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식품독성학 ) in silico toxicology 또는 In silico ADME study에 대한 동향 조사

"식품독성학 ) in silico toxicology 또는 In silico ADME study에 대한 동향 조사"에 대한 내용입니다.
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식품독성학 in silico toxicology In silico ADME study 동향 조사
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식품독성학 ) in silico toxicology 또는 In silico ADME study에 대한 동향 조사
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    1. 연구동향
    1) In silicotoxicology models and databases as FDA Critical Path Initiative toolkits. (2011)
    이 연구는 인실리코 독성학 모델과 데이터베이스가 FDA의 중요 경로 이니셔티브 도구로써 어떠한 목적을 가지며 어떤 방법을 사용하는지에 대해 설명한다. 이 도구들은 약물 개발과 규제적 평가 과정에서 QT 간격의 연장이나 약물에 의한 인지질증 같은 가능한 부작용들을 예측하기 위해 사용된다. 더 나아가, 이는 약물 대사 및 P450 효소 억제에 대한 인실리코 모델링의 중요성을 강조하며, 식물 성분의 간 독성 평가에 대한 인실리코 스크리닝 방법을 포함한다.

    2) The Pros and Cons of the In-silico Pharmaco-toxicology in Drug Discovery and Development. (2013)
    이 논문은 약물 발견 및 개발 과정에서 화학 구조를 기반으로 한 독성 예측에 기여하는 다양한 인실리코 모델에 대해 다룬다. 특히, 구조-활성 관계(SAR)와 양적 구조-활성 관계(QSAR) 모델을 중심으로 설명한다. 이 모델들은 항산화제가 암 예방 및 치료에 미치는 영향과 같이 논란이 많은 주제를 탐색하는 데 있어 유용하게 사용될 수 있다. 또한, 이 논문은 독성 평가에서 인간 데이터에 대한 민감도를 고려하는 새로운 접근 방법을 제시한다.

    참고자료

    · Arowolo O, Salemme V, Suvorov A. Towards Whole Health Toxicology: In-Silico Prediction of Diseases Sensitive to Multi-Chemical Exposures. Toxics. 2022; 10(12):764. https://doi.org/10.3390/toxics10120764
    · Romualdo Benigni, In silico approaches to genetic toxicology: progress and future, Mutagenesis, Volume 34, Issue 1, January 2019, Pages 1–2, https://doi.org/10.1093/mutage/gey018
    · Lee KM, Corley R, Jarabek AM, Kleinstreuer N, Paini A, Stucki AO, Bell S. Advancing New Approach Methodologies (NAMs) for Tobacco Harm Reduction: Synopsis from the 2021 CORESTA SSPT—NAMs Symposium. Toxics. 2022; 10(12):760. https://doi.org/10.3390/toxics10120760
    · Soodabeh Saeidnia, Azadeh Manayi and Mohammad Abdollahi, 2013. The Pros and Cons of the In-silico Pharmaco-toxicology in Drug Discovery and Development. International Journal of Pharmacology, 9: 176-181.https://doi.org/10.3923/ijp.2013.176.181
    · Valerio, L.G. In silicotoxicology models and databases as FDA Critical Path Initiative toolkits. Hum Genomics 5, 200 (2011). https://doi.org/10.1186/1479-7364-5-3-200
    · Komura H, Watanabe R, Mizuguchi K. The Trends and Future Prospective of In Silico Models from the Viewpoint of ADME Evaluation in Drug Discovery. Pharmaceutics. 2023; 15(11):2619. https://doi.org/10.3390/pharmaceutics15112619
    · Wu F, Zhou Y, Li L, Shen X, Chen G, Wang X, Liang X, Tan M and Huang Z (2020) Computational Approaches in Preclinical Studies on Drug Discovery and Development. Front. Chem. 8:726. https://doi.org/10.3389/fchem.2020.00726
    · Wang Y, Xing J, Xu Y, et al. In silico ADME/T modelling for rational drug design. Quarterly Reviews of Biophysics. 2015;48(4):488-515. https://doi.org/10.1017/S0033583515000190
    · Cheng , Feixiong,Li , Weihua,Liu , Guixia,Tang , Yun,In Silico ADMET Prediction: Recent Advances, Current Challenges and Future Trends,Current Topics in Medicinal Chemistry, volume 13, issue 11, pages 1273-1289, year 2013, https://doi.org/10.2174/15680266113139990033

  • AI와 토픽 톺아보기

    • 1. In silicotoxicology models and databases as FDA Critical Path Initiative toolkits
      In silicotoxicology models and databases have become increasingly important as part of the FDA's Critical Path Initiative, which aims to modernize the drug development process and bring safer, more effective medicines to patients more efficiently. These in silico tools can provide valuable insights into the potential toxicity of drug candidates early in the development process, helping to identify and mitigate risks before costly and time-consuming in vivo studies are conducted. The use of in silico models and databases offers several advantages, including the ability to screen large chemical libraries, explore structure-activity relationships, and predict a wide range of toxicological endpoints. These tools can also help reduce the use of animal testing, which is a key priority for regulatory agencies and the public. Additionally, the integration of in silico approaches with other emerging technologies, such as high-throughput screening and systems biology, can further enhance the predictive power and efficiency of the drug development process. However, the successful implementation of in silicotoxicology models and databases as FDA Critical Path Initiative toolkits also faces several challenges. Ensuring the reliability, reproducibility, and regulatory acceptance of these models is crucial, as is the need for continued investment in research and development to improve their predictive capabilities. Collaboration between industry, academia, and regulatory agencies will be essential to address these challenges and fully realize the potential of in silico toxicology in drug discovery and development.
    • 2. The Pros and Cons of the In-silico Pharmaco-toxicology in Drug Discovery and Development
      The use of in-silico pharmaco-toxicology approaches in drug discovery and development has both advantages and disadvantages that must be carefully considered. Pros: 1. Cost and time savings: In-silico models can screen large chemical libraries and predict toxicological endpoints much faster and more cost-effectively than traditional in vivo studies. 2. Reduced animal testing: In-silico approaches can help reduce the need for animal testing, which is a key priority for regulatory agencies and the public. 3. Improved decision-making: In-silico models can provide valuable insights into the potential risks and benefits of drug candidates early in the development process, allowing for more informed decision-making. 4. Exploration of structure-activity relationships: In-silico tools can help researchers better understand the relationship between a drug's chemical structure and its biological activity, including potential toxicity. Cons: 1. Reliability and validation: Ensuring the reliability and regulatory acceptance of in-silico models is a significant challenge, as their predictive capabilities must be thoroughly validated against experimental data. 2. Complexity of biological systems: Accurately modeling the complex interactions and pathways involved in drug pharmacology and toxicology can be extremely challenging, and current in-silico models may not fully capture the nuances of these systems. 3. Limited data availability: The success of in-silico approaches is heavily dependent on the availability of high-quality, diverse datasets, which can be limited for certain endpoints or therapeutic areas. 4. Potential for bias and errors: In-silico models can be susceptible to bias and errors, particularly if the underlying data or algorithms are not carefully curated and validated. Overall, the use of in-silico pharmaco-toxicology approaches in drug discovery and development holds great promise, but careful consideration of both the pros and cons is essential to ensure their effective and responsible implementation.
    • 3. In Silico ADMET Prediction: Recent Advances, Current Challenges and Future Trends
      The field of in silico ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) prediction has seen significant advancements in recent years, with the development of increasingly sophisticated computational models and the availability of larger, more diverse datasets. These in silico approaches have become an integral part of the drug discovery and development process, offering the potential to streamline the identification and optimization of drug candidates. Recent advances in this field include the use of machine learning algorithms, such as deep learning and ensemble methods, to improve the accuracy and reliability of ADMET predictions. Additionally, the integration of multi-scale modeling techniques, which combine molecular-level simulations with physiologically-based pharmacokinetic (PBPK) models, has enabled more comprehensive and realistic predictions of drug behavior in the body. However, the field of in silico ADMET prediction still faces several challenges that need to be addressed. One of the key challenges is the limited availability of high-quality, diverse experimental data, which is essential for training and validating these computational models. Additionally, the complexity of biological systems and the inherent variability in ADMET processes can make it difficult to develop universally applicable models. Looking to the future, the continued advancement of in silico ADMET prediction is likely to be driven by several key trends. These include the integration of multi-omics data (e.g., genomics, proteomics, metabolomics) to better capture the underlying biological mechanisms, the development of more sophisticated modeling approaches that can handle the inherent uncertainty and variability in ADMET processes, and the increased use of hybrid modeling strategies that combine in silico and in vitro/in vivo data. Overall, the field of in silico ADMET prediction holds great promise for accelerating and improving the drug discovery and development process, but ongoing research and collaboration between industry, academia, and regulatory agencies will be essential to address the current challenges and realize the full potential of these computational tools.
    • 4. In silico ADME/T modelling for rational drug design
      In silico ADME/T (Absorption, Distribution, Metabolism, Excretion, and Toxicity) modeling has become an increasingly important tool in the rational design of new drug candidates. These computational approaches offer the potential to predict key pharmacokinetic and toxicological properties of drug molecules early in the development process, allowing researchers to make more informed decisions and optimize the drug's properties before investing in costly and time-consuming in vitro and in vivo studies. The advantages of in silico ADME/T modeling for rational drug design are numerous. By screening large chemical libraries and predicting a wide range of ADME/T endpoints, researchers can identify promising lead compounds and prioritize them for further development. This can help to reduce attrition rates and improve the overall efficiency of the drug discovery pipeline. Additionally, in silico models can provide valuable insights into the structure-activity relationships (SAR) that govern a drug's ADME/T properties, enabling the design of more targeted and effective molecules. However, the successful implementation of in silico ADME/T modeling for rational drug design also faces several challenges. Ensuring the reliability and regulatory acceptance of these computational models is crucial, as is the need for continuous improvement in their predictive capabilities. This requires ongoing investment in research and development, as well as close collaboration between industry, academia, and regulatory agencies. Furthermore, the complexity of biological systems and the inherent variability in ADME/T processes can make it difficult to develop universally applicable in silico models. Addressing this challenge may require the integration of multi-scale modeling approaches, which combine molecular-level simulations with physiologically-based pharmacokinetic (PBPK) models, as well as the incorporation of multi-omics data to better capture the underlying biological mechanisms. Despite these challenges, the potential of in silico ADME/T modeling for rational drug design remains immense. As the field continues to evolve and the predictive capabilities of these computational tools improve, they are likely to play an increasingly central role in the drug discovery and development process, helping to bring safer and more effective medicines to patients more efficiently.
    • 5. In silico approaches to genetic toxicology: progress and future
      In silico approaches to genetic toxicology have made significant progress in recent years, offering the potential to streamline the assessment of the genotoxic potential of drug candidates and other chemicals. These computational methods can provide valuable insights into the mechanisms of genetic toxicity, as well as help to identify and prioritize compounds for further testing. The key advantages of in silico approaches to genetic toxicology include the ability to screen large chemical libraries, explore structure-activity relationships, and predict a wide range of genotoxic endpoints, such as mutagenicity, chromosomal aberrations, and DNA damage. These computational tools can also help to reduce the use of animal testing, which is a priority for regulatory agencies and the public. However, the successful implementation of in silico approaches to genetic toxicology also faces several challenges. Ensuring the reliability and regulatory acceptance of these models is crucial, as is the need for continuous improvement in their predictive capabilities. This requires ongoing investment in research and development, as well as close collaboration between industry, academia, and regulatory agencies. One of the key challenges in this field is the inherent complexity of genetic toxicology, which involves multiple mechanisms and pathways that can be influenced by a wide range of factors, including chemical structure, metabolism, and cellular processes. Addressing this complexity may require the integration of multi-scale modeling approaches, which combine molecular-level simulations with systems biology models, as well as the incorporation of multi-omics data to better capture the underlying biological mechanisms. Despite these challenges, the potential of in silico approaches to genetic toxicology remains significant. As the field continues to evolve and the predictive capabilities of these computational tools improve, they are likely to play an increasingly important role in the assessment and management of chemical safety, helping to protect human health and the environment more effectively.
    • 6. Computational Approaches in Preclinical Studies on Drug Discovery and Development
      Computational approaches have become increasingly important in the preclinical stages of drug discovery and development, offering the potential to streamline the identification and optimization of drug candidates. These computational methods can be applied to a wide range of preclinical activities, including target identification and validation, lead compound screening and optimization, and the prediction of pharmacokinetic and toxicological properties. By leveraging the power of in silico models and simulations, researchers can explore a larger chemical space, identify promising compounds more efficiently, and gain valuable insights into the underlying biological mechanisms. Some of the key advantages of computational approaches in preclinical studies include: 1. Cost and time savings: In silico models can screen large chemical libraries and predict a wide range of endpoints much faster and more cost-effectively than traditional in vitro and in vivo studies. 2. Reduced animal testing: Computational methods can help to reduce the need for animal testing, which is a key priority for regulatory agencies and the public. 3. Improved decision-making: In silico tools can provide valuable insights into the potential risks and benefits of drug candidates early in the development process, allowing for more informed decision-making. 4. Exploration of structure-activity relationships: Computational approaches can help researchers better understand the relationship between a drug's chemical structure and its biological activity, including potential efficacy and toxicity. However, the successful implementation of computational approaches in preclinical studies also faces several challenges. Ensuring the reliability and regulatory acceptance of these models is crucial, as is the need for continuous improvement in their predictive capabilities. This requires ongoing investment in research and development, as well as close collaboration between industry, academia, and regulatory agencies. Additionally, the complexity of biological systems and the inherent variability in drug behavior can make it difficult to develop universally applicable computational models. Addressing this challenge may require the integration of multi-scale modeling approaches, which combine molecular-level simulations with physiologically-based pharmacokinetic (PBPK) models, as well as the incorporation of multi-omics data to better capture the underlying biological mechanisms. Despite these challenges, the potential of computational approaches in preclinical studies on drug discovery and development remains significant. As the field continues to evolve and the predictive capabilities of these computational tools improve, they are likely to play an increasingly central role in the drug development process, helping to bring safer and more effective medicines to patients more efficiently.
    • 7. Towards Whole Health Toxicology: In-Silico Prediction of Diseases Sensitive to Multi-Chemical Exposures
      The concept of

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