the inhibitory effect of eravacycline on human PDAC cells in vivo, BxPC-3 cells were subcutaneously injected into nude mice to establish a xenograft model for in vivo experiments. Mice were randomly divided into three groups: control, gemcitabine (25 mg / kg), and eravacycline (10 mg / kg). Following the last dose of treatment, mice were monitored for an additional 6 days before the experiment was terminated. Mice were sacrificed, and tumors were excised, photographed, and weighed (Figure 6B-6D). Results revealed that eravacycline treatment significantly inhibited tumor growth by 76% (Figure 6C). However, there was no significant overall weight loss in mice in the eravacycline group compared to the control group (Figure 6A). Furthermore, eravacycline treatment was superior to gemcitabine treatment, both in terms of tumor volume (Figure 6C) and tumor weight (Figure 6D). These data demonstrate that eravacycline effectively inhibited tumor development in vivo, even more so than gemcitabine.

[0093] While the present invention has been described in conjunction with specific embodiments thereof, it is evident that many alternatives, modifications, and variations will be apparent to those skilled in the art. Accordingly, it is intended to embrace all such alternatives, modifications, and variations that fall within the spirit and broad scope of the appended claims.

Claims

1. 1. A method for treating or preventing pancreatic cancer or metastasis thereof in a subject in need thereof, comprising administering to the subject a therapeutically effective amount of eravacycline or a derivative thereof, thereby treating or preventing pancreatic cancer or metastasis thereof in the subject.

2. A method for treating or preventing cancer involving expression of mutant p53 carrying a mutation in which tyrosine at position 220 is replaced by cysteine ​​(Y220C) in a subject in need of such treatment or prevention, comprising administering a therapeutically effective amount of eravacycline or a derivative thereof to the subject, thereby treating or preventing cancer involving expression of mutant p53 carrying the Y220C mutation in the subject.

3. The method of claim 2, wherein the cancer is pancreatic cancer or a metastasis thereof.

4. The eravacycline has formula I: 【Chemical 1】 The method of any one of claims 1 to 3, wherein

5. 5. The method of any one of claims 1 to 4, wherein the eravacycline is a salt or crystalline form of eravacycline.

6. 6. The method of claim 5, wherein said eravacycline is eravacycline dihydrochloride.

7. The method of any one of claims 1 to 6, wherein the pancreatic cancer is pancreatic ductal adenocarcinoma (PDAC).

8. The method of any one of claims 1 to 7, wherein the administration is systemic administration.

9. 9. The method of any one of claims 1 to 8, wherein said administering comprises administering a pharmaceutical composition comprising a therapeutically effective amount of said eravacycline and a pharmaceutically acceptable carrier, excipient, or adjuvant.

10. 10. The method of any one of claims 1 to 9, wherein the subject does not have an eravacycline-treatable bacterial infection.

11. 11. The method of any one of claims 1 to 10, wherein the treatment comprises at least one of increasing cancer cell apoptosis, increasing expression of truncated poly(ADP-ribose) polymerase 1 (cPARP1) in cancer cells, decreasing expression of DNA polymerase kappa (POLK) in cancer cells, decreasing expression of mutant p53 in cancer cells, and decreasing migration of cancer cells in the subject.

12. 12. The method of any one of claims 1 to 11, further comprising administering at least one other conventional cancer treatment.

13. A pharmaceutical composition comprising a therapeutically effective amount of eravacycline or a derivative thereof for use in treating or preventing pancreatic cancer or metastasis thereof in a subject in need thereof.

14. A pharmaceutical composition comprising a therapeutically effective amount of eravacycline or a derivative thereof for use in treating or preventing cancer involving expression of a mutant p53 carrying the Y220C mutation in a subject in need thereof.

15. The pharmaceutical composition of claim 14, wherein the cancer is pancreatic cancer or a metastasis thereof.

16. The eravacycline has formula I: 【Chemistry 2】 The pharmaceutical composition of any one of claims 13 to 15, wherein

17. 17. The pharmaceutical composition of any one of claims 13 to 16, wherein the eravacycline is a salt or crystalline form of eravacycline.

18. 18. The pharmaceutical composition of claim 17, wherein the eravacycline is eravacycline dihydrochloride.

19. The pharmaceutical composition of any one of claims 13 to 18, wherein the pancreatic cancer is pancreatic ductal adenocarcinoma (PDAC).

20. 20. The pharmaceutical composition of any one of claims 13 to 19, formulated for systemic administration.

21. 21. The pharmaceutical composition of any one of claims 13 to 20, further comprising a pharmaceutically acceptable carrier, excipient, or adjuvant.

22. 22. The pharmaceutical composition of any one of claims 13 to 21, wherein the subject does not have an eravacycline-treatable bacterial infection.

23. The pharmaceutical composition of any one of claims 13 to 22, wherein the treatment comprises at least one of increasing cancer cell apoptosis, increasing expression of cPARP1 in cancer cells, decreasing expression of POLK in cancer cells, and decreasing migration of cancer cells in the subject.

24. The pharmaceutical composition of any one of claims 13 to 23, further in combination with at least one other conventional cancer treatment.

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