WO2020225737A1

WO2020225737A1 - Integrated 3d bioprinted human disease in a dish model to simultaneously study drug discovery and development parameters

Info

Publication number: WO2020225737A1
Application number: PCT/IB2020/054272
Authority: WO
Inventors: Uday Saxena; Subrahmanyam VANGALA
Original assignee: Reagene Innovations Pvt. Ltd.
Priority date: 2019-05-08
Filing date: 2020-05-06
Publication date: 2020-11-12

Abstract

This application provides an integrated 3D bioprinted human disease in a dish model or system that serves the purpose to study and better predict the outcome of test drugs and drug combinations for human diseases and conditions. The model system provided herein is especially useful for simultaneous analyses of various parameters including drug metabolism, efficacy and safety, in conditions that do not have optimal animal models for testing novel drugs. The invention further establishes a unique system to design specific drugs and test them thus providing a platform for personalized medicine.

Description

Integrated 3D bioprinted human disease in a dish model to simultaneously study drug discovery and development parameters

RELATED APPLICATION

This application is related to and claims priority from Indian Provisional Applications

201941018457 and 201941018458 filed on 8^th May 2019 and is incorporated herein in its entirety.

FIELD OF THE INVENTION

This application is related to an integrated 3D bioprinted human disease in a dish model or system that serves the purpose to study and better predict the outcome of test drugs and drug combinations for human diseases and conditions. The model system provided herein is especially useful for simultaneous analyses of various parameters including drug metabolism, efficacy and safety, in conditions that do not have optimal animal models for testing novel drugs as well as in personalized medicine.

BACKGROUND OF THE INVENTION

It is well known that there is low productivity of pharma industry where the probability of a success from drug discovery stage to market entry is less than 1%. Added to this is the time taken (10-12 years) for a drug to be discovered and launched as well as high costs which is upwards of $300 million per drug launch. The major reasons for failure are toxicity of the drug, poor bioavailability and sub optimal efficacy. There is an urgent need for innovative paradigms that can improve the discovery process.

An important reason for the high failure rates is the way drug discovery process works now. Most if not all of the discovery work currently is done using animal models for measuring efficacy, pharmacokinetic properties, and toxicity. The animal models used are typically chemically induced or engineered to create human disease like profile. Unfortunately, the animal models are poor in mimicking human physiology and disease. The problem is very clearly demonstrated in therapy areas of cancer and central nervous system (CNS) diseases where most compounds that look good in animal models are dismal failures in humans because the animal models are simply inadequate. A combination of poor success rates in cancer with a highly susceptible population like pediatric patients with cancer the result is even lower likelihood of success.

75% of the preclinical work in drug discovery and development is performed using animal models for profiling efficacy, safety and drug metabolism. But translation of these activities from animal to humans has failed in drug discovery to a point that 90% of drugs fail in humans even after being effective in animal models. Typical disease models use either diet or chemical induction of diseases, transgenic models of target gene overexpression, knock out models of target gene knocked out or diet induced models using inbred strains which have nor human like heterogeneity. None of these models reflect human physiology and pathology

Most drugs fail due to either poor drug metabolism, efficacy or toxicity not predicted by the preclinical models.

Drug such as troglitazone is a great example of a drug that looked good in animal models but showed toxicity in humans and had to be withdrawn. Most human Diseases cannot be mimicked in animals e.g. Alzheimer is a recent and great example of several failures of drugs in human trials despite being very effective in sophisticated animal models of disease. Billions of dollars and time was wasted. Similarly in certain diseases such cancer and sepsis, as mentioned in the Provisional applications 201941018457 and 201941018458 which are incorporated herein, the animal models are either not predictive or fail to capture human disease process resulting in failure. In cancer there are patient derived xenograft(PDX) models where human patient derived cells are used in mice, most of the physiology, drug metabolism and toxicity is still reflective of that animal. Similarly sepsis has not had a targeted approved drug despite decades of animal studies where drugs tested worked effectively

Thus, there is a dire need of human like tools that are better predictors of human disease, drug metabolism, its efficacy and toxicity .The other major areas where human like model are desperately needed are the Rare diseases which have no animal models. A case in point is infantile Pompe disease in which the infants die within first two years of life due to cardiac failure but the only animal model available does not replicate this and continues to develop into adult mice.

Drugs are currently profiled using series ofhuman purified enzymes/protein targetsor cell free and single cell based systems and animal models for demonstrating metabolism, efficacy and toxicity but it does not give the holistic picture. Real scenario only emerges in clinical trials , mostly in late stages, afterthe sunk costsrun in billions of dollars. FDA and EMEA are

providingregulatory push now to develop alternate discovery tools and platforms to animal human like tools, that are already in practice for cosmetics. Keeping in view of the above, the applicants have created a unique platform to study human disease, drug metabolism, efficacy and safety all in one model which helps to test drug or a combination of drugs for a disease. While the applicants have used lung cancer to establish the disease model, it is reiterated that the said platform can be applied to testing any disease condition.

Critical Drawbacks in the current practice of drug discovery

1. The way drug discovery is conducted now it is a trial and error process

2. Tens of thousands of compounds have to be screened before a candidate is identified

3. Discovery is done in isolation, compounds are often screened in a single cell system thus completely overlooking the synergy and cross-talk that occurs in real life physiology where several organs exist together

4. All of the discovery work is performed in animal models which do not mimic human disease and the compounds are finally tested in clinical trials

5. The highest failure rates in drug discovery are in the therapy areas of cancer and central nervous system (CNS) diseases where compounds that look very good in animal models have failed miserably in clinical trials. This is because the animal models used have very low predictive value for translation into humans.

SUMMARY OF THE INVENTION

The present invention provides an integrated system to test drug discovery and development parameters of a test drug or a combination of drugs. In one aspect, the present invention provides an integrated system to test drug discovery and development parameters of a test drug or a combination of drugs, the said system comprising multiple cell layered scaffolds in communication with each other and in a sequence wherein the said sequence comprises 3D printed target cells from a patient, suffering from the disease or condition for which the test drug is tested, as the bottom-most layer.

In another aspect the invention provides an unique integrated system comprising a 3D printed primary human cells layered in a specific sequence wherein the said sequence comprises intestinal cells as top layer, liver cells as middle layer and 3D printed target cells from patient, suffering from the disease or condition for which the novel drug is tested, as the bottom layer for testing drugs and drug combinations for human diseases.

The invention also provides an integrated system to test drug discovery and development parameters and the said parameters include a variety of parameters including absorption, drug metabolism, efficacy, toxicity, drug-drug interaction (DDI), pharmacokinetics and therapeutic index.

In yet another aspect the invention provides a process to test drug discovery and development parameters of a test drug or a combination of drugs wherein the said process comprising sequential layering of primary human cell linesin a scaffold, the said sequence comprising: a) a top most layer is intestinal cells;

b) a middle layer is liver cells;

c) a bottom layer is 3D printed layer of target cells from a patient, suffering from the disease or condition for which the test drug is tested; and

wherein the test drug is added to the top most layer.

The scaffold used to grow the primary cells is selected from the group consisting of collagen, alginate, agar, hydrogels, extracellular matrix proteins, porous filter paper, cross linking agents, gelatin and a combination thereof.

The invention also provides an integrated model for personalized medicine, wherein the said model comprises addition of a drug or a combination of drugs to a system comprising multiple cell layered scaffolds in communication with each other and in a sequence wherein the said 5 IB20/054272

(15.05.20) sequence comprises 3D printed target cells from a patient who needs the personalized medicine, as the bottom-most layer.

BRIEF DESCRIPTION OF DRAWINGS

Scheme 1. Specific sequence of a "3D printed human disease in a dish" platform

Scheme 2. Workflow of 3D bioprinted human diseases in a dish platform

Figure 1. Viability of 3D bioprinted human cells A549, HepG2 and Colo205.

Figure 2. Morphology of 3D bioprinted cells under normal phase contrast microscopy (A: Bottom layer of A549 lung cancer cells, B: Middle layer of HepG2 cells, C: Top layer of colo205 cells)

Figure 3: H and E stained cross section of 3D bioprinted system showing 3 layers of cells, Colo205, HepG2 and A549 cells.

Figure 4. Reproducibility of activity of cisplatin in 3D model - Three independent experiments with cisplatin efficacy yield similar IC50 values.

Figure 5. Drug cell number Comparison: HepG2 cell layer.

Figure 6. Drug cell number Comparison: A549 cell layer.

Figure 7. Comparison of metabolism of cisplatin by 3D bioprinted system versus lung cancer A549 cells as monolayer. A) metabolites of cisplatin in 3D printed system B) Metabolite of cisplatin in A549 monolayer.

Figure 8. Kinetics of cisplatin metabolism over time (6-24 h) showing greater metabolic activity of 3D bioprinted system versus A549 monolayer.

Figure 9. Comparison of metabolism of paracetamol by 3D bioprinted system versus lung cancer A549 cells as monolayer. A) Metabolite of paracetamol in A549 monolayer

B) metabolites of paracetamol in 3D printed system.

Figure 10. Comparison of metabolism of metformin by 3D bioprinted system versus lung cancer A549 cells as monolayer. A) Metabolite of metformin in A549 monolayer B) metabolites of metformin in 3D printed system.

Figure 11. Comparison of metabolism of cyclophosphamide by 3D bioprinted system versus lung cancer A549 cells as monolayer. While there was no detectable metabolism of cyclophosphamide in A549 monolayer, the HPLC read out of metabolism of cyclophosphamide in 3D bio printed system is shown in the figure.

Figure 12. Comparison of metabolism of testosterone by 3D bioprinted system versus lung cancer A549 cells as monolayer. A) Metabolite of testosterone in A549 monolayer B) metabolites of testosterone in 3D printed system.

Figure 13. Comparison of metabolism of indomethacin by 3D bioprinted system versus lung cancer A549 cells as monolayer. A) Metabolite of indomethacin in A549 monolayer B) metabolites of indomethacin in 3D printed system.

DESCRIPTION OF THE INVENTION

The present invention provides an integrated 3D bioprinted human disease in a dish model or system that serves the purpose to study and better predict the outcome of test drugs and drug combinations for human diseases and conditions.

As working example, applicants herein provide a human 3D system to simulate human drug metabolism in the same sequence as humans i.e. drug absorption by intestine, metabolism by liver and effect of drug and its metabolites on target lung cancer A549 cells.

In one embodiment, the cells are layered in similar fashion to what an orally delivered drug will encounter in humans .i.e., the drug is absorbed by intestinal cells, then metabolized by the liver during first pass and its metabolites and parent drug are exposed to the target tissue as shown in Schemes 1 and 2.

In another embodiment the present model for drug discovery is also extended for pediatric leukemia. In this platform which is the invention, 3D printed primary human cells layered in a specific sequence will be used (Scheme 1).

The topography of the platform is provided below:

a) The top layer will have intestinal cells layered on collagen scaffold;

b) Below (a) is human liver cells layered on to collagen scaffold;

c) The bottom layer below (b) consists of cells from patients suffering from the disease for which the drug is being tested. By establishing these layers, the test drug that is added to the top layer encounters the cell types in a physiologically relevant sequence, of first contact with intestinal, then by the liver and finally with target tissues. While the collagen scaffold is used herein, it can be appreciated by a person skilled in the art that various scaffolds can be used successfully to grow the cells.

As an example, in Scheme 1, the target cell is represented by cells extracted from patient suffering from pediatric acute lymphocytic leukemia, a rare disease which is currently largely treated by cytotoxic agents.

In this model, the applicants establish that a rapid readout of the drug (s) absorption by human intestine, metabolism by human liver, efficacy towards the leukemia, drug-drug interactions (DDI) if any and a projected therapeutic index is rapidly obtained.

Pediatric ALL is currently treated by a cocktail of cytotoxic drugs that are used in series. The present invention is to repurpose existing drugs that work with complementary targeted mechanisms to combine with the cytotoxic drugs so as to reduce the side effects of cytotoxic drugs and to improve efficacy. The test drug that can be added, for pediatric ALL, include but not limited to several non-oncological drug candidates with recent evidence of anti-cancer activity such as EPA/DHA, PUFAs, losartan/ARBs, chloroquine/hydroxychloroquine, statins, propranolol/beta blockers, omeprazole/PPI, metformin and PDE5 inhibitors.

The present invention is particularly important in quickly finding a "new' combination to a) improve efficacy and b) reduce the use of cytotoxic drug and decrease side effects.

The invention also provides a platform to test several known and repurposed drugs for uses that are hitherto not established (known drug or drug combinations for new use). Such endeavors have not been undertaken merely because of the a) non-affordability of cost involved in extending the use of a known drug to treat a different disease or condition b) it is difficult to mimic certain disease conditions in animal models; for example there are no drugs designed for pediatric leukemia but adult leukemia drugs are simply extended for pediatric conditions. This is because of lack of animal models specific for pediatric leukemia. In such scenario, the present 3D printed human disease model can quickly screen multiple drugs and drug combinations and provide validation in shortest period of time. The present invention of developing human disease in a dish platform uses human cell lines as well as primary human cells since these cells can mimic human biology. Moreover, using multiple cells, instead of isolated cells, involved in drug action, metabolism and safety will all be studied together for drug testing which is a key advantage. Most importantly, a single study can analyze different parameters such as potential absorption by intestinal cells, drug metabolism by liver cells, efficacy on target tissue cells and safety index against non target cells of a drug.

The model/platform of the present invention entails 3D printed cells in scaffold layered in a specific sequence. The top most layer is human intestinal cells, underlying that is human primary liver cells, grown on layers of collagen scaffolding. At the bottom, 3D printed layer of target cells (patient derived cells, for example pediatric ALL lymphocytic cell as in Scheme 1) is layered in a 12 or 24 well plate.

While collagen scaffolding is used in the present invention, other scaffolds that can be used in the present invention includes but not limited to alginate, agar, hydrogels, extracellular matrix proteins, porous filter paper, cross linking agents, gelatin and a combination thereof. The extracellular matrix proteins is firbronectin or proteoglycans and the likes thereof. The cross linking agents is polyethylene glycol (PEG) or calcium chloride and the likes thereof. The scaffolding used is also dependent on the test drug and the disease being modeled for.

The intestinal cells that are used in the invention is selected from human Colo205 or Caco2 or primary human intestinal epithelial cells.

Theliver cells that are used in the invention is selected from Hepg2 or plateable human cryopreserved primary hepatocytes.

The target cells are typically cells extracted from patients suffering from the disease or condition for which the drug is tested. These cells are then 3D printed and added as the bottom most layer in a dish. All three layers described are in communication with each other as shown in Scheme 2. The test drug or test drug combination to be tested is added on top of the top most layer. It is also provided herein that more layers of specific cells could be added to the system, depending on the drug tested, for example, kidney cells, to understand kidney excretion functionality for kidney toxicity. The invention of the 3D system described here further provides for robust validation of structurally diverse set of drugs that have different profile, efficacy, toxicity and drug metabolism thus immensely useful in drug discovery and development. Further extending the invention, using the present 3D bio printed system a variety of critical parameters important in the discovery of drugs and their development can be ascertained. These parameters include but are not limited to

• Identification of drugtarget proteins and genes, their isoforms and their validation in the role of a disease;

• Development of drug screening assays for the specific targets and diseases;

• Identification of compound hits in the screening assays and optimizing these hit

compounds into lead compounds and finally into drugs;

• Choosing compounds with desirable properties based on their ability to penetrate and distribute across multiple cell layers and scaffolds;

• Assessment of in vitro activity of the drugs and projecting potential doses for in vivo studies based on the IC50 or ED50 obtained;

• Demonstration of proof of efficacy of the drugs by phenotypic and genotypic

measurements;

• Testing of new formulation for the drugs to optimize drug delivery;

• Collection of proteomics, genomics and metabolomics information impacted by a drug;

• Identification of biomarkers that may be impacted by the drugs and their potential utility in human studies;

• Predicting the potential viability of a drug for human development based on its profile in the 3D bioprinted system;

The present invention provides a platform/system to test several drugs and drug combination for diseases or conditions where animal models of the said disease are unpredictable. The present system will therefore accelerate drug discovery, save money and markedly increase probability of success by bypassing bulk of the futile animal studies.

It is also reiterated that the present invention of '3D printed human disease in a dish' platform is not limited solely to the disease or condition discussed herein but can be applied to discover drugs for various diseases by tweaking the sequence of cells in the platform model which can be envisaged by a person skilled in the art.

Advantages of the Invention

1. Human cells important in absorption and metabolism are in the same environment as the target tissues and are in communication with each other;

2. The test compound(s) will be added to the top layer and will encounter the cell types in a physiologically relevant sequence, of first contact with intestinal, then by the liver and finally with target tissues;

3. The potential absorption of the compound by intestine, its metabolism by liver and the efficacy of the compound and its metabolites can all be studied in a single study;

4. The potential toxicity of the compound can also be scored by looking at the cell death of the three cell types and its selectivity for a particular cell type; any cell type can be used as per the disease or condition for which the drug is tested. Furthermore, patient cells and primary cells can be used to make the model personalized

5. Several compounds, biologies, nutraceuticals or combinations can be tested in one shot by adding them together as well as potential for any drug-drug interaction issues can be seen.

6. mechanism of action of a drug across multiple cell types grown together can be studied especially in diseases where multiple cell/tissue types are involved

7. This model can be used to prioritize and rank order compound libraries and also study PKPD relationships from human phase I samples.

8. The model further can be further extended to study and predict disease outcomes using patient cells.

9. Superior to traditional co-culture models or single cell models because of defined

architecture obtained by 3 D bioprinting, cell communication, interactions with matrix milieu.

Examples The Examples provided herein further enhance the understanding of the invention in general and enable the invention. It is for illustrative purposes only and cannot be construed as limiting the scope of the invention.

Example 1: 3D Model Development and optimization

a) 3D bioprinting

Human intestinal cell Colo205 or Caco2, liver cell (Hepg2) and lung cancer (A549), all from Lonza or ATCC were used as representative cell lines and maintained in Dulbecco's modification of Eagle's Medium (DMEM, ATCC, USA) supplemented with 15% fetal bovine serum (Thermo Scientific) and 1% Penicillin/Streptomycin. This media was also used for 3D bioprinting. 3D bioprinter from 3D Cultures (Philadelphia, USA, Model No. 3D Printer Serial#QH18100328, version 2.0, software: Cura_15.04.2.exe) was used for printing.

The above cells were layered as shown in Scheme 2: target cells A549 at the bottom, followed by hepg2 liver cells and finally at the very top were the colo205 or caco2 cells.

Cell to bebioprinted were loaded in a 10 ml syringe (which serves as a printer cartridge) in DMEM. Prior to printing the cells, the 12 or 24 well plate was first layered with collagen as the scaffold between each cell layer. Collagen hydrogel precursor (Rat tail, type I; BD Bio- sciences) was used as the scaffold at dilutions of 1.5 to 3.0 mg/mL pH adjusted in Dulbecco's phosphate- buffered saline (without calcium and magnesium) and was maintained on ice until it was ready to be loaded into a syringe for printing.

Printing pressure and dispenser valve opening times (pulse duration) are critical parameters for the proper printing of various biomaterials, hydrogels, and cells. The following parameters (Table 1) were found to be optimal for maintain cell morphology and viability.

Table 1. Printing Parameters

(computer assisted design) was developed to dispense the cells by the bioprinted in 12 or 24 well cell culture plates. Between layering of each cell type, the system was placed in a cell culture incubator for 24 hours.

To study the metabolism, efficacy and toxicity of a drug, the drug was added to the 3D bioprinted layers in 12/24 well plates in the media at the top of the well. The media was later collected after defined time, from 0-48 hours, for HPLC analysis and the cell layers are examined for cell viability and morphology as shown in Scheme 2.

b) Cell viability

The viability of cells grown in layers by 3D bioprinting was assessed by addition of MTT and then counting the stained cells under a phase contrast microscope. For every layer at least five representative fields were counted and data analyzed by ImageWare. The different layers were counted by adjusting the microscope to focus on a particular layer. In some experiments the cells were then lysed and MTT colour OD was measured using a spectrophotometer.

Haematoxylin and Eosin staining was used on unfixed 3D bioprinted cell layers to visualize the cell layers.

The cells in each layer were viable up to at least 48 hrs after the assembly of the 3D bioprinted system (Figure 1). So drug properties after addition to the 3D bioprinted system was studied for at least 48 hrs after addition of the drug. During this time a drug can be added once or multiple dosing and can also be added a few hours apart if needed to simulate single dose versus multiple dose regimens. c) Cell morphology

Cell morphology by phase contrast microscopy (Figure 2) and H and E stained (Figure 3) visuals provide each of the three cell layers. It was found that the cell morphology was in line with what is expected of each layer suggesting that the cells grow well in this 3D dish and are healthy. Similarly, H and E staining of the 3D bioprinted layers showed three separate cell layers in between the collagen scaffold. Collectively, it is established that the 3D bioprinted cells can be used for studying drug metabolism, efficacy and toxicity in one single system.

Example 2

Efficacy and toxicity scoring of cancer drugs in the 3D system of the present invention

Since as an example, lung cancer A549 cells are used herein, this system can be used to study drugs that are active against lung cancer. Three groups of drugs for validation were selected - those that are known to be chemotherapeutic, those that have shown some activity and then those that were not expected to have any activity.

Efficacy

The data provided herein shows that the 3D bioprinted system shows more effective activity by cancer chemotherapy drugs but not other two groups of drugs. The reproducibility of the system for demonstrating drug activity was ascertained by demonstrating that similar degree of inhibition was obtained in three separate experiments using cisplatin, which is a known anti cancer chemotherapeutic (Figure 4). The applicants also profiled other classes of structurally diverse drugs as mentioned above such as pro drug cyclophosphamide, a chemotherapeutic which needs to be converted to an active metabolite by the liver cells.

Table 2. Efficacy of various drugs against lung cancer A549 cells in the 3D bioprinter system

As shown in Table 2, the chemotherapeutic drugs cisplatin and cyclophosphamide were most active as shown by their IC50 (drug concentration needed for 50% of activity). Indomethacin shows moderate activity as has been shown to be active in some clinical studies but there are no reports of its activity in PDX animal models. Surprisingly, this drug is found to be active in the present 3D system (Table 2) establishing this 3D model a very effective one for drug validation. Metformin and testosterone which are not effective for lung cancer showed no activity. Overall these data strongly support that the present system is reflective of drug activities against lung cancer and indomethacin activity supports activity similar to human reports.

Toxicity

Besides testing efficacy, the present 3D printed platform is also used to simultaneously test the general toxicity of the test drug or drug combination.

Toxicity was analyzed by looking at a drug's activity on the target A549 cells (Figure 6) versus any effect on the liver hepG2 cells (Figure 5). Any killing of the hepG2 cells is considered general toxicity since it is not the target cell. It was then scored for cell death of the target A549 cells versus the liver Hepg2 cells. We then calculated the ratio of cell death between how many heg2 cells: target A549 cells. A higher ratio would indicate more/selective death of the target cancer cells relative to the liver Hepg2 cells. This ratio is termed as Toxicity Index of the drug as shown in Table3 , the chemotherapeutic drugs cisplatin and cyclophosphamide which work by being cytotoxic has a ratio of close to 1, suggesting that they killed the targetA549 cells and the liver HepG2 cells equally. In contrast indomethacin had a ratio of more than 6 suggesting that it was mainly killing the target A549 cells but not the liver HepG2 cells. Paracetamol which is known to kill liver cells showed a ratio of 2 suggesting some toxicity toward liver cells. Metformin and testosterone tested as negative controls did not kill either liver Hepg2 or A549 cells. Thus, our system can be used to look at efficacy and toxicity at the same time an establish a Toxicity Index for it which can be used to make decisions.

Table 3. Toxicity Index of various drugs in 3D bioprinted model

Example 3.

Analysis of Drug metabolism using 3D bioprinted system of the invention

One of the advantages of the 3D bioprinted system is the ability to study drug metabolism of the test drug alongside efficacy and toxicity. The drug metabolic profile of every drug tested above in the 3D bioprinted system versus just the target cells A549 cells alone was compared using HPLC .

As shown in Figures 7-13, it was found that the metabolic activity in 3D bioprinted system was distinct and superior in terms of the number of metabolite peaks seen as compared to incubating drugs with target cells A549 cells alone. In case of the prodrug cyclophosphamide no peaks of metabolites were found in A549 cells but several metabolite peaks were found when incubated with 3D bioprinted system. This points out the advantage of profiling drugs in the present 3D bioprinted system versus the traditional way of screening drugs with just the target cells or enzymes. Table. 4 shows the number of cytochrome p450 enzymes that have a role in metabolism of drugs in the 3D bioprinted system in order to metabolize the drugs. Overall the data strongly support the ability of the 3D bioprinted system to metabolize drugs much better than just target cells and thus provide a better platform for testing drugs and drug

combinations for specific disease/condition.

Table 4. Metabolism of structurally different drugs in 3D bioprinted system is significantly more than cancer A549 cells alone assessed by HPLC - Demonstration of activity of cytochrome p450 enzymes

Claims

Claims We Claim

1. An integrated system to test drug discovery and development parameters of a test drug or a combination of drugs, the said system comprising multiple cell layered scaffolds in

communication with each other and in a sequence wherein the said sequence comprises 3D printed target cells from a patient, suffering from the disease or condition for which the test drug is tested, as the bottom-most layer.

2. An integrated system to test drug discovery and development parameters of a test drug or a combination of drugs, the said system comprising a 3D printed primary human cells layered in a specific sequence wherein the said sequence comprises intestinal cells as top layer, liver cells as middle layer and 3D printed target cells from patient, suffering from the disease or condition for which the novel drug is tested, as the bottom layer.

3. The integrated system to test drug discovery and development parameters of a test drug or a combination of drugs as claimed in claims 1 and 2, the said parameters are selected from absorption, drug metabolism, efficacy, toxicity, drug-drug interaction (DDI), pharmacokinetics and therapeutic index.

4. The integrated system to test drug discovery and development parameters of a test drug or a combination of drugs as claimed in claims 1 wherein the scaffold is selected from the group consisting of collagen, alginate, agar, hydrogels, extracellular matrix proteins, porous filter paper, cross linking agents, gelatin and a combination thereof.

5. A process to test drug discovery and development parameters of a test drug or a

combination of drugs wherein the said process comprising sequential layering of primary human cell linesin a scaffold, the said sequence comprising:

a) a top most layer is intestinal cells;

b) a middle layer is liver cells; c) a bottom layer is 3D printed layer of target cells from a patient, suffering from the disease or condition for which the test drug is tested; and

wherein the test drug is added to the top most layer.

6. The process to test drug discovery and development parameters of a test drug or a combination of drugs as claimed in claim 4 wherein the said scaffold is selected from the group consisting of collagen, alginate, agar, hydrogels, extracellular matrix proteins, porous filter paper, cross linking agents, gelatin and a combination thereof.

7. An integrated model for personalized medicine, wherein the said model comprises addition of a drug or a combination of drugs to a system comprising multiple cell layered scaffolds in communication with each other and in a sequence wherein the said sequence comprises 3D printed target cells from a patient who needs the personalized medicine, as the bottom-most layer.