Advantages and challenges of the most commonly used NAMs in cardiovascular biomedical research
| . | Advantages . | Challenges . |
|---|---|---|
| In silico | Fast | Standardization is lagging behind |
| Reliable/more accurate vs. animal methods in predicting cardiotoxicity/making prediction of the ability of a compound to bind to a receptor even before compound synthesis | Can be closed systems—transparency is an issue/wider use is prevented Conditions of access to patient data for training/using models are not regulated yet | |
| Can provide mechanistic insights into special high-risk populations for CVDs Expected to reduce the cost for R&D by ∼35% Decrease/prevent altogether the use of animals | Complex models may require access to large computing facilities (not part of standard healthcare facilities) | |
| Can combine with AI/ML and high throughput to deliver huge amounts of data in less time | ||
| Can integrate a wide range of data from different disciplines, towards the digital twin | ||
| OoC | Ease of use—user-friendly and portability | Standardization is lagging; validation/qualification of OoC models is also missing |
| Fast | Compatibility with other lab equipment, imaging, analytical instrumentation, etc. can be an issue | |
| Fabrication of chips is quite cheap | Integration of sensors might be problematic in some cases | |
| Expected to reduce the cost for drug R&D by ∼10–26% | Adsorption effects on the surface of OoC, which can affect the results (use of alternative materials for the manufacturing of the chip) | |
| Decrease/prevent altogether the use of animals | 3D cultures are hard to maintain long term | |
| Integration of sensors can measure in real-time several parameters (pH, O2, etc.) | Human-derived cardiac cells (e.g. stem cells or primary cell lines) are not mature enough/variability of maturation protocols | |
| Good mimicking of the cardiovascular (myocardial/vessel) microenvironment | Need to acquire human-derived cells from several donors | |
| iPSC-derived CMs and CFs can be produced | Mostly ventricle derived (atrial-derived models are under-represented) | |
| Can obtain iPSCs from donors with specific background (age, sex, race/ethnicity, and pathologies) to study specific CVDs | Chronic aspect of development of CVDs is challenging to reproduce | |
| Possible to test a variety of drug compounds in a wide range of doses at the same time | ||
| Ability to integrate several chips together (multi-chips), e.g. heart-on-chip with liver-on-chip and/or kidney-on-chip, to better mimic the human physiology |
| . | Advantages . | Challenges . |
|---|---|---|
| In silico | Fast | Standardization is lagging behind |
| Reliable/more accurate vs. animal methods in predicting cardiotoxicity/making prediction of the ability of a compound to bind to a receptor even before compound synthesis | Can be closed systems—transparency is an issue/wider use is prevented Conditions of access to patient data for training/using models are not regulated yet | |
| Can provide mechanistic insights into special high-risk populations for CVDs Expected to reduce the cost for R&D by ∼35% Decrease/prevent altogether the use of animals | Complex models may require access to large computing facilities (not part of standard healthcare facilities) | |
| Can combine with AI/ML and high throughput to deliver huge amounts of data in less time | ||
| Can integrate a wide range of data from different disciplines, towards the digital twin | ||
| OoC | Ease of use—user-friendly and portability | Standardization is lagging; validation/qualification of OoC models is also missing |
| Fast | Compatibility with other lab equipment, imaging, analytical instrumentation, etc. can be an issue | |
| Fabrication of chips is quite cheap | Integration of sensors might be problematic in some cases | |
| Expected to reduce the cost for drug R&D by ∼10–26% | Adsorption effects on the surface of OoC, which can affect the results (use of alternative materials for the manufacturing of the chip) | |
| Decrease/prevent altogether the use of animals | 3D cultures are hard to maintain long term | |
| Integration of sensors can measure in real-time several parameters (pH, O2, etc.) | Human-derived cardiac cells (e.g. stem cells or primary cell lines) are not mature enough/variability of maturation protocols | |
| Good mimicking of the cardiovascular (myocardial/vessel) microenvironment | Need to acquire human-derived cells from several donors | |
| iPSC-derived CMs and CFs can be produced | Mostly ventricle derived (atrial-derived models are under-represented) | |
| Can obtain iPSCs from donors with specific background (age, sex, race/ethnicity, and pathologies) to study specific CVDs | Chronic aspect of development of CVDs is challenging to reproduce | |
| Possible to test a variety of drug compounds in a wide range of doses at the same time | ||
| Ability to integrate several chips together (multi-chips), e.g. heart-on-chip with liver-on-chip and/or kidney-on-chip, to better mimic the human physiology |
AI/ML, artificial intelligence/machine learning; CFs, cardiac fibroblasts; CMs, cardiomyocytes; CVDs, cardiovascular diseases; iPSC, induced pluripotent stem cells; NAMs, new approach methodologies; OoC, organ-on-chip; R&D, research and development.
Advantages and challenges of the most commonly used NAMs in cardiovascular biomedical research
| . | Advantages . | Challenges . |
|---|---|---|
| In silico | Fast | Standardization is lagging behind |
| Reliable/more accurate vs. animal methods in predicting cardiotoxicity/making prediction of the ability of a compound to bind to a receptor even before compound synthesis | Can be closed systems—transparency is an issue/wider use is prevented Conditions of access to patient data for training/using models are not regulated yet | |
| Can provide mechanistic insights into special high-risk populations for CVDs Expected to reduce the cost for R&D by ∼35% Decrease/prevent altogether the use of animals | Complex models may require access to large computing facilities (not part of standard healthcare facilities) | |
| Can combine with AI/ML and high throughput to deliver huge amounts of data in less time | ||
| Can integrate a wide range of data from different disciplines, towards the digital twin | ||
| OoC | Ease of use—user-friendly and portability | Standardization is lagging; validation/qualification of OoC models is also missing |
| Fast | Compatibility with other lab equipment, imaging, analytical instrumentation, etc. can be an issue | |
| Fabrication of chips is quite cheap | Integration of sensors might be problematic in some cases | |
| Expected to reduce the cost for drug R&D by ∼10–26% | Adsorption effects on the surface of OoC, which can affect the results (use of alternative materials for the manufacturing of the chip) | |
| Decrease/prevent altogether the use of animals | 3D cultures are hard to maintain long term | |
| Integration of sensors can measure in real-time several parameters (pH, O2, etc.) | Human-derived cardiac cells (e.g. stem cells or primary cell lines) are not mature enough/variability of maturation protocols | |
| Good mimicking of the cardiovascular (myocardial/vessel) microenvironment | Need to acquire human-derived cells from several donors | |
| iPSC-derived CMs and CFs can be produced | Mostly ventricle derived (atrial-derived models are under-represented) | |
| Can obtain iPSCs from donors with specific background (age, sex, race/ethnicity, and pathologies) to study specific CVDs | Chronic aspect of development of CVDs is challenging to reproduce | |
| Possible to test a variety of drug compounds in a wide range of doses at the same time | ||
| Ability to integrate several chips together (multi-chips), e.g. heart-on-chip with liver-on-chip and/or kidney-on-chip, to better mimic the human physiology |
| . | Advantages . | Challenges . |
|---|---|---|
| In silico | Fast | Standardization is lagging behind |
| Reliable/more accurate vs. animal methods in predicting cardiotoxicity/making prediction of the ability of a compound to bind to a receptor even before compound synthesis | Can be closed systems—transparency is an issue/wider use is prevented Conditions of access to patient data for training/using models are not regulated yet | |
| Can provide mechanistic insights into special high-risk populations for CVDs Expected to reduce the cost for R&D by ∼35% Decrease/prevent altogether the use of animals | Complex models may require access to large computing facilities (not part of standard healthcare facilities) | |
| Can combine with AI/ML and high throughput to deliver huge amounts of data in less time | ||
| Can integrate a wide range of data from different disciplines, towards the digital twin | ||
| OoC | Ease of use—user-friendly and portability | Standardization is lagging; validation/qualification of OoC models is also missing |
| Fast | Compatibility with other lab equipment, imaging, analytical instrumentation, etc. can be an issue | |
| Fabrication of chips is quite cheap | Integration of sensors might be problematic in some cases | |
| Expected to reduce the cost for drug R&D by ∼10–26% | Adsorption effects on the surface of OoC, which can affect the results (use of alternative materials for the manufacturing of the chip) | |
| Decrease/prevent altogether the use of animals | 3D cultures are hard to maintain long term | |
| Integration of sensors can measure in real-time several parameters (pH, O2, etc.) | Human-derived cardiac cells (e.g. stem cells or primary cell lines) are not mature enough/variability of maturation protocols | |
| Good mimicking of the cardiovascular (myocardial/vessel) microenvironment | Need to acquire human-derived cells from several donors | |
| iPSC-derived CMs and CFs can be produced | Mostly ventricle derived (atrial-derived models are under-represented) | |
| Can obtain iPSCs from donors with specific background (age, sex, race/ethnicity, and pathologies) to study specific CVDs | Chronic aspect of development of CVDs is challenging to reproduce | |
| Possible to test a variety of drug compounds in a wide range of doses at the same time | ||
| Ability to integrate several chips together (multi-chips), e.g. heart-on-chip with liver-on-chip and/or kidney-on-chip, to better mimic the human physiology |
AI/ML, artificial intelligence/machine learning; CFs, cardiac fibroblasts; CMs, cardiomyocytes; CVDs, cardiovascular diseases; iPSC, induced pluripotent stem cells; NAMs, new approach methodologies; OoC, organ-on-chip; R&D, research and development.
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