Pharmaceutical Research最新文献

{"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":"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":"697-709"},"PeriodicalIF":3.5,"publicationDate":"2025-04-01","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":"Correction: 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 Stjepanović, Mihalj Poša","doi":"10.1007/s11095-025-03862-y","DOIUrl":"https://doi.org/10.1007/s11095-025-03862-y","url":null,"abstract":"","PeriodicalId":20027,"journal":{"name":"Pharmaceutical Research","volume":"42 4","pages":"725"},"PeriodicalIF":3.5,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144040135","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":"Evaluation of Process Parameters for Continuous Manufacturing of Quetiapine Fumarate Immediate Release Tablets Using Twin Screw Wet Granulation.","authors":"Tejaswini Naguboyina, Preethi Lakkala, Siva Ram Munnangi, Sateesh Kumar Vemula, Michael Repka","doi":"10.1007/s11095-025-03859-7","DOIUrl":"https://doi.org/10.1007/s11095-025-03859-7","url":null,"abstract":"<p><strong>Purpose: </strong>Granulation is one of the important unit operations in the manufacturing of solid dosage forms like tablets and capsules that regulate the quality of end products. It is a process of particle enlargement by agglomeration technique, which improves flow properties, compressibility, reduction of dust formation, drug content uniformity, dissolution rates, and overall product stability. Traditionally, it has been a batch process due to a better understanding of the process. However, there has been a shift towards continuous manufacturing using a Twin-screw granulator, which is more robust, scalable, and versatile for a wide range of applications.</p><p><strong>Methods: </strong>This work presents the innovative use of Twin screw wet granulation (TSWG) in the development of Quetiapine fumarate (QTF) immediate release tablets. Various process parameters (Screw configuration, liquid-to-solid (L/S) ratios), and binders (HPC and PVP) were evaluated to determine their effect on granule quality. Further, the obtained granules were tested for particle size distribution and flow properties.</p><p><strong>Results: </strong>A higher percentage of uniform-sized granules were yielded with three mixing zones even with a lower liquid addition compared to that of one mixing zone with a higher liquid addition. These granules were further tableted and tested for their hardness, friability, disintegration, and dissolution. The tablets disintegrated and released the drug (~ 95%) rapidly within 5 min in 0.1 N HCl due to QTF's high solubility and porosity of granules.</p><p><strong>Conclusions: </strong>Overall, the understanding of process parameters and their influence on granule and tablet characteristics would help establish a more robust and continuous manufacturing of dosage forms.</p>","PeriodicalId":20027,"journal":{"name":"Pharmaceutical Research","volume":"42 4","pages":"685-696"},"PeriodicalIF":3.5,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12055873/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144032848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Learning Chemotherapy Drug Action via Universal Physics-Informed Neural Networks.","authors":"Lena Podina, Ali Ghodsi, Mohammad Kohandel","doi":"10.1007/s11095-025-03858-8","DOIUrl":"https://doi.org/10.1007/s11095-025-03858-8","url":null,"abstract":"<p><strong>Objective: </strong>Quantitative systems pharmacology (QSP) is widely used to assess drug effects and toxicity before the drug goes to clinical trial. However, significant manual distillation of the literature is needed in order to construct a QSP model. Parameters may need to be fit, and simplifying assumptions of the model need to be made. In this work, we apply Universal Physics-Informed Neural Networks (UPINNs) to learn unknown components of various differential equations that model chemotherapy pharmacodynamics.</p><p><strong>Methods: </strong>Using UPINNs, we learn three commonly employed chemotherapeutic drug actions (log-kill, Norton-Simon, and <math><msub><mi>E</mi> <mo>max</mo></msub> </math> ) from synthetic data. Then, we use the UPINN method to fit the parameters for several synthetic datasets simultaneously. Finally, we learn the net proliferation rate in a model of doxorubicin (a chemotherapeutic) pharmacodynamics.</p><p><strong>Results: </strong>We show that the UPINN can successfully learn the hidden terms and unknown parameters in a variety of differential equations (with differing time and variable scales) that model the effect of chemotherapeutics on cancer cells.</p><p><strong>Conclusions: </strong>As the examples we study are only toy examples, we highlight the usefulness of UPINNs in learning unknown terms in pharmacodynamic and pharmacokinetic models. UPINNs can be used to find these terms and analyze them further to understand new chemotherapeutics and biological mechanisms that interact with them.</p>","PeriodicalId":20027,"journal":{"name":"Pharmaceutical Research","volume":"42 4","pages":"593-612"},"PeriodicalIF":3.5,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144023611","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 Powder Blend Flowability from Individual Constituent Properties Using Machine Learning.","authors":"Anna Owasit, Siddharth Tripathi, Rajesh Davé, Joshua Young","doi":"10.1007/s11095-025-03855-x","DOIUrl":"https://doi.org/10.1007/s11095-025-03855-x","url":null,"abstract":"<p><strong>Purpose: </strong>Predicting powder blend flowability is necessary for pharmaceutical manufacturing but challenging and resource-intensive. The purpose was to develop machine learning (ML) models to help predict flowability across multiple flow categories, identify key predictive features, and arrive at formulations with improved flow properties.</p><p><strong>Methods: </strong>A dataset of 410 blends, composed of 9 active pharmaceutical ingredients (APIs) and 18 excipients with varying silica dry-coating parameters, was analyzed. Supervised ML models were trained to predict various flowability categories (very cohesive, cohesive, semi-cohesive, well-flowing, and free-flowing). Particle size, morphology, surface properties, and coating parameters were used as features. Classification algorithms, including Random Forest (RF) and Extreme Gradient Boosting (XGBoost), were evaluated. Unsupervised clustering identified natural groupings within flowability data.</p><p><strong>Results: </strong>The best-performing models achieved up to 85% accuracy for predicting flowability regimes of individual components and 87% for blends. Individual components generally showed higher accuracy than blends, except in the uncoated scenario with 2 flow regimes, where blends outperformed with 94.67%. SHapley Additive exPlanations (SHAP) and Feature Importance analysis indicated dry coating parameters as the most influential factors, followed by particle size and morphology. ML models effectively identified category transitions between flow regimes, offering insights into blend optimization.</p><p><strong>Conclusion: </strong>Integrating ML with mechanistic approaches effectively predicted powder blend flowability across diverse categories and elucidated feature-property relationships. These outcomes can facilitate the rational design of blends having enhanced flow properties at reduced experimental effort through judiciously selected dry coating of a blend constituent; making this approach promising for advancing pharmaceutical process and product development.</p>","PeriodicalId":20027,"journal":{"name":"Pharmaceutical Research","volume":"42 4","pages":"665-683"},"PeriodicalIF":3.5,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12055667/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144035294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","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":"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":"613-622"},"PeriodicalIF":3.5,"publicationDate":"2025-04-01","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":"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":"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":"623-637"},"PeriodicalIF":3.5,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12055928/pdf/","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":"OA","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":"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":"579-591"},"PeriodicalIF":3.5,"publicationDate":"2025-04-01","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":"An In Vitro Dissolution Method for Inhaled Drugs Depositing in the Tracheobronchial Lung Region.","authors":"Alexander Huang, Scott Tavernini, Dino J Farina, Warren H Finlay, Andrew R Martin","doi":"10.1007/s11095-025-03860-0","DOIUrl":"https://doi.org/10.1007/s11095-025-03860-0","url":null,"abstract":"<p><strong>Purpose: </strong>To evaluate and develop a novel dissolution test method using tracheobronchial (TB) mimic filters for assessing the dissolution behavior of inhaled drugs targeting the tracheobronchial lung region.</p><p><strong>Methods: </strong>Fluticasone propionate (FP), a poorly soluble corticosteroid, was selected as the test drug. A novel filter-based apparatus (FBA) fractionated the inhaled dose into extrathoracic, tracheobronchial, and alveolar fractions. FP was delivered via dry powder inhaler (DPI) (Flovent Diskus, 250 µg) and pressurized metered-dose inhaler (pMDI) (Flovent HFA, 250 µg). Regional deposition estimates were compared between inhalers. Dissolution tests were performed on the captured TB dose using phosphate-buffered saline + 0.5% sodium dodecyl sulfate at 37 °C. First-order dissolution rate constants ( <math><msub><mi>k</mi> <mn>1</mn></msub> </math> ), difference ( <math><msub><mi>f</mi> <mn>1</mn></msub> </math> ), and similarity ( <math><msub><mi>f</mi> <mn>2</mn></msub> </math> ) factors were calculated. Particle distribution and loading effects on the TB filter were assessed using scanning electron microscopy (SEM).</p><p><strong>Results: </strong>The TB filter demonstrated consistent performance, with no drug loading effects observed for up to the highest drug loading tested, which was 7 actuations of the DPI (~ 110 µg FP collected on the TB filter), or 5 actuations of the pMDI (~ 170 µg). Dissolution profiles revealed no significant differences across DPI doses, and slower dissolution rates for the pMDI compared to the DPI, with <math><msub><mi>k</mi> <mn>1</mn></msub> </math> values indicating significant differences (p < 0.05). SEM showed no particle aggregation or filter clogging. Similarity and difference factors supported these findings.</p><p><strong>Conclusions: </strong>The dissolution method discriminated between the two inhalers and is a promising new tool for use in the dissolution testing of orally inhaled drug products.</p>","PeriodicalId":20027,"journal":{"name":"Pharmaceutical Research","volume":"42 4","pages":"639-650"},"PeriodicalIF":3.5,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144009254","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":"Nitrosamine Drug Substance-Related Impurities (NDSRIs) in Pharmaceuticals: Formation, Mitigation Strategies, and Emphasis on Mutagenicity Risks.","authors":"Dande Aishwarya, Vaishnavi Ramakant Dhampalwar, Nikhil Pallaprolu, Ramalingam Peraman","doi":"10.1007/s11095-025-03857-9","DOIUrl":"https://doi.org/10.1007/s11095-025-03857-9","url":null,"abstract":"<p><strong>Objectives: </strong>To investigate the formation, detection, mutagenicity, and control strategies of nitrosamine drug substance-related impurities (NDSRIs) in pharmaceutical formulations, emphasizing regulatory compliance, risk mitigation, and the establishment of acceptable intake (AI) limits for enhanced drug safety.</p><p><strong>Methods: </strong>This study reviews the NDSRI formation and mutagenicity assessment methods, including in silico, in vitro, and in vivo assays. It also explores mitigation strategies and approaches for determining AI limits.</p><p><strong>Results: </strong>The findings indicate that NDSRIs are primarily formed through the nitrosation of APIs containing amine groups, with key risk factors including reactive functional groups and interactions between drugs and excipients. Mutagenicity evaluation revealed that while in silico and in vitro assays provide initial insights, in vivo assays offer more comprehensive and biologically relevant data by capturing complex metabolic processes and systemic interactions. Effective mitigation strategies, such as optimizing the manufacturing conditions and using nitrosation inhibitors, are crucial in reducing NDSRI formation. Approaches like the carcinogenic potency categorization (CPCA) and read-across methods are proposed for determining AI limits, facilitating safer exposure thresholds and supporting regulatory compliance.</p><p><strong>Conclusion: </strong>A multifaceted approach is vital for managing NDSRIs in pharmaceuticals. Comprehensive mutagenicity testing, especially in vivo assays, provides biologically relevant insights into NDSRI-associated risks. Implementing control strategies and, determining AI limits are key to minimizing exposure. Strengthening regulatory frameworks and industry practices improves drug safety, quality, and public health protection.</p>","PeriodicalId":20027,"journal":{"name":"Pharmaceutical Research","volume":"42 4","pages":"547-578"},"PeriodicalIF":3.5,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144006028","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}