Compounds with immunomodulatory activity and their therapeutic uses

Abstract

The present invention generally relates to new compounds for therapeutic use. In particular, a series of disclosed compounds with immunomodulatory activity are useful for treating immune system dysfunction and various cancers. Pharmaceutical compositions and methods for treating patients with immune disorders and / or cancer by administering therapeutically effective amounts of such compounds, alone or in combination with other therapeutic agents, are within the scope of this disclosure. Programmed cell death protein 1 (PD-1) is an immune checkpoint receptor involved in the generation of new cancer therapeutics.

Description

[Technical Field]

[0001] Government Assistance Clause This invention was made with government support under CA023168 awarded by the National Institutes of Health. The government has certain rights in this invention.

[0002] CROSS-REFERENCE TO RELATED APPLICATIONS This patent application is related to and claims priority to U.S. Provisional Patent Application No. 62 / 987,914, filed March 11, 2020, the contents of which are incorporated herein by reference in their entirety.

[0003] Technical Field The present invention relates generally to compounds with immunomodulatory activity and their therapeutic uses. Also described herein are pharmaceutical compositions of such compounds and methods for treating cancer patients by administering a therapeutically effective amount of such compounds alone, in combination with other therapeutic agents, or in a pharmaceutical composition. [Background technology]

[0004] background This section introduces aspects that may help to facilitate a better understanding of the present disclosure. Accordingly, these statements are to be read in this light and not understood as admissions about what is prior art or what is not prior art. Summary of the Invention [Means for solving the problem]

[0005] Programmed cell death protein 1 (PD-1) is an immune checkpoint receptor involved in the generation of novel cancer therapeutics 1 Prolonged interaction between the T cell receptor and major histocompatibility complex (MHC) leads to upregulation of PD-1 on the surface of activated T cells. 2Activated T cells produce cytokines such as interferon-γ, which in turn induce tumor cells to express programmed death-ligand 1 (PD-L1) on their cell surface. 2 Tumors evade the immune system by exploiting the interaction between PD-1 and its ligand PD-L1, which results in downstream effector T cell function and survival, thus resulting in a suppressive immune response in the tumor microenvironment. 2 Blockade of the PD-1 / PD-L1 interaction can enhance antitumor immunity, and a great deal of work has been done to develop monoclonal antibodies as inhibitors of the PD-1 / PD-L1 interaction. 3、4 For example, pembrolizumab, cemiplimab, and nivolumab are three FDA-approved anti-PD-1 antibodies. 4 The discovery of small molecule inhibitors would offer advantages over antibodies, such as rapid action, simplicity for in vivo administration, and the ability to penetrate cell membranes and interact with the cytoplasmic domains of cell surface receptors. 5 Over the past few years, significant progress has been made in designing PD-1 / PD-L1 inhibitors. 6、7 Specifically, Bristol-Myers Squibb (BMS) has discovered a set of potent PD-1 / PD-L1 small molecule inhibitors based on peptidomimetics and non-peptide small molecules. 6、7 In particular, BMS revealed a chemical library containing the 2-methyl-3-biphenyl-methanol scaffold. Later, Holak et al. investigated the interaction of BMS molecules with PD-L1, suggesting that BMS molecules induce PD-L1 dimerization, and also reported the crystal structure of a compound with dimeric PD-L1. 8、9 Based on these findings, the inventors envisioned developing a machine learning (ML) framework for selecting and testing new PD-1 / PD-L1 inhibitors. There is an unmet medical need for more effective treatments of cancer, especially for PD-1 / PD-L1 inhibitors with immunomodulatory activity.

[0006] The above and other objects, features and advantages of the present invention will become more apparent when taken in conjunction with the following description and drawings. [Brief explanation of the drawings]

[0007] [Figure 1] Figure 1. The EGNN model utilizes a combination of local (A) and global (B) features. Local features are calculated using a GNN to assign weights to various subgraphs of a molecule from the molecule's molecular graph. Global features are a collection of docking scores used to describe the interaction between a compound and a protein. These two features are combined to create a concatenated vector (C), which is passed through a softmax layer and bootstrapped to classify molecules as having "low" or "high" potency for PD-1 / PD-L1 interactions.

[0008] [Figure 2A] Figure 2A, top: Classification of training data in BMS and Incyte patents; bottom left: Main PD-L1 inhibitor scaffolds in BMS patents. R groups can be CN, Cl, Br, or CH3; and bottom right: Main PD-L1 inhibitor scaffolds in Incyte patents. Here, A and B denote sub-scaffolds.

[0009] [Figure 2B-C] Figures 2B and 2C show heat maps of pairwise Tanimoto similarity scores for BMS and Incyte compounds, respectively.

[0010] [Figure 3]Figures 3A-3C. In the PD-1 / PD-L1 complex crystal structure (PDB ID: 4ZQK), the light pink chains represent PD-1 protein, and the light cyan chains represent PD-L1 protein. In the PD-L1 homodimer crystal structure (PDB ID: 5N2F), the tan chains represent PD-L1 chain A, and the pale blue chains represent PD-L1 chain B. (Figure 3A) Overlapping and aligned PD-1 / PD-L1 (4ZQK) and PD-L1 dimer (5N2F) crystal structures. (Figure 3B) Overlapping and aligned two crystal structures, in which the binding site of PD-L1 dimer (5N2F) (gray mesh) is determined. (Figure 3C) Crystal structure of PD-L1 dimer (5N2F) with a small molecule (ligand ID: 8HW) in its binding site (gray mesh).

[0011] [Figure 4A-B] Figure 4A shows the training-validation and testing scheme used for the model. Figure 4B depicts Cohen's kappa scores for EGNN and GNN with different training-validation and testing sets.

[0012] [Figure 4C-D] Figure 4C shows the F1 scores for EGNN and GNN models with different training-validation and test sets; Figure 4D shows a heatmap of pairwise Tanimoto similarity scores between BMS and Incyte compound precision-recall curves for EGNN, GNN, RF, and SVM models trained on Incyte data.

[0013] [Figure 5A] Figure 5A shows that EGNN predicted a new PD-1 / PD-L1 inhibitor, compound 4b, by scaffold hopping between BMS compound 4a or BMS-1 ​​and BMS-1002. The blue portion of 4b was added from BMS-1002, and the pink portion was added from 4a (BMS-1).

[0014] [Figure 5B]Figure 5B shows the location of the top docking pose of compound 4b in the PD-L1 homodimer crystal structure (PDB ID: 5N2F). The inset shows the hydrophobic tunnel for compound 4b.

[0015] [Figure 5C] Figure 5C shows the chemical interactions of the top docking pose of compound 4b in the PD-L1 homodimer. The blue and pink areas indicate sticks to 4b. The yellow dotted lines between the compound and residues AThr20 and AAla121 represent hydrogen bonds. The orientation of the aromatic ring of tyrosine, ATyr56, suggests plausible π-π interactions with the blue aromatic ring of 2,3-dihydro-1,4-benzodioxin in compound 4b.

[0016] [Figure 5D] Figure 5D shows a comparison of the IC values ​​of 4a (BMS-1 ​​control compound, red) and new compound 4b (blue). DMSO controls for the positive (PC-DMSO, purple) and negative (NC-DMSO, green) controls of the assay are shown for each test concentration. DETAILED DESCRIPTION OF THE INVENTION

[0017] Detailed Description

[0018] For the purposes of promoting an understanding of the principles of the present disclosure, reference will now be made to the embodiments illustrated in the drawings, and specific language will be used to describe the same, it being understood, however, that no limitation of the scope of the disclosure is thereby intended.

[0019] As used herein, the following terms and phrases shall have the meanings set forth below: Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art.

[0020] In the present disclosure, the term "about" can allow for a degree of variation of a value or range, e.g., within 10%, within 5%, or within 1% of a stated value or a stated limit of a range. In the present disclosure, the term "substantially" can allow for a degree of variation of a value or range, e.g., within 90%, within 95%, or within 99% of a stated value or a stated limit of a range.

[0021] In this document, the terms "a," "an," or "the" are used to include one or more than one, unless the context clearly dictates otherwise. The term "or" is used to refer to a non-exclusive "or" unless otherwise indicated. Additionally, any phrases or terms of art used herein that are not otherwise defined should be understood to be for descriptive purposes only and not for limiting purposes. Any use of section headings is intended to aid in the reading and comprehension of the document and should not be construed as limiting. Furthermore, information related to a section heading may appear within or outside of that particular section. Furthermore, all publications, patents, and patent documents referenced herein are incorporated herein by reference in their entirety, as if individually incorporated by reference. In the event of conflicting usage between this document and a document so incorporated by reference, the usage in the incorporated reference should be considered supplementary to the usage in this document, and in the event of any irreconcilable conflict, the usage in this document shall control.

[0022] "Halogen" designates F, CI, Br, or I. "Halogen substituted" or "halo" substitution designates the replacement of one or more hydrogen atoms with F, CI, Br, or I.

[0023] As used herein, the term "alkyl" refers to a saturated monovalent chain of carbon atoms, which may be optionally branched. In embodiments containing alkyl, it is understood that illustrative variations of those embodiments include lower alkyl, such as C1-C6 alkyl, methyl, ethyl, propyl, 3-methylpentyl, and the like.

[0024] As used herein, the term "alkenyl" refers to an unsaturated monovalent chain of carbon atoms that contains at least one double bond, which may be optionally branched. In embodiments that contain alkenyl, it is understood that illustrative variations of those embodiments include lower alkenyl, such as C2-C6, C2-C4 alkenyl, etc.

[0025] As used herein, the term "alkynyl" refers to an unsaturated monovalent chain of carbon atoms that includes at least one triple bond, which may be optionally branched. In embodiments that include alkynyl, it is understood that illustrative variations of those embodiments include lower alkynyl, such as C2-C6, C2-C4 alkynyl, etc.

[0026] As used herein, the term "cycloalkyl" refers to a monovalent chain of carbon atoms, portions of which form a ring. In embodiments containing cycloalkyl, it is understood that exemplary variations of those embodiments include lower cylcoalkyls, such as C-C cycloalkyl, cyclopropyl, cyclohexyl, 3-ethylcyclopentyl, and the like.

[0027] As used herein, the term "cycloalkenyl" refers to an unsaturated monovalent chain of carbon atoms, portions of which form a ring. In embodiments that include cycloalkenyl, it is understood that illustrative variations of those embodiments include lower cycloalkenyl, such as C-C, C-C cycloalkenyl, etc.

[0028] As used herein, the term "alkylene" refers to a saturated divalent chain of carbon atoms, which may be optionally branched. In embodiments containing alkylene, it is understood that illustrative variations of those embodiments include lower alkylenes such as C2-C4 alkylene, methylene, ethylene, propylene, 3-methylpentylene, and the like.

[0029] It is understood that each of the alkyl, cycloalkyl, alkenyl, cycloalkenyl, alkylene, and heterocycle may be optionally substituted with independently selected groups such as alkyl, haloalkyl, hydroxyalkyl, aminoalkyl, carboxylic acid and derivatives thereof including esters, amides, and nitrites, hydroxy, alkoxy, acyloxy, amino, alky- and dialkylamino, acylamino, thio, etc., and combinations thereof.

[0030] As used herein, the term "heterocyclic" or "heterocycle" refers to a monovalent chain of carbon and heteroatoms, where the heteroatoms are selected from nitrogen, oxygen, and sulfur, some of which, at least one heteroatom, forms a ring. The term "heterocycle" may include both "aromatic heterocycles" and "non-aromatic heterocycles." Heterocycles include 4- to 7-membered monocyclic and 8- to 12-membered bicyclic rings such as imidazolyl, thiazolyl, oxazolyl, oxazinyl, thiazinyl, dithianyl, dioxanyl, isoxazolyl, isothiazolyl, triazolyl, furanyl, tetrahydrofuranyl, dihydrofuranyl, pyranyl, tetrazolyl, pyrazolyl, pyrazinyl, pyridazinyl, imidazolyl, pyridinyl, pyrrolyl, dihydropyrrolyl, pyrrolidinyl, piperidinyl, piperazinyl, pyrimidinyl, morpholinyl, tetrahydrothiophenyl, thiophenyl, azetidinyl, oxetanyl, thiiranyl, oxiranyl, aziridinyl, etc. A "heterocycle" may be optionally substituted at any one or more positions capable of bearing a hydrogen atom.

[0031] As used herein, the term "aryl" includes monocyclic and polycyclic aromatic carbocyclic groups, each of which may be optionally substituted. The term "optionally substituted aryl" refers to an aromatic mono- or polycyclic ring of carbon atoms, such as phenyl, naphthyl, and the like, optionally substituted with one or more independently selected substituents, such as halo, hydroxyl, amino, alkyl or alkoxy, alkylsulfonyl, cyano, nitro, and the like.

[0032] The term "heteroaryl" or "heteroaromatic ring" includes substituted or unsubstituted aromatic monocyclic ring structures, preferably 5- to 7-membered rings, more preferably 5- to 6-membered rings, whose ring structures include at least one heteroatom, preferably 1 to 4 heteroatoms, more preferably 1 or 2 heteroatoms. The term "heteroaryl" may also include ring systems having one or two rings, where at least one of the rings is heteroaromatic; for example, the other cyclic ring can be a cycloalkyl, cycloalkenyl, cycloalkynyl, aromatic carbocycle, heteroaryl, and / or heterocycle. Heteroaryl groups include, but are not limited to, pyridyl, N-oxopyridyl, pyrimidinyl, pyrazinyl, pyridazinyl, triazinyl, furyl, quinolyl, isoquinolyl, thienyl, imidazolyl, thiazolyl, indolyl, pyrryl, oxazolyl, benzofuryl, benzothienyl, benzthiazolyl, isoxazolyl, pyrazolyl, triazolyl, tetrazolyl, indazolyl, 1,2,4-thiadiazolyl, isothiazolyl, benzothienyl, purinyl, carbazolyl, benzimidazolyl, indolinyl, and the like. In some embodiments, heteroaryl groups have 1 to about 20 carbon atoms, and in further embodiments, from about 3 to about 20 carbon atoms. In some embodiments, heteroaryl groups contain 3 to about 14, 3 to about 7, or 5 to 6 ring-forming atoms. In some embodiments, heteroaryl groups have 1 to about 4, 1 to about 3, or 1 to 2 heteroatoms.

[0033] In some embodiments, "heterocycloalkyl" refers to a non-aromatic heterocycle in which one or more of the ring-forming atoms is a heteroatom such as an O, N, or S atom. Heterocycloalkyl groups can include mono- or polycyclic (e.g., having 2, 3, or 4 fused rings) ring systems and spirocycles. Examples of heterocycloalkyl groups include morpholino, thiomorpholino, piperazinyl, tetrahydrofuranyl, tetrahydrothienyl, 2,3-dihydrobenzofuryl, 1,3-benzodioxole, benzo-1,4-dioxane, piperidinyl, pyrrolidinyl, isoxazolidinyl, isothiazolidinyl, pyrazolidinyl, oxazolidinyl, thiazolidinyl, imidazolidinyl, and the like. Also included within the definition of heterocycloalkyl are moieties having one or more aromatic rings fused (i.e., having a common bond) with a non-aromatic heterocyclic ring, such as phthalimidyl, naphthalimidyl, and benzo derivatives of heterocycles. Heterocycloalkyl groups having one or more fused aromatic rings can be attached through either the aromatic or non-aromatic portion.

[0034] The term "optionally substituted" or "optionally substituents," as used herein, means that the group in question is either unsubstituted or substituted with one or more of the specified substituents. When the group in question is substituted with more than one substituent, the substituents may be the same or different. Furthermore, when the terms "independently," "independently is," and "independently selected from" are used, it means that the groups in question may be the same or different. Certain of the terms defined herein may occur more than one time in the structures, and upon such occurrence, each term shall be defined independently of the others.

[0035] The term "patient" includes humans and non-human animals, such as companion animals (such as dogs and cats) and livestock animals. Livestock animals are animals that are raised for food production. The patient to be treated is preferably a mammal, particularly a human.

[0036] The term "pharmaceutically acceptable carrier" is art-recognized and refers to a pharmaceutically acceptable material, composition, or vehicle, such as a liquid or solid filler, diluent, excipient, solvent, or encapsulating material, which is involved in carrying or transporting any subject composition or its components. Each carrier must be "acceptable" in the sense of being compatible with the subject composition and its components and not harmful to the patient. Some examples of materials that can serve as pharmaceutically acceptable carriers include: (1) sugars, such as lactose, glucose, and sucrose; (2) starches, such as corn starch and potato starch; (3) cellulose and its derivatives, such as sodium carboxymethylcellulose, ethylcellulose, and cellulose acetate; (4) powdered tragacanth; (5) malt; (6) gelatin; (7) talc; (8) excipients, such as cocoa butter and suppository wax; (9) oils, such as peanut oil, cottonseed oil, safflower oil, sesame oil, olive oil, corn oil, and soybean oil; (10) glycols such as propylene glycol; (11) polyols such as glycerin, sorbitol, mannitol, and polyethylene glycol; (12) esters such as ethyl oleate and ethyl laurate; (13) agar; (14) buffers such as magnesium hydroxide and aluminum hydroxide; (15) alginic acid; (16) pyrogen-free water; (17) isotonic saline; (18) Ringer's solution; (19) ethyl alcohol; (20) phosphate buffer solution; and (21) other non-toxic compatible substances used in pharmaceutical formulations.

[0037] As used herein, the term "administering" includes all means of introducing the compounds and compositions described herein into a patient, including, but not limited to, oral (po), intravenous (iv), intramuscular (im), subcutaneous (sc), transdermal, inhalation, buccal, ocular, sublingual, vaginal, rectal, etc. The compounds and compositions described herein may be administered in unit dosage forms and / or formulations containing conventional non-toxic pharmaceutically acceptable carriers, adjuvants and vehicles.

[0038] Solid pharmaceutical forms can contain inactive ingredients and carrier materials such as calcium carbonate, calcium phosphate, sodium phosphate, lactose, starch, mannitol, alginate, gelatin, guar gum, magnesium stearate, aluminum stearate, methylcellulose, talc, highly dispersed silicic acid, silicone oil, high molecular weight fatty acids (such as stearic acid), gelatin, agar, or vegetable or animal fats and oils, or solid high molecular weight polymers (such as polyethylene glycol), and preparations suitable for oral administration can contain additional flavorings and / or sweetening agents, if desired.

[0039] Liquid pharmaceutical forms can be sterilized and / or contain auxiliary substances, such as preservatives, stabilizers, wetting agents, osmotic agents, emulsifiers, spreading agents, solubilizers, salts, sugars or sugar alcohols for adjusting or buffering osmotic pressure, and / or viscosity adjusters, as appropriate. Examples of such additives are tartaric acid and citrate buffers, ethanol, and sequestrants (such as ethylenediaminetetraacetic acid and its non-toxic salts). Liquid polyethylene oxide, microcrystalline cellulose, carboxymethylcellulose, polyvinylpyrrolidone, dextran, or high molecular weight polymers such as gelatin are suitable for adjusting viscosity. Examples of solid carrier materials are starch, lactose, mannitol, methylcellulose, talc, highly dispersed silicic acid, high molecular weight fatty acids (such as stearic acid), gelatin, agar, calcium phosphate, magnesium stearate, animal and vegetable fats, and solid high molecular weight polymers such as polyethylene glycol.

[0040] Oily suspensions for parenteral or topical application can be liquid fatty acid esters, in each case having from 8 to 22 carbon atoms in the fatty acid chain, such as vegetable, synthetic or semi-synthetic oils, such as palmitic, lauric, tridecanoic, margaric, stearic, arachidic, myristic, behenic, pentadecanoic, linoleic, elaidic, brassidic, erucic or oleic acid, which are esterified with mono- to trihydric alcohols having from 1 to 6 carbon atoms, such as methanol, ethanol, propanol, butanol, pentanol or their isomers, glycol or glycerol. Examples of such fatty acid esters include commercially available Miglyol, isopropyl myristate, isopropyl palmitate, isopropyl stearate, PEG-6-caprate, caprylic / capric acid esters of saturated fatty alcohols, polyoxyethylene glycerol trioleate, ethyl oleate, waxy fatty acid esters, such as artificial duck gland fat, coconut fatty acid isopropyl esters, oleyl oleate, decyl oleate, ethyl lactate, dibutyl phthalate, diisopropyl adipate, and polyol fatty acid esters. Silicone oils of different viscosities, as well as fatty alcohols such as isotridecyl alcohol, 2-octyldodecanol, cetylstearyl alcohol, or oleyl alcohol, or fatty acids such as oleic acid, are also suitable. Vegetable oils such as castor oil, almond oil, olive oil, sesame oil, cottonseed oil, peanut oil, and soybean oil can also be used.

[0041] Suitable solvents, gelling agents and solubilizers are water or water-miscible solvents. Examples of suitable substances are alcohols such as ethanol or isopropyl alcohol, benzyl alcohol, 2-octyldodecanol, polyethylene glycol, phthalates, adipates, propylene glycol, glycerol, di- or tripropylene glycol, waxes, methyl cellosolve, cellosolve, esters, morpholine, dioxane, dimethyl sulfoxide, dimethylformamide, tetrahydrofuran, cyclohexanone, etc.

[0042] Mixtures of gelling agents and film-forming agents are also entirely possible. In this case, in particular, ionic macromolecules are used, such as sodium carboxymethylcellulose, polyacrylic acid, polymethacrylic acid and their salts, sodium amylopectin semiglycolate, alginic acid or propylene glycol alginate sodium salts, gum arabic, xanthan gum, guar gum or carrageenan. The following can be used as additional formulation aids: glycerol, paraffins of different viscosities, triethanolamine, collagen, allantoin and novantisolic acid. The use of surfactants, emulsifiers, or wetting agents, such as sodium lauryl sulfate, fatty alcohol sulfate ethers, di-sodium N-lauryl-iminodipropionate, polyethoxylated castor oil or sorbitan monooleate, sorbitan monostearate, polysorbates (e.g., Tween), cetyl alcohol, lecithin, glycerol monostearate, polyoxyethylene stearate, alkylphenol polyglycol ethers, cetyltrimethylammonium chloride, or mono- / di-alkyl polyglycol ether orthophosphate monoethanolamine salts, may also be required for formulation. Stabilizers such as montmorillonite or colloidal silicic acid can also be used to stabilize emulsions or prevent the destruction of active substances, such as antioxidants, for example, tocopherol or butylhydroxyanisole, or preservatives such as p-hydroxybenzoic acid esters, to prepare the desired formulation.

[0043] The preparation for parenteral administration can be in the form of separate dosage units such as ampoules or vials.Preferably, also use the solution of active compound, preferably aqueous solution, and especially isotonic solution and suspension.These injection forms can be made available as ready-to-use preparations, or can be prepared just before use by mixing the active compound, for example, lyophilized product, containing other solid carrier material if appropriate, with desired solvent or suspending agent.

[0044] Intranasal preparations can be in the form of aqueous or oily solutions or suspensions. They can also be in the form of lyophilisates which are prepared before use using suitable solvents or suspending agents.

[0045] Inhalable preparations can be present as powders, solutions or suspensions. Preferably, the inhalable preparations are in the form of a powder, e.g., of the active ingredient in admixture with suitable formulation auxiliaries such as lactose.

[0046] The preparation is manufactured, aliquoted and sealed under customary antimicrobial and aseptic conditions.

[0047] As mentioned above, the compound of the present invention can be administered as a combination therapy with other active agents, for example, therapeutically active compounds useful in the treatment of cancer, such as prostate cancer, ovarian cancer, lung cancer or breast cancer.In combination therapy, active ingredients can be formulated as a composition containing several active ingredients in a single dosage form, and / or as a kit containing each active ingredient in separate dosage forms.The active ingredients used in combination therapy can be administered together or separately.

[0048] It should be understood that the total daily amount of compound and composition described herein can be determined by the attending physician within the scope of sound medical judgment.The specific therapeutically effective dose level for any specific patient will depend on various factors, including the disorder being treated and the severity of the disorder; the activity of the specific compound used; the specific composition used; the age, weight, general health, sex and dietary habits of patient; the administration time and excretion rate of the specific compound used, the duration of treatment, the drug used in combination with or simultaneously with the specific compound used; and similar factors that are well known to researchers, veterinarians, physicians or other clinicians of ordinary skill.

[0049] A wide range of acceptable dosages is contemplated herein, including doses falling within the range of about 1 μg / kg to about 1 g / kg, depending on the route of administration. Dosages may be single or divided and may be administered according to a variety of dosing protocols, including once daily, twice daily, three times daily, or even every other day, once weekly, once monthly, etc. In each case, the therapeutically effective amounts described herein correspond to the administration example, or alternatively, to the total daily, weekly, or monthly dose.

[0050] As used herein, the term "therapeutically effective amount" refers to an amount of an active compound or pharmaceutical agent that elicits the biological or medicinal response in a tissue system, animal, or human that is desired by a researcher, veterinarian, physician, or other clinician, including alleviation of the symptoms of the disease or disorder being treated. In one aspect, a therapeutically effective amount is one that can treat or alleviate the disease or symptoms of the disease at a reasonable benefit / risk ratio applicable to any medical treatment.

[0051] As used herein, the term "therapeutically effective amount" refers to the amount administered to a patient and may be based on body surface area, patient weight, and / or patient condition. Additionally, it is understood that there is a correlation (exemplary based on milligrams per square meter of body surface) between dosages determined for humans and dosages determined for animals, including test animals, as described by Freireich, EJ, et al., Cancer Chemother. Rep. 1966, 50 (4), 219, the disclosure of which is incorporated herein by reference. Body surface area may be approximately determined from the patient's height and weight (see, for example, Scientific Tables, Geigy Pharmaceuticals, Ardley, New York, pages 537-538 (1970)). A therapeutically effective amount of a compound described herein may be defined as any amount useful for inhibiting the growth (or killing) of a population of malignant or cancerous cells, for example, an amount that may be observed in a patient in need of alleviation of such cancer or malignancy. Typically, such effective amounts range from about 5 mg / kg to about 500 mg / kg, about 5 mg / kg to about 250 mg / kg, and / or about 5 mg / kg to about 150 mg of compound per patient body weight. It is understood that effective doses may also vary depending on the route of administration, optional excipient usage, and the possible use of the compound in combination with other conventional and non-conventional therapeutic treatments, including other anti-tumor agents, radiation therapy, etc.

[0052] The term "patient," as used herein, includes humans and non-human animals, such as companion animals (dogs, cats, etc.) and livestock animals. Livestock animals are animals that are raised for food production. The patient to be treated is preferably a mammal, particularly a human.

[0053] PD-1 programmed cell death-1; PD-L1 programmed death ligand-1; GNN graph neural network; EGNN energy graph neural network; SVM support vector machine; RF random forest; FDA, US Food and Drug Administration; HTRF homogeneous time-resolved fluorescence; PPh3, triphenylphosphine; DIAD, diisopropyl azodicarboxylate; THF, tetrahydrofuran; Me4Si tetramethylsilane; ML, machine learning; AUROC, area under the receiver operating characteristic curve.

[0054] The present invention relates generally to new compounds for therapeutic use. In particular, the disclosure relates to novel compounds with immunomodulatory activity useful in the treatment of various cancers.

[0055] Also described herein are pharmaceutical compositions of such compounds and methods for treating cancer patients by administering a therapeutically effective amount of such compounds, alone, in combination with other therapeutic agents, or in a pharmaceutical composition.

[0056] In some exemplary embodiments, the present invention relates to pharmaceutical compositions comprising one or more of the compounds as disclosed herein or pharmaceutically acceptable salts thereof, together with one or more diluents, excipients or carriers.

[0057] In some exemplary embodiments, the present invention provides a compound having formula (I): [ka] or a pharmaceutically acceptable salt thereof [In the formula, Ar1 is optionally substituted aryl or heteroaryl; R1 and R2 are independently hydrogen, halo, azido, nitro, cyano, alkyl, alkenyl, alkynyl, alkylalkynyl, alkyloxy, hydroxyalkyl, aminoalkyl, thiolalkyl, mercaptoalkyl, heteroalkyl, heteroalkenyl, heteroalkynyl, heterocyclyl, cycloalkyl, cycloalkenyl, cycloheteroalkyl, cycloheteroalkenyl, acyl, aryl, heteroaryl, arylalkyl, arylalkenyl, or arylalkynyl, each of which is optionally substituted; R3 is halo, azido, nitro, cyano, alkyl, alkenyl, alkynyl, alkylalkynyl, alkyloxy, hydroxyalkyl, aminoalkyl, thiolalkyl, mercaptoalkyl, heteroalkyl, heteroalkenyl, heteroalkynyl, heterocyclyl, cycloalkyl, cycloalkenyl, cycloheteroalkyl, cycloheteroalkenyl, acyl, aryl, heteroaryl, arylalkyl, arylalkenyl, or arylalkynyl, each of which is optionally substituted; Q is [ka] or one or several amino acid residues] Regarding.

[0058] In some exemplary embodiments, the present invention relates to compounds having formula (I) as disclosed herein, wherein Ar1 is phenyl, 2,3-dihydrobenzo[b][1,4]-dioxine, or phenyl(thiazol-2-yl)methanol.

[0059] In some exemplary embodiments, the invention relates to compounds having formula (I) as disclosed herein, wherein R and R are independently hydrogen, methyl, hydroxyl, methoxyl, or —OCHAr.

[0060] In some exemplary embodiments, the invention relates to compounds having formula (I) as disclosed herein, wherein R3 is CH3, CN, or Cl.

[0061] In some exemplary embodiments, the present invention provides a method for producing a medicament for the treatment of a pulmonary arthritis. [ka] The present invention relates to compounds having formula (I) as disclosed herein, including:

[0062] In some other illustrative embodiments, the present invention provides a compound having formula (II): [ka] or a pharmaceutically acceptable salt thereof [In the formula, R1 is aryl, substituted aryl, or heteroaryl; R2 is an alkyl, alkenyl, alkynyl, alkylalkynyl, alkyloxy, hydroxyalkyl, aminoalkyl, thiolalkyl, mercaptoalkyl, heteroalkyl, heteroalkenyl, heteroalkynyl, heterocyclyl, cycloalkyl, cycloalkenyl, cycloheteroalkyl, cycloheteroalkenyl, acyl, aryl, heteroaryl, arylalkyl, arylalkenyl, or arylalkynyl, each of which is optionally substituted, primary or secondary amine; R3 is halo, azido, nitro, cyano, alkyl, alkenyl, alkynyl, alkylalkynyl, alkyloxy, hydroxyalkyl, aminoalkyl, thiolalkyl, mercaptoalkyl, heteroalkyl, heteroalkenyl, heteroalkynyl, heterocyclyl, cycloalkyl, cycloalkenyl, cycloheteroalkyl, cycloheteroalkenyl, acyl, aryl, heteroaryl, arylalkyl, arylalkenyl, or arylalkynyl, each of which is optionally substituted; Ar1 is aryl, heteroaryl, arylalkyl, arylalkenyl, or arylalkynyl, each of which is optionally substituted. Regarding.

[0063] In some exemplary embodiments, the present invention relates to a compound having formula (II) as disclosed herein, wherein Ar1 is phenyl, 2,3-dihydrobenzo[b][1,4]-dioxine, or phenyl(thiazol-2-yl)methanol.

[0064] In some exemplary embodiments, the present invention relates to compounds having formula (II) as disclosed herein, wherein R1 and R2 are independently piperidine, pyrrolidine, phenyl, 4-halophenyl (halo = fluoro, bromo, iodo), and / or one or more amino acid residues, either singly or in combination with other amino acids.

[0065] In some exemplary embodiments, the invention relates to compounds having Formula (II), as disclosed herein, wherein R3 is methyl, CN, or halo.

[0066] In some exemplary embodiments, the present invention provides a method for producing a medicament for the treatment of a pulmonary arthritis. [ka] The present invention relates to compounds having formula (II), as disclosed herein, wherein

[0067] In some exemplary embodiments, the present invention provides a compound having formula (III): [ka] or a pharmaceutically acceptable salt thereof [In the formula, Ar1 is phenyl, 2,3-dihydrobenzo[b][1,4]dioxine or phenyl(thiazol-2-yl)methanol; Ar2 is piperidine or pyrrolidine; Ar3 is phenyl, 4-halophenyl (halo = fluoro, bromo, iodo), X is independently halo, azido, nitro, cyano, alkyl, alkenyl, alkynyl, alkylalkynyl, alkyloxy, hydroxyalkyl, aminoalkyl, thiolalkyl, mercaptoalkyl, heteroalkyl, heteroalkenyl, heteroalkynyl, heterocyclyl, cycloalkyl, cycloalkenyl, cycloheteroalkyl, cycloheteroalkenyl, acyl, aryl, heteroaryl, arylalkyl, arylalkenyl, or arylalkynyl, each of which is optionally substituted. Regarding.

[0068] In some exemplary embodiments, the invention relates to compounds having formula (III), as disclosed herein, where X is methyl, cyano, or chloro.

[0069] In some exemplary embodiments, the present invention provides a method for producing a medicament for the treatment of a pulmonary arthritis. [ka] The present invention relates to compounds having formula (III), as disclosed herein, wherein

[0070] In some illustrative embodiments, the present invention relates to pharmaceutical compositions comprising one or more compounds having formula (I), (II), (III), or pharmaceutically acceptable salts thereof, together with one or more diluents, excipients, or carriers.

[0071] In some illustrative embodiments, the present invention relates to one or more compounds having Formula (I), (II), (III), or pharmaceutically acceptable salts thereof, that are immunomodulators, together with one or more diluents, excipients, or carriers.

[0072] In some illustrative embodiments, the present invention relates to one or more compounds having Formula (I), (II), (III), or a pharmaceutically acceptable salt thereof, that are inhibitors of the PD-1 and PDL-1 signaling pathway, together with one or more diluents, excipients, or carriers.

[0073] In some illustrative embodiments, the present invention relates to one or more compounds having Formula (I), (II), (III) or pharmaceutically acceptable salts thereof, together with one or more diluents, excipients or carriers, for use in the treatment of cancer.

[0074] In some exemplary embodiments, the present invention relates to a method for treating a patient with cancer, comprising administering to a patient in need of relief from said cancer a therapeutically effective amount of one or more compounds having Formula (I), (II), (III) or pharmaceutically acceptable salts thereof, and one or more carriers, diluents, or excipients.

[0075] In some exemplary embodiments, the present invention relates to a method for treating a patient with cancer, comprising administering to a patient in need of relief from said cancer a therapeutically effective amount of one or more compounds having Formula (I), (II), (III) or pharmaceutically acceptable salts thereof, and one or more carriers, diluents, or excipients, wherein the compounds are immunomodulators.

[0076] In some exemplary embodiments, the present invention relates to a method for treating a patient with cancer, comprising administering to a patient in need of relief from said cancer a therapeutically effective amount of one or more compounds having Formula (I), (II), (III) or pharmaceutically acceptable salts thereof, in combination with one or more other compounds of the same or different mechanism of action, and one or more carriers, diluents, or excipients, wherein the compounds are immunomodulators.

[0077] In some exemplary embodiments, the present invention relates to a method for treating a patient with cancer, comprising administering to a patient in need of relief from said cancer a therapeutically effective amount of one or more compounds having Formula (I), (II), (III) or pharmaceutically acceptable salts thereof, and one or more carriers, diluents, or excipients, wherein said cancer is castration-resistant prostate cancer.

[0078] In some exemplary embodiments, the present invention relates to a method for treating a patient with cancer, comprising administering to a patient in need of relief from said cancer a therapeutically effective amount of one or more compounds having Formula (I), (II), (III) or pharmaceutically acceptable salts thereof, and one or more carriers, diluents, or excipients, wherein the compounds are immunomodulators.

[0079] In some illustrative embodiments, the present invention relates to a pharmaceutical composition comprising one or more compounds having formula (I), (II), (III) or pharmaceutically acceptable salts thereof, together with one or more diluents, excipients or carriers, for use as a medicament for cancer.

[0080] In some exemplary embodiments, the present invention relates to a drug conjugate comprising one or more compounds having formula (I), (II), (III) or pharmaceutically acceptable salts thereof, and one or more carriers, diluents, or excipients.

[0081] In some illustrative embodiments, the present invention relates to drug conjugates comprising one or more compounds having formula (I), (II), (III) or pharmaceutically acceptable salts thereof, and one or more carriers, diluents, or excipients, wherein the conjugates provide cell-type or tissue-type targeting, or the conjugates target another pathway that synergizes with the action of the compounds.

[0082] In some exemplary embodiments, the present invention relates to a method for treating a cancer patient, comprising administering to a patient in need of relief from said cancer a therapeutically effective amount of one or more compounds, together with one or more carriers, diluents, or excipients, wherein the compounds have the formula (I), (II), or (III):

[0083] In some exemplary embodiments, the present invention provides a compound having formula (IV): [ka] or a pharmaceutically acceptable salt thereof [In the formula, [ka] represents a single or double bond, [ka] represents an optional cyclic ring; R1 is hydrogen, halo, azido, nitro, cyano, alkyl, alkenyl, alkynyl, alkylalkynyl, alkyloxy, hydroxyalkyl, aminoalkyl, thiolalkyl, mercaptoalkyl, heteroalkyl, heteroalkenyl, heteroalkynyl, heterocyclyl, cycloalkyl, cycloalkenyl, cycloheteroalkyl, cycloheteroalkenyl, acyl, aryl, heteroaryl, arylalkyl, arylalkenyl, or arylalkynyl, each of which is optionally substituted; R2 is hydrogen, halo, azido, nitro, cyano, alkyl, alkenyl, alkynyl, alkylalkynyl, alkyloxy, hydroxyalkyl, aminoalkyl, thiolalkyl, mercaptoalkyl, heteroalkyl, heteroalkenyl, heteroalkynyl, heterocyclyl, cycloalkyl, cycloalkenyl, cycloheteroalkyl, cycloheteroalkenyl, acyl, aryl, heteroaryl, arylalkyl, arylalkenyl, or arylalkynyl, each of which is optionally substituted; and X is carbon or nitrogen. Regarding.

[0084] In some exemplary embodiments, the present invention provides a method for producing a medicament for the treatment of a pulmonary arthritis. [ka] The present invention relates to compounds having formula (IV), as disclosed herein, wherein

[0085] In some exemplary embodiments, the present invention provides a compound having formula (V): [ka] or a pharmaceutically acceptable salt thereof [In the formula, R1, R2, and R3 are independently selected from the group consisting of hydrogen, halo, azido, nitro, cyano, alkyl, alkenyl, alkynyl, alkylalkynyl, alkyloxy, hydroxyalkyl, aminoalkyl, thiolalkyl, mercaptoalkyl, heteroalkyl, heteroalkenyl, heteroalkynyl, heterocyclyl, cycloalkyl, cycloalkenyl, cycloheteroalkyl, cycloheteroalkenyl, acyl, aryl, heteroaryl, arylalkyl, arylalkenyl, or arylalkynyl, each of which is optionally substituted, or any two adjacent substituents joined together to form a cyclic or heterocyclic moiety. Regarding.

[0086] In some exemplary embodiments, the invention relates to compounds having formula (V), as disclosed herein, wherein R1 = CH3.

[0087] In some exemplary embodiments, the invention relates to compounds having formula (V), as disclosed herein, wherein R1 = Cl.

[0088] In some exemplary embodiments, the present invention provides a method for producing a medicament for the treatment of a pulmonary arthritis. [ka] [ka]

[0023] The present invention relates to compounds having formula (V), as disclosed herein, wherein:

[0089] In some exemplary embodiments, the present invention provides compounds having formula VI or VII: [ka] or a pharmaceutically acceptable salt thereof, A is carbon or nitrogen; L is (CH2)n , -SO, -SO2, -CO, -CO(CH2)O, where n is 0, 1, or 2; Ar1 is aryl, heteroaryl, arylalkyl, arylalkenyl, or arylalkynyl, each of which is optionally substituted; R1 is halo, azido, nitro, cyano, alkyl, alkenyl, alkynyl, alkylalkynyl, alkyloxy, hydroxyalkyl, aminoalkyl, thiolalkyl, mercaptoalkyl, heteroalkyl, heteroalkenyl, heteroalkynyl, heterocyclyl, cycloalkyl, cycloalkenyl, cycloheteroalkyl, cycloheteroalkenyl, acyl, aryl, heteroaryl, arylalkyl, arylalkenyl, or arylalkynyl, each of which is optionally substituted; R2 is H, methyl, ethyl or any alkyl; R3 is halo, -OR, -NO2, cyano, -NR a R b , -N3, -S(O)2R a , -C(alkyl), -C(cycloalkyl), C(alkynyl), C(haloalkyl), acyl, aryl, heteroaryl, arylalkyl, arylalkenyl, or arylalkynyl, each of which is optionally substituted; and wherein R, R a and R b are independently alkyl; R4 is halo, an amino acid, a saturated or unsaturated aromatic or heteroaromatic ring, a carbohydrate derivative, or -(CH2) m NR a R b where m=0, 1, 2, and R a and R b are independently alkyl or or R3 and R4 joined together form a cyclic or heterocyclic moiety; R5 is halo, -OR, -NO2, cyano, -NR a R b , -N3, -S(O)2R a, -C(alkyl), -C(cycloalkyl), C(alkynyl), C(haloalkyl), acyl, aryl, heteroaryl, arylalkyl, arylalkenyl, or arylalkynyl, each of which is optionally substituted; and wherein R, R a and R b are independently alkyl. Regarding.

[0090] In some exemplary embodiments, the present invention provides a compound having formula VIII: [ka] or a pharmaceutically acceptable salt thereof [In the formula, n is 0, 1, or 2, A is carbon or nitrogen; R1 is halo, azido, nitro, cyano, alkyl, alkenyl, alkynyl, alkylalkynyl, alkyloxy, hydroxyalkyl, aminoalkyl, thiolalkyl, mercaptoalkyl, heteroalkyl, heteroalkenyl, heteroalkynyl, heterocyclyl, cycloalkyl, cycloalkenyl, cycloheteroalkyl, cycloheteroalkenyl, acyl, aryl, heteroaryl, arylalkyl, arylalkenyl, or arylalkynyl, each of which is optionally substituted; R2 is independently halo, -OR, -NO2, cyano, or -NR a R b , -N3, -S(O)2R a , -C(alkyl), -C(cycloalkyl), C(alkynyl), C(haloalkyl), acyl, aryl, heteroaryl, arylalkyl, arylalkenyl, or arylalkynyl, each of which is optionally substituted; and wherein R, R a and R b are independently alkyl, where R, R a and R b are independently alkyl or or R2 and R3 joined together form a cyclic or heterocyclic moiety; R3 is independently -(CH2) m NR a R b {where m=0 to 2}, halo, any amino acid, any saturated or unsaturated aromatic or heteroaromatic ring or carbohydrate derivative, where R a and R b are independently alkyl; R4 is independently halo, -OR, -NO2, cyano, or -NR a R b , -N3, -S(O)2R a , -C(alkyl), -C(cycloalkyl), C(alkynyl), C(haloalkyl), acyl, aryl, heteroaryl, arylalkyl, arylalkenyl, or arylalkynyl, each of which is optionally substituted; and wherein R, R a and R b are independently alkyl, where R, R a and R b are independently alkyl; Ar1 is aryl, heteroaryl, arylalkyl, arylalkenyl, or arylalkynyl, each of which is optionally substituted. Regarding.

[0091] In some exemplary embodiments, the present invention provides a compound having formula IX: [ka] or a pharmaceutically acceptable salt thereof [In the formula, n is 0, 1, or 2, R1 is halo, azido, nitro, cyano, alkyl, alkenyl, alkynyl, alkylalkynyl, alkyloxy, hydroxyalkyl, aminoalkyl, thiolalkyl, mercaptoalkyl, heteroalkyl, heteroalkenyl, heteroalkynyl, heterocyclyl, cycloalkyl, cycloalkenyl, cycloheteroalkyl, cycloheteroalkenyl, acyl, aryl, heteroaryl, arylalkyl, arylalkenyl, or arylalkynyl, each of which is optionally substituted; R2 is independently halo, -OR, -NO2, cyano, or -NR a R b , -N3, -S(O)2R a , -C(alkyl), -C(cycloalkyl), C(alkynyl), C(haloalkyl), acyl, aryl, heteroaryl, arylalkyl, arylalkenyl, or arylalkynyl, each of which is optionally substituted; and wherein R, R a and R b are independently alkyl; R3 is independently -(CH2) m NR a R b {where m=0 to 2}, halo, any amino acid, any saturated or unsaturated aromatic or heteroaromatic ring or carbohydrate derivative, where R a and R b are independently alkyl; R4 is independently halo, -OR, -NO2, cyano, or -NR a R b , -N3, -S(O)2R a , -C(alkyl), -C(cycloalkyl), C(alkynyl), C(haloalkyl), acyl, aryl, heteroaryl, arylalkyl, arylalkenyl, or arylalkynyl, each of which is optionally substituted; and wherein R, R a and R b are independently alkyl; Ar1 is aryl, heteroaryl, arylalkyl, arylalkenyl, or arylalkynyl, each of which is optionally substituted. Regarding.

[0092] In some exemplary embodiments, the present invention relates to pharmaceutical compositions comprising one or more compounds having formula (IV)-(IX) as disclosed herein or a pharmaceutically acceptable salt thereof, together with one or more diluents, excipients, or carriers.

[0093] In some exemplary embodiments, the invention relates to a pharmaceutical composition comprising one or more compounds having formula (IV)-(IX) as disclosed herein or a pharmaceutically acceptable salt thereof, together with one or more diluents, excipients or carriers, wherein the compounds are immunomodulators.

[0094] In some exemplary embodiments, the present invention relates to pharmaceutical compositions comprising one or more compounds having formula (IV)-(IX) as disclosed herein or a pharmaceutically acceptable salt thereof, together with one or more diluents, excipients, or carriers, wherein the compounds are inhibitors of the PD-1 and PDL-1 signaling pathways.

[0095] In some exemplary embodiments, the present invention relates to a method for treating a cancer patient, comprising administering a therapeutically effective amount of one or more compounds having formula (IV)-(IX) as disclosed herein or a pharmaceutically acceptable salt thereof, together with one or more diluents, excipients, or carriers.

[0096] In some exemplary embodiments, the present invention relates to pharmaceutical compositions comprising one or more compounds having formula (IV)-(IX) as disclosed herein or a pharmaceutically acceptable salt thereof, together with one or more diluents, excipients, or carriers, wherein the compounds are inhibitors of the PD-1 and PDL-1 signaling pathways.

[0097] In some exemplary embodiments, the present invention relates to a method for treating a cancer patient, comprising administering to a cancer patient in need of relief from said cancer a therapeutically effective amount of one or more compounds having formula (IV)-(IX) as disclosed herein, and one or more carriers, diluents, or excipients.

[0098] In some exemplary embodiments, the present invention relates to a method for treating a cancer patient, comprising administering to a cancer patient in need of relief from said cancer a therapeutically effective amount of one or more compounds having formula (IV)-(IX) as disclosed herein in combination with one or more other compounds of the same or different mechanism of action, and one or more carriers, diluents, or excipients.

[0099] In some exemplary embodiments, the present invention relates to a method for treating a cancer patient, comprising administering to a cancer patient in need of relief from said cancer a therapeutically effective amount of one or more compounds having formula (IV)-(IX) as disclosed herein, and one or more carriers, diluents, or excipients, wherein the compounds are inhibitors of the PD-1 and PDL-1 signaling pathways.

[0100] In some exemplary embodiments, the present invention relates to a method for treating a cancer patient, comprising administering a therapeutically effective amount of one or more compounds having formula (IV)-(IX) as disclosed herein, and one or more carriers, diluents, or excipients to a cancer patient in need of relief of said cancer, wherein said cancer is castration-resistant prostate cancer.

[0101] In some exemplary embodiments, the present invention relates to a pharmaceutical composition comprising one or more compounds having formula (I)-(IX) as disclosed herein and one or more carriers, diluents, or excipients, for use as a medicament for cancer.

[0102] The programmed cell death protein 1 / programmed death ligand 1 (PD-1 / PD-L1) interaction is an immune checkpoint exploited by cancer cells to enhance immunosuppression. There is a significant need to develop small molecule drugs that are fast-acting, cost-effective, and readily bioavailable compared to antibodies. Unfortunately, brute-force synthesis and validation of large libraries of small molecules that inhibit the PD-1 / PD-L1 interaction are both time-consuming and expensive. To improve this drug discovery pipeline, we developed a machine learning methodology trained on patent data to identify, synthesize, and validate small molecule PD-1 / PD-L1 inhibitors. Our model incorporates two features: docking scores to represent binding energy (E) as a global feature and subgraph features via a molecular topology graph neural network (GNN) to represent local features. This energy-graph neural network (EGNN) model outperformed conventional machine learning methods and simple GNNs with an F1 score of 0.9524 and a Cohen's kappa score of 0.8861 on the holdout test set, suggesting that small molecule topology, structural interactions in the binding pocket, and chemical diversity in the training data are all important considerations for enhancing model performance. Using the bootstrapped EGNN model, compounds with predicted high and low potencies for inhibiting the PD-1 / PD-L1 interaction were selected for synthesis and experimental validation. The potent inhibitor, (4-((3-(2,3-dihydrobenzo[b][1,4]dioxin-6-yl)-2-methylbenzyl)oxy)-2,6-dimethoxybenzyl)-D-serine, is a hybrid of two known bioactive scaffolds and has an IC50 value of 339.9 nM, which is relatively better than known bioactive compounds. We conclude that our bootstrapped EGNN model will be useful for identifying target-specific, highly potent molecules designed by scaffold hopping, a well-known medicinal chemistry technique.

[0103] Traditionally, the development of small molecule inhibitors relies on high-throughput screening of large libraries of diverse drug-like compounds. 10 Or medicinal chemists iterating on scaffolds with weak receptor activity to enhance potency. 11 This entire process is (i) time-consuming, (ii) requires expensive equipment and robotics, (iii) is based on trial and error, and (iv) is highly inefficient for quickly identifying several new scaffolds. 12 In addition, virtual screening using docking methods has been developed to improve this process, but with limited success. 13 Furthermore, support vector machines (SVM) 14~16 , Random Forest (RF) 17~19 , graph convolutional networks 20 and Graph Neural Networks (GNNs) 21、22 ML architectures such as these have been used to design drugs and predict drug-target interactions. 23、24 Recently, a new architecture that utilizes a combination of graph features in protein binding sites has shown great promise for calculating binding affinities and determining whether a compound binds to a target. 20、22 .

[0104] Several new neural network-based architectures have also been proposed that show promise for identifying strong scaffolds, but many remain experimentally untested. 15、16、25~28 We hope that advances in our ability to retrieve and characterize protein crystallography data will drive the creation of these models. 29 Recently, it has been shown that molecular subgraph features incorporated via GNNs and the protein features encoded by their sequences can be combined to predict whether a compound can target a given protein. 24 Inspired by this work and based on our interest in developing methods for drug design and immunology, 29~36In this paper, we developed a new machine learning model to predict whether a compound can inhibit the PD-1 / PD-L1 interaction. Our method replaces protein sequence features with docking scores that represent the binding free energy. Due to this global energy interaction of small molecules in the binding pocket, we named this model "Energy Graph Neural Network" (EGNN). The three-dimensional atomic interaction energy scores are calculated using the CANDOCK algorithm. 31 The predictions are calculated using the EGNN model (Figure 1B) and combined with local molecular graph features (Figure 1A) using an end-to-end training methodology (Figure 1C-D). In this work, we use this EGNN model to select designs for synthesis and experimentally test a curated list of compounds from these predictions to proactively identify potent PD-L1 small molecule inhibitors using a homogeneous time-resolved fluorescence (HTRF) assay. We also tested negative predictions, suggesting the usefulness of the model for use in selecting potent lead drugs as PD-1 / PD-L1 inhibitors.

[0105] Patent data for training the EGNN model

[0106] We used PD-1 / PD-L1 small molecule inhibition data for 762 compounds from four patents to train our model: WO2015 / 034820A1 by BMS. 7 and WO2015 / 160641A2 (674 compounds) 6 , and WO2018 / 119263A1 by Incyte Corporation. 37 and US2018 / 0273519A1 38 (88 compounds). A homogeneous time-resolved fluorescence (HTRF) binding assay was used to demonstrate activity against the PD-1 / PD-L1 interaction in the patents. However, the patents do not provide individual IC values ​​for all compounds. 50The data set did not include a single value, providing a range of inhibition by different molecules. Therefore, we trained a binary classifier using a cutoff for both datasets to treat molecules as "high potency" or "low potency" (Figure 1). If a molecule's reported IC50 was less than or equal to 100 nM in the patent, it was considered a "high potency" molecule; otherwise, it was considered a "low potency" molecule. This threshold was chosen because it was the only common threshold among the four patents (Table S4). It should be noted that our experiments using multiple replicates did not allow us to obtain accurately reported results for some molecules in the patents, so the actual value of the IC50 should not be considered here (see the IC50 value of compound 4a in Table 2 and BMS-1 ​​annotated as 6-100 nM in WO2015 / 034820A1 patent 7). Therefore, we consider the positive prediction (high potency) based on the IC50 values ​​of our experiments relative to the upper limit of the BMS control molecule (compound 4a / BMS-1) in WO2015 / 034820A1 patent 7. A training dataset of 762 small molecules with BMS or Incyte annotations is shown in the Supporting Information file (TrainingData.xlsx).

[0107] We selected the BMS and Incyte patents to encompass the chemical diversity of molecules in the training dataset. Figure 2A shows the distribution of low- and high-potency molecules and common scaffolds in the BMS and Incyte patents. The BMS patent has 372 high-potency compounds and 302 low-potency compounds, while the Incyte patent has 47 high-potency compounds and 41 low-potency compounds, respectively. The BMS patent scaffold contains 417 derivatives of (2-methyl-3-biphenylyl)methanol and 257 derivatives of [3-(2,3-dihydro-1,4-benzodioxin-6-yl)-2-methylphenyl]methanol, shown in Figure 2A (bottom left) with R groups as CN, Cl, Br, and CH. On the other hand, the Incyte patent scaffold has distinct sub-scaffolds, labeled A and B in Figure 2A (bottom right). In the Incyte scaffold, X represents either an N or CR group (R: alkyl group). Because the general structure of compounds in the Incyte patents has more structural diversity in terms of sub-scaffolds and atoms, these scaffolds suggest that the chemical diversity of Incyte compounds is higher than that of BMS compounds. We verified this observation using pairwise Tanimoto similarity scores for BMS and Incyte compounds, as shown as heat maps in Figures 2B and 2C, respectively. Pairwise Tanimoto similarity was calculated using Morgan fingerprints with a radius of 2 and a bit length of 1024. High red areas in the BMS heat map indicate that molecular pairs are structurally similar to each other. Low red areas in the Incyte heat map suggest that they have more chemical diversity in molecular structure. Furthermore, the average pairwise Tanimoto similarity scores for all BMS compounds were found to be 0.4434 and 0.3920 for all Incyte compounds, confirming the higher chemical diversity of Incyte compounds compared to BMS compounds.

[0108] PD-L1 homodimer and PD-1 / PD-L1 crystal structures reveal binding sites for docking

[0109] BMS compounds have previously been shown to inhibit PD-1 / PD-L1 interaction by inducing PD-L1 dimerization 8、9 Therefore, the PD-L1 homodimer crystal structure (PDB ID: 5N2F) was selected for docking all compounds in this manuscript. The PD-1 / PD-L1 crystal structure (PDB ID: 4ZQK) was also used to verify whether the locations of the PD-L1 binding sites in the homodimer crystal structure (5N2F) overlapped and aligned with each other using the PyMol software package. 40 (Figure 3A). In Figure 3B, the selected binding site of the PD-L1 homodimer in the overlapping and aligned crystal structure is shown, indicating that the formation of a PD-L1 homodimer with a small molecule blocks the PD-1 / PD-L1 interaction. A known inhibitor of the PD-1 / PD-L1 interaction (ligand ID: 8HW) in the selected binding site (Figure 3C) is shown. 8 suggested that the selected binding site corresponding to the PD-L1 homodimer is relevant for developing PD-1 / PD-L1 inhibitors. Therefore, the docking interaction of the PD-L1 homodimer will be relevant for identifying PD-1 / PD-L1 inhibitors. Furthermore, direct docking with PD-1 / PD-L1 has not been performed because the binding site between PD-1 and PD-L1 is filled with interacting amino acid residues from both proteins. Therefore, there is no space for small compounds to dock with the PD-1 / PD-L1 complex.

[0110] CANDOCK 31 (For details, see the Experimental section on Docking in EGNN and Generation of Energy Features with Energy Vectors (E)). Prior to developing the machine learning method, we used 96 potential energy scoring functions in CANDOCK to classify high-potency versus low-potency molecules. 31We also assessed the ability to use only the docking scores of compounds in the training set for each of the classes. Cohen's kappa scores were used to select the best scoring function for discriminating between the two classes (Table S1). The scoring function, radial cumulative complete 15 (RCC15), achieved the highest Cohen's kappa score of 0.41447. However, the RCC15 score was unable to clearly separate all high- and low-potency molecules in the training data (see the violin plot in Figure S1). Using only one scoring function is not sufficient to capture different states of PD-1 / PD-L1 inhibition with small molecules. Therefore, we developed an EGNN model using the top scoring function from each class that demonstrated a positive Cohen's kappa value (Table S1) to represent the overall trait (see the Experimental section for details on docking in EGNN and generation of energy traits with energy vectors (E)). This included the RCR15 (kappa = 0.37746) and RCC15 (kappa = 0.41447) scoring functions. Models with kappa scores between 0.21 and 0.40 were considered fair agreement models, and models with kappa scores between 0.41 and 0.60 were considered moderate agreement models. 42 .

[0111] The hyperparameter-optimized EGNN model outperforms GNN and other baseline models.

[0112] A detailed description of the EGNN model, which includes the combination of molecular GNNs combined with docking, is provided in the experimental section. Figure 1 shows that the EGNN model combines local features of small molecules, represented as GNNs (see Graph Neural Networks for molecular graphs in EGNN), together with global features of protein-ligand interactions, represented as docking scores (see Docking in EGNN and Generation of Energy Features with Energy Vectors (E) in EGNN). The EGNN was trained on 88 small molecules with high and low potency for PD-1 / PD-L1 inhibition extracted from two Incyte patents (see patent data for training the EGNN model). We calculated the mean F1 score (by 5-fold cross-validation) versus the variation in the number of epochs for different hyperparameters (Figure S2). Optimal hyperparameters were chosen to avoid overfitting and underfitting, and for EGNN, they included dimension (dim) of hidden molecule vectors = 10, subgraph radius = 2, and number of hidden layers = 1 (see Experimental Section for EGNN training and hyperparameter optimization).

[0113] The EGNN and GNN models were trained on different training sets to examine the effect of chemical diversity on model performance for classifying high- and low-potency molecules. Two datasets (BMS and Incyte) were used separately and in combination to train the EGNN model and determine the best dataset for predicting PD-1 / PD-L1 inhibitors. Splitting the dataset into training-validation and test sets (4:1) was performed using two different methods: (1) using a random splitter on shuffled data, and (2) using a scaffold splitting method with the DeepChem library. 43Training was then performed using 5-fold cross-validation, and the predictive ability of the models was evaluated using a test set. Cohen's kappa, F1 score, and area under the receiver operating characteristic curve (AUROC) were measured to compare three models trained on BMS data only, Incyte data only, and the combined BMS-Incyte data. Furthermore, in separate experiments, all measurements were taken for the EGNN and GNN models trained on BMS data only, while predicting on Incyte data, and vice versa.

[0114] Figure 4A shows how the data set is divided in the training-validation-test set scheme.The initial data set is divided into two sets at a ratio of 4:1 based on scaffold division or random shuffle division.Then, 80% of the data set is used as training and validation data set, while 20% of the data set is used as holdout test set to evaluate model performance.

[0115] Figure 4A shows the average F1 scores (by 5-fold cross-validation) for both models trained on BMS compounds, Incyte compounds, and a union of these sets. The average F1 scores for the EGNN and GNN models trained on the Incyte data were 0.956 (±0.051) and 0.678 (±0.157), respectively (Figure 4A). This result suggests that the EGNN model trained on the Incyte data, which contains diverse chemical scaffolds (Figure 2C), performs significantly better than the GNN trained on the same dataset. However, when the same test was performed with only BMS compounds, which have lower chemical diversity than Incyte, the average F1 scores were comparable for both models: 0.992 (±0.007) for the EGNN model and 0.948 (±0.022) for the GNN model. This suggests that the GNN model performs better with less chemical diversity in the training data compared to greater chemical diversity in the training data. However, the EGNN model performs well on both datasets, indicating that it is a better model than the GNN.

[0116] The GNN model with scaffold splitting appeared to produce comparable results with EGNN (Figure S4). However, this was expected because graph neural networks use a two-dimensional molecular framework / topology in training. GNNs perform well when the framework distribution of compounds is similar in the training-validation and test sets. However, our intention is to develop a model that can be used to screen large compound libraries, which would not be necessary to share the same scaffold distribution as the training set (i.e., Incyte or BMS). Therefore, we chose a random splitter with shuffling to create a test set for performance evaluation to develop a more generalized model.

[0117] Cohen's kappa scores for the different test sets (the holdout test set was based on a random split) for both models trained on BMS compounds, Incyte compounds, and a union of these sets are shown in Figure 4B. The kappa scores for the EGNN and GNN models trained on Incyte data and tested on the holdout test set were 0.8861 and 0.4304, respectively (Figure 4B). This result suggests that the EGNN model trained on the Incyte data (Figure 2C), which contains diverse chemical scaffolds, performs significantly better than the GNN trained on the same dataset. However, when the same test was performed on only BMS compounds, which have lower chemical diversity than Incyte, the Cohen's kappa scores were comparable for both models: 0.6416 for the EGNN model and 0.7164 for the GNN model. This suggests that the GNN model performs better with less chemical diversity in the training and test data compared to greater chemical diversity. Both models also perform comparable with the combined dataset. When both the BMS and Incyte datasets were combined, the kappa score for the holdout test set for the EGNN model was 0.6072, and for the GNN model it was 0.6729. A similar trend in F1 scores is observed for the three different training set comparisons (Figure 4C). These results suggest that the EGNN model outperforms the GNN model for chemically diverse datasets such as the Incyte data. We believe this is due to the addition of "global" energy features captured by the docking scores of PD-L1 homodimers as training data in EGNN, compared with only "local" structural features of small molecules in the training data for the GNN model.

[0118] We also investigated the ability of EGNN and GNN models trained on one compound set to predict high- and low-potency PD-1 / PD-L1 inhibitors in another compound set. These results are presented in Figures 4B and 4C (kappa and F1 scores, respectively), with different bar patterns representing different test sets. The Tanimoto similarity between Incyte and BMS compounds is also shown in the heatmap in Figure 4D. The average pairwise Tanimoto similarity score of 0.3044 indicates that the compounds in these two datasets are very dissimilar to each other. When EGNN and GNN models were trained on BMS compounds and used to predict Incyte compounds, Cohen's kappa scores of 0.1505 for EGNN and 0.1200 for GNN were observed, along with F1 scores of 0.3810 and 0.2264, respectively. On the other hand, both the F1 and kappa scores for both models improved when they were trained on Incyte data and used to predict BMS compounds (EGNN kappa score = 0.3852, GNN = 0.3196 and EGNN F1 score = 0.7400, GNN = 0.6958). These results show that there is a significant improvement in F1 score and Cohen's kappa score for both the EGNN and GNN models when trained on Incyte data and tested on BMS. However, the AUROC score cannot accurately distinguish these models (Figure S3). This was expected because AUROC is not a good measure for evaluating models trained on skewed / imbalanced datasets and may interfere with poor model performance. 44These results also suggest that the EGNN model outperformed the GNN model in both cases (see Table S2 for details). This highlights the importance of chemical diversity in the training data, even when there is not much compound similarity between the training and test sets. Therefore, using only BMS or combined BMS and Incyte data to train the final model to make predictions about unknown molecules is not suitable. Therefore, we chose only the Incyte dataset to train the EGNN model, which significantly improved EGNN model performance (Table S2).

[0119] We also confirmed the ability to classify training set compounds into low- and high-potency classes simply by comparing Tanimoto 2D similarity. A violin plot showing the distribution of Tanimoto 2D similarity scores for low-potency, high-potency, and all compounds is shown in Figure S5. This clearly shows that compounds in either the high- or low-potency class exhibit a high probability of having low pairwise similarity scores, even when all compounds are considered equally. Therefore, simply considering topological similarity is not sufficient to select potent PD-1 / PD-L1 inhibitors.

[0120] Finally, we compared the cross-validated EGNN model with GNN, support vector machine (SVM), and random forest (RF) baseline models trained on the Incyte training data using their test set performance. Both the SVM and RF models were also trained on local and global features. The Weisfeiler-Lehman algorithm 45Fingerprints extracted from molecular graphs with a radius of 2 using the scikit-learn library were used as "local" features similar to the EGNN and GNN models. Here, we zero-padded to the maximum fingerprint length to maintain the same fingerprint dimensions. The same preselected docking scores (RCR15 and RCC15) obtained by CANDOCK were used as "global" energy features. The obtained AUROC, AUPRC, precision, recall, F1 score, and Cohen's kappa values ​​are tabulated in Table 1 for all four models. The SVM models were derived from the scikit-learn library using the "linear" kernel. 46 RF is trained using the "svm" package in the scikit-learn library using 500 trees. 46 It was trained using the "Random Forest Classifier" in the scikit-learn library. 46 The "Evaluation Metrics" module in was used for the statistics AUROC, precision, recall, F1 score, and Cohen's Kappa. Precision-recall curves and AUPRC values ​​for the models were generated using the "precrec" library in the R programming language. 47 The EGNN model outperformed all other models with AUROC, AUPRC, precision, recall, F1 score, and Cohen's kappa values ​​of 0.9250, 0.9212, 0.9091, 1.0000, 0.9524, and 0.8861, respectively (Table 1). Comparing the precision-recall curves of these four models (Figure 4E) also confirmed that the EGNN model outperformed all other three models. In summary, the combined local and global features in EGNN gave the best performance using the Incyte dataset.

[0121] [Table 1]

[0122] Synthetic selection and validation of EGNN predictions for PD-1 / PD-L1 inhibition

[0123] Using the optimal hyperparameters and an EGNN model trained on the Incyte dataset, predictions were obtained for an in-house database of small molecule designs. We developed a bootstrapped EGNN model to predict compounds with high and low potency for PD-1 / PD-L1 inhibition using the 1000EGNN model (see Bootstrapping the EGNN Model). Bootstrapping is an essential statistical technique that can be used to select reliable molecules for synthesis and experimental validation based on the consensus among multiple models. The bootstrapped EGNN model identified high- and low-potency small molecules synthesized and then experimentally validated in HTRF binding assays as PD-1 / PD-L1 inhibitors (see Table 2 for an overview). Specifically, we selected four molecules predicted to have high or low potency for PD-1 / PD-L1 inhibition for testing based on the bootstrapped EGNN softmax mean score and standard deviation (see EGNN softmax scores in Table 2).

[0124] Among the EGNN bootstrapped predictions, we selected one molecule (compound 4b) as highly potent and three low-potency molecules with different scaffolds (compounds 4c, 4d, and 4e) for further testing. We defined a new parameter called "count," which records the number of models out of 100 that give a softmax score of 0.5 or greater for the molecule of interest. Specifically, compound 4b was predicted to be a highly potent PD-1 / PD-L1 inhibitor with a count of 99 and an average softmax score of 0.7771 (±0.1193). In contrast, only 69 counts and an average Softmax value of 0.5786 (±0.1406) were obtained for compound 4c, only 5 counts and an average Softmax value of 0.1821 (±0.1514) for compound 4d, and 62 counts and an average Softmax value of 0.5280 (±0.1259) for compound 4e, suggesting a low predicted potency. We also synthesized a BMS scaffold (compound 4a, a known PD-1 / PD-L1 inhibitor) for use as a positive control for HTRF experiments. The compound structures are shown in Schemes 1 and 2 (see Experimental Section for procedures and characterization). The predicted highly potent molecule (compound 4b) is a hybrid of two BMS molecules, 4a (BMS-1) and BMS-1002, containing (4-((3-(2,3-dihydrobenzo[b][1,4]dioxin-6-yl)-2-methylbenzyl)oxy)-2,6-dimethoxybenzyl)-D-serine, (2-methyl-3-biphenylyl)methanol, and [3-(2,3-dihydro-1,4-benzodioxin-6-yl)-2-methylphenyl]methanol, respectively (Figure 5A), suggesting the ability of the EGNN model to undergo scaffold hopping.

[0125] The EGNN trained only on the Incyte data contained three [3-(2,3-dihydro-1,4-benzodioxin-6-yl)-2-methylphenyl]methanol scaffold-containing compounds. In a separate experiment, we removed these and performed predictions on our synthetic library. The EGNN model was still able to predict compound 4b (a compound based on the [3-(2,3-dihydro-1,4-benzodioxin-6-yl)-2-methylphenyl]methanol scaffold) as a highly potent compound with a softmax score of 0.8285 ± 0.1396 and 971 counts. This result demonstrates that the EGNN model can identify highly potent PD-1 / PD-L1 inhibitors with the [3-(2,3-dihydro-1,4-benzodioxin-6-yl)-2-methylphenyl]methanol scaffold without having to learn on similar scaffolds.

[0126] Scheme 1. Representative synthesis scheme a [ka] [ka]

[0127] Scheme 2. Synthesis of 4d (GCL2) [ka]

[0128] The top docking pose of the PD-L1 homodimer (PDB ID: 5N2F) for compound 4b interacts in a similar manner as previously shown for the co-crystal structure 8 (Figure 5B). Specifically, for compound 4b, the 2,3-dihydro-1,4-benzodioxin group binds to amino acid residue Tyr56 ( A This made it easier to operate. ATyr56 is the 2,3-dihydro-1,4-benzodioxin group 8 It is known that the hydrophobic pocket does not close from one end, creating a hydrophobic tunnel (Figure 5B inset) rather than a hydrophobic cleft in the docked conformation. In addition, the aromatic ring of compound 4b (2,3-dihydro-1,4-benzodioxin) is located between the amino acid residues A It is stabilized by π-π stacking interactions with Tyr56 (Figure 5C). The central methylbenzyl ring in the structure (magenta in Figure 5A, 4b) is rotated by approximately 30° relative to the 2,3-dihydro-1,4-benzodioxin ring, orienting the methyl group of the methylbenzyl ring toward strand B of the PD-L1 homodimer. This orientation is mediated by Met115 and Met125 of both strands A and B of the homodimer. B The D-serine terminus of compound 4b provides a hydrophobic interaction with Ala121. A along with plausible hydrogen bond formation between the main chain NH of Tyr123 and the oxygen in one of the two methoxy groups of the 4b molecule. A Thr20 and A It forms a hydrogen bond with Ala121 (Figure 5C). These results suggest a favorable interaction of compound 4b, which can dimerize PD-L1, which would result in PD-1 / PD-L1 inhibition.

[0129] HTRF assay demonstrated that compound 4b had an IC of 339.9 nM for inhibiting the PD-1 / PD-L1 interaction. 50 (See Experimental Section for details) (Figure 5D), which is consistent with the IC50 of 521.5 nM for BMS compound 4a (BMS-1 ​​molecule in BMS patent WO2015 / 034820A1), which was synthesized and tested in our laboratory. 50 The BMS-1 ​​molecule has an IC of 6-100 nM in the HTRF assay in the BMS patent. 50 It should be noted that 7 However, multiple replicates of our experiments demonstrated ICs of less than 100 nM to inhibit PD-1 / PD-L1 interaction. 50 did not yield any value (IC50 (See the Calculation of Values ​​section and supporting file HTRF_IC50_Data.xlsx). As previously mentioned, we have 50 This result does not affect our machine learning method, as we have classified molecules based on high and low potency rather than estimating values. A possible explanation for this difference in experimental results between our work and the patent is the difference in the HTRF assay and IC 50 The difference may be in the protocol used to perform the calculation of IC values. For this reason, we provide a detailed description of the HTRF assay protocol, IC 50 The supporting data files used by the scientific community and for the analysis of data for the calculation of IC (see Experimental Section). To test the validity of our bootstrapped EGNN model and accurately identify low potency predictions, we also tested compounds 4c, 4d, and 4e, which resulted in no / poor inhibition of the PD-1 / PD-L1 interaction (Table 2). The IC for each compound tested was 50 Plots (Fig. S6) and 13 C and 1 H NMR spectra are provided as supporting information. The pairwise Tanimoto similarity scores between 4a and 4e (Table S3) demonstrate the ability of the EGNN model to identify high- and low-potency inhibitors, regardless of structural similarity. Compound 4e shows high similarity to the control BMS compound (4e), with a Tanimoto similarity score of 0.8018. However, the model recognized it as a low-potency molecule, and actual testing showed it to be a poor inhibitor of PD1 / PD-L1 with an IC50 of 1261 nM. On the other hand, the model recognized compound 4b as a high-potency PD1 / PD-L1 inhibitor, and HTRF assays showed it to have a very good IC50 of 339.9 nM. 50However, its pairwise Tanimoto similarity score with the reference compound (4a) is only 0.5074. Collectively, these results suggest that the bootstrapped EGNN model can be used to select molecules for synthesis and experimental validation of PD-1 / PD-L1 inhibition, and to identify low-potency molecules that are structurally similar to the reference compound 4a.

[0130] [Table 2]

[0131] Discussion and Conclusions

[0132] Cancer immunotherapy has made great strides in treating cancer, and the development of PD-1 / PD-L1 immune checkpoint inhibitors has become a key area of ​​research for the treatment of several tumors. Currently, six therapeutic antibodies targeting both PD-1 (pembrolizumab, nivolumab, and cemiplimab) and PD-L1 (atezolizumab, durvalumab, and avelumab) have been approved by the US FDA. Recently, structure determination of the human PD-1 / PD-L1 complex and cocrystals of inhibitory ligands has been demonstrated. 44~46 Along with this, several new small molecule PD-1 / PD-L1 inhibitors have been developed. 43 Still, the field is very active in the search for new small molecules to inhibit this important checkpoint, and we hope to enhance the pace of this quest through the use of novel structure-based ML methods that have been extensively benchmarked and prospectively tested.

[0133] We developed a novel ML methodology, EGNN, based on a combination of local features of small molecule topology and global features of small molecules interacting within the binding pocket as an energy score for selecting, synthesizing, and experimentally validating potent inhibitors of the PD-1 / PD-L1 interaction. Specifically, EGNN outperforms traditional ML architectures, such as RF and SVM, which incorporate both local and global features, as well as GNN models, which use only local features of small molecule topology. When benchmarked with known PD-1 / PD-L1 inhibitors from BMS and Incyte patent data, we concluded that the topology of the small molecule, the structural interactions in the binding pocket, and the chemical diversity of the training data are all important considerations for enhancing model performance.

[0134] The inventors used a bootstrapped EGNN model (based on the 1000EGNN model) to predict and reliably select new molecules for chemical synthesis and subsequent inhibition testing using an HTRF PD-1 / PD-L1 inhibition assay. The inventors believe that bootstrapping is an important statistical technique for use with ML methods to confidently select molecules for experimental validation in drug design. The predicted high-potency molecule, (4-((3-(2,3-dihydrobenzo[b][1,4]dioxin-6-yl)-2-methylbenzyl)oxy)-2,6-dimethoxybenzyl)-D-serine, is a hybrid of two BMS high-potency molecular scaffolds and exhibited an IC of 339.9 nM for inhibiting the PD-1 / PD-L1 interaction. 50 This suggests the ability of the EGNN model to perform scaffold hopping to identify novel inhibitors. The accurate selection of low-potency molecules with different scaffolds suggests the utility of our bootstrapped model for the selection of compounds for synthesis, a challenging problem in the field of ML-based drug design.

[0135] Our EGNN methodology can be further developed by adding more chemically diverse data and incorporating reinforced iterative learning with experiments performed at each step to develop a library of structurally diverse small molecules that inhibit PD-1 / PD-L1 interactions and derive structure-activity relationships. Given the general nature of the machine learning model and readily available docking methodology, this approach can be adapted to identify small molecule immunomodulators by targeting other immune checkpoints, as well as be used to incorporate local and global features for target-based drug design in general. [Example]

[0136] Experimental section

[0137] Homogeneous time-resolved fluorescence (HTRF) assay for testing the inhibition of prospective compounds

[0138] Inhibition of PD-1 / PD-L1 interaction was tested for four high- and low-potency predicted compounds using a PD1 / PD-L1 HTRF assay kit from Cisbio US, Inc. The assay protocol was used for each predicted compound (4b, 4c, 4d, and 4e) and the BMS control compound (4a) as described in the kit. Briefly, 2 μL of compound, 4 μL from a 25 nM Tag1-PD-L1 protein solution, and 4 μL from a 250 nM Tag2-PD1 protein solution were added to a Cisbio HTRF 96-well low-volume white plate. The plate was then incubated at room temperature for 15 minutes. Next, 10 μL of premixed anti-tag detection reagent (1× Anti-Tag1-Eu) was added to the 96-well low-volume white plate. 3+5 μL of 1× Anti-tag2-XL665 and 5 μL of 1× Anti-tag2-XL665 were added, and the sealed plate was incubated at room temperature for 2 hours. Finally, the plate sealer was removed, and measurements were performed using an HTRF®-compatible reader. This protocol used 12 different concentrations of each compound, with the maximum and minimum assay concentrations being 10,000 nM and 0.001 nM, respectively. Several replicates of different concentrations were performed for the high-potency predicted compound 4b (36 data points) and the positive control compound 4a (48 data points). A fitted curve for the normalized signal, displayed as ΔF / ΔFmax (calculated using the HTRF ratio 665 nm / 620 nm) versus log [concentration], was used to determine the 50% inhibitory concentration (IC) of the compounds. 50 ) was determined (IC 50 See next section on calculating values).

[0139] To calculate ΔF / ΔF max, first calculate the HTRF ratio as follows:

number

[0140] A multiplication factor of 10,000 was used to improve the accuracy of the data during calculations without dealing with decimal values. ΔR ratios, which indicate the "specific signal" of compounds that disrupt the PD-1 / PD-L1 interaction, were calculated by subtracting the background HTRF ratio (negative DMSO control in our work) from each compound (sample) HTRF ratio as follows: ΔR = HTRF ratio (sample) - HTRF ratio (background)

[0141] Data normalization was then performed to minimize variation in values ​​between different days, different plate reader instruments, or when assays were performed by different individuals. Normalization was performed to the background HTRF ratio and calculated as follows:

number

[0142] Finally, the ΔF / ΔF max ratio was calculated to allow comparison of values ​​between multiple experiments.

number

[0143] I C 50 Calculating values

[0144] IC for PD-1 / PD-L1 inhibition 50 IC values ​​were determined by analyzing the logarithm of the concentration-response curves and fitting them to a sigmoidal curve using four-parameter logistic (4PL) regression using GraphPad Prism software version 8.3.0 for Windows, GraphPad Software, La Jolla, California USA, www.graphpad.com. 50 The values ​​are provided in Table 2. The following equation defines the regression curve:

number

[0145] Machine learning architecture of the EGNN model

[0146] The EGNN model was developed using PyTorch. 47All scripts and results for implementing the machine learning model are provided on GitHub at https: / / github.com / chopralab / egnn. Figure 1 shows an overview of the EGNN machine learning architecture. We implemented a graph neural network for molecular graphs by Tsubaki and colleagues. 24 Briefly, ChemAxon Molar Converter 48 The molecular structures were converted into SMILES strings using the RDKit software. 49 Using the software package and the Weisfeiler-Lehman algorithm, an r-radius subgraph graph for the molecule was extracted (Figure 1A). The following section contains details of the EGNN architecture.

[0147] Graph Neural Networks for Molecular Graphs in EGNN

[0148] The following equations and annotations for molecular GNN details are taken from the original paper with minor modifications for clarity. 24 It is reproduced here from the lowercase boldface text (e.g.

number

number

number

number

[0149] A graph can be defined as G = (V, E), where V and E are the set of vertices and edges, respectively. When applied to chemistry, atoms can be defined as vertices, and chemical bonds can be defined as edges. Initially, all atoms and chemical bonds are embedded as real-valued vectors with dimension d based on their different types. Due to the limited diversity of atoms (e.g., C, N, O, etc.) and bonds (e.g., single, double, triple bonds, etc.) in small molecules, the number of learning parameters is limited. Therefore, a strategy called r-radius subgraph is used. 50 was used to circumvent this limitation.

[0150] r-radius subgraph

[0151] The set of all atoms within a defined radius r, atom i, can be represented as N(i,r). If r=0, then N(i,r)={i}, which is the set of all atoms in the molecule. The i-th vertex (v i ) is defined as follows: i (r) =(V i (r) ,E i (r) ), in the formula, V i (r) ={v j |j∈N(i,r)} E i (r) ={e mn∈E|(m,n)∈N(i,r)×N(i,r-1)} The i-th and j-th atoms (e ij ) is defined as follows: e ij (r) =(V i (r-1) ∪V j (r-1) ,E i (r-1) ∩E j (r-1) )

[0152] The randomly initialized embedding (Figure 1) is then assigned to each r-radius edge e based on its type. ij (r) and vertices (v i (r) ) Backpropagation has been used to train these random embeddings.

[0153] vertex transition function

[0154] Suppose,

number

number

number

number

number

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number

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[0155] Edge transition function

[0156] As mentioned before, the edge transition function is

number

number

number

number

[0157] Molecular vector output of molecular GNN

[0158] The transition function is an updated set of atomic (vertex) vectors.

number

number

number

[0159] First, all reported molecules are compared with the Marvin sketch. 51 All drawn molecules were then cleaned in 3D and converted into sybyl.mol2 files, which were then analyzed using our in-house CANDOCK software using the default parameters of 20,000 maximum number possible and 20% top seed percent. 31 The software package (version 0.6.0) was used for docking (Figure 1B). The CANDOCK source code is available on GitHub at https: / / github.com / chopralab / candock / releases / tag / v0.6.0. Docking was performed using the PD-L1 homodimer crystal structure (PDB ID: 5N2F). We selected the binding site based on the coordinates of the crystal ligand (ligand ID: 8HW) in the protein structure. Then, radial-mean-reduced (RMR6) was used. 31 The top pose31 was used as the "selector" parameter for docking to select the top poses for each docked compound. 31 The docking scores were recalculated using all 96 different potential energy functions available in the software. All 96 CANDOCK docking energy scores for each molecule were normalized for each potential energy function for use as vectors in the EGNN model;

number

number

number

[0160] EGNN output

[0161] As shown in Figure 1C, the normalized docking energy score vector

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number

number

number

number

number

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[0162] Bootstrapping the EGNN model

[0163] The model uses random numbers to initialize edge and vertex vectors. Therefore, bootstrapping was used on the final model to obtain predictions. 1000 different models with distinct random seeds were trained to obtain predictions on the in-house molecular design test set. The averaged softmax score was used as the final prediction result of the bootstrapped model. Finally, the molecules in the synthetic test set were classified as high or low potency based on the averaged softmax score. If it was greater than or equal to 0.5, it was considered high potency, otherwise it was considered low potency (Figure 1). Thus, the EGNN model was trained on a given SMILES string, CANDOCK 31 The model was trained with backpropagation using vectors of RCR15 and RCC15 scores generated by

[0000] and their high or low potency status according to the PD-L1 protein. The trained model can be used to predict the probability that a given molecule is a high or low potency molecule for the PD-L1 protein.

[0164] EGNN training and hyperparameter optimization

[0165] The model takes as input the SMILES string and docking energy score string for a given molecule. The hyperparameters of the model were optimized before use for prediction. The dimension (dim) of the GNN hidden vector, the number of hidden layers of the GNN, and the subgraph radius were optimized by considering the 5-fold cross-validated F1 score. Three values ​​were used for the dimension of the GNN hidden molecule vector output (i.e., dim = 5, 10, and 15). The numbers 1, 2, and 3 were used to ascertain the optimal number of hidden layers in the GNN. Finally, the optimal subgraph radius for the model was selected from radius = 1, 2, and 3.

[0166] Calculating F1 scores and Cohen's kappa

[0167] The following terms were used to calculate Cohen's kappa and F1 score: The number of compounds predicted to be highly potent and experimentally reported to be highly potent was considered as true positives (TP). The number of compounds that were deemed highly potent but experimentally reported in the patent to be of low potency was interpreted as false positives (FP). True negatives (TN) are defined as the number of compounds predicted to be of low potency and also experimentally reported to be of low potency. False negatives (FN) are then defined as the number of compounds predicted to be of low potency but experimentally reported to be of high potency. The F1 score is defined as follows:

number

number

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[0168] synthesis

[0169] Unless otherwise noted, all reagents and solvents were purchased from commercial sources and used as received. All reactions were carried out in screw-cap vials. Proton ( 1 H) and carbon ( 13 C) NMR spectra were obtained using a 500 MHz system with Me4Si as the internal standard and are reported in units of δ. Coupling constants (J values) are reported in Hz. Column chromatography was performed on silica gel using flash chromatography (Teledyne ISCO EZprep). High-resolution mass spectra (HRMS) were obtained using electron spray ionization (ESI) techniques on a TOF mass analyzer. Organic solvents and starting materials were used as received. BMS Compound 4a (BMS-1 ​​or KPGC01S94) 7 Compounds 4b-c were synthesized according to the reported procedures starting from compounds 1, 2a-b, and 3a-b, and the spectral data were consistent with the reported data. 6~8 .

[0170] Compound 4a (BMS-1 ​​or KPGC01S94), (S)-1-(2,6-dimethoxy-4-((2-methyl-[1,1'-biphenyl]-3-yl)methoxy)benzyl)piperidine-2-carboxylic acid: 3b from Scheme 1 (45 mg, 0.125 mmol), (S)-piperidine-2-carboxylic acid (64.5 mg, 4 equiv., 0.5 mmol), sodium cyanoborohydride (40.8 mg, 5.2 equiv., 0.65 mmol) were dissolved in DMF (1 mL), and then acetic acid (2 drops) was added. The reaction mixture was stirred at 80 °C for 1 h. The reaction was monitored by TLC. The crude was purified with 0-20% DCM:MeOH to give the desired product as an off-white solid (31.5 mg, 53% yield). 1 H NMR (500 MHz, DMSO-d6) δ 7.49 - 7.41 (m, 3H), 7.39 - 7.34 (m, 1H), 7.32 - 7.25 (m, 3H), 7.19 (dd, J = 7.7, 1.5 Hz, 1H), 6.41 (s, 2H), 5.17 (s, 2H), 4.08 (s, 2H), 3.78 (s, 7H), 3.11 (t, J = 5.5, 5.5 Hz, 1H), 3.08 - 2.99 (m, 1H), 2.60 (dd, J = 13.5, 6.7 Hz, 1H), 2.20 (s, 3H), 1.80 (q, J = 6.0, 5.9, 5.9 Hz, 2H), 1.55 (q, J = 6.7, 6.1, 6.1 Hz, 2H), 1.37 (ddt, J = 18.4, 12.8, 6.5, 6.5 Hz, 3H); 13 C NMR (126 MHz, DMSO-d6) δ 172.0, 161.47, 160.32, 142.70, 141.85, 135.83, 134.59, 130.25, 129.63, 128.86, 128.72, 127.44, 126.04, 92.05, 69.23, 64.35, 56.42, 48.70, 46.21, 31.16, 26.11, 22.13, 21.27, 16.41.

[0171] Compound 4b (KPGC01S32), (4-((3-(2,3-dihydrobenzo[b][1,4]dioxin-6-yl)-2-methylbenzyl)oxy)-2,6-dimethoxybenzyl)-D-serine: 3a from Scheme 1 (35.6 mg, 0.104 mmol), D-serine (32.8 mg, 3 equiv.), sodium cyanoborohydride (19.6 mg, 3 equiv.) were dissolved in DMF (1 mL), and then acetic acid (0.104 mmol, 1 equiv., 2 drops) was added. The reaction mixture was allowed to stir at room temperature overnight. The reaction was monitored by TLC. The crude was purified with 0-20% MeOH:DCM to give the desired product as an off-white solid (42% yield). 1 H NMR (500 MHz, DMSO-d6) δ 7.42 (dd, J = 7.6, 1.5 Hz, 1H), 7.22 (t, J = 7.6, 7.6 Hz, 1H), 7.15 (dd, J = 7.6, 1.5 Hz, 1H), 6.90 (d, J = 8.2 Hz, 1H), 6.76 (d, J = 2.1 Hz, 1H), 6.73 (dd, J = 8.2, 2.1 Hz, 1H), 6.37 (s, 2H), 5.13 (s, 2H), 4.26 (s, 4H), 3.86 (s, 2H), 3.77 (s, 6H), 3.58 (dt, J = 8.5, 3.4, 3.4 Hz, 2H), 2.94 (t, J = 6.0, 6.0 Hz, 1H, NH), 2.20 (s, 3H); 13 C NMR (126 MHz, DMSO-d6) δ 172.85, 160.75, 159.69, 159.53, 143.42, 142.96, 142.12, 135.85, 134.96, 134.65, 130.20, 128.56, HRMS(ESI):C 28 H 32 NO8[M+H] + Measured mass 510.2132 m / z; calculated mass 510.2128.

[0172] Compound 4c (KPGC01S138), N-(2,6-dimethoxy-4-((2-methyl-[1,1'-biphenyl]-3-yl)methoxy)benzyl)-3,3,3-trifluoro-1-phenylpropan-1-amine: 3b from Scheme 1 (8 mg, 0.022 mmol), 3,3,3-trifluoro-1-phenylpropan-1-amine (16.7 mg, 0.088 mmol, 4 equiv.), sodium cyanoborohydride (7.2 mg, 0.114 mmol, 5.2 equiv.) were dissolved in DMF (0.5 mL), and then acetic acid (1 drop) was added. The reaction mixture was stirred at 80 °C for 3 h. The reaction was monitored by TLC. The crude was purified with 0-20% MeOH:DCM to give the desired product as an oil (68% yield). 1 H NMR (500 MHz, CDCl3) δ 7.45 - 7.38 (m, 5H), 7.38 - 7.30 (m, 6H), 7.30 - 7.27 (m, 1H), 7.26 (d, J = 5.4 Hz, 1H), 6.22 (s, 2H), 5.08 (s, 2H), 3.96 (t, J = 6.5, 6.5 Hz, 1H), 3.75 (s, 6H), 3.73 (d, J = 6.5 Hz, 2H), 2.46 (s, 1H), 2.27 (s, 3H), 1.29 - 1.24 (m, 1H); 13 C NMR (126 MHz, CDCl3) δ 160.23, 159.41, 143.06, 141.94, 135.1, 134.49, 132.81, 130.34, 129.40, 128.49, 128.32, 128.11, 127.91, 127.62, 127.18, 126.90, 125.65, 91.17, 69.34, 56.21, 55.53, 39.11, 16.23;HRMS(ESI):C 32 H 33 F3NO3[M+H] + Measured mass 536.2419 m / z; calculated mass 536.2413.

[0173] Compound 4d (GCL.2), (7R,8R,9S,13S,14S,17R)-17-ethynyl-17-hydroxy-7,13-dimethyl-1,2,6,7,8,9,10,11,12,13,14,15,16,17-tetradecahydro-3H-cyclopenta[a]phenanthren-3-one: Tibolone (156 mg, 0.5 mmol) was placed in a round-bottom flask containing 10 mL of THF and 100 μL of water. p-Toluenesulfonic acid (85 mg, 0.5 mmol) was then added, and the mixture was refluxed at 80 °C for 48 h. The reaction progress was monitored by TLC. The organic solvent was then evaporated to dryness to give the crude product, which was purified by flash column chromatography using 20% ​​ethyl acetate in petroleum ether solvent mixture as the eluent to give an off-white solid pure compound GCL2 (53% yield). 1 H NMR (500 MHz, MeOD) δ 5.80 (t, J = 2.1, 2.1 Hz, 1H), 2.88 (s, 1H), 2.56 (ddt, J = 14.1, 5.4, 1.6, 1.6 Hz, 1H), 2.42 - 2.28 (m, 4H), 2.27 - 2.19 (m, 1H), 2.18 - 2.13 (m, 1H), 2.06 - 1.90 (m, 3H), 1.77 - 1.53 (m, 6H), 1.44 - 1.25 (m, 2H), 1.14 (qd, J = 11.0, 11.0, 10.9, 4.2Hz, 1H), 0.91 (d, J = 0.7 Hz, 3H), 0.79 (d, J = 7.1 Hz, 3H). 13 C NMR (126 MHz, MeOD) δ 201.06, 167.84, 125.32, 87.28, 78.82, 73.45, 48.12, 47.95, 47.78, 47.61, 47.44, 47.27, 47.10, 46.67, 45.82, 43.05, 42.76, 42.18, 38.28, 36.02, 32.25, 30.66, 26.44, 26.41, 21.71, 11.79, 11.77.

[0174] Compound 4e (KPGC01S42), (S)-1-(2,6-dimethoxy-4-((2-methyl-[1,1'-biphenyl]-3-yl)methoxy)benzyl)piperidine-3-carboxylic acid: 3b from Scheme 1 (24 mg, 0.0066 mmol), (D)-nipecotic acid (34.2 mg, 0.0265 mmol, 4 equiv.), sodium cyanoborohydride (5.2 mg, 0.0343 mmol, 5.2 equiv.) were dissolved in DMF (1 mL), and then acetic acid (1 drop) was added. The reaction mixture was allowed to stir at room temperature for 14 h. The reaction was monitored by TLC (silica, 5% DCM:MeOH). The crude was purified by flash chromatography using 0-20% DCM:MeOH to give the desired product as an oil (43% yield). 1 H NMR (500 MHz, DMSO): δ 7.49 - 7.41 (m, 3H), 7.39 - 7.34 (m, 1H), 7.32 - 7.25 (m, 3H), 7.19 (d, J = 7.7 Hz, 1H), 6.37 (s, 2H), 5.15 (s, 2H), 3.75 (s, 6H), 3.53 (s, 2H), 2.38 - 2.30 (m, 2H), 2.20 (s, 3H), 1.97 (s, 4H), 1.16 (s, 2H); 13 C NMR (126 MHz, DMSO): δ 170.81, 160.50, 160.06, 142.67, 141.87, 135.99, 134.56, 130.18, 129.63, 128.81, 128.71, 127.42, 126.01, 92.00, 69.09, 60.22, 56.27, 55.37, 55.04, 52.65, 49.05, 31.14, 26.68, 21.21, 16.40, 14.54;LCMS / MS (ESI): 29 H 33 NO5[M+H] + Measured mass 476.3 m / z; calculated mass 476.24. [ka]

[0175] Synthesis of 2a: [ka]

[0176] Step-1: 2-(3-Bromo-2-methylphenyl)-5-((4-fluorophenyl)(piperidin-1-yl)methyl)-1,3,4-oxadiazole: In a clean, dry screw-cap vial with a magnetic stir bar, a mixture of piperidine (1 equiv.), 4-fluorobenzaldehyde (1 equiv.), and N-(isocyanoimino)triphenylphosphorane (1 equiv.) was dissolved in DCM (5 mL / mmol). A solution of 3-bromo-2-methyl-benzoic acid (1 equiv.) in DCM was slowly added to the reaction mixture at room temperature and allowed to stir at 50-60 °C for 2 h. The solvent was removed in vacuo, and the crude was purified by flash chromatography using hexane:ethyl acetate (0-60%) as the eluent to give a pale yellow oily product.

[0177] Step-2: 2-((4-fluorophenyl)(piperidin-1-yl)methyl)-5-(2-methyl-[1,1'-biphenyl]-3-yl)-1,3,4-oxadiazole (2a): In a clean, dry screw-cap vial with a magnetic stir bar, a mixture of 2-(3-bromo-2-methylphenyl)-5-((4-fluorophenyl)(piperidin-1-yl)methyl)-1,3,4-oxadiazole (1 equivalent), phenylboronic acid (2 equivalents), and PdCl(dppf)-CHCl (3 mol%) was taken and purged once with argon. Toluene (4.5 mL) and ethanol (1.5 mL) were added, and the reaction mixture was purged with argon. While purging, 1.5 mL of 1 M NaHCO was added and allowed to stir at 80 °C for 45 min. The reaction progress was monitored by TLC. Upon completion of the reaction, ethyl acetate (20 mL) was added and washed with water (2 x 20 mL). The organic solvent was removed under reduced pressure and the crude material was purified by flash chromatography (0-60%, hexane:ethyl acetate) to give a clear oily product.

[0178] Synthesis of 2b [ka]

[0179] A mixture of 2-(3-bromo-2-methylphenyl)-5-((4-fluorophenyl)(piperidin-1-yl)methyl)-1,3,4-oxadiazole (1 equivalent), (2,3-dihydrobenzo[b][1,4]dioxin-6-yl)boronic acid (2 equivalents), and PdCl(dppf)-CHCl (3 mol%) was placed in a clean, dry screw-cap vial equipped with a magnetic stir bar and purged once with argon. Toluene (4.5 mL) and ethanol (1.5 mL) were added, and the reaction mixture was purged with argon. While purging, 1.5 mL of 1 M NaHCO was added and allowed to stir at 80 °C for 45 minutes. The reaction progress was monitored by TLC. Upon completion of the reaction, ethyl acetate (20 mL) was added and washed with water (2 × 20 mL). The organic solvent was removed under reduced pressure and the crude material was purified by flash chromatography (0-60%, hexanes:ethyl acetate) to give a clear oily product.

[0180] Synthesis of 2c [ka]

[0181] [ka]

[0182] Preparation of methyl N-(tert-butoxycarbonyl)-O-(tert-butyl)-D-serinate: A mixture of Boc-D-serine (1 equivalent) and iodomethane (3 equivalents) in DMF was stirred at room temperature for 2 hours. Ice-cold water was added to the reaction mixture, and it was extracted with ethyl acetate. The organic layer was then washed with water, and the ethyl acetate was removed in vacuo. The oily crude product was quantitatively obtained and used in the next step without further purification.

[0183] Preparation of tert-butyl (R)-(3-(tert-butoxy)-1-hydrazineyl-1-oxopropan-2-yl)carbamate: Hydrazine monohydrate (2 equivalents) was slowly added to a mixture of the methyl ester of Boc-D-serine (1 equivalent) in DCM kept in an ice bath. It was stirred at room temperature for 2 hours until a white suspension was observed. Ice-cold water was added to the reaction mixture, which was extracted with ethyl acetate. The organic layer was then washed with water, and the ethyl acetate was removed in vacuo. The white solid product was quantitatively obtained and used in the next step without further purification.

[0184] [ka]

[0185] A mixture of 3-bromo-2-methylbenzoic acid (1 eq.), EDCI (1.5 eq.), DIPEA (5 eq.) in DMF was stirred for 10 minutes, and then tert-butyl (R)-(3-(tert-butoxy)-1-hydrazinyl-1-oxopropan-2-yl)carbamate (1 eq.) was added to the reaction mixture, which was allowed to stir at room temperature overnight. The reaction was monitored by TLC. The crude was extracted with ethyl acetate:ice-cold water, and the ethyl acetate layer was removed under reduced pressure. The product was purified by flash column chromatography using 0-60% hexane:ethyl acetate to give a white solid (65% yield).

[0186] [ka]

[0187] A mixture of tert-butyl (R)-(1-(2-(3-bromo-2-methylbenzoyl)hydrazinyl)-3-(tert-butoxy)-1-oxopropan-2-yl)carbamate (1 equivalent, 0.5 mmol), PPh3 (1.1 equivalents), iodine (2 equivalents), and triethylamine (2 equivalents) in THF (5 mL) was stirred at room temperature overnight. The reaction was monitored by TLC. The product was purified by flash column chromatography using 0-60% hexanes:ethyl acetate to give an oily product (78% yield).

[0188] [ka]

[0189] A mixture of tert-butyl (R)-(1-(5-(3-bromo-2-methylphenyl)-1,3,4-oxadiazol-2-yl)-2-(tert-butoxy)ethyl)carbamate (1 equivalent), (2,3-dihydrobenzo[b][1,4]dioxin-6-yl)boronic acid (1.5 equivalents), PdCl(dppf)·CHCl (3 mol%) in toluene:ethanol (1.5:0.5 mL) was purged twice with argon. 1 M NaHCO (1.5 mL) was added under an inert atmosphere, and it was allowed to stir at 80 °C for 45 min. The reaction was monitored by TLC. The product was purified by flash column chromatography using 0–60% hexane:ethyl acetate to give an oily product (63% yield).

[0190] [ka]

[0191] To a solution of tert-butyl (R)-(2-(tert-butoxy)-1-(5-(3-(2,3-dihydrobenzo[b][1,4]dioxin-6-yl)-2-methylphenyl)-1,3,4-oxadiazol-2-yl)ethyl)carbamate (50 mg) in DCM (2 mL), trifluoroacetic acid (0.5 mL) was added and allowed to stir at room temperature for 3 hours. The reaction was monitored by TLC. The product was purified by flash column chromatography using 0-50% DCM:methanol to give an oily product (88% yield).

[0192] [ka]

[0193] Synthesis of (IIIa)

[0194] [ka]

[0195] Step 1: 3-Bromo-N,2-dimethylaniline (1 equiv.), (2,3-dihydrobenzo[b][1,4]dioxin-6-yl)boronic acid (1.5 equiv.), PdCl(dppf)·CHCl (3 mol%) in toluene:ethanol (1.5:0.5 mL) was purged twice with argon. 1 M NaHCO (1.5 mL) was added under an inert atmosphere, and it was stirred at 80 °C for 45 min. The reaction was monitored by TLC. The product was purified by flash column chromatography using 0-60% hexane:ethyl acetate to give an oily product (77% yield).

[0196] [ka]

[0197] Step-2: To a mixture of 3-(2,3-dihydrobenzo[b][1,4]dioxin-6-yl)-N,2-dimethylaniline (1 equiv.), propionaldehyde (1.1 equiv.), and (N-isocyanoimino)triphenylphosphorane in DCM, benzoic acid (1.1 equiv.) was added portionwise. The reaction mixture was stirred at 40°C for 2-3 hours. The reaction was monitored by TLC. The product was purified by flash column chromatography using 0-60% hexane:ethyl acetate to give an oily product (54% yield).

[0198] Synthesis of (IIIb) [ka]

[0199] Step 1: 3-Bromo-N,2-dimethylaniline (1 equiv.), phenylboronic acid (1.5 equiv.), PdCl(dppf)·CHCl (3 mol%) in toluene:ethanol (1.5:0.5 mL) was purged twice with argon. 1 M NaHCO (1.5 mL) was added under an inert atmosphere and stirred at 80 °C for 45 min. The reaction was monitored by TLC. The product was purified by flash column chromatography using 0-60% hexane:ethyl acetate to give an oily product (74% yield).

[0200] [ka]

[0201] Step-2: To a mixture of N,2-dimethyl-[1,1'-biphenyl]-3-amine (1 eq.), propionaldehyde (1.1 eq.), and (N-isocyanoimino)triphenylphosphorane in DCM, benzoic acid (1.1 eq.) was added portionwise. The reaction mixture was stirred at 40°C for 2-3 hours. The reaction was monitored by TLC. The product was purified by flash column chromatography using 0-60% hexane:ethyl acetate to give an oily product (48% yield).

[0202] [ka]

[0203] Representative Procedure: 4-(5-phenyl-1,3,4-oxadiazol-2-yl)-3,4-dihydrobenzo[e][1,2,3]oxathiazine 2,2-dioxide [Formula IV derivative]:

[0204] [ka]

[0205] In a clean, oven-dried screw-cap vial, sulfonylimine 1a (0.2 mmol), benzoic acid (1.1 equiv.), and (N-isocyanoimino)triphenylphosphorane (1.1 equiv.) were placed and cooled to -10 °C. Next, CHCl (3 mL) was added and stirred at the same temperature for 5–10 min. The solvent was partially removed under reduced pressure, and the crude product was loaded directly onto a silica cartridge and purified by flash chromatography (Teledyne ISCO) using EtOAc:hexane as the eluent. The major fractions were collected and repurified by reverse-phase preparative HPLC (Teledyne ISCO) using water:MeCN as the eluent.

[0206] A compound having formula V: [ka]

[0207] When R1=CH3, the compound [ka] and When R1=Cl, the compound [ka] A compound according to formula (V),

[0208] Representative procedure for the synthesis of 4-methyl-N-(phenyl(5-phenyl-1,3,4-oxadiazol-2-yl)methyl)benzenesulfonamide [Formula V derivative]: In a clean, oven-dried screw-cap vial, N-tosylimine (0.2 mmol), benzoic acid (1.1 equiv.), and (N-isocyanoimino)triphenylphosphorane (1.1 equiv.) were taken and cooled to -10 °C. Then, CHCl (3 mL) was added and allowed to stir at the same temperature for 5–10 min. The solvent was partially removed under reduced pressure, and the crude was directly loaded onto a silica cartridge and purified by flash chromatography (Teledyne ISCO) using EtOAc:hexane as the eluent to give a solid product.

[0209] Those skilled in the art will recognize that numerous modifications may be made to the specific implementations described above, and that the implementation should not be limited to the specific limitations described; other implementations may be possible.

[0210] While the invention has been illustrated and described in detail in the drawings and foregoing description, it is to be considered illustrative and not restrictive in character, it being understood that only certain particular embodiments have been shown and described, and that all changes and modifications that fall within the spirit of the invention are desired to be protected. The scope of the method and apparatus of the present invention is intended to be defined by the following claims. It must be understood, however, that the present disclosure may be practiced otherwise than as specifically explained and illustrated without departing from its spirit or scope. [ka] [ka] [ka] [ka]

[0211] (Item 1) Compounds having formula (I): [ka] or a pharmaceutically acceptable salt thereof [In the formula, Ar1 is optionally substituted aryl or heteroaryl; R1 and R2 are independently hydrogen, halo, azido, nitro, cyano, alkyl, alkenyl, alkynyl, alkylalkynyl, alkyloxy, hydroxyalkyl, aminoalkyl, thiolalkyl, mercaptoalkyl, heteroalkyl, heteroalkenyl, heteroalkynyl, heterocyclyl, cycloalkyl, cycloalkenyl, cycloheteroalkyl, cycloheteroalkenyl, acyl, aryl, heteroaryl, arylalkyl, arylalkenyl, or arylalkynyl, each of which is optionally substituted; R3 is halo, azido, nitro, cyano, alkyl, alkenyl, alkynyl, alkylalkynyl, alkyloxy, hydroxyalkyl, aminoalkyl, thiolalkyl, mercaptoalkyl, heteroalkyl, heteroalkenyl, heteroalkynyl, heterocyclyl, cycloalkyl, cycloalkenyl, cycloheteroalkyl, cycloheteroalkenyl, acyl, aryl, heteroaryl, arylalkyl, arylalkenyl, or arylalkynyl, each of which is optionally substituted; Q is [ka] or one or several amino acid residues]. (Item 2) The compound according to item 1, wherein Ar1 is phenyl, 2,3-dihydrobenzo[b][1,4]-dioxine or phenyl(thiazol-2-yl)methanol. (Item 3) The compound according to item 1, wherein R1 and R2 are independently hydrogen, methyl, hydroxyl, methoxyl or -OCH2Ar. (Item 4) The compound according to item 1, wherein R3 is CH3, CN or Cl. (Item 5) [ka] Item 1. The compound according to item 1, comprising: (Item 6) Compounds having formula (II): [ka] or a pharmaceutically acceptable salt thereof [In the formula, R1 is aryl, substituted aryl, or heteroaryl; R2 is an alkyl, alkenyl, alkynyl, alkylalkynyl, alkyloxy, hydroxyalkyl, aminoalkyl, thiolalkyl, mercaptoalkyl, heteroalkyl, heteroalkenyl, heteroalkynyl, heterocyclyl, cycloalkyl, cycloalkenyl, cycloheteroalkyl, cycloheteroalkenyl, acyl, aryl, heteroaryl, arylalkyl, arylalkenyl, or arylalkynyl, each of which is optionally substituted, primary or secondary amine; R3 is halo, azido, nitro, cyano, alkyl, alkenyl, alkynyl, alkylalkynyl, alkyloxy, hydroxyalkyl, aminoalkyl, thiolalkyl, mercaptoalkyl, heteroalkyl, heteroalkenyl, heteroalkynyl, heterocyclyl, cycloalkyl, cycloalkenyl, cycloheteroalkyl, cycloheteroalkenyl, acyl, aryl, heteroaryl, arylalkyl, arylalkenyl, or arylalkynyl, each of which is optionally substituted; Ar1 is aryl, heteroaryl, arylalkyl, arylalkenyl, or arylalkynyl, each of which is optionally substituted. (Item 7) The compound according to item 6, wherein Ar1 is phenyl, 2,3-dihydrobenzo[b][1,4]-dioxine, phenyl(thiazol-2-yl)methanol. (Item 8) The compound according to item 6, wherein R1 and R2 are independently piperidine, pyrrolidine, phenyl, 4-halophenyl (halo = fluoro, bromo, iodo), and / or one or more amino acid residues, either singly or in combination of amino acids. (Item 9) Item 7. The compound according to item 6, wherein R3 is methyl, CN or halo. (Item 10) [ka] Item 7. The compound according to item 6, wherein (Item 11) Compounds having formula (III): [ka] or a pharmaceutically acceptable salt thereof [In the formula, Ar1 is phenyl, 2,3-dihydrobenzo[b][1,4]dioxine or phenyl(thiazol-2-yl)methanol; Ar2 is piperidine or pyrrolidine; Ar3 is phenyl, 4-halophenyl (halo = fluoro, bromo, iodo), X is independently halo, azido, nitro, cyano, alkyl, alkenyl, alkynyl, alkylalkynyl, alkyloxy, hydroxyalkyl, aminoalkyl, thiolalkyl, mercaptoalkyl, heteroalkyl, heteroalkenyl, heteroalkynyl, heterocyclyl, cycloalkyl, cycloalkenyl, cycloheteroalkyl, cycloheteroalkenyl, acyl, aryl, heteroaryl, arylalkyl, arylalkenyl, or arylalkynyl, each of which is optionally substituted. (Item 12) [ka] Item 12. The compound according to item 11, wherein (Item 13) Item 12. The compound according to item 11, wherein X is methyl, cyano or chloro. (Item 14) 14. A pharmaceutical composition comprising one or more of the compounds according to items 1 to 13 or pharmaceutically acceptable salts thereof together with one or more diluents, excipients or carriers. (Item 15) 14. The compound according to items 1 to 13, which is an immunomodulator. (Item 16) 14. The compound according to items 1 to 13, which is an inhibitor of the PD-1 and PDL-1 signaling pathway. (Item 17) 14. The compound according to items 1 to 13, for the treatment of cancer. (Item 18) 14. A method for treating a cancer patient, comprising administering a therapeutically effective amount of one or more of the compounds according to items 1 to 13, and one or more carriers, diluents or excipients, to a patient in need of relief from said cancer. (Item 19) 14. A method for treating a cancer patient, comprising administering to a patient in need of relief from said cancer a therapeutically effective amount of a compound according to items 1 to 13 in combination with one or more other compounds of the same or different mechanism of action, and one or more carriers, diluents or excipients. (Item 20) 20. The method of item 19, wherein the cancer is castration-resistant prostate cancer. (Item 21) 14. A pharmaceutical composition comprising one or more of the compounds according to items 1 to 13 or pharmaceutically acceptable salts thereof, together with one or more diluents, excipients or carriers, for use as a medicament for cancer. (Item 22) A drug conjugate comprising one or more of the compounds described in items 1 to 13, wherein the conjugate provides cell-type or tissue-type targeting, or the conjugate targets another pathway that synergizes with the action of the compound described in items 1 to 13. (Item 23) 1. A method for treating a cancer patient, comprising administering to a patient in need of relief of said cancer a therapeutically effective amount of one or more compounds together with one or more carriers, diluents or excipients, wherein said compounds have the formula (I), (II) or (III). (Item 24) Compounds having formula (IV): [ka] or a pharmaceutically acceptable salt thereof [In the formula, [ka] represents a single or double bond, [ka] represents an optional cyclic ring; R1 is hydrogen, halo, azido, nitro, cyano, alkyl, alkenyl, alkynyl, alkylalkynyl, alkyloxy, hydroxyalkyl, aminoalkyl, thiolalkyl, mercaptoalkyl, heteroalkyl, heteroalkenyl, heteroalkynyl, heterocyclyl, cycloalkyl, cycloalkenyl, cycloheteroalkyl, cycloheteroalkenyl, acyl, aryl, heteroaryl, arylalkyl, arylalkenyl, or arylalkynyl, each of which is optionally substituted; R2 is hydrogen, halo, azido, nitro, cyano, alkyl, alkenyl, alkynyl, alkylalkynyl, alkyloxy, hydroxyalkyl, aminoalkyl, thiolalkyl, mercaptoalkyl, heteroalkyl, heteroalkenyl, heteroalkynyl, heterocyclyl, cycloalkyl, cycloalkenyl, cycloheteroalkyl, cycloheteroalkenyl, acyl, aryl, heteroaryl, arylalkyl, arylalkenyl, or arylalkynyl, each of which is optionally substituted; and X is carbon or nitrogen. (Item 25) [ka] [ka] 25. The compound according to item 24, wherein (Item 26) A compound having formula V: [ka] or a pharmaceutically acceptable salt thereof [In the formula, R1, R2, and R3 are independently selected from the group consisting of hydrogen, halo, azido, nitro, cyano, alkyl, alkenyl, alkynyl, alkylalkynyl, alkyloxy, hydroxyalkyl, aminoalkyl, thiolalkyl, mercaptoalkyl, heteroalkyl, heteroalkenyl, heteroalkynyl, heterocyclyl, cycloalkyl, cycloalkenyl, cycloheteroalkyl, cycloheteroalkenyl, acyl, aryl, heteroaryl, arylalkyl, arylalkenyl, or arylalkynyl, each of which is optionally substituted; or any two adjacent substituents joined together to form a cyclic or heterocyclic moiety. (Item 27) 27. The compound according to item 26, wherein R1=CH3. (Item 28) 27. The compound according to item 26, wherein R1=Cl. (Item 29) [ka] [ka] 27. The compound according to item 26, wherein (Item 30) Compounds having formula VI or VII: [ka] or a pharmaceutically acceptable salt thereof [In the formula, A is carbon or nitrogen; L is (CH2) n , -SO, -SO2, -CO, -CO(CH2)O, where n is 0, 1, or 2; Ar1 is aryl, heteroaryl, arylalkyl, arylalkenyl, or arylalkynyl, each of which is optionally substituted; R1 is halo, azido, nitro, cyano, alkyl, alkenyl, alkynyl, alkylalkynyl, alkyloxy, hydroxyalkyl, aminoalkyl, thiolalkyl, mercaptoalkyl, heteroalkyl, heteroalkenyl, heteroalkynyl, heterocyclyl, cycloalkyl, cycloalkenyl, cycloheteroalkyl, cycloheteroalkenyl, acyl, aryl, heteroaryl, arylalkyl, arylalkenyl, or arylalkynyl, each of which is optionally substituted; R2 is H, methyl, ethyl or any alkyl; R3 is halo, -OR, -NO2, cyano, -NR a R b , -N3, -S(O)2R a, -C(alkyl), -C(cycloalkyl), C(alkynyl), C(haloalkyl), acyl, aryl, heteroaryl, arylalkyl, arylalkenyl, or arylalkynyl, each of which is optionally substituted; and wherein R, R a and R b are independently alkyl; R4 is halo, an amino acid, a saturated or unsaturated aromatic or heteroaromatic ring, a carbohydrate derivative, or -(CH2) m NR a R b where m=0, 1, 2, and R a and R b are independently alkyl or or R3 and R4 joined together form a cyclic or heterocyclic moiety; R5 is halo, -OR, -NO2, cyano, -NR a R b , -N3, -S(O)2R a , -C(alkyl), -C(cycloalkyl), C(alkynyl), C(haloalkyl), acyl, aryl, heteroaryl, arylalkyl, arylalkenyl, or arylalkynyl, each of which is optionally substituted; and wherein R, R a and R b are independently alkyl. (Item 31) Compounds having formula VIII: [ka] or a pharmaceutically acceptable salt thereof [In the formula, n is 0, 1, or 2, A is carbon or nitrogen; R1 is halo, azido, nitro, cyano, alkyl, alkenyl, alkynyl, alkylalkynyl, alkyloxy, hydroxyalkyl, aminoalkyl, thiolalkyl, mercaptoalkyl, heteroalkyl, heteroalkenyl, heteroalkynyl, heterocyclyl, cycloalkyl, cycloalkenyl, cycloheteroalkyl, cycloheteroalkenyl, acyl, aryl, heteroaryl, arylalkyl, arylalkenyl, or arylalkynyl, each of which is optionally substituted; R2 is independently halo, -OR, -NO2, cyano, or -NR a R b , -N3, -S(O)2R a , -C(alkyl), -C(cycloalkyl), C(alkynyl), C(haloalkyl), acyl, aryl, heteroaryl, arylalkyl, arylalkenyl, or arylalkynyl, each of which is optionally substituted; and wherein R, R a and R b are independently alkyl, where R, R a and R b are independently alkyl or or R2 and R3 joined together form a cyclic or heterocyclic moiety; R3 is independently -(CH2) m NR a R b {where m=0 to 2}, halo, any amino acid, any saturated or unsaturated aromatic or heteroaromatic ring or carbohydrate derivative, where R a and R b are independently alkyl; R4 is independently halo, -OR, -NO2, cyano, or -NR a R b , -N3, -S(O)2R a , -C(alkyl), -C(cycloalkyl), C(alkynyl), C(haloalkyl), acyl, aryl, heteroaryl, arylalkyl, arylalkenyl, or arylalkynyl, each of which is optionally substituted; and wherein R, Ra and R b are independently alkyl, where R, R a and R b are independently alkyl; Ar1 is aryl, heteroaryl, arylalkyl, arylalkenyl, or arylalkynyl, each of which is optionally substituted. (Item 32) Compounds having formula IX: [ka] or a pharmaceutically acceptable salt thereof [In the formula, n is 0, 1, or 2, R1 is halo, azido, nitro, cyano, alkyl, alkenyl, alkynyl, alkylalkynyl, alkyloxy, hydroxyalkyl, aminoalkyl, thiolalkyl, mercaptoalkyl, heteroalkyl, heteroalkenyl, heteroalkynyl, heterocyclyl, cycloalkyl, cycloalkenyl, cycloheteroalkyl, cycloheteroalkenyl, acyl, aryl, heteroaryl, arylalkyl, arylalkenyl, or arylalkynyl, each of which is optionally substituted; R2 is independently halo, -OR, -NO2, cyano, or -NR a R b , -N3, -S(O)2R a , -C(alkyl), -C(cycloalkyl), C(alkynyl), C(haloalkyl), acyl, aryl, heteroaryl, arylalkyl, arylalkenyl, or arylalkynyl, each of which is optionally substituted; and wherein R, R a and R b are independently alkyl; R3 is independently -(CH2) m NR a R b{where m=0 to 2}, halo, any amino acid, any saturated or unsaturated aromatic or heteroaromatic ring or carbohydrate derivative, where R a and R b are independently alkyl; R4 is independently halo, -OR, -NO2, cyano, or -NR a R b , -N3, -S(O)2R a , -C(alkyl), -C(cycloalkyl), C(alkynyl), C(haloalkyl), acyl, aryl, heteroaryl, arylalkyl, arylalkenyl, or arylalkynyl, each of which is optionally substituted; and wherein R, R a and R b are independently alkyl; Ar1 is aryl, heteroaryl, arylalkyl, arylalkenyl, or arylalkynyl, each of which is optionally substituted. (Item 33) A pharmaceutical composition comprising one or more compounds according to items 24 to 32 or pharmaceutically acceptable salts thereof, together with one or more diluents, excipients or carriers. (Item 34) 33. The compound according to items 24 to 32, which is an immunomodulator. (Item 35) 33. The compound according to items 24 to 32, which is an inhibitor of the PD-1 and PDL-1 signaling pathway. (Item 36) 33. The compound according to items 24 to 32, for the treatment of cancer. (Item 37) 34. A method for treating a cancer patient, comprising administering to a patient in need of relief from said cancer a therapeutically effective amount of one or more of the compounds according to items 24 to 32, and one or more carriers, diluents or excipients. (Item 38) A method for treating a cancer patient, comprising administering a therapeutically effective amount of a compound according to items 24 to 32 in combination with one or more other compounds of the same or different mechanism of action, and one or more carriers, diluents or excipients to a cancer patient in need of relief of said cancer. (Item 39) 39. The method of claim 38, wherein the cancer is castration-resistant prostate cancer. (Item 40) A pharmaceutical composition comprising one or more of the compounds according to items 24 to 32 or pharmaceutically acceptable salts thereof, together with one or more diluents, excipients or carriers, for use as a medicament for cancer. (Item 41) A drug conjugate comprising one or more of the compounds described in items 24 to 32, wherein the conjugate provides cell-type or tissue-type targeting, or the conjugate targets another pathway that synergizes with the action of the compound described in items 24 to 32. (Item 42) 34. A method for treating a cancer patient, comprising administering to a patient in need of relief from said cancer a therapeutically effective amount of one or more of the compounds according to items 24 to 32, together with one or more carriers, diluents or excipients. (Item 43) A pharmaceutical composition comprising one or more compounds having formula (I) to (IX), or a pharmaceutically acceptable salt thereof, together with one or more diluents, excipients or carriers, for use as a medicament for the treatment of cancer.

Claims

1. Compounds having formula (I): 【Chemistry 39】 or a pharmaceutically acceptable salt thereof [In the formula, Ar １ is 2,3-dihydrobenzo[b][1,4]-dioxinyl, R １ and R ２ is independently alkyloxy; R ３ is alkyl, Q is, 【Transformation 55】 is.

2. R １ and R ２ 2. The compound of claim 1, or a pharmaceutically acceptable salt thereof, wherein is methoxyl.

3. R ３ But CH ３ 3. The compound of claim 2, wherein:

4. The compound has the formula: 【Transformation 56】 2. The compound of claim 1, wherein

5. 10. A pharmaceutical composition comprising one or more compounds according to claims 1 to 4 or pharmaceutically acceptable salts thereof, together with one or more diluents, excipients or carriers.

6. 10. A composition comprising a compound according to claims 1 to 4 for use in the treatment of cancer.

7. 7. The composition of claim 6, wherein the composition is administered in combination with one or more other compounds of the same or different mechanism of action.

8. The compound according to claim 1 or 2, or a pharmaceutically acceptable salt thereof, wherein R 3 is C 1-6 alkyl.

9. The composition described in claim 7, characterized in that the cancer is castration-resistant prostate cancer.

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