Integrating Model‐Informed Drug Development With AI: A Synergistic Approach to Accelerating Pharmaceutical Innovation
ABSTRACT The pharmaceutical industry constantly strives to improve drug development processes to reduce costs, increase efficiencies, and enhance therapeutic outcomes for patients. Model‐Informed Drug Development (MIDD) uses mathematical models to simulate intricate processes involved in drug absorp...
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| Format: | Article |
| Language: | English |
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Wiley
2025-01-01
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| Series: | Clinical and Translational Science |
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| Online Access: | https://doi.org/10.1111/cts.70124 |
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| author | Karthik Raman Rukmini Kumar Cynthia J. Musante Subha Madhavan |
| author_facet | Karthik Raman Rukmini Kumar Cynthia J. Musante Subha Madhavan |
| author_sort | Karthik Raman |
| collection | DOAJ |
| description | ABSTRACT The pharmaceutical industry constantly strives to improve drug development processes to reduce costs, increase efficiencies, and enhance therapeutic outcomes for patients. Model‐Informed Drug Development (MIDD) uses mathematical models to simulate intricate processes involved in drug absorption, distribution, metabolism, and excretion, as well as pharmacokinetics and pharmacodynamics. Artificial intelligence (AI), encompassing techniques such as machine learning, deep learning, and Generative AI, offers powerful tools and algorithms to efficiently identify meaningful patterns, correlations, and drug–target interactions from big data, enabling more accurate predictions and novel hypothesis generation. The union of MIDD with AI enables pharmaceutical researchers to optimize drug candidate selection, dosage regimens, and treatment strategies through virtual trials to help derisk drug candidates. However, several challenges, including the availability of relevant, labeled, high‐quality datasets, data privacy concerns, model interpretability, and algorithmic bias, must be carefully managed. Standardization of model architectures, data formats, and validation processes is imperative to ensure reliable and reproducible results. Moreover, regulatory agencies have recognized the need to adapt their guidelines to evaluate recommendations from AI‐enhanced MIDD methods. In conclusion, integrating model‐driven drug development with AI offers a transformative paradigm for pharmaceutical innovation. By integrating the predictive power of computational models and the data‐driven insights of AI, the synergy between these approaches has the potential to accelerate drug discovery, optimize treatment strategies, and usher in a new era of personalized medicine, benefiting patients, researchers, and the pharmaceutical industry as a whole. |
| format | Article |
| id | doaj-art-cadacafd946e471cb8fb821e3ff37808 |
| institution | Kabale University |
| issn | 1752-8054 1752-8062 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Clinical and Translational Science |
| spelling | doaj-art-cadacafd946e471cb8fb821e3ff378082025-01-24T08:17:46ZengWileyClinical and Translational Science1752-80541752-80622025-01-01181n/an/a10.1111/cts.70124Integrating Model‐Informed Drug Development With AI: A Synergistic Approach to Accelerating Pharmaceutical InnovationKarthik Raman0Rukmini Kumar1Cynthia J. Musante2Subha Madhavan3Centre for Integrative Biology and Systems mEdicine (IBSE), Wadhwani School of Data Science and AI Indian Institute of Technology (IIT) Madras Chennai IndiaVantage Research Inc Lewes Delaware USATranslational Clinical Sciences Pfizer Research and Development Cambridge Massachusetts USAGlobal Biometrics and Data Management Pfizer Research and Development New York New York USAABSTRACT The pharmaceutical industry constantly strives to improve drug development processes to reduce costs, increase efficiencies, and enhance therapeutic outcomes for patients. Model‐Informed Drug Development (MIDD) uses mathematical models to simulate intricate processes involved in drug absorption, distribution, metabolism, and excretion, as well as pharmacokinetics and pharmacodynamics. Artificial intelligence (AI), encompassing techniques such as machine learning, deep learning, and Generative AI, offers powerful tools and algorithms to efficiently identify meaningful patterns, correlations, and drug–target interactions from big data, enabling more accurate predictions and novel hypothesis generation. The union of MIDD with AI enables pharmaceutical researchers to optimize drug candidate selection, dosage regimens, and treatment strategies through virtual trials to help derisk drug candidates. However, several challenges, including the availability of relevant, labeled, high‐quality datasets, data privacy concerns, model interpretability, and algorithmic bias, must be carefully managed. Standardization of model architectures, data formats, and validation processes is imperative to ensure reliable and reproducible results. Moreover, regulatory agencies have recognized the need to adapt their guidelines to evaluate recommendations from AI‐enhanced MIDD methods. In conclusion, integrating model‐driven drug development with AI offers a transformative paradigm for pharmaceutical innovation. By integrating the predictive power of computational models and the data‐driven insights of AI, the synergy between these approaches has the potential to accelerate drug discovery, optimize treatment strategies, and usher in a new era of personalized medicine, benefiting patients, researchers, and the pharmaceutical industry as a whole.https://doi.org/10.1111/cts.70124clinical trialsdeep learningdrug discoverygenerative AIlarge language modelsmachine learning |
| spellingShingle | Karthik Raman Rukmini Kumar Cynthia J. Musante Subha Madhavan Integrating Model‐Informed Drug Development With AI: A Synergistic Approach to Accelerating Pharmaceutical Innovation Clinical and Translational Science clinical trials deep learning drug discovery generative AI large language models machine learning |
| title | Integrating Model‐Informed Drug Development With AI: A Synergistic Approach to Accelerating Pharmaceutical Innovation |
| title_full | Integrating Model‐Informed Drug Development With AI: A Synergistic Approach to Accelerating Pharmaceutical Innovation |
| title_fullStr | Integrating Model‐Informed Drug Development With AI: A Synergistic Approach to Accelerating Pharmaceutical Innovation |
| title_full_unstemmed | Integrating Model‐Informed Drug Development With AI: A Synergistic Approach to Accelerating Pharmaceutical Innovation |
| title_short | Integrating Model‐Informed Drug Development With AI: A Synergistic Approach to Accelerating Pharmaceutical Innovation |
| title_sort | integrating model informed drug development with ai a synergistic approach to accelerating pharmaceutical innovation |
| topic | clinical trials deep learning drug discovery generative AI large language models machine learning |
| url | https://doi.org/10.1111/cts.70124 |
| work_keys_str_mv | AT karthikraman integratingmodelinformeddrugdevelopmentwithaiasynergisticapproachtoacceleratingpharmaceuticalinnovation AT rukminikumar integratingmodelinformeddrugdevelopmentwithaiasynergisticapproachtoacceleratingpharmaceuticalinnovation AT cynthiajmusante integratingmodelinformeddrugdevelopmentwithaiasynergisticapproachtoacceleratingpharmaceuticalinnovation AT subhamadhavan integratingmodelinformeddrugdevelopmentwithaiasynergisticapproachtoacceleratingpharmaceuticalinnovation |