Pharmaceutical Research最新文献

{"title":"Correlation Between Dissolution Profiles of Salt-Form Drugs in Biorelevant Bicarbonate Buffer and Oral Drug Absorption: Importance of Dose/ Fluid Volume Ratio.","authors":"Yuki Tarumi, Yuji Higashiguchi, Kiyohiko Sugano","doi":"10.1007/s11095-025-03854-y","DOIUrl":"https://doi.org/10.1007/s11095-025-03854-y","url":null,"abstract":"<p><strong>Purpose: </strong>The purpose of this study was to investigate the correlation between the dissolution profiles of salt-form drugs in biorelevant bicarbonate buffer and oral drug absorption.</p><p><strong>Methods: </strong>Ciprofloxacin HCl (CPFX HCl), garenoxacin mesylate (GRNX MS), tosufloxacin tosylate (TFLX TS), levofloxacin free-form (LVFX FF), and sitafloxacin free-form (STFX FF) were employed as model drugs. Bicarbonate buffer fasted state simulated intestinal fluid (BCB-FaSSIF) was used as a biorelevant dissolution medium (pH 6.5, BCB 10 mM (floating lid method), taurocholic acid (3 mM) and lecithin (0.75 mM)). The fraction of a dose absorbed in humans (Fa) was predicted by a simple theoretical framework for oral drug absorption using equilibrium solubility at pH 6.5 (S_eq,pH6.5) or average dissolved drug concentration in the dissolution tests (C_dissolv,AV).</p><p><strong>Results: </strong>Fa was adequately predicted using S_eq,pH6.5 for LVFX FF and STFX FF, however, underpredicted for CPFX HCl (tenfold), GRNX MS (twofold), and TFLX TS (sevenfold). When compendial Dose/FV was used for the dissolution test of CPFX HCl, bulk pH (pH_bulk) remained unchanged and C_dissolv,AV ≈ S_eq,pH6.5, resulting in a tenfold underprediction of Fa. Using clinical Dose/FV, pH_bulk was decreased, C_dissolv,AV was increased, resulting in adequate Fa prediction. Similarly, for GRNX MS and TFLX TS, Fa predictability was improved using C_dissolv,AV at clinical Dose/FV. In these conditions, C_dissolv,AV > S_eq,pH6.5 due to decreased pH_bulk below the first pK_a of the drugs.</p><p><strong>Conclusion: </strong>The use of clinical Dose/FV was important for improving the correlation between the biorelevant dissolution profiles and Fa for salt-form drugs.</p>","PeriodicalId":20027,"journal":{"name":"Pharmaceutical Research","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143788757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Preformulation Experiment: The Influence of Poloxamer 188 and Poloxamer 407 on the Binding Coefficients (Single Molecule) and the Partitioning Coefficients (Micelle) of Ketoprofen (Probe Molecule) with Sodium Cholate, Dodecyl Trimethylammonium Bromide and BrijC10 Surfactants.","authors":"Zita Farkaš Agatić, Vesna Tepavčević, Mladena Lalić-Popović, Nemanja Todorović, Ana Stijepanović, Mihalj Poša","doi":"10.1007/s11095-025-03852-0","DOIUrl":"https://doi.org/10.1007/s11095-025-03852-0","url":null,"abstract":"<p><strong>Introduction: </strong>Ketoprofen, a Biopharmaceutics Classification System (BCS) class II drug, exhibits poor water solubility, necessitating solubilization strategies for effective drug delivery. Surfactants and poloxamers are commonly employed to enhance solubilization via micellar encapsulation and host-guest interactions.</p><p><strong>Aim: </strong>This study investigates the binding interactions, stoichiometry, and partitioning behavior of ketoprofen with surfactants-sodium cholate (SC), dodecyltrimethylammonium bromide (DTAB), and Brij C10 (BC10)-and examines the impact of Poloxamer 188 (P188) and Poloxamer 407 (P407) as modifiers.</p><p><strong>Materials and methods: </strong>Complexation stoichiometry was evaluated using Job's plots, while binding constants (K_b​) were derived from Benesi-Hildebrand plots. Partition coefficients (K_x) and Gibbs energies (ΔG_x​) were determined using Kawamura's equation. Measurements were conducted at 25°C with constant ketoprofen concentrations.</p><p><strong>Results and discussion: </strong>Job's plots indicated 1:1 complexation for most systems, except DTAB + P407, which exhibited a 1.67:1 ratio. DTAB displayed the highest K_x (81386.259 with P188), attributed to electrostatic interactions and micelle stabilization. SC showed moderate K_x​, reduced by poloxamers due to competitive hydrogen bonding. BC10, the least efficient solubilizer, improved slightly with poloxamers by enabling micellar core partitioning. Gibbs energy (ΔG_x < 0) confirmed spontaneous solubilization, with the most favorable values for DTAB + P188. Discrepancies between Job's and Benesi-Hildebrand plots highlighted the limitations of the latter for low-CMC surfactants.</p><p><strong>Conclusion: </strong>DTAB, particularly with P188, demonstrated the greatest potential for ketoprofen solubilization, providing valuable insights for designing surfactant-based drug delivery systems.</p>","PeriodicalId":20027,"journal":{"name":"Pharmaceutical Research","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143788756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Combining High-Throughput Screening and Machine Learning to Predict the Formation of Both Binary and Ternary Amorphous Solid Dispersion Formulations for Early Drug Discovery and Development.","authors":"Tianshu Lu, Yiyang Wu, Ping Xiong, Hao Zhong, Yang Ding, Haifeng Li, Defang Ouyang","doi":"10.1007/s11095-025-03853-z","DOIUrl":"https://doi.org/10.1007/s11095-025-03853-z","url":null,"abstract":"<p><strong>Objective: </strong>Amorphous solid dispersion (ASD) is widely utilized to enhance the solubility and bioavailability of water-insoluble drugs. However, conventional experimental approaches for ASD development are often resource-intensive and time-consuming. Machine learning (ML) algorithms have great potential to predict ASD formulations but face the challenge of extensive data to construct reliable models. Current study aims to predict the formation of both binary and ternary ASD by combined high-throughput screening (HTS) and ML approaches.</p><p><strong>Methods: </strong>Micro-quantity HTS was conducted to generate 1272 binary and ternary solid dispersions using solvent evaporation method. The Powder X-Ray Diffraction (PXRD) was used to characterize the amorphous state of formulations. The results indicated that 188 formulations successfully formed amorphous solid dispersions (ASDs), while 1084 resulted in crystalline formations. Models development employed nested cross-validation with four algorithms: Light Gradient Boosting Machine (LGBM), Random Forest (RF), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP).</p><p><strong>Results: </strong>The RF model for ASD formation achieved 96.7% accuracy on the in-house HTS dataset, with a precision of approximately 87.9% and an F1 score of 83.6%. Furthermore, the RF model trained with milligram-scale HTS experimental data could effectively predict the large-scale ASD formulations from the literature, highlighting its promise as a powerful tool for advancing ASD prediction.</p><p><strong>Conclusion: </strong>In summary, the combination of HTS experiments and ML techniques provides a valuable reference framework for ASD development, greatly minimizing both time and material usage in the selection of formulations during the early stages of drug discovery with a limited quantity of API.</p>","PeriodicalId":20027,"journal":{"name":"Pharmaceutical Research","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143780826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Characterizing the Influence of Relative Humidity and Ethanol Content on the Dynamic Size Distributions of Aerosols Generated from a Soft Mist Inhaler.","authors":"Yiliang Lance Jiang, Jose R Ruiz, Richard Friend, Jonathan P Reid","doi":"10.1007/s11095-025-03851-1","DOIUrl":"https://doi.org/10.1007/s11095-025-03851-1","url":null,"abstract":"<p><strong>Objective: </strong>Inhaled drug delivery systems need to ensure that the delivered aerosol effectively reach the lungs while overcoming challenges related to environmental conditions, such as relative humidity (RH). This study investigates the impact of environmental factors on aqueous aerosol behaviour using a Respimat® Soft Mist Inhaler (SMI) formulated with and without ethanol content.</p><p><strong>Methods: </strong>Comparative Hygroscopic Aerosol Particle Sizing (CHAPS) was used to measure aerosol size distribution under varying RH levels, while single droplet analysis was conducted using Comparative Kinetics-Electrodynamic Balance (CK-EDB) to assess particle behaviour.</p><p><strong>Results: </strong>The findings reveal that increased RH results in larger particle sizes, while elevated ethanol content consistently decreases both particle size and mass. The strong agreement between CHAPS measurements and CK-EDB data suggests that aerosol plume behaviour can be accurately modelled from single droplet data.</p><p><strong>Conclusion: </strong>The study highlights ethanol's role in optimizing particle size distribution, which is crucial for enhancing the therapeutic efficiency of inhaled medications. These results underscore the importance of tailoring formulation and environmental conditions to improve drug delivery outcomes in pulmonary therapies and the importance of recognising that aerosol particle size distributions are dynamic and highly compositionally dependent.</p>","PeriodicalId":20027,"journal":{"name":"Pharmaceutical Research","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143764615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advances in Physiologically Based Pharmacokinetic (PBPK) Modeling and its Regulatory Utility to Support Oral Drug Product Development and Harmonization.","authors":"Yi-Hsien Cheng, Sherin Thomas, Yu Chung Tsang, Susana Almeida, Muhammad Ashraf, Nikoletta Fotaki, Tycho Heimbach, Nikunjkumar Patel, Harshil Shah, Xiaojian Jiang, Myong-Jin Kim, Rebecca Moody, Amin Rostami-Hodjegan, Romi Singh, Liang Zhao, Andrew Babiskin, Fang Wu","doi":"10.1007/s11095-025-03849-9","DOIUrl":"https://doi.org/10.1007/s11095-025-03849-9","url":null,"abstract":"<p><p>This report summarizes the proceedings of Session 1 of the one-day public workshop titled \"Advances in PBPK Modeling and its Regulatory Utility for Oral Drug Product Development\" hosted by the U.S. Food and Drug Administration (FDA) and the Center for Research on Complex Generics (CRCG) on October 12, 2023. This report focuses on cutting-edge developments, ongoing challenges, and potential solutions in the field of physiologically based pharmacokinetic (PBPK) absorption modeling for systemic and gastrointestinal (GI) locally acting oral drug products, as well as exploring opportunities to enhance global harmonization for generic drug development. Despite significant advancements and several successful case studies of utilizing PBPK models in generic drug development, developing patient-centric dissolution quality standards using PBPK modeling that account for food effects or different disease states remains challenging. Combining multiple dissolution studies at different pH ranges can aid in developing patient-centric dissolution specifications. Additionally, a major challenge for GI locally acting drug products is the inability to validate the PBPK model for local bioavailability due to the lack of measured data for local drug concentration along the different sections of the GI tract. A totality of evidence-based approach, taking account of all available data in addition to PBPK modeling-based evidence, should be considered. Moving forward, it is crucial to promote global collaboration and research by sharing knowledge and experiences for utilizing PBPK models in regulatory contexts to advance both internal and international harmonization.</p>","PeriodicalId":20027,"journal":{"name":"Pharmaceutical Research","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143742562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PBPK Modeling to Support Bioavailability and Bioequivalence Assessment in Pediatric Populations.","authors":"Fang Wu, Eleftheria Tsakalozou, Gilbert J Burckart, Rebeka Žakelj, Lu Gaohua, Kazuko Sagawa, Viera Lukacova, Siva Vaithiyalingam, Jianghong Fan, Nikoletta Fotaki, Nikunjkumar Patel, Lanyan Fang","doi":"10.1007/s11095-025-03846-y","DOIUrl":"https://doi.org/10.1007/s11095-025-03846-y","url":null,"abstract":"<p><p>This report summarizes the proceedings for Session 3 of the one-day public workshop entitled \"Advances in PBPK Modeling and its Regulatory Utility for Oral Drug Product Development\" a jointly sponsored workshop by U.S. Food and Drug Administration (FDA) and the Center for Research on Complex Generics (CRCG) on October 12, 2023. The theme of this session was the application and relevant considerations for PBPK modeling in supporting bioavailability (BA) and BE assessment in pediatric populations. The takeaway message from this session was that PBPK modeling can support relative BA and BE assessment in pediatrics since such studies are generally performed in adults or healthy subjects. PBPK absorption modeling can incorporate characteristics of the drug substance and formulation as well as pediatric physiology to assess the potential differences in absorption of different formulations in pediatrics for new and generic drugs. It is necessary to consider the totality of data and use all available evidence integrated into a mechanistic PBPK model to support decision-making. Global research efforts are needed to bridge critical data gaps.</p>","PeriodicalId":20027,"journal":{"name":"Pharmaceutical Research","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143720971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting Distribution Coefficients (LogD) of Cyclic Peptides Using Molecular Dynamics Simulations.","authors":"Hao Lou, Mei Feng, Zahraa Al-Tamimi, Krzysztof Kuczera, Michael J Hageman","doi":"10.1007/s11095-025-03850-2","DOIUrl":"https://doi.org/10.1007/s11095-025-03850-2","url":null,"abstract":"<p><strong>Purpose: </strong>The distribution coefficient (LogD) is a critical property for oral peptide drug design. In this study, we focused on cyclic peptides (octreotide and its analogs) and aimed to determine their LogD values at four pHs using both the simulation and experimental approaches.</p><p><strong>Methods: </strong>For the experimental approach, the shake-flask method with LCMS quantification was employed to determine LogD values. For the simulation approach, the partition coefficient (LogP) was obtained from the solvation free energy calculations using molecular dynamics (MD) simulation. The LogD values were then calculated from the obtained LogP values considering the predicted pKa and ionization states of each peptide residue. More peptide properties such as polar surface area (PSA), number of intramolecular hydrogen bonds, solvent accessible surface area (SASA), and radius of gyration (R_g) were also calculated with the aid of MD simulation.</p><p><strong>Results: </strong>For a total of 28 LogD values across four pHs, the predicted values from the simulation under the OPLS-AA forcefield agreed with the experimental values, with an average deviation of 1.39 ± 0.86 log units, displaying better predictions compared to the data generated under the CHARMM forcefield or using commercial software. In addition, the analysis of PSA, SASA, and R_g data suggested the peptides exhibited some conformational flexibility in both aqueous and organic phases.</p><p><strong>Conclusions: </strong>The method developed in this study can predict the LogD values at a wide pH range covering multiple formulation/physiological conditions and therefore can provide insights into designing oral peptide drugs, especially for early-stage projects.</p>","PeriodicalId":20027,"journal":{"name":"Pharmaceutical Research","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143721000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GraphDeep-hERG: Graph Neural Network PharmacoAnalytics for Assessing hERG-Related Cardiotoxicity.","authors":"Yankang Jing, Yiyang Zhang, Guangyi Zhao, Terence McGuire, Jack Zhao, Ben Gibbs, Ganqian Hou, Zhiwei Feng, Ying Xue, Xiang-Qun Xie","doi":"10.1007/s11095-025-03848-w","DOIUrl":"https://doi.org/10.1007/s11095-025-03848-w","url":null,"abstract":"<p><strong>Purpose: </strong>The human Ether-a-go-go Related-Gene (hERG) encodes rectifying potassium channels that play a significant role during action potential repolarization of cardiomyocytes. Blockade of the hERG channel by off-target drugs can lead to long QT syndrome, significantly increasing the risk of proarrhythmic cardiotoxicity. Traditional hERG screening methods are effort-demanding and time-consuming. Thus, it is essential to develop computational methods to utilize the existing knowledge for faster and more accurate in silico screening. Although with wide use of deep learning/machine learning algorithms, existing computational models often rely on manually defined atomic features to represent atom nodes, which may overlook critical underlying information. Thus, we want to provide a new method to learn the atom representation automatically.</p><p><strong>Methods: </strong>We first developed an automated atom embedding model using deep neural networks (DNNs), trained with 118,312 compounds collected from the ZINC database. We then trained a Graph neural networks (GNNs) model with 7909 ChEMBL compounds as the classifying part. The integration of our atom embedding model and GNN models formed a classifier that could effectively distinguish between hERG inhibitors and non-inhibitors.</p><p><strong>Results: </strong>Our atom embedding model achieved 0.93 accuracy in representing structures. Our best GNN model achieved an accuracy of 0.84 and outcompeted traditional machine-learning models, as well as published AI-driven models, in external testing.</p><p><strong>Conclusions: </strong>These results highlight the potential of our automated atom embedding model as a standard for generating robust molecular representations. Its integration with advanced GNN algorithms offers promising assistance for screening hERG inhibitors and accelerating drug discovery and repurposing.</p>","PeriodicalId":20027,"journal":{"name":"Pharmaceutical Research","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143720968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Immunogenicity of Generic Peptide Impurities: Current Orthogonal Approaches.","authors":"Anne S De Groot, Aimee Mattei, Benjamin Gabriel, Jennifer Calderini, Brian J Roberts, Sandra Lelias, Mitchell McAllister, Christine Boyle, William Martin, Guilhem Richard","doi":"10.1007/s11095-025-03843-1","DOIUrl":"https://doi.org/10.1007/s11095-025-03843-1","url":null,"abstract":"<p><p>Generic drugs have saved consumers billions of dollars in the United States. The demand for lower-cost and effective drugs, particularly for well-known peptide drugs like Ozempic and Wegovy (brand names for semaglutide), has resulted in a surge of generic drug development to address perceived shortages in the supply of the reference listed drugs (RLD). To address this demand for generics and expedite consumer access to lower-cost generic versions of approved drugs, the U.S. Food and Drug Administration (FDA) has developed an \"Abbreviated New Drug Application\" (ANDA) pathway that simplifies the generic drug review process and expands access to these much-needed medicines without compromising quality and safety standards. Guidelines for this pathway require sponsors to identify and characterize both process- and product-related impurities in drug formulations that differ in nature or concentration from the RLD. The ANDA pathway devotes specific attention to immunogenicity and recommends the use of orthogonal methods of assessment to demonstrate that a proposed generic drug is immunologically equivalent to its RLD and therefore suitable for submission via the ANDA pathway. In this perspective, we describe several orthogonal methods for immunogenicity risk assessment of generic peptide impurities and contrast these with other methods such as MHC-Associated Peptide Proteomics peptide elution (MAPPs) assays. Given their importance in the generic drug approval pathway, we have submitted the \"PANDA^®\" immunogenicity risk assessment methods as a 'model master file'.</p>","PeriodicalId":20027,"journal":{"name":"Pharmaceutical Research","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143701146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Industry Perspectives on Implementation of Model Master File (MMF) Framework for Generics and Innovator Drugs: Opportunities, Challenges and Future Outlook.","authors":"Sivacharan Kollipara, Markus Friden, Tycho Heimbach, Pratik Saha, Jan De Backer, Tausif Ahmed, Timothy Nicholas","doi":"10.1007/s11095-025-03844-0","DOIUrl":"10.1007/s11095-025-03844-0","url":null,"abstract":"<p><p>Modeling and simulation (M&S) based approaches have proven significant utility in both new drug and generic product development. Considering the plethora of applications of such novel approaches, the concept of model master file (MMF) has been introduced recently to streamline the regulatory submission process as well as to facilitate the use of M&S approaches. The MMF has potential to reduce the applicant's efforts in preparing and submitting modeling-based applications and can result in reduced review timelines. Approved MMF's are considered as reusable, sharable, portable and generalizable and thus can be used by the same applicant in multiple submissions or by multiple applicants. To further increase the understanding of the MMF framework and to understand potential applications, and limitations, the USFDA and the Center for Research on Complex Generics (CRCG, https://www.complexgenerics.org ) co-hosted a hybrid public workshop titled \"Considerations and Potential Regulatory Applications for a Model Master File\". This article summarizes the industry perspectives of MMF implementation from both new drug and generic product development perspectives. With the help of diverse case studies, an effort was made in the manuscript to discuss potential challenges, opportunities and benefits. The objective of this article is to portray industry thinking on the MMF concept and the use and implementation of the concept during drug discovery and development. The views presented in this manuscript are of industry participants present at the workshop and not the industry at large.</p>","PeriodicalId":20027,"journal":{"name":"Pharmaceutical Research","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143616762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}