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Giannuzzi V, Bertolani A, Torretta S, Reggiardo G, Toich E, Bonifazi D, Ceci A. Innovative research methodologies in the EU regulatory framework: an analysis of EMA qualification procedures from a pediatric perspective. Front Med (Lausanne) 2024; 11:1369547. [PMID: 38606157 PMCID: PMC11007141 DOI: 10.3389/fmed.2024.1369547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Accepted: 03/13/2024] [Indexed: 04/13/2024] Open
Abstract
Introduction The European Medicines Agency (EMA) offers scientific advice to support the qualification procedure of novel methodologies, such as preclinical and in vitro models, biomarkers, and pharmacometric methods, thereby endorsing their acceptability in medicine research and development (R&D). This aspect is particularly relevant to overcome the scarcity of data and the lack of validated endpoints and biomarkers in research fields characterized by small samples, such as pediatrics. Aim This study aimed to analyze the potential pediatric interest in methodologies qualified as "novel methodologies for medicine development" by the EMA. Methods The positive qualification opinions of novel methodologies for medicine development published on the EMA website between 2008 and 2023 were identified. Multi-level analyses were conducted to investigate data with a hierarchical structure and the effects of cluster-level variables and cluster-level variances and to evaluate their potential pediatric interest, defined as the possibility of using the novel methodology in pediatric R&D and the availability of pediatric data. The duration of the procedure, the type of methodology, the specific disease or disease area addressed, the type of applicant, and the availability of pediatric data at the time of the opinion release were also investigated. Results Most of the 27 qualifications for novel methodologies issued by the EMA (70%) were potentially of interest to pediatric patients, but only six of them reported pediatric data. The overall duration of qualification procedures with pediatric interest was longer than that of procedures without any pediatric interest (median time: 7 months vs. 3.5 months, respectively; p = 0.082). In parallel, qualification procedures that included pediatric data lasted for a longer period (median time: 8 months vs. 6 months, respectively; p = 0.150). Nephrology and neurology represented the main disease areas (21% and 16%, respectively), while endpoints, biomarkers, and registries represented the main types of innovative methodologies (32%, 26%, and 16%, respectively). Discussion Our results underscore the importance of implementing innovative methodologies in regulatory-compliant pediatric research activities. Pediatric-dedicated research infrastructures providing regulatory support and strategic advice during research activities could be crucial to the design of ad hoc pediatric methodologies or to extend and validate them for pediatrics.
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Affiliation(s)
- Viviana Giannuzzi
- Department of Research, Fondazione per la Ricerca Farmacologica Gianni Benzi Onlus, Bari, Italy
| | - Arianna Bertolani
- Department of Project Development, Consorzio per Valutazioni Biologiche e Farmacologiche (CVBF), Pavia, Italy
- TEDDY, European Network of Excellence for Paediatric Research, Pavia, Italy
| | - Silvia Torretta
- TEDDY, European Network of Excellence for Paediatric Research, Pavia, Italy
| | - Giorgio Reggiardo
- Department of Project Development, Consorzio per Valutazioni Biologiche e Farmacologiche (CVBF), Pavia, Italy
| | - Eleonora Toich
- Department of Project Development, Consorzio per Valutazioni Biologiche e Farmacologiche (CVBF), Pavia, Italy
| | - Donato Bonifazi
- Department of Project Development, Consorzio per Valutazioni Biologiche e Farmacologiche (CVBF), Pavia, Italy
- TEDDY, European Network of Excellence for Paediatric Research, Pavia, Italy
| | - Adriana Ceci
- Department of Research, Fondazione per la Ricerca Farmacologica Gianni Benzi Onlus, Bari, Italy
- TEDDY, European Network of Excellence for Paediatric Research, Pavia, Italy
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Bonate PL, Barrett JS, Ait-Oudhia S, Brundage R, Corrigan B, Duffull S, Gastonguay M, Karlsson MO, Kijima S, Krause A, Lovern M, Riggs MM, Neely M, Ouellet D, Plan EL, Rao GG, Standing J, Wilkins J, Zhu H. Training the next generation of pharmacometric modelers: a multisector perspective. J Pharmacokinet Pharmacodyn 2024; 51:5-31. [PMID: 37573528 DOI: 10.1007/s10928-023-09878-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 07/17/2023] [Indexed: 08/15/2023]
Abstract
The current demand for pharmacometricians outmatches the supply provided by academic institutions and considerable investments are made to develop the competencies of these scientists on-the-job. Even with the observed increase in academic programs related to pharmacometrics, this need is unlikely to change in the foreseeable future, as the demand and scope of pharmacometrics applications keep expanding. Further, the field of pharmacometrics is changing. The field largely started when Lewis Sheiner and Stuart Beal published their seminal papers on population pharmacokinetics in the late 1970's and early 1980's and has continued to grow in impact and use since its inception. Physiological-based pharmacokinetics and systems pharmacology have grown rapidly in scope and impact in the last decade and machine learning is just on the horizon. While all these methodologies are categorized as pharmacometrics, no one person can be an expert in everything. So how do you train future pharmacometricians? Leading experts in academia, industry, contract research organizations, clinical medicine, and regulatory gave their opinions on how to best train future pharmacometricians. Their opinions were collected and synthesized to create some general recommendations.
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Affiliation(s)
| | | | | | - Richard Brundage
- Metrum Research Group, University of Minnesota, Minneapolis, MN, USA
| | | | - Stephen Duffull
- Certara, Princeton, NJ, USA
- School of Pharmacy, University of Otago, Dunedin, New Zealand
| | | | | | - Shinichi Kijima
- Office of New Drug V, Pharmaceuticals and Medical Devices Agency (PMDA), Tokyo, Japan
| | | | - Mark Lovern
- Certara, Princeton, NJ, USA
- Certara, Raleigh, NC, USA
| | | | - Michael Neely
- Children's Hospital Los Angeles, University of Southern California Keck School of Medicine, Los Angeles, CA, USA
| | | | | | - Gauri G Rao
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA
| | - Joseph Standing
- Great Ormond Street Institute of Child Health, University College London, London, UK
- Department of Pharmacy, Great Ormond Street Hospital for Children, London, UK
| | | | - Hao Zhu
- Food and Drug Administration, Silver Springs, MD, USA
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Michelet R, Aulin LBS, Borghardt JM, Costa TD, Denti P, Ibarra M, Ma G, Meibohm B, Pillai G(C, Schmidt S, Hennig S, Kloft C. Barriers to global pharmacometrics: educational challenges and opportunities across the globe. CPT Pharmacometrics Syst Pharmacol 2023; 12:743-747. [PMID: 36960632 PMCID: PMC10272295 DOI: 10.1002/psp4.12940] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 12/23/2022] [Accepted: 02/08/2023] [Indexed: 03/25/2023] Open
Affiliation(s)
- Robin Michelet
- Department of Clinical Pharmacy and Biochemistry, Institute of PharmacyFreie Universitaet BerlinBerlinGermany
- qPharmetra LLCNijmegenThe Netherlands
| | - Linda B. S. Aulin
- Department of Clinical Pharmacy and Biochemistry, Institute of PharmacyFreie Universitaet BerlinBerlinGermany
| | - Jens M. Borghardt
- Research DMPK, Drug Discovery SciencesBoehringer Ingelheim Pharma GmbH & Co. KGBiberachGermany
| | - Teresa Dalla Costa
- Pharmaceutical Sciences Graduate ProgramFederal University of Rio Grande do SulPorto AlegreBrazil
| | - Paolo Denti
- Division of Clinical Pharmacology, Department of MedicineUniversity of Cape TownCape TownSouth Africa
| | - Manuel Ibarra
- Department of Pharmaceutical Sciences, Faculty of ChemistryUniversidad de la RepúblicaMontevideoUruguay
| | - Guangda Ma
- Department of Pharmacology & Clinical PharmacologyThe University of AucklandAucklandNew Zealand
| | - Bernd Meibohm
- Department of Pharmaceutical Sciences, College of PharmacyUniversity of Tennessee Health Science CenterMemphisTennesseeUSA
| | - Goonaseelan (Colin) Pillai
- Division of Clinical Pharmacology, Department of MedicineUniversity of Cape TownCape TownSouth Africa
- CP+ Associates GmbHBaselSwitzerland
- Phamacometrics Africa NPCCape TownSouth Africa
| | - Stephan Schmidt
- Center for Pharmacometrics and Systems Pharmacology, Department of Pharmaceutics, College of PharmacyUniversity of FloridaOrlandoFloridaUSA
| | - Stefanie Hennig
- Certara, Inc.PrincetonNew JerseyUSA
- School of Clinical Sciences, Faculty of HealthQueensland University of TechnologyBrisbaneQueenslandAustralia
- Department of Clinical Pharmacy, Institute of PharmacyFreie Universitaet BerlinBerlinGermany
| | - Charlotte Kloft
- Department of Clinical Pharmacy and Biochemistry, Institute of PharmacyFreie Universitaet BerlinBerlinGermany
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4
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Keutzer L, You H, Farnoud A, Nyberg J, Wicha SG, Maher-Edwards G, Vlasakakis G, Moghaddam GK, Svensson EM, Menden MP, Simonsson USH. Machine Learning and Pharmacometrics for Prediction of Pharmacokinetic Data: Differences, Similarities and Challenges Illustrated with Rifampicin. Pharmaceutics 2022; 14:pharmaceutics14081530. [PMID: 35893785 PMCID: PMC9330804 DOI: 10.3390/pharmaceutics14081530] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 07/16/2022] [Accepted: 07/18/2022] [Indexed: 01/27/2023] Open
Abstract
Pharmacometrics (PM) and machine learning (ML) are both valuable for drug development to characterize pharmacokinetics (PK) and pharmacodynamics (PD). Pharmacokinetic/pharmacodynamic (PKPD) analysis using PM provides mechanistic insight into biological processes but is time- and labor-intensive. In contrast, ML models are much quicker trained, but offer less mechanistic insights. The opportunity of using ML predictions of drug PK as input for a PKPD model could strongly accelerate analysis efforts. Here exemplified by rifampicin, a widely used antibiotic, we explore the ability of different ML algorithms to predict drug PK. Based on simulated data, we trained linear regressions (LASSO), Gradient Boosting Machines, XGBoost and Random Forest to predict the plasma concentration-time series and rifampicin area under the concentration-versus-time curve from 0–24 h (AUC0–24h) after repeated dosing. XGBoost performed best for prediction of the entire PK series (R2: 0.84, root mean square error (RMSE): 6.9 mg/L, mean absolute error (MAE): 4.0 mg/L) for the scenario with the largest data size. For AUC0–24h prediction, LASSO showed the highest performance (R2: 0.97, RMSE: 29.1 h·mg/L, MAE: 18.8 h·mg/L). Increasing the number of plasma concentrations per patient (0, 2 or 6 concentrations per occasion) improved model performance. For example, for AUC0–24h prediction using LASSO, the R2 was 0.41, 0.69 and 0.97 when using predictors only (no plasma concentrations), 2 or 6 plasma concentrations per occasion as input, respectively. Run times for the ML models ranged from 1.0 s to 8 min, while the run time for the PM model was more than 3 h. Furthermore, building a PM model is more time- and labor-intensive compared with ML. ML predictions of drug PK could thus be used as input into a PKPD model, enabling time-efficient analysis.
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Affiliation(s)
- Lina Keutzer
- Department of Pharmaceutical Biosciences, Uppsala University, 75124 Uppsala, Sweden; (L.K.); (H.Y.)
| | - Huifang You
- Department of Pharmaceutical Biosciences, Uppsala University, 75124 Uppsala, Sweden; (L.K.); (H.Y.)
| | - Ali Farnoud
- Computational Health Center, Helmholtz Munich, 85764 Neuherberg, Germany; (A.F.); (M.P.M.)
| | - Joakim Nyberg
- Department of Pharmacy, Uppsala University, 75123 Uppsala, Sweden; (J.N.); (E.M.S.)
| | - Sebastian G. Wicha
- Department of Clinical Pharmacy, Institute of Pharmacy, University of Hamburg, 20146 Hamburg, Germany;
| | - Gareth Maher-Edwards
- Research, Clinical Pharmacology Modelling & Simulation, GlaxoSmithKline, London TW8 9GS, UK; (G.M.-E.); (G.V.); (G.K.M.)
| | - Georgios Vlasakakis
- Research, Clinical Pharmacology Modelling & Simulation, GlaxoSmithKline, London TW8 9GS, UK; (G.M.-E.); (G.V.); (G.K.M.)
| | - Gita Khalili Moghaddam
- Research, Clinical Pharmacology Modelling & Simulation, GlaxoSmithKline, London TW8 9GS, UK; (G.M.-E.); (G.V.); (G.K.M.)
- Department of Clinical Neurosciences, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Elin M. Svensson
- Department of Pharmacy, Uppsala University, 75123 Uppsala, Sweden; (J.N.); (E.M.S.)
- Department of Pharmacy, Radboud Institute of Health Sciences, Radboud University Medical Center, 6525 EZ Nijmegen, The Netherlands
| | - Michael P. Menden
- Computational Health Center, Helmholtz Munich, 85764 Neuherberg, Germany; (A.F.); (M.P.M.)
- Department of Biology, Ludwig-Maximilian University Munich, 82152 Planegg-Martinsried, Germany
- German Center for Diabetes Research (DZD e.V.), 85764 Neuherberg, Germany
| | - Ulrika S. H. Simonsson
- Department of Pharmaceutical Biosciences, Uppsala University, 75124 Uppsala, Sweden; (L.K.); (H.Y.)
- Correspondence:
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5
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Towards regulatory endorsement of drug development tools to promote the application of model-informed drug development in Duchenne muscular dystrophy. J Pharmacokinet Pharmacodyn 2019; 46:441-455. [DOI: 10.1007/s10928-019-09642-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2019] [Accepted: 05/15/2019] [Indexed: 12/16/2022]
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6
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Ioannidis JPA. Reproducible pharmacokinetics. J Pharmacokinet Pharmacodyn 2019; 46:111-116. [PMID: 31004315 DOI: 10.1007/s10928-019-09621-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2018] [Accepted: 02/05/2019] [Indexed: 01/31/2023]
Abstract
Reproducibility is a highly desired feature of scientific investigation in general, and it has special connotations for research in pharmacokinetics, a vibrant field with over 500,000 publications to-date. It is important to be able to differentiate between genuine heterogeneity in pharmacokinetic parameters from heterogeneity that is due to errors and biases. This overview discusses efforts and opportunities to diminish the latter type of undesirable heterogeneity. Several reporting and research guidance documents and standards have been proposed for pharmacokinetic studies, but their adoption is still rather limited. Quality problems in the methods used and model evaluations have been examined in some empirical studies of the literature. Standardization of statistical and laboratory tools and procedures can be improved in the field. Only a small fraction of pharmacokinetic studies become pre-registered and only 9995 such studies have been registered in ClinicalTrials.gov as of August 2018. It is likely that most pharmacokinetic studies remain unpublished. Publication bias affecting the results and inferences has been documented in case studies, but its exact extent is unknown for the field at-large. The use of meta-analyses in the field is still limited. Availability of raw data, detailed protocols, software and codes is hopefully improving with multiple ongoing initiatives. Several research practices can contribute to greater transparency and reproducibility for pharmacokinetic investigations.
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Affiliation(s)
- John P A Ioannidis
- Departments of Medicine, Health Research and Policy, Biomedical Data Science, and Statistics, Stanford Prevention Research Center, Meta-Research Innovation Center at Stanford (METRICS), Stanford University, 1265 Welch Road, Medical School Office Building Room X306, Stanford, CA, 94305, USA.
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7
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Krause A, Kloft C, Huisinga W, Karlsson M, Pinheiro J, Bies R, Rogers J, Mentré F, Musser BJ. Comment on Jaki et al., A proposal for a new PhD level curriculum on quantitative methods for drug development. Pharmaceutical Statistics 17 (5):593-606, Sep/Oct 2018, DOI: 10.1002/pst.1873. Pharm Stat 2019; 18:278-281. [PMID: 30932340 DOI: 10.1002/pst.1940] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2018] [Accepted: 02/15/2019] [Indexed: 11/08/2022]
Affiliation(s)
- Andreas Krause
- Clinical Pharmacology, Idorsia Pharmaceuticals Ltd, Allschwil, Switzerland
| | - Charlotte Kloft
- Department of Clinical Pharmacy and Biochemistry, Institute of Pharmacy, Freie Universität Berlin, Germany
| | - Wilhelm Huisinga
- Computational Physiology, Institute of Mathematics, University of Potsdam, Germany
| | - Mats Karlsson
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | - José Pinheiro
- Quantitative Sciences, Janssen Pharmaceutical Companies of Johnson and Johnson, Titusville, New Jersey
| | - Robert Bies
- Department of Pharmaceutical Sciences, School of Pharmacy, State University of New York at Buffalo, New York.,Computational and Data Enabled Science and Engineering Program, University at Buffalo, New York
| | - James Rogers
- Statistical Modeling and Data Sciences, Metrum Research Group, Tariffvile, Connecticut
| | - France Mentré
- IAME, UMR1137, Inserm and Université de Paris Diderot, Paris, France
| | - Bret J Musser
- Biostatistics and Data Management, Regeneron Pharmaceuticals, Inc., Tarrytown, New York
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8
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Radivojevic A, Corrigan B, Downie N, Fox R, Fiedler-Kelly J, Liu H, Melhem M, Radke D, Schaefer P, Su J, Swat MJ, Teuscher NS, Thanneer N, Zong A, Wilkins JJ. Data standards for model-informed drug development: an ISoP initiative. J Pharmacokinet Pharmacodyn 2018; 45:659-661. [PMID: 30046963 DOI: 10.1007/s10928-018-9595-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2018] [Accepted: 05/31/2018] [Indexed: 10/28/2022]
Affiliation(s)
| | | | | | | | | | | | | | - David Radke
- Eli Lilly and Company, Indianapolis, IN, USA
| | | | - Jing Su
- Merck & Co., Kenilworth, NJ, USA
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9
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Goulooze SC, Krekels EH, van Dijk M, Tibboel D, van der Graaf PH, Hankemeier T, Knibbe CA, van Hasselt JC. Towards personalized treatment of pain using a quantitative systems pharmacology approach. Eur J Pharm Sci 2017; 109S:S32-S38. [DOI: 10.1016/j.ejps.2017.05.027] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2017] [Accepted: 05/11/2017] [Indexed: 02/08/2023]
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10
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Szalkai B, Grolmusz VK, Grolmusz VI. Identifying combinatorial biomarkers by association rule mining in the CAMD Alzheimer's database. Arch Gerontol Geriatr 2017; 73:300-307. [PMID: 28918286 DOI: 10.1016/j.archger.2017.08.006] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2017] [Revised: 08/09/2017] [Accepted: 08/12/2017] [Indexed: 11/29/2022]
Abstract
The concept of combinatorial biomarkers was conceived when it was noticed that simple biomarkers are often inadequate for recognizing and characterizing complex diseases. Here we present an algorithmic search method for complex biomarkers which may predict or indicate Alzheimer's disease (AD) and other kinds of dementia. We show that our method is universal since it can describe any Boolean function for biomarker discovery. We applied data mining techniques that are capable to uncover implication-like logical schemes with detailed quality scoring. The new SCARF program was applied for the Tucson, Arizona based Critical Path Institute's CAMD database, containing laboratory and cognitive test data for 5821 patients from the placebo arm of clinical trials of large pharmaceutical companies, and consequently, the data is much more reliable than numerous other databases for dementia. The results of our study on this larger than 5800-patient cohort suggest beneficial effects of high B12 vitamin level, negative effects of high sodium levels or high AST (aspartate aminotransferase) liver enzyme levels to cognition. As an example for a more complex and quite surprising rule: Low or normal blood glucose level with either low cholesterol or high serum sodium would also increase the probability of bad cognition with a 3.7 multiplier. The source code of the new SCARF program is publicly available at http://pitgroup.org/static/scarf.zip.
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Affiliation(s)
- Balázs Szalkai
- PIT Bioinformatics Group, Eötvös University, H-1117 Budapest, Hungary.
| | - Vince K Grolmusz
- 2nd Department of Internal Medicine, Semmelweis University, Budapest, Hungary..
| | - Vince I Grolmusz
- PIT Bioinformatics Group, Eötvös University, H-1117 Budapest, Hungary; Uratim Ltd., H-1118 Budapest, Hungary.
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Mehrotra S, Gobburu J. Communicating to Influence Drug Development and Regulatory Decisions: A Tutorial. CPT Pharmacometrics Syst Pharmacol 2016; 5:163-72. [PMID: 27299706 PMCID: PMC4846777 DOI: 10.1002/psp4.12073] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2016] [Revised: 02/24/2016] [Accepted: 02/25/2016] [Indexed: 01/07/2023] Open
Abstract
Pharmacometricians require three skills to be influential: technical, business (e.g., drug development), and soft skills (e.g., communication). Effective communication is required to translate technical and often complicated quantitative findings to interdisciplinary team members in order to influence drug development or regulatory decisions. In this tutorial, we highlight important aspects related to communicating pharmacometric analysis to influence decisions.
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Affiliation(s)
- S Mehrotra
- Center for Translational Medicine, School of Pharmacy, University of Maryland, Baltimore, Maryland, USA
| | - J Gobburu
- Center for Translational Medicine, School of Pharmacy, University of Maryland, Baltimore, Maryland, USA
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Dykstra K, Mehrotra N, Tornøe CW, Kastrissios H, Patel B, Al-Huniti N, Jadhav P, Wang Y, Byon W. Reporting guidelines for population pharmacokinetic analyses. J Clin Pharmacol 2016; 55:875-87. [PMID: 26148467 DOI: 10.1002/jcph.532] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2015] [Accepted: 04/28/2015] [Indexed: 11/10/2022]
Abstract
The purpose of this work was to develop a consolidated set of guiding principles for the reporting of population pharmacokinetic (PK) analyses based on input from a survey of practitioners as well as discussions between industry, consulting, and regulatory scientists. The survey found that identification of population covariate effects on drug exposure and support for dose selection (in which population PK frequently serves as preparatory analysis for exposure-response modeling) are the main areas of influence for population PK analysis. The proposed guidelines consider 2 main purposes of population PK reports: (1) to present key analysis findings and their impact on drug development decisions, and (2) as documentation of the analysis methods for the dual purpose of enabling review of the analysis and facilitating future use of the models. This work also identified 2 main audiences for the reports: (1) a technically competent group responsible for in-depth review of the data, methodology, and results; and (2) a scientifically literate but not technically adept group, whose main interest is in the implications of the analysis for the broader drug development program. We recommend a generalized question-based approach with 6 questions that need to be addressed throughout the report. We recommend 8 sections (Synopsis, Introduction, Data, Methods, Results, Discussion, Conclusions, Appendix) with suggestions for the target audience and level of detail for each section. A section providing general expectations regarding population PK reporting from a regulatory perspective is also included. We consider this an important step toward industrialization of the field of pharmacometrics such that a nontechnical audience also understands the role of pharmacometric analyses in decision making. Population PK reports were chosen as representative reports to derive these recommendations; however, the guiding principles presented here are applicable for all pharmacometric reports including pharmacokinetics/pharmacodynamics and simulation reports.
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Affiliation(s)
| | - Nitin Mehrotra
- Division of Pharmacometrics, US Food and Drug Administration, Silver Spring, MD, USA
| | | | | | - Bela Patel
- Clinical Pharmacology, Quantitative Sciences, GlaxoSmithKline, King of Prussia, PA, USA
| | - Nidal Al-Huniti
- Quantitative Clinical Pharmacology, AstraZeneca, Waltham, MA, USA
| | - Pravin Jadhav
- Quantitative Pharmacology and Pharmacometrics, Merck and Co., Whitehouse Station, NJ, USA
| | - Yaning Wang
- Division of Pharmacometrics, US Food and Drug Administration, Silver Spring, MD, USA
| | - Wonkyung Byon
- Global Innovative Pharma Business Clinical Pharmacology, Pfizer Inc., Groton, CT, USA
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Dykstra K, Mehrotra N, Tornøe CW, Kastrissios H, Patel B, Al-Huniti N, Jadhav P, Wang Y, Byon W. Reporting guidelines for population pharmacokinetic analyses. J Pharmacokinet Pharmacodyn 2015; 42:301-14. [PMID: 25925797 PMCID: PMC4432104 DOI: 10.1007/s10928-015-9417-1] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2015] [Accepted: 04/15/2015] [Indexed: 12/02/2022]
Abstract
The purpose of this work was to develop a consolidated set of guiding principles for reporting of population pharmacokinetic (PK) analyses based on input from a survey of practitioners as well as discussions between industry, consulting and regulatory scientists. The survey found that identification of population covariate effects on drug exposure and support for dose selection (where population PK frequently serves as preparatory analysis to exposure–response modeling) are the main areas of influence for population PK analysis. The proposed guidelines consider two main purposes of population PK reports (1) to present key analysis findings and their impact on drug development decisions, and (2) as documentation of the analysis methods for the dual purpose of enabling review of the analysis and facilitating future use of the models. This work also identified two main audiences for the reports: (1) a technically competent group responsible for in-depth review of the data, methodology, and results, and (2) a scientifically literate, but not technically adept group, whose main interest is in the implications of the analysis for the broader drug development program. We recommend a generalized question-based approach with six questions that need to be addressed throughout the report. We recommend eight sections (Synopsis, Introduction, Data, Methods, Results, Discussion, Conclusions, Appendix) with suggestions for the target audience and level of detail for each section. A section providing general expectations regarding population PK reporting from a regulatory perspective is also included. We consider this an important step towards industrialization of the field of pharmacometrics such that non-technical audience also understands the role of pharmacometrics analyses in decision making. Population PK reports were chosen as representative reports to derive these recommendations; however, the guiding principles presented here are applicable for all pharmacometric reports including PKPD and simulation reports.
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14
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Enhancing population pharmacokinetic modeling efficiency and quality using an integrated workflow. J Pharmacokinet Pharmacodyn 2014; 41:319-34. [DOI: 10.1007/s10928-014-9370-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2014] [Accepted: 07/08/2014] [Indexed: 10/25/2022]
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15
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Trivedi A, Lee RE, Meibohm B. Applications of pharmacometrics in the clinical development and pharmacotherapy of anti-infectives. Expert Rev Clin Pharmacol 2013; 6:159-70. [PMID: 23473593 DOI: 10.1586/ecp.13.6] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
With the increased emergence of anti-infective resistance in recent years, much focus has recently been drawn to the development of new anti-infectives and the optimization of treatment regimens and combination therapies for established antimicrobials. In this context, the field of pharmacometrics using quantitative numerical modeling and simulation techniques has in recent years emerged as an invaluable tool in the pharmaceutical industry, academia and regulatory agencies to facilitate the integration of preclinical and clinical development data and to provide a scientifically based framework for rational dosage regimen design and treatment optimization. This review highlights the usefulness of pharmacometric analyses in anti-infective drug development and applied pharmacotherapy with select examples.
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Affiliation(s)
- Ashit Trivedi
- Department of Pharmaceutical Sciences, University of Tennessee Health Science Center, Memphis, TN, USA
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Byon W, Smith MK, Chan P, Tortorici MA, Riley S, Dai H, Dong J, Ruiz-Garcia A, Sweeney K, Cronenberger C. Establishing best practices and guidance in population modeling: an experience with an internal population pharmacokinetic analysis guidance. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2013; 2:e51. [PMID: 23836283 PMCID: PMC6483270 DOI: 10.1038/psp.2013.26] [Citation(s) in RCA: 128] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/27/2012] [Accepted: 04/02/2013] [Indexed: 02/03/2023]
Abstract
This tutorial describes the development of a population pharmacokinetic (Pop PK) analysis guidance within Pfizer, which strives for improved consistency and efficiency, and a more systematic approach to model building. General recommendations from the Pfizer internal guidance and a suggested workflow for Pop PK model building are discussed. A description is also provided for mechanisms by which conflicting opinions were captured and resolved across the organization to arrive at the final guidance. CPT: Pharmacometrics & Systems Pharmacology (2013) 2, e51; doi:10.1038/psp.2013.26; advance online publication 3 July 2013
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Affiliation(s)
- W Byon
- Global Clinical Pharmacology, Pfizer, Groton, Connecticut, USA
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Zhang L, Allerheiligen SR, Lalonde RL, Stanski DR, Pfister M. Fostering Culture and Optimizing Organizational Structure for Implementing Model-Based Drug Development. J Clin Pharmacol 2013; 50:146S-150S. [DOI: 10.1177/0091270010376976] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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18
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Romero K, Corrigan B, Neville J, Kopko S, Cantillon M. Striving for an integrated drug-development process for neurodegeneration: the coalition against major diseases. Neurodegener Dis Manag 2011. [DOI: 10.2217/nmt.11.51] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
SUMMARY Alzheimer’s and Parkinson’s diseases are the two main neurodegenerative disorders and despite the public health need, drug development for these conditions has been plagued by a high attrition rate in the late phases of evaluation. In order to improve the efficiency of the drug development process for these conditions, the Coalition Against Major Diseases was formed by the Critical Path Institute in September 2008, in collaboration with the Engelberg Center for Health Care Reform at the Brookings Institution (Washington, DC, USA), with the aim of sharing precompetitive patient level data from legacy clinical trials, and transforming those data into generalizable and shareable knowledge in the form of drug development tools for Alzheimer’s and Parkinson’s diseases. As of May 2011, Coalition Against Major Diseases has 21 members (14 pharmaceutical companies and seven patient groups), joined by the US FDA, the European Medicines Agency, the National Institute of Aging and the National Institute of Neurological Disorders and Stroke. The drug development tools in development will take the form of biomarkers and modeling and simulation frameworks, and will be submitted for regulatory evaluation and qualification as ‘fit for purpose’ in the specific context of the drug development process for these diseases. This article constitutes a report of the progress of the work of the coalition in data standards, disease models and biomarkers.
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Affiliation(s)
| | - Brian Corrigan
- Pfizer Global Research & Development, 50 Pequot Avenue, New London, CT 06320, USA
| | - Jon Neville
- Critical Path Institute, 1730 East River Road, Tucson, AZ 85718, USA
| | - Steve Kopko
- CDISC, PO Box 2068, Round Rock, TX 78680, USA
| | - Marc Cantillon
- Critical Path Institute, 1730 East River Road, Tucson, AZ 85718, USA
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