Advancing drug development with “Fit-for-Purpose” modeling informed approaches Article Swipe
Jennifer Sheng
,
Tongli Zhang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1007/s10928-025-09995-2
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1007/s10928-025-09995-2
Model-informed Drug Development (MIDD) is an essential framework for advancing drug development and supporting regulatory decision-making. The current review presents a strategic blueprint to closely align MIDD tools with key questions of interests (QOI), content of use (COU), and model impact across stages of development —from early discovery to post-market lifecycle management. To demonstrate how the strategy works, we have also provided examples of how the MIDD tools can be applied to enhance the target identification, assist with lead compound optimization, improve preclinical prediction accuracy, facilitate First-in-Human (FIH) studies, optimize clinical trial design including dosage optimization, describe clinical population pharmacokinetics/exposure-response (PPK/ER) characteristics, and support label updates during post-approval stages. Additionally, the roles of some commonly used modeling methodologies, such as quantitative structure-activity relationship (QSAR), physiologically based pharmacokinetic (PBPK), semi-mechanistic pharmacokinetics/pharmacodynamics (PK/PD), PPK/ER, and quantitative systems pharmacology (QSP), are highlighted. What is more, we also explored the evolving role of MIDD in the context of emerging technologies, such as artificial intelligence (AI) and machine learning (ML) approaches. Further, MIDD utilities in development and regulatory evaluation of 505(b) (2) and generic drug products, as well as practical considerations of MIDD in regulatory interactions and asset acquisitions, are briefly discussed. At the end of the review, we briefly addressed the potential challenges faced by MIDD, such as lack of appropriate resources and slow organizational acceptance and alignment, as well as our perspectives on future opportunities of how MIDD could be further expanded.
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- Type
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All OpenAlex metadata
Raw OpenAlex JSON
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https://doi.org/10.1007/s10928-025-09995-2Digital Object Identifier
- Title
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Advancing drug development with “Fit-for-Purpose” modeling informed approachesWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-09-15Full publication date if available
- Authors
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Jennifer Sheng, Tongli ZhangList of authors in order
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-
https://doi.org/10.1007/s10928-025-09995-2Publisher landing page
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https://link.springer.com/content/pdf/10.1007/s10928-025-09995-2.pdfDirect link to full text PDF
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://link.springer.com/content/pdf/10.1007/s10928-025-09995-2.pdfDirect OA link when available
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0Total citation count in OpenAlex
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50Number of works referenced by this work
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| abstract_inverted_index.(ML) | 165 |
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| abstract_inverted_index.What | 140 |
| abstract_inverted_index.also | 61, 144 |
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| abstract_inverted_index.tools | 28, 68 |
| abstract_inverted_index.trial | 92 |
| abstract_inverted_index.(COU), | 38 |
| abstract_inverted_index.(MIDD) | 4 |
| abstract_inverted_index.(QOI), | 34 |
| abstract_inverted_index.(QSP), | 137 |
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| abstract_inverted_index.(PBPK), | 128 |
| abstract_inverted_index.(QSAR), | 124 |
| abstract_inverted_index.PPK/ER, | 132 |
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| abstract_inverted_index.review, | 203 |
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| abstract_inverted_index.support | 104 |
| abstract_inverted_index.systems | 135 |
| abstract_inverted_index.updates | 106 |
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| abstract_inverted_index.(PK/PD), | 131 |
| abstract_inverted_index.(PPK/ER) | 101 |
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| abstract_inverted_index.modeling | 117 |
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| abstract_inverted_index.strategy | 57 |
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| abstract_inverted_index.discovery | 48 |
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| abstract_inverted_index.potential | 208 |
| abstract_inverted_index.practical | 185 |
| abstract_inverted_index.products, | 181 |
| abstract_inverted_index.questions | 31 |
| abstract_inverted_index.resources | 218 |
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| abstract_inverted_index.utilities | 169 |
| abstract_inverted_index.acceptance | 222 |
| abstract_inverted_index.alignment, | 224 |
| abstract_inverted_index.artificial | 159 |
| abstract_inverted_index.challenges | 209 |
| abstract_inverted_index.discussed. | 197 |
| abstract_inverted_index.evaluation | 174 |
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| abstract_inverted_index.population | 99 |
| abstract_inverted_index.prediction | 84 |
| abstract_inverted_index.regulatory | 15, 173, 190 |
| abstract_inverted_index.supporting | 14 |
| abstract_inverted_index.Development | 3 |
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| abstract_inverted_index.development | 12, 45, 171 |
| abstract_inverted_index.management. | 52 |
| abstract_inverted_index.post-market | 50 |
| abstract_inverted_index.preclinical | 83 |
| abstract_inverted_index.highlighted. | 139 |
| abstract_inverted_index.intelligence | 160 |
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| abstract_inverted_index.acquisitions, | 194 |
| abstract_inverted_index.opportunities | 232 |
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| abstract_inverted_index.technologies, | 156 |
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| abstract_inverted_index.Model-informed | 1 |
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| abstract_inverted_index.methodologies, | 118 |
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| abstract_inverted_index.physiologically | 125 |
| abstract_inverted_index.characteristics, | 102 |
| abstract_inverted_index.decision-making. | 16 |
| abstract_inverted_index.semi-mechanistic | 129 |
| abstract_inverted_index.structure-activity | 122 |
| abstract_inverted_index.pharmacokinetics/pharmacodynamics | 130 |
| abstract_inverted_index.pharmacokinetics/exposure-response | 100 |
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| institutions_distinct_count | 2 |
| citation_normalized_percentile.value | 0.40821218 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |