Pharmaceutical Platform Technology for Drug Discovery and Consumer Health Product Development

Abstract

In one embodiment, the present invention describes a method for identifying an optimized natural medicine containing a prescribed dose of active and contributing ingredients. In one embodiment, the method disclosed in the present invention develops a composition comprising cannabinoids for the treatment of hepatocellular carcinoma.

Description

[Technical field]

[0001] The present invention relates to a platform technology that incorporates drug design methodology into identifying and quantifying the active and contributing components of herbal formulations, such as traditional Chinese medicines.

[0002] Throughout this application, various references are referenced, the disclosures of these publications in their entireties are hereby incorporated by reference into this application in order to more fully describe the state of the art to which this invention pertains. [Background technology]

[0003] Traditional medicine, including Traditional Chinese Medicine (TCM), has been used for thousands of years. Most of these traditional medicinal formulas consist of either plant, animal, or mineral-based formulas. These formulas are designed to treat specific conditions unique to individuals who have been diagnosed using traditional paradigms. The aim of deploying TCM is to treat disease and return the patient to a state of equilibrium, unlike traditional medicines.

[0004] TCM is gaining popularity around the world. In 2018, the Hong Kong South China Morning Post reported that the TCM market was approaching US$50 billion worldwide. (https: / / www.scmp.com / news / china / society / article / 2166278 / traditional-chinese-medicine-closes-us50-billion-market-long.)

[0005] China as a country has invested significant resources in promoting and advancing TCM research.13 th In the Five-Year Plan, the State Council of China ordered the modernization of TCM (KPMG, 2016, The 13th th(Five-Year Plan-China's Transformation and Integration with the Global Economy). In early 2016, the Chinese government released a blueprint for the development of TCM over the next 15 years. It stated that TCM should have equal legal status to modern medicine and be regulated as such (https: / / www.economist.com / news / china / 21727945-unproven-remedies-promoted-state-why-chinas-traditional-medicine-boom-dangerous).). In late 2016, a "White Paper" was released stating that TCM will play a major role in reforming the healthcare system due to its relatively low cost (http: / / english.gov.cn / archive / white_paper / 2016 / 12 / 06 / content_281475509333700.htm) (Wang,Zhou et al.2021)).

[0006] Traditional medicine, including TCM, reached a new milestone in April 2019. th The World Health Organization's (WHO) International Statistical Classification of Diseases and Related Health Problems (ICD), Chapter 26, describes traditional treatments.

[0007] Despite the popularity of TCM and the support of the Chinese government, the mainstream scientific, medical, and pharmaceutical communities have been highly skeptical and critical of the therapeutic value of TCM. This criticism is primarily due to the complexity of TCM with regard to the identity and quantity, and therefore consistency and quality, of the active ingredients, the limited understanding of the mechanism of action, and the lack of clinical evidence received through the rigor of pharmaceutical testing (Graziose, Lila et al. 2010). It is clear that the pharmacological, clinical, and qualitative gaps between TCM and modern medicine need to be bridged before traditional medicine can be fully accepted by the mainstream. This void creates an unmet need for research in this field.

[0008] To address the knowledge gap between TCM and modern medicine, it is important to understand the structure of TCM formulas. TCM formulas are composed of four main components: Jun (the Emperor, the main component, a must in the formula), Chen (the Emperor's assistant, enhancing its activity), Zuo (a facilitator to reduce secondary symptoms and toxicity of active substances), and Si (guiding activity to the site of action).

[0009] In China and around the world, there is a driving force to link the concepts of TCM formulations with the mechanisms of drug action, which has become the basis of modern science. Several approaches such as data mining, in silico modeling and prediction, systems biology, multi-omics, genetics, bioinformatics, network pharmacology and quality markers have been used to study TCM formulas. Although there have been successful cases in identifying active ingredients and their mechanisms of action (Jiang, Zhang et al. 2012, Liang, Jiang et al. 2012, Shi, Zhao et al. 2012, Sun, Dai et al. 2012, Zhang, Li et al. 2013, Su, Jia et al. 2014, Dai 2019, Zhao, Liu et al. 2019), the therapeutic value of these ingredients acting individually or in combination has not been thoroughly clinically tested.

[0010] TCM scientists recognize that active ingredients must have the right drug-like properties, in other words, the right pharmacokinetic properties, and be therapeutically active. Attempts are made to narrow the list of actives to those with the right drug-like properties. Unfortunately, the pharmacokinetic properties of these ingredients are usually derived from animals (Wang, Sun et al. 2011), which may have limited applicability to humans. A method to rapidly and accurately predict the human pharmacokinetic properties of test compounds is desirable.

[0011] Moreover, these studies barely consider the contribution of active metabolites and their drug-like properties. Metabolism of herbal ingredients frequently occurs in the intestinal lumen, mainly by the microbiome, enterocytes and liver. A well-known example is Panax ginseng, whose ginsenosides are not active per se, but colonic metabolites such as compound K (Hasegawa 2004). With regard to active metabolites, the site of production adds complexity for the quantification of these compounds, especially metabolites produced by the microbiome.

[0012] As TCM pharmacology research advances, it is recognized that to successfully develop a medicine with the attributes of a TCM formula, several hurdles must be overcome, as the final product will contain multiple elements that address different problems within the body.

[0013] In silico modeling, including bioinformatics, systems biology, network pharmacology, data mining, and docking, is an excellent tool to identify potential active ingredients. This is the initial stage for both traditional drug discovery and TCM research. Two branches are found: looking for single compounds in the former case and groups of compounds in the latter case, addressing a large number of targets in a network that may or may not be related.

[0014] These in silico exercises provide direction for in vitro studies. A major complication in TCM research is the large number of studies and associated samples, which are often processed at high throughput, especially when multi-omics and network pharmacology methods are incorporated into the protocols.

[0015] Although the workflows of traditional drug discovery and TCM development are similar, due to the complexity of TCM formulas that contain multiple herbs, each herb can easily contain more than 1000 compounds, different strategies must be devised.

[0016] Before a methodology can be designed to develop medicines while preserving the characteristics of TCM, a "translation" of the TCM formula design from the perspective of modern science will be necessary. Furthermore, several issues must be identified, addressed, and included in the methodology.

[0017] Jun, Emperor, denotes the active component, Chen is a component that is less active and / or can interact with the active enhancing its effect, Zou is a component that is an inhibitor of Jun's toxic activity, and Shi is a component that directs the active to the active site. In this design, there are components with less activity (Jun). There are components with activity, but they act on the same receptor or pathway / network that enhances the effect of the active (Chen). There are compounds that treat the symptoms of the disease or inhibit the toxicity of the active (Zou). There are compounds that direct the activity to the site of action or change its fate in the body, like pharmacokinetics (Shi).

[0018] An ingredient does not have to be active if it contributes to the activity of the overall formulation. Interactions between ingredients can be active-active, active-inactive, or potentially inactive-inactive. Given these characteristics, the development process is not remotely similar to that used in conventional medicine.

[0019] Components from A. annua (Weathers, Elfawal et al. 2014, Weathers, Towler et al. 2014) have improved the pharmacokinetic properties of active ingredients, e.g., artemisinin. This example is a good example of an "Shi" candidate. Traditional screening processes will not target components without activity.

[0020] Tam et al. (2019) showed that genistein, an active ingredient in less than 1 min, enhances the activity of the main ingredient, biochanin A. In turn, biochanin A counteracts the potential toxicity of genistein in one extract (Jun, Chen, Zou).

[0021] In multicomponent product development, conventional wisdom is that two-body interactions are the primary reaction between molecules. Higher-order interactions were assumed to have negligible contributions. Recent publications in yeast have clearly shown that higher-order interactions in biological systems have significant contributions (Tekin, White et al. 2018). When considering higher-order interactions, the burden of unraveling these contributions becomes heavy and tracking is impractical (Figure 1). Potentially, billions of data points could be required, and the sampling burden increases when multiple targets are involved. It is clear that the process of evaluating higher-order interactions needs to be simplified.

[0022] Although our mechanistic understanding of TCM is improving with modern science, it is not surprising that there are no medicines derived from traditional TCM formulations that contain ingredients with attributes related to the four pillars of TCM formulations. Jun, Chen, Zou and Shi.

[0023] Using an integrative approach, Tam & Tuszynski (2008) successfully developed a Pharmaceutical Platform Technology (PPT) for identifying and quantifying active ingredients from complex mixtures, similar to those present in medicinal herbs.

[0024] This technique has been applied to identify contributing components of clinically tested plants. Unlike traditional pharmaceutical approaches, which aim to search for the most potent chemicals, the aim with PPT is to reveal the group of active / contributing components responsible for their pharmacological action.

[0025] The inventors reasoned that botanical medicines developed using PPT would have a greater chance of success because they have a longer history of use and, quite frequently, have supporting clinical data.

[0026] Tam and Tuszynski's (2008) approach requires randomization of unknowns in a complex mixture. This entire process, from start to finish, may require hundreds of thousands of samples (Figure 1(?)).

[0027] In the present invention, a simplified and improved method is described for identifying and quantifying the active and contributing ingredients in herbs or herbal formulations. Summary of the Invention

[0028] In one embodiment, the present invention discloses a pharmaceutical platform technology (generation II) (PPT-II), which engages drug development methodology to mine and quantify active and contributing components in herbal formulas such as traditional Chinese medicines, which are responsible for and consistent with the paradigm of TCM or other fields of herbal medicine.

[0029] In one embodiment, in silico methods are used to screen herbs or herbal formulas, the goal being to rapidly obtain a number of compounds of interest.

[0030] In one embodiment, compounds of interest are subjected to in vitro screening to determine their efficacy and likelihood of possessing suitable drug-like properties.

[0031] In one embodiment, compounds of interest are screened in specific in vitro models in conjunction with in silico strategies.

[0032] In one embodiment, the pharmacokinetic and pharmacodynamic properties of the compounds are examined in vitro.

[0033] In one embodiment, the ratio of target compounds is optimized prior to lead declaration.

[0034] In one embodiment, systems biology, systems pharmacology, and bioinformatics are incorporated into the present invention to increase the probability of identifying components responsible for the clinical response of botanical mixtures.

[0035] In one embodiment, a machine learning-based approach is incorporated into the PPT-II workflow to assist in data mining and analysis, as well as the selection of preclinical models for validation of identified candidates and their clinical relevance. In another embodiment, the selection of potential activities is supported by mechanism of action.

[0036] In one embodiment, a cluster expansion method is engaged to model the many-body interactions between the contributing components.

[0037] In one embodiment, the methods described in the present invention are used to develop a cannabis-derived product composition for the treatment of liver cancer.

[0038] In one embodiment, the cannabis derived composition consists of 2-3 active moieties, in another embodiment the compositions act together additively or synergistically. [Brief description of the drawings]

[0039] [Figure 1] We show the relationship between the number of interactions (N) and the number of interactions required to evaluate multiple body interactions. [Diagram 2] The workflow of PPT-II is shown. [Diagram 3] Figure 2 shows step 2 of the PPT-II flowchart (PD = pharmacodynamics, PK = pharmacokinetics, MTII = metabolic transporter inducers or inhibitors, and *iteration of step 2.1 where more than one compound may be present). [Figure 4] The integrated platform of PPT-II is shown. [Diagram 5] Images of 3D HepG2 spheroids are shown. [Figure 6] Relationship between oral bioavailability (Fb), systemic exposure, and area under the plasma concentration-time curve (AUC) Insert a. Cannabinoids with Fb of 90% or less, b. Cannabinoids with Fb of 90% or more [Figure 7]The chemical structures of cannabinol (CBN), cannabidiol (CBD), and cannabichromene (CBC) are shown below. [Figure 8A] FIG. 1 shows a comparison of IC50 values ​​obtained for CBD, CBC and CBN on HepG2 cells in IMDM medium with various concentrations of fetal bovine serum (FBS). [Figure 8B] 1 shows a repeat of the IC50 experiment of CBC on HepG2 cells over a wide range of FBS concentrations. [Figure 8C] 1 shows the reported IC50 values ​​of CBD, CBC, and CBN in relation to sorafenib observed in IMDM medium containing 1%, 10% and 20% FBS. [Figure 9] Comparison of observed IC50 values ​​for ternary ratios of CBD:CBC:CBN obtained for HepG2 cells in IMDM medium containing 20% ​​FBS, 1% ethanol. The CBN ratio is 1 compared to the other cannabinoid components. [Figure 10A] Comparison of IC50 values ​​of CBD, CBC, CBN, there-of, and sorafenib (performed in IMDM medium containing 20% ​​FBS and 1% ethanol) (SCI-931: CBD:CBC:CBN 9:3:1, SCI-421: CBD:CBC:CBN 4:2:1, SCI-111: CBD:CBC:CBN 1:1:1). [Figure 10B] Comparison of IC50 values ​​of three triplicate combinations of CBD:CBC:CBN on HepG2 cells in medium containing various amounts of FBS. Numbers in the legend correspond to ratio values. [Figure 11A] CBD:CBC:CBN 9:3:1 IC50 plot of HepG2 spheroids performed after 48 hours in DMEM medium with 1% FBS. [Figure 11B] Phase contrast (top) and fluorescence (bottom) microscopy images of HepG2 spheroids treated with a combination of CBD:CBC:CBN (9:3:1) for 48 hours. Fluorescence images obtained after 1 hour of incubation at 37°C using CyQuant™. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0040] The present invention discloses a specifically designed set of procedures consisting of a blend of in silico and in vitro methodologies, allowing to decipher active and related compounds from botanical or natural formulas. The uniqueness of this method is that it provides a means to develop multi-ingredient or consumer drug candidates in a short period of time with a high probability of clinical success.

[0041] In one embodiment, in silico methodologies include systems biology, systems pharmacology and bioinformatics for data mining and data analysis.

[0042] In one embodiment, machine learning based approaches are used in different areas of the procedure to enhance the thoroughness of data mining (step 1-1 in Figure 2) and the accuracy of strategic screening (steps 2-3 in Figure 3), to infer potential mechanisms of biological activity in disease networks, or to select appropriate in vitro models to provide clinically relevant results (steps 1-2, Figure 2).

[0043] In one embodiment, the structure of the research module of the PPT-II described in this invention includes three interacting units (computational unit, Figure 4) managed by a control center, which are bioinformatics, in vitro and analytical units. The computational unit plays the role of the brain, while the bioinformatics unit is responsible for data mining, data analysis, modeling, simulation, and provides insights into the feasibility of potential projects. The in vitro and analytical units receive instructions on the experimental design and, in return, receive data for processing and analysis. There is also a direct communication between the analytical and in vitro units, as the analytical group supplies samples for PK and PD measurements to the in vitro unit.

[0044] The flow charts shown in Figures 2 and 3 illustrate the workflow of the entire process. The validity of each step described in these schematics is demonstrated in the Examples.

[0045] In one embodiment, the primary objective of the present invention is to establish a set of clinically relevant parameters to describe the efficacy and drug-like properties of the active and contributing ingredients in herbal formulations.

[0046] In one embodiment, the method described in this invention is designed to rapidly and efficiently discard extraneous ingredients that have no or very limited contribution to the overall effect of the herbal formula (Figures 2 and 3).

[0047] In one embodiment, in vitro models, e.g., 3D cell cultures (Figure 5), organoids, patient-derived xenografts (PDX), cells on a chip (CoC), are used to mimic a patient's disease condition in vitro.

[0048] In one embodiment, a suitable in vitro model is established to estimate the efficacy, or pharmacodynamics (PD), or absorption (A), distribution (D), metabolism (M), excretion (E), toxicity (T), or PK of the individual ingredients in the herb or herbal formulation.

[0049] In one embodiment, key biomarkers for known and unknown in vitro models are selected from a small number of signaling pathways using a newly designed high-throughput system.

[0050] In one embodiment, these markers are used to quantify the effectiveness of the active and contributing ingredients.

[0051] In one embodiment, the absorption of individual components is assessed using either conventional CaCO-2 or MDCK cell models. Alternatively, 3D intestinal models, such as organ-on-a-chip models, are used to estimate the extent of absorption of herbal compounds.

[0052] In one embodiment, metabolism in enterocytes is estimated using human intestinal microsomes. Alternatively, 3D intestinal models, such as organ-on-a-chip models, are used to estimate in vivo intestinal metabolism.

[0053] In one embodiment, the luminal stability of the components is estimated by incubating the components in simulated gastric or intestinal fluids, mimicking fasting and fed states.

[0054] In one embodiment, established anaerobic methods are used to estimate the fecal metabolism of components.

[0055] In one embodiment, the hepatic metabolism of the components is assessed using either human liver microsomes, S-9 fractions, hepatocytes or a 3D model of the human liver.

[0056] In one embodiment, the excretion of components is estimated using established in silico methods. Alternatively, the renal excretion of components is estimated using 2D renal models such as MDCK cell monolayers or 3D model organoid models.

[0057] In one embodiment, the distribution of the components is estimated using the in vitro method reported by Mayumi, Tachibana et al. (2020). The volume of distribution and the profile of the active substance at the site of action are estimated by incorporating in vitro measurements of distribution to organs.

[0058] In one embodiment, the potential activity and pharmacokinetic parameters of MTII are estimated using the in vitro model established in the present invention.

[0059] In one embodiment, the in vitro estimated PD and PK parameters are scaled to a human physiologically based pharmacodynamics and pharmacokinetics (PBPKPD) model.

[0060] In one embodiment, dosages of the active and contributing components are calculated to achieve optimal concentrations at the site of action using a parameterized PBPKPD model.

[0061] Based on the predictions of the model, appropriate routes of administration and dosage forms are determined to achieve an optimized time course of activity at the site of action.

[0062] In one embodiment, the present invention provides a method for efficiently identifying a composition comprising an active compound from an herb or herbal formula for treating a disease, the method comprising: 1) obtaining a chemical profile in the herb or herbal formula from existing databases, or by creating a chemical profile using data mining and machine learning algorithms, or using high resolution mass spectrometry if there is no record in the existing database; 2) Identify and develop appropriate in vitro models that have clinical relevance to the disease; 3) Three criteria: systems biology, systems pharmacology, bioinformatics, and machine learning; a. Effectiveness in treating disease; b.Effects on absorption and metabolism of active ingredients C. Their drug-like properties and metabolites computationally identifying potential active ingredients as lead candidate compounds from the chemical profile obtained in step 1 using methods from a methodology based approach; 4) strategically screening the list of lead candidate compounds obtained in step 3) according to a set of predefined and adjustable pharmacodynamic and pharmacokinetic criteria based on a pairwise-based experimental approach using the in vitro model developed in step 2, resulting in a list of secondary candidate compounds; 5) validating and testing the secondary candidate compounds for efficacy and side effects in vitro to obtain a list of active compounds; and 6) Preparing compositions containing active compounds in terms of compound-compound interactions, the compositions having maximum efficacy with minimum side effects in treating the disease.

[0063] In one embodiment, the chemical profile of a herb or herbal formula refers to the list of chemicals in the herb or herbal formula.In one embodiment, the chemical profile includes the chemicals that are metabolized to or derived from the chemicals in the herb or herbal formula.In one aspect, the chemical profile includes the chemicals that can induce or inhibit the metabolism or change the transport of certain compounds in vitro or in vivo.

[0064] In one embodiment, the compound-compound interaction comprises a pairwise interaction. In one embodiment, the pairwise interaction is quantified by a two-dimensional array based in vitro model.

[0065] In one embodiment, the compound-compound interaction comprises a higher order interaction. In one embodiment, the higher order interaction is predicted by using cluster expansion with data from pairwise interactions validated with experimental input.

[0066] In one embodiment, drug-like properties are estimated using in vitro and in silico parameters as input for a physiologically based pharmacokinetic model.

[0067] In one embodiment, in vitro and in silico generated drug-like properties are used to estimate in vivo pharmacokinetic parameters with appropriate scaling using one or more of the following: 1) Human intestinal microsomes, 2) 3D intestinal model, 3) Simulated gastric or intestinal fluids, 4) Established anaerobic methods; 5) Human liver microsomes 6) S-9 fraction, 7) liver cells, 8) 3D model of the human liver, 9) 2D kidney model, 10) 3D organoid models, and 11) Organ-on-a-chip model.

[0068] In one embodiment, drug-like properties are estimated using existing algorithms, commercial or open source software.

[0069] In one embodiment, the chemical profile of the herb or herbal formula compound that can induce or inhibit metabolism or alter transport of potential active ingredients and metabolites.

[0070] In one embodiment, the chemical profile of the herb or herbal formula includes compounds that can increase or decrease the effects or side effects associated with one or more of the potential active ingredients and metabolites in treating a disease.

[0071] In one embodiment, the compositions are formulated in view of compound-compound interactions established using in vitro methods.

[0072] In one embodiment, the composition is formulated for easy or efficient delivery.

[0073] In one embodiment, the composition is formulated as a tablet, solution, suspension, cream, emulsion, or nanoencapsulated emulsion.

[0074] In one embodiment, the composition is formulated for oral, sublingual, topical, subcutaneous, intramuscular, intravenous or intraarterial administration.

[0075] In one embodiment, the disease is selected from the group consisting of cancer, cardiovascular, liver, renal, pulmonary, neurodegenerative, arthritis, immune, autoimmune diseases, and diseases described in the TCM literature.

[0076] In one embodiment, the herb or herbal formulation is or comprises cannabis or a cannabinoid.

[0077] In one embodiment, cannabinoids include, but are not limited to, one or more of the following: 1) Cannabichromene (CBC), 2) cannabinol (CBN), and 3) Cannabidiol (CBD). The purpose of the following examples is to illustrate the sequence of events and innovations in in vitro, in silico, and analytical methods to improve parameter prediction and enhance data mining and processing. EXAMPLES

[0078] Example 1 The purpose of this example is to illustrate the integrated approach of the present invention, encompassing computational, in vitro and analytical processes designed to efficiently and accurately generate multi-compound leads with herbal formula characteristics (Figures 2 and 3).

[0079] After the decision was made to develop a herbal formulation, a four-step approach was adopted to devise a group of bioactive substances and contributing compounds that play key roles in Jun, Chen, Zuoo, and Shi.

[0080] The first step is to use computational methodologies to screen the chemical profile of the herbal formula for potentially active and contributing compounds, their metabolic pathways and metabolite production. Compounds containing metabolites with suitable ADME or drug-like properties are included for further study (Step 1 in Figure 2).

[0081] The first step involves the use of in silico methods to predict the metabolism of active compounds, as well as inactive compounds that enhance or inhibit the metabolism of bioactive substances. The potential significance of these compounds depends on their PK properties, which are calculated using software such as ADME predictors.

[0082] Another part of the first phase is to establish appropriate and standardized in vitro models and tools approved by the US FDA to accurately describe the disease process (steps 1-2 in Figure 2). The in vitro tools will be used to generate activity data for compounds that are not available in databases, as part of in silico screening.

[0083] The second step is to perform a strategic screening using a combination of one in vitro model or one in vitro model system, an analysis based on the cluster growing framework and in silico methodologies. The goal is to quantify the efficacy of individual compounds and their potential interactions (step 2, Figures 2 and 3).

[0084] In the second stage, potential contributing compounds identified in the in-silico search with enzyme induction or inhibition properties are prepared and separated into two sets of samples, solo and paired compounds, for further screening using standardized in vitro models approved or tested by the US FDA (steps 2-1 and 2-2, Figure 3).

[0085] The second stage involves the use of a decision loop based on cluster expansion based on in silico models of potential interactions of compounds for the in vitro PD response to selected compounds (steps 2-3, Figure 3). If the in vitro PD validation of higher order interactions does not match the predictions of the in-silico model, refine the in-silico model using the in vitro PD observed interactions and screen the compounds in steps 2-4.

[0086] The second phase involves the use of high-throughput MTII studies of compounds obtained from steps 2-3 or 2-4 (steps 2-5, Figure 3).

[0087] The third step is to use in vitro models designed to predict the human drug-like properties of bioactive and contributing compounds (Figure 2).

[0088] The fourth step is optimizing the ratio of these compounds, resulting in leads for preclinical testing.

[0089] Unique features of this workflow include: 1. Potential active and contributing compounds are identified in silico, so fractionation is not necessary. 2. Potential active metabolites are identified early. Because most TCMs are orally administered and most naturally occurring compounds are in glycoside form, the actual activity is in the deglycosylated aglycone form. The number of aglycones is usually less than the number of the corresponding glycosides. 3. When including certain compounds in the analysis, consider the abundance of the components. 4. Higher-order interactions are often ignored. To include this aspect of interactions, an astronomical number of samples is required (Figure 1). Methodologies including cluster expansion based on pairwise PD interaction studies and tools for data analysis including nonlinear regression based on machine learning were used to reduce the number of samples tested. 5. By incorporating the above four points, your workload will be significantly reduced.

[0090] Compared to traditional drug discovery approaches, the risk is low as the rate of lead generation is faster and the efficacy and toxicity of herbal formulations are mostly known, thus greatly increasing the chances of success in clinical trials. Quality control issues faced by herbal medicines are overcome as lead content and dosage are well defined.

[0091] Example 2 The purpose of this example is to provide an overview of the computational process described in the present invention. The process as it is implemented is described and the process as it is developed is exposed.

[0092] Several algorithms and database management systems have been developed to accommodate the TCM database, data mining and analysis, dose-response and interaction analysis, and cluster expansion-based interaction analysis.

[0093] An SQLite database management system was developed to store and organize data from open source databases and in-house developed chemical, medicinal herb, and disease networks. A Graphic User Interface (GUI) was developed using PhP language to access the databases. For chemicals, the PubChem database from the National Institutes of Health (https: / / pubchem.ncbi.nlm.nih.gov / ) was used. For medicinal herbs, two TCM databases, TM-MC (http: / / informatics.kiom.re.kr / compound / browse.do) and ETCM (http: / / www.tcmip.cn / ETCM / ) were used. For disease networks, the KEGG: Kyoto Encyclopedia of Genes and Genomes database (https: / / www.kegg.jp / ) was used.

[0094] The data was analyzed using Python language with various bioinformatics and cheminformatics packages to mine physicochemical, biological, and pharmacokinetic information of the chemicals of interest through the PubChem database (KEGG (https: / / www.genome.jp / kegg / pathway.html). and signaling pathway information for the disease of interest). The algorithm can also work with several analytical tools including basic descriptive statistical analysis, partition coefficient and minimum dose prediction, and chemical similarity comparison.

[0095] We designed prototype algorithms for analyzing dose-response and compound-compound interactions. We performed cellular dose-response regressions for single and two-compound mixtures using the Python language.

[0096] We developed a mathematical theory of interaction analysis based on cluster expansion. The validity of this theory requires experimental data input for validation. This aspect of interaction takes into account higher order processes that are missing in drug discovery.

[0097] An in vivo physiologically based pharmacokinetic (PBPK) simulation program was established. It supports one, two, and multi-compartment models. The compartment model program was designed using Matlab and Simulink to emulate human physiological conditions. By inputting appropriate human physiological parameters, the absorption, distribution, metabolism, and excretion profiles of the target compound can be estimated in humans. The current version supports both command line and simple GUI operations (Matlab 2014b or later is required for the GUI).

[0098] The goal of the computational unit is to seamlessly organize the in vitro and analytical data accumulation, and the computational database to create an integrated workflow for efficient screening of compounds of interest, as described in Figures 2 and 3. The lead information, PD / PK properties of the lead's components, and mechanism of action form a template for data presentation. This workflow will be the backbone of PPT-II.

[0099] To achieve an integrated workflow, several areas need to be enhanced and new algorithms are needed to fulfill various aspects of the computational screening and analysis process.

[0100] In the scope of data mining, algorithms need to be expanded to include databases like Reactome for assessing protein-protein interactions (https: / / reactome.org / ), Biomodels (combining networks and in vitro cellular reactions to provide a more sound understanding of disease models https: / / www.ebi.ac.uk / biomodels / ), the Genomics of Drug Sensitivity in Cancer (GDSC) database (https: / / www.cancerrxgene.org / ) for signal transduction and clinical applications (Sakellaropoulos, Vougas et al. 2019), Zinc (https: / / zinc.docking.org / ), and Drugbank (https: / / go.drugbank.com / ) for FDA approved drugs, consisting of chemical and clinical information), thus having the potential to enable in vitro and in vivo scaling in cancer treatments. This expansion will also include more comprehensive PK and PD information for in silico evaluation. Additionally, the authors explore the use of machine learning-based natural language processing methods to mine data through both categories of literature and web content to enrich their database.

[0101] Computational methods and tools for predicting phase I and phase II metabolism for drug discovery (Tyzack and Kirchmair 2019) are used to predict the metabolism of target compounds.

[0102] Metabolic inhibitors will be identified using the method published in Tyzack and Kirchmair (2019), and metabolic inducers will be identified using the method of Banerjee, Dunkel et al. (2020).

[0103] To improve the accuracy of in vitro PD prediction in humans using gene expression data, we use published machine learning algorithms and databases utilized by Sakellaropoulos, Vougas et al. (2019) and Geeleher, Cox et al. (2014) to bridge the gap between in vitro models and humans.

[0104] A machine learning based nonlinear regression method is used to adequately model higher order interactions based on in vitro PD data, including nonlinear contributions from multi-compound combinations.

[0105] We improved two parts of the existing PBPK simulation program. The first part is the introduction of a better in vitro in vivo correlation method to systematically improve the in vitro scaling. The second part focuses on the development of a computational fluid dynamics model to simulate drug dissolution and absorption in the gastrointestinal tract.

[0106] Example 3 The in vitro assays described in the database are suitable for assessing compound potency, but are of limited usefulness for predicting physiologically effective concentrations.

[0107] One of the main objectives of the present invention is to establish in vitro tools to accurately describe disease processes. 3-D cell models or organoids, and patient-derived xenografts (PDx) have characteristics of organs or tissues in the human body. In disease states, multiple pathways are affected across cell types. These are usually expressed at alterations in gene, protein, and marker levels. Single-cell 2-D models are useful for initial screening, but do not provide information related to the entire disease process. For example, measuring changes in dopaminergic neurons in Parkinson's disease does not provide information about the leaky blood-brain barrier and its impact on Parkinson's disease. Efficacy quantified using the 3-D system has a high accuracy of projection to the patient. This type of system also allows for the evaluation of the effects of multiple compounds on multiple targets, making it the most suitable system for TCM research. Use commercially available organoids and patient-derived xenografts unless they are not commercially available (https: / / www.corning.com / worldwide / en / products / life-sciences / resources / stories / in-the-field / glioma-stem-cells-enable-the-production-of-3d-human-mini-brain-to-improve-glioblastoma-treatment.html).

[0108] Example 4 The purpose of this example is to disclose a strategy to minimize the number of samples required to quantify pairwise and higher order interactions, i.e., many-body interactions.

[0109] Regarding compound-compound interactions, traditional pharmacological and toxicological studies focus only on pairwise interactions. Chou (2006) proposed a general equation (Equation 1) that equates the cellular response to the proportion of affected cells:

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[0121] Example 5 Currently, there is no way to accurately estimate hepatic clearance and intrinsic clearance in humans without conducting human studies.One of the aims of this embodiment is to disclose how to better estimate the hepatic clearance of compounds in vivo using a new method to estimate in vivo metabolism using data obtained from 3-D liver model and existing human data of a known set of substrates.Two approaches are used to estimate in vivo hepatic clearance in humans.

[0122] The first approach includes: 1. Establishment of a 3-D liver model, 2. Selection of a cocktail of substrates with known human hepatic clearance values ​​and low (~0.1), medium (~0.5) to high (~1.0) extraction ratios, e.g., antipyrine, E = 0.1, midazolam, E = 0.5, propranolol, E = 0.99. These substrates are used for quality control of the model. The in vivo hepatic extraction ratio (E) is calculated using the following formula:

[0123]

number

[0124]

number

[0125] The metabolic rate of compounds in the fractions is measured using a 3-D model. The intrinsic clearance (V max / K m The in vitro extraction ratio is estimated using the following formula:

[0126]

number

[0127]

number

[0128]

number

[0129] From in vitro to in vivo

[0130]

number

[0131]

number

[0132] In vivo E values ​​can be estimated using Equation 3.

[0133] Another objective of this example is to couple the 3-D hepatocyte model to identify MTII compounds in herbal formulations.

[0134] Following US Food and Drug Administration guidelines (https: / / www.fda.gov / media / 82734 / download for Phase I, Phase II, and transport substrates), the mixture is subject to evaluation (steps 2-5, Figure 3). Changes in the disappearance rate of these substrates would indicate the presence of potential inducers or inhibitors.

[0135] Example 6 Cannabis sativa consists of over 554 compounds, of which 113 are cannabinoids, 120 are terpenes (Calvi, Pentimalli et al. 2018), and the rest are amides, flavonoids, phenols, alkaloids, fatty acids, etc. The amounts of these compounds vary depending on the strain. There are specialized varieties bred for specific compounds.

[0136] Two major cannabinoids are well known: 9 -Tetrahydrocannabinol (D 9While cannabidiol (THC), psychoactive, is the primary component of recreational consumption, cannabidiol (CBD), non-psychoactive, has numerous medical values ​​for conditions such as pain, neurodegeneration, and gastrointestinal disorders (Walter and Stella 2004, Nagarkatti, Pandey et al. 2009, Urits, Borchart et al. 2019).

[0137] When given in pure form, THC and CBD are not as potent as cannabis. This entropy effect has led researchers to conclude that other components in cannabis are either active, have the ability to enhance the effects of the main component, or act synergistically with each other (Russo 2018).

[0138] The large number of constituents (>500) makes the permutations of cross-reactivity between them unwieldy and impossible to quantify. Initial estimates suggest that for 500 compounds, there are billions of pentagonal (five compounds interacting with each other) interactions, but much more research is needed to initially understand the entropy effect, and it is not a good way to develop medical cannabis products.

[0139] The purpose of this example is to explore possible components that may contribute significantly to the overall effect of Cannabis sativa.

[0140] The parameter used for the evaluation is the Maximum Exposure Index (MEI), which is the product of the AUC and the highest ratio recorded for cannabis. The MEI values ​​of THC and CBD are used as reference components, since these two compounds are highly active, abundant and the most studied.

[0141] Table 1 summarizes the abundance of the seven most studied cannabinoids and 14 terpenes reported in the literature (Tubaro, Giangaspero et al. 2010). Twenty-one compounds (Table 1) account for at least 65%, while the remaining 500 plus compounds account for up to 35% of the plant.

[0142] Systemic exposure (area under the plasma concentration-time curve (AUC) 血漿中濃度 Considering the concentration (MEI) and abundance (MEI) can significantly alter the importance of the components. For example, the systemic exposure of CBD is 6-fold higher than that of THC in terms of AUC values, but considering abundance, THC may account for a larger proportion of the activity when a THC-rich cannabis extract is given (Table 2).

[0143] CBN is a breakdown product of THC, and when cannabis flowers are left to cure, it accumulates in the buds in increasing amounts.

[0144] Among the seven cannabinoids listed in Tables 1 and 2, THC, CBD, THCV and CBG showed the highest MEI values. Among the 17 terpenes, myrcene, -caryophyllene, -pinene, -pinene, terpinolene, trans-ocimene and limonene could contribute significantly to the overall efficacy of Cannabis sativa in terms of MEI values.

[0145] [Table 1]

[0146] [Table 2]

[0147] Consistent with the results of this case, MEI-high cannabinoids and terpenes are the focus of investigation (https: / / www.leafly.com / news / cannabis-101 / list-major-cannabinoids-cannabis-effects).

[0148] High brain exposure is likely to exert central nervous system (CNS) effects. Although there is some variability, all identified cannabinoids have better brain exposure than THC and CBD (ADMET estimates).

[0149] The results of this example highlight several important points: 1. known cannabinoids and terpenes make up a high percentage of cannabis content, 2. these cannabinoids are distributed extensively in the brain, indicating that central nervous system (CNS) effects may be prominent, 3. terpenes, as reported in the literature, had much higher amounts and MEI values ​​and were able to alter the activity of cannabinoids, and 4. cannabinoids and other components with favorable drug-like properties, albeit in small amounts, may contribute to the well-known entropic effects reported in the literature.

[0150] Example 7 The objective of this example is to evaluate the pharmacokinetic properties of known cannabinoids in Cannabis sativa.

[0151] The pharmacokinetic properties of 125 reported cannabinoids were estimated using ADMET, and the results are shown in Figure 6.

[0152] Oral bioavailability ranges from 5.4% for cannabinol methyl ether to 100% for (1'S)-hydroxycannabinol. AUC values ​​normalized over the 1 mg dose range of 0.01 to 479 ng*hr / ml, with a range of 48,000-fold.

[0153] For systemic exposure, AUC, Δ 9 -THC and CBD were ranked 98th and 66th, respectively. The bioavailability values ​​of both cannabinoids were less than 50%, THC (16.7%) and CBD (48.4%) (Figure 6, below a).

[0154] The bioavailability of 25 cannabinoids is greater than 90%, of which 22 are acidic (Figure 6, inset b). These compounds have AUCs that are 6.6-42 times higher than that of CBD, and Δ 9 - 32-203 times higher than that of THC.

[0155] Theoretically, some of these 25 cannabinoids with higher AUC values ​​could be up to 40 times more effective than CBD or have a Δ 9 - Up to 200 times more effective than THC. As a result, doses of these cannabinoids are less effective than CBD or Δ 9 -5-200 times lower than the doses of THC, the two most studied cannabinoids in Cannabis sativa, yet equally effective. Cannabinoids with good drug-like properties, like those in acidic form, have a higher potency than their individual activity. 9 -It may be as effective as or more effective than THC or CBD, or may be more effective if two or more of these compounds act synergistically together.

[0156] Pharmacokinetically, Δ 9 -THC and CBD are in the bottom half of the rankings, with the distinct possibility that more potent cannabinoids have yet to be identified.

[0157] Although the in silico estimation of the pharmacokinetic properties of these cannabinoids may not be accurate, the ranking of these compounds is consistent with the limited clinical data published in the literature.

[0158] The main purpose of this invention is to use PPT-II and its effect is overshadow 9 -To disclose the combination of THC and CBD and the previously undiscovered cannabinoids that result in their interaction. In a subsequent example, the composition of cannabinoids is mined using PPT-II for the treatment of hepatocellular carcinoma (HCC).

[0159] Example 8 The objectives of this example are to: 1. discuss the potential benefits of using a combinatorial approach to treat HCC with a triple cannabinoid formulation; 2. the molecular mechanisms of these combinations; and 3. the importance of the ratio of these active ingredients.

[0160] SUMMARY: Hepatocellular carcinoma (HCC) is a malignant disease with a poor prognosis for patients. Fewer than 35% of diagnosed patients survive for 5 years. This number drops to less than 12% if the cancer metastasizes to nearby tissues and to less than 2% if it metastasizes to other organs (Kitisin, Packiam et al. 2011). To date, there are no approved phytocannabinoid-derived chemotherapy options for HCC. HepG2 cells, isolated in 1975 from a 15-year-old boy, have been characterized as well differentiated and have long been used as a model for hepatocellular carcinoma.

[0161] The medical use of cannabis dates back to 500 BC, when gold vessels containing cannabis residues were unearthed in Scythian tombs. Currently, cannabis is approved in the United States and Canada as an adjunct to chemotherapy to relieve nausea and vomiting, loss of appetite, and pain (Kleckner, Kleckner et al. 2019).

[0162] Anticancer properties of cannabinoids and other components in cannabis, such as terpenes and flavonoids, have been reported (Blasco-Benito, Seijo-Vila et al. 2018). In the cannabis arena, D 9 -Tetrahydrocannabinol (THC) and cannabidiol (CBD) have been the focus of cancer research for several years. CBD has attracted much attention due to its lack of central nervous system (CNS) activity, which has led to a number of concerns about its "side effects."

[0163] CBD is known to have low oral bioavailability and drug-like properties (Meyer, Langos et al. 2018). Its low solubility and high first-pass metabolism make it a poor candidate for oral delivery. CBD oral bioavailability ranges from 9 to 30%, which is accompanied by large inter-individual variability in plasma profiles.

[0164] Nanoparticles, including liposomes, have been used to enhance the oral bioavailability of CBD, but no parenteral formulations designed to deliver CBD specifically to the liver are available.

[0165] In vivo pharmacokinetic data is not available for cannabichromene (CBC) and cannabinol (CBN). In the present invention, the human pharmacokinetics of CBC and CBN are estimated in silico and in vitro. The goal is to determine the optimal route of administration and the desired dosage form for administration.

[0166] The invasive effects of cannabis are well documented. For example, the effects of CBD are less effective when compared to cannabis extracts containing the same amount of CBD (Blasco-Benito, Seijo-Vila et al. 2018). There have been attempts to decipher the contributing components, but the data in the literature are sketchy. Components of cannabis have also been reported to act antagonistically. These two opposing forces may make cannabis extracts with an undefined chemical profile questionable with regard to medicinal use.

[0167] In one embodiment, the potential synergistic anti-cancer activity of cannabinoid combinations was explored with the goal of identifying candidate combinations that are at least as potent as sorafenib in treating HCC.

[0168] In one embodiment, the mechanism of action between the identified candidates is elucidated.

[0169] Although numerous studies on cannabis and its components support their effectiveness against cancer cell proliferation, discrepancies exist in the literature. These may be due to differences in experimental design and / or underlying genetic and environmental factors characteristic of different cancer types. For example, McKallip, Nagarkatti et al. (2005) showed a lack of reactivity to pure THC in various breast cancer cell lines, while Blasco-Benito, Seijo-Vila et al. (2018) reported contradictory results. The latter study further observed an improved response from MDA-MB-231 compared to SUM-159 cells. Interestingly, both of these cell lines are of the overly aggressive triple negative (ER-, PR-, HER2-) type. The authors also found that in addition to the various cell line reactivity, plant extracts containing THC, other cannabinoids, flavonoids, and terpenes showed significant improvements compared to pure THC alone in all cell lines tested.

[0170] The effects of cannabinoids can be explained by their variable binding affinity to multiple G-coupled protein heterodimeric receptors such as CB1, CB2, GPR55, and TPRV1, which carry multiple downstream pathways and result in variable drug responsiveness (Moreno, Cavic et al. 2019). THC has been reported to directly enhance AMPK-mediated autophagy in hepatocellular carcinoma (HCC), HepG2 cells, via direct binding of the CB2 receptor (Vara, Salazar et al. 2011). This occurs in addition to THC-induced autophagy via the ALK receptor in glioma cells (Lorente, Torres et al. 2011). Whether these responses are specific to different cell lines or universal among different cancer cell types remains to be resolved. Furthermore, Torres, Lorente et al. (2011) demonstrated a synergistic response to concomitant treatment with the selective ALK inhibitor, TAE-684, and THC, indicating that additive or synergistic effects can be enhanced by targeting convergent pathways.

[0171] New therapeutic targets are agonist or antagonist compounds that modify heterodimer formation within the numerous C-coupled protein receptors that are part of the cannabinoid receptors. This opens up many new disease pathways, as this superfamily of receptors constitutes about 4% of the protein coding genome (Moreno, Cavic et al. 2019). New targets provide a mechanistic description of cannabinoid and / or other compound interactions.

[0172] THC is known to bind to peripheral CB2 receptors in HepG2 cells and induce pro-apoptotic events, whereas CBD can bind to TRPV1, PPAR, GPR55 and TRPM8 receptors and induce apoptotic events from increased reactive oxygen species (ROS) and ceramide. In comparison, Zhong (2020) reported that CBN mediates apoptosis and cell cycle arrest via MAPK / ERK and PI3K-ATK pathways by downregulating P21. Interestingly, Zhong's study also showed downregulation of CB2 and GPR55 receptors from CBN treatment. This would likely have a direct impact on the efficacy of CBD activity with co-treatment. Meanwhile, CBD is a TPRA1 agonist (De Petrocellis, Vellani et al. 2008) and increases intracellular Ca. 2+It mediates an increase in THC while simultaneously inhibiting AEA reuptake. These reports demonstrate numerous receptor-mediated pathways by which cannabinoids may affect autophagy and apoptosis for the treatment of cancer. Coupling these various receptor selectivities with varying affinities, these drugs may act additively, synergistically, or antagonistically within a given cell line. Indeed, Blasco-Benito, Seijo-Vila et al. (2018) showed improved responses with plant drug extracts compared to pure THC for all cell lines tested. Given the number of permutations and responses that can result from botanical substances, it is not unreasonable to expect variable responsiveness from cannabinoid extracts containing any number of these cannabinoids in any combination or relative ratio, not to mention potential additional anticancer agents such as terpenes and flavonoid antioxidants (Tomko, Whynot et al. 2020). Either way, it would be safe to conclude that treatment with poorly characterized extracts will result in unpredictable responses.

[0173] With this in mind, there has been an increase in studies and patents validating the use of pure monocannabinoid compounds such as tetrahydrocannibinolic acid (THCA), cannabidiolic acid (CBDA), cannabigerolic acid (CBGA), cannabichromenic acid (CBCA) and their decarboxylated derivatives as anticancer agents (Javid, Duncan et al. 2018, Koltai, Poulin et al. 2019). These reports confirmed their in vitro and in vivo activity against various cancer cell lines, but not their relatively low efficacy (IC in the μM range). 50 The low molecular weight (Millar et al. 2018) and poor drug-like properties for the decarboxylated forms make their drug candidates questionable. Indeed, Millar et al. demonstrated this for CBD (Millar, Stone et al. 2018).

[0174] Recent disclosures have reported synergistic interactions with combinations of cannabinoids (Stott, Duncan et al. 2017), but these studies have either focused primarily on only two cannabinoids with a limited number of defined ratios, or have utilized complex trichome extracts that contain numerous components that still characterize some of them (Parolaro, Massi et al. 2013). Drawing conclusions from these results is problematic, given that any number of components in a complex mixture may be involved in the observed effects, and cross traffic may exist between converging biological pathways. It is noted that both CBD and CBN interact with the GPR55 receptor. CBD is a GPR55 agonist. CBN has been shown to downregulate GRP55 expression, which may result in variable responses compared to its concentration and affinity. On the other hand, CBD and CBN are TRPV1 and TRVA1 agonists, increasing intracellular calcium and promoting apoptosis. CBD also acts on different pathways and receptors, some of which may be convergent. CBC and CBN, on the other hand, seem to act on different pathways, so more direct competitive / additive / synergistic reactions may result. With only three cannabinoids, it is easy to know how many overall reactions will be observed depending on the amount of each used. Without empirical evidence, it is difficult to predict what the overall effect will be and what ratios will overload receptors and shunt responses to other pathways.

[0175] Natural variation in cannabinoid ratios within trichomes is due to a variety of factors, including cannabis strain, growth conditions, and harvest time. It can therefore be concluded that the variable response to hepatoma cell lines results from different plant extracts. Furthermore, predicting their anticancer effects is problematic. The degree of predictability is further compromised by other endogenous plant complexes that may promote anticancer activity in many cell studies. This has also been demonstrated in various cell studies (Tomko, Whynot et al. 2020).

[0176] This disclosure reveals a defined combination of cannabinoids with convergent receptor targets and pathways known to induce pro-autophagy and apoptotic events in cancer cells. The CBD and multiple cannabinoid combination formulations described in this invention show IC20 activity comparable to sorafenib on HepG2 cells. 50 value.

[0177] Overview of Combination Candidate Development In one embodiment, three compositions of cannabinoids, CBD, CBC / CBN and CBD / CBC / CBN, have comparable efficacy to the first-line chemotherapy agent sorafenib in an in vitro liver cancer model. In another embodiment, the mechanism of synergy between CBC / CBN and CBD / CBC / CBN is elucidated.

[0178] This disclosure reveals three compositions of cannabinoids consisting of CBD, CBD / CBC and CBD / CBC / CBN that have diverse receptor targets and pathways known to induce pro-autophagy and apoptotic events in cancer cells. The three compositions described in this invention are more effective than sorafenib against HepG2 cells.

[0179] The effects of these three compositions are enhanced when they are nanoencapsulated. These nanoparticles are specifically designed for delivery to the liver to enhance efficacy and safety.

[0180] In one embodiment, three compositions of cannabinoids, CBD, CBC / CBN and CBD / CBC / CBN, have greater efficacy than the first-line chemotherapeutic agent sorafenib in an in vitro liver cancer model.

[0181] In another embodiment, the mechanism of synergy of CBC / CBN and CBD / CBC / CBN is revealed.

[0182] In another embodiment, a delivery mode is disclosed to achieve / provide the best efficacy and safety profile.

[0183] In another embodiment, nanoencapsulated dosage forms for CBD, CBC / CBN and CBD / CBC / CBN for parenteral administration will be disclosed.

[0184] In another embodiment, nanoencapsulated forms of the compositions are shown to be more effective than their respective non-encapsulated compositions.

[0185] The dose of the nanoencapsulated dosage form is at least three times lower than its non-encapsulated counterpart.

[0186] Detailed Description of Cannabinoid Formulas Development: In the present invention, it is disclosed that compositions consisting of CBD, CBC / CBN and CBD / CBC / CBN are more potent than the hepatic chemotherapeutic agent, sorafenib. The relative efficacy and mechanism of interaction were evaluated using in vitro cell models, HepG2, quantitative methods, HPLC / MS / MS, gene expression studies, Western blot studies, and data analysis of compound interactions.

[0187] Materials and Methods: Pure cannabinoids were obtained from Supelco / Cerilliant. Cannabidiol (CBD, C-045-1ML, lot FE10071912), cannabichromene (CBC, C-143-1ML, lot FE06152005), cannabinol (CBN, C-046-1ML, lot FE05052008). Positive control compounds, regorafenib (TCI, R142-25MG, lot 11-88997-23096) and sorafenib (Selleckchem, S7397, lot S739707) were obtained from Fisher. Control chemicals were reconstituted to 10 mM in DMSO and subsequently diluted to working concentrations. Pure cannabinoids in methanol (1 mg / ml) were added directly to the medium at the highest concentration evaluated and serially diluted to the next working concentration.

[0188] Cell assay: HepG2 cells were obtained from American Type Culture Collection (ATCC). Cryopreserved cells in DMEM containing 10% DMSO were thawed and diluted 10-fold in phenol red-free DMEM medium containing 10% fetal bovine serum, 100 μg / ml penicillin / streptomycin, and supplemented with Glutamax™ and sodium pyruvate. Cells were then centrifuged at 20 g for 10 min. Cells were then diluted to 2×10 in the same medium. 5 The cells were resuspended at a concentration of 7.5 × 10 cells / ml in a 96-well cell culture treated flat bottom plate. 3HepG2 cells were seeded and incubated at 37C, 5% CO2, >95% relative humidity for 18 hours to allow cell attachment, after which the medium was replaced with a similar medium containing 1-20% FBS and various test compounds (50μg / ml-50ng / ml). This medium with test compounds was replenished after 24 hours. After 48 hours (2 cell divisions), sodium, 2-(2-methoxy-4-nitro-5-sulfonatophenyl)-3-(2-methoxy-4-nitro-5-sulfophenyl)-N-phenyltetrazole-3-ium-5-carboximidic acid (XTT) (0.3mg / ml) was added and the cells were incubated for 2 hours. Formazan production was measured at 450nm in a Varioskan lux spectrophotometer. Nonspecific absorption was measured at 660nm and subtracted from the reference measurement at 450nm. Sample measurements were performed in triplicate (n=3). After blank subtraction, data were averaged and fitted with a four-point logistic regression curve to determine IC 50 Value (R 2 A 1:1 correlation coefficient (CI) of 0.01 to 0.95 was obtained. Blank samples contained ethanol as a negative control, which is the solvent for the commercially available cannabinoids. The ethanol concentration in the blank standards reflected the concentration used in the serial cannabinoid series dilutions. An initial study was performed to evaluate single cannabinoid activity on HepG2 cell proliferation.

[0189] 10 Cannabinoid assays: For tertiary cannabinoid compound mixtures, the ratio mixtures of CBD:CBC:CBN were randomized between 3:2:1 and 1:1 in IMDM medium supplemented with 20% FBS, the ratio mixtures of CBN:CBC:CBD were randomized between 9:3:1 and 1:1:1 in DMEM medium supplemented with 1% FBS, and the ratio mixtures of CBD:CBC:CBN were randomized between 9:3:1 and 1:1:1 in DMEM medium supplemented with 1% FBS. Cannabinoids were mixed and diluted in medium for serial dilutions. Cell assays and XTT experiments were performed as described above. XTT values ​​were plotted against the first two component ratios to assess the ideal response to the primary components.

[0190] Spheroid (solid tumor) assay: HepG2 cells were thawed and reconstituted in Williams E medium supplemented with Gibco Hepatocyte Maintenance Supplement and 1% FBS. Cells were seeded into Corning® Spheroid black wall, clear round bottom, ultra-low attachment, 96-well microplates at a density of 1500 cells / well. Plates were spun at 10×G for 20 minutes to collect cells in the center of the well. After 60 hours of incubation at 37° C. and 5% CO2, spheroids were treated with a cannabinoid mixture CBD:CBC:CBN 9:3:1 and incubated for an additional 48 hours. Spheroid vitality was measured with Invirogen's CyQuant™ proliferation assay (excitation / emission 508 / 527 nm) after 60 minutes at 37° C.

[0191] Mass spectrometry: Concentrations and ratios of individual cannabinoids were confirmed using HPLC-DAD / MS methodology. After 30 min of incubation, random samples (50 μl) were taken from the test set and crashed in 150 μl of methanol to precipitate media proteins. Samples were centrifuged at 21910 RCF for 5 min and the supernatant was collected in a 2 mL glass vial with an insert. Quantitative data were acquired using an Agilent 6410 Triple Quadrupole Mass Spectrometer coupled to a 1260 Series HPLC and UV detector. A thermostatic autosampler was used to inject the samples onto a 250x4.6mm column fitted with a Phenomenex Kinetex 5 μm XB-C18 100 Å, C18 SecurityGuard ULTRA Cartridge at 40 °C, with a flow rate of 1.00 mL / min and a gradient of 72-100% methanol for 45 min. Mobile phase A consisted of water with 0.05% ammonium acetate, and mobile phase B consisted of methanol with 0.05% ammonium acetate. UV detection was used at 215 nm with a signal bandwidth of 4 nm. Selected ion monitoring (SIM) LC / MS analysis was performed in negative ion mode with the following parameters: feed gas temperature was 320 °C, flow rate was 12 L / min, capillary voltage was -3.8 kV, residence time was 200 ms, fragmentation voltage was 135 V, and gas nebulizer pressure was set at 35.0 psi. Concentrations were determined by quantifying the area response of samples against a standard calibration curve in the range of 0.025–10 μg / ml for the MS detector and 0.5–15 μg / ml for the UV detector. All certified reference materials were purchased from Cerilliant Corporation (CBD, C-045; CBC, C-143; CBN, C-046).

[0192] rt-PCR: Target cancer gene pathways are analyzed using custom Taqman array cards that interrogate 91 unique genes involved in several cancer pathways.

[0193] Seed 2 million HepG2 cells in IMDM medium with 20% FBS and 1% ethanol into a T75 flask and incubate at 37 °C, 7.5% CO2 until 70-80% confluent (approximately 7.5 x 10 5 Cells). Media was supplemented with media containing individual cannabinoids (CBD 17 μg / ml, CBC 23 μg / ml, CBN 20 μg / ml) and a cannabinoid mixture of CBD:CBC:CBN 1:1 (17 μg / ml) and incubated for 6 h before harvesting. Sorafenib (12 μg / ml), a kinase inhibitor with antiangiogenic and antiproliferative activity via Raf, VEGFR, and PDGFR (Roberts and Der 2007), was included as a positive control. Untreated cells were included as a negative control glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and used to normalize the data.

[0194] RNA was extracted using Invitrogen's Dynabead mRNA DIRECT kit using the recommended protocol to obtain 50 L of purified mRNA solution. 50 L of RT master mix was mixed into the mRNA solution and the mixture was left to react at 37°C for 60 minutes. Reverse transcriptase inactivation was performed at 95°C for 5 minutes and the resulting cDNA samples were kept at 4°C until use. 55 microliters of TaqMan Fast Advance master mix was added to an equal volume of cDNA sample containing 1g / 100l of cDNA, which was loaded into each card reservoir. The cards were centrifuged twice for 3 minutes at 1200 RPM in a Legend XFR centrifuge and the rtPCR reaction was performed on a QuantStudio7 PCR machine using standard settings of 40 amplification cycles.

[0195] In-cell Western blot analysis: Protein expression was assessed by seeding 1000 HepG2 cells / well from cyro cryopreserved stocks in 384-well optical bottom black culture plates from Thermo-Scientific (section 142761) in IMDM medium containing 20% ​​FBS and 1% ethanol. After 48 h of recovery, the media was replaced with the same media (CBD: 17 μg / ml, CBC: 23 μg / ml, CBN: 20 μg / ml, sorafenib: 12 μg / ml, SCI-111: 17 μg / ml, SCI-521: 17 μg / ml). After 6 h treatment, cells were fixed with 4% formaldehyde for 15 min, permeabilized with 0.1% Triton-x100 for 10 min, and left overnight at 4 °C in blocking buffer (phosphate-buffered saline (PBS) with 3% bovine serum albumin (BSA)). The next day, cells were rinsed with PBS and incubated with 25 μl of PBS containing primary antibodies (Table 1) for 3 h at 25 °C. Subsequently, the primary antibodies were removed, cells were washed twice with PBS and incubated with PBS containing secondary antibodies (rabbit: 25 μl). Alexa Fluor 790 donkey anti-rabbit IgG (H+L) Lot 2409042, mouse: Alexa Fluor 790 donkey anti-mouse IgG (H+L) Lot 2300923) were administered at a concentration of 1:2000. Draq5 (1:2000) was added and data normalized. After 1 h incubation in secondary antibodies, cells were rinsed with PBS and readings at 700 and 800 nm were acquired on a Licor Odyssey fluorescent imager with a resolution of 21 μm. Analysis was then performed using EmpiriaStudio2.1. A total of 5 samples per antibody were taken and the sample size was reduced to 3 for statistical analysis.

[0196] [Table 3]

[0197] Data collection: Subsequent experiments showed that, in this disclosure, CBD, CBC, and CBN had the lowest IC 50Further studies of these cannabinoids in various media will allow us to determine the relationship between the FBS concentration in the media and the observed IC 50 A correlation was confirmed between the IC values ​​(Figure 8A). Sorafenib, CBD, and CBN were 50 This shows a leveling of the IC values ​​after 12.5% ​​FBS. 50 Although a flattening of values ​​is noted, CBC shows a linear correlation measured further out to 45% FBS (FIG. 8B). This observation led us to estimate the IC of individual cannabinoids against sorafenib in 1%, 10% and 20% FBS. 50 These data are shown in Figure 8C. As can be seen, various IC 50 Despite the value obtained, cannabinoid IC for sorafenib 50 The values ​​remain ~1.25 for CBD, ~1.5 for CBN, and ~2.0 for CBC. This method allows for consistent data comparison across various experimental conditions, as long as sorafenib is used as the benchmark standard. Multiple experimental conditions were explored to reveal toxicity from standard solvent systems reported in the literature (data not shown). Thus, IMDM medium with 20% FBS and 1% ethanol was found to be a non-toxic system that allows for a clear interpretation of the data for HepG2 cells used for the remainder of this disclosure.

[0198] Pairwise Studies: Given the results with pure cannabinoids, we performed similar biological studies with three cannabinoid pairs, CBD-CBC, CBD-CBN, CBC-CBN, with a range of ratios to estimate the effects of the ternary interaction matrix. The resulting IC 50 The corresponding triple ratio concentration values ​​of are shown in Figure 9. Optimal effects were observed when the concentration ratio of CBD:CBC was greater than 2:1 and CBD:CBN was between 2:1 and 4:1 in IMDM medium with 20% FBS.

[0199] Tripartite Interaction: Validation of the XTT results seen in Example 8 was performed to evaluate potential synergistic interactions of the cannabinoids, CBD, CBC, and CBN. Based on the range of potential synergies observed from the plots shown in Figure 9, various combinations of the three cannabinoids were selected for ternary analysis (CBD:CBC:CBN 9:3:1, 4:2:1, and 1:1:1:1). IC 50 Values ​​were obtained using the XTT assay described herein, and values ​​are reported for the total concentration of the cannabinoid combination used in each sample, respectively. The results of this study are shown in Figures 10A and 10B. A CBD:CBC:CBN ratio of 4:2:1 produced IC20s comparable to sorafenib in media containing 20% ​​FBS. 50 values, but this ratio shifted to 9:3:1 as the FBS concentration was reduced to 1% (Figure 10B). This change was hypothesized to be due to a decrease in plasma protein binding of CBC, resulting in an IC 50 This resulted in a simultaneous decrease in IC values ​​(Figure 8A). 50 is seen when a relative 3:1 ratio of CBD:CBC and CBC:CBN in 1% FBS requires an increased ratio to 2:1 in 20% FBS, which likely provides a similar free drug concentration of CBC. This set of results provides evidence that the combination candidate is superior to the individual compounds and more effective than sorafenib. The results also show that the choice of in vitro model, test method and conditions under which the test compounds are evaluated are of utmost importance. This is especially true when combination candidates are being screened.

[0200] Mechanism of interaction: The objective of this study was to understand the mechanism of action of the cannabinoids explored in this disclosure and the optimal effective ratio of these compounds on the proliferation of HepG2 cells in vitro.

[0201] The assessment of cancer gene pathways was performed using a custom-designed TaqMan array card that explored 86 genes associated with numerous cancer-related pathways. These pathways included top G-coupled protein receptors (e.g., CB1, CB2, GPR55, TRPV1) and downstream proteins and enzymes related to apoptosis, autophagy, cell cycle arrest, and also pathways perturbed by cannabinoids previously described in this disclosure. These data are shown in Table 4. The results comparing cannabinoids to the negative control were consistent with previous literature reports. Indeed, this disclosure confirmed that CBD affects the mTor pathway through alterations in several MAP kinase and PI3K pathway genes. It was also confirmed that CBN alters mTOR through the PI3K / ATK pathway, and CBC likely induces CAMK1 and ELK1 genes associated with apoptosis. However, our comprehensive analysis reveals a broader involvement of gene expression. Several other pathways, including TGF signaling, Hedgehog, Jak-STAT, Hippo, and Notch, were perturbed, which have not been reported. Interestingly, Hedgehog is not affected by CBD, whereas Jak-STAT and Notch are only affected by CBC. TGF signaling and PIK3R5 appear to be significantly affected by CBN. These unique targets may explain the additive effects observed from the combination formulation. In fact, a CBD:CBC:CBN ratio of 1:1:1 provided the best overall IC despite retaining only 1 / 3 of the initial concentration of each individual cannabinoid. 50We do not present a single-cell assay, and in fact show some of the strongest gene folding changes (MAP3K, SMAD3, SMO, CCNE1, and BCL2). These effects suggest that there are upstream interactions not identified in this study, ultimately resulting in a more synergistic effect of the mixture than the individual cannabinoids alone. In addition to individual pathways, genes associated with specific cellular processes were also explored. As expected, genes involved in cell cycle arrest and apoptosis were altered with all three cannabinoids tested. However, genes involved in angiogenesis (VEGFA and CUL2) and cell adhesion (GSK3, DVLD2, and Catenin B) were also altered. These processes are important for metastasis, cannot be assessed from simple 2D cell assays, and show that the IC measured from the XTT assay was significantly higher than that of the control. 50 Does not affect the value.

[0202] In addition to the aforementioned genes, the inventors noted that neither CB1 nor CB2 genes are expressed in the studied HepG2 cell line. Rather, PPARG, ERBB2 and MET proteins show changes. It is unclear whether cannabinoids directly bind to these receptors or affect their expression. PPARG is a transcription factor associated with the expression / activity of G-coupled protein receptors and may affect potential cannabinoid receptors (GRP55 and GRP119). MET is also known as hepatocyte growth factor and is directly related to hepatocyte proliferation.

[0203] [Table 4-1]

[0204] [Table 4-2]

[0205] Genetic analysis: To validate the PCR observations, candidate genes were selected from Table 4 for protein expression. These genes are shown in Table 4. It is common that the observed gene expression changes do not necessarily correlate with protein expression due to recycling / scavenger pathways, lag times between gene and protein expression, and a complex network of homeostatic mechanisms in the game. In any case, protein expression of all target genes validated the PCR results for at least one of the screened drug candidates. These fold changes are shown in Table 5. Most notable are the universal changes observed for the apoptosis-related proteins p53 and c-MYC. Cell adhesion proteins were also affected by all candidates examined, and angiogenesis-related proteins were affected by the combination of CBD and cannabinoids. MAPK / PI3K / ATK proteins were also affected by cannabinoids confirming previous literature findings. Interestingly, the tested combinations showed comparable IC in XTT experiments, despite a reduction in the individual doses of cannabinoids. 50 Not only did these compounds show IC values, but also showed similar or more significant gene and protein expression changes compared to their individual component counterparts. For example, SCI-521, which contains only 62.5%, 25%, and 12.5% ​​of the individual cannabinoids CBD, CBC, and CBN, showed IC values ​​less than those of CBD and sorafenib alone in IMDM medium containing 20% ​​FBS. 50 Produces IC similar to 。The combination also produced a similar reduction in phosphorylated PTEN protein as CBD alone (Table 5). There was also more VEGA expression and reduced CUL2 compared to CBD alone, which was not observed with CBD in the given time frame of sample collection (6 hours). These data suggest a cooperative effect of the individual cannabinoids in providing additive / synergistic effects. Similar results have been observed with SCI-111, which contains only 33% of each cannabinoid. This formulation produced more significant protein changes for p53 compared to the individual cannabinoids, although with XTT, neither 1% nor 20% FBS results showed an ideal IC50. Based on the data presented in 10B, it can be assumed that this ratio presents optimal results when FBS approaches 50%.

[0206] [Table 5-1]

[0207] [Table 5-2]

[0208] 3D HepG2 Study: The efficacy of a cannabinoid mixture was evaluated against an in vitro solid tumor model, HepG2 cells, in a spheroid format. The cannabinoid mixture (SCI-931):CBD:CBC:CBN 9:3:1) showed IC 50 The antibody showed activity at a concentration of 2.95 μg / ml (FIGS. 11A and 11B).

[0209] Example 9 The aim of this study was to estimate the drug-like properties of three cannabinoids in a ternary system with the goal of determining the method of administration and optimal delivery. The drug-like properties of CBD, CBC, and CBN were evaluated using a combination of ADMET Predictor® and Poulin and Theil (2002). A proprietary pharmacokinetic model was used to simulate plasma and organ profiles.

[0210] [Table 6-1]

[0211] [Table 6-2]

[0212] Table 6 summarizes the in silico estimates of the drug-like properties of CBD, CBC and CBN. Besides CBD, there is a lack of pharmacokinetic data on CBC and CBN in humans. The total body clearance values ​​of CBD estimated here are consistent with those reported in the literature (Meyer, Langos et al. 2018), providing qualitative support that this dataset can be used at higher levels.

[0213] CBD, CBC and CBN have extremely low solubility and high intestinal and hepatic first-pass metabolism, making them poor candidates for oral administration. These three cannabinoids are primarily cleared by the liver and have high Cl levels. TB showed a value close to the hepatic blood flow (1.5 L / min).

[0214] Drugs with high first-pass metabolism have high inter-individual variability, with >10-fold variations not uncommon. Intra-subject variability is also problematic. These drug concentration variations are problematic for cancer treatment because the effects of chemotherapeutic agents are highly non-specific and therefore toxic. Dose escalation is not practical. Moreover, for multi-component formulas, such as those disclosed in this invention, where concentration ratios are crucial to elicit synergistic effects, individual variations in pharmacokinetic profiles may pose a threat to the success of treatment.

[0215] By administering the candidate via the vascular route, inter- and intra-subject variability can be avoided. The AUC value of CBD changed approximately 2-fold after intravenous injection (Meyer, Langos et al. 2018). This variability is within the range of the synergistic ratio measured in this disclosure.

[0216] To improve management of delivery, maximize efficacy, and minimize toxicity, candidates can be injected intravascularly. Table 7 shows the estimated infusion rates to achieve steady-state blood and liver levels, which are the respective IC 50 Equivalent to the value.

[0217] [Table 7]

[0218] Although in silico prediction of pharmacokinetic parameters has improved over the past decades, there is still room for improvement: predicted values ​​that deviate from clinical values ​​by more than 5-fold are not uncommon.

[0219] The prediction method of clinical concentration has not yet been developed. A general approach is not yet available. In this disclosure, the physiological effective concentration of the formula described herein is derived using positive controls with both in vitro and in vivo data.

[0220] Nanocapsulation of combination drug candidates based on in silico prediction may enhance the efficacy of the candidates and reduce their toxicity.

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Claims

1. A method for efficiently identifying an optimal composition containing an active compound from an herb or herbal preparation for treating a disease susceptible to treatment with said herb or herbal preparation, comprising: 1) obtaining a chemical profile of the herb or herbal preparation from an existing database, or if no record is found in said existing database, creating a chemical profile using high resolution mass spectrometry for the herb or herbal preparation, and optionally determining the chemical structure of the compound or compounds identified in the created chemical profile; 2) identifying and developing a suitable in vitro model that is clinically relevant for said disease; 3) Three criteria a) efficacy in treating said diseases; b) Potential effects on the absorption and metabolism of active ingredients; c) Drug-like properties and metabolites computationally identifying potential active ingredients as primary candidate compounds from the chemical profile obtained in step 1) using methods from systems biology, systems pharmacology, bioinformatics, and machine learning-based approaches based on 4) A series of predefined, adjustable pharmacodynamic and pharmacokinetic criteria and five iterative procedures: a) evaluating the biological effects of primary candidate compounds using the in vitro model developed in step 2); b) evaluating pairwise interactions of primary candidate compounds based on a pairwise-based experimental approach using the in vitro model developed in step 2), and as a result, determining whether a pair of compounds synergistically / antagonistically regulates the disease; c. Using the results obtained in steps 4.a) and 4.b) and a cluster expansion method to predict the synergistic effect of multiple compounds for treating the disease, and generate a list of secondary candidate compounds with specific concentration ratios; d) verifying and testing the efficacy and side effects of the secondary candidate compounds obtained in step 4 c) in vitro; and e) Repeat steps 4b) to 4d) until step 4d) is verified to obtain a list of active compounds. screening the primary candidate compounds obtained in step 3) according to 5) formulating an optimal composition comprising said active compound taking into account compound-compound interactions, said composition having maximum efficacy with minimum side effects in treating said disease.

2. An optimal composition comprising one or more of CBD, CBC, and CBN for treating hepatocellular carcinoma.

3. The optimal composition of claim 2, wherein the composition comprises cannabichromene (CBC) and cannabinol (CBN) in an optimal ratio, or the composition comprises CBC, CBN, and CBD in an optimal ratio.

4. The optimal composition of claim 3, wherein the composition comprises CBC and CBN in a weight ratio ranging from 16:1 to 1:2, or the ratio of CBD:CBC:CBN is in the range of 1:1:1 to 9:3:

1.

5. The optimal composition described in claim 2 for inhibiting hepatocellular carcinoma.

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