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

Prediction-Corrected Visual Predictive Checks for Diagnosing Nonlinear Mixed-Effects Models

Bergstrand, M., Hooker, A. C., Wallin, J. E., & Karlsson, M. O. (2011) · The AAPS Journal · doi:10.1208/s12248-011-9255-z · Cited by 1,435

Ways to Fit a PK Model with Some Data Below the Quantification Limit

Beal, S. L. (2001) · Journal of Pharmacokinetics and Pharmacodynamics · doi:10.1023/a:1012299115260 · Cited by 1,156

Understanding the Dose-Effect Relationship

Holford, N. H. G., & Sheiner, L. B. (1981) · Clinical Pharmacokinetics · doi:10.2165/00003088-198106060-00002 · Cited by 1,147

Modeling good research practices--overview: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force--1.

Caro, J., Briggs, A., Siebert, U., & Kuntz, K. M. (2012) · Value in Health · doi:10.1016/j.jval.2012.06.012 · Cited by 1,006

Basic Concepts in Population Modeling, Simulation, and Model‐Based Drug Development—Part 2: Introduction to Pharmacokinetic Modeling Methods

Mould, D., & Upton, R. N. (2013) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1038/psp.2013.14 · Cited by 906

Importance of Shrinkage in Empirical Bayes Estimates for Diagnostics: Problems and Solutions

Savić, R. M., & Karlsson, M. O. (2009) · The AAPS Journal · doi:10.1208/s12248-009-9133-0 · Cited by 641

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

Basic Concepts in Physiologically Based Pharmacokinetic Modeling in Drug Discovery and Development

Jones, H., & Rowland‐Yeo, K. (2013) · CPT: Pharmacometrics & Systems Pharmacology · doi:10.1038/psp.2013.41 · Cited by 626

Basic Concepts in Population Modeling, Simulation, and Model‐Based Drug Development

Mould, D., & Upton, R. N. (2012) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1038/psp.2012.4 · Cited by 589

Physiologically Based Pharmacokinetic (PBPK) Modeling and Simulation Approaches: A Systematic Review of Published Models, Applications, and Model Verification

Sager, J. E., Yu, J., Ragueneau, I., & Isoherranen, N. (2015) · Drug Metabolism and Disposition · doi:10.1124/dmd.115.065920 · Cited by 541

A Model‐Informed Drug Discovery and Development Strategy for the Novel Glucose‐Responsive Insulin MK‐2640 Enabled Rapid Decision Making

Visser, S. A. G., Kandala, B., Fancourt, C., Krug, A. W., & Cho, C. R. (2019) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.1729 · Cited by 18

Model Description Language (MDL): A Standard for Modeling and Simulation

Smith, M. K., Moodie, S. L., Bizzotto, R., Blaudez, E., Borella, E., Carrara, L., Chan, P., Chenel, M., Comets, E., Gieschke, R., et al. (2017) · CPT: Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12222 · Cited by 17

An automated approach to identify scientific publications reporting pharmacokinetic parameters

Hernandez, F. G., Carter, S. J., Iso-Sipilä, J., Goldsmith, P., Almousa, A., Gastine, S., Lilaonitkul, W., Kloprogge, F., & Standing, J. (2021) · Wellcome Open Research · doi:10.12688/wellcomeopenres.16718.1 · Cited by 18

Pharmacometrics meets statistics—A synergy for modern drug development

Ryeznik, Y. V., Sverdlov, O., Svensson, E. M., Montepiedra, G., Hooker, A. C., & Wong, W. K. (2021) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12696 · Cited by 17

PK/PD model‐informed dose selection for oncology phase I expansion: Case study based on PF‐06939999, a PRMT5 inhibitor

Guo, C., Liao, K., Li, M., Wang, I., Shaik, M. N., & Yin, D. (2022) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12882 · Cited by 17

New challenges in drug discovery

Bano, I., Butt, U. D., & Mohsan, S. A. H. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-91376-8.00021-5 · Cited by 17

Proposed best practice for projects that involve modelling and simulation

O’Kelly, M., Anisimov, V. V., Campbell, C., & Hamilton, S. (2016) · Pharmaceutical Statistics · doi:10.1002/pst.1789 · Cited by 17

Model-Based Adaptive Optimal Design (MBAOD) Improves Combination Dose Finding Designs: an Example in Oncology

Pierrillas, P. B., Fouliard, S., Chenel, M., Hooker, A. C., Friberg, L. E., & Karlsson, M. O. (2018) · The AAPS Journal · doi:10.1208/s12248-018-0206-9 · Cited by 17

Guidelines for mechanistic modeling and analysis in cardiovascular research

Colebank, M. J., Oomen, P. J. A., Witzenburg, C. M., Grosberg, A., Beard, D. A., Husmeier, D., Olufsen, M. S., & Chesler, N. C. (2024) · American Journal of Physiology-Heart and Circulatory Physiology · doi:10.1152/ajpheart.00766.2023 · Cited by 18

Computational techniques for enhancing PK/PD modeling and simulation and ADMET prediction

Ghani, S., Khan, N., Sable, H., Yao, F., & Shafiq, M. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-33024-7.00001-1 · Cited by 16

Modeling of the Weight Status and Risk of Nonalcoholic Fatty Liver Disease in Elderly Individuals: The Potential Impact of the Disulfide Bond‐Forming Oxidoreductase A‐Like Protein (DsbA‐L) Polymorphism on the Weight Status

Oniki, K., Watanabe, T., Kudo, M., Izuka, T., Ono, T., Matsuda, K. M., Sakamoto, Y., Nagaoka, K., Imafuku, T., Ishima, Y., et al. (2018) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12292 · Cited by 16

Clinical trial simulations of the interaction between cannabidiol and clobazam and effect on drop‐seizure frequency

Bergmann, K. R., Broekhuizen, K., & Groeneveld, G. J. (2019) · British Journal of Clinical Pharmacology · doi:10.1111/bcp.14158 · Cited by 17

Bayesian prediction-based individualized dosing of anti-methicillin-resistant Staphylococcus aureus treatment: Recent advancements and prospects in therapeutic drug monitoring

Oda, K., Saito, H., & Jono, H. (2023) · Pharmacology & Therapeutics · doi:10.1016/j.pharmthera.2023.108433 · Cited by 17

Pharmacokinetics and pharmacodynamics of finerenone in patients with chronic kidney disease and type 2 diabetes: Insights based on FIGARO‐DKD and FIDELIO‐DKD

Eißing, T., Goulooze, S. C., van den Berg, P., van Noort, M., Ruppert, M., Snelder, N., Garmann, D., Lippert, J., Heinig, R., BRINKER, M. D., et al. (2023) · Diabetes Obesity and Metabolism · doi:10.1111/dom.15387 · Cited by 18

A Systematic Evaluation of Effect of Adherence Patterns on the Sample Size and Power of a Clinical Study

Mallayasamy, S., Chaturvedula, A., Blaschke, T. F., & Fossler, M. J. (2018) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12361 · Cited by 16

Conditional distribution modeling as an alternative method for covariates simulation: Comparison with joint multivariate normal and bootstrap techniques

Smania, G., & Jonsson, E. N. (2021) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12613 · Cited by 16

Unleashing the future: The revolutionary role of machine learning and artificial intelligence in drug discovery

Yadav, M. K., Dahiya, V., Tripathi, M. K., Chaturvedi, N., Rashmi, M., Ghosh, A., & Raj, V. S. (2024) · European Journal of Pharmacology · doi:10.1016/j.ejphar.2024.177103 · Cited by 19

Assessing Similarity Among Individual Tumor Size Lesion Dynamics: The CICIL Methodology

Terranova, N., Girard, P., Ioannou, K., Klinkhardt, U., & Munafo, A. (2018) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12284 · Cited by 15

dOFV distributions: a new diagnostic for the adequacy of parameter uncertainty in nonlinear mixed-effects models applied to the bootstrap

Dosne, A., Niebecker, R., & Karlsson, M. O. (2016) · Journal of Pharmacokinetics and Pharmacodynamics · doi:10.1007/s10928-016-9496-7 · Cited by 15

Can lifecycle management safeguard innovation in the pharmaceutical industry?

Hering, S., Loretz, B., Friedli, T. K., Lehr, C., & Stieneker, F. (2018) · Drug Discovery Today · doi:10.1016/j.drudis.2018.10.008 · Cited by 15

Food Effect Study Design With Oral Drugs: Lessons Learned From Recently Approved Drugs in Oncology

Farha, M. W., Masson, E., Tomkinson, H. K., & Mugundu, G. M. (2018) · The Journal of Clinical Pharmacology · doi:10.1002/jcph.1351 · Cited by 15

Mvlogncorrest: An R Package for Sampling from Multivariate Lognormal Distributions and Estimating Correlations from Uncomplete Correlation Matrix

Carlo, A. D., Tosca, E. M., Melillo, N., & Magni, P. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107517 · Cited by 14

Population pharmacokinetic analysis of rivaroxaban in children and comparison to prospective physiologically‐based pharmacokinetic predictions

Willmann, S., Coboeken, K., Zhang, Y., Mayer, H., Ince, I., Mešić, E., Thelen, K., Kubitza, D., Lensing, A. W. A., Yang, H., et al. (2021) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12688 · Cited by 14

Model-Based Meta-Analysis to Optimize Staphylococcus aureus‒Targeted Therapies for Atopic Dermatitis

Miyano, T., Irvine, A. D., & Tanaka, R. J. (2022) · JID Innovations · doi:10.1016/j.xjidi.2022.100110 · Cited by 14

Physiologically Based Pharmacokinetics as a Component of Model‐Informed Drug Development: Where We Were, Where We Are, and Where We Are Heading

Rostami‐Hodjegan, A., & Toon, S. (2020) · The Journal of Clinical Pharmacology · doi:10.1002/jcph.1654 · Cited by 14

Use of Real‐World Data and Real‐World Evidence in Rare Disease Drug Development: A Statistical Perspective

Chen, J., Gruber, S., Lee, H., Chu, H., Lee, S., Tian, H., Wang, Y., He, W., Jemielita, T., Song, Y., et al. (2025) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.3576 · Cited by 13

Transforming Translation Through Quantitative Pharmacology for High‐Impact Decision Making in Drug Discovery and Development

Gupta, N., Bottino, D. C., Simonsson, U. S. H., Musante, C. J., Bueters, T., Rieger, T. R., Macha, S., Chenel, M., Fancourt, C., Kanodia, J., et al. (2019) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.1667 · Cited by 13

Therapeutic drug monitoring of anti-tumor necrosis factor agents: lessons learned and remaining issues

Wang, Z., & Dreesen, E. (2020) · Current Opinion in Pharmacology · doi:10.1016/j.coph.2020.09.007 · Cited by 13

Quantitative system pharmacology as a legitimate approach to examine extrapolation strategies used to support pediatric drug development

Azer, K., & Barrett, J. S. (2022) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12801 · Cited by 13

Generation and application of avatars in pharmacometric modelling

Chasseloup, E., Hooker, A. C., & Karlsson, M. O. (2023) · Journal of Pharmacokinetics and Pharmacodynamics · doi:10.1007/s10928-023-09873-9 · Cited by 13

Improving the tools of clinical pharmacology: Goals for 2017 and beyond

Zineh, I., Abernethy, D. R., Hop, C. E. C. A., Bello, A., McClellan, M., Daniel, G., & Romine, M. H. (2016) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.530 · Cited by 13

Structural identifiability for mathematical pharmacology: models of myelosuppression

Evans, N. D., Cheung, S. Y. A., & Yates, J. W. T. (2018) · Journal of Pharmacokinetics and Pharmacodynamics · doi:10.1007/s10928-018-9569-x · Cited by 13

Pediatric Extrapolation in Type 2 Diabetes: Future Implications of a Workshop

Barrett, J. S., Bucci-Rechtweg, C. M., Cheung, S. Y. A., Gamalo, M., Haertter, S., Karres, J., Marquard, J., Mulugeta, Y. A., Ollivier, C., Strougo, A., et al. (2020) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.1805 · Cited by 13

Comparing Circulating Tumor Cell Counts with Dynamic Tumor Size Changes as Predictor of Overall Survival: A Quantitative Modeling Framework

Netterberg, I., Karlsson, M. O., Terstappen, L. W. M. M., Koopman, M., Punt, C. J. A., & Friberg, L. E. (2020) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-19-2570 · Cited by 13

An AI Approach to Generating MIDD Assets Across the Drug Development Continuum

Barrett, J. S., Goyal, R. K., Gobburu, J. V. S., Baran, S. W., & Varshney, J. (2023) · The AAPS Journal · doi:10.1208/s12248-023-00838-x · Cited by 13

End‐to‐end application of model‐informed drug development for ertugliflozin, a novel sodium‐glucose cotransporter 2 inhibitor

Fediuk, D. J., Nucci, G., Dawra, V. K., Callegari, E., Zhou, S., Musante, C. J., Liang, Y., Sweeney, K., & Sahasrabudhe, V. (2021) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12633 · Cited by 12

Disease progression joint model predicts time to type 1 diabetes onset: Optimizing future type 1 diabetes prevention studies

Morales, J. F., Muse, R., Podichetty, J. T., Burton, J. K., David, S. E., Lang, P. M., Schmidt, S., Romero, K., O’Doherty, I. M., Martin, F., et al. (2023) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12973 · Cited by 12

Opportunities and pitfalls in clinical proof-of-concept: principles and examples

Chen, C. (2018) · Drug Discovery Today · doi:10.1016/j.drudis.2018.01.045 · Cited by 12

Pharmacometrics, Physiologically Based Pharmacokinetics , Quantitative Systems Pharmacology— What's Next?—Joining Mechanistic and Epidemiological Approaches

Schmidt, S., Kim, S., Vozmediano, V., Cristofoletti, R., Winterstein, A. G., & Brown, J. D. (2019) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12425 · Cited by 12

Dose Finding in the Clinical Development of 60 US Food and Drug Administration–Approved Drugs Compared With Learning vs. Confirming Recommendations

Lyauk, Y. K., Jonker, D. M., & Lund, T. M. (2019) · Clinical and Translational Science · doi:10.1111/cts.12641 · Cited by 12

Modeling tumor growth inhibition and toxicity outcome after administration of anticancer agents in xenograft mice: A Dynamic Energy Budget (DEB) approach

Terranova, N., Tosca, E. M., Borella, E., Pesenti, E. A., Rocchetti, M., & Magni, P. (2018) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2018.04.012 · Cited by 12

Machine Learning Prediction and Validation of Plasma Concentration–Time Profiles

Iwata, H., Kageyama, M., & D, K. H. M. (2025) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.4c01431 · Cited by 11

Model-Informed Drug Development, Pharmacokinetic/Pharmacodynamic Cutoff Value Determination, and Antibacterial Efficacy of Benapenem against Enterobacteriaceae

Ji, X., Xue, F., Kang, Z., Zhong, W., Kuan, I., Yang, X., Zhu, X., Li, Y., & Lv, Y. (2019) · Antimicrobial Agents and Chemotherapy · doi:10.1128/aac.01751-19 · Cited by 11

Assessment of translational risk in drug research: Role of biomarker classification and mechanism-based PKPD concepts

Visser, S. A. G., & Bueters, T. (2017) · European Journal of Pharmaceutical Sciences · doi:10.1016/j.ejps.2017.08.006 · Cited by 11

A multi-scale model for low-density lipoprotein cholesterol (LDL-C) regulation in the human body: Application to quantitative systems pharmacology

Toroghi, M. K., Cluett, W. R., & Mahadevan, R. (2019) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2019.06.032 · Cited by 11

Model‐Informed Drug Development for Ixazomib, an Oral Proteasome Inhibitor

Gupta, N., Hanley, M. J., Diderichsen, P. M., Yang, H., Ke, A. B., Teng, Z., Labotka, R., Berg, D. T., Patel, C. A., Liu, G., et al. (2018) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.1047 · Cited by 11

Translational PK/PD: a retrospective analysis of performance and impact from a drug portfolio

Jansson‐Löfmark, R., Fridén, M., Badolo, L., Ahlström, C., Gurrell, I. K., Pangalos, M. N., & Jones, R. D. O. (2025) · Drug Discovery Today · doi:10.1016/j.drudis.2025.104417 · Cited by 10

How Well Are We Applying Quantitative Methods to Reverse Translation to Inform Early Clinical Development?

Heatherington, A. C., Kasichayanula, S., & Venkatakrishnan, K. (2017) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.948 · Cited by 10

Mechanistic Pharmacokinetic/Pharmacodynamic Model of Sunitinib and Dopamine in MCF-7/Adr Xenografts: Linking Cellular Heterogeneity to Tumour Burden

Wang, S., Zhu, X., Han, M., Hao, F., Lu, W., & Zhou, T. (2020) · The AAPS Journal · doi:10.1208/s12248-020-0428-5 · Cited by 10

Population Pharmacokinetics of Oral Brepocitinib in Healthy Volunteers and Patients

Hughes, J. H., Qiu, R., Banfield, C. R., Dowty, M. E., & Nicholas, T. (2022) · Clinical Pharmacology in Drug Development · doi:10.1002/cpdd.1163 · Cited by 10

Regulatory Framework for Drug Development in Rare Diseases

Korth-Bradley, J. M. (2022) · The Journal of Clinical Pharmacology · doi:10.1002/jcph.2171 · Cited by 11

A “middle‐out approach” for the prediction of human drug disposition from preclinical data using simplified physiologically based pharmacokinetic (PBPK) models

Yau, E., Gertz, M., Ogungbenro, K., Aarons, L. J., & Olivares‐Morales, A. (2023) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12915 · Cited by 10

Perspective on the State of Pharmacometrics and Systems Pharmacology Integration

Trame, M. N., Riggs, M. M., Biliouris, K., Marathe, D. D., Mettetal, J. T., Post, T. M., Rizk, M. L., Visser, S. A. G., & Musante, C. J. (2018) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12313 · Cited by 10

Toward Progress in Quantitative Translational Medicine: A Call to Action

Venkatakrishnan, K., Zheng, S., Musante, C. J., Jin, J. Y., Riggs, M. M., Krishnaswami, S., & Visser, S. A. G. (2019) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.1687 · Cited by 10

Low‐dose empagliflozin as adjunct‐to‐insulin therapy in type 1 diabetes: A valid modelling and simulation analysis to confirm efficacy

Perkins, B. A., Soleymanlou, N., Rosenstock, J., Skyler, J. S., Laffel, L. M., Liesenfeld, K., Neubacher, D., Riggs, M. M., Johnston, C., Eudy‐Byrne, R. J., et al. (2019) · Diabetes Obesity and Metabolism · doi:10.1111/dom.13945 · Cited by 11

Integrated Multi-stakeholder Systems Thinking Strategy: Decision-making with Biopharmaceutics Risk Assessment Roadmap (BioRAM) to Optimize Clinical Performance of Drug Products

Selen, A., Müllertz, A., Kesisoglou, F., Ho, R. J. Y., Cook, J., Dickinson, P. A., & Flanagan, T. R. (2020) · The AAPS Journal · doi:10.1208/s12248-020-00470-z · Cited by 10

Thoughtflow: Standards and Tools for Provenance Capture and Workflow Definition to Support Model‐Informed Drug Discovery and Development

Wilkins, J. J., Chan, P., Chard, J., Smith, G. A. M., Smith, M. K., Beer, M., Dunn, A., Flandorfer, C., Franklin, C., Goméni, R., et al. (2017) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12171 · Cited by 10

The feasibility of physiologically based pharmacokinetic modeling in forensic medicine illustrated by the example of morphine

Schaefer, N., Moj, D., Lehr, T., Schmidt, P. H., & Ramsthaler, F. (2017) · International Journal of Legal Medicine · doi:10.1007/s00414-017-1754-8 · Cited by 10

Accelerating virtual patient generation with a Bayesian optimization and machine learning surrogate model

Iwata, H., & Saito, R. (2024) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.13288 · Cited by 9

Systematic review and quantitative meta-analysis of age-dependent human T-lymphocyte homeostasis

Kulesh, V., Peskov, K. V., Helmlinger, G., & Bocharov, G. A. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1475871 · Cited by 9

Pharmacometrics in the Age of Large Language Models: A Vision of the Future

Tosca, E. M., Aiello, L., Carlo, A. D., & Magni, P. (2025) · Pharmaceutics · doi:10.3390/pharmaceutics17101274 · Cited by 10

Advancing rare disease therapeutics through digital twins: Opportunities in drug development and precision dosing

Dette, C. M. U., Alberg, V., Rüdesheim, S., Selzer, D., Marok, F. Z., Bragazzi, N. L., Fuhr, L. M., Brunak, S., Pearson, E. R., Zahn, T. R., et al. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.11.047 · Cited by 11

Combining Model‐Based Clinical Trial Simulation, Pharmacoeconomics, and Value of Information to Optimize Trial Design

Hill‐McManus, D., & Hughes, D. A. (2020) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12579 · Cited by 9

Quantitative Clinical Pharmacology Supports the Bridging From i.v. Dosing and Approval of s.c. Rituximab in B‐Cell Hematological Malignancies

Jamois, C., Gibiansky, E., Gibiansky, L. V., Chavanne, C., Morcos, P. N., McIntyre, C., Barrett, M., Lundberg, L., Zharkov, A., Boehnke, A., et al. (2021) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.2308 · Cited by 9

Application of quantitative protein mass spectrometric data in the early predictive analysis of target engagement by monoclonal antibodies

Muliaditan, M., & Sepp, A. (2022) · Clinical and Translational Science · doi:10.1111/cts.13278 · Cited by 9

Pediatric oncology drug development and dosage optimization

Cheung, S. Y. A., Hay, J. L., Lin, Y.-W., de Greef, R., & Bullock, J. M. (2024) · Frontiers in Oncology · doi:10.3389/fonc.2023.1235947 · Cited by 12

The Case for a Bayesian Approach to Benefit-Risk Assessment: Overview and Future Directions

Costa, M. J., He, W., Jemiai, Y., Zhao, Y., & DiCasoli, C. (2017) · Therapeutic Innovation & Regulatory Science · doi:10.1177/2168479017698190 · Cited by 9

Common Best Practice in Modeling and Simulation Across Quantitative Disciplines: A Comparison of Independently Emerging Proposals

Visser, S. A. G., Norton, J. D., Marshall, S. F., & O’Kelly, M. (2017) · Statistics in Biopharmaceutical Research · doi:10.1080/19466315.2017.1385520 · Cited by 9

The use of extrapolation based on modeling and simulation to support high‐dose regimens of ceftaroline fosamil in pediatric patients with complicated skin and soft‐tissue infections

Chan, P. L. S., McFadyen, L., Quaye, A., Leister-Tebbe, H. K., Hendrick, V. M., Hammond, J., & Raber, S. R. (2021) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12608 · Cited by 9

Prediction of Real-World Drug Effectiveness Prelaunch: Case Study in Rheumatoid Arthritis

Didden, E., Ruffieux, Y., Hummel, N., Efthimiou, O., Reichenbach, S., Gsteiger, S., Finckh, A., Fletcher, C., Salanti, G., & Egger, M. (2018) · Medical Decision Making · doi:10.1177/0272989x18775975 · Cited by 8

In Silico Pharmacology for Evidence-Based and Precision Medicine

Spanakis, M. (2023) · Pharmaceutics · doi:10.3390/pharmaceutics15031014 · Cited by 9

Model-informed drug development in pediatric, pregnancy and geriatric drug development: States of the art and future

Wu, Y., Zheng, Y., Li, Q., Yao, B., Cao, J., Liu, H., Hao, G., van den Anker, J., Zheng, Y., & Zhao, W. (2024) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2024.115364 · Cited by 8

A case‐study of model‐informed drug development of a novel PCSK9 anti sense oligonucleotide. Part 1: First time in man to phase II

Knöchel, J., Nilsson, C. A. M., Carlsson, B., Wernevik, L. C., Hofherr, A., Gennemark, P., Jansson‐Löfmark, R., Isaksson, R., Rydén‐Bergsten, T., Hamrén, B., et al. (2022) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12866 · Cited by 8

In silico trial for the assessment of givinostat dose adjustment rules based on the management of key hematological parameters in polycythemia vera patients

Tosca, E. M., Carlo, A. D., Bartolucci, R., Fiorentini, F., Tollo, S. D., Caserini, M., Rocchetti, M., Bettica, P. U., & Magni, P. (2024) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.13087 · Cited by 8

Dose rationale for the use of meropenem/vaborbactam combination in paediatric patients with Gram‐negative bacterial infections

Fornari, C., Arrieta, A. C., Bradley, J. S., Tout, M., Magalhães, P. J., Koraïchi, F., Borella, E., Piana, C., Pasqua, O. E. D., Vallespir, B. P., et al. (2024) · British Journal of Clinical Pharmacology · doi:10.1111/bcp.16145 · Cited by 8

Advancing research and development of anti-infectives for children with a focus on antiretroviral therapy: A clinical development perspective

Buchanan, A. M., Bekker, A., Chandasana, H., DeMasi, R. A., Lulic, Z., Ernest, T. B., Brothers, C. H., Min, S., Ruel, T., & Tan, L. (2024) · International Journal of Antimicrobial Agents · doi:10.1016/j.ijantimicag.2024.107306 · Cited by 8

Application of Pharmacometrics in Advancing the Clinical Research of Antibody-Drug Conjugates: Principles and Modeling Strategies

Li, X., Liu, D., Liu, S., Yu, M., Wu, X., & Wang, H. (2024) · Clinical Pharmacokinetics · doi:10.1007/s40262-024-01423-x · Cited by 8

Pharmacokinetic/Pharmacodynamic Modeling of the Anti-Cancer Effect of Dexamethasone in Pancreatic Cancer Xenografts and Anticipation of Human Efficacious Doses

Yao, Y., Yao, Q., Fu, Y., Tian, X., An, Q., Yang, L., Su, H., Lu, W., Hao, C., & Zhou, T. (2019) · Journal of Pharmaceutical Sciences · doi:10.1016/j.xphs.2019.10.035 · Cited by 8

How Semiphysiological Population Pharmacokinetic Modeling Incorporating Active Hepatic Uptake Supports Phase II Dose Selection of RO7049389, A Novel Anti–Hepatitis B Virus Drug

Cosson, V. F., Feng, S., Jaminion, F., Lemenuel‐Diot, A., Parrott, N. J., Paehler, A., Bo, Q., & Jin, Y. (2021) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.2184 · Cited by 8

Concept and utility of population pharmacokinetic and pharmacokinetic/pharmacodynamic models in drug development and clinical practice

Roganović, M., Homšek, A., Jovanović, M., Vučenović, V. T., Ćulafić, M., Miljković, B., & Vučićević, K. M. (2021) · Arhiv za farmaciju · doi:10.5937/arhfarm71-32901 · Cited by 8

Editorial: Model-informed drug development and evidence-based translational pharmacology

Zhao, J., Zhu, X., Tan, S., Chen, C., Kaddoumi, A., Guo, X., Lin, Y.-W., & Cheung, S. Y. A. (2022) · Frontiers in Pharmacology · doi:10.3389/fphar.2022.1086551 · Cited by 8

Current status and challenges of model-informed drug discovery and development in China

Wang, Y., Ji, J., Yao, Y., Nie, J., Xie, F., Xie, Y., & Li, G. (2024) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2024.115459 · Cited by 7

Model-informed drug discovery and development approaches to inform clinical trial design and regulatory decisions: A primer for the MENA region

Alasmari, M. S., Albusaysi, S., Elhefnawy, M. E., Ali, A. M., Altigani, K., Almoslem, M., Alharbi, M., Alghamdi, J., & Alsultan, A. (2024) · Saudi Pharmaceutical Journal · doi:10.1016/j.jsps.2024.102207 · Cited by 7

Model-Informed Drug Development of New Cefoperazone Sodium and Sulbactam Sodium Combination (3:1): Pharmacokinetic/Pharmacodynamic Analysis and Antibacterial Efficacy Against Enterobacteriaceae

Ji, X., Zhu, X., Li, Y., Xue, F., Kuan, I., He, Q., Meng, X.-R., Xiang, X., Yi-min, C., & Zheng, B. (2022) · Frontiers in Pharmacology · doi:10.3389/fphar.2022.856792 · Cited by 7

Drug Exposure to Establish Pharmacokinetic–Response Relationships in Oncology

Solans, B. P., Garrido, M. J., & Trocóniz, I. F. (2019) · Clinical Pharmacokinetics · doi:10.1007/s40262-019-00828-3 · Cited by 7

Bayesian forecasting of tumor size metrics and overall survival

Krishnan, S. M., & Friberg, L. E. (2022) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12869 · Cited by 7

Body of evidence and approaches applied in the clinical development programme of fixed‐dose combinations in the European Union from 2010 to 2016

Nøhr‐Nielsen, A., Bruin, M. L. D., Thomsen, M. S., Pipper, C. B., Lange, T., Bjerrum, O. J., & Lund, T. M. (2019) · British Journal of Clinical Pharmacology · doi:10.1111/bcp.13986 · Cited by 7

Industry Perspective on Using MIDD for Pediatric Studies Requiring Integration of Ontogeny

Corriol‐Rohou, S., & Cheung, S. Y. A. (2019) · The Journal of Clinical Pharmacology · doi:10.1002/jcph.1495 · Cited by 7

Safety and pharmacokinetics of IBI112, an IL-23 monoclonal antibody, in Chinese healthy volunteers: a first-in-human phase 1 study

He, J., Du, W., Yang, H., Wang, J., Cai, C., Ma, Q., Li, N., Yu, J., Wu, X., Wu, J., et al. (2023) · Expert Opinion on Investigational Drugs · doi:10.1080/13543784.2023.2230122 · Cited by 6

Recommended Phase II Doses of Talquetamab in Patients With Relapsed/Refractory Multiple Myeloma From MonumenTAL‐1: Clinical Pharmacology Results

Gong, J., Zhou, J., Yuan, D., Ma, X., Vishwamitra, D., Hilder, B. W., Masterson, T. J., Tolbert, J. A., Renaud, T. M., Heuck, C. J., et al. (2025) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.70004 · Cited by 6

Quantum mechanics in drug design: Progress, challenges, and future frontiers

Niazi, S. K. (2025) · Communicative & Integrative Biology · doi:10.1080/19420889.2025.2603140 · Cited by 10

Application of Population Balance Model to Simulate Precipitation of Weak Base and Zwitterionic Drugs in Gastrointestinal pH Environment

Yamamoto, H., Shanker, R., & Sugano, K. (2023) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.3c00088 · Cited by 6

Systems biology platform for efficient development and translation of multitargeted therapeutics

Azer, K., & Leaf, I. A. (2023) · Frontiers in Systems Biology · doi:10.3389/fsysb.2023.1229532 · Cited by 6

An integrative mechanistic model of thymocyte dynamics

Kulesh, V., Peskov, K. V., Helmlinger, G., & Bocharov, G. A. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1321309 · Cited by 6

Bioequivalence risk assessment of oral formulations containing racemic ibuprofen through a chiral physiologically based pharmacokinetic model of ibuprofen enantiomers

Reig-López, J., Cuquerella-Gilabert, M., Bandín‐Vilar, E., Merino‐Sanjuán, M., Sanjuan, V. M., & García‐Arieta, A. (2024) · European Journal of Pharmaceutics and Biopharmaceutics · doi:10.1016/j.ejpb.2024.114293 · Cited by 6

Stably Accelerating Stiff Quantitative Systems Pharmacology Models: Continuous-Time Echo State Networks as Implicit Machine Learning

Anantharaman, R., Abdelrehim, A., Jain, A., Pal, A., Sharp, D., Utkarsh, Edelman, A. S., & Rackauckas, C. (2022) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2023.01.004 · Cited by 6

Experience Learned and Perspectives on Using Model-Integrated Evidence in the Regulatory Context for Generic Drug Products—a Meeting Report

Tsakalozou, E., Fang, L., Bi, Y., van den Heuvel, M. W., Ahmed, T., Tsang, Y. C., Lionberger, R. A., Rostami‐Hodjegan, A., & Zhao, L. Y. (2024) · The AAPS Journal · doi:10.1208/s12248-023-00884-5 · Cited by 6

Application of Pharmacokinetics and Pharmacodynamics in Product Life Cycle Management. A Case Study with a Carbidopa-Levodopa Extended-Release Formulation

Modi, N. B. (2017) · The AAPS Journal · doi:10.1208/s12248-016-0032-x · Cited by 6

Data standards for model-informed drug development: an ISoP initiative

Radivojevic, A., Corrigan, B., Downie, N., Fox, R., Fiedler‐Kelly, J., Liu, H., Melhem, M. R., Radke, D., Schaefer, P., Su, J., et al. (2018) · Journal of Pharmacokinetics and Pharmacodynamics · doi:10.1007/s10928-018-9595-8 · Cited by 6

V2ACHER: Visualization of complex trial data in pharmacometric analyses with covariates

Lommerse, J., Plock, N., Cheung, S. Y. A., & Sachs, J. R. (2021) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12679 · Cited by 6

Model-Informed Drug Discovery and Development: Current Industry Good Practice and Regulatory Expectations and Future Perspectives

Marshall, S., Madabushi, R., Manolis, E., Krudys, K., Staab, A., Dykstra, K., & Visser, S. A. G. (2019) · CPT: Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12372 · Cited by 138

Strategic adoption of model-informed drug development (MIDD) as an overarching business model in biotechnology and life science industry

Krishna, R. (2026) · Drug Discovery Today · doi:10.1016/j.drudis.2026.104668 · Cited by 3

Common Best Practice in Modeling and Simulation Across Quantitative Disciplines: A Comparison of Independently Emerging Proposals

Visser, S. A. G., Norton, J. D., Marshall, S. F., & O’Kelly, M. (2017) · Statistics in Biopharmaceutical Research · doi:10.1080/19466315.2017.1385520 · Cited by 9

A Holistic and Integrative Approach for Advancing Model‐Informed Drug Development

Madabushi, R., Wang, Y., & Zineh, I. (2018) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12379 · Cited by 41

Model‐Informed Drug Development: Steps Toward Harmonized Guidance

Marshall, S. F., Ahamadi, M., Chien, J. Y., Iwata, D., Farkas, P., Filipe, A., Frey, N., Greene, E., Kawai, N., Li, J., et al. (2023) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.3006 · Cited by 36