Model‐Informed Drug Development: Current US Regulatory Practice and Future Considerations

Radiomics: the bridge between medical imaging and personalized medicine

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CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep LearningPreprint

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Real-World Evidence — What Is It and What Can It Tell Us?

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A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study

Sun, R., Limkin, E. J., Vakalopoulou, M., Dercle, L., Champiat, S., Han, S. R., Verlingue, L., Brandao, D., Lancia, A., Ammari, S., et al. (2018) · The Lancet Oncology · doi:10.1016/s1470-2045(18)30413-3 · Cited by 1,236 · cited 2× · see citing works

Learning versus confirming in clinical drug development*

Sheiner, L. B. (1997) · Clinical Pharmacology & Therapeutics · doi:10.1016/s0009-9236(97)90160-0 · Cited by 633 · see citing works

Ipilimumab 10 mg/kg versus ipilimumab 3 mg/kg in patients with unresectable or metastatic melanoma: a randomised, double-blind, multicentre, phase 3 trial

Ascierto, P. A., Vecchio, M. D., Robert, C., Mackiewicz, A. A., Chiarion‐Sileni, V., Arance, A. M., Lebbé, C., Bastholt, L., Hamid, O., Rutkowski, P. L., et al. (2017) · The Lancet Oncology · doi:10.1016/s1470-2045(17)30231-0 · Cited by 508 · see citing works

Evaluation of the novel factor Xa inhibitor edoxaban compared with warfarin in patients with atrial fibrillation: Design and rationale for the Effective aNticoaGulation with factor xA next GEneration in Atrial Fibrillation–Thrombolysis In Myocardial Infarction study 48 (ENGAGE AF–TIMI 48)

Ruff, C. T., Giugliano, R. P., Antman, E. M., Crugnale, S. E., Bocanegra, T. S., Mercuri, M. F., HANYOK, J. J., Patel, I. J., Shi, M., Salazar, D., et al. (2010) · American Heart Journal · doi:10.1016/j.ahj.2010.06.042 · Cited by 480 · see citing works

Model-based Drug Development

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Good Practices in Model‐Informed Drug Discovery and Development: Practice, Application, and Documentation

Workgroup, E. M., Marshall, S., Burghaus, R., Cosson, V, Cheung, S., Chenel, M., DellaPasqua, O., Frey, N., Hamrén, B., Harnisch, L., et al. (2016) · CPT: Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12049 · Cited by 373 · see citing works

Everolimus With Reduced Tacrolimus Improves Renal Function in De Novo Liver Transplant Recipients: A Randomized Controlled Trial

SIMONE, P. D., Nevens, F., Carlis, L. G. D., Metselaar, H. J., Beckebaum, S., Saliba, F., Jonas, S., Sudan, D. L., Fung, J. J., Fischer, L., et al. (2012) · American Journal of Transplantation · doi:10.1111/j.1600-6143.2012.04212.x · Cited by 328 · see citing works

Scientific white paper on concentration-QTc modeling

Garnett, C. E., Bonate, P. L., Dang, Q., Ferber, G., Huang, D. P., Liu, J., Mehrotra, D. V., Riley, S., Sager, P. T., Tornøe, C. W., et al. (2017) · Journal of Pharmacokinetics and Pharmacodynamics · doi:10.1007/s10928-017-9558-5 · Cited by 249 · see citing works

Model-Based Drug Development: A Rational Approach to Efficiently Accelerate Drug Development

Milligan, P. A., Brown, M. J., Marchant, B., Martin, S. W., van der Graaf, P. H., Benson, N., Nucci, G., Nichols, D. J., Boyd, R. A., Mandema, J. W., et al. (2013) · Clinical Pharmacology & Therapeutics · doi:10.1038/clpt.2013.54 · Cited by 238 · see citing works

Elucidation of Relationship Between Tumor Size and Survival in Non-Small-Cell Lung Cancer Patients Can Aid Early Decision Making in Clinical Drug Development

Wang, Y., Sung, C. W., Dartois, C., Ramchandani, R., Booth, B. P., Rock, E. P., & Gobburu, J. V. S. (2009) · Clinical Pharmacology & Therapeutics · doi:10.1038/clpt.2009.64 · Cited by 229 · see citing works

Concentration‐QT Relationships Play a Key Role in the Evaluation of Proarrhythmic Risk During Regulatory Review

Garnett, C. E., Beasley, B. N., Bhattaram, V. A., Jadhav, P. R., Madabushi, R., Stockbridge, N., Tornøe, C. W., Wang, Y., Zhu, H., & Gobburu, J. V. (2007) · The Journal of Clinical Pharmacology · doi:10.1177/0091270007307881 · Cited by 219 · see citing works

Opportunities for integration of pharmacokinetics, pharmacodynamics, and toxicokinetics in rational drug development

Peck, C. C., Barr, W. H., Benet, L. Z., Collins, J. M., Desjardins, R. E., Furst, D. E., Harter, J. G., Levy, G., Ludden, T. M., Rodman, J. H., et al. (1992) · Clinical Pharmacology & Therapeutics · doi:10.1038/clpt.1992.47 · Cited by 192 · see citing works

Results From the IQ‐CSRC Prospective Study Support Replacement of the Thorough QT Study by QT Assessment in the Early Clinical Phase

Darpö, B., Benson, C., Dota, C., Ferber, G., Garnett, C. E., Green, C., Jarugula, V. R., Johannesen, L., Keirns, J. J., Krudys, K. M., et al. (2015) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.60 · Cited by 181 · see citing works

Concepts and Challenges in Quantitative Pharmacology and Model-Based Drug Development

Zhang, L., Pfister, M., & Meibohm, B. (2008) · The AAPS Journal · doi:10.1208/s12248-008-9062-3 · Cited by 93 · see citing works

Leveraging Prior Quantitative Knowledge to Guide Drug Development Decisions and Regulatory Science Recommendations: Impact of FDA Pharmacometrics During 2004–2006

Wang, Y., Bhattaram, A., Jadhav, P. R., Lesko, L. J., Madabushi, R., Powell, J. R., Qiu, W., Sun, H., Yim, D., Zheng, J. J., et al. (2008) · The Journal of Clinical Pharmacology · doi:10.1177/0091270007311111 · Cited by 73 · see citing works

Endpoints and Analyses to Discern Disease-Modifying Drug Effects in Early Parkinson’s Disease

Bhattaram, V. A., Siddiqui, O. I., Kapcala, L. P., & Gobburu, J. V. S. (2009) · The AAPS Journal · doi:10.1208/s12248-009-9123-2 · Cited by 71 · see citing works

Clinical Drug–Drug Interaction Evaluations to Inform Drug Use and Enable Drug Access

Rekić, D., Reynolds, K. S., Zhao, P., Zhang, L., Yoshida, K. J., Sachar, M., Piquette‐Miller, M., Huang, S. M., & Zineh, I. (2017) · Journal of Pharmaceutical Sciences · doi:10.1016/j.xphs.2017.04.016 · Cited by 65 · see citing works

Accelerating development of scientific evidence for medical products within the existing US regulatory framework

Sherman, R. E., Davies, K. M., Robb, M. A., Hunter, N. L., & Califf, R. M. (2017) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2017.25 · Cited by 60 · see citing works

Physiologically Based Pharmacokinetic Modeling: From Regulatory Science to Regulatory Policy

Sinha, V. P., Zhao, P., Huang, S. M., & Zineh, I. (2014) · Clinical Pharmacology & Therapeutics · doi:10.1038/clpt.2014.46 · Cited by 58 · see citing works

Pharmacokinetic–pharmacodynamic assessment of topiramate dosing regimens for children with epilepsy 2 to <10 years of age

Girgis, I. G., Nandy, P., Nye, J. S., Ford, L., Mohanty, S., Wang, S., Ochalski, S. J., Eerdekens, M., & Cox, E. H. (2010) · Epilepsia · doi:10.1111/j.1528-1167.2010.02598.x · Cited by 45 · see citing works

Statistical Issues of QT Prolongation Assessment Based on Linear Concentration Modeling

Tsong, Y., Shen, M., Zhong, J., & Zhang, J. (2008) · Journal of Biopharmaceutical Statistics · doi:10.1080/10543400801995502 · Cited by 43 · see citing works

Pharmacometrics-Based Dose Selection of Levofloxacin as a Treatment for Postexposure Inhalational Anthrax in Children

Li, F., Nandy, P., Chien, S., Noel, G. J., & Tornøe, C. W. (2009) · Antimicrobial Agents and Chemotherapy · doi:10.1128/aac.00667-09 · Cited by 41 · see citing works

Operational Characteristics of Linear Concentration‐QT Models for Assessing QTc Interval in the Thorough QT and Phase I Clinical Studies

Garnett, C. E., Needleman, K., Liu, J., Brundage, R. C., & Wang, Y. (2016) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.361 · Cited by 37 · see citing works

Clinical Pharmacology and the Catalysis of Regulatory Science: Opportunities for the Advancement of Drug Development and Evaluation

Zineh, I., & Woodcock, J. (2013) · Clinical Pharmacology & Therapeutics · doi:10.1038/clpt.2013.32 · Cited by 34 · see citing works

A Perspective on the Use of Concentration‐QT Modeling in Drug Development

Russell, T., Riley, S., Cook, J., & Lalonde, R. L. (2007) · The Journal of Clinical Pharmacology · doi:10.1177/0091270007311115 · Cited by 26 · see citing works

Quantification of disease progression and dropout for Alzheimer’s disease

Faltaos, D. W., Chen, Y., Wang, Y., Gobburu, J. V. S., & Zhu, H. (2012) · International Journal of Clinical Pharmacology and Therapeutics · doi:10.5414/cp201787 · Cited by 25 · see citing works

Commentary on the Clinical Relevance of Concentration/QTc Relationships for New Drug Candidates

Bloomfield, D. M., & Krishna, R. (2007) · The Journal of Clinical Pharmacology · doi:10.1177/0091270007312257 · Cited by 18 · see citing works

Development of a placebo effect model combined with a dropout model for bipolar disorder

Sun, W., LAUGHREN, T. P., Zhu, H., Hochhaus, G., & Wang, Y. (2013) · Journal of Pharmacokinetics and Pharmacodynamics · doi:10.1007/s10928-013-9305-5 · Cited by 15 · see citing works

Bayesian Quantitative Disease–Drug–Trial Models for Parkinson’s Disease to Guide Early Drug Development

Lee, J. Y., & Gobburu, J. V. S. (2011) · The AAPS Journal · doi:10.1208/s12248-011-9293-6 · Cited by 11 · see citing works

Justification of noninferiority margin: Methodology considerations in an exposure–response analysis

Wang, Y., Harigaya, Y., Cavaillé-Coll, M. W., Colangelo, P. M., & Reynolds, K. (2015) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.44 · Cited by 11 · see citing works

Is This the Dose for You?: The Role of Modeling

Sm, H., Bhattaram, A., Mehrotra, N., & Wang, Y. (2012) · Clinical Pharmacology & Therapeutics · doi:10.1038/clpt.2012.228 · Cited by 8 · see citing works

Letter to the Editor: Statistical Issues of QT Prolongation Assessment Based on Linear Concentration Modeling by Yi Tsong et al.

Wang, Y., & Garnett, C. E. (2010) · Journal of Biopharmaceutical Statistics · doi:10.1080/10543400903094881 · Cited by 5 · see citing works

Communicating With the FDA: The “Third Rail” of a New Model for Drug Development

Stanski, D. R., & Orloff, J. J. (2008) · The Journal of Clinical Pharmacology · doi:10.1177/0091270007313560 · Cited by 4 · see citing works

They Are From the Government and They Really Are Here to Help You

Woosley, R. L. (2008) · The Journal of Clinical Pharmacology · doi:10.1177/0091270007313559 · Cited by 3 · see citing works

Response to Letter to the Editor: Statistical Issues of QT Prolongation Assessment Based on Linear Concentration Modeling by Yi Tsong et al.

Tsong, Y., Shen, M., Zhong, J., & Zhang, J. (2010) · Journal of Biopharmaceutical Statistics · doi:10.1080/10543400903582083 · Cited by 1 · see citing works

Personalized Cancer Vaccines: Clinical Landscape, Challenges, and Opportunities

Shemesh, C. S., Hsu, J., Hosseini, I., Shen, B., Rotte, A., Twomey, P. L., Girish, S., & Wu, B. M. (2020) · Molecular Therapy · doi:10.1016/j.ymthe.2020.09.038 · Cited by 289 · see citing works

3D cell culture models: Drug pharmacokinetics, safety assessment, and regulatory consideration

Wang, H., Brown, P. C., Chow, E. C. Y., Ewart, L. C., Ferguson, S., Fitzpatrick, S., Freedman, B., Guo, G. L., Hedrich, W. D., Heyward, S. A., et al. (2021) · Clinical and Translational Science · doi:10.1111/cts.13066 · Cited by 219 · see citing works

Consideration of a Credibility Assessment Framework in Model‐Informed Drug Development: Potential Application to Physiologically‐Based Pharmacokinetic Modeling and Simulation

Kuemmel, C., Yang, Y., Zhang, X., Florian, J., Zhu, H., Tegenge, M., Huang, S., Wang, Y., Morrison, T., & Zineh, I. (2020) · CPT: Pharmacometrics &amp; Systems Pharmacology · doi:10.1002/psp4.12479 · Cited by 177 · see citing works

Scientific and regulatory evaluation of mechanistic in silico drug and disease models in drug development: Building model credibility

Musuamba, F. T., Rusten, I. S., Lesage, R., Russo, G. L., Bursi, R., Emili, L., Wangorsch, G., Manolis, E., Karlsson, K. E., Kulesza, A. J., et al. (2021) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12669 · Cited by 175 · see citing works

Towards artificial intelligence-enabled extracellular vesicle precision drug delivery

Greenberg, Z., Graim, K. S., & He, M. (2023) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2023.114974 · Cited by 160 · see citing works

Translational precision medicine: an industry perspective

Hartl, D., Luca, V. D., Kostikova, A., Laramie, J. M., Kennedy, S., Ferrero, E., Siegel, R. W., Fink, M., Ahmed, S., Millholland, J. M., et al. (2021) · Journal of Translational Medicine · doi:10.1186/s12967-021-02910-6 · Cited by 153 · see citing works

IVIVE: Facilitating the Use of In Vitro Toxicity Data in Risk Assessment and Decision Making

Chang, X., Tan, Y., Allen, D., Bell, S. M., Brown, P. C., Browning, L., Ceger, P. C., Gearhart, J. M., Hakkinen, P. J., Kabadi, S. V., et al. (2022) · Toxics · doi:10.3390/toxics10050232 · Cited by 133 · see citing works

Virtual patients, digital twins and causal disease models: Paving the ground for in silico clinical trials

Moingeon, P. E., Chenel, M., Rousseau, C. F., Voisin, E., & Guedj, M. (2023) · Drug Discovery Today · doi:10.1016/j.drudis.2023.103605 · Cited by 124 · see citing works

Immune digital twins for complex human pathologies: applications, limitations, and challenges

Niarakis, A., Laubenbacher, R., An, G. C., Ilan, Y., Fisher, J., Flobak, Å., Reiche, K., Martínez, M. R., Geris, L., Ladeira, L., et al. (2024) · npj Systems Biology and Applications · doi:10.1038/s41540-024-00450-5 · Cited by 104 · see citing works

Machine Learning and Pharmacometrics for Prediction of Pharmacokinetic Data: Differences, Similarities and Challenges Illustrated with Rifampicin

Keutzer, L., You, H., Farnoud, A., Nyberg, J., Wicha, S. G., Maher‐Edwards, G., Vlasakakis, G., Moghaddam, G. K., Svensson, E. M., Menden, M. P., et al. (2022) · Pharmaceutics · doi:10.3390/pharmaceutics14081530 · Cited by 98 · see citing works

Progress and Opportunities to Advance Clinical Cancer Therapeutics Using Tumor Dynamic Models

Bruno, R., Bottino, D. C., de Alwis, D. P., Fojo, A. T., Guedj, J., Liu, C., Swanson, K. R., Zheng, J., Zheng, Y., & Jin, J. Y. (2019) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-19-0287 · Cited by 85 · see citing works

How can natural language processing help model informed drug development?: a review

Bhatnagar, R., Sardar, S., Beheshti, M., & Podichetty, J. T. (2022) · JAMIA Open · doi:10.1093/jamiaopen/ooac043 · Cited by 79 · see citing works

Reinforcement learning strategies in cancer chemotherapy treatments: A review

Yang, C., Shiranthika, C., Wang, C.-Y., Chen, K.-W., & Sumathipala, S. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.107280 · Cited by 74 · see citing works

Two heads are better than one: current landscape of integrating QSP and machine learning

Zhang, T., Androulakis, I. P., Bonate, P. L., Cheng, L., Helikar, T., Parikh, J., Rackauckas, C., Subramanian, K., Cho, C. R., on behalf of the Working Group, et al. (2022) · Journal of Pharmacokinetics and Pharmacodynamics · doi:10.1007/s10928-022-09805-z · Cited by 62 · see citing works

Computational Frontiers in Aptamer-Based Nanomedicine for Precision Therapeutics: A Comprehensive Review

Kumar, S., Mohan, A., Sharma, N. R., Kumar, A., Girdhar, M., Malik, T., & Verma, A. K. (2024) · ACS Omega · doi:10.1021/acsomega.4c02466 · Cited by 55 · see citing works

Systems biology of angiogenesis signaling: Computational models and omics

Zhang, Y., Wang, H., Oliveira, R. H. D. M., Zhao, C., & Popel, A. S. (2021) · WIREs Mechanisms of Disease · doi:10.1002/wsbm.1550 · Cited by 54 · see citing works

A comprehensive regulatory and industry review of modeling and simulation practices in oncology clinical drug development

Ruiz-Garcı́a, A., Baverel, P., Bottino, D. C., Dolton, M., Feng, Y., González-García, I., Kim, J., Robey, S., Singh, I., Turner, D. C., et al. (2023) · Journal of Pharmacokinetics and Pharmacodynamics · doi:10.1007/s10928-023-09850-2 · Cited by 54 · see citing works

Role of Model‐Informed Drug Development in Pediatric Drug Development, Regulatory Evaluation, and Labeling

Bi, Y., Liu, J., Li, L., Yu, J., Bhattaram, A., Bewernitz, M., Li, R., Liu, C., Earp, J. C., Ma, L., et al. (2019) · The Journal of Clinical Pharmacology · doi:10.1002/jcph.1478 · Cited by 54 · see citing works

Perspective on model‐informed drug development

Lesko, L. J. (2021) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12699 · Cited by 52 · see citing works

Application of Machine Learning in Drug Development and Regulation: Current Status and Future Potential

Liu, Q., Zhu, H., Liu, C., Jean, D., Huang, S. M., ElZarrad, M. K., Blumenthal, G. M., & Wang, Y. (2020) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.1771 · Cited by 49 · see citing works

Incorporating Ontogeny in Physiologically Based Pharmacokinetic Modeling to Improve Pediatric Drug Development: What We Know About Developmental Changes in Membrane Transporters

Cheung, K. W. K., van Groen, B. D., Burckart, G. J., Zhang, L., de Wildt, S. N., & Huang, S. M. (2019) · The Journal of Clinical Pharmacology · doi:10.1002/jcph.1489 · Cited by 48 · see citing works

Model-Informed Drug Development Approach Supporting Approval of Adalimumab (HUMIRA) in Adolescent Patients with Hidradenitis Suppurativa: a Regulatory Perspective

Bi, Y., Liu, J., Wang, J., Epps, R. E., Kettl, D., Marcus, K. A., Seo, S. K., Zhu, H., & Wang, Y. (2019) · The AAPS Journal · doi:10.1208/s12248-019-0363-5 · Cited by 46 · see citing works

Nanomedicine Ex Machina: Between Model-Informed Development and Artificial Intelligence

Nova, M. V., Lin, T. P., Shanehsazzadeh, S., Jain, K., Ng, S. C. Y., Wacker, R., Chichakly, K. J., & Wacker, M. G. (2022) · Frontiers in Digital Health · doi:10.3389/fdgth.2022.799341 · Cited by 44 · see citing works

Model-based Target Pharmacology Assessment (mTPA): An Approach Using PBPK/PD Modeling and Machine Learning to Design Medicinal Chemistry and DMPK Strategies in Early Drug Discovery

Chen, E. P., Bondi, R. W., & Michalski, P. J. (2021) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.0c02033 · Cited by 44 · see citing works

Clinical pharmacology applications in clinical drug development and clinical care: A focus on Saudi Arabia

Alsultan, A., Alghamdi, W. A., Alghamdi, J., Alharbi, A., Aljutayli, A., Albassam, A. A., Almazroo, O. A., & Alqahtani, S. (2020) · Saudi Pharmaceutical Journal · doi:10.1016/j.jsps.2020.08.012 · Cited by 40 · see citing works

The <scp>US</scp> Food and Drug Administration's Model‐Informed Drug Development Paired Meeting Pilot Program: Early Experience and Impact

Madabushi, R., Benjamin, J. M., Grewal, R., Pacanowski, M. A., Strauss, D. G., Wang, Y., Zhu, H., & Zineh, I. (2019) · Clinical Pharmacology &amp; Therapeutics · doi:10.1002/cpt.1457 · Cited by 39 · see citing works

Monoclonal Antibodies and Fc‐Fusion Proteins for Pediatric Use: Dosing, Immunogenicity, and Modeling and Simulation in Data Submitted to the US Food and Drug Administration

Liu, X. I., Dallmann, A., Wang, Y., Green, D. J., Burnham, J. M., Chiang, B., Wu, P., Sheng, M., Lu, K., van den Anker, J. N., et al. (2019) · The Journal of Clinical Pharmacology · doi:10.1002/jcph.1406 · Cited by 35 · see citing works

Cytokine Release Syndrome By T-cell–Redirecting Therapies: Can We Predict and Modulate Patient Risk?

Vyver, A. J. V. D., Marrer‐Berger, E., Wang, K., Lehr, T., & Walz, A. (2021) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-21-0470 · Cited by 35 · see citing works

Innovative approaches and recent advances in the study of ontogeny of drug metabolism and transport

van Groen, B. D., Allegaert, K., Tibboel, D., & de Wildt, S. N. (2020) · British Journal of Clinical Pharmacology · doi:10.1111/bcp.14534 · Cited by 34 · see citing works

Application of systems biology-based in silico tools to optimize treatment strategy identification in Still’s disease

Segú-Vergés, C., Coma, M., Kessel, C., Smeets, S. J., Foell, D., & Aldea, A. (2021) · Arthritis Research & Therapy · doi:10.1186/s13075-021-02507-w · Cited by 34 · see citing works

Evolution of Drug Development and Regulatory Affairs: The Demonstrated Power of Artificial Intelligence

Nene, L. N. Z., Flepisi, B. T., Brand, S. J., Basson, C., & Balmith, M. (2024) · Clinical Therapeutics · doi:10.1016/j.clinthera.2024.05.012 · Cited by 33 · see citing works

Quantitative Proteomics in Translational Absorption, Distribution, Metabolism, and Excretion and Precision Medicine

Ahire, D. S., Kruger, L., Sharma, S., Mettu, V. S., Basit, A., & Prasad, B. (2022) · Pharmacological Reviews · doi:10.1124/pharmrev.121.000449 · Cited by 33 · see citing works

Quantitative Proteomic Map of Enzymes and Transporters in the Human Kidney: Stepping Closer to Mechanistic Kidney Models to Define Local Kinetics

Al‐Majdoub, Z. M., Scotcher, D., Achour, B., Barber, J., Galetin, A., & Rostami‐Hodjegan, A. (2021) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.2396 · Cited by 30 · see citing works

Model‐Informed Approach Supporting Drug Development and Regulatory Evaluation for Rare Diseases

Li, R., Ma, L., Li, F., Li, L., Bi, Y., Yuan, Y., Li, Y., Xu, Y., Zhang, X., Liu, J., et al. (2022) · The Journal of Clinical Pharmacology · doi:10.1002/jcph.2143 · Cited by 30 · see citing works

Model‐Informed Drug Development: A Regulatory Perspective on Progress

Zhu, H., Huang, S. M., Madabushi, R., Strauss, D. G., Wang, Y., & Zineh, I. (2019) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.1475 · Cited by 30 · see citing works

There is Only One Valid Definition of Clearance: Critical Examination of Clearance Concepts Reveals the Potential for Errors in Clinical Drug Dosing Decisions

Benet, L. Z., Sodhi, J. K., Makrygiorgos, G., & Mesbah, A. (2021) · The AAPS Journal · doi:10.1208/s12248-021-00591-z · Cited by 30 · see citing works

Opportunities and challenges for applying model‐informed drug development approaches to gene therapies

Belov, A. A., Schultz, K. L. W., Forshee, R. A., & Tegenge, M. A. (2021) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12597 · Cited by 29 · see citing works

Clinical Pharmacology Regulatory Sciences in Drug Development and Precision Medicine: Current Status and Emerging Trends

Liu, Q., Ahadpour, M., Rocca, M., & Huang, S. M. (2021) · The AAPS Journal · doi:10.1208/s12248-021-00563-3 · Cited by 28 · see citing works

In Vitro to In Vivo Extrapolation Linked to Physiologically Based Pharmacokinetic Models for Assessing the Brain Drug Disposition

Murata, Y., Neuhoff, S., Rostami‐Hodjegan, A., Takita, H., Al‐Majdoub, Z. M., & Ogungbenro, K. (2022) · The AAPS Journal · doi:10.1208/s12248-021-00675-w · Cited by 28 · see citing works

Intranasal Naloxone Repeat Dosing Strategies and Fentanyl Overdose

Strauss, D. G., Li, Z., Chaturbedi, A., Chakravartula, S., Samieegohar, M., Mann, J., Nallani, S. C., Prentice, K. W., Shah, A., Burkhart, K., et al. (2024) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.51839 · Cited by 27 · see citing works

Prediction of Human Pharmacokinetics and Clinical Effective Dose of SI–B001, an EGFR/HER3 Bi-specific Monoclonal Antibody

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Application of Machine Learning for Tumor Growth Inhibition – Overall Survival Modeling Platform

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Pharmacological Basis for Abrogating Myocardial Reperfusion Injury Through a Multi-Target Combined Antioxidant Therapy

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Zhang, M., Yu, Z., Liu, H., Wang, X., Li, H., Yao, X., & Liu, D. (2023) · Biopharmaceutics & Drug Disposition · doi:10.1002/bdd.2368 · Cited by 12 · see citing works

Optimizing dose-schedule regimens with bayesian adaptive designs: opportunities and challenges

Chen, X., He, R., Chen, X., Jiang, L., & Wang, F. (2023) · Frontiers in Pharmacology · doi:10.3389/fphar.2023.1261312 · Cited by 12 · see citing works

The emerging role of physiologically-based pharmacokinetic/biopharmaceutics modeling in formulation development

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Translational Pharmacokinetic/Pharmacodynamic Modeling and Simulation of Oxaliplatin and Irinotecan in Colorectal Cancer

Zhu, J., Zhang, Y., Zhao, Y., Zhang, J., Hao, K., & He, H. (2023) · Pharmaceutics · doi:10.3390/pharmaceutics15092274 · Cited by 11 · see citing works

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Liu, C., Xu, Y., Liu, Q., Zhu, H., & Wang, Y. (2022) · Journal of Pharmacokinetics and Pharmacodynamics · doi:10.1007/s10928-022-09802-2 · Cited by 11 · see citing works

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Machine Learning Models for Predicting Personalized Tacrolimus Stable Dosages in Pediatric Renal Transplant Patients

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Mi, K., Pu, S., Hou, Y. J., Sun, L., Zhou, K., Ma, W., Xu, X., Huo, M., Liu, Z., Xie, C., et al. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23073722 · Cited by 10 · see citing works

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Wang, L., Wang, J., Feng, J., Doi, M., Pepe, S., Pacanowski, M. A., & Schuck, R. N. (2022) · Orphanet Journal of Rare Diseases · doi:10.1186/s13023-022-02298-6 · Cited by 10 · see citing works

Integration of Pharmacokinetics, Pharmacodynamics, Safety, and Efficacy into Model‐Informed Dose Selection in Oncology First‐in‐Human Study: A Case of Roblitinib ( FGF401 )

Wilbaux, M., Yang, S., Jullion, A., Demanse, D., Porta, D. G., Myers, A. P., Meille, C., & Gu, Y. (2022) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.2752 · Cited by 10 · see citing works

Pharmacokinetics and Pharmacodynamics of Glucarpidase Rescue Treatment After High-dose Methotrexate Therapy Based on Modeling and Simulation

Kimura, T., Fukaya, Y., Hamada, Y., Yoshimura, K., & Kawamoto, H. (2023) · Anticancer Research · doi:10.21873/anticanres.16351 · Cited by 10 · see citing works

Empirical methods for the validation of time-to-event mathematical models taking into account uncertainty and variability: application to EGFR + lung adenocarcinoma

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Exploring the synergistic effects of cabozantinib and a programmed cell death protein 1 inhibitor in metastatic renal cell carcinoma with machine learning

Durán, I., Castellano, D., Puente, J., Martín-Couce, L., Bello, E., Anido, U., Mas, J. M., & Costa, L. (2022) · Oncotarget · doi:10.18632/oncotarget.28183 · Cited by 10 · see citing works

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Xiong, Y., Samtani, M. N., & Ouellet, D. (2024) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2024.115503 · Cited by 9 · see citing works

A Multiscale Quantitative Systems Pharmacology Model for the Development and Optimization of mRNA Vaccines

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Courcelles, E., Boissel, J., Massol, J., Klingmann, I., Kahoul, R., Hommel, M., Pham, E., & Kulesza, A. J. (2022) · Frontiers in Medical Technology · doi:10.3389/fmedt.2022.810315 · Cited by 9 · see citing works

PDUFA VI: It Is Time to Unleash the Full Potential of Model‐Informed Drug Development

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Evidence Mapping of Model-Informed Drug Development Across Regulatory and Clinical Decisions

Hassan, A., Siddiqui, N., Khan, B., Malik, S., & Ali, H. (2026) · International Journal Of Pharmaceutical Research And Allied Sciences · doi:10.51847/xhrxdmxmuk